From a7ff426677615abb8c52e11e54bda88f04b4617b Mon Sep 17 00:00:00 2001 From: Carl Date: Sat, 10 Feb 2024 04:56:56 +0000 Subject: [PATCH] :artificial_satellite: --- .../R/2-earthdata/execute-results/html.json | 4 +- .../figure-html/unnamed-chunk-8-1.png | Bin 0 -> 40671 bytes .../figure-html/unnamed-chunk-9-1.png | Bin 0 -> 38670 bytes .../R/earthdata/execute-results/html.json | 14 -- .../R/intro-R/execute-results/html.json | 16 --- .../intro-R/figure-html/unnamed-chunk-5-1.png | Bin 1327524 -> 0 bytes .../intro-R/figure-html/unnamed-chunk-8-1.png | Bin 817473 -> 0 bytes drafts/gdalcubes-stac-cog.Rmd | 18 +-- drafts/sst-gdal.qmd | 76 ++++++---- drafts/sst-xarray.ipynb | 4 +- drafts/{sst.qmd => turtles-sst.qmd} | 0 tutorials/R/2-earthdata.qmd | 134 +++++++++++++++++- .../figure-html/unnamed-chunk-8-1.png | Bin 0 -> 40671 bytes .../figure-html/unnamed-chunk-9-1.png | Bin 0 -> 38670 bytes tutorials/python/1-intro-python.ipynb | 2 - ...-download.ipynb => 2-NASA-EarthData.ipynb} | 13 +- 16 files changed, 199 insertions(+), 82 deletions(-) create mode 100644 _freeze/tutorials/R/2-earthdata/figure-html/unnamed-chunk-8-1.png create mode 100644 _freeze/tutorials/R/2-earthdata/figure-html/unnamed-chunk-9-1.png delete mode 100644 _freeze/tutorials/R/earthdata/execute-results/html.json delete mode 100644 _freeze/tutorials/R/intro-R/execute-results/html.json delete mode 100644 _freeze/tutorials/R/intro-R/figure-html/unnamed-chunk-5-1.png delete mode 100644 _freeze/tutorials/R/intro-R/figure-html/unnamed-chunk-8-1.png rename drafts/{sst.qmd => turtles-sst.qmd} (100%) create mode 100644 tutorials/R/2-earthdata_files/figure-html/unnamed-chunk-8-1.png create mode 100644 tutorials/R/2-earthdata_files/figure-html/unnamed-chunk-9-1.png rename tutorials/python/{2-xarray-without-download.ipynb => 2-NASA-EarthData.ipynb} (99%) diff --git a/_freeze/tutorials/R/2-earthdata/execute-results/html.json b/_freeze/tutorials/R/2-earthdata/execute-results/html.json index 5bd0e19..a1ebbb9 100644 --- a/_freeze/tutorials/R/2-earthdata/execute-results/html.json +++ b/_freeze/tutorials/R/2-earthdata/execute-results/html.json @@ -1,7 +1,7 @@ { - "hash": "936e34da6da3eac5bbee72becc66c013", + "hash": "4819be6ba03f2a0e18b3442281c34a39", "result": { - "markdown": "---\ntitle: \"NASA EarthData\"\nformat: html\n---\n\n\nThe NASA EarthData program provides access to an extensive collection of spatial data products from each of its 12 Distributed Active Archive Centers ('DAACs') on the high-performance S3 storage system of Amazon Web Services (AWS). We can take advantage of range requests with NASA EarthData URLs, but unlike the previous examples,\nNASA requires an authentication step. NASA offers several different mechanisms, including `netrc` authentication, token-based authentication, and S3 credentials, but only the first of these works equally well from locations both inside and outside of AWS-based compute, so there really is very little reason to learn the other two.\n\nThe [`earthdatalogin` package in R](https://boettiger-lab.github.io/earthdatalogin/) or the `earthaccess` package in Python handle the authentication. The R package sets up authentication behind the scenes using environmental variables.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nearthdatalogin::edl_netrc()\n```\n:::\n\n\n(A default login is supplied though users are encouraged to [register](https://urs.earthdata.nasa.gov/home) for their own individual accounts.) Once this is in place, EarthData's protected URLs can be used like any other: \n\n\n::: {.cell}\n\n```{.r .cell-code}\nterra::rast(\"https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T56JKT.2023246T235950.v2.0/HLS.L30.T56JKT.2023246T235950.v2.0.SAA.tif\",\n vsi=TRUE)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nclass : SpatRaster \ndimensions : 3660, 3660, 1 (nrow, ncol, nlyr)\nresolution : 30, 30 (x, y)\nextent : 199980, 309780, 7190200, 7300000 (xmin, xmax, ymin, ymax)\ncoord. ref. : WGS 84 / UTM zone 56N (EPSG:32656) \nsource : HLS.L30.T56JKT.2023246T235950.v2.0.SAA.tif \nname : HLS.L30.T56JKT.2023246T235950.v2.0.SAA \n```\n:::\n:::\n", + "markdown": "---\ntitle: \"NASA EarthData\"\n---\n\n\nThis tutorial demonstrates how begin to generalize the pattern introduced in the introductory example to work with other data sources, in particular, with NASA EarthData. \n\nNASA recently announced completion of the transfer of some [59 petabytes of data](https://www.earthdata.nasa.gov/eosdis/cloud-evolution) to the Amazon cloud -- a core component of NASA's Transformation to Open Science (TOPS) mission. Researchers are frequently told that to take advantage of such \"cloud data\" they will need to pay (or find a grant or other program to pay) for cloud computing resources. This approach is sometimes describes as \"send the compute to the data\". While this narrative is no doubt beneficial to Amazon Inc, it exacerbates inequity and is often misleading. The purpose of having data in a cloud storage platform is not just to make it faster to access that data on rented cloud computing platforms. The high bandwith and high disk speeds provided by these systems can be just as powerful when you provide your own compute. Consistent with NASA's vision, this means that high-performance access is free\n\n> NASA Earth science data have been freely openly and available to all users since EOSDIS became operational in 1994. Under NASA's full and open data policy, all NASA mission data (along with the algorithms, metadata, and documentation associated with these data) must be freely available to the public. This means that anyone, anywhere in the world, can access the more than 59 PB of NASA Earth science data without restriction\n\nAll we need is software that can treat the cloud storage as if it were local storage: _a virtual filesystem_. The ability to do this -- the HTTP range request standard -- has been around for over two decades and is widely implemented in open source software. Unfortunately, many users and workflows are stuck in an old model that assumes individual files must always be downloaded first. \n\n\n\n\n\n\nTo make this work with NASA EarthData however, we have one additional challenge involving the problem of _authentication_. NASA offers several different mechanisms, including (1) `netrc` authentication, (2) token-based authentication, and (3) S3 credentials, but only the first of these works equally well from locations both inside and outside of AWS-based compute, so there really is very little reason to learn the other two. \n\nThe [`earthdatalogin` package in R](https://boettiger-lab.github.io/earthdatalogin/) or the `earthaccess` package in Python handle the authentication. The R package sets up authentication behind the scenes using environmental variables.\n\n\n::: {.cell hash='2-earthdata_cache/html/unnamed-chunk-2_44795769054d73bef405fd262dac49bd'}\n\n```{.r .cell-code}\nearthdatalogin::edl_netrc()\n```\n:::\n\n\n(A default login is supplied though users are encouraged to [register](https://urs.earthdata.nasa.gov/home) for their own individual accounts.) Once this is in place, EarthData's protected URLs can be used like any other. For instance, after authenticating, we can read this NASA harmonized LandSat-Sentinel2 `tif` whether we are running locally:\n\n\n::: {.cell hash='2-earthdata_cache/html/unnamed-chunk-3_fd847ddb31b3bf7630ca7a7821656a37'}\n\n```{.r .cell-code}\nterra::rast(\"https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T56JKT.2023246T235950.v2.0/HLS.L30.T56JKT.2023246T235950.v2.0.SAA.tif\",\n vsi=TRUE)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nclass : SpatRaster \ndimensions : 3660, 3660, 1 (nrow, ncol, nlyr)\nresolution : 30, 30 (x, y)\nextent : 199980, 309780, 7190200, 7300000 (xmin, xmax, ymin, ymax)\ncoord. ref. : WGS 84 / UTM zone 56N (EPSG:32656) \nsource : HLS.L30.T56JKT.2023246T235950.v2.0.SAA.tif \nname : HLS.L30.T56JKT.2023246T235950.v2.0.SAA \n```\n:::\n:::\n\n\nAn important aspect about this approach is that it does not require any custom wrappers or specialized functions to access data. Most R packages that work with spatial data, including `terra`, `sf`, `stars` and others, do so through the use of GDAL for parsing spatial data formats. This means that they all support the [GDAL Virtual Filesystem](https://gdal.org/user/virtual_file_systems.html) for cloud-native reads out of the box. `earthdatalogin()` takes advantage of this by setting authentication credentials as GDAL environmental variable configuration, allowing these existing to seamlessly read NASA data. No need to learn any additional access functions.\n\n\n## Working with STAC & gdalcubes\n\n\n\n::: {.cell hash='2-earthdata_cache/html/setup_3baf4c1b5077bcfc0fab4dd82164e88f'}\n\n```{.r .cell-code}\nlibrary(earthdatalogin)\nlibrary(rstac)\nlibrary(gdalcubes)\n```\n:::\n\n\n`earthdatalogin` also includes optional configuration settings for GDAL which can improve performance of cloud-based data access. Set the GDAL environmental variables using `gdal_cloud_config()`. An additional helper function exposes the usual GDAL environmental variable to the `gdalcubes` R package. \n\n\n::: {.cell hash='2-earthdata_cache/html/unnamed-chunk-4_17b81e9d4ad30b73c77d4ed861c8ed0f'}\n\n```{.r .cell-code}\nedl_netrc() # Authenticate\ngdal_cloud_config() # Optimize GDAL for cloud\nwith_gdalcubes() # Export settings to gdalcubes package\n\ngdalcubes_options(parallel = TRUE)\n```\n:::\n\n\n\nNASA provides their own search system. NASA also provides a STAC-based search (see below), which allows us to use the standard syntax we have already seen. However, NASA's STAC API is significantly slower and more prone to server errors than the STAC APIs provided by Element84, Microsoft Planetary Computer, and others. \n\nHere, we use NASA's own search protocol, which is less general, but gives us a chance to illustrate the use of `gdalcubes` using arbitrary URL lists when no STAC catalog is available. We search a handful of dates for illustrative purposes, but this approach can easily scale to larger lists without needing additional RAM or disk space. \n\n\n::: {.cell hash='2-earthdata_cache/html/unnamed-chunk-5_e81ae49aa355f949da68dc25eb5b178f'}\n\n```{.r .cell-code}\nstart <- \"2020-01-01\"\nend <- \"2020-01-03\" \nurls <- edl_search(short_name = \"MUR-JPL-L4-GLOB-v4.1\",\n temporal = c(start, end))\n```\n:::\n\n\nThese netcdf files lack appropriate metadata (projection, extent) that GDAL expects. We can provide this manually using the GDAL VRT mechanism:\n\n\n::: {.cell hash='2-earthdata_cache/html/unnamed-chunk-6_eccb2df8eb45a7dfde434c506295b91e'}\n\n```{.r .cell-code}\nvrt <- function(url) {\n prefix <- \"vrt://NETCDF:/vsicurl/\"\n suffix <- \":analysed_sst?a_srs=OGC:CRS84&a_ullr=-180,90,180,-90\"\n paste0(prefix, url, suffix)\n}\n\n# date associated with each file\nurl_dates <- as.Date(gsub(\".*(\\\\d{8})\\\\d{6}.*\", \"\\\\1\", urls), format=\"%Y%m%d\")\n```\n:::\n\n\nBecause each file in this list of URLs has the same spatial extent, resolution, and projection, we can now manually construct our space-time data cube from these netcdf slices:\n\n\n::: {.cell hash='2-earthdata_cache/html/unnamed-chunk-7_bd828ad3710695c92bb3dd2d2cb09e61'}\n\n```{.r .cell-code}\ndata_gd <- gdalcubes::stack_cube(vrt(urls), datetime_values = url_dates)\n```\n:::\n\n\nWe use gdalcubes to crop each file \n\n\n::: {.cell hash='2-earthdata_cache/html/unnamed-chunk-8_dd8e17f912864a356a164b576e4a0513'}\n\n```{.r .cell-code}\nextent = list(left=-93, right=-76, bottom=41, top=49,\n t0=start, t1=end)\n\nbench::bench_time({\n data_gd |> \n gdalcubes::crop(extent) |> \n aggregate_time(dt=\"P3D\", method=\"mean\") |> \n plot(col = viridisLite::viridis(10))\n})\n```\n\n::: {.cell-output-display}\n![](2-earthdata_files/figure-html/unnamed-chunk-8-1.png){width=672}\n:::\n\n::: {.cell-output .cell-output-stdout}\n```\nprocess real \n 2.29s 27.34s \n```\n:::\n:::\n\n\n\n\n# Search via STAC\n\n\nThe STAC Catalog search system we illustrated in the introductory example is a widely used standard. This means that we can use the same packages and same code we have already learned to access entirely different geospatial data products prepared by an entirely different provider. In this example, we will illustrate searching NASA's EarthData catalog using the STAC interface. We can also browse the collection of [NASA Earthdata STAC Catalogs](https://radiantearth.github.io/stac-browser/#/external/cmr.earthdata.nasa.gov/stac/) in a web browser. (***NOTE***: Unfortunately, at this time, NASA's implementation of the STAC standard is incomplete. The web browser only shows the first 10 entries under any heading, despite the NASA STAC API actually having the complete records. Also, NASA's STAC service is considerably slower than those provided by Element84 or Microsoft Planetary Computer.)\n\n\n::: {.cell hash='2-earthdata_cache/html/unnamed-chunk-9_c10ec0cfdcda2a6e6a18ad1f8a87ade7'}\n\n```{.r .cell-code}\nitems <- stac(\"https://cmr.earthdata.nasa.gov/stac/POCLOUD\") |> \n stac_search(collections = \"MUR-JPL-L4-GLOB-v4.1\",\n datetime = paste(start,end, sep = \"/\")) |>\n post_request() |>\n items_fetch()\n\nitems\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n###STACItemCollection\n- matched feature(s): 3\n- features (3 item(s) / 0 not fetched):\n - 20200101090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n - 20200102090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n - 20200103090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1\n- assets: data, metadata, opendap\n- item's fields: \nassets, bbox, collection, geometry, id, links, properties, stac_extensions, stac_version, type\n```\n:::\n:::\n\n", "supporting": [], "filters": [ 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zoEh|hT(-?;*#${CfXypvy%SFVRltIvm3!W=5E2~%2phe=(MddKjIZH_;D3kIb2c8x ziH`aJ8w1n{(7|%JD|mnq+EM`KfH~dJkyRiuIzkYqB3GWt8dbNQFfi>q2NzKVG-V+6 zhE#Ki|EFMgZ3hp$6IRaTNZT^E;i(=1xUP REmF&9sOqW|DE<5F{{Vg}cv%1d literal 0 HcmV?d00001 diff --git a/_freeze/tutorials/R/earthdata/execute-results/html.json b/_freeze/tutorials/R/earthdata/execute-results/html.json deleted file mode 100644 index 5bd0e19..0000000 --- a/_freeze/tutorials/R/earthdata/execute-results/html.json +++ /dev/null @@ -1,14 +0,0 @@ -{ - "hash": "936e34da6da3eac5bbee72becc66c013", - "result": { - "markdown": "---\ntitle: \"NASA EarthData\"\nformat: html\n---\n\n\nThe NASA EarthData program provides access to an extensive collection of spatial data products from each of its 12 Distributed Active Archive Centers ('DAACs') on the high-performance S3 storage system of Amazon Web Services (AWS). We can take advantage of range requests with NASA EarthData URLs, but unlike the previous examples,\nNASA requires an authentication step. NASA offers several different mechanisms, including `netrc` authentication, token-based authentication, and S3 credentials, but only the first of these works equally well from locations both inside and outside of AWS-based compute, so there really is very little reason to learn the other two.\n\nThe [`earthdatalogin` package in R](https://boettiger-lab.github.io/earthdatalogin/) or the `earthaccess` package in Python handle the authentication. The R package sets up authentication behind the scenes using environmental variables.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nearthdatalogin::edl_netrc()\n```\n:::\n\n\n(A default login is supplied though users are encouraged to [register](https://urs.earthdata.nasa.gov/home) for their own individual accounts.) Once this is in place, EarthData's protected URLs can be used like any other: \n\n\n::: {.cell}\n\n```{.r .cell-code}\nterra::rast(\"https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T56JKT.2023246T235950.v2.0/HLS.L30.T56JKT.2023246T235950.v2.0.SAA.tif\",\n vsi=TRUE)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nclass : SpatRaster \ndimensions : 3660, 3660, 1 (nrow, ncol, nlyr)\nresolution : 30, 30 (x, y)\nextent : 199980, 309780, 7190200, 7300000 (xmin, xmax, ymin, ymax)\ncoord. ref. : WGS 84 / UTM zone 56N (EPSG:32656) \nsource : HLS.L30.T56JKT.2023246T235950.v2.0.SAA.tif \nname : HLS.L30.T56JKT.2023246T235950.v2.0.SAA \n```\n:::\n:::\n", - "supporting": [], - "filters": [ - "rmarkdown/pagebreak.lua" - ], - "includes": {}, - "engineDependencies": {}, - "preserve": {}, - "postProcess": true - } -} \ No newline at end of file diff --git a/_freeze/tutorials/R/intro-R/execute-results/html.json b/_freeze/tutorials/R/intro-R/execute-results/html.json deleted file mode 100644 index 5cd378c..0000000 --- a/_freeze/tutorials/R/intro-R/execute-results/html.json +++ /dev/null @@ -1,16 +0,0 @@ -{ - "hash": "afd4598181f8271ffdf654399c470105", - "result": { - "markdown": "---\ntitle: Introduction\nicon: play-btn\n---\n\n\n\n## Exploring the Legacy of Redlining\n\nThis executable notebook provides an opening example to illustrate a cloud-native workflow in both R and python. \nPedagogy research emphasizes the importance of \"playing the whole game\" before breaking down every pitch and hit.\nWe intentionally focus on powerful high-level tools (STAC API, COGs, datacubes) to illustrate how a few chunks of\ncode can perform a task that would be far slower and more verbose in a traditional file-based, download-first workflow.\nNote the close parallels between R and Python syntax. This arises because both languages wrap the same underlying \ntools (the STAC API and GDAL warper) and handle many of the nuisances of spatial data -- from re-projections and\nresampling to mosaic tiles -- without us noticing.\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(rstac)\nlibrary(gdalcubes)\nlibrary(stars)\nlibrary(tmap)\nlibrary(dplyr)\ngdalcubes::gdalcubes_options(parallel = TRUE)\n```\n:::\n\n\n\n## Data discovery\n\n\n\nThe first step in many workflows involves discovering individual spatial data files covering the space, time, and variables of interest. Here we use a [STAC](https://stacspec.org/en) Catalog API to recover a list of candidate data. \nWe dig deeper into how this works and what it returns in later recipes. This example searches for images in a lon-lat bounding box from a collection of Cloud-Optimized-GeoTIFF (COG) images taken by Sentinel2 satellite mission.\nThis function will not download any imagery, it merely gives us a list of metadata about available images, including the access URLs.\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nbox <- c(xmin=-122.51, ymin=37.71, xmax=-122.36, ymax=37.81) \nstart_date <- \"2022-06-01\"\nend_date <- \"2022-08-01\"\nitems <-\n stac(\"https://earth-search.aws.element84.com/v0/\") |>\n stac_search(collections = \"sentinel-s2-l2a-cogs\",\n bbox = box,\n datetime = paste(start_date, end_date, sep=\"/\"),\n limit = 100) |>\n ext_query(\"eo:cloud_cover\" < 20) |>\n post_request()\n```\n:::\n\n\nWe pass this list of images to a high-level utilty (`gdalcubes` in R, `odc.stac` in python) that will do all of the heavy lifting. Using the URLs and metadata provided by STAC, \nthese functions can extract only our data of interest (given by the bounding box) without downloading unnecessary regions or bands. While streaming the data, these functions\nwill also reproject it into the desired coordinate reference system -- (an often costly operation to perform in R) and can potentially resample or aggregate the data to a desired \nspatial resolution. (The R code will also resample from images in overlapping areas to replace pixels masked by clouds)\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\ncol <- stac_image_collection(items$features, asset_names = c(\"B08\", \"B04\", \"SCL\"))\n\ncube <- cube_view(srs =\"EPSG:4326\",\n extent = list(t0 = start_date, t1 = end_date,\n left = box[1], right = box[3],\n top = box[4], bottom = box[2]),\n dx = 0.0001, dy = 0.0001, dt = \"P1D\",\n aggregation = \"median\", resampling = \"average\")\n\nmask <- image_mask(\"SCL\", values=c(3, 8, 9)) # mask clouds and cloud shadows\n\ndata <- raster_cube(col, cube, mask = mask)\n```\n:::\n\n\n\nWe can do arbitrary calculations on this data as well. Here we calculate NDVI, a widely used measure of greenness that can be used to determine tree cover. \n(Note that the R example uses lazy evaluation, and can thus perform these calculations while streaming)\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nndvi <- data |>\n select_bands(c(\"B04\", \"B08\")) |>\n apply_pixel(\"(B08-B04)/(B08+B04)\", \"NDVI\") |>\n reduce_time(c(\"mean(NDVI)\"))\n\nndvi_stars <- st_as_stars(ndvi)\n```\n:::\n\n\n\nAnd we plot the result. The long rectangle of Golden Gate Park is clearly visible in the North-West.\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nmako <- tm_scale_continuous(values = viridisLite::mako(30))\nfill <- tm_scale_continuous(values = \"Greens\")\n\ntm_shape(ndvi_stars) + tm_raster(col.scale = mako)\n```\n\n::: {.cell-output-display}\n![](intro-R_files/figure-html/unnamed-chunk-5-1.png){width=672}\n:::\n:::\n\n\n\n# From NDVI to Environmental Justice\n\nWe examine the present-day impact of historic \"red-lining\" of US cities during the Great Depression using data from the [Mapping Inequality](https://dsl.richmond.edu/panorama/redlining) project. All though this racist practice was banned by federal law under the Fair Housing Act of 1968, the systemic scars of that practice are still so deeply etched on our landscape that the remain visible from space -- \"red-lined\" areas (graded \"D\" under the racist HOLC scheme) show systematically lower greenness than predominately-white neighborhoods (Grade \"A\"). Trees provide many benefits, from mitigating urban heat to biodiversity, real-estate value, to health.\n\n\n## Zonal statistics \n\nIn addition to large scale raster data such as satellite imagery, the analysis of vector shapes such as polygons showing administrative regions is a central component of spatial analysis, and particularly important to spatial social sciences. The red-lined areas of the 1930s are one example of spatial vectors. One common operation is to summarise the values of all pixels falling within a given polygon, e.g. computing the average greenness (NDVI) \n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsf <- \"/vsicurl/https://dsl.richmond.edu/panorama/redlining/static/citiesData/CASanFrancisco1937/geojson.json\" |>\n st_read() |>\n st_make_valid() |>\n select(-label_coords)\n```\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\npoly <- ndvi |> extract_geom(sf, FUN = mean, reduce_time = TRUE)\nsf$NDVI <- poly$NDVI\n```\n:::\n\n\n\nWe plot the underlying NDVI as well as the average NDVI of each polygon, along with it's textual grade, using `tmap`. Note that \"A\" grades tend to be darkest green (high NDVI) while \"D\" grades are frequently the least green. (Regions not zoned for housing at the time of the 1937 housing assessment are not displayed as polygons.)\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\ntm_shape(ndvi_stars) + tm_raster(col.scale = mako) +\n tm_shape(sf) + tm_polygons('NDVI', fill.scale = fill) +\n tm_shape(sf) + tm_text(\"grade\", col=\"darkblue\", size=0.6) +\n tm_legend_hide()\n```\n\n::: {.cell-output-display}\n![](intro-R_files/figure-html/unnamed-chunk-8-1.png){width=672}\n:::\n:::\n\n\n\nAre historically redlined areas still less green?\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsf |> \n as_tibble() |>\n group_by(grade) |> \n summarise(ndvi = mean(NDVI), \n sd = sd(NDVI)) |>\n knitr::kable()\n```\n\n::: {.cell-output-display}\n|grade | ndvi| sd|\n|:-----|---------:|---------:|\n|A | 0.3201204| 0.0611414|\n|B | 0.2138501| 0.0783221|\n|C | 0.1956334| 0.0564822|\n|D | 0.1949736| 0.0385805|\n|NA | 0.0962092| NA|\n:::\n:::\n", - "supporting": [ - "intro-R_files" - ], - "filters": [ - "rmarkdown/pagebreak.lua" - ], - 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