From 7ac48d1ea6db835e32a32d36ed8730ec3c1e23c3 Mon Sep 17 00:00:00 2001 From: RRC_GHA Date: Tue, 20 Feb 2024 16:52:01 +0000 Subject: [PATCH] Create automated build --- public/2024-02-arctic/search.json | 12 +- public/2024-02-arctic/session_11.html | 12 +- public/2024-02-arctic/session_15.html | 4 +- public/2024-02-arctic/session_19.html | 701 +++++++++++++----- .../figure-html/unnamed-chunk-19-1.png | Bin 0 -> 113002 bytes .../figure-html/unnamed-chunk-20-1.png | Bin 0 -> 110700 bytes .../figure-html/unnamed-chunk-21-1.png | Bin 0 -> 128868 bytes .../figure-html/unnamed-chunk-22-1.png | Bin 0 -> 125611 bytes .../figure-html/unnamed-chunk-25-1.png | Bin 0 -> 250400 bytes .../figure-html/unnamed-chunk-4-1.png | Bin 0 -> 64329 bytes .../figure-html/unnamed-chunk-9-1.png | Bin 0 -> 146779 bytes 11 files changed, 537 insertions(+), 192 deletions(-) create mode 100644 public/2024-02-arctic/session_19_files/figure-html/unnamed-chunk-19-1.png create mode 100644 public/2024-02-arctic/session_19_files/figure-html/unnamed-chunk-20-1.png create mode 100644 public/2024-02-arctic/session_19_files/figure-html/unnamed-chunk-21-1.png create mode 100644 public/2024-02-arctic/session_19_files/figure-html/unnamed-chunk-22-1.png create mode 100644 public/2024-02-arctic/session_19_files/figure-html/unnamed-chunk-25-1.png create mode 100644 public/2024-02-arctic/session_19_files/figure-html/unnamed-chunk-4-1.png create mode 100644 public/2024-02-arctic/session_19_files/figure-html/unnamed-chunk-9-1.png diff --git a/public/2024-02-arctic/search.json b/public/2024-02-arctic/search.json index 5b6e6d60..5b078827 100644 --- a/public/2024-02-arctic/search.json +++ b/public/2024-02-arctic/search.json @@ -1294,7 +1294,7 @@ "href": "session_19.html#about-the-data", "title": "19  Working with Spatial Data", "section": "19.2 About the data", - "text": "19.2 About the data\nAll of the data used in this tutorial are simplified versions of real datasets available on the KNB Data Repository. We are using simplified datasets to ease the processing burden on all our computers since the original geospatial datasets are high-resolution. These simplified versions of the datasets may contain topological errors.\nThe spatial data we will be using to create the map are:\n\n\n\nData\nOriginal datasets\n\n\n\n\nAlaska regional boundaries\nJared Kibele and Jeanette Clark. 2018. State of Alaska’s Salmon and People Regional Boundaries. Knowledge Network for Biocomplexity. doi:10.5063/F1125QWP.\n\n\nCommunity locations and population\nJeanette Clark, Sharis Ochs, Derek Strong, and National Historic Geographic Information System. 2018. Languages used in Alaskan households, 1990-2015. Knowledge Network for Biocomplexity. doi:10.5063/F11G0JHX.\n\n\nAlaska rivers\nThe rivers shapefile is a simplified version of Jared Kibele and Jeanette Clark. Rivers of Alaska grouped by SASAP region, 2018. Knowledge Network for Biocomplexity. doi:10.5063/F1SJ1HVW.\n\n\n\n\n\n\n\n\n\nSetup\n\n\n\n\nNavigate to this dataset on KNB’s test site and download the zip folder.\nUpload the zip folder to the data folder in the training_{USERNAME} project. You don’t need to unzip the folder ahead of time, uploading will automatically unzip the folder.\n\nAlternatively, programatically download and extract the demo data with:\n\n\n\nknb_url <- 'https://dev.nceas.ucsb.edu/knb/d1/mn/v2/object/urn%3Auuid%3Aaceaecb2-1ce0-4d41-a839-d3607d32bb58'\ndownload.file(url = knb_url, destfile = 'demo_data.zip')\nunzip('demo_data.zip', exdir = 'data')\nfile.remove('demo_data.zip')\n\n\nCreate a new R Markdown file.\n\nTitle it “Intro to sf package for Spatial Data and Making Maps”\nSave the file and name it “intro-sf-spatial-data-maps”.\n\nLoad the following libraries at the top of your R Markdown file.\n\n\nlibrary(readr)\nlibrary(sf)\nlibrary(ggplot2)\nlibrary(leaflet)\nlibrary(scales)\nlibrary(ggmap)\nlibrary(dplyr)", + "text": "19.2 About the data\nAll of the data used in this tutorial are simplified versions of real datasets available on the KNB Data Repository. We are using simplified datasets to ease the processing burden on all our computers since the original geospatial datasets are high-resolution. These simplified versions of the datasets may contain topological errors.\nThe spatial data we will be using to create the map are:\n\n\n\nData\nOriginal datasets\n\n\n\n\nAlaska regional boundaries\nJared Kibele and Jeanette Clark. 2018. State of Alaska’s Salmon and People Regional Boundaries. Knowledge Network for Biocomplexity. doi:10.5063/F1125QWP.\n\n\nCommunity locations and population\nJeanette Clark, Sharis Ochs, Derek Strong, and National Historic Geographic Information System. 2018. Languages used in Alaskan households, 1990-2015. Knowledge Network for Biocomplexity. doi:10.5063/F11G0JHX.\n\n\nAlaska rivers\nThe rivers shapefile is a simplified version of Jared Kibele and Jeanette Clark. Rivers of Alaska grouped by SASAP region, 2018. Knowledge Network for Biocomplexity. doi:10.5063/F1SJ1HVW.\n\n\n\n\n\n\n\n\n\nSetup\n\n\n\n\nNavigate to this dataset on KNB’s test site and download the zip folder.\nUpload the zip folder to the data folder in the training_{USERNAME} project. You don’t need to unzip the folder ahead of time, uploading will automatically unzip the folder.\n\nAlternatively, programatically download and extract the demo data with: \n\n\n\nknb_url <- \"https://dev.nceas.ucsb.edu/knb/d1/mn/v2/object/urn%3Auuid%3Aaceaecb2-1ce0-4d41-a839-d3607d32bb58\"\n\ndownload.file(url = knb_url, destfile = 'shapefile_demo_data.zip')\n\nunzip('shapefile_demo_data.zip', exdir = 'data')\n\nfile.remove('demo_data.zip')\n\n\nCreate a new Quarto file.\n\nTitle it “Working with Spatial Data in R”\nSave the file and name it “intro-to-spatial-data”.\n\nLoad the following libraries at the top of your Quarto file.\n\n\nlibrary(readr)\nlibrary(sf)\nlibrary(ggplot2)\nlibrary(leaflet)\nlibrary(scales)\nlibrary(ggmap)\nlibrary(dplyr)", "crumbs": [ "19  Working with Spatial Data" ] @@ -1304,7 +1304,7 @@ "href": "session_19.html#exploring-the-data-using-plot-and-st_crs", "title": "19  Working with Spatial Data", "section": "19.3 Exploring the data using plot() and st_crs()", - "text": "19.3 Exploring the data using plot() and st_crs()\nFirst let’s read in the shapefile of regional boundaries in Alaska using read_sf() and then create a basic plot of the data plot().\n\n# read in shapefile using read_sf()\nak_regions <- read_sf(\"data/ak_regions_simp.shp\")\n\n\n# quick plot\nplot(ak_regions)\n\nWe can also examine it’s class using class().\n\nclass(ak_regions)\n\nsf objects usually have two types of classes: sf and data.frame.\nUnlike a typical data.frame, an sf object has spatial metadata (geometry type, dimension, bbox, epsg (SRID), proj4string) and an additional column typically named geometry that contains the spatial data.\nSince our shapefile object has the data.frame class, viewing the contents of the object using the head() function or other exploratory functions shows similar results as if we read in data using read.csv() or read_csv().\n\nhead(ak_regions)\n\nglimpse(ak_regions)\n\n\n19.3.1 Coordinate Reference System (CRS)\n\n\n\nSource: ESRI\n\n\nEvery sf object needs a coordinate reference system (or crs) defined in order to work with it correctly. A coordinate reference system contains both a datum and a projection. The datum is how you georeference your points (in 3 dimensions!) onto a spheroid. The projection is how these points are mathematically transformed to represent the georeferenced point on a flat piece of paper. All coordinate reference systems require a datum. However, some coordinate reference systems are “unprojected” (also called geographic coordinate systems). Coordinates in latitude/longitude use a geographic (unprojected) coordinate system. One of the most commonly used geographic coordinate systems is WGS 1984.\nESRI has a blog post that explains these concepts in more detail with very helpful diagrams and examples.\nYou can view what crs is set by using the function st_crs().\n\nst_crs(ak_regions)\n\nThis is pretty confusing looking. Without getting into the details, that long string says that this data has a geographic coordinate system (WGS84) with no projection. A convenient way to reference crs quickly is by using the EPSG code, a number that represents a standard projection and datum. You can check out a list of (lots!) of EPSG codes here.\nWe will use multiple EPSG codes in this lesson. Here they are, along with their more readable names:\n\n3338: Alaska Albers (projected CRS)\n4326: WGS84 (World Geodetic System 1984), used in GPS (unprojected CRS)\n3857: Pseudo-Mercator, used in Google Maps, OpenStreetMap, Bing, ArcGIS, ESRI (projected CRS)\n\nYou will often need to transform your geospatial data from one coordinate system to another. The st_transform() function does this quickly for us. You may have noticed the maps above looked wonky because of the dateline. We might want to set a different projection for this data so it plots nicer. A good one for Alaska is called the Alaska Albers projection, with an EPSG code of 3338.\n\nak_regions_3338 <- ak_regions %>%\n st_transform(crs = 3338)\n\nst_crs(ak_regions_3338)\n\n\nplot(ak_regions_3338)\n\nMuch better!", + "text": "19.3 Exploring the data using plot() and st_crs()\nFirst let’s read in the shapefile of regional boundaries in Alaska using read_sf() and then create a basic plot of the data plot().\n\n# read in shapefile using read_sf()\nak_regions <- read_sf(\"data/ak_regions_simp.shp\")\n\n\n# quick plot\nplot(ak_regions)\n\n\n\n\n\n\n\n\nWe can also examine it’s class using class().\n\nclass(ak_regions)\n\n[1] \"sf\" \"tbl_df\" \"tbl\" \"data.frame\"\n\n\nsf objects usually have two types of classes: sf and data.frame.\nUnlike a typical data.frame, an sf object has spatial metadata (geometry type, dimension, bbox, epsg (SRID), proj4string) and an additional column typically named geometry that contains the spatial data.\nSince our shapefile object has the data.frame class, viewing the contents of the object using the head() function or other exploratory functions shows similar results as if we read in data using read.csv() or read_csv().\n\nhead(ak_regions)\n\nSimple feature collection with 6 features and 3 fields\nGeometry type: MULTIPOLYGON\nDimension: XY\nBounding box: xmin: -179.2296 ymin: 51.15702 xmax: 179.8567 ymax: 71.43957\nGeodetic CRS: WGS 84\n# A tibble: 6 × 4\n region_id region mgmt_area geometry\n <int> <chr> <dbl> <MULTIPOLYGON [°]>\n1 1 Aleutian Islands 3 (((-171.1345 52.44974, -171.1686 52.4174…\n2 2 Arctic 4 (((-139.9552 68.70597, -139.9893 68.7051…\n3 3 Bristol Bay 3 (((-159.8745 58.62778, -159.8654 58.6137…\n4 4 Chignik 3 (((-155.8282 55.84638, -155.8049 55.8655…\n5 5 Copper River 2 (((-143.8874 59.93931, -143.9165 59.9403…\n6 6 Kodiak 3 (((-151.9997 58.83077, -152.0358 58.8271…\n\nglimpse(ak_regions)\n\nRows: 13\nColumns: 4\n$ region_id <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13\n$ region <chr> \"Aleutian Islands\", \"Arctic\", \"Bristol Bay\", \"Chignik\", \"Cop…\n$ mgmt_area <dbl> 3, 4, 3, 3, 2, 3, 4, 4, 2, 4, 2, 1, 4\n$ geometry <MULTIPOLYGON [°]> MULTIPOLYGON (((-171.1345 5..., MULTIPOLYGON (((-139.9552 6.…\n\n\n\n19.3.1 Coordinate Reference System (CRS)\n\n\n\nSource: ESRI\n\n\nEvery sf object needs a coordinate reference system (or crs) defined in order to work with it correctly. A coordinate reference system contains both a datum and a projection. The datum is how you georeference your points (in 3 dimensions!) onto a spheroid. The projection is how these points are mathematically transformed to represent the georeferenced point on a flat piece of paper. All coordinate reference systems require a datum. However, some coordinate reference systems are “unprojected” (also called geographic coordinate systems). Coordinates in latitude/longitude use a geographic (unprojected) coordinate system. One of the most commonly used geographic coordinate systems is WGS 1984.\nESRI has a blog post that explains these concepts in more detail with very helpful diagrams and examples.\nYou can view what crs is set by using the function st_crs().\n\nst_crs(ak_regions)\n\nCoordinate Reference System:\n User input: WGS 84 \n wkt:\nGEOGCRS[\"WGS 84\",\n DATUM[\"World Geodetic System 1984\",\n ELLIPSOID[\"WGS 84\",6378137,298.257223563,\n LENGTHUNIT[\"metre\",1]]],\n PRIMEM[\"Greenwich\",0,\n ANGLEUNIT[\"degree\",0.0174532925199433]],\n CS[ellipsoidal,2],\n AXIS[\"latitude\",north,\n ORDER[1],\n ANGLEUNIT[\"degree\",0.0174532925199433]],\n AXIS[\"longitude\",east,\n ORDER[2],\n ANGLEUNIT[\"degree\",0.0174532925199433]],\n ID[\"EPSG\",4326]]\n\n\nThis is pretty confusing looking. Without getting into the details, that long string says that this data has a geographic coordinate system (WGS84) with no projection. A convenient way to reference crs quickly is by using the EPSG code, a number that represents a standard projection and datum. You can check out a list of (lots!) of EPSG codes here.\nWe will use multiple EPSG codes in this lesson. Here they are, along with their more readable names:\n\n3338: Alaska Albers (projected CRS)\n4326: WGS84 (World Geodetic System 1984), used in GPS (unprojected CRS)\n3857: Pseudo-Mercator, used in Google Maps, OpenStreetMap, Bing, ArcGIS, ESRI (projected CRS)\n\nYou will often need to transform your geospatial data from one coordinate system to another. The st_transform() function does this quickly for us. You may have noticed the maps above looked wonky because of the dateline. We might want to set a different projection for this data so it plots nicer. A good one for Alaska is called the Alaska Albers projection, with an EPSG code of 3338.\n\nak_regions_3338 <- ak_regions %>%\n st_transform(crs = 3338)\n\nst_crs(ak_regions_3338)\n\nCoordinate Reference System:\n User input: EPSG:3338 \n wkt:\nPROJCRS[\"NAD83 / Alaska Albers\",\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[\"Alaska Albers (meters)\",\n METHOD[\"Albers Equal Area\",\n ID[\"EPSG\",9822]],\n PARAMETER[\"Latitude of false origin\",50,\n ANGLEUNIT[\"degree\",0.0174532925199433],\n ID[\"EPSG\",8821]],\n PARAMETER[\"Longitude of false origin\",-154,\n ANGLEUNIT[\"degree\",0.0174532925199433],\n ID[\"EPSG\",8822]],\n PARAMETER[\"Latitude of 1st standard parallel\",55,\n ANGLEUNIT[\"degree\",0.0174532925199433],\n ID[\"EPSG\",8823]],\n PARAMETER[\"Latitude of 2nd standard parallel\",65,\n ANGLEUNIT[\"degree\",0.0174532925199433],\n ID[\"EPSG\",8824]],\n PARAMETER[\"Easting at false origin\",0,\n LENGTHUNIT[\"metre\",1],\n ID[\"EPSG\",8826]],\n PARAMETER[\"Northing at false origin\",0,\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[\"Topographic mapping (small scale).\"],\n AREA[\"United States (USA) - Alaska.\"],\n BBOX[51.3,172.42,71.4,-129.99]],\n ID[\"EPSG\",3338]]\n\n\n\nplot(ak_regions_3338)\n\n\n\n\n\n\n\n\nMuch better!", "crumbs": [ "19  Working with Spatial Data" ] @@ -1314,7 +1314,7 @@ "href": "session_19.html#sf-the-tidyverse", "title": "19  Working with Spatial Data", "section": "19.4 sf & the Tidyverse", - "text": "19.4 sf & the Tidyverse\nsf objects can be used as a regular data.frame object in many operations. We already saw the results of plot() and head().\nSince sf objects are data.frames, they play nicely with packages in the tidyverse. Here are a couple of simple examples:\n\n19.4.1 select()\n\n# returns the names of all the columns in dataset\ncolnames(ak_regions_3338)\n\n\nak_regions_3338 %>%\n select(region)\n\nNote the sticky geometry column! The geometry column will stay with your sf object even if it is not called explicitly.\n\n\n19.4.2 filter()\n\nunique(ak_regions_3338$region)\n\n\nak_regions_3338 %>%\n filter(region == \"Southeast\")", + "text": "19.4 sf & the Tidyverse\nsf objects can be used as a regular data.frame object in many operations. We already saw the results of plot() and head().\nSince sf objects are data.frames, they play nicely with packages in the tidyverse. Here are a couple of simple examples:\n\n19.4.1 select()\n\n# returns the names of all the columns in dataset\ncolnames(ak_regions_3338)\n\n[1] \"region_id\" \"region\" \"mgmt_area\" \"geometry\" \n\n\n\nak_regions_3338 %>%\n select(region)\n\nSimple feature collection with 13 features and 1 field\nGeometry type: MULTIPOLYGON\nDimension: XY\nBounding box: xmin: -2175328 ymin: 405653 xmax: 1579226 ymax: 2383770\nProjected CRS: NAD83 / Alaska Albers\n# A tibble: 13 × 2\n region geometry\n <chr> <MULTIPOLYGON [m]>\n 1 Aleutian Islands (((-1156666 420855.1, -1159837 417990.3, -1161898 41694…\n 2 Arctic (((571289.9 2143072, 569941.5 2142691, 569158.2 2142146…\n 3 Bristol Bay (((-339688.6 973904.9, -339302 972297.3, -339229.2 9710…\n 4 Chignik (((-114381.9 649966.8, -112866.8 652065.8, -108836.8 65…\n 5 Copper River (((561012 1148301, 559393.7 1148169, 557797.7 1148492, …\n 6 Kodiak (((115112.5 983293, 113051.3 982825.9, 110801.3 983211.…\n 7 Kotzebue (((-678815.3 1819519, -677555.2 1820698, -675557.8 1821…\n 8 Kuskokwim (((-1030125 1281198, -1029858 1282333, -1028980 1284032…\n 9 Cook Inlet (((35214.98 1002457, 36660.3 1002038, 36953.11 1001186,…\n10 Norton Sound (((-848357 1636692, -846510 1635203, -840513.7 1632225,…\n11 Prince William Sound (((426007.1 1087250, 426562.5 1088591, 427711.6 1089991…\n12 Southeast (((1287777 744574.1, 1290183 745970.8, 1292940 746262.7…\n13 Yukon (((-375318 1473998, -373723.9 1473487, -373064.8 147393…\n\n\nNote the sticky geometry column! The geometry column will stay with your sf object even if it is not called explicitly.\n\n\n19.4.2 filter()\n\nunique(ak_regions_3338$region)\n\n [1] \"Aleutian Islands\" \"Arctic\" \"Bristol Bay\" \n [4] \"Chignik\" \"Copper River\" \"Kodiak\" \n [7] \"Kotzebue\" \"Kuskokwim\" \"Cook Inlet\" \n[10] \"Norton Sound\" \"Prince William Sound\" \"Southeast\" \n[13] \"Yukon\" \n\n\n\nak_regions_3338 %>%\n filter(region == \"Southeast\")\n\nSimple feature collection with 1 feature and 3 fields\nGeometry type: MULTIPOLYGON\nDimension: XY\nBounding box: xmin: 559475.7 ymin: 722450 xmax: 1579226 ymax: 1410576\nProjected CRS: NAD83 / Alaska Albers\n# A tibble: 1 × 4\n region_id region mgmt_area geometry\n* <int> <chr> <dbl> <MULTIPOLYGON [m]>\n1 12 Southeast 1 (((1287777 744574.1, 1290183 745970.8, 1292940 …", "crumbs": [ "19  Working with Spatial Data" ] @@ -1324,7 +1324,7 @@ "href": "session_19.html#spatial-joins", "title": "19  Working with Spatial Data", "section": "19.5 Spatial Joins", - "text": "19.5 Spatial Joins\nYou can also use the sf package to create spatial joins, useful for when you want to utilize two datasets together.\n\n\n\n\n\n\nExercise: How many people live in each of these Alaska regions?\n\n\n\nWe have some population data, but it gives the population by city, not by region. To determine the population per region we will need to:\n\nRead in the population data from a csv and turn it into an sf object\nUse a spatial join (st_join()) to assign each city to a region\nUse group_by() and summarize() to calculate the total population by region\nSave the spatial object you created using write_sf()\n\n\n\n1. Read in alaska_population.csv using read.csv()\n\n# read in population data\npop <- read_csv(\"data/alaska_population.csv\")\n\nTurn pop into a spatial object\nThe st_join() function is a spatial left join. The arguments for both the left and right tables are objects of class sf which means we will first need to turn our population data.frame with latitude and longitude coordinates into an sf object.\nWe can do this easily using the st_as_sf() function, which takes as arguments the coordinates and the crs. The remove = F specification here ensures that when we create our geometry column, we retain our original lat lng columns, which we will need later for plotting. Although it isn’t said anywhere explicitly in the file, let’s assume that the coordinate system used to reference the latitude longitude coordinates is WGS84, which has a crs number of 4326.\n\npop_4326 <- st_as_sf(pop,\n coords = c('lng', 'lat'),\n crs = 4326,\n remove = F)\n\nhead(pop_4326)\n\n2. Join population data with Alaska regions data using st_join()\nNow we can do our spatial join! You can specify what geometry function the join uses (st_intersects, st_within, st_crosses, st_is_within_distance…) in the join argument. The geometry function you use will depend on what kind of operation you want to do, and the geometries of your shapefiles.\nIn this case, we want to find what region each city falls within, so we will use st_within.\n\npop_joined <- st_join(pop_4326, ak_regions_3338, join = st_within)\n\nThis gives an error!\nError: st_crs(x) == st_crs(y) is not TRUE\nTurns out, this won’t work right now because our coordinate reference systems are not the same. Luckily, this is easily resolved using st_transform(), and projecting our population object into Alaska Albers.\n\npop_3338 <- st_transform(pop_4326, crs = 3338)\n\n\npop_joined <- st_join(pop_3338, ak_regions_3338, join = st_within)\n\nhead(pop_joined)\n\n\n\n\n\n\n\nExploring types of joins\n\n\n\nThere are many different types of joins you can do with geospatial data. Examine the help page for these joins (?st_within() will get you there). What other joins types might be appropriate for examining the relationship between points and polygyons? What about two sets of polygons?\n\n\n3. Calculate the total population by region using group_by() and summarize()\nNext we compute the total population for each region. In this case, we want to do a group_by() and summarise() as this were a regular data.frame. Otherwise all of our point geometries would be included in the aggregation, which is not what we want. Our goal is just to get the total population by region. We remove the sticky geometry using as.data.frame(), on the advice of the sf::tidyverse help page.\n\npop_region <- pop_joined %>%\n as.data.frame() %>%\n group_by(region) %>%\n summarise(total_pop = sum(population))\n\nhead(pop_region)\n\nAnd use a regular left_join() to get the information back to the Alaska region shapefile. Note that we need this step in order to regain our region geometries so that we can make some maps.\n\npop_region_3338 <- left_join(ak_regions_3338, pop_region, by = \"region\")\n\n# plot to check\nplot(pop_region_3338[\"total_pop\"])\n\nSo far, we have learned how to use sf and dplyr to use a spatial join on two datasets and calculate a summary metric from the result of that join.\n\n\n\n\n\n\nsf and tidyverse best practices\n\n\n\nThe group_by() and summarize() functions can also be used on sf objects to summarize within a dataset and combine geometries. Many of the tidyverse functions have methods specific for sf objects, some of which have additional arguments that wouldn’t be relevant to the data.frame methods. You can run ?sf::tidyverse to get documentation on the tidyverse sf methods.\n\n\nSay we want to calculate the population by Alaska management area, as opposed to region.\n\npop_mgmt_338 <- pop_region_3338 %>%\n group_by(mgmt_area) %>%\n summarize(total_pop = sum(total_pop))\n\nplot(pop_mgmt_338[\"total_pop\"])\n\nNotice that the region geometries were combined into a single polygon for each management area.\nIf we don’t want to combine geometries, we can specify do_union = F as an argument.\n\npop_mgmt_3338 <- pop_region_3338 %>%\n group_by(mgmt_area) %>%\n summarize(total_pop = sum(total_pop), do_union = F)\n\nplot(pop_mgmt_3338[\"total_pop\"])\n\n4. Save the spatial object to a new file using write_sf()\nSave the spatial object to disk using write_sf() and specifying the filename. Writing your file with the extension .shp will assume an ESRI driver driver, but there are many other format options available.\n\nwrite_sf(pop_region_3338, \"data/ak_regions_population.shp\")\n\n\n19.5.1 Visualize with ggplot\nggplot2 now has integrated functionality to plot sf objects using geom_sf().\nWe can plot sf objects just like regular data.frames using geom_sf.\n\nggplot(pop_region_3338) +\n geom_sf(aes(fill = total_pop)) +\n labs(fill = \"Total Population\") +\n scale_fill_continuous(low = \"khaki\",\n high = \"firebrick\",\n labels = comma) +\n theme_bw()\n\nWe can also plot multiple shapefiles in the same plot. Say if we want to visualize rivers in Alaska, in addition to the location of communities, since many communities in Alaska are on rivers. We can read in a rivers shapefile, doublecheck the crs to make sure it is what we need, and then plot all three shapefiles - the regional population (polygons), the locations of cities (points), and the rivers (linestrings).\n\nrivers_3338 <- read_sf(\"data/ak_rivers_simp.shp\")\nst_crs(rivers_3338)\n\nNote that although no EPSG code is set explicitly, with some sluething we can determine that this is EPSG:3338. This site is helpful for looking up EPSG codes.\n\nggplot() +\n geom_sf(data = pop_region_3338, aes(fill = total_pop)) +\n geom_sf(data = pop_3338, size = 0.5) +\n geom_sf(data = rivers_3338,\n aes(linewidth = StrOrder)) +\n scale_linewidth(range = c(0.05, 0.5), guide = \"none\") +\n labs(title = \"Total Population by Alaska Region\",\n fill = \"Total Population\") +\n scale_fill_continuous(low = \"khaki\",\n high = \"firebrick\",\n labels = comma) +\n theme_bw()", + "text": "19.5 Spatial Joins\nYou can also use the sf package to create spatial joins, useful for when you want to utilize two datasets together.\n\n\n\n\n\n\nExercise: How many people live in each of these Alaska regions?\n\n\n\nWe have some population data, but it gives the population by city, not by region. To determine the population per region we will need to:\n\nRead in the population data from a csv and turn it into an sf object\nUse a spatial join (st_join()) to assign each city to a region\nUse group_by() and summarize() to calculate the total population by region\nSave the spatial object you created using write_sf()\n\n\n\n1. Read in alaska_population.csv using read.csv()\n\n# read in population data\npop <- read_csv(\"data/alaska_population.csv\")\n\nTurn pop into a spatial object\nThe st_join() function is a spatial left join. The arguments for both the left and right tables are objects of class sf which means we will first need to turn our population data.frame with latitude and longitude coordinates into an sf object.\nWe can do this easily using the st_as_sf() function, which takes as arguments the coordinates and the crs. The remove = F specification here ensures that when we create our geometry column, we retain our original lat lng columns, which we will need later for plotting. Although it isn’t said anywhere explicitly in the file, let’s assume that the coordinate system used to reference the latitude longitude coordinates is WGS84, which has a crs number of 4326.\n\npop_4326 <- st_as_sf(pop,\n coords = c('lng', 'lat'),\n crs = 4326,\n remove = F)\n\nhead(pop_4326)\n\nSimple feature collection with 6 features and 5 fields\nGeometry type: POINT\nDimension: XY\nBounding box: xmin: -176.6581 ymin: 51.88 xmax: -154.1703 ymax: 62.68889\nGeodetic CRS: WGS 84\n# A tibble: 6 × 6\n year city lat lng population geometry\n <dbl> <chr> <dbl> <dbl> <dbl> <POINT [°]>\n1 2015 Adak 51.9 -177. 122 (-176.6581 51.88)\n2 2015 Akhiok 56.9 -154. 84 (-154.1703 56.94556)\n3 2015 Akiachak 60.9 -161. 562 (-161.4314 60.90944)\n4 2015 Akiak 60.9 -161. 399 (-161.2139 60.91222)\n5 2015 Akutan 54.1 -166. 899 (-165.7731 54.13556)\n6 2015 Alakanuk 62.7 -165. 777 (-164.6153 62.68889)\n\n\n2. Join population data with Alaska regions data using st_join()\nNow we can do our spatial join! You can specify what geometry function the join uses (st_intersects, st_within, st_crosses, st_is_within_distance…) in the join argument. The geometry function you use will depend on what kind of operation you want to do, and the geometries of your shapefiles.\nIn this case, we want to find what region each city falls within, so we will use st_within.\n\npop_joined <- st_join(pop_4326, ak_regions_3338, join = st_within)\n\nThis gives an error!\nError: st_crs(x) == st_crs(y) is not TRUE\nTurns out, this won’t work right now because our coordinate reference systems are not the same. Luckily, this is easily resolved using st_transform(), and projecting our population object into Alaska Albers.\n\npop_3338 <- st_transform(pop_4326, crs = 3338)\n\n\npop_joined <- st_join(pop_3338, ak_regions_3338, join = st_within)\n\nhead(pop_joined)\n\nSimple feature collection with 6 features and 8 fields\nGeometry type: POINT\nDimension: XY\nBounding box: xmin: -1537925 ymin: 472626.9 xmax: -10340.71 ymax: 1456223\nProjected CRS: NAD83 / Alaska Albers\n# A tibble: 6 × 9\n year city lat lng population geometry region_id region \n <dbl> <chr> <dbl> <dbl> <dbl> <POINT [m]> <int> <chr> \n1 2015 Adak 51.9 -177. 122 (-1537925 472626.9) 1 Aleutian I…\n2 2015 Akhiok 56.9 -154. 84 (-10340.71 770998.4) 6 Kodiak \n3 2015 Akiac… 60.9 -161. 562 (-400885.5 1236460) 8 Kuskokwim \n4 2015 Akiak 60.9 -161. 399 (-389165.7 1235475) 8 Kuskokwim \n5 2015 Akutan 54.1 -166. 899 (-766425.7 526057.8) 1 Aleutian I…\n6 2015 Alaka… 62.7 -165. 777 (-539724.9 1456223) 13 Yukon \n# ℹ 1 more variable: mgmt_area <dbl>\n\n\n\n\n\n\n\n\nExploring types of joins\n\n\n\nThere are many different types of joins you can do with geospatial data. Examine the help page for these joins (?st_within() will get you there). What other joins types might be appropriate for examining the relationship between points and polygyons? What about two sets of polygons?\n\n\n3. Calculate the total population by region using group_by() and summarize()\nNext we compute the total population for each region. In this case, we want to do a group_by() and summarise() as this were a regular data.frame. Otherwise all of our point geometries would be included in the aggregation, which is not what we want. Our goal is just to get the total population by region. We remove the sticky geometry using as.data.frame(), on the advice of the sf::tidyverse help page.\n\npop_region <- pop_joined %>%\n as.data.frame() %>%\n group_by(region) %>%\n summarise(total_pop = sum(population))\n\nhead(pop_region)\n\n# A tibble: 6 × 2\n region total_pop\n <chr> <dbl>\n1 Aleutian Islands 8840\n2 Arctic 8419\n3 Bristol Bay 6947\n4 Chignik 311\n5 Cook Inlet 408254\n6 Copper River 2294\n\n\nAnd use a regular left_join() to get the information back to the Alaska region shapefile. Note that we need this step in order to regain our region geometries so that we can make some maps.\n\npop_region_3338 <- left_join(ak_regions_3338, pop_region, by = \"region\")\n\n# plot to check\nplot(pop_region_3338[\"total_pop\"])\n\n\n\n\n\n\n\n\nSo far, we have learned how to use sf and dplyr to use a spatial join on two datasets and calculate a summary metric from the result of that join.\n\n\n\n\n\n\nsf and tidyverse best practices\n\n\n\nThe group_by() and summarize() functions can also be used on sf objects to summarize within a dataset and combine geometries. Many of the tidyverse functions have methods specific for sf objects, some of which have additional arguments that wouldn’t be relevant to the data.frame methods. You can run ?sf::tidyverse to get documentation on the tidyverse sf methods.\n\n\nSay we want to calculate the population by Alaska management area, as opposed to region.\n\npop_mgmt_338 <- pop_region_3338 %>%\n group_by(mgmt_area) %>%\n summarize(total_pop = sum(total_pop))\n\nplot(pop_mgmt_338[\"total_pop\"])\n\n\n\n\n\n\n\n\nNotice that the region geometries were combined into a single polygon for each management area.\nIf we don’t want to combine geometries, we can specify do_union = F as an argument.\n\npop_mgmt_3338 <- pop_region_3338 %>%\n group_by(mgmt_area) %>%\n summarize(total_pop = sum(total_pop), do_union = F)\n\nplot(pop_mgmt_3338[\"total_pop\"])\n\n\n\n\n\n\n\n\n4. Save the spatial object to a new file using write_sf()\nSave the spatial object to disk using write_sf() and specifying the filename. Writing your file with the extension .shp will assume an ESRI driver driver, but there are many other format options available.\n\nwrite_sf(pop_region_3338, \"data/ak_regions_population.shp\")\n\n\n19.5.1 Visualize with ggplot\nggplot2 now has integrated functionality to plot sf objects using geom_sf().\nWe can plot sf objects just like regular data.frames using geom_sf.\n\nggplot(pop_region_3338) +\n geom_sf(aes(fill = total_pop)) +\n labs(fill = \"Total Population\") +\n scale_fill_continuous(low = \"khaki\",\n high = \"firebrick\",\n labels = comma) +\n theme_bw()\n\n\n\n\n\n\n\n\nWe can also plot multiple shapefiles in the same plot. Say if we want to visualize rivers in Alaska, in addition to the location of communities, since many communities in Alaska are on rivers. We can read in a rivers shapefile, doublecheck the crs to make sure it is what we need, and then plot all three shapefiles - the regional population (polygons), the locations of cities (points), and the rivers (linestrings).\n\n\nCoordinate Reference System:\n User input: Albers \n wkt:\nPROJCRS[\"Albers\",\n BASEGEOGCRS[\"GCS_GRS 1980(IUGG, 1980)\",\n DATUM[\"D_unknown\",\n ELLIPSOID[\"GRS80\",6378137,298.257222101,\n LENGTHUNIT[\"metre\",1,\n ID[\"EPSG\",9001]]]],\n PRIMEM[\"Greenwich\",0,\n ANGLEUNIT[\"Degree\",0.0174532925199433]]],\n CONVERSION[\"unnamed\",\n METHOD[\"Albers Equal Area\",\n ID[\"EPSG\",9822]],\n PARAMETER[\"Latitude of false origin\",50,\n ANGLEUNIT[\"Degree\",0.0174532925199433],\n ID[\"EPSG\",8821]],\n PARAMETER[\"Longitude of false origin\",-154,\n ANGLEUNIT[\"Degree\",0.0174532925199433],\n ID[\"EPSG\",8822]],\n PARAMETER[\"Latitude of 1st standard parallel\",55,\n ANGLEUNIT[\"Degree\",0.0174532925199433],\n ID[\"EPSG\",8823]],\n PARAMETER[\"Latitude of 2nd standard parallel\",65,\n ANGLEUNIT[\"Degree\",0.0174532925199433],\n ID[\"EPSG\",8824]],\n PARAMETER[\"Easting at false origin\",0,\n LENGTHUNIT[\"metre\",1],\n ID[\"EPSG\",8826]],\n PARAMETER[\"Northing at false origin\",0,\n LENGTHUNIT[\"metre\",1],\n ID[\"EPSG\",8827]]],\n CS[Cartesian,2],\n AXIS[\"(E)\",east,\n ORDER[1],\n LENGTHUNIT[\"metre\",1,\n ID[\"EPSG\",9001]]],\n AXIS[\"(N)\",north,\n ORDER[2],\n LENGTHUNIT[\"metre\",1,\n ID[\"EPSG\",9001]]]]\n\n\n\nrivers_3338 <- read_sf(\"data/ak_rivers_simp.shp\")\nst_crs(rivers_3338)\n\nNote that although no EPSG code is set explicitly, with some sluething we can determine that this is EPSG:3338. This site is helpful for looking up EPSG codes.\n\nggplot() +\n geom_sf(data = pop_region_3338, aes(fill = total_pop)) +\n geom_sf(data = pop_3338, size = 0.5) +\n geom_sf(data = rivers_3338,\n aes(linewidth = StrOrder)) +\n scale_linewidth(range = c(0.05, 0.5), guide = \"none\") +\n labs(title = \"Total Population by Alaska Region\",\n fill = \"Total Population\") +\n scale_fill_continuous(low = \"khaki\",\n high = \"firebrick\",\n labels = comma) +\n theme_bw()", "crumbs": [ "19  Working with Spatial Data" ] @@ -1334,7 +1334,7 @@ "href": "session_19.html#incorporate-base-maps-into-static-maps-using-ggmap", "title": "19  Working with Spatial Data", "section": "19.6 Incorporate base maps into static maps using ggmap", - "text": "19.6 Incorporate base maps into static maps using ggmap\nThe ggmap package has some functions that can render base maps (as raster objects) from open tile servers like Google Maps, Stamen, OpenStreetMap, and others.\nWe’ll need to transform our shapefile with population data by community to EPSG:3857 which is the crs used for rendering maps in Google Maps, Stamen, and OpenStreetMap, among others.\n\npop_3857 <- pop_3338 %>%\n st_transform(crs = 3857)\n\nNext, let’s grab a base map from the Stamen map tile server covering the region of interest. First we include a function that transforms the bounding box (which starts in EPSG:4326) to also be in the EPSG:3857 CRS, which is the projection that the map raster is returned in from Stamen. This is an issue with ggmap described in more detail here\n\n# Define a function to fix the bbox to be in EPSG:3857\n# See https://github.com/dkahle/ggmap/issues/160#issuecomment-397055208\nggmap_bbox_to_3857 <- function(map) {\n if (!inherits(map, \"ggmap\"))\n stop(\"map must be a ggmap object\")\n # Extract the bounding box (in lat/lon) from the ggmap to a numeric vector,\n # and set the names to what sf::st_bbox expects:\n map_bbox <- setNames(unlist(attr(map, \"bb\")),\n c(\"ymin\", \"xmin\", \"ymax\", \"xmax\"))\n \n # Coonvert the bbox to an sf polygon, transform it to 3857,\n # and convert back to a bbox (convoluted, but it works)\n bbox_3857 <-\n st_bbox(st_transform(st_as_sfc(st_bbox(map_bbox, crs = 4326)), 3857))\n \n # Overwrite the bbox of the ggmap object with the transformed coordinates\n attr(map, \"bb\")$ll.lat <- bbox_3857[\"ymin\"]\n attr(map, \"bb\")$ll.lon <- bbox_3857[\"xmin\"]\n attr(map, \"bb\")$ur.lat <- bbox_3857[\"ymax\"]\n attr(map, \"bb\")$ur.lon <- bbox_3857[\"xmax\"]\n map\n}\n\nNext, we define the bounding box of interest, and use get_stamenmap() to get the basemap. Then we run our function defined above on the result of the get_stamenmap() call.\n\nbbox <- c(-170, 52,-130, 64) # this is roughly southern Alaska\nak_map <- get_stamenmap(bbox, zoom = 4) # get base map\nak_map_3857 <- ggmap_bbox_to_3857(ak_map) # fix the bbox to be in EPSG:3857\n\nFinally, plot both the base raster map with the population data overlayed, which is easy now that everything is in the same projection (3857):\n\nggmap(ak_map_3857) +\n geom_sf(data = pop_3857,\n aes(color = population),\n inherit.aes = F) +\n scale_color_continuous(low = \"khaki\",\n high = \"firebrick\",\n labels = comma)", + "text": "19.6 Incorporate base maps into static maps using ggmap\nThe ggmap package has some functions that can render base maps (as raster objects) from open tile servers like Google Maps, Stamen, OpenStreetMap, and others.\nWe’ll need to transform our shapefile with population data by community to EPSG:3857 which is the crs used for rendering maps in Google Maps, Stamen, and OpenStreetMap, among others.\n\npop_3857 <- pop_3338 %>%\n st_transform(crs = 3857)\n\nNext, let’s grab a base map from the Stamen map tile server covering the region of interest. First we include a function that transforms the bounding box (which starts in EPSG:4326) to also be in the EPSG:3857 CRS, which is the projection that the map raster is returned in from Stamen. This is an issue with ggmap described in more detail here\n\n# Define a function to fix the bbox to be in EPSG:3857\n# See https://github.com/dkahle/ggmap/issues/160#issuecomment-397055208\nggmap_bbox_to_3857 <- function(map) {\n if (!inherits(map, \"ggmap\"))\n stop(\"map must be a ggmap object\")\n # Extract the bounding box (in lat/lon) from the ggmap to a numeric vector,\n # and set the names to what sf::st_bbox expects:\n map_bbox <- setNames(unlist(attr(map, \"bb\")),\n c(\"ymin\", \"xmin\", \"ymax\", \"xmax\"))\n \n # Coonvert the bbox to an sf polygon, transform it to 3857,\n # and convert back to a bbox (convoluted, but it works)\n bbox_3857 <-\n st_bbox(st_transform(st_as_sfc(st_bbox(map_bbox, crs = 4326)), 3857))\n \n # Overwrite the bbox of the ggmap object with the transformed coordinates\n attr(map, \"bb\")$ll.lat <- bbox_3857[\"ymin\"]\n attr(map, \"bb\")$ll.lon <- bbox_3857[\"xmin\"]\n attr(map, \"bb\")$ur.lat <- bbox_3857[\"ymax\"]\n attr(map, \"bb\")$ur.lon <- bbox_3857[\"xmax\"]\n map\n}\n\nNext, we define the bounding box of interest, and use get_stamenmap() to get the basemap. Then we run our function defined above on the result of the get_stamenmap() call.\n\nbbox <- c(-170, 52,-130, 64) # this is roughly southern Alaska\nak_map <- get_stamenmap(bbox, zoom = 4) # get base map\nak_map_3857 <- ggmap_bbox_to_3857(ak_map) # fix the bbox to be in EPSG:3857\n\nak_map_ggspatial <- annotation_map_tile(zoomin = 4)\n\nFinally, plot both the base raster map with the population data overlayed, which is easy now that everything is in the same projection (3857):\n\nggmap(ak_map_3857) +\n geom_sf(data = pop_3857,\n aes(color = population),\n inherit.aes = F) +\n scale_color_continuous(low = \"khaki\",\n high = \"firebrick\",\n labels = comma)", "crumbs": [ "19  Working with Spatial Data" ] @@ -1344,7 +1344,7 @@ "href": "session_19.html#visualize-sf-objects-with-leaflet", "title": "19  Working with Spatial Data", "section": "19.7 Visualize sf objects with leaflet", - "text": "19.7 Visualize sf objects with leaflet\nWe can also make an interactive map from our data above using leaflet.\nleaflet (unlike ggplot) will project data for you. The catch is that you have to give it both a projection (like Alaska Albers), and that your shapefile must use a geographic coordinate system. This means that we need to use our shapefile with the 4326 EPSG code. Remember you can always check what crs you have set using st_crs.\nHere we define a leaflet projection for Alaska Albers, and save it as a variable to use later.\n\nepsg3338 <- leaflet::leafletCRS(\n crsClass = \"L.Proj.CRS\",\n code = \"EPSG:3338\",\n proj4def = \"+proj=aea +lat_1=55 +lat_2=65 +lat_0=50 +lon_0=-154 +x_0=0 +y_0=0 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs\",\n resolutions = 2 ^ (16:7)\n)\n\nYou might notice that this looks familiar! The syntax is a bit different, but most of this information is also contained within the crs of our shapefile:\n\nst_crs(pop_region_3338)\n\nSince leaflet requires that we use an unprojected coordinate system, let’s use st_transform() yet again to get back to WGS84.\n\npop_region_4326 <- pop_region_3338 %>% st_transform(crs = 4326)\n\n\nm <- leaflet(options = leafletOptions(crs = epsg3338)) %>%\n addPolygons(data = pop_region_4326,\n fillColor = \"gray\",\n weight = 1)\n\nm\n\nWe can add labels, legends, and a color scale.\n\npal <- colorNumeric(palette = \"Reds\", domain = pop_region_4326$total_pop)\n\nm <- leaflet(options = leafletOptions(crs = epsg3338)) %>%\n addPolygons(\n data = pop_region_4326,\n fillColor = ~ pal(total_pop),\n weight = 1,\n color = \"black\",\n fillOpacity = 1,\n label = ~ region\n ) %>%\n addLegend(\n position = \"bottomleft\",\n pal = pal,\n values = range(pop_region_4326$total_pop),\n title = \"Total Population\"\n )\n\nm\n\nWe can also add the individual communities, with popup labels showing their population, on top of that!\n\npal <- colorNumeric(palette = \"Reds\", domain = pop_region_4326$total_pop)\n\nm <- leaflet(options = leafletOptions(crs = epsg3338)) %>%\n addPolygons(\n data = pop_region_4326,\n fillColor = ~ pal(total_pop),\n weight = 1,\n color = \"black\",\n fillOpacity = 1\n ) %>%\n addCircleMarkers(\n data = pop_4326,\n lat = ~ lat,\n lng = ~ lng,\n radius = ~ log(population / 500),\n # arbitrary scaling\n fillColor = \"gray\",\n fillOpacity = 1,\n weight = 0.25,\n color = \"black\",\n label = ~ paste0(pop_4326$city, \", population \", comma(pop_4326$population))\n ) %>%\n addLegend(\n position = \"bottomleft\",\n pal = pal,\n values = range(pop_region_4326$total_pop),\n title = \"Total Population\"\n )\n\nm", + "text": "19.7 Visualize sf objects with leaflet\nWe can also make an interactive map from our data above using leaflet.\nleaflet (unlike ggplot) will project data for you. The catch is that you have to give it both a projection (like Alaska Albers), and that your shapefile must use a geographic coordinate system. This means that we need to use our shapefile with the 4326 EPSG code. Remember you can always check what crs you have set using st_crs.\nHere we define a leaflet projection for Alaska Albers, and save it as a variable to use later.\n\nepsg3338 <- leaflet::leafletCRS(\n crsClass = \"L.Proj.CRS\",\n code = \"EPSG:3338\",\n proj4def = \"+proj=aea +lat_1=55 +lat_2=65 +lat_0=50 +lon_0=-154 +x_0=0 +y_0=0 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs\",\n resolutions = 2 ^ (16:7)\n)\n\nYou might notice that this looks familiar! The syntax is a bit different, but most of this information is also contained within the crs of our shapefile:\n\nst_crs(pop_region_3338)\n\nCoordinate Reference System:\n User input: EPSG:3338 \n wkt:\nPROJCRS[\"NAD83 / Alaska Albers\",\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[\"Alaska Albers (meters)\",\n METHOD[\"Albers Equal Area\",\n ID[\"EPSG\",9822]],\n PARAMETER[\"Latitude of false origin\",50,\n ANGLEUNIT[\"degree\",0.0174532925199433],\n ID[\"EPSG\",8821]],\n PARAMETER[\"Longitude of false origin\",-154,\n ANGLEUNIT[\"degree\",0.0174532925199433],\n ID[\"EPSG\",8822]],\n PARAMETER[\"Latitude of 1st standard parallel\",55,\n ANGLEUNIT[\"degree\",0.0174532925199433],\n ID[\"EPSG\",8823]],\n PARAMETER[\"Latitude of 2nd standard parallel\",65,\n ANGLEUNIT[\"degree\",0.0174532925199433],\n ID[\"EPSG\",8824]],\n PARAMETER[\"Easting at false origin\",0,\n LENGTHUNIT[\"metre\",1],\n ID[\"EPSG\",8826]],\n PARAMETER[\"Northing at false origin\",0,\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[\"Topographic mapping (small scale).\"],\n AREA[\"United States (USA) - Alaska.\"],\n BBOX[51.3,172.42,71.4,-129.99]],\n ID[\"EPSG\",3338]]\n\n\nSince leaflet requires that we use an unprojected coordinate system, let’s use st_transform() yet again to get back to WGS84.\n\npop_region_4326 <- pop_region_3338 %>% st_transform(crs = 4326)\n\n\nm <- leaflet(options = leafletOptions(crs = epsg3338)) %>%\n addPolygons(data = pop_region_4326,\n fillColor = \"gray\",\n weight = 1)\n\nm\n\n\n\n\n\nWe can add labels, legends, and a color scale.\n\npal <- colorNumeric(palette = \"Reds\", domain = pop_region_4326$total_pop)\n\nm <- leaflet(options = leafletOptions(crs = epsg3338)) %>%\n addPolygons(\n data = pop_region_4326,\n fillColor = ~ pal(total_pop),\n weight = 1,\n color = \"black\",\n fillOpacity = 1,\n label = ~ region\n ) %>%\n addLegend(\n position = \"bottomleft\",\n pal = pal,\n values = range(pop_region_4326$total_pop),\n title = \"Total Population\"\n )\n\nm\n\n\n\n\n\nWe can also add the individual communities, with popup labels showing their population, on top of that!\n\npal <- colorNumeric(palette = \"Reds\", domain = pop_region_4326$total_pop)\n\nm <- leaflet(options = leafletOptions(crs = epsg3338)) %>%\n addPolygons(\n data = pop_region_4326,\n fillColor = ~ pal(total_pop),\n weight = 1,\n color = \"black\",\n fillOpacity = 1\n ) %>%\n addCircleMarkers(\n data = pop_4326,\n lat = ~ lat,\n lng = ~ lng,\n radius = ~ log(population / 500),\n # arbitrary scaling\n fillColor = \"gray\",\n fillOpacity = 1,\n weight = 0.25,\n color = \"black\",\n label = ~ paste0(pop_4326$city, \", population \", comma(pop_4326$population))\n ) %>%\n addLegend(\n position = \"bottomleft\",\n pal = pal,\n values = range(pop_region_4326$total_pop),\n title = \"Total Population\"\n )\n\nm", "crumbs": [ "19  Working with Spatial Data" ] diff --git a/public/2024-02-arctic/session_11.html b/public/2024-02-arctic/session_11.html index 3a365b55..1cbb9a24 100644 --- a/public/2024-02-arctic/session_11.html +++ b/public/2024-02-arctic/session_11.html @@ -1013,8 +1013,8 @@

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- +
+


diff --git a/public/2024-02-arctic/session_15.html b/public/2024-02-arctic/session_15.html index 7a569fcb..92a5ba81 100644 --- a/public/2024-02-arctic/session_15.html +++ b/public/2024-02-arctic/session_15.html @@ -538,8 +538,8 @@

-
- +
+

Once we have a dataset like that, we might want to plot it. One thing that we do repeatedly is set a consistent set of display elements for creating graphs and plots. By using a function to create a custom ggplot theme, we can enable to keep key parts of the formatting flexible. For example, in the custom_theme function, we provide a base_size argument that defaults to using a font size of 9 points. Because it has a default set, it can safely be omitted. But if it is provided, then that value is used to set the base font size for the plot.

diff --git a/public/2024-02-arctic/session_19.html b/public/2024-02-arctic/session_19.html index 3ac63220..fc12b21a 100644 --- a/public/2024-02-arctic/session_19.html +++ b/public/2024-02-arctic/session_19.html @@ -103,6 +103,15 @@ "search-label": "Search" } } + + + + + + + + + @@ -395,22 +404,25 @@

Navigate to this dataset on KNB’s test site and download the zip folder.
  • Upload the zip folder to the data folder in the training_{USERNAME} project. You don’t need to unzip the folder ahead of time, uploading will automatically unzip the folder.
      -
    1. Alternatively, programatically download and extract the demo data with:
    2. +
    3. Alternatively, programatically download and extract the demo data with:
  • -
    knb_url <- 'https://dev.nceas.ucsb.edu/knb/d1/mn/v2/object/urn%3Auuid%3Aaceaecb2-1ce0-4d41-a839-d3607d32bb58'
    -download.file(url = knb_url, destfile = 'demo_data.zip')
    -unzip('demo_data.zip', exdir = 'data')
    -file.remove('demo_data.zip')
    +
    knb_url <- "https://dev.nceas.ucsb.edu/knb/d1/mn/v2/object/urn%3Auuid%3Aaceaecb2-1ce0-4d41-a839-d3607d32bb58"
    +
    +download.file(url = knb_url, destfile = 'shapefile_demo_data.zip')
    +
    +unzip('shapefile_demo_data.zip', exdir = 'data')
    +
    +file.remove('demo_data.zip')
      -
    1. Create a new R Markdown file. +
    2. Create a new Quarto file.
        -
      1. Title it “Intro to sf package for Spatial Data and Making Maps”
      2. -
      3. Save the file and name it “intro-sf-spatial-data-maps”.
      4. +
      5. Title it “Working with Spatial Data in R”
      6. +
      7. Save the file and name it “intro-to-spatial-data”.
    3. -
    4. Load the following libraries at the top of your R Markdown file.
    5. +
    6. Load the following libraries at the top of your Quarto file.
    library(readr)
    @@ -434,18 +446,51 @@ 

    # quick plot
     plot(ak_regions)
    +
    +
    +
    +

    +
    +
    +

    We can also examine it’s class using class().

    class(ak_regions)
    +
    +
    [1] "sf"         "tbl_df"     "tbl"        "data.frame"
    +

    sf objects usually have two types of classes: sf and data.frame.

    Unlike a typical data.frame, an sf object has spatial metadata (geometry type, dimension, bbox, epsg (SRID), proj4string) and an additional column typically named geometry that contains the spatial data.

    Since our shapefile object has the data.frame class, viewing the contents of the object using the head() function or other exploratory functions shows similar results as if we read in data using read.csv() or read_csv().

    -
    head(ak_regions)
    -
    -glimpse(ak_regions)
    +
    head(ak_regions)
    +
    +
    Simple feature collection with 6 features and 3 fields
    +Geometry type: MULTIPOLYGON
    +Dimension:     XY
    +Bounding box:  xmin: -179.2296 ymin: 51.15702 xmax: 179.8567 ymax: 71.43957
    +Geodetic CRS:  WGS 84
    +# A tibble: 6 × 4
    +  region_id region           mgmt_area                                  geometry
    +      <int> <chr>                <dbl>                        <MULTIPOLYGON [°]>
    +1         1 Aleutian Islands         3 (((-171.1345 52.44974, -171.1686 52.4174…
    +2         2 Arctic                   4 (((-139.9552 68.70597, -139.9893 68.7051…
    +3         3 Bristol Bay              3 (((-159.8745 58.62778, -159.8654 58.6137…
    +4         4 Chignik                  3 (((-155.8282 55.84638, -155.8049 55.8655…
    +5         5 Copper River             2 (((-143.8874 59.93931, -143.9165 59.9403…
    +6         6 Kodiak                   3 (((-151.9997 58.83077, -152.0358 58.8271…
    +
    +
    glimpse(ak_regions)
    +
    +
    Rows: 13
    +Columns: 4
    +$ region_id <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13
    +$ region    <chr> "Aleutian Islands", "Arctic", "Bristol Bay", "Chignik", "Cop…
    +$ mgmt_area <dbl> 3, 4, 3, 3, 2, 3, 4, 4, 2, 4, 2, 1, 4
    +$ geometry  <MULTIPOLYGON [°]> MULTIPOLYGON (((-171.1345 5..., MULTIPOLYGON (((-139.9552 6.…
    +

    19.3.1 Coordinate Reference System (CRS)

    @@ -459,7 +504,26 @@

    blog post that explains these concepts in more detail with very helpful diagrams and examples.

    You can view what crs is set by using the function st_crs().

    -
    st_crs(ak_regions)
    +
    st_crs(ak_regions)
    +
    +
    Coordinate Reference System:
    +  User input: WGS 84 
    +  wkt:
    +GEOGCRS["WGS 84",
    +    DATUM["World Geodetic System 1984",
    +        ELLIPSOID["WGS 84",6378137,298.257223563,
    +            LENGTHUNIT["metre",1]]],
    +    PRIMEM["Greenwich",0,
    +        ANGLEUNIT["degree",0.0174532925199433]],
    +    CS[ellipsoidal,2],
    +        AXIS["latitude",north,
    +            ORDER[1],
    +            ANGLEUNIT["degree",0.0174532925199433]],
    +        AXIS["longitude",east,
    +            ORDER[2],
    +            ANGLEUNIT["degree",0.0174532925199433]],
    +    ID["EPSG",4326]]
    +

    This is pretty confusing looking. Without getting into the details, that long string says that this data has a geographic coordinate system (WGS84) with no projection. A convenient way to reference crs quickly is by using the EPSG code, a number that represents a standard projection and datum. You can check out a list of (lots!) of EPSG codes here.

    We will use multiple EPSG codes in this lesson. Here they are, along with their more readable names:

    @@ -470,13 +534,66 @@

    3338.

    -
    ak_regions_3338 <- ak_regions %>%
    -    st_transform(crs = 3338)
    -
    -st_crs(ak_regions_3338)
    +
    ak_regions_3338 <- ak_regions %>%
    +    st_transform(crs = 3338)
    +
    +st_crs(ak_regions_3338)
    +
    +
    Coordinate Reference System:
    +  User input: EPSG:3338 
    +  wkt:
    +PROJCRS["NAD83 / Alaska Albers",
    +    BASEGEOGCRS["NAD83",
    +        DATUM["North American Datum 1983",
    +            ELLIPSOID["GRS 1980",6378137,298.257222101,
    +                LENGTHUNIT["metre",1]]],
    +        PRIMEM["Greenwich",0,
    +            ANGLEUNIT["degree",0.0174532925199433]],
    +        ID["EPSG",4269]],
    +    CONVERSION["Alaska Albers (meters)",
    +        METHOD["Albers Equal Area",
    +            ID["EPSG",9822]],
    +        PARAMETER["Latitude of false origin",50,
    +            ANGLEUNIT["degree",0.0174532925199433],
    +            ID["EPSG",8821]],
    +        PARAMETER["Longitude of false origin",-154,
    +            ANGLEUNIT["degree",0.0174532925199433],
    +            ID["EPSG",8822]],
    +        PARAMETER["Latitude of 1st standard parallel",55,
    +            ANGLEUNIT["degree",0.0174532925199433],
    +            ID["EPSG",8823]],
    +        PARAMETER["Latitude of 2nd standard parallel",65,
    +            ANGLEUNIT["degree",0.0174532925199433],
    +            ID["EPSG",8824]],
    +        PARAMETER["Easting at false origin",0,
    +            LENGTHUNIT["metre",1],
    +            ID["EPSG",8826]],
    +        PARAMETER["Northing at false origin",0,
    +            LENGTHUNIT["metre",1],
    +            ID["EPSG",8827]]],
    +    CS[Cartesian,2],
    +        AXIS["easting (X)",east,
    +            ORDER[1],
    +            LENGTHUNIT["metre",1]],
    +        AXIS["northing (Y)",north,
    +            ORDER[2],
    +            LENGTHUNIT["metre",1]],
    +    USAGE[
    +        SCOPE["Topographic mapping (small scale)."],
    +        AREA["United States (USA) - Alaska."],
    +        BBOX[51.3,172.42,71.4,-129.99]],
    +    ID["EPSG",3338]]
    +
    -
    plot(ak_regions_3338)
    +
    plot(ak_regions_3338)
    +
    +
    +
    +

    +
    +
    +

    Much better!

    @@ -488,23 +605,67 @@

    19.4.1 select()

    -
    # returns the names of all the columns in dataset
    -colnames(ak_regions_3338)
    +
    # returns the names of all the columns in dataset
    +colnames(ak_regions_3338)
    +
    +
    [1] "region_id" "region"    "mgmt_area" "geometry" 
    +
    -
    ak_regions_3338 %>%
    -    select(region)
    +
    ak_regions_3338 %>%
    +    select(region)
    +
    +
    Simple feature collection with 13 features and 1 field
    +Geometry type: MULTIPOLYGON
    +Dimension:     XY
    +Bounding box:  xmin: -2175328 ymin: 405653 xmax: 1579226 ymax: 2383770
    +Projected CRS: NAD83 / Alaska Albers
    +# A tibble: 13 × 2
    +   region                                                               geometry
    +   <chr>                                                      <MULTIPOLYGON [m]>
    + 1 Aleutian Islands     (((-1156666 420855.1, -1159837 417990.3, -1161898 41694…
    + 2 Arctic               (((571289.9 2143072, 569941.5 2142691, 569158.2 2142146…
    + 3 Bristol Bay          (((-339688.6 973904.9, -339302 972297.3, -339229.2 9710…
    + 4 Chignik              (((-114381.9 649966.8, -112866.8 652065.8, -108836.8 65…
    + 5 Copper River         (((561012 1148301, 559393.7 1148169, 557797.7 1148492, …
    + 6 Kodiak               (((115112.5 983293, 113051.3 982825.9, 110801.3 983211.…
    + 7 Kotzebue             (((-678815.3 1819519, -677555.2 1820698, -675557.8 1821…
    + 8 Kuskokwim            (((-1030125 1281198, -1029858 1282333, -1028980 1284032…
    + 9 Cook Inlet           (((35214.98 1002457, 36660.3 1002038, 36953.11 1001186,…
    +10 Norton Sound         (((-848357 1636692, -846510 1635203, -840513.7 1632225,…
    +11 Prince William Sound (((426007.1 1087250, 426562.5 1088591, 427711.6 1089991…
    +12 Southeast            (((1287777 744574.1, 1290183 745970.8, 1292940 746262.7…
    +13 Yukon                (((-375318 1473998, -373723.9 1473487, -373064.8 147393…
    +

    Note the sticky geometry column! The geometry column will stay with your sf object even if it is not called explicitly.

    19.4.2 filter()

    -
    unique(ak_regions_3338$region)
    +
    unique(ak_regions_3338$region)
    +
    +
     [1] "Aleutian Islands"     "Arctic"               "Bristol Bay"         
    + [4] "Chignik"              "Copper River"         "Kodiak"              
    + [7] "Kotzebue"             "Kuskokwim"            "Cook Inlet"          
    +[10] "Norton Sound"         "Prince William Sound" "Southeast"           
    +[13] "Yukon"               
    +
    -
    ak_regions_3338 %>%
    -    filter(region == "Southeast")
    +
    ak_regions_3338 %>%
    +    filter(region == "Southeast")
    +
    +
    Simple feature collection with 1 feature and 3 fields
    +Geometry type: MULTIPOLYGON
    +Dimension:     XY
    +Bounding box:  xmin: 559475.7 ymin: 722450 xmax: 1579226 ymax: 1410576
    +Projected CRS: NAD83 / Alaska Albers
    +# A tibble: 1 × 4
    +  region_id region    mgmt_area                                         geometry
    +*     <int> <chr>         <dbl>                               <MULTIPOLYGON [m]>
    +1        12 Southeast         1 (((1287777 744574.1, 1290183 745970.8, 1292940 …
    +
    @@ -532,36 +693,69 @@

    1. Read in alaska_population.csv using read.csv()

    -
    # read in population data
    -pop <- read_csv("data/alaska_population.csv")
    +
    # read in population data
    +pop <- read_csv("data/alaska_population.csv")

    Turn pop into a spatial object

    The st_join() function is a spatial left join. The arguments for both the left and right tables are objects of class sf which means we will first need to turn our population data.frame with latitude and longitude coordinates into an sf object.

    We can do this easily using the st_as_sf() function, which takes as arguments the coordinates and the crs. The remove = F specification here ensures that when we create our geometry column, we retain our original lat lng columns, which we will need later for plotting. Although it isn’t said anywhere explicitly in the file, let’s assume that the coordinate system used to reference the latitude longitude coordinates is WGS84, which has a crs number of 4326.

    -
    pop_4326 <- st_as_sf(pop,
    -                     coords = c('lng', 'lat'),
    -                     crs = 4326,
    -                     remove = F)
    -
    -head(pop_4326)
    +
    pop_4326 <- st_as_sf(pop,
    +                     coords = c('lng', 'lat'),
    +                     crs = 4326,
    +                     remove = F)
    +
    +head(pop_4326)
    +
    +
    Simple feature collection with 6 features and 5 fields
    +Geometry type: POINT
    +Dimension:     XY
    +Bounding box:  xmin: -176.6581 ymin: 51.88 xmax: -154.1703 ymax: 62.68889
    +Geodetic CRS:  WGS 84
    +# A tibble: 6 × 6
    +   year city       lat   lng population             geometry
    +  <dbl> <chr>    <dbl> <dbl>      <dbl>          <POINT [°]>
    +1  2015 Adak      51.9 -177.        122    (-176.6581 51.88)
    +2  2015 Akhiok    56.9 -154.         84 (-154.1703 56.94556)
    +3  2015 Akiachak  60.9 -161.        562 (-161.4314 60.90944)
    +4  2015 Akiak     60.9 -161.        399 (-161.2139 60.91222)
    +5  2015 Akutan    54.1 -166.        899 (-165.7731 54.13556)
    +6  2015 Alakanuk  62.7 -165.        777 (-164.6153 62.68889)
    +

    2. Join population data with Alaska regions data using st_join()

    Now we can do our spatial join! You can specify what geometry function the join uses (st_intersects, st_within, st_crosses, st_is_within_distance…) in the join argument. The geometry function you use will depend on what kind of operation you want to do, and the geometries of your shapefiles.

    In this case, we want to find what region each city falls within, so we will use st_within.

    -
    pop_joined <- st_join(pop_4326, ak_regions_3338, join = st_within)
    +
    pop_joined <- st_join(pop_4326, ak_regions_3338, join = st_within)

    This gives an error!

    -
    Error: st_crs(x) == st_crs(y) is not TRUE
    +
    Error: st_crs(x) == st_crs(y) is not TRUE

    Turns out, this won’t work right now because our coordinate reference systems are not the same. Luckily, this is easily resolved using st_transform(), and projecting our population object into Alaska Albers.

    -
    pop_3338 <- st_transform(pop_4326, crs = 3338)
    +
    pop_3338 <- st_transform(pop_4326, crs = 3338)
    -
    pop_joined <- st_join(pop_3338, ak_regions_3338, join = st_within)
    -
    -head(pop_joined)
    +
    pop_joined <- st_join(pop_3338, ak_regions_3338, join = st_within)
    +
    +head(pop_joined)
    +
    +
    Simple feature collection with 6 features and 8 fields
    +Geometry type: POINT
    +Dimension:     XY
    +Bounding box:  xmin: -1537925 ymin: 472626.9 xmax: -10340.71 ymax: 1456223
    +Projected CRS: NAD83 / Alaska Albers
    +# A tibble: 6 × 9
    +   year city     lat   lng population             geometry region_id region     
    +  <dbl> <chr>  <dbl> <dbl>      <dbl>          <POINT [m]>     <int> <chr>      
    +1  2015 Adak    51.9 -177.        122  (-1537925 472626.9)         1 Aleutian I…
    +2  2015 Akhiok  56.9 -154.         84 (-10340.71 770998.4)         6 Kodiak     
    +3  2015 Akiac…  60.9 -161.        562  (-400885.5 1236460)         8 Kuskokwim  
    +4  2015 Akiak   60.9 -161.        399  (-389165.7 1235475)         8 Kuskokwim  
    +5  2015 Akutan  54.1 -166.        899 (-766425.7 526057.8)         1 Aleutian I…
    +6  2015 Alaka…  62.7 -165.        777  (-539724.9 1456223)        13 Yukon      
    +# ℹ 1 more variable: mgmt_area <dbl>
    +
    @@ -579,19 +773,37 @@

    3. Calculate the total population by region using group_by() and summarize()

    Next we compute the total population for each region. In this case, we want to do a group_by() and summarise() as this were a regular data.frame. Otherwise all of our point geometries would be included in the aggregation, which is not what we want. Our goal is just to get the total population by region. We remove the sticky geometry using as.data.frame(), on the advice of the sf::tidyverse help page.

    -
    pop_region <- pop_joined %>%
    -    as.data.frame() %>%
    -    group_by(region) %>%
    -    summarise(total_pop = sum(population))
    -
    -head(pop_region)
    +
    pop_region <- pop_joined %>%
    +    as.data.frame() %>%
    +    group_by(region) %>%
    +    summarise(total_pop = sum(population))
    +
    +head(pop_region)
    +
    +
    # A tibble: 6 × 2
    +  region           total_pop
    +  <chr>                <dbl>
    +1 Aleutian Islands      8840
    +2 Arctic                8419
    +3 Bristol Bay           6947
    +4 Chignik                311
    +5 Cook Inlet          408254
    +6 Copper River          2294
    +

    And use a regular left_join() to get the information back to the Alaska region shapefile. Note that we need this step in order to regain our region geometries so that we can make some maps.

    -
    pop_region_3338 <- left_join(ak_regions_3338, pop_region, by = "region")
    -
    -# plot to check
    -plot(pop_region_3338["total_pop"])
    +
    pop_region_3338 <- left_join(ak_regions_3338, pop_region, by = "region")
    +
    +# plot to check
    +plot(pop_region_3338["total_pop"])
    +
    +
    +
    +

    +
    +
    +

    So far, we have learned how to use sf and dplyr to use a spatial join on two datasets and calculate a summary metric from the result of that join.

    @@ -609,58 +821,131 @@

    Say we want to calculate the population by Alaska management area, as opposed to region.

    -
    pop_mgmt_338 <- pop_region_3338 %>%
    -    group_by(mgmt_area) %>%
    -    summarize(total_pop = sum(total_pop))
    -
    -plot(pop_mgmt_338["total_pop"])
    +
    pop_mgmt_338 <- pop_region_3338 %>%
    +    group_by(mgmt_area) %>%
    +    summarize(total_pop = sum(total_pop))
    +
    +plot(pop_mgmt_338["total_pop"])
    +
    +
    +
    +

    +
    +
    +

    Notice that the region geometries were combined into a single polygon for each management area.

    If we don’t want to combine geometries, we can specify do_union = F as an argument.

    -
    pop_mgmt_3338 <- pop_region_3338 %>%
    -    group_by(mgmt_area) %>%
    -    summarize(total_pop = sum(total_pop), do_union = F)
    -
    -plot(pop_mgmt_3338["total_pop"])
    +
    pop_mgmt_3338 <- pop_region_3338 %>%
    +    group_by(mgmt_area) %>%
    +    summarize(total_pop = sum(total_pop), do_union = F)
    +
    +plot(pop_mgmt_3338["total_pop"])
    +
    +
    +
    +

    +
    +
    +

    4. Save the spatial object to a new file using write_sf()

    Save the spatial object to disk using write_sf() and specifying the filename. Writing your file with the extension .shp will assume an ESRI driver driver, but there are many other format options available.

    -
    write_sf(pop_region_3338, "data/ak_regions_population.shp")
    +
    write_sf(pop_region_3338, "data/ak_regions_population.shp")

    19.5.1 Visualize with ggplot

    ggplot2 now has integrated functionality to plot sf objects using geom_sf().

    We can plot sf objects just like regular data.frames using geom_sf.

    -
    ggplot(pop_region_3338) +
    -    geom_sf(aes(fill = total_pop)) +
    -    labs(fill = "Total Population") +
    -    scale_fill_continuous(low = "khaki",
    -                          high =  "firebrick",
    -                          labels = comma) +
    -    theme_bw()
    +
    ggplot(pop_region_3338) +
    +    geom_sf(aes(fill = total_pop)) +
    +    labs(fill = "Total Population") +
    +    scale_fill_continuous(low = "khaki",
    +                          high =  "firebrick",
    +                          labels = comma) +
    +    theme_bw()
    +
    +
    +
    +

    +
    +
    +

    We can also plot multiple shapefiles in the same plot. Say if we want to visualize rivers in Alaska, in addition to the location of communities, since many communities in Alaska are on rivers. We can read in a rivers shapefile, doublecheck the crs to make sure it is what we need, and then plot all three shapefiles - the regional population (polygons), the locations of cities (points), and the rivers (linestrings).

    -
    rivers_3338 <- read_sf("data/ak_rivers_simp.shp")
    -st_crs(rivers_3338)
    +
    +
    Coordinate Reference System:
    +  User input: Albers 
    +  wkt:
    +PROJCRS["Albers",
    +    BASEGEOGCRS["GCS_GRS 1980(IUGG, 1980)",
    +        DATUM["D_unknown",
    +            ELLIPSOID["GRS80",6378137,298.257222101,
    +                LENGTHUNIT["metre",1,
    +                    ID["EPSG",9001]]]],
    +        PRIMEM["Greenwich",0,
    +            ANGLEUNIT["Degree",0.0174532925199433]]],
    +    CONVERSION["unnamed",
    +        METHOD["Albers Equal Area",
    +            ID["EPSG",9822]],
    +        PARAMETER["Latitude of false origin",50,
    +            ANGLEUNIT["Degree",0.0174532925199433],
    +            ID["EPSG",8821]],
    +        PARAMETER["Longitude of false origin",-154,
    +            ANGLEUNIT["Degree",0.0174532925199433],
    +            ID["EPSG",8822]],
    +        PARAMETER["Latitude of 1st standard parallel",55,
    +            ANGLEUNIT["Degree",0.0174532925199433],
    +            ID["EPSG",8823]],
    +        PARAMETER["Latitude of 2nd standard parallel",65,
    +            ANGLEUNIT["Degree",0.0174532925199433],
    +            ID["EPSG",8824]],
    +        PARAMETER["Easting at false origin",0,
    +            LENGTHUNIT["metre",1],
    +            ID["EPSG",8826]],
    +        PARAMETER["Northing at false origin",0,
    +            LENGTHUNIT["metre",1],
    +            ID["EPSG",8827]]],
    +    CS[Cartesian,2],
    +        AXIS["(E)",east,
    +            ORDER[1],
    +            LENGTHUNIT["metre",1,
    +                ID["EPSG",9001]]],
    +        AXIS["(N)",north,
    +            ORDER[2],
    +            LENGTHUNIT["metre",1,
    +                ID["EPSG",9001]]]]
    +
    +
    +
    +
    rivers_3338 <- read_sf("data/ak_rivers_simp.shp")
    +st_crs(rivers_3338)

    Note that although no EPSG code is set explicitly, with some sluething we can determine that this is EPSG:3338. This site is helpful for looking up EPSG codes.

    -
    ggplot() +
    -    geom_sf(data = pop_region_3338, aes(fill = total_pop)) +
    -    geom_sf(data = pop_3338, size = 0.5) +
    -    geom_sf(data = rivers_3338,
    -            aes(linewidth = StrOrder)) +
    -    scale_linewidth(range = c(0.05, 0.5), guide = "none") +
    -    labs(title = "Total Population by Alaska Region",
    -         fill = "Total Population") +
    -    scale_fill_continuous(low = "khaki",
    -                          high =  "firebrick",
    -                          labels = comma) +
    -    theme_bw() 
    +
    ggplot() +
    +    geom_sf(data = pop_region_3338, aes(fill = total_pop)) +
    +    geom_sf(data = pop_3338, size = 0.5) +
    +    geom_sf(data = rivers_3338,
    +            aes(linewidth = StrOrder)) +
    +    scale_linewidth(range = c(0.05, 0.5), guide = "none") +
    +    labs(title = "Total Population by Alaska Region",
    +         fill = "Total Population") +
    +    scale_fill_continuous(low = "khaki",
    +                          high =  "firebrick",
    +                          labels = comma) +
    +    theme_bw() 
    +
    +
    +
    +

    +
    +
    +
    @@ -669,49 +954,51 @@

    -
    pop_3857 <- pop_3338 %>%
    -    st_transform(crs = 3857)
    +
    pop_3857 <- pop_3338 %>%
    +    st_transform(crs = 3857)

    Next, let’s grab a base map from the Stamen map tile server covering the region of interest. First we include a function that transforms the bounding box (which starts in EPSG:4326) to also be in the EPSG:3857 CRS, which is the projection that the map raster is returned in from Stamen. This is an issue with ggmap described in more detail here

    -
    # Define a function to fix the bbox to be in EPSG:3857
    -# See https://github.com/dkahle/ggmap/issues/160#issuecomment-397055208
    -ggmap_bbox_to_3857 <- function(map) {
    -    if (!inherits(map, "ggmap"))
    -        stop("map must be a ggmap object")
    -    # Extract the bounding box (in lat/lon) from the ggmap to a numeric vector,
    -    # and set the names to what sf::st_bbox expects:
    -    map_bbox <- setNames(unlist(attr(map, "bb")),
    -                         c("ymin", "xmin", "ymax", "xmax"))
    -    
    -    # Coonvert the bbox to an sf polygon, transform it to 3857,
    -    # and convert back to a bbox (convoluted, but it works)
    -    bbox_3857 <-
    -        st_bbox(st_transform(st_as_sfc(st_bbox(map_bbox, crs = 4326)), 3857))
    -    
    -    # Overwrite the bbox of the ggmap object with the transformed coordinates
    -    attr(map, "bb")$ll.lat <- bbox_3857["ymin"]
    -    attr(map, "bb")$ll.lon <- bbox_3857["xmin"]
    -    attr(map, "bb")$ur.lat <- bbox_3857["ymax"]
    -    attr(map, "bb")$ur.lon <- bbox_3857["xmax"]
    -    map
    -}
    +
    # Define a function to fix the bbox to be in EPSG:3857
    +# See https://github.com/dkahle/ggmap/issues/160#issuecomment-397055208
    +ggmap_bbox_to_3857 <- function(map) {
    +    if (!inherits(map, "ggmap"))
    +        stop("map must be a ggmap object")
    +    # Extract the bounding box (in lat/lon) from the ggmap to a numeric vector,
    +    # and set the names to what sf::st_bbox expects:
    +    map_bbox <- setNames(unlist(attr(map, "bb")),
    +                         c("ymin", "xmin", "ymax", "xmax"))
    +    
    +    # Coonvert the bbox to an sf polygon, transform it to 3857,
    +    # and convert back to a bbox (convoluted, but it works)
    +    bbox_3857 <-
    +        st_bbox(st_transform(st_as_sfc(st_bbox(map_bbox, crs = 4326)), 3857))
    +    
    +    # Overwrite the bbox of the ggmap object with the transformed coordinates
    +    attr(map, "bb")$ll.lat <- bbox_3857["ymin"]
    +    attr(map, "bb")$ll.lon <- bbox_3857["xmin"]
    +    attr(map, "bb")$ur.lat <- bbox_3857["ymax"]
    +    attr(map, "bb")$ur.lon <- bbox_3857["xmax"]
    +    map
    +}

    Next, we define the bounding box of interest, and use get_stamenmap() to get the basemap. Then we run our function defined above on the result of the get_stamenmap() call.

    -
    bbox <- c(-170, 52,-130, 64) # this is roughly southern Alaska
    -ak_map <- get_stamenmap(bbox, zoom = 4) # get base map
    -ak_map_3857 <- ggmap_bbox_to_3857(ak_map) # fix the bbox to be in EPSG:3857
    +
    bbox <- c(-170, 52,-130, 64) # this is roughly southern Alaska
    +ak_map <- get_stamenmap(bbox, zoom = 4) # get base map
    +ak_map_3857 <- ggmap_bbox_to_3857(ak_map) # fix the bbox to be in EPSG:3857
    +
    +ak_map_ggspatial <-  annotation_map_tile(zoomin = 4)

    Finally, plot both the base raster map with the population data overlayed, which is easy now that everything is in the same projection (3857):

    -
    ggmap(ak_map_3857) +
    -    geom_sf(data = pop_3857,
    -            aes(color = population),
    -            inherit.aes = F) +
    -    scale_color_continuous(low = "khaki",
    -                           high =  "firebrick",
    -                           labels = comma)
    +
    ggmap(ak_map_3857) +
    +    geom_sf(data = pop_3857,
    +            aes(color = population),
    +            inherit.aes = F) +
    +    scale_color_continuous(low = "khaki",
    +                           high =  "firebrick",
    +                           labels = comma)
    @@ -720,83 +1007,141 @@

    -
    epsg3338 <- leaflet::leafletCRS(
    -    crsClass = "L.Proj.CRS",
    -    code = "EPSG:3338",
    -    proj4def =  "+proj=aea +lat_1=55 +lat_2=65 +lat_0=50 +lon_0=-154 +x_0=0 +y_0=0 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs",
    -    resolutions = 2 ^ (16:7)
    -)
    +
    epsg3338 <- leaflet::leafletCRS(
    +    crsClass = "L.Proj.CRS",
    +    code = "EPSG:3338",
    +    proj4def =  "+proj=aea +lat_1=55 +lat_2=65 +lat_0=50 +lon_0=-154 +x_0=0 +y_0=0 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs",
    +    resolutions = 2 ^ (16:7)
    +)

    You might notice that this looks familiar! The syntax is a bit different, but most of this information is also contained within the crs of our shapefile:

    -
    st_crs(pop_region_3338)
    +
    st_crs(pop_region_3338)
    +
    +
    Coordinate Reference System:
    +  User input: EPSG:3338 
    +  wkt:
    +PROJCRS["NAD83 / Alaska Albers",
    +    BASEGEOGCRS["NAD83",
    +        DATUM["North American Datum 1983",
    +            ELLIPSOID["GRS 1980",6378137,298.257222101,
    +                LENGTHUNIT["metre",1]]],
    +        PRIMEM["Greenwich",0,
    +            ANGLEUNIT["degree",0.0174532925199433]],
    +        ID["EPSG",4269]],
    +    CONVERSION["Alaska Albers (meters)",
    +        METHOD["Albers Equal Area",
    +            ID["EPSG",9822]],
    +        PARAMETER["Latitude of false origin",50,
    +            ANGLEUNIT["degree",0.0174532925199433],
    +            ID["EPSG",8821]],
    +        PARAMETER["Longitude of false origin",-154,
    +            ANGLEUNIT["degree",0.0174532925199433],
    +            ID["EPSG",8822]],
    +        PARAMETER["Latitude of 1st standard parallel",55,
    +            ANGLEUNIT["degree",0.0174532925199433],
    +            ID["EPSG",8823]],
    +        PARAMETER["Latitude of 2nd standard parallel",65,
    +            ANGLEUNIT["degree",0.0174532925199433],
    +            ID["EPSG",8824]],
    +        PARAMETER["Easting at false origin",0,
    +            LENGTHUNIT["metre",1],
    +            ID["EPSG",8826]],
    +        PARAMETER["Northing at false origin",0,
    +            LENGTHUNIT["metre",1],
    +            ID["EPSG",8827]]],
    +    CS[Cartesian,2],
    +        AXIS["easting (X)",east,
    +            ORDER[1],
    +            LENGTHUNIT["metre",1]],
    +        AXIS["northing (Y)",north,
    +            ORDER[2],
    +            LENGTHUNIT["metre",1]],
    +    USAGE[
    +        SCOPE["Topographic mapping (small scale)."],
    +        AREA["United States (USA) - Alaska."],
    +        BBOX[51.3,172.42,71.4,-129.99]],
    +    ID["EPSG",3338]]
    +

    Since leaflet requires that we use an unprojected coordinate system, let’s use st_transform() yet again to get back to WGS84.

    -
    pop_region_4326 <- pop_region_3338 %>% st_transform(crs = 4326)
    +
    pop_region_4326 <- pop_region_3338 %>% st_transform(crs = 4326)
    -
    m <- leaflet(options = leafletOptions(crs = epsg3338)) %>%
    -    addPolygons(data = pop_region_4326,
    -                fillColor = "gray",
    -                weight = 1)
    -
    -m
    +
    m <- leaflet(options = leafletOptions(crs = epsg3338)) %>%
    +    addPolygons(data = pop_region_4326,
    +                fillColor = "gray",
    +                weight = 1)
    +
    +m
    +
    +
    + +

    We can add labels, legends, and a color scale.

    -
    pal <- colorNumeric(palette = "Reds", domain = pop_region_4326$total_pop)
    -
    -m <- leaflet(options = leafletOptions(crs = epsg3338)) %>%
    -    addPolygons(
    -        data = pop_region_4326,
    -        fillColor = ~ pal(total_pop),
    -        weight = 1,
    -        color = "black",
    -        fillOpacity = 1,
    -        label = ~ region
    -    ) %>%
    -    addLegend(
    -        position = "bottomleft",
    -        pal = pal,
    -        values = range(pop_region_4326$total_pop),
    -        title = "Total Population"
    -    )
    -
    -m
    +
    pal <- colorNumeric(palette = "Reds", domain = pop_region_4326$total_pop)
    +
    +m <- leaflet(options = leafletOptions(crs = epsg3338)) %>%
    +    addPolygons(
    +        data = pop_region_4326,
    +        fillColor = ~ pal(total_pop),
    +        weight = 1,
    +        color = "black",
    +        fillOpacity = 1,
    +        label = ~ region
    +    ) %>%
    +    addLegend(
    +        position = "bottomleft",
    +        pal = pal,
    +        values = range(pop_region_4326$total_pop),
    +        title = "Total Population"
    +    )
    +
    +m
    +
    +
    + +

    We can also add the individual communities, with popup labels showing their population, on top of that!

    -
    pal <- colorNumeric(palette = "Reds", domain = pop_region_4326$total_pop)
    -
    -m <- leaflet(options = leafletOptions(crs = epsg3338)) %>%
    -    addPolygons(
    -        data = pop_region_4326,
    -        fillColor = ~ pal(total_pop),
    -        weight = 1,
    -        color = "black",
    -        fillOpacity = 1
    -    ) %>%
    -    addCircleMarkers(
    -        data = pop_4326,
    -        lat = ~ lat,
    -        lng = ~ lng,
    -        radius = ~ log(population / 500),
    -        # arbitrary scaling
    -        fillColor = "gray",
    -        fillOpacity = 1,
    -        weight = 0.25,
    -        color = "black",
    -        label = ~ paste0(pop_4326$city, ", population ", comma(pop_4326$population))
    -    ) %>%
    -    addLegend(
    -        position = "bottomleft",
    -        pal = pal,
    -        values = range(pop_region_4326$total_pop),
    -        title = "Total Population"
    -    )
    -
    -m
    +
    pal <- colorNumeric(palette = "Reds", domain = pop_region_4326$total_pop)
    +
    +m <- leaflet(options = leafletOptions(crs = epsg3338)) %>%
    +    addPolygons(
    +        data = pop_region_4326,
    +        fillColor = ~ pal(total_pop),
    +        weight = 1,
    +        color = "black",
    +        fillOpacity = 1
    +    ) %>%
    +    addCircleMarkers(
    +        data = pop_4326,
    +        lat = ~ lat,
    +        lng = ~ lng,
    +        radius = ~ log(population / 500),
    +        # arbitrary scaling
    +        fillColor = "gray",
    +        fillOpacity = 1,
    +        weight = 0.25,
    +        color = "black",
    +        label = ~ paste0(pop_4326$city, ", population ", comma(pop_4326$population))
    +    ) %>%
    +    addLegend(
    +        position = "bottomleft",
    +        pal = pal,
    +        values = range(pop_region_4326$total_pop),
    +        title = "Total Population"
    +    )
    +
    +m
    +
    +
    + +
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