fix (spatial): updating library loading now that raster/vector data a…

…re in separate files

_freeze/spatial_raster/execute-results/html.json

@@ -1,9 +1,11 @@
 {
-  "hash": "d58375b2f0c0d274f21408fa7be77a0b",
+  "hash": "e29acb041ba3a1a52be880493fbe2fbc",
   "result": {
     "engine": "knitr",
-    "markdown": "---\ntitle: \"Raster Data\"\nengine: knitr\n---\n\n\n\n\n## Library Loading\n\nBefore we can dive into \"actual\" spatial work we'll need to load some libraries.\n\n:::panel-tabset\n## [{{< fa brands r-project >}} R]{.r}\n\nLoad `terra` and `sf` to work with raster and vector data respectively.\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# Load needed libraries\nlibrary(terra)\n```\n:::\n\n\n\n\n## [{{< fa brands python >}} Python]{.py}\n\nLoad the `rioxarray`, `rasterio`, and `geopandas` libraries to work with raster and vector data. We'll also load the `os` library to deal with file path issues.\n\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\n# Load needed libraries\nimport os\nimport rasterio as rio\nimport rioxarray as rxr\n```\n:::\n\n\n\n:::\n\nTo demonstrate raster data operations we'll use NASA's [Shuttle Radar Topography Mission Global 3 arc second](https://lpdaac.usgs.gov/products/srtmgl3v003/) elevation data. Note that some minor preparatory work was necessary to get the data ready for our purposes here and is preserved in [this folder](https://github.com/njlyon0/collab_bilingualism/tree/main/dev) of the website's {{< fa brands github >}} GitHub repository.\n\n## Loading Rasters\n\nWe can begin by reading in the data.\n\n:::panel-tabset\n## [{{< fa brands r-project >}} R]{.r}\n\nThere are several [{{< fa brands r-project >}} R]{.r} packages for working with raster data but we'll focus on `terra`.\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# Load a raster of elevation data\nrast_r <- terra::rast(x = file.path(\"data\", \"elevation.tif\"))\n\n# Check the class of this object\nclass(rast_r)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] \"SpatRaster\"\nattr(,\"package\")\n[1] \"terra\"\n```\n\n\n:::\n:::\n\n\n\n\n## [{{< fa brands python >}} Python]{.py}\n\nThe `rioxarray` library will be our focal library for raster operations with [{{< fa brands python >}} Python].\n\npd.read_csv(os.path.join(\"data\", \"verts.csv\"))\n\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\n# Load a raster of elevation data\nrast_py = rxr.open_rasterio(os.path.join(\"data\", \"elevation.tif\"), masked = True).squeeze()\n\n# Check the type of this variable\ntype(rast_py.rio.crs)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n<class 'rasterio.crs.CRS'>\n```\n\n\n:::\n:::\n\n\n\n:::\n\n## Checking Raster CRS\n\nAs noted earlier, the very first thing we should do after reading in spatial data is _check the coordinate reference system!_\n\n:::panel-tabset\n## [{{< fa brands r-project >}} R]{.r}\n\nWe can check the CRS with the `crs` function (from `terra`).\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# Check the CRS of the elevation data\nterra::crs(x = rast_r)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] \"GEOGCRS[\\\"WGS 84\\\",\\n    ENSEMBLE[\\\"World Geodetic System 1984 ensemble\\\",\\n        MEMBER[\\\"World Geodetic System 1984 (Transit)\\\"],\\n        MEMBER[\\\"World Geodetic System 1984 (G730)\\\"],\\n        MEMBER[\\\"World Geodetic System 1984 (G873)\\\"],\\n        MEMBER[\\\"World Geodetic System 1984 (G1150)\\\"],\\n        MEMBER[\\\"World Geodetic System 1984 (G1674)\\\"],\\n        MEMBER[\\\"World Geodetic System 1984 (G1762)\\\"],\\n        MEMBER[\\\"World Geodetic System 1984 (G2139)\\\"],\\n        ELLIPSOID[\\\"WGS 84\\\",6378137,298.257223563,\\n            LENGTHUNIT[\\\"metre\\\",1]],\\n        ENSEMBLEACCURACY[2.0]],\\n    PRIMEM[\\\"Greenwich\\\",0,\\n        ANGLEUNIT[\\\"degree\\\",0.0174532925199433]],\\n    CS[ellipsoidal,2],\\n        AXIS[\\\"geodetic latitude (Lat)\\\",north,\\n            ORDER[1],\\n            ANGLEUNIT[\\\"degree\\\",0.0174532925199433]],\\n        AXIS[\\\"geodetic longitude (Lon)\\\",east,\\n            ORDER[2],\\n            ANGLEUNIT[\\\"degree\\\",0.0174532925199433]],\\n    USAGE[\\n        SCOPE[\\\"Horizontal component of 3D system.\\\"],\\n        AREA[\\\"World.\\\"],\\n        BBOX[-90,-180,90,180]],\\n    ID[\\\"EPSG\\\",4326]]\"\n```\n\n\n:::\n:::\n\n\n\n\n## [{{< fa brands python >}} Python]{.py}\n\n[{{< fa brands python >}} Python]{.py} stores raster CRS as an attribute.\n\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\n# Check the CRS of the elevation data\nrast_py.rio.crs\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\nCRS.from_wkt('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\n:::\n:::\n\n\n\n:::\n\nYou may note in the above operations that [{{< fa brands python >}} Python]{.py} and [{{< fa brands r-project >}} R]{.r} seem to be returning different information for the same data. In fact they are saying the same thing just in slightly different ways. [{{< fa brands r-project >}} R]{.r} is telling us the name of the coordinate reference system (World Geodetic System 1984) while [{{< fa brands python >}} Python]{.py} is giving us the four-digit \"EPSG\" code. For context, the European Petroleum Survey Group (EPSG) is a major authority on accepted coordinate reference systems. Each CRS is given a unique, four-digit code. As you may now be able to guess, \"4326\" is the EPSG code for the World Geodetic System 1984 CRS!\n \nThis CRS (WGS84) is an especially common one so you may eventually commit its EPSG code to memory but often you'll encounter either CRS names or EPSG codes with which you are not familiar. It may not sound like sage advice but it can be simplest to just use whichever piece of information your coding language returns to Google the \"missing\" information.\n\n## Transforming Raster CRS\n\nOnce we know the current CRS, we can transform into a different coordinate reference system as needed. Let's transform from WGS84 into another commonly-used CRS: Albers Equal Area (EPSG code 3083).\n\nQuick warning, transforming rasters can take a **long** time and is very computationally intensive. It'll work for a relatively small raster like the one we're working with here but in general you should be careful with attempting to re-project a raster into a different CRS.\n\n:::panel-tabset\n## [{{< fa brands r-project >}} R]{.r}\n\nFor CRS transformations in [{{< fa brands r-project >}} R]{.r} we can use the `project` function (pronounced like the verb not the noun).\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# Transform the raster CRS\nrast_alber_r <- terra::project(x = rast_r, y = \"epsg:3083\")\n\n# Re-check the CRS to confirm it worked\nterra::crs(rast_alber_r)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] \"PROJCRS[\\\"NAD83 / Texas Centric Albers Equal Area\\\",\\n    BASEGEOGCRS[\\\"NAD83\\\",\\n        DATUM[\\\"North American Datum 1983\\\",\\n            ELLIPSOID[\\\"GRS 1980\\\",6378137,298.257222101,\\n                LENGTHUNIT[\\\"metre\\\",1]]],\\n        PRIMEM[\\\"Greenwich\\\",0,\\n            ANGLEUNIT[\\\"degree\\\",0.0174532925199433]],\\n        ID[\\\"EPSG\\\",4269]],\\n    CONVERSION[\\\"Texas Centric Albers Equal Area\\\",\\n        METHOD[\\\"Albers Equal Area\\\",\\n            ID[\\\"EPSG\\\",9822]],\\n        PARAMETER[\\\"Latitude of false origin\\\",18,\\n            ANGLEUNIT[\\\"degree\\\",0.0174532925199433],\\n            ID[\\\"EPSG\\\",8821]],\\n        PARAMETER[\\\"Longitude of false origin\\\",-100,\\n            ANGLEUNIT[\\\"degree\\\",0.0174532925199433],\\n            ID[\\\"EPSG\\\",8822]],\\n        PARAMETER[\\\"Latitude of 1st standard parallel\\\",27.5,\\n            ANGLEUNIT[\\\"degree\\\",0.0174532925199433],\\n            ID[\\\"EPSG\\\",8823]],\\n        PARAMETER[\\\"Latitude of 2nd standard parallel\\\",35,\\n            ANGLEUNIT[\\\"degree\\\",0.0174532925199433],\\n            ID[\\\"EPSG\\\",8824]],\\n        PARAMETER[\\\"Easting at false origin\\\",1500000,\\n            LENGTHUNIT[\\\"metre\\\",1],\\n            ID[\\\"EPSG\\\",8826]],\\n        PARAMETER[\\\"Northing at false origin\\\",6000000,\\n            LENGTHUNIT[\\\"metre\\\",1],\\n            ID[\\\"EPSG\\\",8827]]],\\n    CS[Cartesian,2],\\n        AXIS[\\\"easting (X)\\\",east,\\n            ORDER[1],\\n            LENGTHUNIT[\\\"metre\\\",1]],\\n        AXIS[\\\"northing (Y)\\\",north,\\n            ORDER[2],\\n            LENGTHUNIT[\\\"metre\\\",1]],\\n    USAGE[\\n        SCOPE[\\\"State-wide spatial data presentation requiring true area measurements.\\\"],\\n        AREA[\\\"United States (USA) - Texas.\\\"],\\n        BBOX[25.83,-106.66,36.5,-93.5]],\\n    ID[\\\"EPSG\\\",3083]]\"\n```\n\n\n:::\n:::\n\n\n\n\n## [{{< fa brands python >}} Python]{.py}\n\n[{{< fa brands python >}} Python]{.py} CRS re-projections have slightly more steps to work through.\n\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\n# Create a CRS variable for the desired ending CRS\nwgs84_crs = rio.crs.CRS.from_string(\"EPSG:3083\")\n\n# Reproject the raster we had\nrast_alber_py = rast_py.rio.reproject(wgs84_crs)\n\n# Re-check the CRS to confirm it worked\nrast_alber_py.rio.crs\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\nCRS.from_wkt('PROJCS[\"NAD83 / Texas Centric Albers Equal Area\",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\"]],AUTHORITY[\"EPSG\",\"4269\"]],PROJECTION[\"Albers_Conic_Equal_Area\"],PARAMETER[\"latitude_of_center\",18],PARAMETER[\"longitude_of_center\",-100],PARAMETER[\"standard_parallel_1\",27.5],PARAMETER[\"standard_parallel_2\",35],PARAMETER[\"false_easting\",1500000],PARAMETER[\"false_northing\",6000000],UNIT[\"metre\",1,AUTHORITY[\"EPSG\",\"9001\"]],AXIS[\"Easting\",EAST],AXIS[\"Northing\",NORTH],AUTHORITY[\"EPSG\",\"3083\"]]')\n```\n\n\n:::\n:::\n\n\n\n:::\n",
-    "supporting": [],
+    "markdown": "---\ntitle: \"Raster Data\"\nengine: knitr\n---\n\n\n\n\n## Library Loading\n\nBefore we can dive into \"actual\" spatial work we'll need to load some libraries.\n\n:::panel-tabset\n## [{{< fa brands r-project >}} R]{.r}\n\nLoad `terra` to work with raster data.\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# Load needed libraries\nlibrary(terra)\n```\n:::\n\n\n\n\n## [{{< fa brands python >}} Python]{.py}\n\nLoad the `rioxarray` and `rasterio` libraries to work with raster data. We'll also load the `os` library to deal with file path issues.\n\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\n# Load needed libraries\nimport os\nimport rasterio as rio\nimport rioxarray as rxr\n```\n:::\n\n\n\n:::\n\nTo demonstrate raster data operations we'll use NASA's [Shuttle Radar Topography Mission Global 3 arc second](https://lpdaac.usgs.gov/products/srtmgl3v003/) elevation data. Note that some minor preparatory work was necessary to get the data ready for our purposes here and is preserved in [this folder](https://github.com/njlyon0/collab_bilingualism/tree/main/dev) of the website's {{< fa brands github >}} GitHub repository.\n\n## Loading Rasters\n\nWe can begin by reading in the data.\n\n:::panel-tabset\n## [{{< fa brands r-project >}} R]{.r}\n\nThere are several [{{< fa brands r-project >}} R]{.r} packages for working with raster data but we'll focus on `terra`.\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# Load a raster of elevation data\nrast_r <- terra::rast(x = file.path(\"data\", \"elevation.tif\"))\n\n# Check the class of this object\nclass(rast_r)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] \"SpatRaster\"\nattr(,\"package\")\n[1] \"terra\"\n```\n\n\n:::\n:::\n\n\n\n\n## [{{< fa brands python >}} Python]{.py}\n\nThe `rioxarray` library will be our focal library for raster operations with [{{< fa brands python >}} Python].\n\npd.read_csv(os.path.join(\"data\", \"verts.csv\"))\n\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\n# Load a raster of elevation data\nrast_py = rxr.open_rasterio(os.path.join(\"data\", \"elevation.tif\"), masked = True).squeeze()\n\n# Check the type of this variable\ntype(rast_py.rio.crs)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n<class 'rasterio.crs.CRS'>\n```\n\n\n:::\n:::\n\n\n\n:::\n\n## Checking Raster CRS\n\nAs noted earlier, the very first thing we should do after reading in spatial data is _check the coordinate reference system!_\n\n:::panel-tabset\n## [{{< fa brands r-project >}} R]{.r}\n\nWe can check the CRS with the `crs` function (from `terra`).\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# Check the CRS of the elevation data\nterra::crs(x = rast_r)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] \"GEOGCRS[\\\"WGS 84\\\",\\n    ENSEMBLE[\\\"World Geodetic System 1984 ensemble\\\",\\n        MEMBER[\\\"World Geodetic System 1984 (Transit)\\\"],\\n        MEMBER[\\\"World Geodetic System 1984 (G730)\\\"],\\n        MEMBER[\\\"World Geodetic System 1984 (G873)\\\"],\\n        MEMBER[\\\"World Geodetic System 1984 (G1150)\\\"],\\n        MEMBER[\\\"World Geodetic System 1984 (G1674)\\\"],\\n        MEMBER[\\\"World Geodetic System 1984 (G1762)\\\"],\\n        MEMBER[\\\"World Geodetic System 1984 (G2139)\\\"],\\n        ELLIPSOID[\\\"WGS 84\\\",6378137,298.257223563,\\n            LENGTHUNIT[\\\"metre\\\",1]],\\n        ENSEMBLEACCURACY[2.0]],\\n    PRIMEM[\\\"Greenwich\\\",0,\\n        ANGLEUNIT[\\\"degree\\\",0.0174532925199433]],\\n    CS[ellipsoidal,2],\\n        AXIS[\\\"geodetic latitude (Lat)\\\",north,\\n            ORDER[1],\\n            ANGLEUNIT[\\\"degree\\\",0.0174532925199433]],\\n        AXIS[\\\"geodetic longitude (Lon)\\\",east,\\n            ORDER[2],\\n            ANGLEUNIT[\\\"degree\\\",0.0174532925199433]],\\n    USAGE[\\n        SCOPE[\\\"Horizontal component of 3D system.\\\"],\\n        AREA[\\\"World.\\\"],\\n        BBOX[-90,-180,90,180]],\\n    ID[\\\"EPSG\\\",4326]]\"\n```\n\n\n:::\n:::\n\n\n\n\n## [{{< fa brands python >}} Python]{.py}\n\n[{{< fa brands python >}} Python]{.py} stores raster CRS as an attribute.\n\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\n# Check the CRS of the elevation data\nrast_py.rio.crs\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\nCRS.from_wkt('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\n:::\n:::\n\n\n\n:::\n\nYou may note in the above operations that [{{< fa brands python >}} Python]{.py} and [{{< fa brands r-project >}} R]{.r} seem to be returning different information for the same data. In fact they are saying the same thing just in slightly different ways. [{{< fa brands r-project >}} R]{.r} is telling us the name of the coordinate reference system (World Geodetic System 1984) while [{{< fa brands python >}} Python]{.py} is giving us the four-digit \"EPSG\" code. For context, the European Petroleum Survey Group (EPSG) is a major authority on accepted coordinate reference systems. Each CRS is given a unique, four-digit code. As you may now be able to guess, \"4326\" is the EPSG code for the World Geodetic System 1984 CRS!\n \nThis CRS (WGS84) is an especially common one so you may eventually commit its EPSG code to memory but often you'll encounter either CRS names or EPSG codes with which you are not familiar. It may not sound like sage advice but it can be simplest to just use whichever piece of information your coding language returns to Google the \"missing\" information.\n\n## Transforming Raster CRS\n\nOnce we know the current CRS, we can transform into a different coordinate reference system as needed. Let's transform from WGS84 into another commonly-used CRS: Albers Equal Area (EPSG code 3083).\n\nQuick warning, transforming rasters can take a **long** time and is very computationally intensive. It'll work for a relatively small raster like the one we're working with here but in general you should be careful with attempting to re-project a raster into a different CRS.\n\n:::panel-tabset\n## [{{< fa brands r-project >}} R]{.r}\n\nFor CRS transformations in [{{< fa brands r-project >}} R]{.r} we can use the `project` function (pronounced like the verb not the noun).\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# Transform the raster CRS\nrast_alber_r <- terra::project(x = rast_r, y = \"epsg:3083\")\n\n# Re-check the CRS to confirm it worked\nterra::crs(rast_alber_r)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] \"PROJCRS[\\\"NAD83 / Texas Centric Albers Equal Area\\\",\\n    BASEGEOGCRS[\\\"NAD83\\\",\\n        DATUM[\\\"North American Datum 1983\\\",\\n            ELLIPSOID[\\\"GRS 1980\\\",6378137,298.257222101,\\n                LENGTHUNIT[\\\"metre\\\",1]]],\\n        PRIMEM[\\\"Greenwich\\\",0,\\n            ANGLEUNIT[\\\"degree\\\",0.0174532925199433]],\\n        ID[\\\"EPSG\\\",4269]],\\n    CONVERSION[\\\"Texas Centric Albers Equal Area\\\",\\n        METHOD[\\\"Albers Equal Area\\\",\\n            ID[\\\"EPSG\\\",9822]],\\n        PARAMETER[\\\"Latitude of false origin\\\",18,\\n            ANGLEUNIT[\\\"degree\\\",0.0174532925199433],\\n            ID[\\\"EPSG\\\",8821]],\\n        PARAMETER[\\\"Longitude of false origin\\\",-100,\\n            ANGLEUNIT[\\\"degree\\\",0.0174532925199433],\\n            ID[\\\"EPSG\\\",8822]],\\n        PARAMETER[\\\"Latitude of 1st standard parallel\\\",27.5,\\n            ANGLEUNIT[\\\"degree\\\",0.0174532925199433],\\n            ID[\\\"EPSG\\\",8823]],\\n        PARAMETER[\\\"Latitude of 2nd standard parallel\\\",35,\\n            ANGLEUNIT[\\\"degree\\\",0.0174532925199433],\\n            ID[\\\"EPSG\\\",8824]],\\n        PARAMETER[\\\"Easting at false origin\\\",1500000,\\n            LENGTHUNIT[\\\"metre\\\",1],\\n            ID[\\\"EPSG\\\",8826]],\\n        PARAMETER[\\\"Northing at false origin\\\",6000000,\\n            LENGTHUNIT[\\\"metre\\\",1],\\n            ID[\\\"EPSG\\\",8827]]],\\n    CS[Cartesian,2],\\n        AXIS[\\\"easting (X)\\\",east,\\n            ORDER[1],\\n            LENGTHUNIT[\\\"metre\\\",1]],\\n        AXIS[\\\"northing (Y)\\\",north,\\n            ORDER[2],\\n            LENGTHUNIT[\\\"metre\\\",1]],\\n    USAGE[\\n        SCOPE[\\\"State-wide spatial data presentation requiring true area measurements.\\\"],\\n        AREA[\\\"United States (USA) - Texas.\\\"],\\n        BBOX[25.83,-106.66,36.5,-93.5]],\\n    ID[\\\"EPSG\\\",3083]]\"\n```\n\n\n:::\n:::\n\n\n\n\n## [{{< fa brands python >}} Python]{.py}\n\n[{{< fa brands python >}} Python]{.py} CRS re-projections have slightly more steps to work through.\n\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\n# Create a CRS variable for the desired ending CRS\nwgs84_crs = rio.crs.CRS.from_string(\"EPSG:3083\")\n\n# Reproject the raster we had\nrast_alber_py = rast_py.rio.reproject(wgs84_crs)\n\n# Re-check the CRS to confirm it worked\nrast_alber_py.rio.crs\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\nCRS.from_wkt('PROJCS[\"NAD83 / Texas Centric Albers Equal Area\",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\"]],AUTHORITY[\"EPSG\",\"4269\"]],PROJECTION[\"Albers_Conic_Equal_Area\"],PARAMETER[\"latitude_of_center\",18],PARAMETER[\"longitude_of_center\",-100],PARAMETER[\"standard_parallel_1\",27.5],PARAMETER[\"standard_parallel_2\",35],PARAMETER[\"false_easting\",1500000],PARAMETER[\"false_northing\",6000000],UNIT[\"metre\",1,AUTHORITY[\"EPSG\",\"9001\"]],AXIS[\"Easting\",EAST],AXIS[\"Northing\",NORTH],AUTHORITY[\"EPSG\",\"3083\"]]')\n```\n\n\n:::\n:::\n\n\n\n:::\n",
+    "supporting": [
+      "spatial_raster_files"
+    ],
     "filters": [
       "rmarkdown/pagebreak.lua"
     ],