diff --git a/previews/PR23/chapters/01-spatial-data.html b/previews/PR23/chapters/01-spatial-data.html new file mode 100644 index 0000000..a2f2bed --- /dev/null +++ b/previews/PR23/chapters/01-spatial-data.html @@ -0,0 +1,1096 @@ + + + + + + + + + + +1  Geographic data in Julia โ€“ Geocomputation with Julia + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + +
+ + + +
+ +
+
+

1  Geographic data in Julia

+
+ + + + +
+ + + + +
+ + + +
+ + +
+
using Pkg
+Pkg.status()
+
+
Status `~/work/geocompjl/geocompjl/Project.toml`
+  [c9ce4bd3] ArchGDAL v0.10.4
+  [13f3f980] CairoMakie v0.12.11
+  [a93c6f00] DataFrames v1.6.1
+  [0703355e] DimensionalData v0.28.1 `https://github.com/rafaqz/DimensionalData.jl#main`
+  [62cb38b5] GeoDataFrames v0.3.10
+  [68eda718] GeoFormatTypes v0.4.2
+  [cf35fbd7] GeoInterface v1.3.6
+  [61d90e0f] GeoJSON v0.8.1
+  [db073c08] GeoMakie v0.7.4
+  [3251bfac] GeometryOps v0.1.12
+  [a90b1aa1] LibGEOS v0.9.2
+  [c94c279d] Proj v1.7.1
+  [a3a2b9e3] Rasters v0.11.7 `https://github.com/asinghvi17/Rasters.jl#as/dd28`
+  [2913bbd2] StatsBase v0.34.3
+  [10745b16] Statistics v1.10.0
+
+
+
+
+
"output"
+
+
+
+

1.1 Introduction

+
+
using GeoDataFrames
+df = GeoDataFrames.read("data/world.gpkg")
+
+
177ร—11 DataFrame
171 rows omitted
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Rowgeomiso_a2name_longcontinentregion_unsubregiontypearea_km2poplifeExpgdpPercap
IGeometrโ€ฆString?StringStringStringStringStringFloat64Float64?Float64?Float64?
1Geometry: wkbMultiPolygonFJFijiOceaniaOceaniaMelanesiaSovereign country19290.0885806.069.968222.25
2Geometry: wkbMultiPolygonTZTanzaniaAfricaAfricaEastern AfricaSovereign country9.32746e55.22349e764.1632402.1
3Geometry: wkbMultiPolygonEHWestern SaharaAfricaAfricaNorthern AfricaIndeterminate96270.6missingmissingmissing
โ‹ฎโ‹ฎโ‹ฎโ‹ฎโ‹ฎโ‹ฎโ‹ฎโ‹ฎโ‹ฎโ‹ฎโ‹ฎโ‹ฎ
175Geometry: wkbMultiPolygonXKKosovoEuropeEuropeSouthern EuropeSovereign country11230.31.8218e671.09768698.29
176Geometry: wkbMultiPolygonTTTrinidad and TobagoNorth AmericaAmericasCaribbeanSovereign country7737.811.35449e670.42631181.8
177Geometry: wkbMultiPolygonSSSouth SudanAfricaAfricaEastern AfricaSovereign country6.24909e51.1531e755.8171935.88
+
+
+
+
+
using CairoMakie
+using GeoMakie
+
+f, a, p = poly(df.geom)
+
+
+
+

+
+
+
+
+
+

1.1.1 Raster from scratch

+

In this section, we are going to demonstrate the creation of rasters from scratch. We will construct two small rasters, elev and grain, which we will use in examples later in the book. Unlike creating a vector layer (see ?sec-vector-layer-from-scratch), creating a raster from scratch is rarely needed in practice because aligning a raster with the proper spatial extent is challenging to do programmatically (โ€œgeoreferencingโ€ tools in GIS software are a better fit for the job). Nevertheless, the examples will be helpful to become more familiar with the Rasters.jl data structures.

+
+
using Rasters
+import GeoFormatTypes as GFT
+
+
WARNING: Method definition convert_arguments(MakieCore.CellGrid, Rasters.AbstractRaster{var"#s115", 2, D, A} where A where D where var"#s115") in module RastersMakieExt at /home/runner/.julia/packages/Rasters/JcYNY/ext/RastersMakieExt/plotrecipes.jl:304 overwritten at /home/runner/.julia/packages/Rasters/JcYNY/ext/RastersMakieExt/plotrecipes.jl:308.
+ERROR: Method overwriting is not permitted during Module precompilation. Use `__precompile__(false)` to opt-out of precompilation.
+
+
+

Conceptually, a raster is an array combined with georeferencing information, whereas the latter comprises:

+
    +
  • Lookup vectors for the axes, encoding the spatial coordinates for each grid cell. These take the form of the X and Y dimensions in the raster that youโ€™ll see below.
  • +
  • A coordinate reference system (CRS) definition, specifying the association of the rasterโ€™s coordinates with the surface of the earth.
  • +
+

Therefore, to create a raster, we first need to have an array with the values, and then supplement it with the georeferencing information. Letโ€™s create the arrays elev and grain. The elev array is a \(6 \times 6\) array with sequential values from 1 to 36. It can be created as follows using base Julia functions.

+
+
elev = reshape(UInt8(1):UInt8(36), (6, 6))
+
+
6ร—6 reshape(::UnitRange{UInt8}, 6, 6) with eltype UInt8:
+ 0x01  0x07  0x0d  0x13  0x19  0x1f
+ 0x02  0x08  0x0e  0x14  0x1a  0x20
+ 0x03  0x09  0x0f  0x15  0x1b  0x21
+ 0x04  0x0a  0x10  0x16  0x1c  0x22
+ 0x05  0x0b  0x11  0x17  0x1d  0x23
+ 0x06  0x0c  0x12  0x18  0x1e  0x24
+
+
+

The grain array represents a categorical raster with values 0, 1, 2, corresponding to categories โ€œclayโ€, โ€œsiltโ€, โ€œsandโ€, respectively. We will create it from a specific arrangement of pixel values, using reshape.

+
+
v = UInt8[
+  1, 0, 1, 2, 2, 2, 
+  0, 2, 0, 0, 2, 1, 
+  0, 2, 2, 0, 0, 2, 
+  0, 0, 1, 1, 1, 1, 
+  1, 1, 1, 2, 1, 1, 
+  2, 1, 2, 2, 0, 2
+]
+grain = reshape(v, (6, 6))
+
+
6ร—6 Matrix{UInt8}:
+ 0x01  0x00  0x00  0x00  0x01  0x02
+ 0x00  0x02  0x02  0x00  0x01  0x01
+ 0x01  0x00  0x02  0x01  0x01  0x02
+ 0x02  0x00  0x00  0x01  0x02  0x02
+ 0x02  0x02  0x00  0x01  0x01  0x00
+ 0x02  0x01  0x02  0x01  0x01  0x02
+
+
+

Note that in both cases, we are using the uint8 (unsigned integer in 8 bits, i.e., 0-255) data type, which is sufficient to represent all possible values of the given rasters (see ?tbl-numpy-data-types). This is the recommended approach for a minimal memory footprint.

+

What is missing now is the georeferencing information (see ?sec-using-rasters-jl). In this case, since the rasters are arbitrary, we also set up arbitrary dimension lookups for the x and y axes, where:

+
    +
  • The origin (\(x_{min}\), \(y_{max}\)) is at -1.5,1.5
  • +
  • The raster resolution (\(delta_{x}\), \(delta_{y}\)) is 0.5,-0.5
  • +
+

We can add this information using rasterio.transform.from_origin, and specifying west, north, xsize, and ysize parameters. The resulting transformation matrix object is hereby named new_transform.

+
+
new_x = X(range(-1.5, step=0.5, length=6))
+new_y = Y(range(1.0, step=-0.5, length=6))
+
+
Y 1.0:-0.5:-1.5
+
+
+

We can now construct a Raster object, from the elev array and the dimensions new_x and new_y.
+We assign to it a CRS of EPSG:4326 (which encodes that the coordinate system is longitude/latitude on the โ€œstandardโ€ WGS84 definition of the Earthโ€™s curvature).

+

Here, we use the GFT.EPSG(code) constructor to create an object that encodes a reference code under the European Petroleum Survey Group (EPSG) authorityโ€™s database of coordinate reference systems.

+
+
elev_raster = Raster(elev, (new_x, new_y); crs = GFT.EPSG(4326))
+
+
โ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ
+โ”‚ 6ร—6 Raster{UInt8,2} โ”‚
+โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ dims โ”
+  โ†“ X Projected{Float64} -1.5:0.5:1.0 ForwardOrdered Regular Points,
+  โ†’ Y Projected{Float64} 1.0:-0.5:-1.5 ReverseOrdered Regular Points
+โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ raster โ”ค
+  extent: Extent(X = (-1.5, 1.0), Y = (-1.5, 1.0))
+
+  crs: EPSG:4326
+โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜
+  โ†“ โ†’     1.0     0.5     0.0    -0.5    -1.0    -1.5
+ -1.5  0x01    0x07    0x0d    0x13    0x19    0x1f
+ -1.0  0x02    0x08    0x0e    0x14    0x1a    0x20
+ -0.5  0x03    0x09    0x0f    0x15    0x1b    0x21
+  0.0  0x04    0x0a    0x10    0x16    0x1c    0x22
+  0.5  0x05    0x0b    0x11    0x17    0x1d    0x23
+  1.0  0x06    0x0c    0x12    0x18    0x1e    0x24
+
+
+

The raster can now be plotted in its coordinate system, passing the array elev along with the transformation matrix new_transform to rasterio.plot.show (Figure 1.1).

+
+
plot(elev_raster)
+
+
+
+
+ +
+
+Figure 1.1: Plot of the elev raster, a minimal example of a continuous raster, created from scratch +
+
+
+
+
+

The grain raster can be plotted the same way, as we are going to use the same transformation matrix for it as well (Figure 1.2).

+
+
plot(Raster(grain, (new_x, new_y); crs = GFT.EPSG(4326)))
+
+
+
+
+ +
+
+Figure 1.2: Plot of the grain raster, a minimal example of a categorical raster, created from scratch +
+
+
+
+
+

At this point, we have two rasters, each composed of an array and related dimension lookups. We can work with the raster using Rasters.jl by:

+
    +
  • Keeping in mind that any other layer we use in the analysis is in the same CRS
  • +
+

Finally, to export the raster for permanent storage, along with the spatial metadata, we need to go through the following steps:

+
    +
  1. Create a raster file (where we set the lookups and the CRS, among other settings)
  2. +
  3. Write the array with raster values into the connection
  4. +
  5. Close the connection
  6. +
+

Donโ€™t worry if the code below is unclear; the concepts related to writing raster data to file will be explained in ?sec-data-output-raster. For now, for completeness, and also to use these rasters in subsequent chapters without having to re-create them from scratch, we just provide the code for exporting the elev and grain rasters into the output directory. In the case of elev, we do it as follows with the Rasters.write functions and methods of the Rasters.jl package.

+
+
write("output/elev.tif", elev_raster; force = true)
+
+
"output/elev.tif"
+
+
+

Note that the CRS we (arbitrarily) set for the elev raster is WGS84, defined using crs=4326 according to the EPSG code.

+

Exporting the grain raster is done in the same way, with the only differences being the file name and the array we write into the connection.

+
+
write("output/grain.tif", Raster(grain, (new_x, new_y); crs = GFT.EPSG(4326)); force = true)
+
+
"output/grain.tif"
+
+
+

As a result, the files elev.tif and grain.tif are written into the output directory. We are going to use these small raster files later on in the examples (for example, ?sec-raster-subsetting).

+

Note that the transform matrices and dimensions of elev and grain are identical. This means that the rasters are overlapping, and can be combined into one two-band raster, processed in raster algebra operations (?sec-map-algebra), etc.

+ + +
+
+ +
+ + +
+ + + + + + \ No newline at end of file diff --git a/previews/PR23/chapters/01-spatial-data_files/figure-html/cell-5-output-1.svg b/previews/PR23/chapters/01-spatial-data_files/figure-html/cell-5-output-1.svg new file mode 100644 index 0000000..0febef9 --- /dev/null +++ b/previews/PR23/chapters/01-spatial-data_files/figure-html/cell-5-output-1.svg @@ -0,0 +1,387 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/previews/PR23/chapters/01-spatial-data_files/figure-html/fig-rasterio-plot-elev-output-1.svg b/previews/PR23/chapters/01-spatial-data_files/figure-html/fig-rasterio-plot-elev-output-1.svg new file mode 100644 index 0000000..0f578a9 --- /dev/null +++ b/previews/PR23/chapters/01-spatial-data_files/figure-html/fig-rasterio-plot-elev-output-1.svg @@ -0,0 +1,118 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/previews/PR23/chapters/01-spatial-data_files/figure-html/fig-rasterio-plot-grain-output-1.svg b/previews/PR23/chapters/01-spatial-data_files/figure-html/fig-rasterio-plot-grain-output-1.svg new file mode 100644 index 0000000..e6b63c9 --- /dev/null +++ b/previews/PR23/chapters/01-spatial-data_files/figure-html/fig-rasterio-plot-grain-output-1.svg @@ -0,0 +1,118 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/previews/PR23/chapters/02-attribute-operations.html b/previews/PR23/chapters/02-attribute-operations.html new file mode 100644 index 0000000..e3d4cdc --- /dev/null +++ b/previews/PR23/chapters/02-attribute-operations.html @@ -0,0 +1,698 @@ + + + + + + + + + + +2  Attribute data operations โ€“ Geocomputation with Julia + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + +
+ + + +
+ +
+
+

2  Attribute data operations

+
+ + + + +
+ + + + +
+ + + +
+ + +
+

Prerequisites

+ + +
+ +
+ + +
+ + + + + + \ No newline at end of file diff --git a/previews/PR23/chapters/03-spatial-operations.html b/previews/PR23/chapters/03-spatial-operations.html new file mode 100644 index 0000000..fd61dd7 --- /dev/null +++ b/previews/PR23/chapters/03-spatial-operations.html @@ -0,0 +1,698 @@ + + + + + + + + + + +3  Spatial data operations โ€“ Geocomputation with Julia + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + +
+ + + +
+ +
+
+

3  Spatial data operations

+
+ + + + +
+ + + + +
+ + + +
+ + +
+

Prerequisites

+ + +
+ +
+ + +
+ + + + + + \ No newline at end of file diff --git a/previews/PR23/chapters/04-geometry-operations.html b/previews/PR23/chapters/04-geometry-operations.html new file mode 100644 index 0000000..4e5f0ac --- /dev/null +++ b/previews/PR23/chapters/04-geometry-operations.html @@ -0,0 +1,698 @@ + + + + + + + + + + +4  Geometry operations โ€“ Geocomputation with Julia + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + +
+ + + +
+ +
+
+

4  Geometry operations

+
+ + + + +
+ + + + +
+ + + +
+ + +
+

Prerequisites

+ + +
+ +
+ + +
+ + + + + + \ No newline at end of file diff --git a/previews/PR23/chapters/05-raster-vector.html b/previews/PR23/chapters/05-raster-vector.html new file mode 100644 index 0000000..7a53b58 --- /dev/null +++ b/previews/PR23/chapters/05-raster-vector.html @@ -0,0 +1,2829 @@ + + + + + + + + + + +5  Raster-vector interactions โ€“ Geocomputation with Julia + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + +
+ + + +
+ +
+
+

5  Raster-vector interactions

+
+ + + + +
+ + + + +
+ + + +
+ + +
+

Prerequisites

+

This chapter requires importing the following packages:

+
+
using GeoDataFrames, DataFrames
+using Rasters, ArchGDAL # Raster I/O and operations
+using Proj # activate reprojection capabilities
+import GeoInterface as GI, GeometryOps as GO, LibGEOS as LG # Vector operations
+import GeoFormatTypes as GFT # for CRS types
+using GeoMakie, CairoMakie # plotting
+
+
WARNING: Method definition convert_arguments(MakieCore.CellGrid, Rasters.AbstractRaster{var"#s115", 2, D, A} where A where D where var"#s115") in module RastersMakieExt at /home/runner/.julia/packages/Rasters/JcYNY/ext/RastersMakieExt/plotrecipes.jl:304 overwritten at /home/runner/.julia/packages/Rasters/JcYNY/ext/RastersMakieExt/plotrecipes.jl:308.
+ERROR: Method overwriting is not permitted during Module precompilation. Use `__precompile__(false)` to opt-out of precompilation.
+
+
+

Here, we will also set the theme in Makie.jl to ensure that all surface and heatmap plots are represented in file as bitmap images, rather than the raw data.
+This is necessary only because Quarto breaks on SVGs above a certain size.
+You can feel free to set this for your own work, but it is not required.

+
+
Makie.set_theme!(
+    Heatmap = (; rasterize = 2),
+    Surface = (; rasterize = 2),
+)
+
+

It also relies on the following data files:

+
+
src_srtm = Raster("data/srtm.tif")
+src_nlcd = Raster("data/nlcd.tif")
+src_grain = Raster("output/grain.tif")
+src_elev = Raster("output/elev.tif")
+src_dem = Raster("data/dem.tif")
+zion = GeoDataFrames.read("data/zion.gpkg")
+zion_points = GeoDataFrames.read("data/zion_points.gpkg")
+cycle_hire_osm = GeoDataFrames.read("data/cycle_hire_osm.gpkg")
+us_states = GeoDataFrames.read("data/us_states.gpkg")
+nz = GeoDataFrames.read("data/nz.gpkg")
+src_nz_elev = Raster("data/nz_elev.tif")
+
+
โ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ
+โ”‚ 1115ร—1450 Raster{Float32,2} โ”‚
+โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ dims โ”
+  โ†“ X Projected{Float64} LinRange{Float64}(995456.5461976258, 2.109456546197626e6, 1115) ForwardOrdered Regular Intervals{Start},
+  โ†’ Y Projected{Float64} LinRange{Float64}(6.190960927303582e6, 4.741960927303582e6, 1450) ReverseOrdered Regular Intervals{Start}
+โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ metadata โ”ค
+  Metadata{Rasters.GDALsource} of Dict{String, Any} with 4 entries:
+  "units"    => ""
+  "offset"   => 0.0
+  "filepath" => "data/nz_elev.tif"
+  "scale"    => 1.0
+โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ raster โ”ค
+  extent: Extent(X = (995456.5461976258, 2.110456546197626e6), Y = (4.741960927303582e6, 6.191960927303582e6))
+  missingval: NaN32
+  crs: PROJCS["unknown",GEOGCS["unknown",DATUM["Unknown based on GRS80 ellipsoid",SPHEROID["GRS 1980",6378137,298.257222101004,AUTHORITY["EPSG","7019"]],TOWGS84[0,0,0,0,0,0,0]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",173],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",1600000],PARAMETER["false_northing",10000000],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH]]
+โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜
+ โ†“ โ†’          6.19096e6    6.18996e6  โ€ฆ    4.74296e6    4.74196e6
+ 9.95457e5  NaN          NaN             NaN          NaN
+ โ‹ฎ                                    โ‹ฑ                 โ‹ฎ
+ 2.10946e6  NaN          NaN          โ€ฆ  NaN          NaN
+
+
+
+
+

5.1 Introduction

+

This chapter focuses on interactions between raster and vector geographic data models, both introduced in Chapter 1. It includes four main techniques:

+
    +
  • Raster cropping and masking using vector objects (Section 5.2)
  • +
  • Extracting raster values using different types of vector data (Section 5.3)
  • +
  • Raster to vector conversion (Section 5.4)
  • +
  • Vector to raster conversion (Section 5.5)
  • +
+

These concepts are demonstrated using data from previous chapters, to understand their potential real-world applications.

+
+
+

5.2 Raster masking and cropping

+

Many geographic data projects involve integrating data from many different sources, such as remote sensing images (rasters) and administrative boundaries (vectors). Often the extent of input raster datasets is larger than the area of interest. In this case, raster masking, cropping, or both, are useful for unifying the spatial extent of input data (Figure 5.1 (b) and (c), and the following two examples, illustrate the difference between masking and cropping). Both operations reduce memory use and computational demand for subsequent analysis, and may be a necessary preprocessing step before creating attractive maps involving raster data.

+

We will use two layers to illustrate raster cropping:

+
    +
  • The srtm.tif raster representing elevation, in meters above sea level, in south-western Utah: a Rasters.jl file connection named src_srtm (see Figure 5.1 (a))
  • +
  • The zion.gpkg vector layer representing the Zion National Park boundaries (a DataFrame named zion)
  • +
+

Both target and cropping objects must have the same projection. Since it is easier and more precise to reproject vector layers, compared to rasters, we use the following expression to reproject (?sec-reprojecting-vector-geometries) the vector layer zion into the coordinate reference system (CRS) of the raster src_srtm. The CRS defines how the coordinates of the geometry relate to locations on the surface of the Earth.

+
+
zion = GO.reproject(zion; target_crs = GI.crs(src_srtm))
+
+
1ร—12 DataFrame
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
RowgeomUNIT_CODEGIS_NotesUNIT_NAMEDATE_EDITSTATEREGIONGNIS_IDUNIT_TYPECREATED_BYMETADATAPARKNAME
Polygonโ€ฆStringStringStringDateTimeStringStringStringStringStringStringString
1Polygon{false, false, Vector{LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Nothing, WellKnownText{CRS}}}, Nothing, WellKnownText{CRS}}(LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Nothing, WellKnownText{CRS}}[LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Nothing, WellKnownText{CRS}}([(-113.084, 37.1713), (-113.086, 37.1716), (-113.091, 37.1722), (-113.091, 37.1744), (-113.091, 37.178), (-113.091, 37.1817), (-113.091, 37.1853), (-113.091, 37.1889), (-113.091, 37.1926), (-113.091, 37.1962) โ€ฆ (-113.06, 37.1713), (-113.062, 37.1713), (-113.064, 37.1713), (-113.069, 37.1713), (-113.071, 37.1713), (-113.073, 37.1713), (-113.078, 37.1713), (-113.08, 37.1713), (-113.082, 37.1713), (-113.084, 37.1713)], nothing, WellKnownText{CRS}(CRS(), "GEOGCS[\\"WGS 84\\",DATUM[\\"WGS_1984\\",SPHEROID[\\"WGS 84\\",6378137,298.257223563,AUTHORITY[\\"EPSG\\",\\"7030\\"]],AUTHORITY[\\"EPSG\\",\\"6326\\"]],PRIMEM[\\"Greenwich\\",0,AUTHORITY[\\"EPSG\\",\\"8901\\"]],UNIT[\\"degree\\",0.0174532925199433,AUTHORITY[\\"EPSG\\",\\"9122\\"]],AXIS[\\"Latitude\\",NORTH],AXIS[\\"Longitude\\",EAST],AUTHORITY[\\"EPSG\\",\\"4326\\"]]"))], nothing, WellKnownText{CRS}(CRS(), "GEOGCS[\\"WGS 84\\",DATUM[\\"WGS_1984\\",SPHEROID[\\"WGS 84\\",6378137,298.257223563,AUTHORITY[\\"EPSG\\",\\"7030\\"]],AUTHORITY[\\"EPSG\\",\\"6326\\"]],PRIMEM[\\"Greenwich\\",0,AUTHORITY[\\"EPSG\\",\\"8901\\"]],UNIT[\\"degree\\",0.0174532925199433,AUTHORITY[\\"EPSG\\",\\"9122\\"]],AXIS[\\"Latitude\\",NORTH],AXIS[\\"Longitude\\",EAST],AUTHORITY[\\"EPSG\\",\\"4326\\"]]"))ZIONLands - http://landsnet.nps.gov/tractsnet/documents/ZION/Metadata/zion_metadata.xmlZion National Park2017-06-22T00:00:00UTIM1455157National ParkLandshttps://irma.nps.gov/App/Reference/Profile/2181118#Zion National MonumentZion
+
+
+
+

To mask the image, i.e., convert all pixels which do not intersect with the zion polygon to missing, we use the Rasters.mask function.
+mask supports any geometry, vector of geometries, feature collection, or table with a geometry column!

+

The tabset below shows all the different ways to mask a raster. Weโ€™ll go forward with the approach of using a DataFrame.

+
+ +
+
+
+
out_image_mask = Rasters.mask(src_srtm; with = zion)
+
+
โ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ
+โ”‚ 465ร—457 Raster{UInt16,2} โ”‚
+โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ dims โ”
+  โ†“ X Projected{Float64} LinRange{Float64}(-113.23958321278403, -112.85291654614313, 465) ForwardOrdered Regular Intervals{Start},
+  โ†’ Y Projected{Float64} LinRange{Float64}(37.51208342983253, 37.13208342985786, 457) ReverseOrdered Regular Intervals{Start}
+โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ metadata โ”ค
+  Metadata{Rasters.GDALsource} of Dict{String, Any} with 4 entries:
+  "units"    => ""
+  "offset"   => 0.0
+  "filepath" => "data/srtm.tif"
+  "scale"    => 1.0
+โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ raster โ”ค
+  extent: Extent(X = (-113.23958321278403, -112.85208321280986), Y = (37.13208342985786, 37.512916763165805))
+  missingval: 0xffff
+  crs: GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST],AUTHORITY["EPSG","4326"]]
+โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜
+    โ†“ โ†’        37.5121      37.5113      37.5104  โ€ฆ      37.1329      37.1321
+ -113.24   0xffff       0xffff       0xffff          0xffff       0xffff
+    โ‹ฎ                                             โ‹ฑ               
+ -112.853  0xffff       0xffff       0xffff       โ€ฆ  0xffff       0xffff
+
+
+
+
+
+
masker = zion.geom[1]
+
+
GeoInterface.Wrappers.Polygon{false, false, Vector{GeoInterface.Wrappers.LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Nothing, WellKnownText{GeoFormatTypes.CRS}}}, Nothing, WellKnownText{GeoFormatTypes.CRS}}(GeoInterface.Wrappers.LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Nothing, WellKnownText{GeoFormatTypes.CRS}}[GeoInterface.Wrappers.LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Nothing, WellKnownText{GeoFormatTypes.CRS}}([(-113.08438684283465, 37.17130388117393), (-113.0859893948904, 37.17162779353809), (-113.09132351665151, 37.17215207288513), (-113.09132530000656, 37.17440980000173), (-113.09132830000658, 37.17803750000173), (-113.09133150000655, 37.18166840000168), (-113.09133470000658, 37.18529940000173), (-113.09134070000657, 37.188925000001696), (-113.09134660000655, 37.19255060000173), (-113.09135260000657, 37.19617620000171)  โ€ฆ  (-113.05962130000604, 37.17131270000172), (-113.06185720000607, 37.171310800001734), (-113.06415140000612, 37.17131020000169), (-113.06868130000619, 37.17130650000175), (-113.07101920000618, 37.17130460000174), (-113.07321130000625, 37.17130450000173), (-113.07773970000633, 37.17130420000172), (-113.08035690000636, 37.17130410000175), (-113.0822681000064, 37.171304000001705), (-113.08438684283465, 37.17130388117393)], nothing, WellKnownText{GeoFormatTypes.CRS}(GeoFormatTypes.CRS(), "GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]"))], nothing, WellKnownText{GeoFormatTypes.CRS}(GeoFormatTypes.CRS(), "GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]"))
+
+
+
+
Rasters.mask(src_srtm; with = masker)
+
+
โ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ
+โ”‚ 465ร—457 Raster{UInt16,2} โ”‚
+โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ dims โ”
+  โ†“ X Projected{Float64} LinRange{Float64}(-113.23958321278403, -112.85291654614313, 465) ForwardOrdered Regular Intervals{Start},
+  โ†’ Y Projected{Float64} LinRange{Float64}(37.51208342983253, 37.13208342985786, 457) ReverseOrdered Regular Intervals{Start}
+โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ metadata โ”ค
+  Metadata{Rasters.GDALsource} of Dict{String, Any} with 4 entries:
+  "units"    => ""
+  "offset"   => 0.0
+  "filepath" => "data/srtm.tif"
+  "scale"    => 1.0
+โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ raster โ”ค
+  extent: Extent(X = (-113.23958321278403, -112.85208321280986), Y = (37.13208342985786, 37.512916763165805))
+  missingval: 0xffff
+  crs: GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST],AUTHORITY["EPSG","4326"]]
+โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜
+    โ†“ โ†’        37.5121      37.5113      37.5104  โ€ฆ      37.1329      37.1321
+ -113.24   0xffff       0xffff       0xffff          0xffff       0xffff
+    โ‹ฎ                                             โ‹ฑ               
+ -112.853  0xffff       0xffff       0xffff       โ€ฆ  0xffff       0xffff
+
+
+
+
+
+
masker = zion.geom
+
+
1-element Vector{GeoInterface.Wrappers.Polygon{false, false, Vector{GeoInterface.Wrappers.LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Nothing, WellKnownText{GeoFormatTypes.CRS}}}, Nothing, WellKnownText{GeoFormatTypes.CRS}}}:
+ GeoInterface.Wrappers.Polygon{false, false, Vector{GeoInterface.Wrappers.LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Nothing, WellKnownText{GeoFormatTypes.CRS}}}, Nothing, WellKnownText{GeoFormatTypes.CRS}}(GeoInterface.Wrappers.LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Nothing, WellKnownText{GeoFormatTypes.CRS}}[GeoInterface.Wrappers.LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Nothing, WellKnownText{GeoFormatTypes.CRS}}([(-113.08438684283465, 37.17130388117393), (-113.0859893948904, 37.17162779353809), (-113.09132351665151, 37.17215207288513), (-113.09132530000656, 37.17440980000173), (-113.09132830000658, 37.17803750000173), (-113.09133150000655, 37.18166840000168), (-113.09133470000658, 37.18529940000173), (-113.09134070000657, 37.188925000001696), (-113.09134660000655, 37.19255060000173), (-113.09135260000657, 37.19617620000171)  โ€ฆ  (-113.05962130000604, 37.17131270000172), (-113.06185720000607, 37.171310800001734), (-113.06415140000612, 37.17131020000169), (-113.06868130000619, 37.17130650000175), (-113.07101920000618, 37.17130460000174), (-113.07321130000625, 37.17130450000173), (-113.07773970000633, 37.17130420000172), (-113.08035690000636, 37.17130410000175), (-113.0822681000064, 37.171304000001705), (-113.08438684283465, 37.17130388117393)], nothing, WellKnownText{GeoFormatTypes.CRS}(GeoFormatTypes.CRS(), "GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]"))], nothing, WellKnownText{GeoFormatTypes.CRS}(GeoFormatTypes.CRS(), "GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]"))
+
+
+
+
Rasters.mask(src_srtm; with = masker)
+
+
โ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ
+โ”‚ 465ร—457 Raster{UInt16,2} โ”‚
+โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ dims โ”
+  โ†“ X Projected{Float64} LinRange{Float64}(-113.23958321278403, -112.85291654614313, 465) ForwardOrdered Regular Intervals{Start},
+  โ†’ Y Projected{Float64} LinRange{Float64}(37.51208342983253, 37.13208342985786, 457) ReverseOrdered Regular Intervals{Start}
+โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ metadata โ”ค
+  Metadata{Rasters.GDALsource} of Dict{String, Any} with 4 entries:
+  "units"    => ""
+  "offset"   => 0.0
+  "filepath" => "data/srtm.tif"
+  "scale"    => 1.0
+โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ raster โ”ค
+  extent: Extent(X = (-113.23958321278403, -112.85208321280986), Y = (37.13208342985786, 37.512916763165805))
+  missingval: 0xffff
+  crs: GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST],AUTHORITY["EPSG","4326"]]
+โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜
+    โ†“ โ†’        37.5121      37.5113      37.5104  โ€ฆ      37.1329      37.1321
+ -113.24   0xffff       0xffff       0xffff          0xffff       0xffff
+    โ‹ฎ                                             โ‹ฑ               
+ -112.853  0xffff       0xffff       0xffff       โ€ฆ  0xffff       0xffff
+
+
+ +
+
+
+
+
+
+ +
+
+Note +
+
+
+

Note that since Julia has a native missing/NODATA value type, we donโ€™t need to specify a NODATA value for the mask function.

+

However, it can sometimes be useful and more efficient to specify a sentinel value which Rasters treats as missing.

+

You can do this by specifying the missingval keyword argument, like so:

+
out_image_mask = Rasters.mask(src_srtm; with = zion, missingval = 9999)
+
+
+

We can write this masked raster to file with Rasters.write:

+
+
Rasters.write("output/srtm_masked.tif", out_image_mask; force = true
+)
+
+
"output/srtm_masked.tif"
+
+
+

In Rasters.jl, cropping and masking are distinct operations. Cropping, which reduces the raster extent to the extent of the vector layer, is accomplished with the crop function.

+

Here, we simply pass the zion feature table to the to keyword argument, which indicates what to crop the raster โ€œtoโ€. We also set the touches keyword argument to true, to specify that pixels that partially overlap with the vector layer are included in the output.

+
+
out_image_crop = Rasters.crop(src_srtm; to = zion, touches = true)
+
+
โ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ
+โ”‚ 439ร—436 Raster{UInt16,2} โ”‚
+โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ dims โ”
+  โ†“ X Projected{Float64} LinRange{Float64}(-113.22874987945141, -112.86374987947575, 439) ForwardOrdered Regular Intervals{Start},
+  โ†’ Y Projected{Float64} LinRange{Float64}(37.50375009649975, 37.14125009652391, 436) ReverseOrdered Regular Intervals{Start}
+โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ metadata โ”ค
+  Metadata{Rasters.GDALsource} of Dict{String, Any} with 4 entries:
+  "units"    => ""
+  "offset"   => 0.0
+  "filepath" => "data/srtm.tif"
+  "scale"    => 1.0
+โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ raster โ”ค
+  extent: Extent(X = (-113.22874987945141, -112.86291654614247), Y = (37.14125009652391, 37.504583429833026))
+  missingval: 0xffff
+  crs: GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST],AUTHORITY["EPSG","4326"]]
+โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜
+    โ†“ โ†’        37.5038      37.5029      37.5021  โ€ฆ      37.1421      37.1413
+ -113.229  0x0668       0x065f       0x0659          0x05a0       0x05df
+    โ‹ฎ                                             โ‹ฑ       โ‹ฎ       
+ -112.864  0x0a00       0x0a1a       0x0a24          0x06da       0x06d4
+
+
+

You can also assemble an extent manually, using Extents.Extent, or extract one using GI.extent.

+

We can crop our masked raster as well:

+
+
out_image_mask_crop = Rasters.crop(out_image_mask; to = zion, touches = true)
+
+
โ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ
+โ”‚ 439ร—436 Raster{UInt16,2} โ”‚
+โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ dims โ”
+  โ†“ X Projected{Float64} LinRange{Float64}(-113.22874987945141, -112.86374987947575, 439) ForwardOrdered Regular Intervals{Start},
+  โ†’ Y Projected{Float64} LinRange{Float64}(37.50375009649975, 37.14125009652391, 436) ReverseOrdered Regular Intervals{Start}
+โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ metadata โ”ค
+  Metadata{Rasters.GDALsource} of Dict{String, Any} with 4 entries:
+  "units"    => ""
+  "offset"   => 0.0
+  "filepath" => "data/srtm.tif"
+  "scale"    => 1.0
+โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ raster โ”ค
+  extent: Extent(X = (-113.22874987945141, -112.86291654614247), Y = (37.14125009652391, 37.504583429833026))
+  missingval: 0xffff
+  crs: GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST],AUTHORITY["EPSG","4326"]]
+โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜
+    โ†“ โ†’        37.5038      37.5029      37.5021  โ€ฆ      37.1421      37.1413
+ -113.229  0xffff       0xffff       0xffff          0xffff       0xffff
+    โ‹ฎ                                             โ‹ฑ       โ‹ฎ       
+ -112.864  0xffff       0xffff       0xffff          0xffff       0xffff
+
+
+

and we write it to file using Rasters.write:

+
+
Rasters.write("output/srtm_masked_cropped.tif", out_image_mask_crop; force = true)
+
+
"output/srtm_masked_cropped.tif"
+
+
+

Figure 5.1 shows the original raster, and the three masking and/or cropping results.

+
+
fig = Figure(size = (600, 600))
+
+ax1 = Axis(fig[1, 1]; title = "Original")
+plot!(ax1, src_srtm)
+poly!(ax1, zion.geom; color = :transparent, strokecolor = :black, strokewidth = 0.75)
+
+ax2 = Axis(fig[1, 2]; title = "Masked")
+plot!(ax2, out_image_mask)
+poly!(ax2, zion.geom; color = :transparent, strokecolor = :black, strokewidth = 0.75)
+
+ax3 = Axis(fig[2, 1]; title = "Cropped")
+plot!(ax3, out_image_crop)
+poly!(ax3, zion.geom; color = :transparent, strokecolor = :black, strokewidth = 0.75)
+
+ax4 = Axis(fig[2, 2]; title = "Masked+Cropped")
+plot!(ax4, out_image_mask_crop)
+poly!(ax4, zion.geom; color = :transparent, strokecolor = :black, strokewidth = 0.75)
+
+display(fig)
+
+
+
+
+
+
+
+ +
+
+(a) Raster masking and cropping +
+
+
+
+
+
+
+
CairoMakie.Screen{PDF}
+
+
+(b) +
+
+
+
+
+Figure 5.1 +
+
+
+
+
+
+

5.3 Raster extraction

+

Raster extraction is the process of identifying and returning the values associated with a โ€˜targetโ€™ raster at specific locations, based on a (typically vector) geographic โ€˜selectorโ€™ object. The reverse of raster extractionโ€”assigning raster cell values based on vector objectsโ€”is rasterization, described in Section 5.4.

+

Rasters.jl provides modular raster extraction and statistics functions, and we use this package in the following examples.

+ +
+

5.3.1 Extraction to points

+

The simplest type of raster extraction is getting the values of raster cells at specific points. To demonstrate extraction to points, we will use zion_points, which contains a sample of 30 locations within the Zion National Park (Figure 5.2).

+
+
fig, ax, plt = plot(src_srtm)
+scatter!(ax, zion_points.geom, color=:black, strokecolor=:white, strokewidth = 1);
+display(fig)
+
+
+
+
+
+
+
+ +
+
+(a) 30 point locations within the Zion National Park, with elevation in the background +
+
+
+
+
+
+
+
CairoMakie.Screen{PDF}
+
+
+(b) +
+
+
+
+
+Figure 5.2 +
+
+
+
+

The following expression extracts elevation values from srtm.tif according to zion_points, using Rasters.extract.

+
+

The API here is not great, can we do better? It currently returns a vector of named tuples, which is not very convenient.

+

One thought is to use a Tables.jl materializer to convert the result if possible. I understand the desire to return the geometry values. But there must be a better way than this.

+
+
+
result1 = DataFrame(Rasters.extract(src_srtm, zion_points; geometry = false))
+
+
30ร—1 DataFrame
24 rows omitted
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Row
UInt16?
11802
22433
31886
โ‹ฎโ‹ฎ
281372
291905
301574
+
+
+
+

The first argument is the raster from which to extract values, and the second is the vector object (or collection of objects) according to which to extract the values.

+
+
+
+ +
+
+Note +
+
+
+

Rasters.jl does not yet support interpolation in extraction, so the values extracted are the values of the nearest cell center.
+This corresponds to interpolate='nearest' in the Python rasterstats package.

+
+
+

Either way, the resulting object is a vector of raster values, corresponding to zion_points. For example, here are the elevations of the first five points.

+
+
result1[1:5, ""]
+
+
5-element Vector{Union{Missing, UInt16}}:
+ 0x070a
+ 0x0981
+ 0x075e
+ 0x055a
+ 0x05ac
+
+
+

To get a DataFrame with the original points geometries (and other attributes, if any), as well as the extracted raster values, we can assign the extraction result into a new column.

+
+
zion_points[!, "elev1"] = result1[!, ""]
+zion_points
+
+
30ร—2 DataFrame
24 rows omitted
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Rowgeomelev1
IGeometrโ€ฆUInt16?
1Geometry: wkbPoint1802
2Geometry: wkbPoint2433
3Geometry: wkbPoint1886
โ‹ฎโ‹ฎโ‹ฎ
28Geometry: wkbPoint1372
29Geometry: wkbPoint1905
30Geometry: wkbPoint1574
+
+
+
+

You can read from a single band by selecting the band in the Raster. TODO finish this text

+
+
+

5.3.2 Extraction to lines

+

Raster extraction is also applicable with line selectors. The typical line extraction algorithm is to extract one value for each raster cell touched by a line. However, this particular approach is not recommended to obtain values along the transects, as it is hard to get the correct distance between each pair of extracted raster values.

+

For line extraction, a better approach is to split the line into many points (at equal distances along the line) and then extract the values for these points using the โ€œextraction to pointsโ€ technique (Section 5.3.1). To demonstrate this, the code below creates (see ?sec-vector-data for recap) zion_transect, a straight line going from northwest to southeast of the Zion National Park.

+
+
coords = [[-113.2, 37.45], [-112.9, 37.2]]
+zion_transect = GI.LineString(coords)
+
+
GeoInterface.Wrappers.LineString{false, false, Vector{Vector{Float64}}, Nothing, Nothing}([[-113.2, 37.45], [-112.9, 37.2]], nothing, nothing)
+
+
+

The utility of extracting heights from a linear selector is illustrated by imagining that you are planning a hike. The method demonstrated below provides an โ€˜elevation profileโ€™ of the route (the line does not need to be straight), useful for estimating how long it will take by determining the cumulative elevation gain of your journey.

+

First, we need to create a layer consisting of points along our line (zion_transect), at specified intervals (e.g., 250). To do that, we need to transform the line into a projected CRS (so that we work with true distances, in \(m\)), such as Universal Transverse Mercator.

+
+
zion_transect_utm = GO.reproject(zion_transect; target_crs = GFT.EPSG(32612), source_crs = GFT.EPSG(4326))
+
+
GeoInterface.Wrappers.LineString{false, false, Vector{Tuple{Float64, Float64}}, Nothing, EPSG{1}}([(305399.67208180577, 4.147066650206682e6), (331380.8917453843, 4.1187500947884847e6)], nothing, EPSG{1}((32612,)))
+
+
+

The printout of the new geometry shows this is still a straight line between two points, only with coordinates in a projected CRS.

+
+

Iโ€™ve chosen to differ from the Python treatment here - instead of selecting some number of points along the line explicitly, I will segmentize the line and extract the points. This is less precise, but we donโ€™t have the API to do arclength interpolation in GeometryOps yet. Hopefully this will be added soon.

+

cf. https://github.com/JuliaGeo/GeometryOps.jl/issues/210

+
+

Here, we interpolate points along the line using GO.segmentize. This operation is sometimes called line densification.

+

We first compute the length of the line, and then use this to segmentize the line into approximately 250 points.

+
+
_centroid, linelen = GO.centroid_and_length(zion_transect_utm)
+
+
((318390.28191359504, 4.1329083724975833e6), 38429.82026966749)
+
+
+

Now that we have the length of the line, we can choose a distance such that we get around 250 points along the line.

+
+
zion_transect_line = GO.segmentize(zion_transect_utm; max_distance = linelen / 250)
+
+
GeoInterface.Wrappers.LineString{false, false, Vector{Tuple{Float64, Float64}}, Nothing, EPSG{1}}([(305399.67208180577, 4.147066650206682e6), (305503.59696046007, 4.146953383985009e6), (305607.5218391144, 4.1468401177633363e6), (305711.44671776873, 4.1467268515416635e6), (305815.37159642304, 4.1466135853199908e6), (305919.29647507734, 4.146500319098318e6), (306023.22135373164, 4.1463870528766452e6), (306127.14623238595, 4.1462737866549725e6), (306231.0711110403, 4.1461605204332997e6), (306334.9959896946, 4.1460472542116265e6)  โ€ฆ  (330445.5678374955, 4.11976949078354e6), (330549.4927161498, 4.119656224561867e6), (330653.41759480414, 4.119542958340194e6), (330757.34247345844, 4.1194296921185213e6), (330861.26735211274, 4.1193164258968486e6), (330965.19223076705, 4.119203159675176e6), (331069.11710942135, 4.119089893453503e6), (331173.0419880757, 4.1189766272318303e6), (331276.96686673, 4.1188633610101575e6), (331380.8917453843, 4.1187500947884847e6)], nothing, EPSG{1}((32612,)))
+
+
+

This gives us a collection of 251 points along the line. We can extract the points that define the line segments by using GI.getpoint on the line, and then reproject the points to the CRS of the raster.

+
+
line_points = GI.getpoint(zion_transect_line)
+zion_transect_pnt = GO.reproject(line_points; target_crs = GI.crs(src_srtm), source_crs = GI.crs(zion_transect_line))
+
+
251-element Vector{Tuple{Float64, Float64}}:
+ (-113.2, 37.45)
+ (-113.1987960226292, 37.449001661297636)
+ (-113.19759207733796, 37.448003309143594)
+ (-113.1963881641247, 37.44700494353919)
+ (-113.1951842829878, 37.44600656448574)
+ (-113.19398043392566, 37.44500817198453)
+ (-113.19277661693668, 37.444009766036885)
+ (-113.19157283201925, 37.4430113466441)
+ (-113.19036907917175, 37.4420129138075)
+ (-113.18916535839257, 37.44101446752837)
+ โ‹ฎ
+ (-112.90956920045984, 37.20801281544639)
+ (-112.90837293949568, 37.20701125948384)
+ (-112.90717671022236, 37.20600969038492)
+ (-112.90598051263827, 37.205008108150906)
+ (-112.90478434674178, 37.2040065127831)
+ (-112.90358821253133, 37.20300490428282)
+ (-112.9023921100053, 37.20200328265134)
+ (-112.90119603916204, 37.201001647889974)
+ (-112.90000000000002, 37.20000000000002)
+
+
+

Finally, we extract the elevation values for each point in our transect and combine the information with zion_transect_pnt (after โ€œpromotingโ€ it to a DataFrame, to accommodate extra attributes), using the point extraction method shown earlier (Section 5.3.1). We also attach the distance of each point along the line, to be used to plot an elevation profile.

+
+
zion_transect_pnt = DataFrame(geometry = zion_transect_pnt)
+
+result = Rasters.extract(src_srtm, zion_transect_pnt; geometry = false)
+# `Rasters.extract` returns a vector of tuples, so we take the first element of each tuple
+result = first.(result)
+# Add elevation data to a new data frame column, named `elevation`
+zion_transect_pnt[!, "elevation"] = result
+
+
251-element Vector{UInt16}:
+ 0x07d1
+ 0x07f1
+ 0x07e4
+ 0x07c5
+ 0x076e
+ 0x074d
+ 0x06fe
+ 0x06e7
+ 0x06d3
+ 0x06ba
+      โ‹ฎ
+ 0x0720
+ 0x079f
+ 0x0769
+ 0x0737
+ 0x072d
+ 0x0737
+ 0x0729
+ 0x070a
+ 0x06e3
+
+
+

We also want to visualize an elevation profile along this line, so we compute the distances along the line manually. Using GO.distance, we can get the distance between successive points along the line. Then, we add 0.0 to the beginning of the array of distances, and sum along that array to get the cumulative distance along the line.

+
+
# Compute distances between successive points along the line
+distances_between_points = GO.distance.(zion_transect_pnt.geometry[1:end-1], zion_transect_pnt.geometry[2:end])
+# Compute cumulative distances along the line
+cumulative_distances = cumsum([0.0; distances_between_points])
+# Assign the distances to a new column in the data frame
+zion_transect_pnt[!, "distance"] = cumulative_distances
+zion_transect_pnt
+
+
251ร—3 DataFrame
245 rows omitted
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Rowgeometryelevationdistance
Tupleโ€ฆUInt16Float64
1(-113.2, 37.45)20010.0
2(-113.199, 37.449)20330.00156405
3(-113.198, 37.448)20200.00312808
โ‹ฎโ‹ฎโ‹ฎโ‹ฎ
249(-112.902, 37.202)18330.387393
250(-112.901, 37.201)18020.388953
251(-112.9, 37.2)17630.390513
+
+
+
+

The information in zion_transect_pnt, namely the "dist" and "elev" attributes, can now be used to draw an elevation profile, as illustrated in Figure 5.3.

+
# Raster and a line transect
+fig, ax, plt = plot(src_srtm)
+lines!(ax, zion_transect; color = :black)
+poly!(ax, zion.geom; color = :transparent, strokecolor = :white, strokewidth = 0.75)
+display(fig)
+# Elevation profile
+fig, ax, plt = lines(
+    zion_transect_pnt.distance, 
+    zion_transect_pnt.elevation;
+    axis = (;
+        xlabel = "Distance (m)",
+        ylabel = "Elevation (m)",
+    )
+)
+display(fig)
+
+
+
+
+
+
+
+
+ +
+
+(a) Raster and a line transect +
+
+
+
+
+
+
+
+ +
+
+(b) Extracted elevation profile +
+
+
+
+
+
+
+
+
+
+
CairoMakie.Screen{PDF}
+
+
+(c) +
+
+
+
+
+
+
+Figure 5.3: Extracting a raster values profile to line +
+
+
+
+
+

5.3.3 Extraction to polygons

+

The final type of geographic vector object that can be used for raster extraction is polygons.
+Like lines, polygons tend to return many raster values per vector geometry. For continuous rasters (Figure 5.4 (a)), we typically want to generate summary statistics for raster values per polygon, for example to characterize a single region or to compare many regions. The generation of raster summary statistics, by polygons, is demonstrated in the code below using Rasters.zonal, which creates a list of summary statistics for โ€œzonesโ€ defined by geometry. In this case, a vector of length 1 is returned, since there is just one polygon in the DataFrame.

+
+
using Statistics # for `mean`
+rmean = Rasters.zonal(mean, src_srtm; of = zion)
+rmin = Rasters.zonal(minimum, src_srtm; of = zion)
+rmax = Rasters.zonal(maximum, src_srtm; of = zion)
+result = (rmean, rmin, rmax)
+
+
(Union{Missing, Float64}[1818.211830154405], Union{Missing, UInt16}[0x0462], Union{Missing, UInt16}[0x0a65])
+
+
+

Rasters.zonal accepts any function that works on iterables (collections of elements that can be looped over) and returns a single value, like mean, minimum, maximum, std, median, mode, sum, prod, etc.

+
+ +
+
+
+
# This is about the worst possible implementation of a mean function,
+# please don't use it in real life!
+function my_mean(iterable)
+    result = 0.0
+    count = 0
+    for value in iterable
+        result += value
+        count += 1
+    end
+    result /= count
+    return result
+end
+
+Rasters.zonal(my_mean, src_srtm; of = zion)
+
+
1-element Vector{Union{Missing, Float64}}:
+ 1818.211830154405
+
+
+
+
+
+
+ +
+
+

Sometimes, in order to be most efficient with raster access, we might want to pass multiple functions to a single Rasters.zonal call. We can do this by passing a function that returns a tuple of values to Rasters.zonal.

+

For example, consider:

+
+
using Statistics # for `mean`
+Rasters.zonal(x -> (mean(x), minimum(x), maximum(x)), src_srtm; of = zion)
+
+
1-element Vector{Union{Missing, Tuple{Float64, UInt16, UInt16}}}:
+ (1818.211830154405, 0x0462, 0x0a65)
+
+
+
+
+
+

We then transform the result to a DataFrame, which makes it easier to track and handle geometry attributes:

+
+
DataFrame(mean = rmean, min = rmin, max = rmax)
+
+
1ร—3 DataFrame
+ + + + + + + + + + + + + + + + + + + + + + + +
Rowmeanminmax
Float64?UInt16?UInt16?
11818.2111222661
+
+
+
+

Because there is only one polygon in the example, single-element vectors are returned. However, if zion was composed of more than one polygon, we would accordingly get more elements in the returned vectors. The result provides useful summaries, for example that the maximum height in the park is 2661 \(m\) above sea level.

+

To count occurrences of categorical raster values within polygons (Figure 5.4 (b)), we can use masking (Section 5.2) combined with StatsBase.countmap, as follows.

+
+
out_image = Rasters.mask(src_nlcd; with = GO.reproject(zion; target_crs = GI.crs(src_nlcd)))
+using StatsBase
+counts = StatsBase.countmap(out_image)
+
+
Dict{UInt8, Int64} with 8 entries:
+  0x05 => 203701
+  0x04 => 298299
+  0x06 => 235
+  0x07 => 62
+  0x02 => 4205
+  0xff => 852741
+  0x08 => 679
+  0x03 => 98285
+
+
+

According to the result, for example, the value 2 (โ€œDevelopedโ€ class) appears in 4205 pixels within the Zion polygon.

+

Figure 5.4 illustrates the two types of raster extraction to polygons described above.

+
# Continuous raster
+fig, ax, plt = plot(src_srtm)
+poly!(ax, zion.geom; color = :transparent, strokecolor = :black, strokewidth = 0.75)
+display(fig)
+# Categorical raster
+fig, ax, plt = plot(src_nlcd; colormap = cgrad(:Set3; categorical = true), source = GI.crs(src_nlcd), axis = (; type = GeoAxis, dest = GI.crs(src_nlcd)))
+poly!(ax, zion.geom; source = GI.crs(zion.geom[1]), color = :transparent, strokecolor = :black, strokewidth = 0.75)
+cm = Colorbar(fig[1, 2], plt)
+ax.xgridvisible = false
+ax.ygridvisible = false
+display(fig) # TODO: make GeoMakie better on small extents
+
+
+
+
+
+
+
+
+ +
+
+(a) Continuous raster +
+
+
+
+
+
+
+
+ +
+
+(b) Categorical raster +
+
+
+
+
+
+
+
+
+
+
CairoMakie.Screen{PDF}
+
+
+(c) +
+
+
+
+
+
+
+Figure 5.4: Sample data used for continuous and categorical raster extraction to a polygon +
+
+
+ + + +
+
+
+

5.4 Rasterization

+

Rasterization is the conversion of vector objects into their representation in raster objects. Usually, the output raster is used for quantitative analysis (e.g., analysis of terrain) or modeling. As we saw in Chapter 1, the raster data model has some characteristics that make it conducive to certain methods. Furthermore, the process of rasterization can help simplify datasets because the resulting values all have the same spatial resolution: rasterization can be seen as a special type of geographic data aggregation.

+

Rasters.jl provides the Rasters.rasterize function for rasterizing vector data. To make this happen, we need to have some definition for a โ€œtemplateโ€ grid, i.e., the โ€œtemplateโ€ raster defining the extent, resolution and CRS of the output. We can also pass a pre-existing raster, in which case Rasters uses the existing grid definition.

+

As for the vector geometries and their associated values, the Rasters.rasterize function can take input in multiple ways: - ; data::FeatureCollection, fill::Symbol means that the values of the column passed to fill will be used to fill the raster. - ; data::Vector{Geometry}, fill::Vector means that the values passed to fill will be associated with each geometry in data and used to fill the raster. - ; data::Any, fill::Function calls fill with the current value

+

Furthermore, we define how to handle multiple values burned into the same pixel, in the first argument called reducer.
+By default, this is last, meaning that the last polygon to be rasterized takes precedence.
+However, we can pass any function that takes in an iterable and returns a single value, like mean, minimum, maximum, std, median, mode, sum, prod, etc.
+Many of these may not make sense to use but they are useful to know of.

+

Furthermore, we define how to deal with multiple values burned into the same pixel, in the first argument called reducer. By default, this is last, meaning that the last polygon to be rasterized takes precedence. However, we can pass any function that takes in an iterable and returns a single value, like mean, minimum, maximum, std, median, mode, sum, prod, etc. Many of these may not make sense to use but they are useful to know of.

+

Finally, we can set the fill value, which is the value that โ€œunaffectedโ€ pixels get, with fill=0 being the default.

+

How the Rasters.rasterize function works with all of these various parameters will be made clear in the next examples.

+

The spatial resolution (area of each โ€œpixelโ€) of the โ€œtemplateโ€ raster has a major impact on the results: if it is too low (cell size is too large), the result may miss the full geographic variability of the vector data; if it is too high, computational times may be excessive. There are no simple rules to follow when deciding an appropriate geographic resolution, which is heavily dependent on the intended use of the results. Often the target resolution is imposed on the user, for example when the output of rasterization needs to be aligned to an existing raster.

+

Depending on the input data, rasterization typically takes one of two forms which we demonstrate next:

+
    +
  • in point rasterization (Section 5.4.1), we typically choose how to treat multiple points: either to summarize presence/absence, point count, or summed attribute values (Figure 5.5)
  • +
  • in line and polygon rasterization (Section 5.4.2), there are typically no such โ€œoverlapsโ€ and we simply โ€œburnโ€ attribute values, or fixed values, into pixels coinciding with the given geometries (Figure 5.6)
  • +
+
+

5.4.1 Rasterizing points

+

To demonstrate point rasterization, we will prepare a โ€œtemplateโ€ raster that has the same extent and CRS as the input vector data cycle_hire_osm_projected (a dataset on cycle hire points in London, illustrated in Figure 5.5 (a)) and a spatial resolution of 1000 \(m\). To do that, we first take our point layer and transform it to a projected CRS.

+
+
cycle_hire_osm_projected = GO.reproject(cycle_hire_osm; target_crs = GFT.EPSG(27700))
+
+
540ร—6 DataFrame
534 rows omitted
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Rowgeomosm_idnamecapacitycyclestreets_iddescription
Tupleโ€ฆStringString?Float64?String?String?
1(5.32354e5, 1.82858e5)108539Windsor Terrace14.0missingmissing
2(5.29848e5, 1.83337e5)598093293Pancras Road, King's Crossmissingmissingmissing
3(5.30636e5, 182609.0)772536185Clerkenwell, Ampton Street11.0missingmissing
โ‹ฎโ‹ฎโ‹ฎโ‹ฎโ‹ฎโ‹ฎโ‹ฎ
538(5.32531e5, 1.78805e5)5121513755missing5.0missingmissing
539(5.38723e5, 1.80836e5)5188912370missingmissingmissingmissing
540(5.38413e5, 1.80774e5)5188912371missingmissingmissingmissing
+
+
+
+

We can then use the Rasters.rasterize function to rasterize the points.

+
+

This isnโ€™t a great way to get an extent, but needs must. Currently we get the extent of cycle_hire_osm_projected by GI.extent(GI.LineString(cycle_hire_osm_projected.geom)).

+

Track https://github.com/geocompx/geocompjl/issues/5 to see if thereโ€™s a better way to get an extent from a vector of geometries.

+
+

As mentioned above, point rasterization can be a very flexible operation: the results depend not only on the nature of the template raster, but also on the the pixel โ€œactivationโ€ method, namely the way we deal with multiple points matching the same pixel.

+

To illustrate this flexibility, we will try three different approaches to point rasterization (Figure 5.5 (b)-(d)). First, we create a raster representing the presence or absence of cycle hire points (known as presence/absence rasters). In this case, we transfer the value of 1 to all pixels where at least one point falls in. In the Rasters.jl framework, we use the Rasters.rasterize function, as described above. In this first example, we want to write the value 1 where the points are present, and 0 otherwise.

+
+
ch_raster1 = Rasters.rasterize(
+    last,                     # reducer
+    cycle_hire_osm_projected; # data
+    fill = 1, 
+    size = (1000, 1000) # specify size in "pixels"
+)
+
+
โ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ
+โ”‚ 1000ร—1000 Raster{Union{Missing, Int64},2} last โ”‚
+โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ dims โ”
+  โ†“ X Projected{Float64} 523038.61452275474:15.684723604717:538707.653403867 ForwardOrdered Regular Intervals{Start},
+  โ†’ Y Projected{Float64} 174934.65727249475:10.03675127048517:184961.37179170942 ForwardOrdered Regular Intervals{Start}
+โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ metadata โ”ค
+  Metadata of Dict{Any, Any}()
+โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ raster โ”ค
+  extent: Extent(X = (523038.61452275474, 538723.3381274717), Y = (174934.65727249475, 184971.40854297992))
+  missingval: missing
+โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜
+      โ†“ โ†’        1.74935e5  1.74945e5  โ€ฆ  1.84941e5  1.84951e5  1.84961e5
+      5.23039e5   missing    missing       missing    missing    missing
+      5.23054e5   missing    missing       missing    missing    missing
+ 523070.0         missing    missing       missing    missing    missing
+      โ‹ฎ                                โ‹ฑ                        โ‹ฎ
+      5.38676e5   missing    missing       missing    missing    missing
+ 538692.0         missing    missing       missing    missing    missing
+      5.38708e5   missing    missing   โ€ฆ   missing    missing    missing
+
+
+

In our second variant of point rasterization, we count the number of bike hire stations. To do that, we use the fixed value of 1 (same as in the last example), but this time combined with the reducer=sum argument. That way, multiple values burned into the same pixel are summed, rather than replaced keeping last (which is the default). The new output, ch_raster2, shows the number of cycle hire points in each grid cell.

+
+
ch_raster2 = Rasters.rasterize(
+    sum,                     # reducer
+    cycle_hire_osm_projected; # data
+    fill = 1, 
+    size = (1000, 1000) # specify size in "pixels"
+)
+
+
โ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ
+โ”‚ 1000ร—1000 Raster{Union{Missing, Int64},2} sum โ”‚
+โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ dims โ”
+  โ†“ X Projected{Float64} 523038.61452275474:15.684723604717:538707.653403867 ForwardOrdered Regular Intervals{Start},
+  โ†’ Y Projected{Float64} 174934.65727249475:10.03675127048517:184961.37179170942 ForwardOrdered Regular Intervals{Start}
+โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ metadata โ”ค
+  Metadata of Dict{Any, Any}()
+โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ raster โ”ค
+  extent: Extent(X = (523038.61452275474, 538723.3381274717), Y = (174934.65727249475, 184971.40854297992))
+  missingval: missing
+โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜
+      โ†“ โ†’        1.74935e5  1.74945e5  โ€ฆ  1.84941e5  1.84951e5  1.84961e5
+      5.23039e5   missing    missing       missing    missing    missing
+      5.23054e5   missing    missing       missing    missing    missing
+ 523070.0         missing    missing       missing    missing    missing
+      โ‹ฎ                                โ‹ฑ                        โ‹ฎ
+      5.38676e5   missing    missing       missing    missing    missing
+ 538692.0         missing    missing       missing    missing    missing
+      5.38708e5   missing    missing   โ€ฆ   missing    missing    missing
+
+
+

The cycle hire locations have different numbers of bicycles described by the capacity variable, raising the question, what is the capacity in each grid cell? To calculate that, in our third point rasterization variant we sum the field ('capacity') rather than the fixed values of 1.

+

This is extremely simple to run, but we will show how to do this two ways: first, by passing the column name in the feature collection to fill.

+
+
ch_raster3 = Rasters.rasterize(
+    sum,                     # reducer
+    cycle_hire_osm_projected; # data
+    fill = :capacity, 
+    size = (1000, 1000) # specify size in "pixels"
+)
+
+
โ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ
+โ”‚ 1000ร—1000 Raster{Union{Missing, Float64},2} capacity โ”‚
+โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ dims โ”
+  โ†“ X Projected{Float64} 523038.61452275474:15.684723604717:538707.653403867 ForwardOrdered Regular Intervals{Start},
+  โ†’ Y Projected{Float64} 174934.65727249475:10.03675127048517:184961.37179170942 ForwardOrdered Regular Intervals{Start}
+โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ metadata โ”ค
+  Metadata of Dict{Any, Any}()
+โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ raster โ”ค
+  extent: Extent(X = (523038.61452275474, 538723.3381274717), Y = (174934.65727249475, 184971.40854297992))
+  missingval: missing
+โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜
+      โ†“ โ†’        1.74935e5  1.74945e5  โ€ฆ  1.84941e5  1.84951e5  1.84961e5
+      5.23039e5   missing    missing       missing    missing    missing
+      5.23054e5   missing    missing       missing    missing    missing
+ 523070.0         missing    missing       missing    missing    missing
+      โ‹ฎ                                โ‹ฑ                        โ‹ฎ
+      5.38676e5   missing    missing       missing    missing    missing
+ 538692.0         missing    missing       missing    missing    missing
+      5.38708e5   missing    missing   โ€ฆ   missing    missing    missing
+
+
+

Second, by passing the vectors of geometries and values separately.

+
+
ch_raster3 = Rasters.rasterize(
+    sum,                     # reducer
+    cycle_hire_osm_projected.geom; # data
+    fill = cycle_hire_osm_projected.capacity, 
+    crs = GI.crs(cycle_hire_osm_projected),
+    size = (1000, 1000) # specify size in "pixels"
+)
+
+
โ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ
+โ”‚ 1000ร—1000 Raster{Union{Missing, Float64},2} sum โ”‚
+โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ dims โ”
+  โ†“ X Projected{Float64} 523038.61452275474:15.684723604717:538707.653403867 ForwardOrdered Regular Intervals{Start},
+  โ†’ Y Projected{Float64} 174934.65727249475:10.03675127048517:184961.37179170942 ForwardOrdered Regular Intervals{Start}
+โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ metadata โ”ค
+  Metadata of Dict{Any, Any}()
+โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ raster โ”ค
+  extent: Extent(X = (523038.61452275474, 538723.3381274717), Y = (174934.65727249475, 184971.40854297992))
+  missingval: missing
+  crs: EPSG:27700
+โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜
+      โ†“ โ†’        1.74935e5  1.74945e5  โ€ฆ  1.84941e5  1.84951e5  1.84961e5
+      5.23039e5   missing    missing       missing    missing    missing
+      5.23054e5   missing    missing       missing    missing    missing
+ 523070.0         missing    missing       missing    missing    missing
+      โ‹ฎ                                โ‹ฑ                        โ‹ฎ
+      5.38676e5   missing    missing       missing    missing    missing
+ 538692.0         missing    missing       missing    missing    missing
+      5.38708e5   missing    missing   โ€ฆ   missing    missing    missing
+
+
+

The input point layer cycle_hire_osm_projected and the three variants of rasterizing it ch_raster1, ch_raster2, and ch_raster3 are shown in Figure 5.5.

+
# Input points
+nonmissing_df = dropmissing(cycle_hire_osm_projected, [:capacity, :geom])
+f, a, p = scatter(nonmissing_df.geom; color = nonmissing_df.capacity)
+Colorbar(f[1, 2], p)
+display(f)
+# Presence/Absence
+plot(ch_raster1) |> display
+# Point counts
+plot(ch_raster2) |> display
+# Summed attribute values
+plot(ch_raster3)
+
+
+
+
+
+
+
+
+ +
+
+(a) Input points +
+
+
+
+
+
+
+
+ +
+
+(b) Presence/Absence +
+
+
+
+
+
+
+
+
+
+ +
+
+(c) Point counts +
+
+
+
+
+
+
+
+ +
+
+(d) Summed attribute values +
+
+
+
+
+
+
+Figure 5.5: Original data and three variants of point rasterization +
+
+
+
+
+

5.4.2 Rasterizing lines and polygons

+

Another dataset based on Californiaโ€™s polygons and borders (created below) illustrates rasterization of lines. There are three preliminary steps. First, we subset the California polygon.

+
+
california = us_states[ us_states[!, "NAME"] .== "California", :]
+
+
1ร—7 DataFrame
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
RowgeomGEOIDNAMEREGIONAREAtotal_pop_10total_pop_15
IGeometrโ€ฆStringStringStringFloat64Float64Float64
1Geometry: wkbMultiPolygon06CaliforniaWest4.09747e53.66373e73.84215e7
+
+
+
+

Second, we obtain the borders of the polygon as a `โ€˜MultiLineStringโ€™

+
+
california_geom = only(california.geom)
+california_borders = GI.MultiLineString(GI.LineString.(GI.getexterior.(GI.getgeom(california_geom))); crs = GI.crs(california_geom)) # TODO: make this a lot better....
+
+
GeoInterface.Wrappers.MultiLineString{false, false, Vector{GeoInterface.Wrappers.LineString{false, false, ArchGDAL.IGeometry{ArchGDAL.wkbLineString}, Nothing, Nothing}}, Nothing, WellKnownText{GeoFormatTypes.CRS}}(GeoInterface.Wrappers.LineString{false, false, ArchGDAL.IGeometry{ArchGDAL.wkbLineString}, Nothing, Nothing}[GeoInterface.Wrappers.LineString{false, false, ArchGDAL.IGeometry{ArchGDAL.wkbLineString}, Nothing, Nothing}(Geometry: LINEARRING (-118.603375 33.478098,-118.368301 33.4 ... 8098), nothing, nothing), GeoInterface.Wrappers.LineString{false, false, ArchGDAL.IGeometry{ArchGDAL.wkbLineString}, Nothing, Nothing}(Geometry: LINEARRING (-118.605534 33.030999,-118.369984 32.8 ... 0999), nothing, nothing), GeoInterface.Wrappers.LineString{false, false, ArchGDAL.IGeometry{ArchGDAL.wkbLineString}, Nothing, Nothing}(Geometry: LINEARRING (-119.919155 34.07728,-119.755521 34.05 ... 7728), nothing, nothing), GeoInterface.Wrappers.LineString{false, false, ArchGDAL.IGeometry{ArchGDAL.wkbLineString}, Nothing, Nothing}(Geometry: LINEARRING (-120.247393 34.001911,-120.073609 34.0 ... 1911), nothing, nothing), GeoInterface.Wrappers.LineString{false, false, ArchGDAL.IGeometry{ArchGDAL.wkbLineString}, Nothing, Nothing}(Geometry: LINEARRING (-124.211605 41.99846,-123.347562 41.99 ... 9846), nothing, nothing)], nothing, WellKnownText{GeoFormatTypes.CRS}(GeoFormatTypes.CRS(), "GEOGCS[\"NAD83\",DATUM[\"North_American_Datum_1983\",SPHEROID[\"GRS 1980\",6378137,298.257222101,AUTHORITY[\"EPSG\",\"7019\"]],AUTHORITY[\"EPSG\",\"6269\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4269\"]]"))
+
+
+

Finally, we rasterize california_borders on a grid with resolution of 0.5 degrees per pixel.

+
+
california_raster1 = Rasters.rasterize(
+    last,
+    california_borders;
+    fill = 1,
+    res = 0.5, # degrees - this is in units of GI.crs(california_borders)
+    boundary = :touches,
+)
+
+
โ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ
+โ”‚ 21ร—19 Raster{Union{Missing, Int64},2} last โ”‚
+โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ dims โ”
+  โ†“ X Projected{Float64} -124.409591:0.5:-114.409591 ForwardOrdered Regular Intervals{Start},
+  โ†’ Y Projected{Float64} 32.534156:0.5:41.534156 ForwardOrdered Regular Intervals{Start}
+โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ metadata โ”ค
+  Metadata of Dict{Any, Any}()
+โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ raster โ”ค
+  extent: Extent(X = (-124.409591, -113.909591), Y = (32.534156, 42.034156))
+  missingval: missing
+โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜
+    โ†“ โ†’   32.5342    33.0342    33.5342    โ€ฆ  40.5342    41.0342    41.5342
+ -124.41    missing    missing    missing      1          1          1
+ -123.91    missing    missing    missing       missing    missing   1
+ -123.41    missing    missing    missing       missing    missing   1
+    โ‹ฎ                                      โ‹ฑ                        
+ -115.41   1           missing    missing       missing    missing    missing
+ -114.91   1          1          1         โ€ฆ    missing    missing    missing
+ -114.41    missing    missing    missing       missing    missing    missing
+
+
+

Compare it to a polygon rasterization, with all_touched=False (the default), which selects only raster cells whose centroids are inside the selector polygon, as illustrated in Figure 5.6 (right).

+
+
california_raster2 = Rasters.rasterize(
+    last, 
+    california;
+    geometrycolumn = :geom,
+    fill = 1,
+    res = 0.5,
+    boundary = :center,
+)
+
+
โ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ
+โ”‚ 21ร—19 Raster{Union{Missing, Int64},2} last โ”‚
+โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ dims โ”
+  โ†“ X Projected{Float64} -124.409591:0.5:-114.409591 ForwardOrdered Regular Intervals{Start},
+  โ†’ Y Projected{Float64} 32.534156:0.5:41.534156 ForwardOrdered Regular Intervals{Start}
+โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ metadata โ”ค
+  Metadata of Dict{Any, Any} with 1 entry:
+  "missed_geometries" => Bool[0]
+โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ raster โ”ค
+  extent: Extent(X = (-124.409591, -113.909591), Y = (32.534156, 42.034156))
+  missingval: missing
+โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜
+    โ†“ โ†’   32.5342    33.0342    33.5342    โ€ฆ  40.5342    41.0342    41.5342
+ -124.41    missing    missing    missing      1           missing   1
+ -123.91    missing    missing    missing      1          1          1
+    โ‹ฎ                                      โ‹ฑ                        
+ -115.41   1          1          1              missing    missing    missing
+ -114.91   1           missing   1         โ€ฆ    missing    missing    missing
+ -114.41    missing    missing    missing       missing    missing    missing
+
+
+

To illustrate which raster pixels are actually selected as part of rasterization, we also show them as points. This also requires the following code section to calculate the points, which we explain in Section 5.5.

+
+ +
+
+
+
dp = DimPoints(california_raster1)
+
+
โ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ
+โ”‚ 21ร—19 DimPoints{Tuple{Float64, Float64},2} โ”‚
+โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ dims โ”
+  โ†“ X Projected{Float64} -124.409591:0.5:-114.409591 ForwardOrdered Regular Intervals{Start},
+  โ†’ Y Projected{Float64} 32.534156:0.5:41.534156 ForwardOrdered Regular Intervals{Start}
+โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜
+    โ†“ โ†’   32.5342               33.0342               โ€ฆ  41.5342
+ -124.41    (-124.41, 32.5342)    (-124.41, 33.0342)       (-124.41, 41.5342)
+ -123.91    (-123.91, 32.5342)    (-123.91, 33.0342)       (-123.91, 41.5342)
+ -123.41    (-123.41, 32.5342)    (-123.41, 33.0342)       (-123.41, 41.5342)
+ -122.91    (-122.91, 32.5342)    (-122.91, 33.0342)       (-122.91, 41.5342)
+    โ‹ฎ                                                 โ‹ฑ  
+ -116.41    (-116.41, 32.5342)    (-116.41, 33.0342)       (-116.41, 41.5342)
+ -115.91    (-115.91, 32.5342)    (-115.91, 33.0342)       (-115.91, 41.5342)
+ -115.41    (-115.41, 32.5342)    (-115.41, 33.0342)       (-115.41, 41.5342)
+ -114.91    (-114.91, 32.5342)    (-114.91, 33.0342)  โ€ฆ    (-114.91, 41.5342)
+ -114.41    (-114.41, 32.5342)    (-114.41, 33.0342)       (-114.41, 41.5342)
+
+
+
+
+

DimensionalData.jl (which underpins Rasters.jl) provides easy ways to get โ€œlookupsโ€, i.e, axis index values, from a raster.

+

Note that these lookups may encode intervals, points, or anything in between - so you should use shiftlocus or set to get the actual point values! DimPoints does this for you.

+

But if you want to see how this can be done automatically, here you go.

+
+
[(x, y) for x in dims(california_raster1, X), y in dims(california_raster1, Y)]
+
+
21ร—19 Matrix{Tuple{Float64, Float64}}:
+ (-124.41, 32.5342)  (-124.41, 33.0342)  โ€ฆ  (-124.41, 41.5342)
+ (-123.91, 32.5342)  (-123.91, 33.0342)     (-123.91, 41.5342)
+ (-123.41, 32.5342)  (-123.41, 33.0342)     (-123.41, 41.5342)
+ (-122.91, 32.5342)  (-122.91, 33.0342)     (-122.91, 41.5342)
+ (-122.41, 32.5342)  (-122.41, 33.0342)     (-122.41, 41.5342)
+ (-121.91, 32.5342)  (-121.91, 33.0342)  โ€ฆ  (-121.91, 41.5342)
+ (-121.41, 32.5342)  (-121.41, 33.0342)     (-121.41, 41.5342)
+ (-120.91, 32.5342)  (-120.91, 33.0342)     (-120.91, 41.5342)
+ (-120.41, 32.5342)  (-120.41, 33.0342)     (-120.41, 41.5342)
+ (-119.91, 32.5342)  (-119.91, 33.0342)     (-119.91, 41.5342)
+ โ‹ฎ                                       โ‹ฑ  
+ (-118.41, 32.5342)  (-118.41, 33.0342)     (-118.41, 41.5342)
+ (-117.91, 32.5342)  (-117.91, 33.0342)     (-117.91, 41.5342)
+ (-117.41, 32.5342)  (-117.41, 33.0342)     (-117.41, 41.5342)
+ (-116.91, 32.5342)  (-116.91, 33.0342)  โ€ฆ  (-116.91, 41.5342)
+ (-116.41, 32.5342)  (-116.41, 33.0342)     (-116.41, 41.5342)
+ (-115.91, 32.5342)  (-115.91, 33.0342)     (-115.91, 41.5342)
+ (-115.41, 32.5342)  (-115.41, 33.0342)     (-115.41, 41.5342)
+ (-114.91, 32.5342)  (-114.91, 33.0342)     (-114.91, 41.5342)
+ (-114.41, 32.5342)  (-114.41, 33.0342)  โ€ฆ  (-114.41, 41.5342)
+
+
+

You can see that this encodes the same values as dp in the other tab.

+

TODO: firm up the description here and add links.

+
+
+
+

Figure 5.6 shows the input vector layer, the rasterization results, and the points pnt.

+
# Line rasterization
+fig, ax, plt = plot(california_raster1; colormap = cgrad(:Set3; categorical = true))
+lines!(ax, california_borders; color = :darkgrey, linewidth = 1)
+scatter!(ax, vec(dp); markersize = 3, color = :black)
+display(fig)
+# Polygon rasterization
+fig, ax, plt = plot(california_raster2; colormap = cgrad(:Set3; categorical = true))
+lines!(ax, california_borders; color = :darkgrey, linewidth = 1)
+scatter!(ax, vec(dp); markersize = 3, color = :black)
+fig
+
+
+
+
+
+
+
+
+ +
+
+(a) Line rasterization w/ boundary=:touches +
+
+
+
+
+
+
+
+ +
+
+(b) Polygon rasterization w/ boundary=:center +
+
+
+
+
+
+
+Figure 5.6: Examples of line and polygon rasterization +
+
+
+
+
+
+

5.5 Spatial vectorization

+

Spatial vectorization is the counterpart of rasterization (Section 5.4). It involves converting spatially continuous raster data into spatially discrete vector data such as points, lines or polygons. There are three standard methods to convert a raster to a vector layer, which we cover next:

+
    +
  • Raster to polygons (Section 5.5.1)โ€”converting raster cells to rectangular polygons, representing pixel areas
  • +
  • Raster to points (Section 5.5.2)โ€”converting raster cells to points, representing pixel centroids
  • +
  • Raster to contours (Section 5.5.3)
  • +
+

Let us demonstrate all three in the given order.

+
+

5.5.1 Raster to polygons

+

Rasters.jl does not currently have a function to convert a raster to a feature collection with one polygon per pixel or cell. This is a similar situation in Python with rasterio.

+

GeometryOps.jl offers a polygonize function that returns a feature collection of polygons, where each feature has a value property that encodes the value of all pixels within that polygon. Each polygon contains pixels with the same value.

+
+
fc = GO.polygonize(src_grain)
+
+
GeoInterface.Wrappers.FeatureCollection{Vector{GeoInterface.Wrappers.Feature{@NamedTuple{geometry::GeoInterface.Wrappers.MultiPolygon{false, false, Vector{GeoInterface.Wrappers.Polygon{false, false, Vector{GeoInterface.Wrappers.LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{GeoFormatTypes.CRS}}}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{GeoFormatTypes.CRS}}}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{GeoFormatTypes.CRS}}, value::UInt8}, WellKnownText{GeoFormatTypes.CRS}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}}}, WellKnownText{GeoFormatTypes.CRS}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}}(GeoInterface.Wrappers.Feature{@NamedTuple{geometry::GeoInterface.Wrappers.MultiPolygon{false, false, 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Tuple{Float64, Float64}}}, WellKnownText{GeoFormatTypes.CRS}}([(2.5, 0.5), (3.5, 0.5), (3.5, 1.5), (2.5, 1.5), (2.5, 0.5)], Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}((X = (2.5, 3.5), Y = (0.5, 1.5))), WellKnownText{GeoFormatTypes.CRS}(GeoFormatTypes.CRS(), "GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]"))], Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}((X = (2.5, 3.5), Y = (0.5, 1.5))), WellKnownText{GeoFormatTypes.CRS}(GeoFormatTypes.CRS(), "GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 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WellKnownText{GeoFormatTypes.CRS}}, value::UInt8}, WellKnownText{GeoFormatTypes.CRS}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}}((geometry = GeoInterface.Wrappers.MultiPolygon{false, false, Vector{GeoInterface.Wrappers.Polygon{false, false, Vector{GeoInterface.Wrappers.LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{GeoFormatTypes.CRS}}}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{GeoFormatTypes.CRS}}}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{GeoFormatTypes.CRS}}(GeoInterface.Wrappers.Polygon{false, false, Vector{GeoInterface.Wrappers.LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{GeoFormatTypes.CRS}}}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{GeoFormatTypes.CRS}}[GeoInterface.Wrappers.Polygon{false, false, Vector{GeoInterface.Wrappers.LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{GeoFormatTypes.CRS}}}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{GeoFormatTypes.CRS}}(GeoInterface.Wrappers.LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{GeoFormatTypes.CRS}}[GeoInterface.Wrappers.LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{GeoFormatTypes.CRS}}([(1.5, 1.5), (2.5, 1.5), (2.5, 2.5), (3.5, 2.5), (3.5, 3.5), (1.5, 3.5), (1.5, 1.5)], Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}((X = (1.5, 3.5), Y = (1.5, 3.5))), WellKnownText{GeoFormatTypes.CRS}(GeoFormatTypes.CRS(), "GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]"))], Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}((X = (1.5, 3.5), Y = (1.5, 3.5))), WellKnownText{GeoFormatTypes.CRS}(GeoFormatTypes.CRS(), "GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]")), GeoInterface.Wrappers.Polygon{false, false, Vector{GeoInterface.Wrappers.LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{GeoFormatTypes.CRS}}}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{GeoFormatTypes.CRS}}(GeoInterface.Wrappers.LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{GeoFormatTypes.CRS}}[GeoInterface.Wrappers.LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{GeoFormatTypes.CRS}}([(5.5, 5.5), (6.5, 5.5), (6.5, 6.5), (5.5, 6.5), (5.5, 5.5)], Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}((X = (5.5, 6.5), Y = (5.5, 6.5))), WellKnownText{GeoFormatTypes.CRS}(GeoFormatTypes.CRS(), "GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]"))], Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}((X = (5.5, 6.5), Y = (5.5, 6.5))), WellKnownText{GeoFormatTypes.CRS}(GeoFormatTypes.CRS(), "GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]")), GeoInterface.Wrappers.Polygon{false, false, Vector{GeoInterface.Wrappers.LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{GeoFormatTypes.CRS}}}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{GeoFormatTypes.CRS}}(GeoInterface.Wrappers.LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{GeoFormatTypes.CRS}}[GeoInterface.Wrappers.LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{GeoFormatTypes.CRS}}([(4.5, 6.5), (2.5, 6.5), (2.5, 5.5), (3.5, 5.5), (3.5, 4.5), (4.5, 4.5), (4.5, 6.5)], Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}((X = (2.5, 4.5), Y = (4.5, 6.5))), WellKnownText{GeoFormatTypes.CRS}(GeoFormatTypes.CRS(), "GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 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Tuple{Float64, Float64}}}, WellKnownText{GeoFormatTypes.CRS}}}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{GeoFormatTypes.CRS}}(GeoInterface.Wrappers.LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{GeoFormatTypes.CRS}}[GeoInterface.Wrappers.LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{GeoFormatTypes.CRS}}([(5.5, 0.5), (6.5, 0.5), (6.5, 1.5), (5.5, 1.5), (5.5, 2.5), (4.5, 2.5), (4.5, 1.5), (3.5, 1.5), (3.5, 0.5), (5.5, 0.5)], Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}((X = (3.5, 6.5), Y = (0.5, 2.5))), WellKnownText{GeoFormatTypes.CRS}(GeoFormatTypes.CRS(), "GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 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Tuple{Float64, Float64}}}, WellKnownText{GeoFormatTypes.CRS}}}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{GeoFormatTypes.CRS}}(GeoInterface.Wrappers.LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{GeoFormatTypes.CRS}}[GeoInterface.Wrappers.LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{GeoFormatTypes.CRS}}([(6.5, 3.5), (5.5, 3.5), (5.5, 2.5), (6.5, 2.5), (6.5, 3.5)], Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}((X = (5.5, 6.5), Y = (2.5, 3.5))), WellKnownText{GeoFormatTypes.CRS}(GeoFormatTypes.CRS(), "GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 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Tuple{Float64, Float64}}}, WellKnownText{GeoFormatTypes.CRS}}}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{GeoFormatTypes.CRS}}(GeoInterface.Wrappers.LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{GeoFormatTypes.CRS}}[GeoInterface.Wrappers.LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{GeoFormatTypes.CRS}}([(1.5, 6.5), (0.5, 6.5), (0.5, 5.5), (1.5, 5.5), (1.5, 6.5)], Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}((X = (0.5, 1.5), Y = (5.5, 6.5))), WellKnownText{GeoFormatTypes.CRS}(GeoFormatTypes.CRS(), "GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 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84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]")), value = 0x02), WellKnownText{GeoFormatTypes.CRS}(GeoFormatTypes.CRS(), "GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]"), Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}((X = (0.5, 6.5), Y = (0.5, 6.5))))], WellKnownText{GeoFormatTypes.CRS}(GeoFormatTypes.CRS(), "GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 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+
+
+

We can convert this โ€œfeature collectionโ€ to a DataFrame as follows. That makes it a lot easier to work with.

+
+
df = DataFrame([GI.properties(f) for f in GI.getfeature(fc)])
+df.geometry = [GI.geometry(f) for f in GI.getfeature(fc)]
+df
+
+
3ร—2 DataFrame
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Rowvaluegeometry
UInt8MultiPolโ€ฆ
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WellKnownText{CRS}(CRS(), "GEOGCS[\\"WGS 84\\",DATUM[\\"WGS_1984\\",SPHEROID[\\"WGS 84\\",6378137,298.257223563,AUTHORITY[\\"EPSG\\",\\"7030\\"]],AUTHORITY[\\"EPSG\\",\\"6326\\"]],PRIMEM[\\"Greenwich\\",0,AUTHORITY[\\"EPSG\\",\\"8901\\"]],UNIT[\\"degree\\",0.0174532925199433,AUTHORITY[\\"EPSG\\",\\"9122\\"]],AXIS[\\"Latitude\\",NORTH],AXIS[\\"Longitude\\",EAST],AUTHORITY[\\"EPSG\\",\\"4326\\"]]")), Polygon{false, false, Vector{LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{CRS}}}, Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{CRS}}(LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{CRS}}[LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{CRS}}([(5.5, 5.5), (6.5, 5.5), (6.5, 6.5), (5.5, 6.5), (5.5, 5.5)], Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}((X = (5.5, 6.5), Y = (5.5, 6.5))), WellKnownText{CRS}(CRS(), "GEOGCS[\\"WGS 84\\",DATUM[\\"WGS_1984\\",SPHEROID[\\"WGS 84\\",6378137,298.257223563,AUTHORITY[\\"EPSG\\",\\"7030\\"]],AUTHORITY[\\"EPSG\\",\\"6326\\"]],PRIMEM[\\"Greenwich\\",0,AUTHORITY[\\"EPSG\\",\\"8901\\"]],UNIT[\\"degree\\",0.0174532925199433,AUTHORITY[\\"EPSG\\",\\"9122\\"]],AXIS[\\"Latitude\\",NORTH],AXIS[\\"Longitude\\",EAST],AUTHORITY[\\"EPSG\\",\\"4326\\"]]"))], Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}((X = (5.5, 6.5), Y = (5.5, 6.5))), WellKnownText{CRS}(CRS(), "GEOGCS[\\"WGS 84\\",DATUM[\\"WGS_1984\\",SPHEROID[\\"WGS 84\\",6378137,298.257223563,AUTHORITY[\\"EPSG\\",\\"7030\\"]],AUTHORITY[\\"EPSG\\",\\"6326\\"]],PRIMEM[\\"Greenwich\\",0,AUTHORITY[\\"EPSG\\",\\"8901\\"]],UNIT[\\"degree\\",0.0174532925199433,AUTHORITY[\\"EPSG\\",\\"9122\\"]],AXIS[\\"Latitude\\",NORTH],AXIS[\\"Longitude\\",EAST],AUTHORITY[\\"EPSG\\",\\"4326\\"]]")), Polygon{false, false, Vector{LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{CRS}}}, Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{CRS}}(LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{CRS}}[LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{CRS}}([(4.5, 6.5), (2.5, 6.5), (2.5, 5.5), (3.5, 5.5), (3.5, 4.5), (4.5, 4.5), (4.5, 6.5)], Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}((X = (2.5, 4.5), Y = (4.5, 6.5))), WellKnownText{CRS}(CRS(), "GEOGCS[\\"WGS 84\\",DATUM[\\"WGS_1984\\",SPHEROID[\\"WGS 84\\",6378137,298.257223563,AUTHORITY[\\"EPSG\\",\\"7030\\"]],AUTHORITY[\\"EPSG\\",\\"6326\\"]],PRIMEM[\\"Greenwich\\",0,AUTHORITY[\\"EPSG\\",\\"8901\\"]],UNIT[\\"degree\\",0.0174532925199433,AUTHORITY[\\"EPSG\\",\\"9122\\"]],AXIS[\\"Latitude\\",NORTH],AXIS[\\"Longitude\\",EAST],AUTHORITY[\\"EPSG\\",\\"4326\\"]]"))], Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}((X = (2.5, 4.5), Y = (4.5, 6.5))), WellKnownText{CRS}(CRS(), "GEOGCS[\\"WGS 84\\",DATUM[\\"WGS_1984\\",SPHEROID[\\"WGS 84\\",6378137,298.257223563,AUTHORITY[\\"EPSG\\",\\"7030\\"]],AUTHORITY[\\"EPSG\\",\\"6326\\"]],PRIMEM[\\"Greenwich\\",0,AUTHORITY[\\"EPSG\\",\\"8901\\"]],UNIT[\\"degree\\",0.0174532925199433,AUTHORITY[\\"EPSG\\",\\"9122\\"]],AXIS[\\"Latitude\\",NORTH],AXIS[\\"Longitude\\",EAST],AUTHORITY[\\"EPSG\\",\\"4326\\"]]")), Polygon{false, false, Vector{LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{CRS}}}, Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{CRS}}(LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{CRS}}[LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{CRS}}([(5.5, 0.5), (6.5, 0.5), (6.5, 1.5), (5.5, 1.5), (5.5, 2.5), (4.5, 2.5), (4.5, 1.5), (3.5, 1.5), (3.5, 0.5), (5.5, 0.5)], Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}((X = (3.5, 6.5), Y = (0.5, 2.5))), WellKnownText{CRS}(CRS(), "GEOGCS[\\"WGS 84\\",DATUM[\\"WGS_1984\\",SPHEROID[\\"WGS 84\\",6378137,298.257223563,AUTHORITY[\\"EPSG\\",\\"7030\\"]],AUTHORITY[\\"EPSG\\",\\"6326\\"]],PRIMEM[\\"Greenwich\\",0,AUTHORITY[\\"EPSG\\",\\"8901\\"]],UNIT[\\"degree\\",0.0174532925199433,AUTHORITY[\\"EPSG\\",\\"9122\\"]],AXIS[\\"Latitude\\",NORTH],AXIS[\\"Longitude\\",EAST],AUTHORITY[\\"EPSG\\",\\"4326\\"]]"))], Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}((X = (3.5, 6.5), Y = (0.5, 2.5))), WellKnownText{CRS}(CRS(), "GEOGCS[\\"WGS 84\\",DATUM[\\"WGS_1984\\",SPHEROID[\\"WGS 84\\",6378137,298.257223563,AUTHORITY[\\"EPSG\\",\\"7030\\"]],AUTHORITY[\\"EPSG\\",\\"6326\\"]],PRIMEM[\\"Greenwich\\",0,AUTHORITY[\\"EPSG\\",\\"8901\\"]],UNIT[\\"degree\\",0.0174532925199433,AUTHORITY[\\"EPSG\\",\\"9122\\"]],AXIS[\\"Latitude\\",NORTH],AXIS[\\"Longitude\\",EAST],AUTHORITY[\\"EPSG\\",\\"4326\\"]]")), Polygon{false, false, Vector{LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{CRS}}}, Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{CRS}}(LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{CRS}}[LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{CRS}}([(6.5, 3.5), (5.5, 3.5), (5.5, 2.5), (6.5, 2.5), (6.5, 3.5)], Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}((X = (5.5, 6.5), Y = (2.5, 3.5))), WellKnownText{CRS}(CRS(), "GEOGCS[\\"WGS 84\\",DATUM[\\"WGS_1984\\",SPHEROID[\\"WGS 84\\",6378137,298.257223563,AUTHORITY[\\"EPSG\\",\\"7030\\"]],AUTHORITY[\\"EPSG\\",\\"6326\\"]],PRIMEM[\\"Greenwich\\",0,AUTHORITY[\\"EPSG\\",\\"8901\\"]],UNIT[\\"degree\\",0.0174532925199433,AUTHORITY[\\"EPSG\\",\\"9122\\"]],AXIS[\\"Latitude\\",NORTH],AXIS[\\"Longitude\\",EAST],AUTHORITY[\\"EPSG\\",\\"4326\\"]]"))], Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}((X = (5.5, 6.5), Y = (2.5, 3.5))), WellKnownText{CRS}(CRS(), "GEOGCS[\\"WGS 84\\",DATUM[\\"WGS_1984\\",SPHEROID[\\"WGS 84\\",6378137,298.257223563,AUTHORITY[\\"EPSG\\",\\"7030\\"]],AUTHORITY[\\"EPSG\\",\\"6326\\"]],PRIMEM[\\"Greenwich\\",0,AUTHORITY[\\"EPSG\\",\\"8901\\"]],UNIT[\\"degree\\",0.0174532925199433,AUTHORITY[\\"EPSG\\",\\"9122\\"]],AXIS[\\"Latitude\\",NORTH],AXIS[\\"Longitude\\",EAST],AUTHORITY[\\"EPSG\\",\\"4326\\"]]")), Polygon{false, false, Vector{LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{CRS}}}, Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{CRS}}(LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{CRS}}[LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{CRS}}([(1.5, 6.5), (0.5, 6.5), (0.5, 5.5), (1.5, 5.5), (1.5, 6.5)], Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}((X = (0.5, 1.5), Y = (5.5, 6.5))), WellKnownText{CRS}(CRS(), "GEOGCS[\\"WGS 84\\",DATUM[\\"WGS_1984\\",SPHEROID[\\"WGS 84\\",6378137,298.257223563,AUTHORITY[\\"EPSG\\",\\"7030\\"]],AUTHORITY[\\"EPSG\\",\\"6326\\"]],PRIMEM[\\"Greenwich\\",0,AUTHORITY[\\"EPSG\\",\\"8901\\"]],UNIT[\\"degree\\",0.0174532925199433,AUTHORITY[\\"EPSG\\",\\"9122\\"]],AXIS[\\"Latitude\\",NORTH],AXIS[\\"Longitude\\",EAST],AUTHORITY[\\"EPSG\\",\\"4326\\"]]"))], Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}((X = (0.5, 1.5), Y = (5.5, 6.5))), WellKnownText{CRS}(CRS(), "GEOGCS[\\"WGS 84\\",DATUM[\\"WGS_1984\\",SPHEROID[\\"WGS 84\\",6378137,298.257223563,AUTHORITY[\\"EPSG\\",\\"7030\\"]],AUTHORITY[\\"EPSG\\",\\"6326\\"]],PRIMEM[\\"Greenwich\\",0,AUTHORITY[\\"EPSG\\",\\"8901\\"]],UNIT[\\"degree\\",0.0174532925199433,AUTHORITY[\\"EPSG\\",\\"9122\\"]],AXIS[\\"Latitude\\",NORTH],AXIS[\\"Longitude\\",EAST],AUTHORITY[\\"EPSG\\",\\"4326\\"]]"))], Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}((X = (0.5, 6.5), Y = (0.5, 6.5))), WellKnownText{CRS}(CRS(), "GEOGCS[\\"WGS 84\\",DATUM[\\"WGS_1984\\",SPHEROID[\\"WGS 84\\",6378137,298.257223563,AUTHORITY[\\"EPSG\\",\\"7030\\"]],AUTHORITY[\\"EPSG\\",\\"6326\\"]],PRIMEM[\\"Greenwich\\",0,AUTHORITY[\\"EPSG\\",\\"8901\\"]],UNIT[\\"degree\\",0.0174532925199433,AUTHORITY[\\"EPSG\\",\\"9122\\"]],AXIS[\\"Latitude\\",NORTH],AXIS[\\"Longitude\\",EAST],AUTHORITY[\\"EPSG\\",\\"4326\\"]]"))
+
+
+
+

The polygon layer df is shown in Figure 5.7.

+
+
f, a, p = poly(df.geometry; color = df.value, strokecolor = :black, strokewidth = 0.75)
+Colorbar(f[1, 2], p)
+f
+
+
+
+
+ +
+
+Figure 5.7: grain.tif converted to a polygon layer +
+
+
+
+
+

As highlighted using edgecolor='black', neighboring pixels sharing the same raster value are dissolved into larger polygons.

+

One suggestion is to add unique values between 0 and 0.9999 to all pixels, convert to polygons, and then get back to the original values using floor.

+
+
+

5.5.2 Raster to points

+

To transform a raster to points, Rasters.jl provides the Rasters.DimTable constructor, which converts a raster into a lazy, table-like form. This can be converted directly to a DataFrame, or operated on independently.

+
+
dt = DimTable(Raster("output/elev.tif"))
+
+
DimensionalData.DimTable with 36 rows, 3 columns, and schema:
+ :X       Float64
+ :Y       Float64
+ :layer1  UInt8
+
+
+

Notice that this has three columns, :X, :Y, and :layer1, corresponding to the pixel centroids and elevation values. But what if we want to treat the X and Y dimensionas as point geometries?

+

DimTable has a mergedims keyword argument for this, which allows us to merge the X and Y dimensions into a single dimension.

+
+
dt = DimTable(Raster("output/elev.tif"), mergedims = (X, Y))
+
+
DimensionalData.DimTable with 36 rows, 2 columns, and schema:
+ :XY      Tuple{Float64, Float64}
+ :layer1  UInt8
+
+
+

This has created a DimTable with a column :XY, which contains the pixel centroids as point-like objects. We can convert this to a DataFrame, set some metadata to indicate that geometry is in :XY, and plot the result.

+
+
df = DataFrame(dt)
+DataFrames.metadata!(df, "GEOINTERFACE:geometrycolumns", (:XY,); style = :note)
+df
+
+
36ร—2 DataFrame
30 rows omitted
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
RowXYlayer1
Tupleโ€ฆUInt8
1(-1.5, 1.0)1
2(-1.0, 1.0)2
3(-0.5, 1.0)3
โ‹ฎโ‹ฎโ‹ฎ
34(-1.11022e-16, -1.5)34
35(0.5, -1.5)35
36(1.0, -1.5)36
+
+
+
+
+
scatter(df.XY; color = df.layer1)
+
+
+
+

+
+
+
+
+

We can even save this to a file trivially easily:

+
+
GeoDataFrames.write("output/elev.gpkg", df)
+GeoDataFrames.read("output/elev.gpkg")
+
+
36ร—2 DataFrame
30 rows omitted
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
RowXYlayer1
IGeometrโ€ฆInt16
1Geometry: wkbPoint1
2Geometry: wkbPoint2
3Geometry: wkbPoint3
โ‹ฎโ‹ฎโ‹ฎ
34Geometry: wkbPoint34
35Geometry: wkbPoint35
36Geometry: wkbPoint36
+
+
+
+

Figure 5.8 shows the input raster and the resulting point layer.

+
# Input raster
+fig, ax, plt = plot(src_elev)
+scatter!(ax, df.XY; color = df.layer1)
+display(fig)
+# Points
+fig, ax, plt = plot(src_elev; alpha = 0.1)
+scatter!(ax, df.XY; color = df.layer1, strokecolor = :black, strokewidth = 1)
+fig
+
+
+
+
+
+
+
+
+ +
+
+(a) Input raster +
+
+
+
+
+
+
+
+ +
+
+(b) Points +
+
+
+
+
+
+
+Figure 5.8: Raster and point representation of elev.tif +
+
+
+

TODO: nodata pixels

+
+
+

5.5.3 Raster to contours

+

Another common type of spatial vectorization is the creation of contour lines, representing lines of continuous height or temperatures (isotherms), for example. We will use a real-world digital elevation model (DEM) because the artificial raster elev.tif produces parallel lines (task for the reader: verify this and explain why this happens). Plotting contour lines is straightforward, using the contour or contourf functions in Makie.

+
+
f, ax, plt = contour(src_dem; levels = LinRange(0, 1200, 50), color = :black)
+
+
+
+

+
+
+
+
+

TODO: gdal_contour (via ArchGDAL??)

+

It would be good to show how to use the provided GDAL executables thoughโ€ฆ

+
+
+
+

5.6 Distance to nearest geometry

+

Calculating a raster of distances to the nearest geometry is an example of a โ€œglobalโ€ raster operation (?sec-global-operations-and-distances). To demonstrate it, suppose that we need to calculate a raster representing the distance to the nearest coast in New Zealand. This example also wraps many of the concepts introduced in this chapter and in previous chapters, such as raster aggregation (?sec-raster-agg-disagg), raster conversion to points (Section 5.5.2), and rasterizing points (Section 5.4.1).

+

For the coastline, we will dissolve the New Zealand administrative division polygon layer and โ€œextractโ€ the boundary as a 'MultiLineString' geometry.

+
+
using LibGEOS
+coastline_linestrings = GI.getexterior.(GI.getgeom(LibGEOS.unaryUnion(GI.GeometryCollection(nz.geom)))) .|> x -> GI.LineString(collect(GI.getpoint(x)))
+coastline = GI.MultiLineString(coastline_linestrings)
+coastline = GO.reproject(coastline; target_crs = GI.crs(src_nz_elev), source_crs = GI.crs(nz))
+
+
GeoInterface.Wrappers.MultiLineString{false, false, Vector{GeoInterface.Wrappers.LineString{false, false, Vector{Tuple{Float64, Float64}}, Nothing, WellKnownText{GeoFormatTypes.CRS}}}, Nothing, WellKnownText{GeoFormatTypes.CRS}}(GeoInterface.Wrappers.LineString{false, false, Vector{Tuple{Float64, Float64}}, Nothing, WellKnownText{GeoFormatTypes.CRS}}[GeoInterface.Wrappers.LineString{false, false, Vector{Tuple{Float64, Float64}}, Nothing, WellKnownText{GeoFormatTypes.CRS}}([(1.2299979013e6, 4.7934322369e6), (1.2216031693e6, 4.7943017685e6), (1.2177895399e6, 4.7904898277e6), (1.2311152067e6, 4.787137924e6), (1.2361839211e6, 4.7843314255e6), (1.2325099445e6, 4.7718700237e6), (1.2173644933e6, 4.7681066692e6), (1.2080683451e6, 4.7605677875e6), (1.199129799e6, 4.7634183939e6), (1.1938565725e6, 4.7574957427e6)  โ€ฆ  (1.1964031733e6, 4.7848147499e6), (1.2011220591e6, 4.7884807601e6), (1.2003610396e6, 4.7969222422e6), (1.1955720567e6, 4.8084895651e6), (1.1989855975e6, 4.8153873633e6), (1.2077044806e6, 4.8171297889e6), (1.2161546733e6, 4.8132662203e6), (1.2184777781e6, 4.8064984488e6), (1.2297297347e6, 4.7987186067e6), (1.2299979013e6, 4.7934322369e6)], nothing, WellKnownText{GeoFormatTypes.CRS}(GeoFormatTypes.CRS(), "PROJCS[\"unknown\",GEOGCS[\"unknown\",DATUM[\"Unknown based on GRS80 ellipsoid\",SPHEROID[\"GRS 1980\",6378137,298.257222101004,AUTHORITY[\"EPSG\",\"7019\"]],TOWGS84[0,0,0,0,0,0,0]],PRIMEM[\"Greenwich\",0],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]]],PROJECTION[\"Transverse_Mercator\"],PARAMETER[\"latitude_of_origin\",0],PARAMETER[\"central_meridian\",173],PARAMETER[\"scale_factor\",0.9996],PARAMETER[\"false_easting\",1600000],PARAMETER[\"false_northing\",10000000],UNIT[\"metre\",1,AUTHORITY[\"EPSG\",\"9001\"]],AXIS[\"Easting\",EAST],AXIS[\"Northing\",NORTH]]")), GeoInterface.Wrappers.LineString{false, false, Vector{Tuple{Float64, Float64}}, Nothing, WellKnownText{GeoFormatTypes.CRS}}([(1.1010682698e6, 4.9169506843e6), (1.0962776826e6, 4.9262213416e6), (1.1070468733e6, 4.9308841834e6), (1.1101943531e6, 4.9284267394e6), (1.1114045691e6, 4.9201779986e6), (1.1088006957e6, 4.914502166e6), (1.1010682698e6, 4.9169506843e6)], nothing, WellKnownText{GeoFormatTypes.CRS}(GeoFormatTypes.CRS(), "PROJCS[\"unknown\",GEOGCS[\"unknown\",DATUM[\"Unknown based on GRS80 ellipsoid\",SPHEROID[\"GRS 1980\",6378137,298.257222101004,AUTHORITY[\"EPSG\",\"7019\"]],TOWGS84[0,0,0,0,0,0,0]],PRIMEM[\"Greenwich\",0],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]]],PROJECTION[\"Transverse_Mercator\"],PARAMETER[\"latitude_of_origin\",0],PARAMETER[\"central_meridian\",173],PARAMETER[\"scale_factor\",0.9996],PARAMETER[\"false_easting\",1600000],PARAMETER[\"false_northing\",10000000],UNIT[\"metre\",1,AUTHORITY[\"EPSG\",\"9001\"]],AXIS[\"Easting\",EAST],AXIS[\"Northing\",NORTH]]")), GeoInterface.Wrappers.LineString{false, false, Vector{Tuple{Float64, Float64}}, Nothing, WellKnownText{GeoFormatTypes.CRS}}([(1.2822947146e6, 4.8243903885e6), (1.2777592139e6, 4.8327835132e6), (1.2629460033e6, 4.8352306146e6), (1.2563895979e6, 4.832365038e6), (1.2436815529e6, 4.8358664271e6), (1.237141582e6, 4.8332854896e6), (1.2365580519e6, 4.8400088904e6), (1.2301828929e6, 4.8518834763e6), (1.2202312165e6, 4.8564065645e6), (1.2167650225e6, 4.8512224604e6)  โ€ฆ  (1.3536635084e6, 4.861558587e6), (1.3540190062e6, 4.8508162544e6), (1.3384831555e6, 4.837432841e6), (1.3299957273e6, 4.8375532567e6), (1.3247154647e6, 4.8316347032e6), (1.3151459743e6, 4.8300668586e6), (1.310825215e6, 4.826096002e6), (1.3025458256e6, 4.8269904113e6), (1.2945187261e6, 4.8232725043e6), (1.2822947146e6, 4.8243903885e6)], nothing, WellKnownText{GeoFormatTypes.CRS}(GeoFormatTypes.CRS(), "PROJCS[\"unknown\",GEOGCS[\"unknown\",DATUM[\"Unknown based on GRS80 ellipsoid\",SPHEROID[\"GRS 1980\",6378137,298.257222101004,AUTHORITY[\"EPSG\",\"7019\"]],TOWGS84[0,0,0,0,0,0,0]],PRIMEM[\"Greenwich\",0],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]]],PROJECTION[\"Transverse_Mercator\"],PARAMETER[\"latitude_of_origin\",0],PARAMETER[\"central_meridian\",173],PARAMETER[\"scale_factor\",0.9996],PARAMETER[\"false_easting\",1600000],PARAMETER[\"false_northing\",10000000],UNIT[\"metre\",1,AUTHORITY[\"EPSG\",\"9001\"]],AXIS[\"Easting\",EAST],AXIS[\"Northing\",NORTH]]")), GeoInterface.Wrappers.LineString{false, false, Vector{Tuple{Float64, Float64}}, Nothing, WellKnownText{GeoFormatTypes.CRS}}([(1.1253763259e6, 4.982869816e6), (1.1248143323e6, 4.976641044e6), (1.1282897921e6, 4.9707170423e6), (1.1254201958e6, 4.9665221375e6), (1.1188173258e6, 4.9720125867e6), (1.1253763259e6, 4.982869816e6)], nothing, WellKnownText{GeoFormatTypes.CRS}(GeoFormatTypes.CRS(), "PROJCS[\"unknown\",GEOGCS[\"unknown\",DATUM[\"Unknown based on GRS80 ellipsoid\",SPHEROID[\"GRS 1980\",6378137,298.257222101004,AUTHORITY[\"EPSG\",\"7019\"]],TOWGS84[0,0,0,0,0,0,0]],PRIMEM[\"Greenwich\",0],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]]],PROJECTION[\"Transverse_Mercator\"],PARAMETER[\"latitude_of_origin\",0],PARAMETER[\"central_meridian\",173],PARAMETER[\"scale_factor\",0.9996],PARAMETER[\"false_easting\",1600000],PARAMETER[\"false_northing\",10000000],UNIT[\"metre\",1,AUTHORITY[\"EPSG\",\"9001\"]],AXIS[\"Easting\",EAST],AXIS[\"Northing\",NORTH]]")), GeoInterface.Wrappers.LineString{false, false, Vector{Tuple{Float64, Float64}}, Nothing, WellKnownText{GeoFormatTypes.CRS}}([(1.7015121629e6, 5.9962048743e6), (1.6940719249e6, 5.9966701668e6), (1.6984728948e6, 5.9883324659e6), (1.7037685647e6, 5.9848085328e6), (1.7060307416e6, 5.9751359042e6), (1.7018195613e6, 5.9719674757e6), (1.6933144403e6, 5.9719708739e6), (1.6883953701e6, 5.9896812008e6), (1.6779656958e6, 6.0055889233e6), (1.662101242e6, 6.0277652897e6)  โ€ฆ  (1.725607508e6, 5.945892267e6), (1.7281682052e6, 5.9584185318e6), (1.7268710844e6, 5.9703482785e6), (1.7127870833e6, 5.9730682541e6), (1.7128762566e6, 5.9791634373e6), (1.7232886065e6, 5.9797407952e6), (1.7331650316e6, 5.9897140167e6), (1.7201965583e6, 5.9800776907e6), (1.709110421e6, 5.9866719829e6), (1.7015121629e6, 5.9962048743e6)], nothing, WellKnownText{GeoFormatTypes.CRS}(GeoFormatTypes.CRS(), "PROJCS[\"unknown\",GEOGCS[\"unknown\",DATUM[\"Unknown based on GRS80 ellipsoid\",SPHEROID[\"GRS 1980\",6378137,298.257222101004,AUTHORITY[\"EPSG\",\"7019\"]],TOWGS84[0,0,0,0,0,0,0]],PRIMEM[\"Greenwich\",0],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]]],PROJECTION[\"Transverse_Mercator\"],PARAMETER[\"latitude_of_origin\",0],PARAMETER[\"central_meridian\",173],PARAMETER[\"scale_factor\",0.9996],PARAMETER[\"false_easting\",1600000],PARAMETER[\"false_northing\",10000000],UNIT[\"metre\",1,AUTHORITY[\"EPSG\",\"9001\"]],AXIS[\"Easting\",EAST],AXIS[\"Northing\",NORTH]]")), GeoInterface.Wrappers.LineString{false, false, Vector{Tuple{Float64, Float64}}, Nothing, WellKnownText{GeoFormatTypes.CRS}}([(1.6650793317e6, 5.4762796377e6), (1.6679650479e6, 5.4828286371e6), (1.6790721723e6, 5.4888496631e6), (1.6808040605e6, 5.4949012903e6), (1.6810365469e6, 5.4869445483e6), (1.6763032282e6, 5.4766970825e6), (1.6685295209e6, 5.4686427381e6), (1.6650793317e6, 5.4762796377e6)], nothing, WellKnownText{GeoFormatTypes.CRS}(GeoFormatTypes.CRS(), "PROJCS[\"unknown\",GEOGCS[\"unknown\",DATUM[\"Unknown based on GRS80 ellipsoid\",SPHEROID[\"GRS 1980\",6378137,298.257222101004,AUTHORITY[\"EPSG\",\"7019\"]],TOWGS84[0,0,0,0,0,0,0]],PRIMEM[\"Greenwich\",0],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]]],PROJECTION[\"Transverse_Mercator\"],PARAMETER[\"latitude_of_origin\",0],PARAMETER[\"central_meridian\",173],PARAMETER[\"scale_factor\",0.9996],PARAMETER[\"false_easting\",1600000],PARAMETER[\"false_northing\",10000000],UNIT[\"metre\",1,AUTHORITY[\"EPSG\",\"9001\"]],AXIS[\"Easting\",EAST],AXIS[\"Northing\",NORTH]]")), GeoInterface.Wrappers.LineString{false, false, Vector{Tuple{Float64, Float64}}, Nothing, WellKnownText{GeoFormatTypes.CRS}}([(1.7939818643e6, 5.9319178426e6), (1.7912570345e6, 5.9199068808e6), (1.7842798937e6, 5.9215788138e6), (1.7818307229e6, 5.9258456737e6), (1.7939818643e6, 5.9319178426e6)], nothing, WellKnownText{GeoFormatTypes.CRS}(GeoFormatTypes.CRS(), "PROJCS[\"unknown\",GEOGCS[\"unknown\",DATUM[\"Unknown based on GRS80 ellipsoid\",SPHEROID[\"GRS 1980\",6378137,298.257222101004,AUTHORITY[\"EPSG\",\"7019\"]],TOWGS84[0,0,0,0,0,0,0]],PRIMEM[\"Greenwich\",0],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]]],PROJECTION[\"Transverse_Mercator\"],PARAMETER[\"latitude_of_origin\",0],PARAMETER[\"central_meridian\",173],PARAMETER[\"scale_factor\",0.9996],PARAMETER[\"false_easting\",1600000],PARAMETER[\"false_northing\",10000000],UNIT[\"metre\",1,AUTHORITY[\"EPSG\",\"9001\"]],AXIS[\"Easting\",EAST],AXIS[\"Northing\",NORTH]]")), GeoInterface.Wrappers.LineString{false, false, Vector{Tuple{Float64, Float64}}, Nothing, WellKnownText{GeoFormatTypes.CRS}}([(1.8266292121e6, 5.9745333354e6), (1.8188737655e6, 5.9791838569e6), (1.8185878044e6, 5.9830890239e6), (1.8112269977e6, 5.9881418267e6), (1.8126655001e6, 5.9932466009e6), (1.8093053687e6, 5.9982083619e6), (1.8108957047e6, 6.0059053369e6), (1.8159541251e6, 6.0078779063e6), (1.8180162185e6, 5.9990038299e6), (1.823326993e6, 5.9918794741e6), (1.8227997093e6, 5.9860917836e6), (1.8283790056e6, 5.9803071686e6), (1.8266292121e6, 5.9745333354e6)], nothing, WellKnownText{GeoFormatTypes.CRS}(GeoFormatTypes.CRS(), "PROJCS[\"unknown\",GEOGCS[\"unknown\",DATUM[\"Unknown based on GRS80 ellipsoid\",SPHEROID[\"GRS 1980\",6378137,298.257222101004,AUTHORITY[\"EPSG\",\"7019\"]],TOWGS84[0,0,0,0,0,0,0]],PRIMEM[\"Greenwich\",0],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]]],PROJECTION[\"Transverse_Mercator\"],PARAMETER[\"latitude_of_origin\",0],PARAMETER[\"central_meridian\",173],PARAMETER[\"scale_factor\",0.9996],PARAMETER[\"false_easting\",1600000],PARAMETER[\"false_northing\",10000000],UNIT[\"metre\",1,AUTHORITY[\"EPSG\",\"9001\"]],AXIS[\"Easting\",EAST],AXIS[\"Northing\",NORTH]]"))], nothing, WellKnownText{GeoFormatTypes.CRS}(GeoFormatTypes.CRS(), "PROJCS[\"unknown\",GEOGCS[\"unknown\",DATUM[\"Unknown based on GRS80 ellipsoid\",SPHEROID[\"GRS 1980\",6378137,298.257222101004,AUTHORITY[\"EPSG\",\"7019\"]],TOWGS84[0,0,0,0,0,0,0]],PRIMEM[\"Greenwich\",0],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]]],PROJECTION[\"Transverse_Mercator\"],PARAMETER[\"latitude_of_origin\",0],PARAMETER[\"central_meridian\",173],PARAMETER[\"scale_factor\",0.9996],PARAMETER[\"false_easting\",1600000],PARAMETER[\"false_northing\",10000000],UNIT[\"metre\",1,AUTHORITY[\"EPSG\",\"9001\"]],AXIS[\"Easting\",EAST],AXIS[\"Northing\",NORTH]]"))
+
+
+

For a โ€œtemplateโ€ raster, we will aggregate the New Zealand DEM, in the nz_elev.tif file, to 5 times coarser resolution. The code section below follows the aggeregation example in ?sec-raster-agg-disagg.

+
+
factor = 2/10
+r = Rasters.resample(src_nz_elev; size = round.(Int, size(src_nz_elev) .* factor), method = :average)
+
+
โ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ
+โ”‚ 223ร—290 Raster{Float32,2} โ”‚
+โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ dims โ”
+  โ†“ X Projected{Float64} LinRange{Float64}(995456.5461976258, 2.105456546197626e6, 223) ForwardOrdered Regular Intervals{Start},
+  โ†’ Y Projected{Float64} LinRange{Float64}(6.186960927303582e6, 4.741960927303582e6, 290) ReverseOrdered Regular Intervals{Start}
+โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ metadata โ”ค
+  Metadata{Rasters.GDALsource} of Dict{String, Any} with 4 entries:
+  "units"    => ""
+  "offset"   => 0.0
+  "filepath" => "/vsimem/tmp"
+  "scale"    => 1.0
+โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ raster โ”ค
+  extent: Extent(X = (995456.5461976258, 2.110456546197626e6), Y = (4.741960927303582e6, 6.191960927303582e6))
+  missingval: NaN32
+  crs: PROJCS["unknown",GEOGCS["unknown",DATUM["Unknown based on GRS80 ellipsoid",SPHEROID["GRS 1980",6378137,298.257222101004,AUTHORITY["EPSG","7019"]],TOWGS84[0,0,0,0,0,0,0]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",173],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",1600000],PARAMETER["false_northing",10000000],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH]]
+โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜
+ โ†“ โ†’          6.18696e6    6.18196e6  โ€ฆ    4.74696e6    4.74196e6
+ 9.95457e5  NaN          NaN             NaN          NaN
+ โ‹ฎ                                    โ‹ฑ                 โ‹ฎ
+ 2.10546e6  NaN          NaN             NaN          NaN
+
+
+

The resulting raster r and the lines layer coastline are plotted in Figure 5.9. Note that the raster values are average elevations based on \(5 \times 5\) pixels, but this is irrelevant for the subsequent calculation; the raster is going to be used as a template, and all of its values will be replaced with distances to coastline (Figure 5.10).

+
+
fig, ax, plt = plot(r)
+lines!(ax, coastline; color = :red)
+fig
+
+
+
+
+ +
+
+Figure 5.9: Template to calculate distance to nearest geometry (coastlines, in red) +
+
+
+
+
+

To calculate the actual distances, we must convert each pixel to a vector (point) geometry. For this purpose, we use the technique demonstrated in Section 5.5.2, but simply select the pixels that are not missing.

+
+
dp = DimPoints(r)
+nonmissing_points = dp[r .=== missingval(r)]
+
+
53316-element Vector{Tuple{Float64, Float64}}:
+ (995456.5461976258, 6.186960927303582e6)
+ (1.0004565461976258e6, 6.186960927303582e6)
+ (1.0054565461976257e6, 6.186960927303582e6)
+ (1.0104565461976259e6, 6.186960927303582e6)
+ (1.0154565461976258e6, 6.186960927303582e6)
+ (1.0204565461976258e6, 6.186960927303582e6)
+ (1.0254565461976258e6, 6.186960927303582e6)
+ (1.0304565461976257e6, 6.186960927303582e6)
+ (1.0354565461976259e6, 6.186960927303582e6)
+ (1.0404565461976258e6, 6.186960927303582e6)
+ โ‹ฎ
+ (2.0654565461976258e6, 4.741960927303582e6)
+ (2.0704565461976258e6, 4.741960927303582e6)
+ (2.0754565461976258e6, 4.741960927303582e6)
+ (2.0804565461976258e6, 4.741960927303582e6)
+ (2.0854565461976258e6, 4.741960927303582e6)
+ (2.0904565461976258e6, 4.741960927303582e6)
+ (2.0954565461976258e6, 4.741960927303582e6)
+ (2.100456546197626e6, 4.741960927303582e6)
+ (2.105456546197626e6, 4.741960927303582e6)
+
+
+

The result is a vector of 2-tuples, which are recognized as GeoInterface point geometries.

+

We can compute the Cartesian distance from each point to the nearest line in the coastline multilinestring using the distance method from GeometryOps.

+
+
distances = GO.distance.((coastline,), nonmissing_points)
+
+
53316-element Vector{Float64}:
+ 572764.2876244619
+ 567764.3239539921
+ 562764.3609290748
+ 557764.3985670706
+ 552764.436885969
+ 547764.4759044152
+ 542764.5156417422
+ 537764.5561180016
+ 532764.5973539978
+ 527764.639371324
+      โ‹ฎ
+ 610809.9189554364
+ 614562.8629679172
+ 618333.460184955
+ 622121.3896252689
+ 625926.3366197749
+ 629747.9927075815
+ 633586.0555316888
+ 637440.2287345697
+ 641310.2218537896
+
+
+

Finally, we rasterize (see Section 5.4.1) the distances into our raster template.

+
+
img = Rasters.rasterize(
+    last,
+    nonmissing_points;
+    to = r,
+    fill = distances,
+)
+
+
โ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ
+โ”‚ 223ร—290 Raster{Union{Missing, Float64},2} last โ”‚
+โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ dims โ”
+  โ†“ X Projected{Float64} LinRange{Float64}(995456.5461976258, 2.105456546197626e6, 223) ForwardOrdered Regular Intervals{Start},
+  โ†’ Y Projected{Float64} LinRange{Float64}(6.186960927303582e6, 4.741960927303582e6, 290) ReverseOrdered Regular Intervals{Start}
+โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ metadata โ”ค
+  Metadata of Dict{Any, Any}()
+โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ raster โ”ค
+  extent: Extent(X = (995456.5461976258, 2.110456546197626e6), Y = (4.741960927303582e6, 6.191960927303582e6))
+  missingval: missing
+  crs: PROJCS["unknown",GEOGCS["unknown",DATUM["Unknown based on GRS80 ellipsoid",SPHEROID["GRS 1980",6378137,298.257222101004,AUTHORITY["EPSG","7019"]],TOWGS84[0,0,0,0,0,0,0]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",173],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",1600000],PARAMETER["false_northing",10000000],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH]]
+โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜
+ โ†“ โ†’             6.18696e6  6.18196e6  โ€ฆ       4.74696e6       4.74196e6
+ 9.95457e5       5.72764e5  5.72767e5     164188.0             1.67786e5
+ 1.00046e6       5.67764e5  5.67767e5          1.60717e5  164391.0
+ 1.00546e6       5.62764e5  5.62767e5          1.57329e5  161080.0
+ โ‹ฎ                                     โ‹ฑ                       โ‹ฎ
+ 2.09546e6       3.31952e5  3.29282e5          6.30403e5       6.33586e5
+ 2.10046e6       3.36173e5  3.33536e5          6.34276e5       6.3744e5
+ 2.10546e6  340415.0        3.37811e5          6.38166e5       6.4131e5
+
+
+

The final result, a raster of distances to the nearest coastline, is shown in Figure 5.10.

+
+
fig, ax, plt = plot(img)
+lines!(ax, coastline; color = :red)
+Colorbar(fig[1, 2], plt; label = "Distance to coastline (m)")
+fig
+
+
+
+
+ +
+
+Figure 5.10: Distance to nearest coastline in New Zealand +
+
+
+
+
+ + + +
+ +
+ + +
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6  Reprojecting geographic data

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7  Geographic data I/O

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8  Making maps with Julia

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Geocomputation with Julia

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Author
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Maarten Pronk, Rafael Schouten, Anshul Singhvi, Felix Cremer and Jakub Nowosad

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Welcome

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This is the online home of Geocomputation with Julia, a book on reproducible geographic data analysis with open source software.

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Inspired by the Free and Open Source Software for Geospatial (FOSS4G) movement this is an open source book. Find the code underlying the geocompjl project on GitHub, ensuring that the content is reproducible, transparent, and accessible. Making the book open source allows you or anyone else, to interact with the project by opening issues, making typo fixes and more, for the benefit of everyone.

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This book is part of the geocompx series, following the popular R and Python editions.

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The bookโ€™s website is built by GitHub Actions, which runs the code every time we make a change, ensuring code correctness and reproducibility. The current build status as follows:

+

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For details on reproducing the book, see the README in the projectโ€™s GitHub repo: https://github.com/geocompx/geocompjl.

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print("Hello geocompx World")
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Hello geocompx World
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References

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+ + + + + + \ No newline at end of file diff --git a/previews/PR23/robots.txt b/previews/PR23/robots.txt new file mode 100644 index 0000000..e824633 --- /dev/null +++ b/previews/PR23/robots.txt @@ -0,0 +1 @@ +Sitemap: https://jl.geocompx.org/sitemap.xml diff --git a/previews/PR23/search.json b/previews/PR23/search.json new file mode 100644 index 0000000..cb52a2a --- /dev/null +++ b/previews/PR23/search.json @@ -0,0 +1,162 @@ +[ + { + "objectID": "index.html", + "href": "index.html", + "title": "Geocomputation with Julia", + "section": "", + "text": "Welcome\nThis is the online home of Geocomputation with Julia, a book on reproducible geographic data analysis with open source software.\nInspired by the Free and Open Source Software for Geospatial (FOSS4G) movement this is an open source book. Find the code underlying the geocompjl project on GitHub, ensuring that the content is reproducible, transparent, and accessible. Making the book open source allows you or anyone else, to interact with the project by opening issues, making typo fixes and more, for the benefit of everyone.\nThis book is part of the geocompx series, following the popular R and Python editions.\nThe bookโ€™s website is built by GitHub Actions, which runs the code every time we make a change, ensuring code correctness and reproducibility. The current build status as follows:\n\nFor details on reproducing the book, see the README in the projectโ€™s GitHub repo: https://github.com/geocompx/geocompjl.\n\nprint(\"Hello geocompx World\")\n\nHello geocompx World", + "crumbs": [ + "Welcome" + ] + }, + { + "objectID": "chapters/01-spatial-data.html", + "href": "chapters/01-spatial-data.html", + "title": "1ย  Geographic data in Julia", + "section": "", + "text": "1.1 Introduction\nusing GeoDataFrames\ndf = GeoDataFrames.read(\"data/world.gpkg\")\n\n177ร—11 DataFrame171 rows omitted\n\n\n\nRow\ngeom\niso_a2\nname_long\ncontinent\nregion_un\nsubregion\ntype\narea_km2\npop\nlifeExp\ngdpPercap\n\n\n\nIGeometrโ€ฆ\nString?\nString\nString\nString\nString\nString\nFloat64\nFloat64?\nFloat64?\nFloat64?\n\n\n\n\n1\nGeometry: wkbMultiPolygon\nFJ\nFiji\nOceania\nOceania\nMelanesia\nSovereign country\n19290.0\n885806.0\n69.96\n8222.25\n\n\n2\nGeometry: wkbMultiPolygon\nTZ\nTanzania\nAfrica\nAfrica\nEastern Africa\nSovereign country\n9.32746e5\n5.22349e7\n64.163\n2402.1\n\n\n3\nGeometry: wkbMultiPolygon\nEH\nWestern Sahara\nAfrica\nAfrica\nNorthern Africa\nIndeterminate\n96270.6\nmissing\nmissing\nmissing\n\n\nโ‹ฎ\nโ‹ฎ\nโ‹ฎ\nโ‹ฎ\nโ‹ฎ\nโ‹ฎ\nโ‹ฎ\nโ‹ฎ\nโ‹ฎ\nโ‹ฎ\nโ‹ฎ\nโ‹ฎ\n\n\n175\nGeometry: wkbMultiPolygon\nXK\nKosovo\nEurope\nEurope\nSouthern Europe\nSovereign country\n11230.3\n1.8218e6\n71.0976\n8698.29\n\n\n176\nGeometry: wkbMultiPolygon\nTT\nTrinidad and Tobago\nNorth America\nAmericas\nCaribbean\nSovereign country\n7737.81\n1.35449e6\n70.426\n31181.8\n\n\n177\nGeometry: wkbMultiPolygon\nSS\nSouth Sudan\nAfrica\nAfrica\nEastern Africa\nSovereign country\n6.24909e5\n1.1531e7\n55.817\n1935.88\nusing CairoMakie\nusing GeoMakie\n\nf, a, p = poly(df.geom)", + "crumbs": [ + "1ย  Geographic data in Julia" + ] + }, + { + "objectID": "chapters/01-spatial-data.html#introduction", + "href": "chapters/01-spatial-data.html#introduction", + "title": "1ย  Geographic data in Julia", + "section": "", + "text": "1.1.1 Raster from scratch\nIn this section, we are going to demonstrate the creation of rasters from scratch. We will construct two small rasters, elev and grain, which we will use in examples later in the book. Unlike creating a vector layer (see ?sec-vector-layer-from-scratch), creating a raster from scratch is rarely needed in practice because aligning a raster with the proper spatial extent is challenging to do programmatically (โ€œgeoreferencingโ€ tools in GIS software are a better fit for the job). Nevertheless, the examples will be helpful to become more familiar with the Rasters.jl data structures.\n\nusing Rasters\nimport GeoFormatTypes as GFT\n\nWARNING: Method definition convert_arguments(MakieCore.CellGrid, Rasters.AbstractRaster{var\"#s115\", 2, D, A} where A where D where var\"#s115\") in module RastersMakieExt at /home/runner/.julia/packages/Rasters/JcYNY/ext/RastersMakieExt/plotrecipes.jl:304 overwritten at /home/runner/.julia/packages/Rasters/JcYNY/ext/RastersMakieExt/plotrecipes.jl:308.\nERROR: Method overwriting is not permitted during Module precompilation. Use `__precompile__(false)` to opt-out of precompilation.\n\n\nConceptually, a raster is an array combined with georeferencing information, whereas the latter comprises:\n\nLookup vectors for the axes, encoding the spatial coordinates for each grid cell. These take the form of the X and Y dimensions in the raster that youโ€™ll see below.\nA coordinate reference system (CRS) definition, specifying the association of the rasterโ€™s coordinates with the surface of the earth.\n\nTherefore, to create a raster, we first need to have an array with the values, and then supplement it with the georeferencing information. Letโ€™s create the arrays elev and grain. The elev array is a \\(6 \\times 6\\) array with sequential values from 1 to 36. It can be created as follows using base Julia functions.\n\nelev = reshape(UInt8(1):UInt8(36), (6, 6))\n\n6ร—6 reshape(::UnitRange{UInt8}, 6, 6) with eltype UInt8:\n 0x01 0x07 0x0d 0x13 0x19 0x1f\n 0x02 0x08 0x0e 0x14 0x1a 0x20\n 0x03 0x09 0x0f 0x15 0x1b 0x21\n 0x04 0x0a 0x10 0x16 0x1c 0x22\n 0x05 0x0b 0x11 0x17 0x1d 0x23\n 0x06 0x0c 0x12 0x18 0x1e 0x24\n\n\nThe grain array represents a categorical raster with values 0, 1, 2, corresponding to categories โ€œclayโ€, โ€œsiltโ€, โ€œsandโ€, respectively. We will create it from a specific arrangement of pixel values, using reshape.\n\nv = UInt8[\n 1, 0, 1, 2, 2, 2, \n 0, 2, 0, 0, 2, 1, \n 0, 2, 2, 0, 0, 2, \n 0, 0, 1, 1, 1, 1, \n 1, 1, 1, 2, 1, 1, \n 2, 1, 2, 2, 0, 2\n]\ngrain = reshape(v, (6, 6))\n\n6ร—6 Matrix{UInt8}:\n 0x01 0x00 0x00 0x00 0x01 0x02\n 0x00 0x02 0x02 0x00 0x01 0x01\n 0x01 0x00 0x02 0x01 0x01 0x02\n 0x02 0x00 0x00 0x01 0x02 0x02\n 0x02 0x02 0x00 0x01 0x01 0x00\n 0x02 0x01 0x02 0x01 0x01 0x02\n\n\nNote that in both cases, we are using the uint8 (unsigned integer in 8 bits, i.e., 0-255) data type, which is sufficient to represent all possible values of the given rasters (see ?tbl-numpy-data-types). This is the recommended approach for a minimal memory footprint.\nWhat is missing now is the georeferencing information (see ?sec-using-rasters-jl). In this case, since the rasters are arbitrary, we also set up arbitrary dimension lookups for the x and y axes, where:\n\nThe origin (\\(x_{min}\\), \\(y_{max}\\)) is at -1.5,1.5\nThe raster resolution (\\(delta_{x}\\), \\(delta_{y}\\)) is 0.5,-0.5\n\nWe can add this information using rasterio.transform.from_origin, and specifying west, north, xsize, and ysize parameters. The resulting transformation matrix object is hereby named new_transform.\n\nnew_x = X(range(-1.5, step=0.5, length=6))\nnew_y = Y(range(1.0, step=-0.5, length=6))\n\nY 1.0:-0.5:-1.5\n\n\nWe can now construct a Raster object, from the elev array and the dimensions new_x and new_y.\nWe assign to it a CRS of EPSG:4326 (which encodes that the coordinate system is longitude/latitude on the โ€œstandardโ€ WGS84 definition of the Earthโ€™s curvature).\nHere, we use the GFT.EPSG(code) constructor to create an object that encodes a reference code under the European Petroleum Survey Group (EPSG) authorityโ€™s database of coordinate reference systems.\n\nelev_raster = Raster(elev, (new_x, new_y); crs = GFT.EPSG(4326))\n\nโ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ\nโ”‚ 6ร—6 Raster{UInt8,2} โ”‚\nโ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ dims โ”\n โ†“ X Projected{Float64} -1.5:0.5:1.0 ForwardOrdered Regular Points,\n โ†’ Y Projected{Float64} 1.0:-0.5:-1.5 ReverseOrdered Regular Points\nโ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ raster โ”ค\n extent: Extent(X = (-1.5, 1.0), Y = (-1.5, 1.0))\n\n crs: EPSG:4326\nโ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜\n โ†“ โ†’ 1.0 0.5 0.0 -0.5 -1.0 -1.5\n -1.5 0x01 0x07 0x0d 0x13 0x19 0x1f\n -1.0 0x02 0x08 0x0e 0x14 0x1a 0x20\n -0.5 0x03 0x09 0x0f 0x15 0x1b 0x21\n 0.0 0x04 0x0a 0x10 0x16 0x1c 0x22\n 0.5 0x05 0x0b 0x11 0x17 0x1d 0x23\n 1.0 0x06 0x0c 0x12 0x18 0x1e 0x24\n\n\nThe raster can now be plotted in its coordinate system, passing the array elev along with the transformation matrix new_transform to rasterio.plot.show (Figureย 1.1).\n\nplot(elev_raster)\n\n\n\n\n\n\n\nFigureย 1.1: Plot of the elev raster, a minimal example of a continuous raster, created from scratch\n\n\n\n\n\nThe grain raster can be plotted the same way, as we are going to use the same transformation matrix for it as well (Figureย 1.2).\n\nplot(Raster(grain, (new_x, new_y); crs = GFT.EPSG(4326)))\n\n\n\n\n\n\n\nFigureย 1.2: Plot of the grain raster, a minimal example of a categorical raster, created from scratch\n\n\n\n\n\nAt this point, we have two rasters, each composed of an array and related dimension lookups. We can work with the raster using Rasters.jl by:\n\nKeeping in mind that any other layer we use in the analysis is in the same CRS\n\nFinally, to export the raster for permanent storage, along with the spatial metadata, we need to go through the following steps:\n\nCreate a raster file (where we set the lookups and the CRS, among other settings)\nWrite the array with raster values into the connection\nClose the connection\n\nDonโ€™t worry if the code below is unclear; the concepts related to writing raster data to file will be explained in ?sec-data-output-raster. For now, for completeness, and also to use these rasters in subsequent chapters without having to re-create them from scratch, we just provide the code for exporting the elev and grain rasters into the output directory. In the case of elev, we do it as follows with the Rasters.write functions and methods of the Rasters.jl package.\n\nwrite(\"output/elev.tif\", elev_raster; force = true)\n\n\"output/elev.tif\"\n\n\nNote that the CRS we (arbitrarily) set for the elev raster is WGS84, defined using crs=4326 according to the EPSG code.\nExporting the grain raster is done in the same way, with the only differences being the file name and the array we write into the connection.\n\nwrite(\"output/grain.tif\", Raster(grain, (new_x, new_y); crs = GFT.EPSG(4326)); force = true)\n\n\"output/grain.tif\"\n\n\nAs a result, the files elev.tif and grain.tif are written into the output directory. We are going to use these small raster files later on in the examples (for example, ?sec-raster-subsetting).\nNote that the transform matrices and dimensions of elev and grain are identical. This means that the rasters are overlapping, and can be combined into one two-band raster, processed in raster algebra operations (?sec-map-algebra), etc.", + "crumbs": [ + "1ย  Geographic data in Julia" + ] + }, + { + "objectID": "chapters/02-attribute-operations.html", + "href": "chapters/02-attribute-operations.html", + "title": "2ย  Attribute data operations", + "section": "", + "text": "Prerequisites", + "crumbs": [ + "2ย  Attribute data operations" + ] + }, + { + "objectID": "chapters/03-spatial-operations.html", + "href": "chapters/03-spatial-operations.html", + "title": "3ย  Spatial data operations", + "section": "", + "text": "Prerequisites", + "crumbs": [ + "3ย  Spatial data operations" + ] + }, + { + "objectID": "chapters/04-geometry-operations.html", + "href": "chapters/04-geometry-operations.html", + "title": "4ย  Geometry operations", + "section": "", + "text": "Prerequisites", + "crumbs": [ + "4ย  Geometry operations" + ] + }, + { + "objectID": "chapters/05-raster-vector.html", + "href": "chapters/05-raster-vector.html", + "title": "5ย  Raster-vector interactions", + "section": "", + "text": "Prerequisites\nThis chapter requires importing the following packages:\nusing GeoDataFrames, DataFrames\nusing Rasters, ArchGDAL # Raster I/O and operations\nusing Proj # activate reprojection capabilities\nimport GeoInterface as GI, GeometryOps as GO, LibGEOS as LG # Vector operations\nimport GeoFormatTypes as GFT # for CRS types\nusing GeoMakie, CairoMakie # plotting\n\nWARNING: Method definition convert_arguments(MakieCore.CellGrid, Rasters.AbstractRaster{var\"#s115\", 2, D, A} where A where D where var\"#s115\") in module RastersMakieExt at /home/runner/.julia/packages/Rasters/JcYNY/ext/RastersMakieExt/plotrecipes.jl:304 overwritten at /home/runner/.julia/packages/Rasters/JcYNY/ext/RastersMakieExt/plotrecipes.jl:308.\nERROR: Method overwriting is not permitted during Module precompilation. Use `__precompile__(false)` to opt-out of precompilation.\nHere, we will also set the theme in Makie.jl to ensure that all surface and heatmap plots are represented in file as bitmap images, rather than the raw data.\nThis is necessary only because Quarto breaks on SVGs above a certain size.\nYou can feel free to set this for your own work, but it is not required.\nMakie.set_theme!(\n Heatmap = (; rasterize = 2),\n Surface = (; rasterize = 2),\n)\nIt also relies on the following data files:\nsrc_srtm = Raster(\"data/srtm.tif\")\nsrc_nlcd = Raster(\"data/nlcd.tif\")\nsrc_grain = Raster(\"output/grain.tif\")\nsrc_elev = Raster(\"output/elev.tif\")\nsrc_dem = Raster(\"data/dem.tif\")\nzion = GeoDataFrames.read(\"data/zion.gpkg\")\nzion_points = GeoDataFrames.read(\"data/zion_points.gpkg\")\ncycle_hire_osm = GeoDataFrames.read(\"data/cycle_hire_osm.gpkg\")\nus_states = GeoDataFrames.read(\"data/us_states.gpkg\")\nnz = GeoDataFrames.read(\"data/nz.gpkg\")\nsrc_nz_elev = Raster(\"data/nz_elev.tif\")\n\nโ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ\nโ”‚ 1115ร—1450 Raster{Float32,2} โ”‚\nโ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ dims โ”\n โ†“ X Projected{Float64} LinRange{Float64}(995456.5461976258, 2.109456546197626e6, 1115) ForwardOrdered Regular Intervals{Start},\n โ†’ Y Projected{Float64} LinRange{Float64}(6.190960927303582e6, 4.741960927303582e6, 1450) ReverseOrdered Regular Intervals{Start}\nโ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ metadata โ”ค\n Metadata{Rasters.GDALsource} of Dict{String, Any} with 4 entries:\n \"units\" => \"\"\n \"offset\" => 0.0\n \"filepath\" => \"data/nz_elev.tif\"\n \"scale\" => 1.0\nโ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ raster โ”ค\n extent: Extent(X = (995456.5461976258, 2.110456546197626e6), Y = (4.741960927303582e6, 6.191960927303582e6))\n missingval: NaN32\n crs: PROJCS[\"unknown\",GEOGCS[\"unknown\",DATUM[\"Unknown based on GRS80 ellipsoid\",SPHEROID[\"GRS 1980\",6378137,298.257222101004,AUTHORITY[\"EPSG\",\"7019\"]],TOWGS84[0,0,0,0,0,0,0]],PRIMEM[\"Greenwich\",0],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]]],PROJECTION[\"Transverse_Mercator\"],PARAMETER[\"latitude_of_origin\",0],PARAMETER[\"central_meridian\",173],PARAMETER[\"scale_factor\",0.9996],PARAMETER[\"false_easting\",1600000],PARAMETER[\"false_northing\",10000000],UNIT[\"metre\",1,AUTHORITY[\"EPSG\",\"9001\"]],AXIS[\"Easting\",EAST],AXIS[\"Northing\",NORTH]]\nโ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜\n โ†“ โ†’ 6.19096e6 6.18996e6 โ€ฆ 4.74296e6 4.74196e6\n 9.95457e5 NaN NaN NaN NaN\n โ‹ฎ โ‹ฑ โ‹ฎ\n 2.10946e6 NaN NaN โ€ฆ NaN NaN", + "crumbs": [ + "5ย  Raster-vector interactions" + ] + }, + { + "objectID": "chapters/05-raster-vector.html#introduction", + "href": "chapters/05-raster-vector.html#introduction", + "title": "5ย  Raster-vector interactions", + "section": "5.1 Introduction", + "text": "5.1 Introduction\nThis chapter focuses on interactions between raster and vector geographic data models, both introduced in Chapter 1. It includes four main techniques:\n\nRaster cropping and masking using vector objects (Section 5.2)\nExtracting raster values using different types of vector data (Section 5.3)\nRaster to vector conversion (Section 5.4)\nVector to raster conversion (Section 5.5)\n\nThese concepts are demonstrated using data from previous chapters, to understand their potential real-world applications.", + "crumbs": [ + "5ย  Raster-vector interactions" + ] + }, + { + "objectID": "chapters/05-raster-vector.html#sec-raster-cropping", + "href": "chapters/05-raster-vector.html#sec-raster-cropping", + "title": "5ย  Raster-vector interactions", + "section": "5.2 Raster masking and cropping", + "text": "5.2 Raster masking and cropping\nMany geographic data projects involve integrating data from many different sources, such as remote sensing images (rasters) and administrative boundaries (vectors). Often the extent of input raster datasets is larger than the area of interest. In this case, raster masking, cropping, or both, are useful for unifying the spatial extent of input data (Figureย 5.1 (b) and (c), and the following two examples, illustrate the difference between masking and cropping). Both operations reduce memory use and computational demand for subsequent analysis, and may be a necessary preprocessing step before creating attractive maps involving raster data.\nWe will use two layers to illustrate raster cropping:\n\nThe srtm.tif raster representing elevation, in meters above sea level, in south-western Utah: a Rasters.jl file connection named src_srtm (see Figureย 5.1 (a))\nThe zion.gpkg vector layer representing the Zion National Park boundaries (a DataFrame named zion)\n\nBoth target and cropping objects must have the same projection. Since it is easier and more precise to reproject vector layers, compared to rasters, we use the following expression to reproject (?sec-reprojecting-vector-geometries) the vector layer zion into the coordinate reference system (CRS) of the raster src_srtm. The CRS defines how the coordinates of the geometry relate to locations on the surface of the Earth.\n\nzion = GO.reproject(zion; target_crs = GI.crs(src_srtm))\n\n1ร—12 DataFrame\n\n\n\nRow\ngeom\nUNIT_CODE\nGIS_Notes\nUNIT_NAME\nDATE_EDIT\nSTATE\nREGION\nGNIS_ID\nUNIT_TYPE\nCREATED_BY\nMETADATA\nPARKNAME\n\n\n\nPolygonโ€ฆ\nString\nString\nString\nDateTime\nString\nString\nString\nString\nString\nString\nString\n\n\n\n\n1\nPolygon{false, false, Vector{LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Nothing, WellKnownText{CRS}}}, Nothing, WellKnownText{CRS}}(LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Nothing, WellKnownText{CRS}}[LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Nothing, WellKnownText{CRS}}([(-113.084, 37.1713), (-113.086, 37.1716), (-113.091, 37.1722), (-113.091, 37.1744), (-113.091, 37.178), (-113.091, 37.1817), (-113.091, 37.1853), (-113.091, 37.1889), (-113.091, 37.1926), (-113.091, 37.1962) โ€ฆ (-113.06, 37.1713), (-113.062, 37.1713), (-113.064, 37.1713), (-113.069, 37.1713), (-113.071, 37.1713), (-113.073, 37.1713), (-113.078, 37.1713), (-113.08, 37.1713), (-113.082, 37.1713), (-113.084, 37.1713)], nothing, WellKnownText{CRS}(CRS(), \"GEOGCS[\\\\\"WGS 84\\\\\",DATUM[\\\\\"WGS_1984\\\\\",SPHEROID[\\\\\"WGS 84\\\\\",6378137,298.257223563,AUTHORITY[\\\\\"EPSG\\\\\",\\\\\"7030\\\\\"]],AUTHORITY[\\\\\"EPSG\\\\\",\\\\\"6326\\\\\"]],PRIMEM[\\\\\"Greenwich\\\\\",0,AUTHORITY[\\\\\"EPSG\\\\\",\\\\\"8901\\\\\"]],UNIT[\\\\\"degree\\\\\",0.0174532925199433,AUTHORITY[\\\\\"EPSG\\\\\",\\\\\"9122\\\\\"]],AXIS[\\\\\"Latitude\\\\\",NORTH],AXIS[\\\\\"Longitude\\\\\",EAST],AUTHORITY[\\\\\"EPSG\\\\\",\\\\\"4326\\\\\"]]\"))], nothing, WellKnownText{CRS}(CRS(), \"GEOGCS[\\\\\"WGS 84\\\\\",DATUM[\\\\\"WGS_1984\\\\\",SPHEROID[\\\\\"WGS 84\\\\\",6378137,298.257223563,AUTHORITY[\\\\\"EPSG\\\\\",\\\\\"7030\\\\\"]],AUTHORITY[\\\\\"EPSG\\\\\",\\\\\"6326\\\\\"]],PRIMEM[\\\\\"Greenwich\\\\\",0,AUTHORITY[\\\\\"EPSG\\\\\",\\\\\"8901\\\\\"]],UNIT[\\\\\"degree\\\\\",0.0174532925199433,AUTHORITY[\\\\\"EPSG\\\\\",\\\\\"9122\\\\\"]],AXIS[\\\\\"Latitude\\\\\",NORTH],AXIS[\\\\\"Longitude\\\\\",EAST],AUTHORITY[\\\\\"EPSG\\\\\",\\\\\"4326\\\\\"]]\"))\nZION\nLands - http://landsnet.nps.gov/tractsnet/documents/ZION/Metadata/zion_metadata.xml\nZion National Park\n2017-06-22T00:00:00\nUT\nIM\n1455157\nNational Park\nLands\nhttps://irma.nps.gov/App/Reference/Profile/2181118#Zion National Monument\nZion\n\n\n\n\n\n\nTo mask the image, i.e., convert all pixels which do not intersect with the zion polygon to missing, we use the Rasters.mask function.\nmask supports any geometry, vector of geometries, feature collection, or table with a geometry column!\nThe tabset below shows all the different ways to mask a raster. Weโ€™ll go forward with the approach of using a DataFrame.\n\nDataFrameSingle geometryVector of geometries\n\n\n\nout_image_mask = Rasters.mask(src_srtm; with = zion)\n\nโ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ\nโ”‚ 465ร—457 Raster{UInt16,2} โ”‚\nโ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ dims โ”\n โ†“ X Projected{Float64} LinRange{Float64}(-113.23958321278403, -112.85291654614313, 465) ForwardOrdered Regular Intervals{Start},\n โ†’ Y Projected{Float64} LinRange{Float64}(37.51208342983253, 37.13208342985786, 457) ReverseOrdered Regular Intervals{Start}\nโ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ metadata โ”ค\n Metadata{Rasters.GDALsource} of Dict{String, Any} with 4 entries:\n \"units\" => \"\"\n \"offset\" => 0.0\n \"filepath\" => \"data/srtm.tif\"\n \"scale\" => 1.0\nโ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ raster โ”ค\n extent: Extent(X = (-113.23958321278403, -112.85208321280986), Y = (37.13208342985786, 37.512916763165805))\n missingval: 0xffff\n crs: GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]\nโ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜\n โ†“ โ†’ 37.5121 37.5113 37.5104 โ€ฆ 37.1329 37.1321\n -113.24 0xffff 0xffff 0xffff 0xffff 0xffff\n โ‹ฎ โ‹ฑ \n -112.853 0xffff 0xffff 0xffff โ€ฆ 0xffff 0xffff\n\n\n\n\n\nmasker = zion.geom[1]\n\nGeoInterface.Wrappers.Polygon{false, false, Vector{GeoInterface.Wrappers.LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Nothing, WellKnownText{GeoFormatTypes.CRS}}}, Nothing, WellKnownText{GeoFormatTypes.CRS}}(GeoInterface.Wrappers.LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Nothing, WellKnownText{GeoFormatTypes.CRS}}[GeoInterface.Wrappers.LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Nothing, WellKnownText{GeoFormatTypes.CRS}}([(-113.08438684283465, 37.17130388117393), (-113.0859893948904, 37.17162779353809), (-113.09132351665151, 37.17215207288513), (-113.09132530000656, 37.17440980000173), (-113.09132830000658, 37.17803750000173), (-113.09133150000655, 37.18166840000168), (-113.09133470000658, 37.18529940000173), (-113.09134070000657, 37.188925000001696), (-113.09134660000655, 37.19255060000173), (-113.09135260000657, 37.19617620000171) โ€ฆ (-113.05962130000604, 37.17131270000172), (-113.06185720000607, 37.171310800001734), (-113.06415140000612, 37.17131020000169), (-113.06868130000619, 37.17130650000175), (-113.07101920000618, 37.17130460000174), (-113.07321130000625, 37.17130450000173), (-113.07773970000633, 37.17130420000172), (-113.08035690000636, 37.17130410000175), (-113.0822681000064, 37.171304000001705), (-113.08438684283465, 37.17130388117393)], nothing, WellKnownText{GeoFormatTypes.CRS}(GeoFormatTypes.CRS(), \"GEOGCS[\\\"WGS 84\\\",DATUM[\\\"WGS_1984\\\",SPHEROID[\\\"WGS 84\\\",6378137,298.257223563,AUTHORITY[\\\"EPSG\\\",\\\"7030\\\"]],AUTHORITY[\\\"EPSG\\\",\\\"6326\\\"]],PRIMEM[\\\"Greenwich\\\",0,AUTHORITY[\\\"EPSG\\\",\\\"8901\\\"]],UNIT[\\\"degree\\\",0.0174532925199433,AUTHORITY[\\\"EPSG\\\",\\\"9122\\\"]],AXIS[\\\"Latitude\\\",NORTH],AXIS[\\\"Longitude\\\",EAST],AUTHORITY[\\\"EPSG\\\",\\\"4326\\\"]]\"))], nothing, WellKnownText{GeoFormatTypes.CRS}(GeoFormatTypes.CRS(), \"GEOGCS[\\\"WGS 84\\\",DATUM[\\\"WGS_1984\\\",SPHEROID[\\\"WGS 84\\\",6378137,298.257223563,AUTHORITY[\\\"EPSG\\\",\\\"7030\\\"]],AUTHORITY[\\\"EPSG\\\",\\\"6326\\\"]],PRIMEM[\\\"Greenwich\\\",0,AUTHORITY[\\\"EPSG\\\",\\\"8901\\\"]],UNIT[\\\"degree\\\",0.0174532925199433,AUTHORITY[\\\"EPSG\\\",\\\"9122\\\"]],AXIS[\\\"Latitude\\\",NORTH],AXIS[\\\"Longitude\\\",EAST],AUTHORITY[\\\"EPSG\\\",\\\"4326\\\"]]\"))\n\n\n\nRasters.mask(src_srtm; with = masker)\n\nโ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ\nโ”‚ 465ร—457 Raster{UInt16,2} โ”‚\nโ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ dims โ”\n โ†“ X Projected{Float64} LinRange{Float64}(-113.23958321278403, -112.85291654614313, 465) ForwardOrdered Regular Intervals{Start},\n โ†’ Y Projected{Float64} LinRange{Float64}(37.51208342983253, 37.13208342985786, 457) ReverseOrdered Regular Intervals{Start}\nโ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ metadata โ”ค\n Metadata{Rasters.GDALsource} of Dict{String, Any} with 4 entries:\n \"units\" => \"\"\n \"offset\" => 0.0\n \"filepath\" => \"data/srtm.tif\"\n \"scale\" => 1.0\nโ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ raster โ”ค\n extent: Extent(X = (-113.23958321278403, -112.85208321280986), Y = (37.13208342985786, 37.512916763165805))\n missingval: 0xffff\n crs: GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]\nโ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜\n โ†“ โ†’ 37.5121 37.5113 37.5104 โ€ฆ 37.1329 37.1321\n -113.24 0xffff 0xffff 0xffff 0xffff 0xffff\n โ‹ฎ โ‹ฑ \n -112.853 0xffff 0xffff 0xffff โ€ฆ 0xffff 0xffff\n\n\n\n\n\nmasker = zion.geom\n\n1-element Vector{GeoInterface.Wrappers.Polygon{false, false, Vector{GeoInterface.Wrappers.LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Nothing, WellKnownText{GeoFormatTypes.CRS}}}, Nothing, WellKnownText{GeoFormatTypes.CRS}}}:\n GeoInterface.Wrappers.Polygon{false, false, Vector{GeoInterface.Wrappers.LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Nothing, WellKnownText{GeoFormatTypes.CRS}}}, Nothing, WellKnownText{GeoFormatTypes.CRS}}(GeoInterface.Wrappers.LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Nothing, WellKnownText{GeoFormatTypes.CRS}}[GeoInterface.Wrappers.LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Nothing, WellKnownText{GeoFormatTypes.CRS}}([(-113.08438684283465, 37.17130388117393), (-113.0859893948904, 37.17162779353809), (-113.09132351665151, 37.17215207288513), (-113.09132530000656, 37.17440980000173), (-113.09132830000658, 37.17803750000173), (-113.09133150000655, 37.18166840000168), (-113.09133470000658, 37.18529940000173), (-113.09134070000657, 37.188925000001696), (-113.09134660000655, 37.19255060000173), (-113.09135260000657, 37.19617620000171) โ€ฆ (-113.05962130000604, 37.17131270000172), (-113.06185720000607, 37.171310800001734), (-113.06415140000612, 37.17131020000169), (-113.06868130000619, 37.17130650000175), (-113.07101920000618, 37.17130460000174), (-113.07321130000625, 37.17130450000173), (-113.07773970000633, 37.17130420000172), (-113.08035690000636, 37.17130410000175), (-113.0822681000064, 37.171304000001705), (-113.08438684283465, 37.17130388117393)], nothing, WellKnownText{GeoFormatTypes.CRS}(GeoFormatTypes.CRS(), \"GEOGCS[\\\"WGS 84\\\",DATUM[\\\"WGS_1984\\\",SPHEROID[\\\"WGS 84\\\",6378137,298.257223563,AUTHORITY[\\\"EPSG\\\",\\\"7030\\\"]],AUTHORITY[\\\"EPSG\\\",\\\"6326\\\"]],PRIMEM[\\\"Greenwich\\\",0,AUTHORITY[\\\"EPSG\\\",\\\"8901\\\"]],UNIT[\\\"degree\\\",0.0174532925199433,AUTHORITY[\\\"EPSG\\\",\\\"9122\\\"]],AXIS[\\\"Latitude\\\",NORTH],AXIS[\\\"Longitude\\\",EAST],AUTHORITY[\\\"EPSG\\\",\\\"4326\\\"]]\"))], nothing, WellKnownText{GeoFormatTypes.CRS}(GeoFormatTypes.CRS(), \"GEOGCS[\\\"WGS 84\\\",DATUM[\\\"WGS_1984\\\",SPHEROID[\\\"WGS 84\\\",6378137,298.257223563,AUTHORITY[\\\"EPSG\\\",\\\"7030\\\"]],AUTHORITY[\\\"EPSG\\\",\\\"6326\\\"]],PRIMEM[\\\"Greenwich\\\",0,AUTHORITY[\\\"EPSG\\\",\\\"8901\\\"]],UNIT[\\\"degree\\\",0.0174532925199433,AUTHORITY[\\\"EPSG\\\",\\\"9122\\\"]],AXIS[\\\"Latitude\\\",NORTH],AXIS[\\\"Longitude\\\",EAST],AUTHORITY[\\\"EPSG\\\",\\\"4326\\\"]]\"))\n\n\n\nRasters.mask(src_srtm; with = masker)\n\nโ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ\nโ”‚ 465ร—457 Raster{UInt16,2} โ”‚\nโ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ dims โ”\n โ†“ X Projected{Float64} LinRange{Float64}(-113.23958321278403, -112.85291654614313, 465) ForwardOrdered Regular Intervals{Start},\n โ†’ Y Projected{Float64} LinRange{Float64}(37.51208342983253, 37.13208342985786, 457) ReverseOrdered Regular Intervals{Start}\nโ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ metadata โ”ค\n Metadata{Rasters.GDALsource} of Dict{String, Any} with 4 entries:\n \"units\" => \"\"\n \"offset\" => 0.0\n \"filepath\" => \"data/srtm.tif\"\n \"scale\" => 1.0\nโ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ raster โ”ค\n extent: Extent(X = (-113.23958321278403, -112.85208321280986), Y = (37.13208342985786, 37.512916763165805))\n missingval: 0xffff\n crs: GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]\nโ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜\n โ†“ โ†’ 37.5121 37.5113 37.5104 โ€ฆ 37.1329 37.1321\n -113.24 0xffff 0xffff 0xffff 0xffff 0xffff\n โ‹ฎ โ‹ฑ \n -112.853 0xffff 0xffff 0xffff โ€ฆ 0xffff 0xffff\n\n\n\n\n\n\n\n\n\n\n\n\nNote\n\n\n\nNote that since Julia has a native missing/NODATA value type, we donโ€™t need to specify a NODATA value for the mask function.\nHowever, it can sometimes be useful and more efficient to specify a sentinel value which Rasters treats as missing.\nYou can do this by specifying the missingval keyword argument, like so:\nout_image_mask = Rasters.mask(src_srtm; with = zion, missingval = 9999)\n\n\nWe can write this masked raster to file with Rasters.write:\n\nRasters.write(\"output/srtm_masked.tif\", out_image_mask; force = true\n)\n\n\"output/srtm_masked.tif\"\n\n\nIn Rasters.jl, cropping and masking are distinct operations. Cropping, which reduces the raster extent to the extent of the vector layer, is accomplished with the crop function.\nHere, we simply pass the zion feature table to the to keyword argument, which indicates what to crop the raster โ€œtoโ€. We also set the touches keyword argument to true, to specify that pixels that partially overlap with the vector layer are included in the output.\n\nout_image_crop = Rasters.crop(src_srtm; to = zion, touches = true)\n\nโ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ\nโ”‚ 439ร—436 Raster{UInt16,2} โ”‚\nโ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ dims โ”\n โ†“ X Projected{Float64} LinRange{Float64}(-113.22874987945141, -112.86374987947575, 439) ForwardOrdered Regular Intervals{Start},\n โ†’ Y Projected{Float64} LinRange{Float64}(37.50375009649975, 37.14125009652391, 436) ReverseOrdered Regular Intervals{Start}\nโ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ metadata โ”ค\n Metadata{Rasters.GDALsource} of Dict{String, Any} with 4 entries:\n \"units\" => \"\"\n \"offset\" => 0.0\n \"filepath\" => \"data/srtm.tif\"\n \"scale\" => 1.0\nโ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ raster โ”ค\n extent: Extent(X = (-113.22874987945141, -112.86291654614247), Y = (37.14125009652391, 37.504583429833026))\n missingval: 0xffff\n crs: GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]\nโ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜\n โ†“ โ†’ 37.5038 37.5029 37.5021 โ€ฆ 37.1421 37.1413\n -113.229 0x0668 0x065f 0x0659 0x05a0 0x05df\n โ‹ฎ โ‹ฑ โ‹ฎ \n -112.864 0x0a00 0x0a1a 0x0a24 0x06da 0x06d4\n\n\nYou can also assemble an extent manually, using Extents.Extent, or extract one using GI.extent.\nWe can crop our masked raster as well:\n\nout_image_mask_crop = Rasters.crop(out_image_mask; to = zion, touches = true)\n\nโ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ\nโ”‚ 439ร—436 Raster{UInt16,2} โ”‚\nโ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ dims โ”\n โ†“ X Projected{Float64} LinRange{Float64}(-113.22874987945141, -112.86374987947575, 439) ForwardOrdered Regular Intervals{Start},\n โ†’ Y Projected{Float64} LinRange{Float64}(37.50375009649975, 37.14125009652391, 436) ReverseOrdered Regular Intervals{Start}\nโ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ metadata โ”ค\n Metadata{Rasters.GDALsource} of Dict{String, Any} with 4 entries:\n \"units\" => \"\"\n \"offset\" => 0.0\n \"filepath\" => \"data/srtm.tif\"\n \"scale\" => 1.0\nโ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ raster โ”ค\n extent: Extent(X = (-113.22874987945141, -112.86291654614247), Y = (37.14125009652391, 37.504583429833026))\n missingval: 0xffff\n crs: GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]\nโ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜\n โ†“ โ†’ 37.5038 37.5029 37.5021 โ€ฆ 37.1421 37.1413\n -113.229 0xffff 0xffff 0xffff 0xffff 0xffff\n โ‹ฎ โ‹ฑ โ‹ฎ \n -112.864 0xffff 0xffff 0xffff 0xffff 0xffff\n\n\nand we write it to file using Rasters.write:\n\nRasters.write(\"output/srtm_masked_cropped.tif\", out_image_mask_crop; force = true)\n\n\"output/srtm_masked_cropped.tif\"\n\n\nFigureย 5.1 shows the original raster, and the three masking and/or cropping results.\n\nfig = Figure(size = (600, 600))\n\nax1 = Axis(fig[1, 1]; title = \"Original\")\nplot!(ax1, src_srtm)\npoly!(ax1, zion.geom; color = :transparent, strokecolor = :black, strokewidth = 0.75)\n\nax2 = Axis(fig[1, 2]; title = \"Masked\")\nplot!(ax2, out_image_mask)\npoly!(ax2, zion.geom; color = :transparent, strokecolor = :black, strokewidth = 0.75)\n\nax3 = Axis(fig[2, 1]; title = \"Cropped\")\nplot!(ax3, out_image_crop)\npoly!(ax3, zion.geom; color = :transparent, strokecolor = :black, strokewidth = 0.75)\n\nax4 = Axis(fig[2, 2]; title = \"Masked+Cropped\")\nplot!(ax4, out_image_mask_crop)\npoly!(ax4, zion.geom; color = :transparent, strokecolor = :black, strokewidth = 0.75)\n\ndisplay(fig)\n\n\n\n\n\n\n\n\n\n\n(a) Raster masking and cropping\n\n\n\n\n\n\n\nCairoMakie.Screen{PDF}\n\n\n(b)\n\n\n\n\n\nFigureย 5.1", + "crumbs": [ + "5ย  Raster-vector interactions" + ] + }, + { + "objectID": "chapters/05-raster-vector.html#sec-raster-extraction", + "href": "chapters/05-raster-vector.html#sec-raster-extraction", + "title": "5ย  Raster-vector interactions", + "section": "5.3 Raster extraction", + "text": "5.3 Raster extraction\nRaster extraction is the process of identifying and returning the values associated with a โ€˜targetโ€™ raster at specific locations, based on a (typically vector) geographic โ€˜selectorโ€™ object. The reverse of raster extractionโ€”assigning raster cell values based on vector objectsโ€”is rasterization, described in Section 5.4.\nRasters.jl provides modular raster extraction and statistics functions, and we use this package in the following examples.\n\nTo points (Section 5.3.1) or to lines (Section 5.3.2), via the Rasters.extract function\nTo polygons (Section 5.3.3), via the Rasters.zonal function\n\n\n5.3.1 Extraction to points\nThe simplest type of raster extraction is getting the values of raster cells at specific points. To demonstrate extraction to points, we will use zion_points, which contains a sample of 30 locations within the Zion National Park (Figureย 5.2).\n\nfig, ax, plt = plot(src_srtm)\nscatter!(ax, zion_points.geom, color=:black, strokecolor=:white, strokewidth = 1);\ndisplay(fig)\n\n\n\n\n\n\n\n\n\n\n(a) 30 point locations within the Zion National Park, with elevation in the background\n\n\n\n\n\n\n\nCairoMakie.Screen{PDF}\n\n\n(b)\n\n\n\n\n\nFigureย 5.2\n\n\n\n\nThe following expression extracts elevation values from srtm.tif according to zion_points, using Rasters.extract.\n\nThe API here is not great, can we do better? It currently returns a vector of named tuples, which is not very convenient.\nOne thought is to use a Tables.jl materializer to convert the result if possible. I understand the desire to return the geometry values. But there must be a better way than this.\n\n\nresult1 = DataFrame(Rasters.extract(src_srtm, zion_points; geometry = false))\n\n30ร—1 DataFrame24 rows omitted\n\n\n\nRow\n\n\n\n\nUInt16?\n\n\n\n\n1\n1802\n\n\n2\n2433\n\n\n3\n1886\n\n\nโ‹ฎ\nโ‹ฎ\n\n\n28\n1372\n\n\n29\n1905\n\n\n30\n1574\n\n\n\n\n\n\nThe first argument is the raster from which to extract values, and the second is the vector object (or collection of objects) according to which to extract the values.\n\n\n\n\n\n\nNote\n\n\n\nRasters.jl does not yet support interpolation in extraction, so the values extracted are the values of the nearest cell center.\nThis corresponds to interpolate='nearest' in the Python rasterstats package.\n\n\nEither way, the resulting object is a vector of raster values, corresponding to zion_points. For example, here are the elevations of the first five points.\n\nresult1[1:5, \"\"]\n\n5-element Vector{Union{Missing, UInt16}}:\n 0x070a\n 0x0981\n 0x075e\n 0x055a\n 0x05ac\n\n\nTo get a DataFrame with the original points geometries (and other attributes, if any), as well as the extracted raster values, we can assign the extraction result into a new column.\n\nzion_points[!, \"elev1\"] = result1[!, \"\"]\nzion_points\n\n30ร—2 DataFrame24 rows omitted\n\n\n\nRow\ngeom\nelev1\n\n\n\nIGeometrโ€ฆ\nUInt16?\n\n\n\n\n1\nGeometry: wkbPoint\n1802\n\n\n2\nGeometry: wkbPoint\n2433\n\n\n3\nGeometry: wkbPoint\n1886\n\n\nโ‹ฎ\nโ‹ฎ\nโ‹ฎ\n\n\n28\nGeometry: wkbPoint\n1372\n\n\n29\nGeometry: wkbPoint\n1905\n\n\n30\nGeometry: wkbPoint\n1574\n\n\n\n\n\n\nYou can read from a single band by selecting the band in the Raster. TODO finish this text\n\n\n5.3.2 Extraction to lines\nRaster extraction is also applicable with line selectors. The typical line extraction algorithm is to extract one value for each raster cell touched by a line. However, this particular approach is not recommended to obtain values along the transects, as it is hard to get the correct distance between each pair of extracted raster values.\nFor line extraction, a better approach is to split the line into many points (at equal distances along the line) and then extract the values for these points using the โ€œextraction to pointsโ€ technique (Section 5.3.1). To demonstrate this, the code below creates (see ?sec-vector-data for recap) zion_transect, a straight line going from northwest to southeast of the Zion National Park.\n\ncoords = [[-113.2, 37.45], [-112.9, 37.2]]\nzion_transect = GI.LineString(coords)\n\nGeoInterface.Wrappers.LineString{false, false, Vector{Vector{Float64}}, Nothing, Nothing}([[-113.2, 37.45], [-112.9, 37.2]], nothing, nothing)\n\n\nThe utility of extracting heights from a linear selector is illustrated by imagining that you are planning a hike. The method demonstrated below provides an โ€˜elevation profileโ€™ of the route (the line does not need to be straight), useful for estimating how long it will take by determining the cumulative elevation gain of your journey.\nFirst, we need to create a layer consisting of points along our line (zion_transect), at specified intervals (e.g., 250). To do that, we need to transform the line into a projected CRS (so that we work with true distances, in \\(m\\)), such as Universal Transverse Mercator.\n\nzion_transect_utm = GO.reproject(zion_transect; target_crs = GFT.EPSG(32612), source_crs = GFT.EPSG(4326))\n\nGeoInterface.Wrappers.LineString{false, false, Vector{Tuple{Float64, Float64}}, Nothing, EPSG{1}}([(305399.67208180577, 4.147066650206682e6), (331380.8917453843, 4.1187500947884847e6)], nothing, EPSG{1}((32612,)))\n\n\nThe printout of the new geometry shows this is still a straight line between two points, only with coordinates in a projected CRS.\n\nIโ€™ve chosen to differ from the Python treatment here - instead of selecting some number of points along the line explicitly, I will segmentize the line and extract the points. This is less precise, but we donโ€™t have the API to do arclength interpolation in GeometryOps yet. Hopefully this will be added soon.\ncf.ย https://github.com/JuliaGeo/GeometryOps.jl/issues/210\n\nHere, we interpolate points along the line using GO.segmentize. This operation is sometimes called line densification.\nWe first compute the length of the line, and then use this to segmentize the line into approximately 250 points.\n\n_centroid, linelen = GO.centroid_and_length(zion_transect_utm)\n\n((318390.28191359504, 4.1329083724975833e6), 38429.82026966749)\n\n\nNow that we have the length of the line, we can choose a distance such that we get around 250 points along the line.\n\nzion_transect_line = GO.segmentize(zion_transect_utm; max_distance = linelen / 250)\n\nGeoInterface.Wrappers.LineString{false, false, Vector{Tuple{Float64, Float64}}, Nothing, EPSG{1}}([(305399.67208180577, 4.147066650206682e6), (305503.59696046007, 4.146953383985009e6), (305607.5218391144, 4.1468401177633363e6), (305711.44671776873, 4.1467268515416635e6), (305815.37159642304, 4.1466135853199908e6), (305919.29647507734, 4.146500319098318e6), (306023.22135373164, 4.1463870528766452e6), (306127.14623238595, 4.1462737866549725e6), (306231.0711110403, 4.1461605204332997e6), (306334.9959896946, 4.1460472542116265e6) โ€ฆ (330445.5678374955, 4.11976949078354e6), (330549.4927161498, 4.119656224561867e6), (330653.41759480414, 4.119542958340194e6), (330757.34247345844, 4.1194296921185213e6), (330861.26735211274, 4.1193164258968486e6), (330965.19223076705, 4.119203159675176e6), (331069.11710942135, 4.119089893453503e6), (331173.0419880757, 4.1189766272318303e6), (331276.96686673, 4.1188633610101575e6), (331380.8917453843, 4.1187500947884847e6)], nothing, EPSG{1}((32612,)))\n\n\nThis gives us a collection of 251 points along the line. We can extract the points that define the line segments by using GI.getpoint on the line, and then reproject the points to the CRS of the raster.\n\nline_points = GI.getpoint(zion_transect_line)\nzion_transect_pnt = GO.reproject(line_points; target_crs = GI.crs(src_srtm), source_crs = GI.crs(zion_transect_line))\n\n251-element Vector{Tuple{Float64, Float64}}:\n (-113.2, 37.45)\n (-113.1987960226292, 37.449001661297636)\n (-113.19759207733796, 37.448003309143594)\n (-113.1963881641247, 37.44700494353919)\n (-113.1951842829878, 37.44600656448574)\n (-113.19398043392566, 37.44500817198453)\n (-113.19277661693668, 37.444009766036885)\n (-113.19157283201925, 37.4430113466441)\n (-113.19036907917175, 37.4420129138075)\n (-113.18916535839257, 37.44101446752837)\n โ‹ฎ\n (-112.90956920045984, 37.20801281544639)\n (-112.90837293949568, 37.20701125948384)\n (-112.90717671022236, 37.20600969038492)\n (-112.90598051263827, 37.205008108150906)\n (-112.90478434674178, 37.2040065127831)\n (-112.90358821253133, 37.20300490428282)\n (-112.9023921100053, 37.20200328265134)\n (-112.90119603916204, 37.201001647889974)\n (-112.90000000000002, 37.20000000000002)\n\n\nFinally, we extract the elevation values for each point in our transect and combine the information with zion_transect_pnt (after โ€œpromotingโ€ it to a DataFrame, to accommodate extra attributes), using the point extraction method shown earlier (Section 5.3.1). We also attach the distance of each point along the line, to be used to plot an elevation profile.\n\nzion_transect_pnt = DataFrame(geometry = zion_transect_pnt)\n\nresult = Rasters.extract(src_srtm, zion_transect_pnt; geometry = false)\n# `Rasters.extract` returns a vector of tuples, so we take the first element of each tuple\nresult = first.(result)\n# Add elevation data to a new data frame column, named `elevation`\nzion_transect_pnt[!, \"elevation\"] = result\n\n251-element Vector{UInt16}:\n 0x07d1\n 0x07f1\n 0x07e4\n 0x07c5\n 0x076e\n 0x074d\n 0x06fe\n 0x06e7\n 0x06d3\n 0x06ba\n โ‹ฎ\n 0x0720\n 0x079f\n 0x0769\n 0x0737\n 0x072d\n 0x0737\n 0x0729\n 0x070a\n 0x06e3\n\n\nWe also want to visualize an elevation profile along this line, so we compute the distances along the line manually. Using GO.distance, we can get the distance between successive points along the line. Then, we add 0.0 to the beginning of the array of distances, and sum along that array to get the cumulative distance along the line.\n\n# Compute distances between successive points along the line\ndistances_between_points = GO.distance.(zion_transect_pnt.geometry[1:end-1], zion_transect_pnt.geometry[2:end])\n# Compute cumulative distances along the line\ncumulative_distances = cumsum([0.0; distances_between_points])\n# Assign the distances to a new column in the data frame\nzion_transect_pnt[!, \"distance\"] = cumulative_distances\nzion_transect_pnt\n\n251ร—3 DataFrame245 rows omitted\n\n\n\nRow\ngeometry\nelevation\ndistance\n\n\n\nTupleโ€ฆ\nUInt16\nFloat64\n\n\n\n\n1\n(-113.2, 37.45)\n2001\n0.0\n\n\n2\n(-113.199, 37.449)\n2033\n0.00156405\n\n\n3\n(-113.198, 37.448)\n2020\n0.00312808\n\n\nโ‹ฎ\nโ‹ฎ\nโ‹ฎ\nโ‹ฎ\n\n\n249\n(-112.902, 37.202)\n1833\n0.387393\n\n\n250\n(-112.901, 37.201)\n1802\n0.388953\n\n\n251\n(-112.9, 37.2)\n1763\n0.390513\n\n\n\n\n\n\nThe information in zion_transect_pnt, namely the \"dist\" and \"elev\" attributes, can now be used to draw an elevation profile, as illustrated in Figureย 5.3.\n# Raster and a line transect\nfig, ax, plt = plot(src_srtm)\nlines!(ax, zion_transect; color = :black)\npoly!(ax, zion.geom; color = :transparent, strokecolor = :white, strokewidth = 0.75)\ndisplay(fig)\n# Elevation profile\nfig, ax, plt = lines(\n zion_transect_pnt.distance, \n zion_transect_pnt.elevation;\n axis = (;\n xlabel = \"Distance (m)\",\n ylabel = \"Elevation (m)\",\n )\n)\ndisplay(fig)\n\n\n\n\n\n\n\n\n\n\n\n(a) Raster and a line transect\n\n\n\n\n\n\n\n\n\n\n\n(b) Extracted elevation profile\n\n\n\n\n\n\n\n\n\n\nCairoMakie.Screen{PDF}\n\n\n(c)\n\n\n\n\n\n\n\nFigureย 5.3: Extracting a raster values profile to line\n\n\n\n\n\n5.3.3 Extraction to polygons\nThe final type of geographic vector object that can be used for raster extraction is polygons.\nLike lines, polygons tend to return many raster values per vector geometry. For continuous rasters (Figureย 5.4 (a)), we typically want to generate summary statistics for raster values per polygon, for example to characterize a single region or to compare many regions. The generation of raster summary statistics, by polygons, is demonstrated in the code below using Rasters.zonal, which creates a list of summary statistics for โ€œzonesโ€ defined by geometry. In this case, a vector of length 1 is returned, since there is just one polygon in the DataFrame.\n\nusing Statistics # for `mean`\nrmean = Rasters.zonal(mean, src_srtm; of = zion)\nrmin = Rasters.zonal(minimum, src_srtm; of = zion)\nrmax = Rasters.zonal(maximum, src_srtm; of = zion)\nresult = (rmean, rmin, rmax)\n\n(Union{Missing, Float64}[1818.211830154405], Union{Missing, UInt16}[0x0462], Union{Missing, UInt16}[0x0a65])\n\n\nRasters.zonal accepts any function that works on iterables (collections of elements that can be looped over) and returns a single value, like mean, minimum, maximum, std, median, mode, sum, prod, etc.\n\n\n\n\n\n\nYou can pass your own custom function too!\n\n\n\n\n\n\n# This is about the worst possible implementation of a mean function,\n# please don't use it in real life!\nfunction my_mean(iterable)\n result = 0.0\n count = 0\n for value in iterable\n result += value\n count += 1\n end\n result /= count\n return result\nend\n\nRasters.zonal(my_mean, src_srtm; of = zion)\n\n1-element Vector{Union{Missing, Float64}}:\n 1818.211830154405\n\n\n\n\n\n\n\n\n\n\n\nYou can pass multiple functions to a single Rasters.zonal call\n\n\n\n\n\nSometimes, in order to be most efficient with raster access, we might want to pass multiple functions to a single Rasters.zonal call. We can do this by passing a function that returns a tuple of values to Rasters.zonal.\nFor example, consider:\n\nusing Statistics # for `mean`\nRasters.zonal(x -> (mean(x), minimum(x), maximum(x)), src_srtm; of = zion)\n\n1-element Vector{Union{Missing, Tuple{Float64, UInt16, UInt16}}}:\n (1818.211830154405, 0x0462, 0x0a65)\n\n\n\n\n\nWe then transform the result to a DataFrame, which makes it easier to track and handle geometry attributes:\n\nDataFrame(mean = rmean, min = rmin, max = rmax)\n\n1ร—3 DataFrame\n\n\n\nRow\nmean\nmin\nmax\n\n\n\nFloat64?\nUInt16?\nUInt16?\n\n\n\n\n1\n1818.21\n1122\n2661\n\n\n\n\n\n\nBecause there is only one polygon in the example, single-element vectors are returned. However, if zion was composed of more than one polygon, we would accordingly get more elements in the returned vectors. The result provides useful summaries, for example that the maximum height in the park is 2661 \\(m\\) above sea level.\nTo count occurrences of categorical raster values within polygons (Figureย 5.4 (b)), we can use masking (Section 5.2) combined with StatsBase.countmap, as follows.\n\nout_image = Rasters.mask(src_nlcd; with = GO.reproject(zion; target_crs = GI.crs(src_nlcd)))\nusing StatsBase\ncounts = StatsBase.countmap(out_image)\n\nDict{UInt8, Int64} with 8 entries:\n 0x05 => 203701\n 0x04 => 298299\n 0x06 => 235\n 0x07 => 62\n 0x02 => 4205\n 0xff => 852741\n 0x08 => 679\n 0x03 => 98285\n\n\nAccording to the result, for example, the value 2 (โ€œDevelopedโ€ class) appears in 4205 pixels within the Zion polygon.\nFigureย 5.4 illustrates the two types of raster extraction to polygons described above.\n# Continuous raster\nfig, ax, plt = plot(src_srtm)\npoly!(ax, zion.geom; color = :transparent, strokecolor = :black, strokewidth = 0.75)\ndisplay(fig)\n# Categorical raster\nfig, ax, plt = plot(src_nlcd; colormap = cgrad(:Set3; categorical = true), source = GI.crs(src_nlcd), axis = (; type = GeoAxis, dest = GI.crs(src_nlcd)))\npoly!(ax, zion.geom; source = GI.crs(zion.geom[1]), color = :transparent, strokecolor = :black, strokewidth = 0.75)\ncm = Colorbar(fig[1, 2], plt)\nax.xgridvisible = false\nax.ygridvisible = false\ndisplay(fig) # TODO: make GeoMakie better on small extents\n\n\n\n\n\n\n\n\n\n\n\n(a) Continuous raster\n\n\n\n\n\n\n\n\n\n\n\n(b) Categorical raster\n\n\n\n\n\n\n\n\n\n\nCairoMakie.Screen{PDF}\n\n\n(c)\n\n\n\n\n\n\n\nFigureย 5.4: Sample data used for continuous and categorical raster extraction to a polygon", + "crumbs": [ + "5ย  Raster-vector interactions" + ] + }, + { + "objectID": "chapters/05-raster-vector.html#sec-rasterization", + "href": "chapters/05-raster-vector.html#sec-rasterization", + "title": "5ย  Raster-vector interactions", + "section": "5.4 Rasterization", + "text": "5.4 Rasterization\nRasterization is the conversion of vector objects into their representation in raster objects. Usually, the output raster is used for quantitative analysis (e.g., analysis of terrain) or modeling. As we saw in Chapter 1, the raster data model has some characteristics that make it conducive to certain methods. Furthermore, the process of rasterization can help simplify datasets because the resulting values all have the same spatial resolution: rasterization can be seen as a special type of geographic data aggregation.\nRasters.jl provides the Rasters.rasterize function for rasterizing vector data. To make this happen, we need to have some definition for a โ€œtemplateโ€ grid, i.e., the โ€œtemplateโ€ raster defining the extent, resolution and CRS of the output. We can also pass a pre-existing raster, in which case Rasters uses the existing grid definition.\nAs for the vector geometries and their associated values, the Rasters.rasterize function can take input in multiple ways: - ; data::FeatureCollection, fill::Symbol means that the values of the column passed to fill will be used to fill the raster. - ; data::Vector{Geometry}, fill::Vector means that the values passed to fill will be associated with each geometry in data and used to fill the raster. - ; data::Any, fill::Function calls fill with the current value\nFurthermore, we define how to handle multiple values burned into the same pixel, in the first argument called reducer.\nBy default, this is last, meaning that the last polygon to be rasterized takes precedence.\nHowever, we can pass any function that takes in an iterable and returns a single value, like mean, minimum, maximum, std, median, mode, sum, prod, etc.\nMany of these may not make sense to use but they are useful to know of.\nFurthermore, we define how to deal with multiple values burned into the same pixel, in the first argument called reducer. By default, this is last, meaning that the last polygon to be rasterized takes precedence. However, we can pass any function that takes in an iterable and returns a single value, like mean, minimum, maximum, std, median, mode, sum, prod, etc. Many of these may not make sense to use but they are useful to know of.\nFinally, we can set the fill value, which is the value that โ€œunaffectedโ€ pixels get, with fill=0 being the default.\nHow the Rasters.rasterize function works with all of these various parameters will be made clear in the next examples.\nThe spatial resolution (area of each โ€œpixelโ€) of the โ€œtemplateโ€ raster has a major impact on the results: if it is too low (cell size is too large), the result may miss the full geographic variability of the vector data; if it is too high, computational times may be excessive. There are no simple rules to follow when deciding an appropriate geographic resolution, which is heavily dependent on the intended use of the results. Often the target resolution is imposed on the user, for example when the output of rasterization needs to be aligned to an existing raster.\nDepending on the input data, rasterization typically takes one of two forms which we demonstrate next:\n\nin point rasterization (Section 5.4.1), we typically choose how to treat multiple points: either to summarize presence/absence, point count, or summed attribute values (Figureย 5.5)\nin line and polygon rasterization (Section 5.4.2), there are typically no such โ€œoverlapsโ€ and we simply โ€œburnโ€ attribute values, or fixed values, into pixels coinciding with the given geometries (Figureย 5.6)\n\n\n5.4.1 Rasterizing points\nTo demonstrate point rasterization, we will prepare a โ€œtemplateโ€ raster that has the same extent and CRS as the input vector data cycle_hire_osm_projected (a dataset on cycle hire points in London, illustrated in Figureย 5.5 (a)) and a spatial resolution of 1000 \\(m\\). To do that, we first take our point layer and transform it to a projected CRS.\n\ncycle_hire_osm_projected = GO.reproject(cycle_hire_osm; target_crs = GFT.EPSG(27700))\n\n540ร—6 DataFrame534 rows omitted\n\n\n\nRow\ngeom\nosm_id\nname\ncapacity\ncyclestreets_id\ndescription\n\n\n\nTupleโ€ฆ\nString\nString?\nFloat64?\nString?\nString?\n\n\n\n\n1\n(5.32354e5, 1.82858e5)\n108539\nWindsor Terrace\n14.0\nmissing\nmissing\n\n\n2\n(5.29848e5, 1.83337e5)\n598093293\nPancras Road, King's Cross\nmissing\nmissing\nmissing\n\n\n3\n(5.30636e5, 182609.0)\n772536185\nClerkenwell, Ampton Street\n11.0\nmissing\nmissing\n\n\nโ‹ฎ\nโ‹ฎ\nโ‹ฎ\nโ‹ฎ\nโ‹ฎ\nโ‹ฎ\nโ‹ฎ\n\n\n538\n(5.32531e5, 1.78805e5)\n5121513755\nmissing\n5.0\nmissing\nmissing\n\n\n539\n(5.38723e5, 1.80836e5)\n5188912370\nmissing\nmissing\nmissing\nmissing\n\n\n540\n(5.38413e5, 1.80774e5)\n5188912371\nmissing\nmissing\nmissing\nmissing\n\n\n\n\n\n\nWe can then use the Rasters.rasterize function to rasterize the points.\n\nThis isnโ€™t a great way to get an extent, but needs must. Currently we get the extent of cycle_hire_osm_projected by GI.extent(GI.LineString(cycle_hire_osm_projected.geom)).\nTrack https://github.com/geocompx/geocompjl/issues/5 to see if thereโ€™s a better way to get an extent from a vector of geometries.\n\nAs mentioned above, point rasterization can be a very flexible operation: the results depend not only on the nature of the template raster, but also on the the pixel โ€œactivationโ€ method, namely the way we deal with multiple points matching the same pixel.\nTo illustrate this flexibility, we will try three different approaches to point rasterization (Figureย 5.5 (b)-(d)). First, we create a raster representing the presence or absence of cycle hire points (known as presence/absence rasters). In this case, we transfer the value of 1 to all pixels where at least one point falls in. In the Rasters.jl framework, we use the Rasters.rasterize function, as described above. In this first example, we want to write the value 1 where the points are present, and 0 otherwise.\n\nch_raster1 = Rasters.rasterize(\n last, # reducer\n cycle_hire_osm_projected; # data\n fill = 1, \n size = (1000, 1000) # specify size in \"pixels\"\n)\n\nโ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ\nโ”‚ 1000ร—1000 Raster{Union{Missing, Int64},2} last โ”‚\nโ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ dims โ”\n โ†“ X Projected{Float64} 523038.61452275474:15.684723604717:538707.653403867 ForwardOrdered Regular Intervals{Start},\n โ†’ Y Projected{Float64} 174934.65727249475:10.03675127048517:184961.37179170942 ForwardOrdered Regular Intervals{Start}\nโ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ metadata โ”ค\n Metadata of Dict{Any, Any}()\nโ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ raster โ”ค\n extent: Extent(X = (523038.61452275474, 538723.3381274717), Y = (174934.65727249475, 184971.40854297992))\n missingval: missing\nโ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜\n โ†“ โ†’ 1.74935e5 1.74945e5 โ€ฆ 1.84941e5 1.84951e5 1.84961e5\n 5.23039e5 missing missing missing missing missing\n 5.23054e5 missing missing missing missing missing\n 523070.0 missing missing missing missing missing\n โ‹ฎ โ‹ฑ โ‹ฎ\n 5.38676e5 missing missing missing missing missing\n 538692.0 missing missing missing missing missing\n 5.38708e5 missing missing โ€ฆ missing missing missing\n\n\nIn our second variant of point rasterization, we count the number of bike hire stations. To do that, we use the fixed value of 1 (same as in the last example), but this time combined with the reducer=sum argument. That way, multiple values burned into the same pixel are summed, rather than replaced keeping last (which is the default). The new output, ch_raster2, shows the number of cycle hire points in each grid cell.\n\nch_raster2 = Rasters.rasterize(\n sum, # reducer\n cycle_hire_osm_projected; # data\n fill = 1, \n size = (1000, 1000) # specify size in \"pixels\"\n)\n\nโ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ\nโ”‚ 1000ร—1000 Raster{Union{Missing, Int64},2} sum โ”‚\nโ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ dims โ”\n โ†“ X Projected{Float64} 523038.61452275474:15.684723604717:538707.653403867 ForwardOrdered Regular Intervals{Start},\n โ†’ Y Projected{Float64} 174934.65727249475:10.03675127048517:184961.37179170942 ForwardOrdered Regular Intervals{Start}\nโ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ metadata โ”ค\n Metadata of Dict{Any, Any}()\nโ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ raster โ”ค\n extent: Extent(X = (523038.61452275474, 538723.3381274717), Y = (174934.65727249475, 184971.40854297992))\n missingval: missing\nโ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜\n โ†“ โ†’ 1.74935e5 1.74945e5 โ€ฆ 1.84941e5 1.84951e5 1.84961e5\n 5.23039e5 missing missing missing missing missing\n 5.23054e5 missing missing missing missing missing\n 523070.0 missing missing missing missing missing\n โ‹ฎ โ‹ฑ โ‹ฎ\n 5.38676e5 missing missing missing missing missing\n 538692.0 missing missing missing missing missing\n 5.38708e5 missing missing โ€ฆ missing missing missing\n\n\nThe cycle hire locations have different numbers of bicycles described by the capacity variable, raising the question, what is the capacity in each grid cell? To calculate that, in our third point rasterization variant we sum the field ('capacity') rather than the fixed values of 1.\nThis is extremely simple to run, but we will show how to do this two ways: first, by passing the column name in the feature collection to fill.\n\nch_raster3 = Rasters.rasterize(\n sum, # reducer\n cycle_hire_osm_projected; # data\n fill = :capacity, \n size = (1000, 1000) # specify size in \"pixels\"\n)\n\nโ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ\nโ”‚ 1000ร—1000 Raster{Union{Missing, Float64},2} capacity โ”‚\nโ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ dims โ”\n โ†“ X Projected{Float64} 523038.61452275474:15.684723604717:538707.653403867 ForwardOrdered Regular Intervals{Start},\n โ†’ Y Projected{Float64} 174934.65727249475:10.03675127048517:184961.37179170942 ForwardOrdered Regular Intervals{Start}\nโ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ metadata โ”ค\n Metadata of Dict{Any, Any}()\nโ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ raster โ”ค\n extent: Extent(X = (523038.61452275474, 538723.3381274717), Y = (174934.65727249475, 184971.40854297992))\n missingval: missing\nโ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜\n โ†“ โ†’ 1.74935e5 1.74945e5 โ€ฆ 1.84941e5 1.84951e5 1.84961e5\n 5.23039e5 missing missing missing missing missing\n 5.23054e5 missing missing missing missing missing\n 523070.0 missing missing missing missing missing\n โ‹ฎ โ‹ฑ โ‹ฎ\n 5.38676e5 missing missing missing missing missing\n 538692.0 missing missing missing missing missing\n 5.38708e5 missing missing โ€ฆ missing missing missing\n\n\nSecond, by passing the vectors of geometries and values separately.\n\nch_raster3 = Rasters.rasterize(\n sum, # reducer\n cycle_hire_osm_projected.geom; # data\n fill = cycle_hire_osm_projected.capacity, \n crs = GI.crs(cycle_hire_osm_projected),\n size = (1000, 1000) # specify size in \"pixels\"\n)\n\nโ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ\nโ”‚ 1000ร—1000 Raster{Union{Missing, Float64},2} sum โ”‚\nโ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ dims โ”\n โ†“ X Projected{Float64} 523038.61452275474:15.684723604717:538707.653403867 ForwardOrdered Regular Intervals{Start},\n โ†’ Y Projected{Float64} 174934.65727249475:10.03675127048517:184961.37179170942 ForwardOrdered Regular Intervals{Start}\nโ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ metadata โ”ค\n Metadata of Dict{Any, Any}()\nโ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ raster โ”ค\n extent: Extent(X = (523038.61452275474, 538723.3381274717), Y = (174934.65727249475, 184971.40854297992))\n missingval: missing\n crs: EPSG:27700\nโ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜\n โ†“ โ†’ 1.74935e5 1.74945e5 โ€ฆ 1.84941e5 1.84951e5 1.84961e5\n 5.23039e5 missing missing missing missing missing\n 5.23054e5 missing missing missing missing missing\n 523070.0 missing missing missing missing missing\n โ‹ฎ โ‹ฑ โ‹ฎ\n 5.38676e5 missing missing missing missing missing\n 538692.0 missing missing missing missing missing\n 5.38708e5 missing missing โ€ฆ missing missing missing\n\n\nThe input point layer cycle_hire_osm_projected and the three variants of rasterizing it ch_raster1, ch_raster2, and ch_raster3 are shown in Figureย 5.5.\n# Input points\nnonmissing_df = dropmissing(cycle_hire_osm_projected, [:capacity, :geom])\nf, a, p = scatter(nonmissing_df.geom; color = nonmissing_df.capacity)\nColorbar(f[1, 2], p)\ndisplay(f)\n# Presence/Absence\nplot(ch_raster1) |> display\n# Point counts\nplot(ch_raster2) |> display\n# Summed attribute values\nplot(ch_raster3)\n\n\n\n\n\n\n\n\n\n\n\n(a) Input points\n\n\n\n\n\n\n\n\n\n\n\n(b) Presence/Absence\n\n\n\n\n\n\n\n\n\n\n\n\n\n(c) Point counts\n\n\n\n\n\n\n\n\n\n\n\n(d) Summed attribute values\n\n\n\n\n\n\n\nFigureย 5.5: Original data and three variants of point rasterization\n\n\n\n\n\n5.4.2 Rasterizing lines and polygons\nAnother dataset based on Californiaโ€™s polygons and borders (created below) illustrates rasterization of lines. There are three preliminary steps. First, we subset the California polygon.\n\ncalifornia = us_states[ us_states[!, \"NAME\"] .== \"California\", :]\n\n1ร—7 DataFrame\n\n\n\nRow\ngeom\nGEOID\nNAME\nREGION\nAREA\ntotal_pop_10\ntotal_pop_15\n\n\n\nIGeometrโ€ฆ\nString\nString\nString\nFloat64\nFloat64\nFloat64\n\n\n\n\n1\nGeometry: wkbMultiPolygon\n06\nCalifornia\nWest\n4.09747e5\n3.66373e7\n3.84215e7\n\n\n\n\n\n\nSecond, we obtain the borders of the polygon as a `โ€˜MultiLineStringโ€™\n\ncalifornia_geom = only(california.geom)\ncalifornia_borders = GI.MultiLineString(GI.LineString.(GI.getexterior.(GI.getgeom(california_geom))); crs = GI.crs(california_geom)) # TODO: make this a lot better....\n\nGeoInterface.Wrappers.MultiLineString{false, false, Vector{GeoInterface.Wrappers.LineString{false, false, ArchGDAL.IGeometry{ArchGDAL.wkbLineString}, Nothing, Nothing}}, Nothing, WellKnownText{GeoFormatTypes.CRS}}(GeoInterface.Wrappers.LineString{false, false, ArchGDAL.IGeometry{ArchGDAL.wkbLineString}, Nothing, Nothing}[GeoInterface.Wrappers.LineString{false, false, ArchGDAL.IGeometry{ArchGDAL.wkbLineString}, Nothing, Nothing}(Geometry: LINEARRING (-118.603375 33.478098,-118.368301 33.4 ... 8098), nothing, nothing), GeoInterface.Wrappers.LineString{false, false, ArchGDAL.IGeometry{ArchGDAL.wkbLineString}, Nothing, Nothing}(Geometry: LINEARRING (-118.605534 33.030999,-118.369984 32.8 ... 0999), nothing, nothing), GeoInterface.Wrappers.LineString{false, false, ArchGDAL.IGeometry{ArchGDAL.wkbLineString}, Nothing, Nothing}(Geometry: LINEARRING (-119.919155 34.07728,-119.755521 34.05 ... 7728), nothing, nothing), GeoInterface.Wrappers.LineString{false, false, ArchGDAL.IGeometry{ArchGDAL.wkbLineString}, Nothing, Nothing}(Geometry: LINEARRING (-120.247393 34.001911,-120.073609 34.0 ... 1911), nothing, nothing), GeoInterface.Wrappers.LineString{false, false, ArchGDAL.IGeometry{ArchGDAL.wkbLineString}, Nothing, Nothing}(Geometry: LINEARRING (-124.211605 41.99846,-123.347562 41.99 ... 9846), nothing, nothing)], nothing, WellKnownText{GeoFormatTypes.CRS}(GeoFormatTypes.CRS(), \"GEOGCS[\\\"NAD83\\\",DATUM[\\\"North_American_Datum_1983\\\",SPHEROID[\\\"GRS 1980\\\",6378137,298.257222101,AUTHORITY[\\\"EPSG\\\",\\\"7019\\\"]],AUTHORITY[\\\"EPSG\\\",\\\"6269\\\"]],PRIMEM[\\\"Greenwich\\\",0,AUTHORITY[\\\"EPSG\\\",\\\"8901\\\"]],UNIT[\\\"degree\\\",0.0174532925199433,AUTHORITY[\\\"EPSG\\\",\\\"9122\\\"]],AXIS[\\\"Latitude\\\",NORTH],AXIS[\\\"Longitude\\\",EAST],AUTHORITY[\\\"EPSG\\\",\\\"4269\\\"]]\"))\n\n\nFinally, we rasterize california_borders on a grid with resolution of 0.5 degrees per pixel.\n\ncalifornia_raster1 = Rasters.rasterize(\n last,\n california_borders;\n fill = 1,\n res = 0.5, # degrees - this is in units of GI.crs(california_borders)\n boundary = :touches,\n)\n\nโ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ\nโ”‚ 21ร—19 Raster{Union{Missing, Int64},2} last โ”‚\nโ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ dims โ”\n โ†“ X Projected{Float64} -124.409591:0.5:-114.409591 ForwardOrdered Regular Intervals{Start},\n โ†’ Y Projected{Float64} 32.534156:0.5:41.534156 ForwardOrdered Regular Intervals{Start}\nโ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ metadata โ”ค\n Metadata of Dict{Any, Any}()\nโ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ raster โ”ค\n extent: Extent(X = (-124.409591, -113.909591), Y = (32.534156, 42.034156))\n missingval: missing\nโ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜\n โ†“ โ†’ 32.5342 33.0342 33.5342 โ€ฆ 40.5342 41.0342 41.5342\n -124.41 missing missing missing 1 1 1\n -123.91 missing missing missing missing missing 1\n -123.41 missing missing missing missing missing 1\n โ‹ฎ โ‹ฑ \n -115.41 1 missing missing missing missing missing\n -114.91 1 1 1 โ€ฆ missing missing missing\n -114.41 missing missing missing missing missing missing\n\n\nCompare it to a polygon rasterization, with all_touched=False (the default), which selects only raster cells whose centroids are inside the selector polygon, as illustrated in Figureย 5.6 (right).\n\ncalifornia_raster2 = Rasters.rasterize(\n last, \n california;\n geometrycolumn = :geom,\n fill = 1,\n res = 0.5,\n boundary = :center,\n)\n\nโ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ\nโ”‚ 21ร—19 Raster{Union{Missing, Int64},2} last โ”‚\nโ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ dims โ”\n โ†“ X Projected{Float64} -124.409591:0.5:-114.409591 ForwardOrdered Regular Intervals{Start},\n โ†’ Y Projected{Float64} 32.534156:0.5:41.534156 ForwardOrdered Regular Intervals{Start}\nโ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ metadata โ”ค\n Metadata of Dict{Any, Any} with 1 entry:\n \"missed_geometries\" => Bool[0]\nโ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ raster โ”ค\n extent: Extent(X = (-124.409591, -113.909591), Y = (32.534156, 42.034156))\n missingval: missing\nโ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜\n โ†“ โ†’ 32.5342 33.0342 33.5342 โ€ฆ 40.5342 41.0342 41.5342\n -124.41 missing missing missing 1 missing 1\n -123.91 missing missing missing 1 1 1\n โ‹ฎ โ‹ฑ \n -115.41 1 1 1 missing missing missing\n -114.91 1 missing 1 โ€ฆ missing missing missing\n -114.41 missing missing missing missing missing missing\n\n\nTo illustrate which raster pixels are actually selected as part of rasterization, we also show them as points. This also requires the following code section to calculate the points, which we explain in Section 5.5.\n\nDimPointsConstructing from lookups\n\n\n\ndp = DimPoints(california_raster1)\n\nโ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ\nโ”‚ 21ร—19 DimPoints{Tuple{Float64, Float64},2} โ”‚\nโ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ dims โ”\n โ†“ X Projected{Float64} -124.409591:0.5:-114.409591 ForwardOrdered Regular Intervals{Start},\n โ†’ Y Projected{Float64} 32.534156:0.5:41.534156 ForwardOrdered Regular Intervals{Start}\nโ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜\n โ†“ โ†’ 32.5342 33.0342 โ€ฆ 41.5342\n -124.41 (-124.41, 32.5342) (-124.41, 33.0342) (-124.41, 41.5342)\n -123.91 (-123.91, 32.5342) (-123.91, 33.0342) (-123.91, 41.5342)\n -123.41 (-123.41, 32.5342) (-123.41, 33.0342) (-123.41, 41.5342)\n -122.91 (-122.91, 32.5342) (-122.91, 33.0342) (-122.91, 41.5342)\n โ‹ฎ โ‹ฑ \n -116.41 (-116.41, 32.5342) (-116.41, 33.0342) (-116.41, 41.5342)\n -115.91 (-115.91, 32.5342) (-115.91, 33.0342) (-115.91, 41.5342)\n -115.41 (-115.41, 32.5342) (-115.41, 33.0342) (-115.41, 41.5342)\n -114.91 (-114.91, 32.5342) (-114.91, 33.0342) โ€ฆ (-114.91, 41.5342)\n -114.41 (-114.41, 32.5342) (-114.41, 33.0342) (-114.41, 41.5342)\n\n\n\n\nDimensionalData.jl (which underpins Rasters.jl) provides easy ways to get โ€œlookupsโ€, i.e, axis index values, from a raster.\nNote that these lookups may encode intervals, points, or anything in between - so you should use shiftlocus or set to get the actual point values! DimPoints does this for you.\nBut if you want to see how this can be done automatically, here you go.\n\n[(x, y) for x in dims(california_raster1, X), y in dims(california_raster1, Y)]\n\n21ร—19 Matrix{Tuple{Float64, Float64}}:\n (-124.41, 32.5342) (-124.41, 33.0342) โ€ฆ (-124.41, 41.5342)\n (-123.91, 32.5342) (-123.91, 33.0342) (-123.91, 41.5342)\n (-123.41, 32.5342) (-123.41, 33.0342) (-123.41, 41.5342)\n (-122.91, 32.5342) (-122.91, 33.0342) (-122.91, 41.5342)\n (-122.41, 32.5342) (-122.41, 33.0342) (-122.41, 41.5342)\n (-121.91, 32.5342) (-121.91, 33.0342) โ€ฆ (-121.91, 41.5342)\n (-121.41, 32.5342) (-121.41, 33.0342) (-121.41, 41.5342)\n (-120.91, 32.5342) (-120.91, 33.0342) (-120.91, 41.5342)\n (-120.41, 32.5342) (-120.41, 33.0342) (-120.41, 41.5342)\n (-119.91, 32.5342) (-119.91, 33.0342) (-119.91, 41.5342)\n โ‹ฎ โ‹ฑ \n (-118.41, 32.5342) (-118.41, 33.0342) (-118.41, 41.5342)\n (-117.91, 32.5342) (-117.91, 33.0342) (-117.91, 41.5342)\n (-117.41, 32.5342) (-117.41, 33.0342) (-117.41, 41.5342)\n (-116.91, 32.5342) (-116.91, 33.0342) โ€ฆ (-116.91, 41.5342)\n (-116.41, 32.5342) (-116.41, 33.0342) (-116.41, 41.5342)\n (-115.91, 32.5342) (-115.91, 33.0342) (-115.91, 41.5342)\n (-115.41, 32.5342) (-115.41, 33.0342) (-115.41, 41.5342)\n (-114.91, 32.5342) (-114.91, 33.0342) (-114.91, 41.5342)\n (-114.41, 32.5342) (-114.41, 33.0342) โ€ฆ (-114.41, 41.5342)\n\n\nYou can see that this encodes the same values as dp in the other tab.\nTODO: firm up the description here and add links.\n\n\n\nFigureย 5.6 shows the input vector layer, the rasterization results, and the points pnt.\n# Line rasterization\nfig, ax, plt = plot(california_raster1; colormap = cgrad(:Set3; categorical = true))\nlines!(ax, california_borders; color = :darkgrey, linewidth = 1)\nscatter!(ax, vec(dp); markersize = 3, color = :black)\ndisplay(fig)\n# Polygon rasterization\nfig, ax, plt = plot(california_raster2; colormap = cgrad(:Set3; categorical = true))\nlines!(ax, california_borders; color = :darkgrey, linewidth = 1)\nscatter!(ax, vec(dp); markersize = 3, color = :black)\nfig\n\n\n\n\n\n\n\n\n\n\n\n(a) Line rasterization w/ boundary=:touches\n\n\n\n\n\n\n\n\n\n\n\n(b) Polygon rasterization w/ boundary=:center\n\n\n\n\n\n\n\nFigureย 5.6: Examples of line and polygon rasterization", + "crumbs": [ + "5ย  Raster-vector interactions" + ] + }, + { + "objectID": "chapters/05-raster-vector.html#sec-spatial-vectorization", + "href": "chapters/05-raster-vector.html#sec-spatial-vectorization", + "title": "5ย  Raster-vector interactions", + "section": "5.5 Spatial vectorization", + "text": "5.5 Spatial vectorization\nSpatial vectorization is the counterpart of rasterization (Section 5.4). It involves converting spatially continuous raster data into spatially discrete vector data such as points, lines or polygons. There are three standard methods to convert a raster to a vector layer, which we cover next:\n\nRaster to polygons (Section 5.5.1)โ€”converting raster cells to rectangular polygons, representing pixel areas\nRaster to points (Section 5.5.2)โ€”converting raster cells to points, representing pixel centroids\nRaster to contours (Section 5.5.3)\n\nLet us demonstrate all three in the given order.\n\n5.5.1 Raster to polygons\nRasters.jl does not currently have a function to convert a raster to a feature collection with one polygon per pixel or cell. This is a similar situation in Python with rasterio.\nGeometryOps.jl offers a polygonize function that returns a feature collection of polygons, where each feature has a value property that encodes the value of all pixels within that polygon. Each polygon contains pixels with the same value.\n\nfc = GO.polygonize(src_grain)\n\nGeoInterface.Wrappers.FeatureCollection{Vector{GeoInterface.Wrappers.Feature{@NamedTuple{geometry::GeoInterface.Wrappers.MultiPolygon{false, false, Vector{GeoInterface.Wrappers.Polygon{false, false, Vector{GeoInterface.Wrappers.LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{GeoFormatTypes.CRS}}}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{GeoFormatTypes.CRS}}}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{GeoFormatTypes.CRS}}, value::UInt8}, WellKnownText{GeoFormatTypes.CRS}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}}}, WellKnownText{GeoFormatTypes.CRS}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}}(GeoInterface.Wrappers.Feature{@NamedTuple{geometry::GeoInterface.Wrappers.MultiPolygon{false, false, Vector{GeoInterface.Wrappers.Polygon{false, false, Vector{GeoInterface.Wrappers.LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{GeoFormatTypes.CRS}}}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{GeoFormatTypes.CRS}}}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{GeoFormatTypes.CRS}}, value::UInt8}, WellKnownText{GeoFormatTypes.CRS}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}}[GeoInterface.Wrappers.Feature{@NamedTuple{geometry::GeoInterface.Wrappers.MultiPolygon{false, false, Vector{GeoInterface.Wrappers.Polygon{false, false, Vector{GeoInterface.Wrappers.LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{GeoFormatTypes.CRS}}}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{GeoFormatTypes.CRS}}}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{GeoFormatTypes.CRS}}, value::UInt8}, WellKnownText{GeoFormatTypes.CRS}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}}((geometry = GeoInterface.Wrappers.MultiPolygon{false, false, Vector{GeoInterface.Wrappers.Polygon{false, false, Vector{GeoInterface.Wrappers.LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{GeoFormatTypes.CRS}}}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{GeoFormatTypes.CRS}}}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{GeoFormatTypes.CRS}}(GeoInterface.Wrappers.Polygon{false, false, Vector{GeoInterface.Wrappers.LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{GeoFormatTypes.CRS}}}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{GeoFormatTypes.CRS}}[GeoInterface.Wrappers.Polygon{false, false, Vector{GeoInterface.Wrappers.LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{GeoFormatTypes.CRS}}}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{GeoFormatTypes.CRS}}(GeoInterface.Wrappers.LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{GeoFormatTypes.CRS}}[GeoInterface.Wrappers.LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{GeoFormatTypes.CRS}}([(1.5, 1.5), (1.5, 3.5), (2.5, 3.5), (2.5, 4.5), (0.5, 4.5), (0.5, 1.5), (1.5, 1.5)], Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}((X = (0.5, 2.5), Y = (1.5, 4.5))), WellKnownText{GeoFormatTypes.CRS}(GeoFormatTypes.CRS(), \"GEOGCS[\\\"WGS 84\\\",DATUM[\\\"WGS_1984\\\",SPHEROID[\\\"WGS 84\\\",6378137,298.257223563,AUTHORITY[\\\"EPSG\\\",\\\"7030\\\"]],AUTHORITY[\\\"EPSG\\\",\\\"6326\\\"]],PRIMEM[\\\"Greenwich\\\",0,AUTHORITY[\\\"EPSG\\\",\\\"8901\\\"]],UNIT[\\\"degree\\\",0.0174532925199433,AUTHORITY[\\\"EPSG\\\",\\\"9122\\\"]],AXIS[\\\"Latitude\\\",NORTH],AXIS[\\\"Longitude\\\",EAST],AUTHORITY[\\\"EPSG\\\",\\\"4326\\\"]]\"))], Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}((X = (0.5, 2.5), Y = (1.5, 4.5))), WellKnownText{GeoFormatTypes.CRS}(GeoFormatTypes.CRS(), \"GEOGCS[\\\"WGS 84\\\",DATUM[\\\"WGS_1984\\\",SPHEROID[\\\"WGS 84\\\",6378137,298.257223563,AUTHORITY[\\\"EPSG\\\",\\\"7030\\\"]],AUTHORITY[\\\"EPSG\\\",\\\"6326\\\"]],PRIMEM[\\\"Greenwich\\\",0,AUTHORITY[\\\"EPSG\\\",\\\"8901\\\"]],UNIT[\\\"degree\\\",0.0174532925199433,AUTHORITY[\\\"EPSG\\\",\\\"9122\\\"]],AXIS[\\\"Latitude\\\",NORTH],AXIS[\\\"Longitude\\\",EAST],AUTHORITY[\\\"EPSG\\\",\\\"4326\\\"]]\")), GeoInterface.Wrappers.Polygon{false, false, Vector{GeoInterface.Wrappers.LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{GeoFormatTypes.CRS}}}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{GeoFormatTypes.CRS}}(GeoInterface.Wrappers.LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{GeoFormatTypes.CRS}}[GeoInterface.Wrappers.LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{GeoFormatTypes.CRS}}([(1.5, 1.5), (1.5, 0.5), (2.5, 0.5), (2.5, 1.5), (1.5, 1.5)], Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}((X = (1.5, 2.5), Y = (0.5, 1.5))), WellKnownText{GeoFormatTypes.CRS}(GeoFormatTypes.CRS(), \"GEOGCS[\\\"WGS 84\\\",DATUM[\\\"WGS_1984\\\",SPHEROID[\\\"WGS 84\\\",6378137,298.257223563,AUTHORITY[\\\"EPSG\\\",\\\"7030\\\"]],AUTHORITY[\\\"EPSG\\\",\\\"6326\\\"]],PRIMEM[\\\"Greenwich\\\",0,AUTHORITY[\\\"EPSG\\\",\\\"8901\\\"]],UNIT[\\\"degree\\\",0.0174532925199433,AUTHORITY[\\\"EPSG\\\",\\\"9122\\\"]],AXIS[\\\"Latitude\\\",NORTH],AXIS[\\\"Longitude\\\",EAST],AUTHORITY[\\\"EPSG\\\",\\\"4326\\\"]]\"))], Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}((X = (1.5, 2.5), Y = (0.5, 1.5))), WellKnownText{GeoFormatTypes.CRS}(GeoFormatTypes.CRS(), \"GEOGCS[\\\"WGS 84\\\",DATUM[\\\"WGS_1984\\\",SPHEROID[\\\"WGS 84\\\",6378137,298.257223563,AUTHORITY[\\\"EPSG\\\",\\\"7030\\\"]],AUTHORITY[\\\"EPSG\\\",\\\"6326\\\"]],PRIMEM[\\\"Greenwich\\\",0,AUTHORITY[\\\"EPSG\\\",\\\"8901\\\"]],UNIT[\\\"degree\\\",0.0174532925199433,AUTHORITY[\\\"EPSG\\\",\\\"9122\\\"]],AXIS[\\\"Latitude\\\",NORTH],AXIS[\\\"Longitude\\\",EAST],AUTHORITY[\\\"EPSG\\\",\\\"4326\\\"]]\")), GeoInterface.Wrappers.Polygon{false, false, Vector{GeoInterface.Wrappers.LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{GeoFormatTypes.CRS}}}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{GeoFormatTypes.CRS}}(GeoInterface.Wrappers.LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{GeoFormatTypes.CRS}}[GeoInterface.Wrappers.LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{GeoFormatTypes.CRS}}([(5.5, 5.5), (5.5, 6.5), (4.5, 6.5), (4.5, 5.5), (5.5, 5.5)], Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}((X = (4.5, 5.5), Y = (5.5, 6.5))), WellKnownText{GeoFormatTypes.CRS}(GeoFormatTypes.CRS(), \"GEOGCS[\\\"WGS 84\\\",DATUM[\\\"WGS_1984\\\",SPHEROID[\\\"WGS 84\\\",6378137,298.257223563,AUTHORITY[\\\"EPSG\\\",\\\"7030\\\"]],AUTHORITY[\\\"EPSG\\\",\\\"6326\\\"]],PRIMEM[\\\"Greenwich\\\",0,AUTHORITY[\\\"EPSG\\\",\\\"8901\\\"]],UNIT[\\\"degree\\\",0.0174532925199433,AUTHORITY[\\\"EPSG\\\",\\\"9122\\\"]],AXIS[\\\"Latitude\\\",NORTH],AXIS[\\\"Longitude\\\",EAST],AUTHORITY[\\\"EPSG\\\",\\\"4326\\\"]]\"))], Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}((X = (4.5, 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Tuple{Float64, Float64}}}, WellKnownText{GeoFormatTypes.CRS}}[GeoInterface.Wrappers.LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{GeoFormatTypes.CRS}}([(4.5, 3.5), (3.5, 3.5), (3.5, 2.5), (2.5, 2.5), (2.5, 1.5), (4.5, 1.5), (4.5, 2.5), (5.5, 2.5), (5.5, 3.5), (4.5, 3.5)], Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}((X = (2.5, 5.5), Y = (1.5, 3.5))), WellKnownText{GeoFormatTypes.CRS}(GeoFormatTypes.CRS(), \"GEOGCS[\\\"WGS 84\\\",DATUM[\\\"WGS_1984\\\",SPHEROID[\\\"WGS 84\\\",6378137,298.257223563,AUTHORITY[\\\"EPSG\\\",\\\"7030\\\"]],AUTHORITY[\\\"EPSG\\\",\\\"6326\\\"]],PRIMEM[\\\"Greenwich\\\",0,AUTHORITY[\\\"EPSG\\\",\\\"8901\\\"]],UNIT[\\\"degree\\\",0.0174532925199433,AUTHORITY[\\\"EPSG\\\",\\\"9122\\\"]],AXIS[\\\"Latitude\\\",NORTH],AXIS[\\\"Longitude\\\",EAST],AUTHORITY[\\\"EPSG\\\",\\\"4326\\\"]]\"))], Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}((X = (2.5, 5.5), Y = (1.5, 3.5))), WellKnownText{GeoFormatTypes.CRS}(GeoFormatTypes.CRS(), \"GEOGCS[\\\"WGS 84\\\",DATUM[\\\"WGS_1984\\\",SPHEROID[\\\"WGS 84\\\",6378137,298.257223563,AUTHORITY[\\\"EPSG\\\",\\\"7030\\\"]],AUTHORITY[\\\"EPSG\\\",\\\"6326\\\"]],PRIMEM[\\\"Greenwich\\\",0,AUTHORITY[\\\"EPSG\\\",\\\"8901\\\"]],UNIT[\\\"degree\\\",0.0174532925199433,AUTHORITY[\\\"EPSG\\\",\\\"9122\\\"]],AXIS[\\\"Latitude\\\",NORTH],AXIS[\\\"Longitude\\\",EAST],AUTHORITY[\\\"EPSG\\\",\\\"4326\\\"]]\"))], Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}((X = (0.5, 5.5), Y = (0.5, 6.5))), WellKnownText{GeoFormatTypes.CRS}(GeoFormatTypes.CRS(), \"GEOGCS[\\\"WGS 84\\\",DATUM[\\\"WGS_1984\\\",SPHEROID[\\\"WGS 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GeoInterface.Wrappers.Feature{@NamedTuple{geometry::GeoInterface.Wrappers.MultiPolygon{false, false, Vector{GeoInterface.Wrappers.Polygon{false, false, Vector{GeoInterface.Wrappers.LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{GeoFormatTypes.CRS}}}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{GeoFormatTypes.CRS}}}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{GeoFormatTypes.CRS}}, value::UInt8}, WellKnownText{GeoFormatTypes.CRS}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}}((geometry = GeoInterface.Wrappers.MultiPolygon{false, false, Vector{GeoInterface.Wrappers.Polygon{false, false, Vector{GeoInterface.Wrappers.LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, 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WellKnownText{GeoFormatTypes.CRS}}(GeoInterface.Wrappers.LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{GeoFormatTypes.CRS}}[GeoInterface.Wrappers.LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{GeoFormatTypes.CRS}}([(0.5, 4.5), (2.5, 4.5), (2.5, 3.5), (6.5, 3.5), (6.5, 5.5), (4.5, 5.5), (4.5, 4.5), (3.5, 4.5), (3.5, 5.5), (2.5, 5.5), (2.5, 6.5), (1.5, 6.5), (1.5, 5.5), (0.5, 5.5), (0.5, 4.5)], Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}((X = (0.5, 6.5), Y = (3.5, 6.5))), WellKnownText{GeoFormatTypes.CRS}(GeoFormatTypes.CRS(), \"GEOGCS[\\\"WGS 84\\\",DATUM[\\\"WGS_1984\\\",SPHEROID[\\\"WGS 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Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}((X = (0.5, 1.5), Y = (5.5, 6.5))), WellKnownText{GeoFormatTypes.CRS}(GeoFormatTypes.CRS(), \"GEOGCS[\\\"WGS 84\\\",DATUM[\\\"WGS_1984\\\",SPHEROID[\\\"WGS 84\\\",6378137,298.257223563,AUTHORITY[\\\"EPSG\\\",\\\"7030\\\"]],AUTHORITY[\\\"EPSG\\\",\\\"6326\\\"]],PRIMEM[\\\"Greenwich\\\",0,AUTHORITY[\\\"EPSG\\\",\\\"8901\\\"]],UNIT[\\\"degree\\\",0.0174532925199433,AUTHORITY[\\\"EPSG\\\",\\\"9122\\\"]],AXIS[\\\"Latitude\\\",NORTH],AXIS[\\\"Longitude\\\",EAST],AUTHORITY[\\\"EPSG\\\",\\\"4326\\\"]]\"))], Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}((X = (0.5, 6.5), Y = (0.5, 6.5))), WellKnownText{GeoFormatTypes.CRS}(GeoFormatTypes.CRS(), \"GEOGCS[\\\"WGS 84\\\",DATUM[\\\"WGS_1984\\\",SPHEROID[\\\"WGS 84\\\",6378137,298.257223563,AUTHORITY[\\\"EPSG\\\",\\\"7030\\\"]],AUTHORITY[\\\"EPSG\\\",\\\"6326\\\"]],PRIMEM[\\\"Greenwich\\\",0,AUTHORITY[\\\"EPSG\\\",\\\"8901\\\"]],UNIT[\\\"degree\\\",0.0174532925199433,AUTHORITY[\\\"EPSG\\\",\\\"9122\\\"]],AXIS[\\\"Latitude\\\",NORTH],AXIS[\\\"Longitude\\\",EAST],AUTHORITY[\\\"EPSG\\\",\\\"4326\\\"]]\")), value = 0x02), WellKnownText{GeoFormatTypes.CRS}(GeoFormatTypes.CRS(), \"GEOGCS[\\\"WGS 84\\\",DATUM[\\\"WGS_1984\\\",SPHEROID[\\\"WGS 84\\\",6378137,298.257223563,AUTHORITY[\\\"EPSG\\\",\\\"7030\\\"]],AUTHORITY[\\\"EPSG\\\",\\\"6326\\\"]],PRIMEM[\\\"Greenwich\\\",0,AUTHORITY[\\\"EPSG\\\",\\\"8901\\\"]],UNIT[\\\"degree\\\",0.0174532925199433,AUTHORITY[\\\"EPSG\\\",\\\"9122\\\"]],AXIS[\\\"Latitude\\\",NORTH],AXIS[\\\"Longitude\\\",EAST],AUTHORITY[\\\"EPSG\\\",\\\"4326\\\"]]\"), Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}((X = (0.5, 6.5), Y = (0.5, 6.5))))], WellKnownText{GeoFormatTypes.CRS}(GeoFormatTypes.CRS(), \"GEOGCS[\\\"WGS 84\\\",DATUM[\\\"WGS_1984\\\",SPHEROID[\\\"WGS 84\\\",6378137,298.257223563,AUTHORITY[\\\"EPSG\\\",\\\"7030\\\"]],AUTHORITY[\\\"EPSG\\\",\\\"6326\\\"]],PRIMEM[\\\"Greenwich\\\",0,AUTHORITY[\\\"EPSG\\\",\\\"8901\\\"]],UNIT[\\\"degree\\\",0.0174532925199433,AUTHORITY[\\\"EPSG\\\",\\\"9122\\\"]],AXIS[\\\"Latitude\\\",NORTH],AXIS[\\\"Longitude\\\",EAST],AUTHORITY[\\\"EPSG\\\",\\\"4326\\\"]]\"), Extents.Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}((X = (0.5, 6.5), Y = (0.5, 6.5))))\n\n\nWe can convert this โ€œfeature collectionโ€ to a DataFrame as follows. That makes it a lot easier to work with.\n\ndf = DataFrame([GI.properties(f) for f in GI.getfeature(fc)])\ndf.geometry = [GI.geometry(f) for f in GI.getfeature(fc)]\ndf\n\n3ร—2 DataFrame\n\n\n\nRow\nvalue\ngeometry\n\n\n\nUInt8\nMultiPolโ€ฆ\n\n\n\n\n1\n0\nMultiPolygon{false, false, Vector{Polygon{false, false, Vector{LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{CRS}}}, Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{CRS}}}, Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{CRS}}(Polygon{false, false, Vector{LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{CRS}}}, Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{CRS}}[Polygon{false, false, Vector{LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{CRS}}}, Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{CRS}}(LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{CRS}}[LinearRing{false, false, Vector{Tuple{Float64, Float64}}, Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}, WellKnownText{CRS}}([(1.5, 1.5), (1.5, 3.5), (2.5, 3.5), (2.5, 4.5), (0.5, 4.5), (0.5, 1.5), (1.5, 1.5)], Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}((X = (0.5, 2.5), Y = (1.5, 4.5))), WellKnownText{CRS}(CRS(), \"GEOGCS[\\\\\"WGS 84\\\\\",DATUM[\\\\\"WGS_1984\\\\\",SPHEROID[\\\\\"WGS 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1.5), (1.5, 0.5), (2.5, 0.5), (2.5, 1.5), (1.5, 1.5)], Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}((X = (1.5, 2.5), Y = (0.5, 1.5))), WellKnownText{CRS}(CRS(), \"GEOGCS[\\\\\"WGS 84\\\\\",DATUM[\\\\\"WGS_1984\\\\\",SPHEROID[\\\\\"WGS 84\\\\\",6378137,298.257223563,AUTHORITY[\\\\\"EPSG\\\\\",\\\\\"7030\\\\\"]],AUTHORITY[\\\\\"EPSG\\\\\",\\\\\"6326\\\\\"]],PRIMEM[\\\\\"Greenwich\\\\\",0,AUTHORITY[\\\\\"EPSG\\\\\",\\\\\"8901\\\\\"]],UNIT[\\\\\"degree\\\\\",0.0174532925199433,AUTHORITY[\\\\\"EPSG\\\\\",\\\\\"9122\\\\\"]],AXIS[\\\\\"Latitude\\\\\",NORTH],AXIS[\\\\\"Longitude\\\\\",EAST],AUTHORITY[\\\\\"EPSG\\\\\",\\\\\"4326\\\\\"]]\"))], Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}((X = (1.5, 2.5), Y = (0.5, 1.5))), WellKnownText{CRS}(CRS(), \"GEOGCS[\\\\\"WGS 84\\\\\",DATUM[\\\\\"WGS_1984\\\\\",SPHEROID[\\\\\"WGS 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5.5), (5.5, 6.5), (4.5, 6.5), (4.5, 5.5), (5.5, 5.5)], Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}((X = (4.5, 5.5), Y = (5.5, 6.5))), WellKnownText{CRS}(CRS(), \"GEOGCS[\\\\\"WGS 84\\\\\",DATUM[\\\\\"WGS_1984\\\\\",SPHEROID[\\\\\"WGS 84\\\\\",6378137,298.257223563,AUTHORITY[\\\\\"EPSG\\\\\",\\\\\"7030\\\\\"]],AUTHORITY[\\\\\"EPSG\\\\\",\\\\\"6326\\\\\"]],PRIMEM[\\\\\"Greenwich\\\\\",0,AUTHORITY[\\\\\"EPSG\\\\\",\\\\\"8901\\\\\"]],UNIT[\\\\\"degree\\\\\",0.0174532925199433,AUTHORITY[\\\\\"EPSG\\\\\",\\\\\"9122\\\\\"]],AXIS[\\\\\"Latitude\\\\\",NORTH],AXIS[\\\\\"Longitude\\\\\",EAST],AUTHORITY[\\\\\"EPSG\\\\\",\\\\\"4326\\\\\"]]\"))], Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}((X = (4.5, 5.5), Y = (5.5, 6.5))), WellKnownText{CRS}(CRS(), \"GEOGCS[\\\\\"WGS 84\\\\\",DATUM[\\\\\"WGS_1984\\\\\",SPHEROID[\\\\\"WGS 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3.5), (3.5, 3.5), (3.5, 2.5), (2.5, 2.5), (2.5, 1.5), (4.5, 1.5), (4.5, 2.5), (5.5, 2.5), (5.5, 3.5), (4.5, 3.5)], Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}((X = (2.5, 5.5), Y = (1.5, 3.5))), WellKnownText{CRS}(CRS(), \"GEOGCS[\\\\\"WGS 84\\\\\",DATUM[\\\\\"WGS_1984\\\\\",SPHEROID[\\\\\"WGS 84\\\\\",6378137,298.257223563,AUTHORITY[\\\\\"EPSG\\\\\",\\\\\"7030\\\\\"]],AUTHORITY[\\\\\"EPSG\\\\\",\\\\\"6326\\\\\"]],PRIMEM[\\\\\"Greenwich\\\\\",0,AUTHORITY[\\\\\"EPSG\\\\\",\\\\\"8901\\\\\"]],UNIT[\\\\\"degree\\\\\",0.0174532925199433,AUTHORITY[\\\\\"EPSG\\\\\",\\\\\"9122\\\\\"]],AXIS[\\\\\"Latitude\\\\\",NORTH],AXIS[\\\\\"Longitude\\\\\",EAST],AUTHORITY[\\\\\"EPSG\\\\\",\\\\\"4326\\\\\"]]\"))], Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}((X = (2.5, 5.5), Y = (1.5, 3.5))), WellKnownText{CRS}(CRS(), \"GEOGCS[\\\\\"WGS 84\\\\\",DATUM[\\\\\"WGS_1984\\\\\",SPHEROID[\\\\\"WGS 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5.5), (6.5, 5.5), (6.5, 6.5), (5.5, 6.5), (5.5, 5.5)], Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}((X = (5.5, 6.5), Y = (5.5, 6.5))), WellKnownText{CRS}(CRS(), \"GEOGCS[\\\\\"WGS 84\\\\\",DATUM[\\\\\"WGS_1984\\\\\",SPHEROID[\\\\\"WGS 84\\\\\",6378137,298.257223563,AUTHORITY[\\\\\"EPSG\\\\\",\\\\\"7030\\\\\"]],AUTHORITY[\\\\\"EPSG\\\\\",\\\\\"6326\\\\\"]],PRIMEM[\\\\\"Greenwich\\\\\",0,AUTHORITY[\\\\\"EPSG\\\\\",\\\\\"8901\\\\\"]],UNIT[\\\\\"degree\\\\\",0.0174532925199433,AUTHORITY[\\\\\"EPSG\\\\\",\\\\\"9122\\\\\"]],AXIS[\\\\\"Latitude\\\\\",NORTH],AXIS[\\\\\"Longitude\\\\\",EAST],AUTHORITY[\\\\\"EPSG\\\\\",\\\\\"4326\\\\\"]]\"))], Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}((X = (5.5, 6.5), Y = (5.5, 6.5))), WellKnownText{CRS}(CRS(), \"GEOGCS[\\\\\"WGS 84\\\\\",DATUM[\\\\\"WGS_1984\\\\\",SPHEROID[\\\\\"WGS 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6.5), (2.5, 6.5), (2.5, 5.5), (3.5, 5.5), (3.5, 4.5), (4.5, 4.5), (4.5, 6.5)], Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}((X = (2.5, 4.5), Y = (4.5, 6.5))), WellKnownText{CRS}(CRS(), \"GEOGCS[\\\\\"WGS 84\\\\\",DATUM[\\\\\"WGS_1984\\\\\",SPHEROID[\\\\\"WGS 84\\\\\",6378137,298.257223563,AUTHORITY[\\\\\"EPSG\\\\\",\\\\\"7030\\\\\"]],AUTHORITY[\\\\\"EPSG\\\\\",\\\\\"6326\\\\\"]],PRIMEM[\\\\\"Greenwich\\\\\",0,AUTHORITY[\\\\\"EPSG\\\\\",\\\\\"8901\\\\\"]],UNIT[\\\\\"degree\\\\\",0.0174532925199433,AUTHORITY[\\\\\"EPSG\\\\\",\\\\\"9122\\\\\"]],AXIS[\\\\\"Latitude\\\\\",NORTH],AXIS[\\\\\"Longitude\\\\\",EAST],AUTHORITY[\\\\\"EPSG\\\\\",\\\\\"4326\\\\\"]]\"))], Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}((X = (2.5, 4.5), Y = (4.5, 6.5))), WellKnownText{CRS}(CRS(), \"GEOGCS[\\\\\"WGS 84\\\\\",DATUM[\\\\\"WGS_1984\\\\\",SPHEROID[\\\\\"WGS 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0.5), (6.5, 0.5), (6.5, 1.5), (5.5, 1.5), (5.5, 2.5), (4.5, 2.5), (4.5, 1.5), (3.5, 1.5), (3.5, 0.5), (5.5, 0.5)], Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}((X = (3.5, 6.5), Y = (0.5, 2.5))), WellKnownText{CRS}(CRS(), \"GEOGCS[\\\\\"WGS 84\\\\\",DATUM[\\\\\"WGS_1984\\\\\",SPHEROID[\\\\\"WGS 84\\\\\",6378137,298.257223563,AUTHORITY[\\\\\"EPSG\\\\\",\\\\\"7030\\\\\"]],AUTHORITY[\\\\\"EPSG\\\\\",\\\\\"6326\\\\\"]],PRIMEM[\\\\\"Greenwich\\\\\",0,AUTHORITY[\\\\\"EPSG\\\\\",\\\\\"8901\\\\\"]],UNIT[\\\\\"degree\\\\\",0.0174532925199433,AUTHORITY[\\\\\"EPSG\\\\\",\\\\\"9122\\\\\"]],AXIS[\\\\\"Latitude\\\\\",NORTH],AXIS[\\\\\"Longitude\\\\\",EAST],AUTHORITY[\\\\\"EPSG\\\\\",\\\\\"4326\\\\\"]]\"))], Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}((X = (3.5, 6.5), Y = (0.5, 2.5))), WellKnownText{CRS}(CRS(), \"GEOGCS[\\\\\"WGS 84\\\\\",DATUM[\\\\\"WGS_1984\\\\\",SPHEROID[\\\\\"WGS 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3.5), (5.5, 3.5), (5.5, 2.5), (6.5, 2.5), (6.5, 3.5)], Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}((X = (5.5, 6.5), Y = (2.5, 3.5))), WellKnownText{CRS}(CRS(), \"GEOGCS[\\\\\"WGS 84\\\\\",DATUM[\\\\\"WGS_1984\\\\\",SPHEROID[\\\\\"WGS 84\\\\\",6378137,298.257223563,AUTHORITY[\\\\\"EPSG\\\\\",\\\\\"7030\\\\\"]],AUTHORITY[\\\\\"EPSG\\\\\",\\\\\"6326\\\\\"]],PRIMEM[\\\\\"Greenwich\\\\\",0,AUTHORITY[\\\\\"EPSG\\\\\",\\\\\"8901\\\\\"]],UNIT[\\\\\"degree\\\\\",0.0174532925199433,AUTHORITY[\\\\\"EPSG\\\\\",\\\\\"9122\\\\\"]],AXIS[\\\\\"Latitude\\\\\",NORTH],AXIS[\\\\\"Longitude\\\\\",EAST],AUTHORITY[\\\\\"EPSG\\\\\",\\\\\"4326\\\\\"]]\"))], Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}((X = (5.5, 6.5), Y = (2.5, 3.5))), WellKnownText{CRS}(CRS(), \"GEOGCS[\\\\\"WGS 84\\\\\",DATUM[\\\\\"WGS_1984\\\\\",SPHEROID[\\\\\"WGS 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6.5), (0.5, 6.5), (0.5, 5.5), (1.5, 5.5), (1.5, 6.5)], Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}((X = (0.5, 1.5), Y = (5.5, 6.5))), WellKnownText{CRS}(CRS(), \"GEOGCS[\\\\\"WGS 84\\\\\",DATUM[\\\\\"WGS_1984\\\\\",SPHEROID[\\\\\"WGS 84\\\\\",6378137,298.257223563,AUTHORITY[\\\\\"EPSG\\\\\",\\\\\"7030\\\\\"]],AUTHORITY[\\\\\"EPSG\\\\\",\\\\\"6326\\\\\"]],PRIMEM[\\\\\"Greenwich\\\\\",0,AUTHORITY[\\\\\"EPSG\\\\\",\\\\\"8901\\\\\"]],UNIT[\\\\\"degree\\\\\",0.0174532925199433,AUTHORITY[\\\\\"EPSG\\\\\",\\\\\"9122\\\\\"]],AXIS[\\\\\"Latitude\\\\\",NORTH],AXIS[\\\\\"Longitude\\\\\",EAST],AUTHORITY[\\\\\"EPSG\\\\\",\\\\\"4326\\\\\"]]\"))], Extent{(:X, :Y), Tuple{Tuple{Float64, Float64}, Tuple{Float64, Float64}}}((X = (0.5, 1.5), Y = (5.5, 6.5))), WellKnownText{CRS}(CRS(), \"GEOGCS[\\\\\"WGS 84\\\\\",DATUM[\\\\\"WGS_1984\\\\\",SPHEROID[\\\\\"WGS 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84\\\\\",6378137,298.257223563,AUTHORITY[\\\\\"EPSG\\\\\",\\\\\"7030\\\\\"]],AUTHORITY[\\\\\"EPSG\\\\\",\\\\\"6326\\\\\"]],PRIMEM[\\\\\"Greenwich\\\\\",0,AUTHORITY[\\\\\"EPSG\\\\\",\\\\\"8901\\\\\"]],UNIT[\\\\\"degree\\\\\",0.0174532925199433,AUTHORITY[\\\\\"EPSG\\\\\",\\\\\"9122\\\\\"]],AXIS[\\\\\"Latitude\\\\\",NORTH],AXIS[\\\\\"Longitude\\\\\",EAST],AUTHORITY[\\\\\"EPSG\\\\\",\\\\\"4326\\\\\"]]\"))\n\n\n\n\n\n\nThe polygon layer df is shown in Figureย 5.7.\n\nf, a, p = poly(df.geometry; color = df.value, strokecolor = :black, strokewidth = 0.75)\nColorbar(f[1, 2], p)\nf\n\n\n\n\n\n\n\nFigureย 5.7: grain.tif converted to a polygon layer\n\n\n\n\n\nAs highlighted using edgecolor='black', neighboring pixels sharing the same raster value are dissolved into larger polygons.\nOne suggestion is to add unique values between 0 and 0.9999 to all pixels, convert to polygons, and then get back to the original values using floor.\n\n\n5.5.2 Raster to points\nTo transform a raster to points, Rasters.jl provides the Rasters.DimTable constructor, which converts a raster into a lazy, table-like form. This can be converted directly to a DataFrame, or operated on independently.\n\ndt = DimTable(Raster(\"output/elev.tif\"))\n\nDimensionalData.DimTable with 36 rows, 3 columns, and schema:\n :X Float64\n :Y Float64\n :layer1 UInt8\n\n\nNotice that this has three columns, :X, :Y, and :layer1, corresponding to the pixel centroids and elevation values. But what if we want to treat the X and Y dimensionas as point geometries?\nDimTable has a mergedims keyword argument for this, which allows us to merge the X and Y dimensions into a single dimension.\n\ndt = DimTable(Raster(\"output/elev.tif\"), mergedims = (X, Y))\n\nDimensionalData.DimTable with 36 rows, 2 columns, and schema:\n :XY Tuple{Float64, Float64}\n :layer1 UInt8\n\n\nThis has created a DimTable with a column :XY, which contains the pixel centroids as point-like objects. We can convert this to a DataFrame, set some metadata to indicate that geometry is in :XY, and plot the result.\n\ndf = DataFrame(dt)\nDataFrames.metadata!(df, \"GEOINTERFACE:geometrycolumns\", (:XY,); style = :note)\ndf\n\n36ร—2 DataFrame30 rows omitted\n\n\n\nRow\nXY\nlayer1\n\n\n\nTupleโ€ฆ\nUInt8\n\n\n\n\n1\n(-1.5, 1.0)\n1\n\n\n2\n(-1.0, 1.0)\n2\n\n\n3\n(-0.5, 1.0)\n3\n\n\nโ‹ฎ\nโ‹ฎ\nโ‹ฎ\n\n\n34\n(-1.11022e-16, -1.5)\n34\n\n\n35\n(0.5, -1.5)\n35\n\n\n36\n(1.0, -1.5)\n36\n\n\n\n\n\n\n\nscatter(df.XY; color = df.layer1)\n\n\n\n\n\n\n\n\nWe can even save this to a file trivially easily:\n\nGeoDataFrames.write(\"output/elev.gpkg\", df)\nGeoDataFrames.read(\"output/elev.gpkg\")\n\n36ร—2 DataFrame30 rows omitted\n\n\n\nRow\nXY\nlayer1\n\n\n\nIGeometrโ€ฆ\nInt16\n\n\n\n\n1\nGeometry: wkbPoint\n1\n\n\n2\nGeometry: wkbPoint\n2\n\n\n3\nGeometry: wkbPoint\n3\n\n\nโ‹ฎ\nโ‹ฎ\nโ‹ฎ\n\n\n34\nGeometry: wkbPoint\n34\n\n\n35\nGeometry: wkbPoint\n35\n\n\n36\nGeometry: wkbPoint\n36\n\n\n\n\n\n\nFigureย 5.8 shows the input raster and the resulting point layer.\n# Input raster\nfig, ax, plt = plot(src_elev)\nscatter!(ax, df.XY; color = df.layer1)\ndisplay(fig)\n# Points\nfig, ax, plt = plot(src_elev; alpha = 0.1)\nscatter!(ax, df.XY; color = df.layer1, strokecolor = :black, strokewidth = 1)\nfig\n\n\n\n\n\n\n\n\n\n\n\n(a) Input raster\n\n\n\n\n\n\n\n\n\n\n\n(b) Points\n\n\n\n\n\n\n\nFigureย 5.8: Raster and point representation of elev.tif\n\n\n\nTODO: nodata pixels\n\n\n5.5.3 Raster to contours\nAnother common type of spatial vectorization is the creation of contour lines, representing lines of continuous height or temperatures (isotherms), for example. We will use a real-world digital elevation model (DEM) because the artificial raster elev.tif produces parallel lines (task for the reader: verify this and explain why this happens). Plotting contour lines is straightforward, using the contour or contourf functions in Makie.\n\nf, ax, plt = contour(src_dem; levels = LinRange(0, 1200, 50), color = :black)\n\n\n\n\n\n\n\n\nTODO: gdal_contour (via ArchGDAL??)\nIt would be good to show how to use the provided GDAL executables thoughโ€ฆ", + "crumbs": [ + "5ย  Raster-vector interactions" + ] + }, + { + "objectID": "chapters/05-raster-vector.html#sec-distance-to-nearest-geometry", + "href": "chapters/05-raster-vector.html#sec-distance-to-nearest-geometry", + "title": "5ย  Raster-vector interactions", + "section": "5.6 Distance to nearest geometry", + "text": "5.6 Distance to nearest geometry\nCalculating a raster of distances to the nearest geometry is an example of a โ€œglobalโ€ raster operation (?sec-global-operations-and-distances). To demonstrate it, suppose that we need to calculate a raster representing the distance to the nearest coast in New Zealand. This example also wraps many of the concepts introduced in this chapter and in previous chapters, such as raster aggregation (?sec-raster-agg-disagg), raster conversion to points (Section 5.5.2), and rasterizing points (Section 5.4.1).\nFor the coastline, we will dissolve the New Zealand administrative division polygon layer and โ€œextractโ€ the boundary as a 'MultiLineString' geometry.\n\nusing LibGEOS\ncoastline_linestrings = GI.getexterior.(GI.getgeom(LibGEOS.unaryUnion(GI.GeometryCollection(nz.geom)))) .|> x -> GI.LineString(collect(GI.getpoint(x)))\ncoastline = GI.MultiLineString(coastline_linestrings)\ncoastline = GO.reproject(coastline; target_crs = GI.crs(src_nz_elev), source_crs = GI.crs(nz))\n\nGeoInterface.Wrappers.MultiLineString{false, false, Vector{GeoInterface.Wrappers.LineString{false, false, Vector{Tuple{Float64, Float64}}, Nothing, WellKnownText{GeoFormatTypes.CRS}}}, Nothing, WellKnownText{GeoFormatTypes.CRS}}(GeoInterface.Wrappers.LineString{false, false, Vector{Tuple{Float64, Float64}}, Nothing, WellKnownText{GeoFormatTypes.CRS}}[GeoInterface.Wrappers.LineString{false, false, Vector{Tuple{Float64, Float64}}, Nothing, WellKnownText{GeoFormatTypes.CRS}}([(1.2299979013e6, 4.7934322369e6), (1.2216031693e6, 4.7943017685e6), (1.2177895399e6, 4.7904898277e6), (1.2311152067e6, 4.787137924e6), (1.2361839211e6, 4.7843314255e6), (1.2325099445e6, 4.7718700237e6), (1.2173644933e6, 4.7681066692e6), (1.2080683451e6, 4.7605677875e6), (1.199129799e6, 4.7634183939e6), (1.1938565725e6, 4.7574957427e6) โ€ฆ (1.1964031733e6, 4.7848147499e6), (1.2011220591e6, 4.7884807601e6), (1.2003610396e6, 4.7969222422e6), (1.1955720567e6, 4.8084895651e6), (1.1989855975e6, 4.8153873633e6), (1.2077044806e6, 4.8171297889e6), (1.2161546733e6, 4.8132662203e6), (1.2184777781e6, 4.8064984488e6), (1.2297297347e6, 4.7987186067e6), (1.2299979013e6, 4.7934322369e6)], nothing, WellKnownText{GeoFormatTypes.CRS}(GeoFormatTypes.CRS(), \"PROJCS[\\\"unknown\\\",GEOGCS[\\\"unknown\\\",DATUM[\\\"Unknown based on GRS80 ellipsoid\\\",SPHEROID[\\\"GRS 1980\\\",6378137,298.257222101004,AUTHORITY[\\\"EPSG\\\",\\\"7019\\\"]],TOWGS84[0,0,0,0,0,0,0]],PRIMEM[\\\"Greenwich\\\",0],UNIT[\\\"degree\\\",0.0174532925199433,AUTHORITY[\\\"EPSG\\\",\\\"9122\\\"]]],PROJECTION[\\\"Transverse_Mercator\\\"],PARAMETER[\\\"latitude_of_origin\\\",0],PARAMETER[\\\"central_meridian\\\",173],PARAMETER[\\\"scale_factor\\\",0.9996],PARAMETER[\\\"false_easting\\\",1600000],PARAMETER[\\\"false_northing\\\",10000000],UNIT[\\\"metre\\\",1,AUTHORITY[\\\"EPSG\\\",\\\"9001\\\"]],AXIS[\\\"Easting\\\",EAST],AXIS[\\\"Northing\\\",NORTH]]\")), GeoInterface.Wrappers.LineString{false, false, Vector{Tuple{Float64, Float64}}, Nothing, WellKnownText{GeoFormatTypes.CRS}}([(1.1010682698e6, 4.9169506843e6), (1.0962776826e6, 4.9262213416e6), (1.1070468733e6, 4.9308841834e6), (1.1101943531e6, 4.9284267394e6), (1.1114045691e6, 4.9201779986e6), (1.1088006957e6, 4.914502166e6), (1.1010682698e6, 4.9169506843e6)], nothing, WellKnownText{GeoFormatTypes.CRS}(GeoFormatTypes.CRS(), \"PROJCS[\\\"unknown\\\",GEOGCS[\\\"unknown\\\",DATUM[\\\"Unknown based on GRS80 ellipsoid\\\",SPHEROID[\\\"GRS 1980\\\",6378137,298.257222101004,AUTHORITY[\\\"EPSG\\\",\\\"7019\\\"]],TOWGS84[0,0,0,0,0,0,0]],PRIMEM[\\\"Greenwich\\\",0],UNIT[\\\"degree\\\",0.0174532925199433,AUTHORITY[\\\"EPSG\\\",\\\"9122\\\"]]],PROJECTION[\\\"Transverse_Mercator\\\"],PARAMETER[\\\"latitude_of_origin\\\",0],PARAMETER[\\\"central_meridian\\\",173],PARAMETER[\\\"scale_factor\\\",0.9996],PARAMETER[\\\"false_easting\\\",1600000],PARAMETER[\\\"false_northing\\\",10000000],UNIT[\\\"metre\\\",1,AUTHORITY[\\\"EPSG\\\",\\\"9001\\\"]],AXIS[\\\"Easting\\\",EAST],AXIS[\\\"Northing\\\",NORTH]]\")), GeoInterface.Wrappers.LineString{false, false, Vector{Tuple{Float64, Float64}}, Nothing, WellKnownText{GeoFormatTypes.CRS}}([(1.2822947146e6, 4.8243903885e6), (1.2777592139e6, 4.8327835132e6), (1.2629460033e6, 4.8352306146e6), (1.2563895979e6, 4.832365038e6), (1.2436815529e6, 4.8358664271e6), (1.237141582e6, 4.8332854896e6), (1.2365580519e6, 4.8400088904e6), (1.2301828929e6, 4.8518834763e6), (1.2202312165e6, 4.8564065645e6), (1.2167650225e6, 4.8512224604e6) โ€ฆ (1.3536635084e6, 4.861558587e6), (1.3540190062e6, 4.8508162544e6), (1.3384831555e6, 4.837432841e6), (1.3299957273e6, 4.8375532567e6), (1.3247154647e6, 4.8316347032e6), (1.3151459743e6, 4.8300668586e6), (1.310825215e6, 4.826096002e6), (1.3025458256e6, 4.8269904113e6), (1.2945187261e6, 4.8232725043e6), (1.2822947146e6, 4.8243903885e6)], nothing, WellKnownText{GeoFormatTypes.CRS}(GeoFormatTypes.CRS(), \"PROJCS[\\\"unknown\\\",GEOGCS[\\\"unknown\\\",DATUM[\\\"Unknown based on GRS80 ellipsoid\\\",SPHEROID[\\\"GRS 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\"PROJCS[\\\"unknown\\\",GEOGCS[\\\"unknown\\\",DATUM[\\\"Unknown based on GRS80 ellipsoid\\\",SPHEROID[\\\"GRS 1980\\\",6378137,298.257222101004,AUTHORITY[\\\"EPSG\\\",\\\"7019\\\"]],TOWGS84[0,0,0,0,0,0,0]],PRIMEM[\\\"Greenwich\\\",0],UNIT[\\\"degree\\\",0.0174532925199433,AUTHORITY[\\\"EPSG\\\",\\\"9122\\\"]]],PROJECTION[\\\"Transverse_Mercator\\\"],PARAMETER[\\\"latitude_of_origin\\\",0],PARAMETER[\\\"central_meridian\\\",173],PARAMETER[\\\"scale_factor\\\",0.9996],PARAMETER[\\\"false_easting\\\",1600000],PARAMETER[\\\"false_northing\\\",10000000],UNIT[\\\"metre\\\",1,AUTHORITY[\\\"EPSG\\\",\\\"9001\\\"]],AXIS[\\\"Easting\\\",EAST],AXIS[\\\"Northing\\\",NORTH]]\")), GeoInterface.Wrappers.LineString{false, false, Vector{Tuple{Float64, Float64}}, Nothing, WellKnownText{GeoFormatTypes.CRS}}([(1.7015121629e6, 5.9962048743e6), (1.6940719249e6, 5.9966701668e6), (1.6984728948e6, 5.9883324659e6), (1.7037685647e6, 5.9848085328e6), (1.7060307416e6, 5.9751359042e6), (1.7018195613e6, 5.9719674757e6), (1.6933144403e6, 5.9719708739e6), (1.6883953701e6, 5.9896812008e6), (1.6779656958e6, 6.0055889233e6), (1.662101242e6, 6.0277652897e6) โ€ฆ (1.725607508e6, 5.945892267e6), (1.7281682052e6, 5.9584185318e6), (1.7268710844e6, 5.9703482785e6), (1.7127870833e6, 5.9730682541e6), (1.7128762566e6, 5.9791634373e6), (1.7232886065e6, 5.9797407952e6), (1.7331650316e6, 5.9897140167e6), (1.7201965583e6, 5.9800776907e6), (1.709110421e6, 5.9866719829e6), (1.7015121629e6, 5.9962048743e6)], nothing, WellKnownText{GeoFormatTypes.CRS}(GeoFormatTypes.CRS(), \"PROJCS[\\\"unknown\\\",GEOGCS[\\\"unknown\\\",DATUM[\\\"Unknown based on GRS80 ellipsoid\\\",SPHEROID[\\\"GRS 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WellKnownText{GeoFormatTypes.CRS}(GeoFormatTypes.CRS(), \"PROJCS[\\\"unknown\\\",GEOGCS[\\\"unknown\\\",DATUM[\\\"Unknown based on GRS80 ellipsoid\\\",SPHEROID[\\\"GRS 1980\\\",6378137,298.257222101004,AUTHORITY[\\\"EPSG\\\",\\\"7019\\\"]],TOWGS84[0,0,0,0,0,0,0]],PRIMEM[\\\"Greenwich\\\",0],UNIT[\\\"degree\\\",0.0174532925199433,AUTHORITY[\\\"EPSG\\\",\\\"9122\\\"]]],PROJECTION[\\\"Transverse_Mercator\\\"],PARAMETER[\\\"latitude_of_origin\\\",0],PARAMETER[\\\"central_meridian\\\",173],PARAMETER[\\\"scale_factor\\\",0.9996],PARAMETER[\\\"false_easting\\\",1600000],PARAMETER[\\\"false_northing\\\",10000000],UNIT[\\\"metre\\\",1,AUTHORITY[\\\"EPSG\\\",\\\"9001\\\"]],AXIS[\\\"Easting\\\",EAST],AXIS[\\\"Northing\\\",NORTH]]\")), GeoInterface.Wrappers.LineString{false, false, Vector{Tuple{Float64, Float64}}, Nothing, WellKnownText{GeoFormatTypes.CRS}}([(1.7939818643e6, 5.9319178426e6), (1.7912570345e6, 5.9199068808e6), (1.7842798937e6, 5.9215788138e6), (1.7818307229e6, 5.9258456737e6), (1.7939818643e6, 5.9319178426e6)], nothing, WellKnownText{GeoFormatTypes.CRS}(GeoFormatTypes.CRS(), \"PROJCS[\\\"unknown\\\",GEOGCS[\\\"unknown\\\",DATUM[\\\"Unknown based on GRS80 ellipsoid\\\",SPHEROID[\\\"GRS 1980\\\",6378137,298.257222101004,AUTHORITY[\\\"EPSG\\\",\\\"7019\\\"]],TOWGS84[0,0,0,0,0,0,0]],PRIMEM[\\\"Greenwich\\\",0],UNIT[\\\"degree\\\",0.0174532925199433,AUTHORITY[\\\"EPSG\\\",\\\"9122\\\"]]],PROJECTION[\\\"Transverse_Mercator\\\"],PARAMETER[\\\"latitude_of_origin\\\",0],PARAMETER[\\\"central_meridian\\\",173],PARAMETER[\\\"scale_factor\\\",0.9996],PARAMETER[\\\"false_easting\\\",1600000],PARAMETER[\\\"false_northing\\\",10000000],UNIT[\\\"metre\\\",1,AUTHORITY[\\\"EPSG\\\",\\\"9001\\\"]],AXIS[\\\"Easting\\\",EAST],AXIS[\\\"Northing\\\",NORTH]]\")), GeoInterface.Wrappers.LineString{false, false, Vector{Tuple{Float64, Float64}}, Nothing, WellKnownText{GeoFormatTypes.CRS}}([(1.8266292121e6, 5.9745333354e6), (1.8188737655e6, 5.9791838569e6), (1.8185878044e6, 5.9830890239e6), (1.8112269977e6, 5.9881418267e6), (1.8126655001e6, 5.9932466009e6), (1.8093053687e6, 5.9982083619e6), (1.8108957047e6, 6.0059053369e6), (1.8159541251e6, 6.0078779063e6), (1.8180162185e6, 5.9990038299e6), (1.823326993e6, 5.9918794741e6), (1.8227997093e6, 5.9860917836e6), (1.8283790056e6, 5.9803071686e6), (1.8266292121e6, 5.9745333354e6)], nothing, WellKnownText{GeoFormatTypes.CRS}(GeoFormatTypes.CRS(), \"PROJCS[\\\"unknown\\\",GEOGCS[\\\"unknown\\\",DATUM[\\\"Unknown based on GRS80 ellipsoid\\\",SPHEROID[\\\"GRS 1980\\\",6378137,298.257222101004,AUTHORITY[\\\"EPSG\\\",\\\"7019\\\"]],TOWGS84[0,0,0,0,0,0,0]],PRIMEM[\\\"Greenwich\\\",0],UNIT[\\\"degree\\\",0.0174532925199433,AUTHORITY[\\\"EPSG\\\",\\\"9122\\\"]]],PROJECTION[\\\"Transverse_Mercator\\\"],PARAMETER[\\\"latitude_of_origin\\\",0],PARAMETER[\\\"central_meridian\\\",173],PARAMETER[\\\"scale_factor\\\",0.9996],PARAMETER[\\\"false_easting\\\",1600000],PARAMETER[\\\"false_northing\\\",10000000],UNIT[\\\"metre\\\",1,AUTHORITY[\\\"EPSG\\\",\\\"9001\\\"]],AXIS[\\\"Easting\\\",EAST],AXIS[\\\"Northing\\\",NORTH]]\"))], nothing, WellKnownText{GeoFormatTypes.CRS}(GeoFormatTypes.CRS(), \"PROJCS[\\\"unknown\\\",GEOGCS[\\\"unknown\\\",DATUM[\\\"Unknown based on GRS80 ellipsoid\\\",SPHEROID[\\\"GRS 1980\\\",6378137,298.257222101004,AUTHORITY[\\\"EPSG\\\",\\\"7019\\\"]],TOWGS84[0,0,0,0,0,0,0]],PRIMEM[\\\"Greenwich\\\",0],UNIT[\\\"degree\\\",0.0174532925199433,AUTHORITY[\\\"EPSG\\\",\\\"9122\\\"]]],PROJECTION[\\\"Transverse_Mercator\\\"],PARAMETER[\\\"latitude_of_origin\\\",0],PARAMETER[\\\"central_meridian\\\",173],PARAMETER[\\\"scale_factor\\\",0.9996],PARAMETER[\\\"false_easting\\\",1600000],PARAMETER[\\\"false_northing\\\",10000000],UNIT[\\\"metre\\\",1,AUTHORITY[\\\"EPSG\\\",\\\"9001\\\"]],AXIS[\\\"Easting\\\",EAST],AXIS[\\\"Northing\\\",NORTH]]\"))\n\n\nFor a โ€œtemplateโ€ raster, we will aggregate the New Zealand DEM, in the nz_elev.tif file, to 5 times coarser resolution. The code section below follows the aggeregation example in ?sec-raster-agg-disagg.\n\nfactor = 2/10\nr = Rasters.resample(src_nz_elev; size = round.(Int, size(src_nz_elev) .* factor), method = :average)\n\nโ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ\nโ”‚ 223ร—290 Raster{Float32,2} โ”‚\nโ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ dims โ”\n โ†“ X Projected{Float64} LinRange{Float64}(995456.5461976258, 2.105456546197626e6, 223) ForwardOrdered Regular Intervals{Start},\n โ†’ Y Projected{Float64} LinRange{Float64}(6.186960927303582e6, 4.741960927303582e6, 290) ReverseOrdered Regular Intervals{Start}\nโ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ metadata โ”ค\n Metadata{Rasters.GDALsource} of Dict{String, Any} with 4 entries:\n \"units\" => \"\"\n \"offset\" => 0.0\n \"filepath\" => \"/vsimem/tmp\"\n \"scale\" => 1.0\nโ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ raster โ”ค\n extent: Extent(X = (995456.5461976258, 2.110456546197626e6), Y = (4.741960927303582e6, 6.191960927303582e6))\n missingval: NaN32\n crs: PROJCS[\"unknown\",GEOGCS[\"unknown\",DATUM[\"Unknown based on GRS80 ellipsoid\",SPHEROID[\"GRS 1980\",6378137,298.257222101004,AUTHORITY[\"EPSG\",\"7019\"]],TOWGS84[0,0,0,0,0,0,0]],PRIMEM[\"Greenwich\",0],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]]],PROJECTION[\"Transverse_Mercator\"],PARAMETER[\"latitude_of_origin\",0],PARAMETER[\"central_meridian\",173],PARAMETER[\"scale_factor\",0.9996],PARAMETER[\"false_easting\",1600000],PARAMETER[\"false_northing\",10000000],UNIT[\"metre\",1,AUTHORITY[\"EPSG\",\"9001\"]],AXIS[\"Easting\",EAST],AXIS[\"Northing\",NORTH]]\nโ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜\n โ†“ โ†’ 6.18696e6 6.18196e6 โ€ฆ 4.74696e6 4.74196e6\n 9.95457e5 NaN NaN NaN NaN\n โ‹ฎ โ‹ฑ โ‹ฎ\n 2.10546e6 NaN NaN NaN NaN\n\n\nThe resulting raster r and the lines layer coastline are plotted in Figureย 5.9. Note that the raster values are average elevations based on \\(5 \\times 5\\) pixels, but this is irrelevant for the subsequent calculation; the raster is going to be used as a template, and all of its values will be replaced with distances to coastline (Figureย 5.10).\n\nfig, ax, plt = plot(r)\nlines!(ax, coastline; color = :red)\nfig\n\n\n\n\n\n\n\nFigureย 5.9: Template to calculate distance to nearest geometry (coastlines, in red)\n\n\n\n\n\nTo calculate the actual distances, we must convert each pixel to a vector (point) geometry. For this purpose, we use the technique demonstrated in Section 5.5.2, but simply select the pixels that are not missing.\n\ndp = DimPoints(r)\nnonmissing_points = dp[r .=== missingval(r)]\n\n53316-element Vector{Tuple{Float64, Float64}}:\n (995456.5461976258, 6.186960927303582e6)\n (1.0004565461976258e6, 6.186960927303582e6)\n (1.0054565461976257e6, 6.186960927303582e6)\n (1.0104565461976259e6, 6.186960927303582e6)\n (1.0154565461976258e6, 6.186960927303582e6)\n (1.0204565461976258e6, 6.186960927303582e6)\n (1.0254565461976258e6, 6.186960927303582e6)\n (1.0304565461976257e6, 6.186960927303582e6)\n (1.0354565461976259e6, 6.186960927303582e6)\n (1.0404565461976258e6, 6.186960927303582e6)\n โ‹ฎ\n (2.0654565461976258e6, 4.741960927303582e6)\n (2.0704565461976258e6, 4.741960927303582e6)\n (2.0754565461976258e6, 4.741960927303582e6)\n (2.0804565461976258e6, 4.741960927303582e6)\n (2.0854565461976258e6, 4.741960927303582e6)\n (2.0904565461976258e6, 4.741960927303582e6)\n (2.0954565461976258e6, 4.741960927303582e6)\n (2.100456546197626e6, 4.741960927303582e6)\n (2.105456546197626e6, 4.741960927303582e6)\n\n\nThe result is a vector of 2-tuples, which are recognized as GeoInterface point geometries.\nWe can compute the Cartesian distance from each point to the nearest line in the coastline multilinestring using the distance method from GeometryOps.\n\ndistances = GO.distance.((coastline,), nonmissing_points)\n\n53316-element Vector{Float64}:\n 572764.2876244619\n 567764.3239539921\n 562764.3609290748\n 557764.3985670706\n 552764.436885969\n 547764.4759044152\n 542764.5156417422\n 537764.5561180016\n 532764.5973539978\n 527764.639371324\n โ‹ฎ\n 610809.9189554364\n 614562.8629679172\n 618333.460184955\n 622121.3896252689\n 625926.3366197749\n 629747.9927075815\n 633586.0555316888\n 637440.2287345697\n 641310.2218537896\n\n\nFinally, we rasterize (see Section 5.4.1) the distances into our raster template.\n\nimg = Rasters.rasterize(\n last,\n nonmissing_points;\n to = r,\n fill = distances,\n)\n\nโ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ\nโ”‚ 223ร—290 Raster{Union{Missing, Float64},2} last โ”‚\nโ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ dims โ”\n โ†“ X Projected{Float64} LinRange{Float64}(995456.5461976258, 2.105456546197626e6, 223) ForwardOrdered Regular Intervals{Start},\n โ†’ Y Projected{Float64} LinRange{Float64}(6.186960927303582e6, 4.741960927303582e6, 290) ReverseOrdered Regular Intervals{Start}\nโ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ metadata โ”ค\n Metadata of Dict{Any, Any}()\nโ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ raster โ”ค\n extent: Extent(X = (995456.5461976258, 2.110456546197626e6), Y = (4.741960927303582e6, 6.191960927303582e6))\n missingval: missing\n crs: PROJCS[\"unknown\",GEOGCS[\"unknown\",DATUM[\"Unknown based on GRS80 ellipsoid\",SPHEROID[\"GRS 1980\",6378137,298.257222101004,AUTHORITY[\"EPSG\",\"7019\"]],TOWGS84[0,0,0,0,0,0,0]],PRIMEM[\"Greenwich\",0],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]]],PROJECTION[\"Transverse_Mercator\"],PARAMETER[\"latitude_of_origin\",0],PARAMETER[\"central_meridian\",173],PARAMETER[\"scale_factor\",0.9996],PARAMETER[\"false_easting\",1600000],PARAMETER[\"false_northing\",10000000],UNIT[\"metre\",1,AUTHORITY[\"EPSG\",\"9001\"]],AXIS[\"Easting\",EAST],AXIS[\"Northing\",NORTH]]\nโ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜\n โ†“ โ†’ 6.18696e6 6.18196e6 โ€ฆ 4.74696e6 4.74196e6\n 9.95457e5 5.72764e5 5.72767e5 164188.0 1.67786e5\n 1.00046e6 5.67764e5 5.67767e5 1.60717e5 164391.0\n 1.00546e6 5.62764e5 5.62767e5 1.57329e5 161080.0\n โ‹ฎ โ‹ฑ โ‹ฎ\n 2.09546e6 3.31952e5 3.29282e5 6.30403e5 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.offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xxl .offcanvas .offcanvas-header{display:none}.navbar-expand-xxl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}.navbar-expand{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand .navbar-nav .dropdown-menu{position:absolute}.navbar-expand .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand .navbar-nav-scroll{overflow:visible}.navbar-expand .navbar-collapse{display:flex !important;display:-webkit-flex 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30'%3e%3cpath stroke='%23ccd1d5' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}[data-bs-theme=dark] .navbar-toggler-icon{--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='%23ccd1d5' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}.card{--bs-card-spacer-y: 1rem;--bs-card-spacer-x: 1rem;--bs-card-title-spacer-y: 0.5rem;--bs-card-title-color: ;--bs-card-subtitle-color: ;--bs-card-border-width: 1px;--bs-card-border-color: rgba(0, 0, 0, 0.175);--bs-card-border-radius: 0.25rem;--bs-card-box-shadow: ;--bs-card-inner-border-radius: calc(0.25rem - 1px);--bs-card-cap-padding-y: 0.5rem;--bs-card-cap-padding-x: 1rem;--bs-card-cap-bg: rgba(52, 58, 64, 0.25);--bs-card-cap-color: ;--bs-card-height: ;--bs-card-color: ;--bs-card-bg: #fff;--bs-card-img-overlay-padding: 1rem;--bs-card-group-margin: 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var(--bs-card-spacer-x);color:var(--bs-card-color)}.card-title{margin-bottom:var(--bs-card-title-spacer-y);color:var(--bs-card-title-color)}.card-subtitle{margin-top:calc(-0.5*var(--bs-card-title-spacer-y));margin-bottom:0;color:var(--bs-card-subtitle-color)}.card-text:last-child{margin-bottom:0}.card-link+.card-link{margin-left:var(--bs-card-spacer-x)}.card-header{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);margin-bottom:0;color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-bottom:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-header:first-child{border-radius:var(--bs-card-inner-border-radius) var(--bs-card-inner-border-radius) 0 0}.card-footer{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-top:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-footer:last-child{border-radius:0 0 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.nav-link.active{background-color:var(--bs-card-bg);border-bottom-color:var(--bs-card-bg)}.card-header-pills{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-left:calc(-0.5*var(--bs-card-cap-padding-x))}.card-img-overlay{position:absolute;top:0;right:0;bottom:0;left:0;padding:var(--bs-card-img-overlay-padding);border-radius:var(--bs-card-inner-border-radius)}.card-img,.card-img-top,.card-img-bottom{width:100%}.card-img,.card-img-top{border-top-left-radius:var(--bs-card-inner-border-radius);border-top-right-radius:var(--bs-card-inner-border-radius)}.card-img,.card-img-bottom{border-bottom-right-radius:var(--bs-card-inner-border-radius);border-bottom-left-radius:var(--bs-card-inner-border-radius)}.card-group>.card{margin-bottom:var(--bs-card-group-margin)}@media(min-width: 576px){.card-group{display:flex;display:-webkit-flex;flex-flow:row wrap;-webkit-flex-flow:row wrap}.card-group>.card{flex:1 0 0%;-webkit-flex:1 0 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6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-focus-border-color: #969fa8;--bs-accordion-btn-focus-box-shadow: 0 0 0 0.25rem rgba(44, 62, 80, 0.25);--bs-accordion-body-padding-x: 1.25rem;--bs-accordion-body-padding-y: 1rem;--bs-accordion-active-color: #121920;--bs-accordion-active-bg: #d5d8dc}.accordion-button{position:relative;display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;width:100%;padding:var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);font-size:1rem;color:var(--bs-accordion-btn-color);text-align:left;background-color:var(--bs-accordion-btn-bg);border:0;border-radius:0;overflow-anchor:none;transition:var(--bs-accordion-transition)}@media(prefers-reduced-motion: reduce){.accordion-button{transition:none}}.accordion-button:not(.collapsed){color:var(--bs-accordion-active-color);background-color:var(--bs-accordion-active-bg);box-shadow:inset 0 calc(-1*var(--bs-accordion-border-width)) 0 var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;-webkit-flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media(prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:first-of-type{border-top-left-radius:var(--bs-accordion-border-radius);border-top-right-radius:var(--bs-accordion-border-radius)}.accordion-item:first-of-type .accordion-button{border-top-left-radius:var(--bs-accordion-inner-border-radius);border-top-right-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:not(:first-of-type){border-top:0}.accordion-item:last-of-type{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-item:last-of-type .accordion-button.collapsed{border-bottom-right-radius:var(--bs-accordion-inner-border-radius);border-bottom-left-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:last-of-type .accordion-collapse{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0;border-radius:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}.accordion-flush .accordion-item .accordion-button,.accordion-flush .accordion-item .accordion-button.collapsed{border-radius:0}[data-bs-theme=dark] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23808b96'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23808b96'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: 0.75rem;--bs-breadcrumb-padding-y: 0.375rem;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: 0.25rem;--bs-breadcrumb-divider-color: rgba(33, 37, 41, 0.75);--bs-breadcrumb-item-padding-x: 0.5rem;--bs-breadcrumb-item-active-color: rgba(33, 37, 41, 0.75);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, ">") /* rtl: var(--bs-breadcrumb-divider, ">") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: 0.75rem;--bs-pagination-padding-y: 0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #fff;--bs-pagination-bg: #18bc9c;--bs-pagination-border-width: 0;--bs-pagination-border-color: transparent;--bs-pagination-border-radius: 0.25rem;--bs-pagination-hover-color: #fff;--bs-pagination-hover-bg: #0f7864;--bs-pagination-hover-border-color: transparent;--bs-pagination-focus-color: #13967d;--bs-pagination-focus-bg: #ecf0f1;--bs-pagination-focus-box-shadow: 0 0 0 0.25rem rgba(44, 62, 80, 0.25);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #0f7864;--bs-pagination-active-border-color: transparent;--bs-pagination-disabled-color: #ecf0f1;--bs-pagination-disabled-bg: #3be6c4;--bs-pagination-disabled-border-color: transparent;display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(0*-1)}.page-item:first-child .page-link{border-top-left-radius:var(--bs-pagination-border-radius);border-bottom-left-radius:var(--bs-pagination-border-radius)}.page-item:last-child .page-link{border-top-right-radius:var(--bs-pagination-border-radius);border-bottom-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: 0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: 0.5rem}.pagination-sm{--bs-pagination-padding-x: 0.5rem;--bs-pagination-padding-y: 0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius: 0.2em}.badge{--bs-badge-padding-x: 0.65em;--bs-badge-padding-y: 0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight: 700;--bs-badge-color: #fff;--bs-badge-border-radius: 0.25rem;display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:var(--bs-badge-border-radius)}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: 1px solid var(--bs-alert-border-color);--bs-alert-border-radius: 0.25rem;--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:1rem}}.progress,.progress-stacked{--bs-progress-height: 1rem;--bs-progress-font-size:0.75rem;--bs-progress-bg: #ecf0f1;--bs-progress-border-radius: 0.25rem;--bs-progress-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.075);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: #2c3e50;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg);border-radius:var(--bs-progress-border-radius)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media(prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media(prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: #212529;--bs-list-group-bg: #fff;--bs-list-group-border-color: #dee2e6;--bs-list-group-border-width: 1px;--bs-list-group-border-radius: 0.25rem;--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: 0.5rem;--bs-list-group-action-color: rgba(33, 37, 41, 0.75);--bs-list-group-action-hover-color: #000;--bs-list-group-action-hover-bg: #ecf0f1;--bs-list-group-action-active-color: #212529;--bs-list-group-action-active-bg: #ecf0f1;--bs-list-group-disabled-color: rgba(33, 37, 41, 0.75);--bs-list-group-disabled-bg: #ecf0f1;--bs-list-group-active-color: #fff;--bs-list-group-active-bg: #2c3e50;--bs-list-group-active-border-color: #2c3e50;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0;border-radius:var(--bs-list-group-border-radius)}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") ". ";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item:first-child{border-top-left-radius:inherit;border-top-right-radius:inherit}.list-group-item:last-child{border-bottom-right-radius:inherit;border-bottom-left-radius:inherit}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1*var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media(min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-sm>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-md>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-lg>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xxl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush{border-radius:0}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #fff;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' 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0.25rem;--bs-toast-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15);--bs-toast-header-color: rgba(33, 37, 41, 0.75);--bs-toast-header-bg: rgba(255, 255, 255, 0.85);--bs-toast-header-border-color: rgba(0, 0, 0, 0.175);width:var(--bs-toast-max-width);max-width:100%;font-size:var(--bs-toast-font-size);color:var(--bs-toast-color);pointer-events:auto;background-color:var(--bs-toast-bg);background-clip:padding-box;border:var(--bs-toast-border-width) solid var(--bs-toast-border-color);box-shadow:var(--bs-toast-box-shadow);border-radius:var(--bs-toast-border-radius)}.toast.showing{opacity:0}.toast:not(.show){display:none}.toast-container{--bs-toast-zindex: 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var(--bg-gradient-start, 36%), #18bc9c var(--bg-gradient-end, 180%)) #29a6c2;color:#fff}.bg-gradient-cyan-teal{--bslib-color-fg: #fff;--bslib-color-bg: #2cacc0;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3498db var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) #2cacc0;color:#fff}@media(min-width: 576px){.nav:not(.nav-hidden){display:flex !important;display:-webkit-flex !important}.nav:not(.nav-hidden):not(.nav-stacked):not(.flex-column){float:none !important}.nav:not(.nav-hidden):not(.nav-stacked):not(.flex-column)>.bslib-nav-spacer{margin-left:auto !important}.nav:not(.nav-hidden):not(.nav-stacked):not(.flex-column)>.form-inline{margin-top:auto;margin-bottom:auto}.nav:not(.nav-hidden).nav-stacked{flex-direction:column;-webkit-flex-direction:column;height:100%}.nav:not(.nav-hidden).nav-stacked>.bslib-nav-spacer{margin-top:auto !important}}.bslib-sidebar-layout{--bslib-sidebar-transition-duration: 500ms;--bslib-sidebar-transition-easing-x: 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#fff}.bslib-page-title{background-color:var(--bslib-page-sidebar-title-bg);color:var(--bslib-page-sidebar-title-color);font-size:1.25rem;font-weight:300;padding:var(--bslib-spacer, 1rem);padding-left:1.5rem;margin-bottom:0;border-bottom:1px solid #dee2e6}.accordion .accordion-header{font-size:calc(1.29rem + 0.48vw);margin-top:0;margin-bottom:.5rem;font-weight:500;line-height:1.2;color:var(--bs-heading-color);margin-bottom:0}@media(min-width: 1200px){.accordion .accordion-header{font-size:1.65rem}}.accordion .accordion-icon:not(:empty){margin-right:.75rem;display:flex}.accordion .accordion-button:not(.collapsed){box-shadow:none}.accordion .accordion-button:not(.collapsed):focus{box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.bslib-card{overflow:auto}.bslib-card .card-body+.card-body{padding-top:0}.bslib-card .card-body{overflow:auto}.bslib-card .card-body p{margin-top:0}.bslib-card .card-body p:last-child{margin-bottom:0}.bslib-card 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!important}}.bslib-full-screen-exit{position:relative;top:1.35rem;font-size:.9rem;cursor:pointer;text-decoration:none;display:flex;float:right;margin-right:2.15rem;align-items:center;color:rgba(var(--bs-body-bg-rgb), 0.8)}.bslib-full-screen-exit:hover{color:rgba(var(--bs-body-bg-rgb), 1)}.bslib-full-screen-exit svg{margin-left:.5rem;font-size:1.5rem}#bslib-full-screen-overlay{position:fixed;inset:0;background-color:rgba(var(--bs-body-color-rgb), 0.6);backdrop-filter:blur(2px);-webkit-backdrop-filter:blur(2px);z-index:1069;animation:bslib-full-screen-overlay-enter 400ms cubic-bezier(0.6, 0.02, 0.65, 1) forwards}@keyframes 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m?m(Object.assign({},e.rects,{placement:e.placement})):m,O="number"==typeof C?{mainAxis:C,altAxis:C}:Object.assign({mainAxis:0,altAxis:0},C),x=e.modifiersData.offset?e.modifiersData.offset[e.placement]:null,k={x:0,y:0};if(A){if(o){var L,S="y"===y?zt:Vt,D="y"===y?Rt:qt,$="y"===y?"height":"width",I=A[y],N=I+g[S],P=I-g[D],M=f?-T[$]/2:0,j=b===Xt?E[$]:T[$],F=b===Xt?-T[$]:-E[$],H=e.elements.arrow,W=f&&H?Ce(H):{width:0,height:0},B=e.modifiersData["arrow#persistent"]?e.modifiersData["arrow#persistent"].padding:{top:0,right:0,bottom:0,left:0},z=B[S],R=B[D],q=Ne(0,E[$],W[$]),V=v?E[$]/2-M-q-z-O.mainAxis:j-q-z-O.mainAxis,K=v?-E[$]/2+M+q+R+O.mainAxis:F+q+R+O.mainAxis,Q=e.elements.arrow&&$e(e.elements.arrow),X=Q?"y"===y?Q.clientTop||0:Q.clientLeft||0:0,Y=null!=(L=null==x?void 0:x[y])?L:0,U=I+K-Y,G=Ne(f?ye(N,I+V-Y-X):N,I,f?ve(P,U):P);A[y]=G,k[y]=G-I}if(a){var J,Z="x"===y?zt:Vt,tt="x"===y?Rt:qt,et=A[w],it="y"===w?"height":"width",nt=et+g[Z],st=et-g[tt],ot=-1!==[zt,Vt].indexOf(_),rt=null!=(J=null==x?void 0:x[w])?J:0,at=ot?nt:et-E[it]-T[it]-rt+O.altAxis,lt=ot?et+E[it]+T[it]-rt-O.altAxis:st,ct=f&&ot?function(t,e,i){var n=Ne(t,e,i);return n>i?i:n}(at,et,lt):Ne(f?at:nt,et,f?lt:st);A[w]=ct,k[w]=ct-et}e.modifiersData[n]=k}},requiresIfExists:["offset"]};function di(t,e,i){void 0===i&&(i=!1);var n,s,o=me(e),r=me(e)&&function(t){var e=t.getBoundingClientRect(),i=we(e.width)/t.offsetWidth||1,n=we(e.height)/t.offsetHeight||1;return 1!==i||1!==n}(e),a=Le(e),l=Te(t,r,i),c={scrollLeft:0,scrollTop:0},h={x:0,y:0};return(o||!o&&!i)&&(("body"!==ue(e)||Ue(a))&&(c=(n=e)!==fe(n)&&me(n)?{scrollLeft:(s=n).scrollLeft,scrollTop:s.scrollTop}:Xe(n)),me(e)?((h=Te(e,!0)).x+=e.clientLeft,h.y+=e.clientTop):a&&(h.x=Ye(a))),{x:l.left+c.scrollLeft-h.x,y:l.top+c.scrollTop-h.y,width:l.width,height:l.height}}function ui(t){var e=new Map,i=new Set,n=[];function s(t){i.add(t.name),[].concat(t.requires||[],t.requiresIfExists||[]).forEach((function(t){if(!i.has(t)){var n=e.get(t);n&&s(n)}})),n.push(t)}return t.forEach((function(t){e.set(t.name,t)})),t.forEach((function(t){i.has(t.name)||s(t)})),n}var fi={placement:"bottom",modifiers:[],strategy:"absolute"};function pi(){for(var t=arguments.length,e=new Array(t),i=0;iNumber.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return(this._inNavbar||"static"===this._config.display)&&(F.setDataAttribute(this._menu,"popper","static"),t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,...g(this._config.popperConfig,[t])}}_selectMenuItem({key:t,target:e}){const i=z.find(".dropdown-menu 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e=/input|textarea/i.test(t.target.tagName),i="Escape"===t.key,n=[Ei,Ti].includes(t.key);if(!n&&!i)return;if(e&&!i)return;t.preventDefault();const s=this.matches(Ii)?this:z.prev(this,Ii)[0]||z.next(this,Ii)[0]||z.findOne(Ii,t.delegateTarget.parentNode),o=qi.getOrCreateInstance(s);if(n)return t.stopPropagation(),o.show(),void o._selectMenuItem(t);o._isShown()&&(t.stopPropagation(),o.hide(),s.focus())}}N.on(document,Si,Ii,qi.dataApiKeydownHandler),N.on(document,Si,Pi,qi.dataApiKeydownHandler),N.on(document,Li,qi.clearMenus),N.on(document,Di,qi.clearMenus),N.on(document,Li,Ii,(function(t){t.preventDefault(),qi.getOrCreateInstance(this).toggle()})),m(qi);const Vi="backdrop",Ki="show",Qi=`mousedown.bs.${Vi}`,Xi={className:"modal-backdrop",clickCallback:null,isAnimated:!1,isVisible:!0,rootElement:"body"},Yi={className:"string",clickCallback:"(function|null)",isAnimated:"boolean",isVisible:"boolean",rootElement:"(element|string)"};class Ui extends H{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return Xi}static get DefaultType(){return Yi}static get NAME(){return Vi}show(t){if(!this._config.isVisible)return void g(t);this._append();const e=this._getElement();this._config.isAnimated&&d(e),e.classList.add(Ki),this._emulateAnimation((()=>{g(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Ki),this._emulateAnimation((()=>{this.dispose(),g(t)}))):g(t)}dispose(){this._isAppended&&(N.off(this._element,Qi),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=r(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),N.on(t,Qi,(()=>{g(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){_(t,this._getElement(),this._config.isAnimated)}}const Gi=".bs.focustrap",Ji=`focusin${Gi}`,Zi=`keydown.tab${Gi}`,tn="backward",en={autofocus:!0,trapElement:null},nn={autofocus:"boolean",trapElement:"element"};class sn extends H{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return en}static get DefaultType(){return nn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),N.off(document,Gi),N.on(document,Ji,(t=>this._handleFocusin(t))),N.on(document,Zi,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,N.off(document,Gi))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=z.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===tn?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?tn:"forward")}}const on=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",rn=".sticky-top",an="padding-right",ln="margin-right";class cn{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,an,(e=>e+t)),this._setElementAttributes(on,an,(e=>e+t)),this._setElementAttributes(rn,ln,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,an),this._resetElementAttributes(on,an),this._resetElementAttributes(rn,ln)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&F.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=F.getDataAttribute(t,e);null!==i?(F.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(o(t))e(t);else for(const i of z.find(t,this._element))e(i)}}const hn=".bs.modal",dn=`hide${hn}`,un=`hidePrevented${hn}`,fn=`hidden${hn}`,pn=`show${hn}`,mn=`shown${hn}`,gn=`resize${hn}`,_n=`click.dismiss${hn}`,bn=`mousedown.dismiss${hn}`,vn=`keydown.dismiss${hn}`,yn=`click${hn}.data-api`,wn="modal-open",An="show",En="modal-static",Tn={backdrop:!0,focus:!0,keyboard:!0},Cn={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class On extends W{constructor(t,e){super(t,e),this._dialog=z.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new cn,this._addEventListeners()}static get Default(){return Tn}static get DefaultType(){return Cn}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||N.trigger(this._element,pn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(wn),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(N.trigger(this._element,dn).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(An),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){N.off(window,hn),N.off(this._dialog,hn),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new Ui({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=z.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),d(this._element),this._element.classList.add(An),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,N.trigger(this._element,mn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){N.on(this._element,vn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():this._triggerBackdropTransition())})),N.on(window,gn,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),N.on(this._element,bn,(t=>{N.one(this._element,_n,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(wn),this._resetAdjustments(),this._scrollBar.reset(),N.trigger(this._element,fn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(N.trigger(this._element,un).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(En)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(En),this._queueCallback((()=>{this._element.classList.remove(En),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=p()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=p()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const 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W{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return zn}static get DefaultType(){return Rn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||N.trigger(this._element,Nn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new cn).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(Dn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add(Sn),this._element.classList.remove(Dn),N.trigger(this._element,Pn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(N.trigger(this._element,Mn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add($n),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove(Sn,$n),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new cn).reset(),N.trigger(this._element,Fn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new Ui({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():N.trigger(this._element,jn)}:null})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_addEventListeners(){N.on(this._element,Bn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():N.trigger(this._element,jn))}))}static jQueryInterface(t){return this.each((function(){const e=qn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}N.on(document,Wn,'[data-bs-toggle="offcanvas"]',(function(t){const e=z.getElementFromSelector(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),l(this))return;N.one(e,Fn,(()=>{a(this)&&this.focus()}));const i=z.findOne(In);i&&i!==e&&qn.getInstance(i).hide(),qn.getOrCreateInstance(e).toggle(this)})),N.on(window,Ln,(()=>{for(const t of z.find(In))qn.getOrCreateInstance(t).show()})),N.on(window,Hn,(()=>{for(const t of z.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&qn.getOrCreateInstance(t).hide()})),R(qn),m(qn);const Vn={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Kn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Qn=/^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i,Xn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Kn.has(i)||Boolean(Qn.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Yn={allowList:Vn,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"
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0000000..b30ce57 --- /dev/null +++ b/previews/PR23/site_libs/quarto-html/quarto-syntax-highlighting.css @@ -0,0 +1,205 @@ +/* quarto syntax highlight colors */ +:root { + --quarto-hl-ot-color: #003B4F; + --quarto-hl-at-color: #657422; + --quarto-hl-ss-color: #20794D; + --quarto-hl-an-color: #5E5E5E; + --quarto-hl-fu-color: #4758AB; + --quarto-hl-st-color: #20794D; + --quarto-hl-cf-color: #003B4F; + --quarto-hl-op-color: #5E5E5E; + --quarto-hl-er-color: #AD0000; + --quarto-hl-bn-color: #AD0000; + --quarto-hl-al-color: #AD0000; + --quarto-hl-va-color: #111111; + --quarto-hl-bu-color: inherit; + --quarto-hl-ex-color: inherit; + --quarto-hl-pp-color: #AD0000; + --quarto-hl-in-color: #5E5E5E; + --quarto-hl-vs-color: #20794D; + --quarto-hl-wa-color: #5E5E5E; + --quarto-hl-do-color: #5E5E5E; + --quarto-hl-im-color: #00769E; + --quarto-hl-ch-color: #20794D; + --quarto-hl-dt-color: #AD0000; + --quarto-hl-fl-color: #AD0000; + --quarto-hl-co-color: #5E5E5E; + --quarto-hl-cv-color: #5E5E5E; + --quarto-hl-cn-color: #8f5902; + --quarto-hl-sc-color: #5E5E5E; + --quarto-hl-dv-color: #AD0000; + --quarto-hl-kw-color: #003B4F; +} + +/* other quarto variables */ +:root { + --quarto-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace; +} + +pre > code.sourceCode > span { + color: #003B4F; +} + +code span { + color: #003B4F; +} + +code.sourceCode > span { + color: #003B4F; +} + +div.sourceCode, +div.sourceCode pre.sourceCode { + color: #003B4F; +} + +code span.ot { + color: #003B4F; + font-style: inherit; +} + +code span.at { + color: #657422; + font-style: inherit; +} + +code span.ss { + color: #20794D; + font-style: inherit; +} + +code span.an { + color: #5E5E5E; + font-style: inherit; +} + +code span.fu { + color: #4758AB; + font-style: inherit; +} + +code span.st { + color: #20794D; + font-style: inherit; +} + +code span.cf { + color: #003B4F; + font-weight: bold; + font-style: inherit; +} + +code span.op { + color: #5E5E5E; + font-style: inherit; +} + +code span.er { + color: #AD0000; + font-style: inherit; +} + +code span.bn { + color: #AD0000; + font-style: inherit; +} + +code span.al { + color: #AD0000; + font-style: inherit; +} + +code span.va { + color: #111111; + font-style: inherit; +} + +code span.bu { + font-style: inherit; +} + +code span.ex { + font-style: inherit; +} + +code span.pp { + color: #AD0000; + font-style: inherit; +} + +code span.in { + color: #5E5E5E; + font-style: inherit; +} + +code span.vs { + color: #20794D; + font-style: inherit; +} + +code span.wa { + color: #5E5E5E; + font-style: italic; +} + +code span.do { + color: #5E5E5E; + font-style: italic; +} + +code span.im { + color: #00769E; + font-style: inherit; +} + +code span.ch { + color: #20794D; + font-style: inherit; +} + +code span.dt { + color: #AD0000; + font-style: inherit; +} + +code span.fl { + color: #AD0000; + font-style: inherit; +} + +code span.co { + color: #5E5E5E; + font-style: inherit; +} + +code span.cv { + color: #5E5E5E; + font-style: italic; +} + +code span.cn { + color: #8f5902; + font-style: inherit; +} + +code span.sc { + color: #5E5E5E; + font-style: inherit; +} + +code span.dv { + color: #AD0000; + font-style: inherit; +} + +code span.kw { + color: #003B4F; + font-weight: bold; + font-style: inherit; +} + +.prevent-inlining { + content: " { + // Find any conflicting margin elements and add margins to the + // top to prevent overlap + const marginChildren = window.document.querySelectorAll( + ".column-margin.column-container > *, .margin-caption, .aside" + ); + + let lastBottom = 0; + for (const marginChild of marginChildren) { + if (marginChild.offsetParent !== null) { + // clear the top margin so we recompute it + marginChild.style.marginTop = null; + const top = marginChild.getBoundingClientRect().top + window.scrollY; + if (top < lastBottom) { + const marginChildStyle = window.getComputedStyle(marginChild); + const marginBottom = parseFloat(marginChildStyle["marginBottom"]); + const margin = lastBottom - top + marginBottom; + marginChild.style.marginTop = `${margin}px`; + } + const styles = window.getComputedStyle(marginChild); + const marginTop = parseFloat(styles["marginTop"]); + lastBottom = top + marginChild.getBoundingClientRect().height + marginTop; + } + } +}; + +window.document.addEventListener("DOMContentLoaded", function (_event) { + // Recompute the position of margin elements anytime the body size changes + if (window.ResizeObserver) { + const resizeObserver = new window.ResizeObserver( + throttle(() => { + layoutMarginEls(); + if ( + window.document.body.getBoundingClientRect().width < 990 && + isReaderMode() + ) { + quartoToggleReader(); + } + }, 50) + ); + resizeObserver.observe(window.document.body); + } + + const tocEl = window.document.querySelector('nav.toc-active[role="doc-toc"]'); + const sidebarEl = window.document.getElementById("quarto-sidebar"); + const leftTocEl = window.document.getElementById("quarto-sidebar-toc-left"); + const marginSidebarEl = window.document.getElementById( + "quarto-margin-sidebar" + ); + // function to determine whether the element has a previous sibling that is active + const prevSiblingIsActiveLink = (el) => { + const sibling = el.previousElementSibling; + if (sibling && sibling.tagName === "A") { + return sibling.classList.contains("active"); + } else { + return false; + } + }; + + // fire slideEnter for bootstrap tab activations (for htmlwidget resize behavior) + function fireSlideEnter(e) { + const event = window.document.createEvent("Event"); + event.initEvent("slideenter", true, true); + window.document.dispatchEvent(event); + } + const tabs = window.document.querySelectorAll('a[data-bs-toggle="tab"]'); + tabs.forEach((tab) => { + tab.addEventListener("shown.bs.tab", fireSlideEnter); + }); + + // fire slideEnter for tabby tab activations (for htmlwidget resize behavior) + document.addEventListener("tabby", fireSlideEnter, false); + + // Track scrolling and mark TOC links as active + // get table of contents and sidebar (bail if we don't have at least one) + const tocLinks = tocEl + ? [...tocEl.querySelectorAll("a[data-scroll-target]")] + : []; + const makeActive = (link) => tocLinks[link].classList.add("active"); + const removeActive = (link) => tocLinks[link].classList.remove("active"); + const removeAllActive = () => + [...Array(tocLinks.length).keys()].forEach((link) => removeActive(link)); + + // activate the anchor for a section associated with this TOC entry + tocLinks.forEach((link) => { + link.addEventListener("click", () => { + if (link.href.indexOf("#") !== -1) { + const anchor = link.href.split("#")[1]; + const heading = window.document.querySelector( + `[data-anchor-id="${anchor}"]` + ); + if (heading) { + // Add the class + heading.classList.add("reveal-anchorjs-link"); + + // function to show the anchor + const handleMouseout = () => { + heading.classList.remove("reveal-anchorjs-link"); + heading.removeEventListener("mouseout", handleMouseout); + }; + + // add a function to clear the anchor when the user mouses out of it + heading.addEventListener("mouseout", handleMouseout); + } + } + }); + }); + + const sections = tocLinks.map((link) => { + const target = link.getAttribute("data-scroll-target"); + if (target.startsWith("#")) { + return window.document.getElementById(decodeURI(`${target.slice(1)}`)); + } else { + return window.document.querySelector(decodeURI(`${target}`)); + } + }); + + const sectionMargin = 200; + let currentActive = 0; + // track whether we've initialized state the first time + let init = false; + + const updateActiveLink = () => { + // The index from bottom to top (e.g. reversed list) + let sectionIndex = -1; + if ( + window.innerHeight + window.pageYOffset >= + window.document.body.offsetHeight + ) { + // This is the no-scroll case where last section should be the active one + sectionIndex = 0; + } else { + // This finds the last section visible on screen that should be made active + sectionIndex = [...sections].reverse().findIndex((section) => { + if (section) { + return window.pageYOffset >= section.offsetTop - sectionMargin; + } else { + return false; + } + }); + } + if (sectionIndex > -1) { + const current = sections.length - sectionIndex - 1; + if (current !== currentActive) { + removeAllActive(); + currentActive = current; + makeActive(current); + if (init) { + window.dispatchEvent(sectionChanged); + } + init = true; + } + } + }; + + const inHiddenRegion = (top, bottom, hiddenRegions) => { + for (const region of hiddenRegions) { + if (top <= region.bottom && bottom >= region.top) { + return true; + } + } + return false; + }; + + const categorySelector = "header.quarto-title-block .quarto-category"; + const activateCategories = (href) => { + // Find any categories + // Surround them with a link pointing back to: + // #category=Authoring + try { + const categoryEls = window.document.querySelectorAll(categorySelector); + for (const categoryEl of categoryEls) { + const categoryText = categoryEl.textContent; + if (categoryText) { + const link = `${href}#category=${encodeURIComponent(categoryText)}`; + const linkEl = window.document.createElement("a"); + linkEl.setAttribute("href", link); + for (const child of categoryEl.childNodes) { + linkEl.append(child); + } + categoryEl.appendChild(linkEl); + } + } + } catch { + // Ignore errors + } + }; + function hasTitleCategories() { + return window.document.querySelector(categorySelector) !== null; + } + + function offsetRelativeUrl(url) { + const offset = getMeta("quarto:offset"); + return offset ? offset + url : url; + } + + function offsetAbsoluteUrl(url) { + const offset = getMeta("quarto:offset"); + const baseUrl = new URL(offset, window.location); + + const projRelativeUrl = url.replace(baseUrl, ""); + if (projRelativeUrl.startsWith("/")) { + return projRelativeUrl; + } else { + return "/" + projRelativeUrl; + } + } + + // read a meta tag value + function getMeta(metaName) { + const metas = window.document.getElementsByTagName("meta"); + for (let i = 0; i < metas.length; i++) { + if (metas[i].getAttribute("name") === metaName) { + return metas[i].getAttribute("content"); + } + } + return ""; + } + + async function findAndActivateCategories() { + const currentPagePath = offsetAbsoluteUrl(window.location.href); + const response = await fetch(offsetRelativeUrl("listings.json")); + if (response.status == 200) { + return response.json().then(function (listingPaths) { + const listingHrefs = []; + for (const listingPath of listingPaths) { + const pathWithoutLeadingSlash = listingPath.listing.substring(1); + for (const item of listingPath.items) { + if ( + item === currentPagePath || + item === currentPagePath + "index.html" + ) { + // Resolve this path against the offset to be sure + // we already are using the correct path to the listing + // (this adjusts the listing urls to be rooted against + // whatever root the page is actually running against) + const relative = offsetRelativeUrl(pathWithoutLeadingSlash); + const baseUrl = window.location; + const resolvedPath = new URL(relative, baseUrl); + listingHrefs.push(resolvedPath.pathname); + break; + } + } + } + + // Look up the tree for a nearby linting and use that if we find one + const nearestListing = findNearestParentListing( + offsetAbsoluteUrl(window.location.pathname), + listingHrefs + ); + if (nearestListing) { + activateCategories(nearestListing); + } else { + // See if the referrer is a listing page for this item + const referredRelativePath = offsetAbsoluteUrl(document.referrer); + const referrerListing = listingHrefs.find((listingHref) => { + const isListingReferrer = + listingHref === referredRelativePath || + listingHref === referredRelativePath + "index.html"; + return isListingReferrer; + }); + + if (referrerListing) { + // Try to use the referrer if possible + activateCategories(referrerListing); + } else if (listingHrefs.length > 0) { + // Otherwise, just fall back to the first listing + activateCategories(listingHrefs[0]); + } + } + }); + } + } + if (hasTitleCategories()) { + findAndActivateCategories(); + } + + const findNearestParentListing = (href, listingHrefs) => { + if (!href || !listingHrefs) { + return undefined; + } + // Look up the tree for a nearby linting and use that if we find one + const relativeParts = href.substring(1).split("/"); + while (relativeParts.length > 0) { + const path = relativeParts.join("/"); + for (const listingHref of listingHrefs) { + if (listingHref.startsWith(path)) { + return listingHref; + } + } + relativeParts.pop(); + } + + return undefined; + }; + + const manageSidebarVisiblity = (el, placeholderDescriptor) => { + let isVisible = true; + let elRect; + + return (hiddenRegions) => { + if (el === null) { + return; + } + + // Find the last element of the TOC + const lastChildEl = el.lastElementChild; + + if (lastChildEl) { + // Converts the sidebar to a menu + const convertToMenu = () => { + for (const child of el.children) { + child.style.opacity = 0; + child.style.overflow = "hidden"; + child.style.pointerEvents = "none"; + } + + nexttick(() => { + const toggleContainer = window.document.createElement("div"); + toggleContainer.style.width = "100%"; + toggleContainer.classList.add("zindex-over-content"); + toggleContainer.classList.add("quarto-sidebar-toggle"); + toggleContainer.classList.add("headroom-target"); // Marks this to be managed by headeroom + toggleContainer.id = placeholderDescriptor.id; + toggleContainer.style.position = "fixed"; + + const toggleIcon = window.document.createElement("i"); + toggleIcon.classList.add("quarto-sidebar-toggle-icon"); + toggleIcon.classList.add("bi"); + toggleIcon.classList.add("bi-caret-down-fill"); + + const toggleTitle = window.document.createElement("div"); + const titleEl = window.document.body.querySelector( + placeholderDescriptor.titleSelector + ); + if (titleEl) { + toggleTitle.append( + titleEl.textContent || titleEl.innerText, + toggleIcon + ); + } + toggleTitle.classList.add("zindex-over-content"); + toggleTitle.classList.add("quarto-sidebar-toggle-title"); + toggleContainer.append(toggleTitle); + + const toggleContents = window.document.createElement("div"); + toggleContents.classList = el.classList; + toggleContents.classList.add("zindex-over-content"); + toggleContents.classList.add("quarto-sidebar-toggle-contents"); + for (const child of el.children) { + if (child.id === "toc-title") { + continue; + } + + const clone = child.cloneNode(true); + clone.style.opacity = 1; + clone.style.pointerEvents = null; + clone.style.display = null; + toggleContents.append(clone); + } + toggleContents.style.height = "0px"; + const positionToggle = () => { + // position the element (top left of parent, same width as parent) + if (!elRect) { + elRect = el.getBoundingClientRect(); + } + toggleContainer.style.left = `${elRect.left}px`; + toggleContainer.style.top = `${elRect.top}px`; + toggleContainer.style.width = `${elRect.width}px`; + }; + positionToggle(); + + toggleContainer.append(toggleContents); + el.parentElement.prepend(toggleContainer); + + // Process clicks + let tocShowing = false; + // Allow the caller to control whether this is dismissed + // when it is clicked (e.g. sidebar navigation supports + // opening and closing the nav tree, so don't dismiss on click) + const clickEl = placeholderDescriptor.dismissOnClick + ? toggleContainer + : toggleTitle; + + const closeToggle = () => { + if (tocShowing) { + toggleContainer.classList.remove("expanded"); + toggleContents.style.height = "0px"; + tocShowing = false; + } + }; + + // Get rid of any expanded toggle if the user scrolls + window.document.addEventListener( + "scroll", + throttle(() => { + closeToggle(); + }, 50) + ); + + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + elRect = undefined; + positionToggle(); + }, 50) + ); + + window.addEventListener("quarto-hrChanged", () => { + elRect = undefined; + }); + + // Process the click + clickEl.onclick = () => { + if (!tocShowing) { + toggleContainer.classList.add("expanded"); + toggleContents.style.height = null; + tocShowing = true; + } else { + closeToggle(); + } + }; + }); + }; + + // Converts a sidebar from a menu back to a sidebar + const convertToSidebar = () => { + for (const child of el.children) { + child.style.opacity = 1; + child.style.overflow = null; + child.style.pointerEvents = null; + } + + const placeholderEl = window.document.getElementById( + placeholderDescriptor.id + ); + if (placeholderEl) { + placeholderEl.remove(); + } + + el.classList.remove("rollup"); + }; + + if (isReaderMode()) { + convertToMenu(); + isVisible = false; + } else { + // Find the top and bottom o the element that is being managed + const elTop = el.offsetTop; + const elBottom = + elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight; + + if (!isVisible) { + // If the element is current not visible reveal if there are + // no conflicts with overlay regions + if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToSidebar(); + isVisible = true; + } + } else { + // If the element is visible, hide it if it conflicts with overlay regions + // and insert a placeholder toggle (or if we're in reader mode) + if (inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToMenu(); + isVisible = false; + } + } + } + } + }; + }; + + const tabEls = document.querySelectorAll('a[data-bs-toggle="tab"]'); + for (const tabEl of tabEls) { + const id = tabEl.getAttribute("data-bs-target"); + if (id) { + const columnEl = document.querySelector( + `${id} .column-margin, .tabset-margin-content` + ); + if (columnEl) + tabEl.addEventListener("shown.bs.tab", function (event) { + const el = event.srcElement; + if (el) { + const visibleCls = `${el.id}-margin-content`; + // walk up until we find a parent tabset + let panelTabsetEl = el.parentElement; + while (panelTabsetEl) { + if (panelTabsetEl.classList.contains("panel-tabset")) { + break; + } + panelTabsetEl = panelTabsetEl.parentElement; + } + + if (panelTabsetEl) { + const prevSib = panelTabsetEl.previousElementSibling; + if ( + prevSib && + prevSib.classList.contains("tabset-margin-container") + ) { + const childNodes = prevSib.querySelectorAll( + ".tabset-margin-content" + ); + for (const childEl of childNodes) { + if (childEl.classList.contains(visibleCls)) { + childEl.classList.remove("collapse"); + } else { + childEl.classList.add("collapse"); + } + } + } + } + } + + layoutMarginEls(); + }); + } + } + + // Manage the visibility of the toc and the sidebar + const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, { + id: "quarto-toc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, { + id: "quarto-sidebarnav-toggle", + titleSelector: ".title", + dismissOnClick: false, + }); + let tocLeftScrollVisibility; + if (leftTocEl) { + tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, { + id: "quarto-lefttoc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + } + + // Find the first element that uses formatting in special columns + const conflictingEls = window.document.body.querySelectorAll( + '[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]' + ); + + // Filter all the possibly conflicting elements into ones + // the do conflict on the left or ride side + const arrConflictingEls = Array.from(conflictingEls); + const leftSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return false; + } + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + className.startsWith("column-") && + !className.endsWith("right") && + !className.endsWith("container") && + className !== "column-margin" + ); + }); + }); + const rightSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return true; + } + + const hasMarginCaption = Array.from(el.classList).find((className) => { + return className == "margin-caption"; + }); + if (hasMarginCaption) { + return true; + } + + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + !className.endsWith("container") && + className.startsWith("column-") && + !className.endsWith("left") + ); + }); + }); + + const kOverlapPaddingSize = 10; + function toRegions(els) { + return els.map((el) => { + const boundRect = el.getBoundingClientRect(); + const top = + boundRect.top + + document.documentElement.scrollTop - + kOverlapPaddingSize; + return { + top, + bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize, + }; + }); + } + + let hasObserved = false; + const visibleItemObserver = (els) => { + let visibleElements = [...els]; + const intersectionObserver = new IntersectionObserver( + (entries, _observer) => { + entries.forEach((entry) => { + if (entry.isIntersecting) { + if (visibleElements.indexOf(entry.target) === -1) { + visibleElements.push(entry.target); + } + } else { + visibleElements = visibleElements.filter((visibleEntry) => { + return visibleEntry !== entry; + }); + } + }); + + if (!hasObserved) { + hideOverlappedSidebars(); + } + hasObserved = true; + }, + {} + ); + els.forEach((el) => { + intersectionObserver.observe(el); + }); + + return { + getVisibleEntries: () => { + return visibleElements; + }, + }; + }; + + const rightElementObserver = visibleItemObserver(rightSideConflictEls); + const leftElementObserver = visibleItemObserver(leftSideConflictEls); + + const hideOverlappedSidebars = () => { + marginScrollVisibility(toRegions(rightElementObserver.getVisibleEntries())); + sidebarScrollVisiblity(toRegions(leftElementObserver.getVisibleEntries())); + if (tocLeftScrollVisibility) { + tocLeftScrollVisibility( + toRegions(leftElementObserver.getVisibleEntries()) + ); + } + }; + + window.quartoToggleReader = () => { + // Applies a slow class (or removes it) + // to update the transition speed + const slowTransition = (slow) => { + const manageTransition = (id, slow) => { + const el = document.getElementById(id); + if (el) { + if (slow) { + el.classList.add("slow"); + } else { + el.classList.remove("slow"); + } + } + }; + + manageTransition("TOC", slow); + manageTransition("quarto-sidebar", slow); + }; + const readerMode = !isReaderMode(); + setReaderModeValue(readerMode); + + // If we're entering reader mode, slow the transition + if (readerMode) { + slowTransition(readerMode); + } + highlightReaderToggle(readerMode); + hideOverlappedSidebars(); + + // If we're exiting reader mode, restore the non-slow transition + if (!readerMode) { + slowTransition(!readerMode); + } + }; + + const highlightReaderToggle = (readerMode) => { + const els = document.querySelectorAll(".quarto-reader-toggle"); + if (els) { + els.forEach((el) => { + if (readerMode) { + el.classList.add("reader"); + } else { + el.classList.remove("reader"); + } + }); + } + }; + + const setReaderModeValue = (val) => { + if (window.location.protocol !== "file:") { + window.localStorage.setItem("quarto-reader-mode", val); + } else { + localReaderMode = val; + } + }; + + const isReaderMode = () => { + if (window.location.protocol !== "file:") { + return window.localStorage.getItem("quarto-reader-mode") === "true"; + } else { + return localReaderMode; + } + }; + let localReaderMode = null; + + const tocOpenDepthStr = tocEl?.getAttribute("data-toc-expanded"); + const tocOpenDepth = tocOpenDepthStr ? Number(tocOpenDepthStr) : 1; + + // Walk the TOC and collapse/expand nodes + // Nodes are expanded if: + // - they are top level + // - they have children that are 'active' links + // - they are directly below an link that is 'active' + const walk = (el, depth) => { + // Tick depth when we enter a UL + if (el.tagName === "UL") { + depth = depth + 1; + } + + // It this is active link + let isActiveNode = false; + if (el.tagName === "A" && el.classList.contains("active")) { + isActiveNode = true; + } + + // See if there is an active child to this element + let hasActiveChild = false; + for (child of el.children) { + hasActiveChild = walk(child, depth) || hasActiveChild; + } + + // Process the collapse state if this is an UL + if (el.tagName === "UL") { + if (tocOpenDepth === -1 && depth > 1) { + // toc-expand: false + el.classList.add("collapse"); + } else if ( + depth <= tocOpenDepth || + hasActiveChild || + prevSiblingIsActiveLink(el) + ) { + el.classList.remove("collapse"); + } else { + el.classList.add("collapse"); + } + + // untick depth when we leave a UL + depth = depth - 1; + } + return hasActiveChild || isActiveNode; + }; + + // walk the TOC and expand / collapse any items that should be shown + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + + // Throttle the scroll event and walk peridiocally + window.document.addEventListener( + "scroll", + throttle(() => { + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 5) + ); + window.addEventListener( + "resize", + throttle(() => { + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 10) + ); + hideOverlappedSidebars(); + highlightReaderToggle(isReaderMode()); +}); + +// grouped tabsets +window.addEventListener("pageshow", (_event) => { + function getTabSettings() { + const data = localStorage.getItem("quarto-persistent-tabsets-data"); + if (!data) { + localStorage.setItem("quarto-persistent-tabsets-data", "{}"); + return {}; + } + if (data) { + return JSON.parse(data); + } + } + + function setTabSettings(data) { + localStorage.setItem( + "quarto-persistent-tabsets-data", + JSON.stringify(data) + ); + } + + function setTabState(groupName, groupValue) { + const data = getTabSettings(); + data[groupName] = groupValue; + setTabSettings(data); + } + + function toggleTab(tab, active) { + const tabPanelId = tab.getAttribute("aria-controls"); + const tabPanel = document.getElementById(tabPanelId); + if (active) { + tab.classList.add("active"); + tabPanel.classList.add("active"); + } else { + tab.classList.remove("active"); + tabPanel.classList.remove("active"); + } + } + + function toggleAll(selectedGroup, selectorsToSync) { + for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) { + const active = selectedGroup === thisGroup; + for (const tab of tabs) { + toggleTab(tab, active); + } + } + } + + function findSelectorsToSyncByLanguage() { + const result = {}; + const tabs = Array.from( + document.querySelectorAll(`div[data-group] a[id^='tabset-']`) + ); + for (const item of tabs) { + const div = item.parentElement.parentElement.parentElement; + const group = div.getAttribute("data-group"); + if (!result[group]) { + result[group] = {}; + } + const selectorsToSync = result[group]; + const value = item.innerHTML; + if (!selectorsToSync[value]) { + selectorsToSync[value] = []; + } + selectorsToSync[value].push(item); + } + return result; + } + + function setupSelectorSync() { + const selectorsToSync = findSelectorsToSyncByLanguage(); + Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => { + Object.entries(tabSetsByValue).forEach(([value, items]) => { + items.forEach((item) => { + item.addEventListener("click", (_event) => { + setTabState(group, value); + toggleAll(value, selectorsToSync[group]); + }); + }); + }); + }); + return selectorsToSync; + } + + const selectorsToSync = setupSelectorSync(); + for (const [group, selectedName] of Object.entries(getTabSettings())) { + const selectors = selectorsToSync[group]; + // it's possible that stale state gives us empty selections, so we explicitly check here. + if (selectors) { + toggleAll(selectedName, selectors); + } + } +}); + +function throttle(func, wait) { + let waiting = false; + return function () { + if (!waiting) { + func.apply(this, arguments); + waiting = true; + setTimeout(function () { + waiting = false; + }, wait); + } + }; +} + +function nexttick(func) { + return setTimeout(func, 0); +} diff --git a/previews/PR23/site_libs/quarto-html/tippy.css b/previews/PR23/site_libs/quarto-html/tippy.css new file mode 100644 index 0000000..e6ae635 --- /dev/null +++ b/previews/PR23/site_libs/quarto-html/tippy.css @@ -0,0 +1 @@ +.tippy-box[data-animation=fade][data-state=hidden]{opacity:0}[data-tippy-root]{max-width:calc(100vw - 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b/previews/PR23/site_libs/quarto-nav/headroom.min.js new file mode 100644 index 0000000..b08f1df --- /dev/null +++ b/previews/PR23/site_libs/quarto-nav/headroom.min.js @@ -0,0 +1,7 @@ +/*! + * headroom.js v0.12.0 - Give your page some headroom. Hide your header until you need it + * Copyright (c) 2020 Nick Williams - http://wicky.nillia.ms/headroom.js + * License: MIT + */ + +!function(t,n){"object"==typeof exports&&"undefined"!=typeof module?module.exports=n():"function"==typeof define&&define.amd?define(n):(t=t||self).Headroom=n()}(this,function(){"use strict";function t(){return"undefined"!=typeof window}function d(t){return function(t){return t&&t.document&&function(t){return 9===t.nodeType}(t.document)}(t)?function(t){var n=t.document,o=n.body,s=n.documentElement;return{scrollHeight:function(){return Math.max(o.scrollHeight,s.scrollHeight,o.offsetHeight,s.offsetHeight,o.clientHeight,s.clientHeight)},height:function(){return t.innerHeight||s.clientHeight||o.clientHeight},scrollY:function(){return void 0!==t.pageYOffset?t.pageYOffset:(s||o.parentNode||o).scrollTop}}}(t):function(t){return{scrollHeight:function(){return Math.max(t.scrollHeight,t.offsetHeight,t.clientHeight)},height:function(){return 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0000000..38cc430 --- /dev/null +++ b/previews/PR23/site_libs/quarto-nav/quarto-nav.js @@ -0,0 +1,325 @@ +const headroomChanged = new CustomEvent("quarto-hrChanged", { + detail: {}, + bubbles: true, + cancelable: false, + composed: false, +}); + +const announceDismiss = () => { + const annEl = window.document.getElementById("quarto-announcement"); + if (annEl) { + annEl.remove(); + + const annId = annEl.getAttribute("data-announcement-id"); + window.localStorage.setItem(`quarto-announce-${annId}`, "true"); + } +}; + +const announceRegister = () => { + const annEl = window.document.getElementById("quarto-announcement"); + if (annEl) { + const annId = annEl.getAttribute("data-announcement-id"); + const isDismissed = + window.localStorage.getItem(`quarto-announce-${annId}`) || false; + if (isDismissed) { + announceDismiss(); + return; + } else { + annEl.classList.remove("hidden"); + } + + const actionEl = annEl.querySelector(".quarto-announcement-action"); + if (actionEl) { + actionEl.addEventListener("click", function (e) { + e.preventDefault(); + // Hide the bar immediately + announceDismiss(); + }); + } + } +}; + +window.document.addEventListener("DOMContentLoaded", function () { + let init = false; + + announceRegister(); + + // Manage the back to top button, if one is present. + let lastScrollTop = window.pageYOffset || document.documentElement.scrollTop; + const scrollDownBuffer = 5; + const scrollUpBuffer = 35; + const btn = document.getElementById("quarto-back-to-top"); + const hideBackToTop = () => { + btn.style.display = "none"; + }; + const showBackToTop = () => { + btn.style.display = "inline-block"; + }; + if (btn) { + window.document.addEventListener( + "scroll", + function () { + const currentScrollTop = + window.pageYOffset || document.documentElement.scrollTop; + + // Shows and hides the button 'intelligently' as the user scrolls + if (currentScrollTop - scrollDownBuffer > lastScrollTop) { + hideBackToTop(); + lastScrollTop = currentScrollTop <= 0 ? 0 : currentScrollTop; + } else if (currentScrollTop < lastScrollTop - scrollUpBuffer) { + showBackToTop(); + lastScrollTop = currentScrollTop <= 0 ? 0 : currentScrollTop; + } + + // Show the button at the bottom, hides it at the top + if (currentScrollTop <= 0) { + hideBackToTop(); + } else if ( + window.innerHeight + currentScrollTop >= + document.body.offsetHeight + ) { + showBackToTop(); + } + }, + false + ); + } + + function throttle(func, wait) { + var timeout; + return function () { + const context = this; + const args = arguments; + const later = function () { + clearTimeout(timeout); + timeout = null; + func.apply(context, args); + }; + + if (!timeout) { + timeout = setTimeout(later, wait); + } + }; + } + + function headerOffset() { + // Set an offset if there is are fixed top navbar + const headerEl = window.document.querySelector("header.fixed-top"); + if (headerEl) { + return headerEl.clientHeight; + } else { + return 0; + } + } + + function footerOffset() { + const footerEl = window.document.querySelector("footer.footer"); + if (footerEl) { + return footerEl.clientHeight; + } else { + return 0; + } + } + + function dashboardOffset() { + const dashboardNavEl = window.document.getElementById( + "quarto-dashboard-header" + ); + if (dashboardNavEl !== null) { + return dashboardNavEl.clientHeight; + } else { + return 0; + } + } + + function updateDocumentOffsetWithoutAnimation() { + updateDocumentOffset(false); + } + + function updateDocumentOffset(animated) { + // set body offset + const topOffset = headerOffset(); + const bodyOffset = topOffset + footerOffset() + dashboardOffset(); + const bodyEl = window.document.body; + bodyEl.setAttribute("data-bs-offset", topOffset); + bodyEl.style.paddingTop = topOffset + "px"; + + // deal with sidebar offsets + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + if (!animated) { + sidebar.classList.add("notransition"); + // Remove the no transition class after the animation has time to complete + setTimeout(function () { + sidebar.classList.remove("notransition"); + }, 201); + } + + if (window.Headroom && sidebar.classList.contains("sidebar-unpinned")) { + sidebar.style.top = "0"; + sidebar.style.maxHeight = "100vh"; + } else { + sidebar.style.top = topOffset + "px"; + sidebar.style.maxHeight = "calc(100vh - " + topOffset + "px)"; + } + }); + + // allow space for footer + const mainContainer = window.document.querySelector(".quarto-container"); + if (mainContainer) { + mainContainer.style.minHeight = "calc(100vh - " + bodyOffset + "px)"; + } + + // link offset + let linkStyle = window.document.querySelector("#quarto-target-style"); + if (!linkStyle) { + linkStyle = window.document.createElement("style"); + linkStyle.setAttribute("id", "quarto-target-style"); + window.document.head.appendChild(linkStyle); + } + while (linkStyle.firstChild) { + linkStyle.removeChild(linkStyle.firstChild); + } + if (topOffset > 0) { + linkStyle.appendChild( + window.document.createTextNode(` + section:target::before { + content: ""; + display: block; + height: ${topOffset}px; + margin: -${topOffset}px 0 0; + }`) + ); + } + if (init) { + window.dispatchEvent(headroomChanged); + } + init = true; + } + + // initialize headroom + var header = window.document.querySelector("#quarto-header"); + if (header && window.Headroom) { + const headroom = new window.Headroom(header, { + tolerance: 5, + onPin: function () { + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + sidebar.classList.remove("sidebar-unpinned"); + }); + updateDocumentOffset(); + }, + onUnpin: function () { + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + sidebar.classList.add("sidebar-unpinned"); + }); + updateDocumentOffset(); + }, + }); + headroom.init(); + + let frozen = false; + window.quartoToggleHeadroom = function () { + if (frozen) { + headroom.unfreeze(); + frozen = false; + } else { + headroom.freeze(); + frozen = true; + } + }; + } + + window.addEventListener( + "hashchange", + function (e) { + if ( + getComputedStyle(document.documentElement).scrollBehavior !== "smooth" + ) { + window.scrollTo(0, window.pageYOffset - headerOffset()); + } + }, + false + ); + + // Observe size changed for the header + const headerEl = window.document.querySelector("header.fixed-top"); + if (headerEl && window.ResizeObserver) { + const observer = new window.ResizeObserver(() => { + setTimeout(updateDocumentOffsetWithoutAnimation, 0); + }); + observer.observe(headerEl, { + attributes: true, + childList: true, + characterData: true, + }); + } else { + window.addEventListener( + "resize", + throttle(updateDocumentOffsetWithoutAnimation, 50) + ); + } + setTimeout(updateDocumentOffsetWithoutAnimation, 250); + + // fixup index.html links if we aren't on the filesystem + if (window.location.protocol !== "file:") { + const links = window.document.querySelectorAll("a"); + for (let i = 0; i < links.length; i++) { + if (links[i].href) { + links[i].dataset.originalHref = links[i].href; + links[i].href = links[i].href.replace(/\/index\.html/, "/"); + } + } + + // Fixup any sharing links that require urls + // Append url to any sharing urls + const sharingLinks = window.document.querySelectorAll( + "a.sidebar-tools-main-item, a.quarto-navigation-tool, a.quarto-navbar-tools, a.quarto-navbar-tools-item" + ); + for (let i = 0; i < sharingLinks.length; i++) { + const sharingLink = sharingLinks[i]; + const href = sharingLink.getAttribute("href"); + if (href) { + sharingLink.setAttribute( + "href", + href.replace("|url|", window.location.href) + ); + } + } + + // Scroll the active navigation item into view, if necessary + const navSidebar = window.document.querySelector("nav#quarto-sidebar"); + if (navSidebar) { + // Find the active item + const activeItem = navSidebar.querySelector("li.sidebar-item a.active"); + if (activeItem) { + // Wait for the scroll height and height to resolve by observing size changes on the + // nav element that is scrollable + const resizeObserver = new ResizeObserver((_entries) => { + // The bottom of the element + const elBottom = activeItem.offsetTop; + const viewBottom = navSidebar.scrollTop + navSidebar.clientHeight; + + // The element height and scroll height are the same, then we are still loading + if (viewBottom !== navSidebar.scrollHeight) { + // Determine if the item isn't visible and scroll to it + if (elBottom >= viewBottom) { + navSidebar.scrollTop = elBottom; + } + + // stop observing now since we've completed the scroll + resizeObserver.unobserve(navSidebar); + } + }); + resizeObserver.observe(navSidebar); + } + } + } +}); diff --git a/previews/PR23/site_libs/quarto-search/autocomplete.umd.js b/previews/PR23/site_libs/quarto-search/autocomplete.umd.js new file mode 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Apache Software License 2.0 + * + * http://www.apache.org/licenses/LICENSE-2.0 + */ +var e,t;e=this,t=function(){"use strict";function e(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter((function(t){return Object.getOwnPropertyDescriptor(e,t).enumerable}))),n.push.apply(n,r)}return n}function t(t){for(var n=1;ne.length)&&(t=e.length);for(var n=0,r=new Array(t);n0&&void 0!==arguments[0]?arguments[0]:1,t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:3,n=new Map,r=Math.pow(10,t);return{get:function(t){var i=t.match(C).length;if(n.has(i))return n.get(i);var o=1/Math.pow(i,.5*e),c=parseFloat(Math.round(o*r)/r);return n.set(i,c),c},clear:function(){n.clear()}}}var $=function(){function e(){var t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{},n=t.getFn,i=void 0===n?I.getFn:n,o=t.fieldNormWeight,c=void 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a/previews/PR23/site_libs/quarto-search/quarto-search.js b/previews/PR23/site_libs/quarto-search/quarto-search.js new file mode 100644 index 0000000..d788a95 --- /dev/null +++ b/previews/PR23/site_libs/quarto-search/quarto-search.js @@ -0,0 +1,1290 @@ +const kQueryArg = "q"; +const kResultsArg = "show-results"; + +// If items don't provide a URL, then both the navigator and the onSelect +// function aren't called (and therefore, the default implementation is used) +// +// We're using this sentinel URL to signal to those handlers that this +// item is a more item (along with the type) and can be handled appropriately +const kItemTypeMoreHref = "0767FDFD-0422-4E5A-BC8A-3BE11E5BBA05"; + +window.document.addEventListener("DOMContentLoaded", function (_event) { + // Ensure that search is available on this page. If it isn't, + // should return early and not do anything + var searchEl = window.document.getElementById("quarto-search"); + if (!searchEl) return; + + const { autocomplete } = window["@algolia/autocomplete-js"]; + + let quartoSearchOptions = {}; + let language = {}; + const searchOptionEl = window.document.getElementById( + "quarto-search-options" + ); + if (searchOptionEl) { + const jsonStr = searchOptionEl.textContent; + quartoSearchOptions = JSON.parse(jsonStr); + language = quartoSearchOptions.language; + } + + // note the search mode + if (quartoSearchOptions.type === "overlay") { + searchEl.classList.add("type-overlay"); + } else { + searchEl.classList.add("type-textbox"); + } + + // Used to determine highlighting behavior for this page + // A `q` query param is expected when the user follows a search + // to this page + const currentUrl = new URL(window.location); + const query = currentUrl.searchParams.get(kQueryArg); + const showSearchResults = currentUrl.searchParams.get(kResultsArg); + const mainEl = window.document.querySelector("main"); + + // highlight matches on the page + if (query && mainEl) { + // perform any highlighting + highlight(escapeRegExp(query), mainEl); + + // fix up the URL to remove the q query param + const replacementUrl = new URL(window.location); + replacementUrl.searchParams.delete(kQueryArg); + window.history.replaceState({}, "", replacementUrl); + } + + // function to clear highlighting on the page when the search query changes + // (e.g. if the user edits the query or clears it) + let highlighting = true; + const resetHighlighting = (searchTerm) => { + if (mainEl && highlighting && query && searchTerm !== query) { + clearHighlight(query, mainEl); + highlighting = false; + } + }; + + // Clear search highlighting when the user scrolls sufficiently + const resetFn = () => { + resetHighlighting(""); + window.removeEventListener("quarto-hrChanged", resetFn); + window.removeEventListener("quarto-sectionChanged", resetFn); + }; + + // Register this event after the initial scrolling and settling of events + // on the page + window.addEventListener("quarto-hrChanged", resetFn); + window.addEventListener("quarto-sectionChanged", resetFn); + + // Responsively switch to overlay mode if the search is present on the navbar + // Note that switching the sidebar to overlay mode requires more coordinate (not just + // the media query since we generate different HTML for sidebar overlays than we do + // for sidebar input UI) + const detachedMediaQuery = + quartoSearchOptions.type === "overlay" ? "all" : "(max-width: 991px)"; + + // If configured, include the analytics client to send insights + const plugins = configurePlugins(quartoSearchOptions); + + let lastState = null; + const { setIsOpen, setQuery, setCollections } = autocomplete({ + container: searchEl, + detachedMediaQuery: detachedMediaQuery, + defaultActiveItemId: 0, + panelContainer: "#quarto-search-results", + panelPlacement: quartoSearchOptions["panel-placement"], + debug: false, + openOnFocus: true, + plugins, + classNames: { + form: "d-flex", + }, + placeholder: language["search-text-placeholder"], + translations: { + clearButtonTitle: language["search-clear-button-title"], + detachedCancelButtonText: language["search-detached-cancel-button-title"], + submitButtonTitle: language["search-submit-button-title"], + }, + initialState: { + query, + }, + getItemUrl({ item }) { + return item.href; + }, + onStateChange({ state }) { + // If this is a file URL, note that + + // Perhaps reset highlighting + resetHighlighting(state.query); + + // If the panel just opened, ensure the panel is positioned properly + if (state.isOpen) { + if (lastState && !lastState.isOpen) { + setTimeout(() => { + positionPanel(quartoSearchOptions["panel-placement"]); + }, 150); + } + } + + // Perhaps show the copy link + showCopyLink(state.query, quartoSearchOptions); + + lastState = state; + }, + reshape({ sources, state }) { + return sources.map((source) => { + try { + const items = source.getItems(); + + // Validate the items + validateItems(items); + + // group the items by document + const groupedItems = new Map(); + items.forEach((item) => { + const hrefParts = item.href.split("#"); + const baseHref = hrefParts[0]; + const isDocumentItem = hrefParts.length === 1; + + const items = groupedItems.get(baseHref); + if (!items) { + groupedItems.set(baseHref, [item]); + } else { + // If the href for this item matches the document + // exactly, place this item first as it is the item that represents + // the document itself + if (isDocumentItem) { + items.unshift(item); + } else { + items.push(item); + } + groupedItems.set(baseHref, items); + } + }); + + const reshapedItems = []; + let count = 1; + for (const [_key, value] of groupedItems) { + const firstItem = value[0]; + reshapedItems.push({ + ...firstItem, + type: kItemTypeDoc, + }); + + const collapseMatches = quartoSearchOptions["collapse-after"]; + const collapseCount = + typeof collapseMatches === "number" ? collapseMatches : 1; + + if (value.length > 1) { + const target = `search-more-${count}`; + const isExpanded = + state.context.expanded && + state.context.expanded.includes(target); + + const remainingCount = value.length - collapseCount; + + for (let i = 1; i < value.length; i++) { + if (collapseMatches && i === collapseCount) { + reshapedItems.push({ + target, + title: isExpanded + ? language["search-hide-matches-text"] + : remainingCount === 1 + ? `${remainingCount} ${language["search-more-match-text"]}` + : `${remainingCount} ${language["search-more-matches-text"]}`, + type: kItemTypeMore, + href: kItemTypeMoreHref, + }); + } + + if (isExpanded || !collapseMatches || i < collapseCount) { + reshapedItems.push({ + ...value[i], + type: kItemTypeItem, + target, + }); + } + } + } + count += 1; + } + + return { + ...source, + getItems() { + return reshapedItems; + }, + }; + } catch (error) { + // Some form of error occurred + return { + ...source, + getItems() { + return [ + { + title: error.name || "An Error Occurred While Searching", + text: + error.message || + "An unknown error occurred while attempting to perform the requested search.", + type: kItemTypeError, + }, + ]; + }, + }; + } + }); + }, + navigator: { + navigate({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + window.location.assign(itemUrl); + } + }, + navigateNewTab({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + const windowReference = window.open(itemUrl, "_blank", "noopener"); + if (windowReference) { + windowReference.focus(); + } + } + }, + navigateNewWindow({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + window.open(itemUrl, "_blank", "noopener"); + } + }, + }, + getSources({ state, setContext, setActiveItemId, refresh }) { + return [ + { + sourceId: "documents", + getItemUrl({ item }) { + if (item.href) { + return offsetURL(item.href); + } else { + return undefined; + } + }, + onSelect({ + item, + state, + setContext, + setIsOpen, + setActiveItemId, + refresh, + }) { + if (item.type === kItemTypeMore) { + toggleExpanded(item, state, setContext, setActiveItemId, refresh); + + // Toggle more + setIsOpen(true); + } + }, + getItems({ query }) { + if (query === null || query === "") { + return []; + } + + const limit = quartoSearchOptions.limit; + if (quartoSearchOptions.algolia) { + return algoliaSearch(query, limit, quartoSearchOptions.algolia); + } else { + // Fuse search options + const fuseSearchOptions = { + isCaseSensitive: false, + shouldSort: true, + minMatchCharLength: 2, + limit: limit, + }; + + return readSearchData().then(function (fuse) { + return fuseSearch(query, fuse, fuseSearchOptions); + }); + } + }, + templates: { + noResults({ createElement }) { + const hasQuery = lastState.query; + + return createElement( + "div", + { + class: `quarto-search-no-results${ + hasQuery ? "" : " no-query" + }`, + }, + language["search-no-results-text"] + ); + }, + header({ items, createElement }) { + // count the documents + const count = items.filter((item) => { + return item.type === kItemTypeDoc; + }).length; + + if (count > 0) { + return createElement( + "div", + { class: "search-result-header" }, + `${count} ${language["search-matching-documents-text"]}` + ); + } else { + return createElement( + "div", + { class: "search-result-header-no-results" }, + `` + ); + } + }, + footer({ _items, createElement }) { + if ( + quartoSearchOptions.algolia && + quartoSearchOptions.algolia["show-logo"] + ) { + const libDir = quartoSearchOptions.algolia["libDir"]; + const logo = createElement("img", { + src: offsetURL( + `${libDir}/quarto-search/search-by-algolia.svg` + ), + class: "algolia-search-logo", + }); + return createElement( + "a", + { href: "http://www.algolia.com/" }, + logo + ); + } + }, + + item({ item, createElement }) { + return renderItem( + item, + createElement, + state, + setActiveItemId, + setContext, + refresh, + quartoSearchOptions + ); + }, + }, + }, + ]; + }, + }); + + window.quartoOpenSearch = () => { + setIsOpen(false); + setIsOpen(true); + focusSearchInput(); + }; + + document.addEventListener("keyup", (event) => { + const { key } = event; + const kbds = quartoSearchOptions["keyboard-shortcut"]; + const focusedEl = document.activeElement; + + const isFormElFocused = [ + "input", + "select", + "textarea", + "button", + "option", + ].find((tag) => { + return focusedEl.tagName.toLowerCase() === tag; + }); + + if ( + kbds && + kbds.includes(key) && + !isFormElFocused && + !document.activeElement.isContentEditable + ) { + event.preventDefault(); + window.quartoOpenSearch(); + } + }); + + // Remove the labeleledby attribute since it is pointing + // to a non-existent label + if (quartoSearchOptions.type === "overlay") { + const inputEl = window.document.querySelector( + "#quarto-search .aa-Autocomplete" + ); + if (inputEl) { + inputEl.removeAttribute("aria-labelledby"); + } + } + + function throttle(func, wait) { + let waiting = false; + return function () { + if (!waiting) { + func.apply(this, arguments); + waiting = true; + setTimeout(function () { + waiting = false; + }, wait); + } + }; + } + + // If the main document scrolls dismiss the search results + // (otherwise, since they're floating in the document they can scroll with the document) + window.document.body.onscroll = throttle(() => { + // Only do this if we're not detached + // Bug #7117 + // This will happen when the keyboard is shown on ios (resulting in a scroll) + // which then closed the search UI + if (!window.matchMedia(detachedMediaQuery).matches) { + setIsOpen(false); + } + }, 50); + + if (showSearchResults) { + setIsOpen(true); + focusSearchInput(); + } +}); + +function configurePlugins(quartoSearchOptions) { + const autocompletePlugins = []; + const algoliaOptions = quartoSearchOptions.algolia; + if ( + algoliaOptions && + algoliaOptions["analytics-events"] && + algoliaOptions["search-only-api-key"] && + algoliaOptions["application-id"] + ) { + const apiKey = algoliaOptions["search-only-api-key"]; + const appId = algoliaOptions["application-id"]; + + // Aloglia insights may not be loaded because they require cookie consent + // Use deferred loading so events will start being recorded when/if consent + // is granted. + const algoliaInsightsDeferredPlugin = deferredLoadPlugin(() => { + if ( + window.aa && + window["@algolia/autocomplete-plugin-algolia-insights"] + ) { + window.aa("init", { + appId, + apiKey, + useCookie: true, + }); + + const { createAlgoliaInsightsPlugin } = + window["@algolia/autocomplete-plugin-algolia-insights"]; + // Register the insights client + const algoliaInsightsPlugin = createAlgoliaInsightsPlugin({ + insightsClient: window.aa, + onItemsChange({ insights, insightsEvents }) { + const events = insightsEvents.flatMap((event) => { + // This API limits the number of items per event to 20 + const chunkSize = 20; + const itemChunks = []; + const eventItems = event.items; + for (let i = 0; i < eventItems.length; i += chunkSize) { + itemChunks.push(eventItems.slice(i, i + chunkSize)); + } + // Split the items into multiple events that can be sent + const events = itemChunks.map((items) => { + return { + ...event, + items, + }; + }); + return events; + }); + + for (const event of events) { + insights.viewedObjectIDs(event); + } + }, + }); + return algoliaInsightsPlugin; + } + }); + + // Add the plugin + autocompletePlugins.push(algoliaInsightsDeferredPlugin); + return autocompletePlugins; + } +} + +// For plugins that may not load immediately, create a wrapper +// plugin and forward events and plugin data once the plugin +// is initialized. This is useful for cases like cookie consent +// which may prevent the analytics insights event plugin from initializing +// immediately. +function deferredLoadPlugin(createPlugin) { + let plugin = undefined; + let subscribeObj = undefined; + const wrappedPlugin = () => { + if (!plugin && subscribeObj) { + plugin = createPlugin(); + if (plugin && plugin.subscribe) { + plugin.subscribe(subscribeObj); + } + } + return plugin; + }; + + return { + subscribe: (obj) => { + subscribeObj = obj; + }, + onStateChange: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onStateChange) { + plugin.onStateChange(obj); + } + }, + onSubmit: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onSubmit) { + plugin.onSubmit(obj); + } + }, + onReset: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onReset) { + plugin.onReset(obj); + } + }, + getSources: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.getSources) { + return plugin.getSources(obj); + } else { + return Promise.resolve([]); + } + }, + data: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.data) { + plugin.data(obj); + } + }, + }; +} + +function validateItems(items) { + // Validate the first item + if (items.length > 0) { + const item = items[0]; + const missingFields = []; + if (item.href == undefined) { + missingFields.push("href"); + } + if (!item.title == undefined) { + missingFields.push("title"); + } + if (!item.text == undefined) { + missingFields.push("text"); + } + + if (missingFields.length === 1) { + throw { + name: `Error: Search index is missing the ${missingFields[0]} field.`, + message: `The items being returned for this search do not include all the required fields. Please ensure that your index items include the ${missingFields[0]} field or use index-fields in your _quarto.yml file to specify the field names.`, + }; + } else if (missingFields.length > 1) { + const missingFieldList = missingFields + .map((field) => { + return `${field}`; + }) + .join(", "); + + throw { + name: `Error: Search index is missing the following fields: ${missingFieldList}.`, + message: `The items being returned for this search do not include all the required fields. Please ensure that your index items includes the following fields: ${missingFieldList}, or use index-fields in your _quarto.yml file to specify the field names.`, + }; + } + } +} + +let lastQuery = null; +function showCopyLink(query, options) { + const language = options.language; + lastQuery = query; + // Insert share icon + const inputSuffixEl = window.document.body.querySelector( + ".aa-Form .aa-InputWrapperSuffix" + ); + + if (inputSuffixEl) { + let copyButtonEl = window.document.body.querySelector( + ".aa-Form .aa-InputWrapperSuffix .aa-CopyButton" + ); + + if (copyButtonEl === null) { + copyButtonEl = window.document.createElement("button"); + copyButtonEl.setAttribute("class", "aa-CopyButton"); + copyButtonEl.setAttribute("type", "button"); + copyButtonEl.setAttribute("title", language["search-copy-link-title"]); + copyButtonEl.onmousedown = (e) => { + e.preventDefault(); + e.stopPropagation(); + }; + + const linkIcon = "bi-clipboard"; + const checkIcon = "bi-check2"; + + const shareIconEl = window.document.createElement("i"); + shareIconEl.setAttribute("class", `bi ${linkIcon}`); + copyButtonEl.appendChild(shareIconEl); + inputSuffixEl.prepend(copyButtonEl); + + const clipboard = new window.ClipboardJS(".aa-CopyButton", { + text: function (_trigger) { + const copyUrl = new URL(window.location); + copyUrl.searchParams.set(kQueryArg, lastQuery); + copyUrl.searchParams.set(kResultsArg, "1"); + return copyUrl.toString(); + }, + }); + clipboard.on("success", function (e) { + // Focus the input + + // button target + const button = e.trigger; + const icon = button.querySelector("i.bi"); + + // flash "checked" + icon.classList.add(checkIcon); + icon.classList.remove(linkIcon); + setTimeout(function () { + icon.classList.remove(checkIcon); + icon.classList.add(linkIcon); + }, 1000); + }); + } + + // If there is a query, show the link icon + if (copyButtonEl) { + if (lastQuery && options["copy-button"]) { + copyButtonEl.style.display = "flex"; + } else { + copyButtonEl.style.display = "none"; + } + } + } +} + +/* Search Index Handling */ +// create the index +var fuseIndex = undefined; +var shownWarning = false; + +// fuse index options +const kFuseIndexOptions = { + keys: [ + { name: "title", weight: 20 }, + { name: "section", weight: 20 }, + { name: "text", weight: 10 }, + ], + ignoreLocation: true, + threshold: 0.1, +}; + +async function readSearchData() { + // Initialize the search index on demand + if (fuseIndex === undefined) { + if (window.location.protocol === "file:" && !shownWarning) { + window.alert( + "Search requires JavaScript features disabled when running in file://... URLs. In order to use search, please run this document in a web server." + ); + shownWarning = true; + return; + } + const fuse = new window.Fuse([], kFuseIndexOptions); + + // fetch the main search.json + const response = await fetch(offsetURL("search.json")); + if (response.status == 200) { + return response.json().then(function (searchDocs) { + searchDocs.forEach(function (searchDoc) { + fuse.add(searchDoc); + }); + fuseIndex = fuse; + return fuseIndex; + }); + } else { + return Promise.reject( + new Error( + "Unexpected status from search index request: " + response.status + ) + ); + } + } + + return fuseIndex; +} + +function inputElement() { + return window.document.body.querySelector(".aa-Form .aa-Input"); +} + +function focusSearchInput() { + setTimeout(() => { + const inputEl = inputElement(); + if (inputEl) { + inputEl.focus(); + } + }, 50); +} + +/* Panels */ +const kItemTypeDoc = "document"; +const kItemTypeMore = "document-more"; +const kItemTypeItem = "document-item"; +const kItemTypeError = "error"; + +function renderItem( + item, + createElement, + state, + setActiveItemId, + setContext, + refresh, + quartoSearchOptions +) { + switch (item.type) { + case kItemTypeDoc: + return createDocumentCard( + createElement, + "file-richtext", + item.title, + item.section, + item.text, + item.href, + item.crumbs, + quartoSearchOptions + ); + case kItemTypeMore: + return createMoreCard( + createElement, + item, + state, + setActiveItemId, + setContext, + refresh + ); + case kItemTypeItem: + return createSectionCard( + createElement, + item.section, + item.text, + item.href + ); + case kItemTypeError: + return createErrorCard(createElement, item.title, item.text); + default: + return undefined; + } +} + +function createDocumentCard( + createElement, + icon, + title, + section, + text, + href, + crumbs, + quartoSearchOptions +) { + const iconEl = createElement("i", { + class: `bi bi-${icon} search-result-icon`, + }); + const titleEl = createElement("p", { class: "search-result-title" }, title); + const titleContents = [iconEl, titleEl]; + const showParent = quartoSearchOptions["show-item-context"]; + if (crumbs && showParent) { + let crumbsOut = undefined; + const crumbClz = ["search-result-crumbs"]; + if (showParent === "root") { + crumbsOut = crumbs.length > 1 ? crumbs[0] : undefined; + } else if (showParent === "parent") { + crumbsOut = crumbs.length > 1 ? crumbs[crumbs.length - 2] : undefined; + } else { + crumbsOut = crumbs.length > 1 ? crumbs.join(" > ") : undefined; + crumbClz.push("search-result-crumbs-wrap"); + } + + const crumbEl = createElement( + "p", + { class: crumbClz.join(" ") }, + crumbsOut + ); + titleContents.push(crumbEl); + } + + const titleContainerEl = createElement( + "div", + { class: "search-result-title-container" }, + titleContents + ); + + const textEls = []; + if (section) { + const sectionEl = createElement( + "p", + { class: "search-result-section" }, + section + ); + textEls.push(sectionEl); + } + const descEl = createElement("p", { + class: "search-result-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + textEls.push(descEl); + + const textContainerEl = createElement( + "div", + { class: "search-result-text-container" }, + textEls + ); + + const containerEl = createElement( + "div", + { + class: "search-result-container", + }, + [titleContainerEl, textContainerEl] + ); + + const linkEl = createElement( + "a", + { + href: offsetURL(href), + class: "search-result-link", + }, + containerEl + ); + + const classes = ["search-result-doc", "search-item"]; + if (!section) { + classes.push("document-selectable"); + } + + return createElement( + "div", + { + class: classes.join(" "), + }, + linkEl + ); +} + +function createMoreCard( + createElement, + item, + state, + setActiveItemId, + setContext, + refresh +) { + const moreCardEl = createElement( + "div", + { + class: "search-result-more search-item", + onClick: (e) => { + // Handle expanding the sections by adding the expanded + // section to the list of expanded sections + toggleExpanded(item, state, setContext, setActiveItemId, refresh); + e.stopPropagation(); + }, + }, + item.title + ); + + return moreCardEl; +} + +function toggleExpanded(item, state, setContext, setActiveItemId, refresh) { + const expanded = state.context.expanded || []; + if (expanded.includes(item.target)) { + setContext({ + expanded: expanded.filter((target) => target !== item.target), + }); + } else { + setContext({ expanded: [...expanded, item.target] }); + } + + refresh(); + setActiveItemId(item.__autocomplete_id); +} + +function createSectionCard(createElement, section, text, href) { + const sectionEl = createSection(createElement, section, text, href); + return createElement( + "div", + { + class: "search-result-doc-section search-item", + }, + sectionEl + ); +} + +function createSection(createElement, title, text, href) { + const descEl = createElement("p", { + class: "search-result-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + + const titleEl = createElement("p", { class: "search-result-section" }, title); + const linkEl = createElement( + "a", + { + href: offsetURL(href), + class: "search-result-link", + }, + [titleEl, descEl] + ); + return linkEl; +} + +function createErrorCard(createElement, title, text) { + const descEl = createElement("p", { + class: "search-error-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + + const titleEl = createElement("p", { + class: "search-error-title", + dangerouslySetInnerHTML: { + __html: ` ${title}`, + }, + }); + const errorEl = createElement("div", { class: "search-error" }, [ + titleEl, + descEl, + ]); + return errorEl; +} + +function positionPanel(pos) { + const panelEl = window.document.querySelector( + "#quarto-search-results .aa-Panel" + ); + const inputEl = window.document.querySelector( + "#quarto-search .aa-Autocomplete" + ); + + if (panelEl && inputEl) { + panelEl.style.top = `${Math.round(panelEl.offsetTop)}px`; + if (pos === "start") { + panelEl.style.left = `${Math.round(inputEl.left)}px`; + } else { + panelEl.style.right = `${Math.round(inputEl.offsetRight)}px`; + } + } +} + +/* Highlighting */ +// highlighting functions +function highlightMatch(query, text) { + if (text) { + const start = text.toLowerCase().indexOf(query.toLowerCase()); + if (start !== -1) { + const startMark = ""; + const endMark = ""; + + const end = start + query.length; + text = + text.slice(0, start) + + startMark + + text.slice(start, end) + + endMark + + text.slice(end); + const startInfo = clipStart(text, start); + const endInfo = clipEnd( + text, + startInfo.position + startMark.length + endMark.length + ); + text = + startInfo.prefix + + text.slice(startInfo.position, endInfo.position) + + endInfo.suffix; + + return text; + } else { + return text; + } + } else { + return text; + } +} + +function clipStart(text, pos) { + const clipStart = pos - 50; + if (clipStart < 0) { + // This will just return the start of the string + return { + position: 0, + prefix: "", + }; + } else { + // We're clipping before the start of the string, walk backwards to the first space. + const spacePos = findSpace(text, pos, -1); + return { + position: spacePos.position, + prefix: "", + }; + } +} + +function clipEnd(text, pos) { + const clipEnd = pos + 200; + if (clipEnd > text.length) { + return { + position: text.length, + suffix: "", + }; + } else { + const spacePos = findSpace(text, clipEnd, 1); + return { + position: spacePos.position, + suffix: spacePos.clipped ? "โ€ฆ" : "", + }; + } +} + +function findSpace(text, start, step) { + let stepPos = start; + while (stepPos > -1 && stepPos < text.length) { + const char = text[stepPos]; + if (char === " " || char === "," || char === ":") { + return { + position: step === 1 ? stepPos : stepPos - step, + clipped: stepPos > 1 && stepPos < text.length, + }; + } + stepPos = stepPos + step; + } + + return { + position: stepPos - step, + clipped: false, + }; +} + +// removes highlighting as implemented by the mark tag +function clearHighlight(searchterm, el) { + const childNodes = el.childNodes; + for (let i = childNodes.length - 1; i >= 0; i--) { + const node = childNodes[i]; + if (node.nodeType === Node.ELEMENT_NODE) { + if ( + node.tagName === "MARK" && + node.innerText.toLowerCase() === searchterm.toLowerCase() + ) { + el.replaceChild(document.createTextNode(node.innerText), node); + } else { + clearHighlight(searchterm, node); + } + } + } +} + +function escapeRegExp(string) { + return string.replace(/[.*+?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string +} + +// highlight matches +function highlight(term, el) { + const termRegex = new RegExp(term, "ig"); + const childNodes = el.childNodes; + + // walk back to front avoid mutating elements in front of us + for (let i = childNodes.length - 1; i >= 0; i--) { + const node = childNodes[i]; + + if (node.nodeType === Node.TEXT_NODE) { + // Search text nodes for text to highlight + const text = node.nodeValue; + + let startIndex = 0; + let matchIndex = text.search(termRegex); + if (matchIndex > -1) { + const markFragment = document.createDocumentFragment(); + while (matchIndex > -1) { + const prefix = text.slice(startIndex, matchIndex); + markFragment.appendChild(document.createTextNode(prefix)); + + const mark = document.createElement("mark"); + mark.appendChild( + document.createTextNode( + text.slice(matchIndex, matchIndex + term.length) + ) + ); + markFragment.appendChild(mark); + + startIndex = matchIndex + term.length; + matchIndex = text.slice(startIndex).search(new RegExp(term, "ig")); + if (matchIndex > -1) { + matchIndex = startIndex + matchIndex; + } + } + if (startIndex < text.length) { + markFragment.appendChild( + document.createTextNode(text.slice(startIndex, text.length)) + ); + } + + el.replaceChild(markFragment, node); + } + } else if (node.nodeType === Node.ELEMENT_NODE) { + // recurse through elements + highlight(term, node); + } + } +} + +/* Link Handling */ +// get the offset from this page for a given site root relative url +function offsetURL(url) { + var offset = getMeta("quarto:offset"); + return offset ? offset + url : url; +} + +// read a meta tag value +function getMeta(metaName) { + var metas = window.document.getElementsByTagName("meta"); + for (let i = 0; i < metas.length; i++) { + if (metas[i].getAttribute("name") === metaName) { + return metas[i].getAttribute("content"); + } + } + return ""; +} + +function algoliaSearch(query, limit, algoliaOptions) { + const { getAlgoliaResults } = window["@algolia/autocomplete-preset-algolia"]; + + const applicationId = algoliaOptions["application-id"]; + const searchOnlyApiKey = algoliaOptions["search-only-api-key"]; + const indexName = algoliaOptions["index-name"]; + const indexFields = algoliaOptions["index-fields"]; + const searchClient = window.algoliasearch(applicationId, searchOnlyApiKey); + const searchParams = algoliaOptions["params"]; + const searchAnalytics = !!algoliaOptions["analytics-events"]; + + return getAlgoliaResults({ + searchClient, + queries: [ + { + indexName: indexName, + query, + params: { + hitsPerPage: limit, + clickAnalytics: searchAnalytics, + ...searchParams, + }, + }, + ], + transformResponse: (response) => { + if (!indexFields) { + return response.hits.map((hit) => { + return hit.map((item) => { + return { + ...item, + text: highlightMatch(query, item.text), + }; + }); + }); + } else { + const remappedHits = response.hits.map((hit) => { + return hit.map((item) => { + const newItem = { ...item }; + ["href", "section", "title", "text", "crumbs"].forEach( + (keyName) => { + const mappedName = indexFields[keyName]; + if ( + mappedName && + item[mappedName] !== undefined && + mappedName !== keyName + ) { + newItem[keyName] = item[mappedName]; + delete newItem[mappedName]; + } + } + ); + newItem.text = highlightMatch(query, newItem.text); + return newItem; + }); + }); + return remappedHits; + } + }, + }); +} + +let subSearchTerm = undefined; +let subSearchFuse = undefined; +const kFuseMaxWait = 125; + +async function fuseSearch(query, fuse, fuseOptions) { + let index = fuse; + // Fuse.js using the Bitap algorithm for text matching which runs in + // O(nm) time (no matter the structure of the text). In our case this + // means that long search terms mixed with large index gets very slow + // + // This injects a subIndex that will be used once the terms get long enough + // Usually making this subindex is cheap since there will typically be + // a subset of results matching the existing query + if (subSearchFuse !== undefined && query.startsWith(subSearchTerm)) { + // Use the existing subSearchFuse + index = subSearchFuse; + } else if (subSearchFuse !== undefined) { + // The term changed, discard the existing fuse + subSearchFuse = undefined; + subSearchTerm = undefined; + } + + // Search using the active fuse + const then = performance.now(); + const resultsRaw = await index.search(query, fuseOptions); + const now = performance.now(); + + const results = resultsRaw.map((result) => { + const addParam = (url, name, value) => { + const anchorParts = url.split("#"); + const baseUrl = anchorParts[0]; + const sep = baseUrl.search("\\?") > 0 ? "&" : "?"; + anchorParts[0] = baseUrl + sep + name + "=" + value; + return anchorParts.join("#"); + }; + + return { + title: result.item.title, + section: result.item.section, + href: addParam(result.item.href, kQueryArg, query), + text: highlightMatch(query, result.item.text), + crumbs: result.item.crumbs, + }; + }); + + // If we don't have a subfuse and the query is long enough, go ahead + // and create a subfuse to use for subsequent queries + if ( + now - then > kFuseMaxWait && + subSearchFuse === undefined && + resultsRaw.length < fuseOptions.limit + ) { + subSearchTerm = query; + subSearchFuse = new window.Fuse([], kFuseIndexOptions); + resultsRaw.forEach((rr) => { + subSearchFuse.add(rr.item); + }); + } + return results; +} diff --git a/previews/PR23/sitemap.xml b/previews/PR23/sitemap.xml new file mode 100644 index 0000000..1cc05e9 --- /dev/null +++ b/previews/PR23/sitemap.xml @@ -0,0 +1,43 @@ + + + + https://jl.geocompx.org/index.html + 2024-09-21T16:08:01.132Z + + + https://jl.geocompx.org/chapters/01-spatial-data.html + 2024-09-21T16:08:00.938Z + + + https://jl.geocompx.org/chapters/02-attribute-operations.html + 2024-09-21T16:08:00.938Z + + + https://jl.geocompx.org/chapters/03-spatial-operations.html + 2024-09-21T16:08:00.938Z + + + https://jl.geocompx.org/chapters/04-geometry-operations.html + 2024-09-21T16:08:00.938Z + + + https://jl.geocompx.org/chapters/05-raster-vector.html + 2024-09-21T16:08:00.938Z + + + https://jl.geocompx.org/chapters/06-reproj.html + 2024-09-21T16:08:00.938Z + + + https://jl.geocompx.org/chapters/07-read-write.html + 2024-09-21T16:08:00.938Z + + + https://jl.geocompx.org/chapters/08-mapping.html + 2024-09-21T16:08:00.938Z + + + https://jl.geocompx.org/references.html + 2024-09-21T16:08:01.133Z + +