class: center, middle, inverse, title-slide # Spatial data & visualization ## <br> <br> Introduction to Data Science ### <a href="https://www.introds.org/">introds.org</a> --- ## 1854 London Cholera Outbreak <img src="img/07/cholera.png" width="50%" style="display: block; margin: auto;" /> --- ## 2013 - 2018 West Nile virus spread <img src="img/07/nextstrain_WNV.png" width="65%" style="display: block; margin: auto;" /> - Map generated from [nextstrain.org](https://nextstrain.org/) -- **Many others!** - [Migrations](http://maps.tnc.org/migrations-in-motion/#4/43.26/-112.02) - [World Population Density](https://luminocity3d.org/WorldPopDen/#3/12.00/10.00) --- ## Spatial data is different Our typical tidy data frame: .midi[ ``` ## # A tibble: 336,776 × 19 ## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time ## <int> <int> <int> <int> <int> <dbl> <int> <int> ## 1 2013 1 1 517 515 2 830 819 ## 2 2013 1 1 533 529 4 850 830 ## 3 2013 1 1 542 540 2 923 850 ## 4 2013 1 1 544 545 -1 1004 1022 ## 5 2013 1 1 554 600 -6 812 837 ## 6 2013 1 1 554 558 -4 740 728 ## 7 2013 1 1 555 600 -5 913 854 ## 8 2013 1 1 557 600 -3 709 723 ## 9 2013 1 1 557 600 -3 838 846 ## 10 2013 1 1 558 600 -2 753 745 ## # … with 336,766 more rows, and 11 more variables: arr_delay <dbl>, ## # carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>, ## # air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm> ``` ] --- ## Spatial data is different Our (new) simple feature object: .midi[ ``` ## Simple feature collection with 100 features and 3 fields ## Geometry type: MULTIPOLYGON ## Dimension: XY ## Bounding box: xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965 ## Geodetic CRS: NAD27 ## First 10 features: ## name regstrd voted geometry ## 1 ASHE 19414 8428 MULTIPOLYGON (((-81.47276 3... ## 2 ALLEGHANY 7556 4101 MULTIPOLYGON (((-81.23989 3... ## 3 SURRY 46666 23660 MULTIPOLYGON (((-80.45634 3... ## 4 CURRITUCK 21803 7536 MULTIPOLYGON (((-76.00897 3... ## 5 NORTHAMPTON 13891 6196 MULTIPOLYGON (((-77.21767 3... ## 6 HERTFORD 14945 6955 MULTIPOLYGON (((-76.74506 3... ## 7 CAMDEN 8128 3472 MULTIPOLYGON (((-76.00897 3... ## 8 GATES 8294 3105 MULTIPOLYGON (((-76.56251 3... ## 9 WARREN 13441 6878 MULTIPOLYGON (((-78.30876 3... ## 10 STOKES 31649 14444 MULTIPOLYGON (((-80.02567 3... ``` ] --- ## Raster versus vector spatial data .vocab[Vector] spatial data describes the world using shapes (points, lines, polygons, etc). <br> .vocab[Raster] spatial data describes the world using cells of constant size. <img src="img/07/vector_raster_comparison.png" width="35%" style="display: block; margin: auto;" /> The choice to use vector or raster data depends on the problem context. We will focus on vector data. <br> .small[*Source:* https://commons.wikimedia.org/wiki/File:Raster_vector_tikz.png] --- ## Simple features A .vocab[simple feature] is a standard way to describe how real-world spatial objects (country, building, tree, road, etc) can be represented by a computer. -- The package `sf` implements simple features and other spatial functionality using **tidy** principles. --- ## Simple features Simple features have a geometry type. Common choices are below. <img src="spatial_files/figure-html/unnamed-chunk-8-1.png" style="display: block; margin: auto;" /> --- ## A simple feature object - Simple features are stored in a data frame, with the geographic information in a column called `geometry`. - Simple features can contain both spatial and non-spatial data. - Functions for spatial data in `sf` begin `st_`. --- class: center, middle # Visualizing spatial data --- ## `nc_votes` This data is from the [North Carolina Early Voting Statistics](https://electproject.github.io/Early-Vote-2020G/NC.html) website, October 2020. The dataset contains the following variables: - `name`: county name - `regstrd`: number of registered voters - `voted`: number of individuals who have voted - `mailed`: number of mail ballots returned - `rejectd`: number of mail ballots rejected - `ml_rqst`: number of mail ballots requested --- ## Getting `sf` objects To read simple features from a file or database use the function `st_read()`. .small[ ```r library(sf) *nc <- st_read("data/nc_votes.shp", quiet = TRUE) nc ``` ``` ## Simple feature collection with 100 features and 6 fields ## Geometry type: MULTIPOLYGON ## Dimension: XY ## Bounding box: xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965 ## Geodetic CRS: NAD27 ## First 10 features: ## name regstrd voted mailed rejectd ml_rqst ## 1 ASHE 19414 8428 NA NA 2666 ## 2 ALLEGHANY 7556 4101 NA NA 971 ## 3 SURRY 46666 23660 4366 7 7088 ## 4 CURRITUCK 21803 7536 NA NA 2472 ## 5 NORTHAMPTON 13891 6196 828 2 1441 ## 6 HERTFORD 14945 6955 NA NA 1524 ## 7 CAMDEN 8128 3472 416 1 739 ## 8 GATES 8294 3105 NA NA 847 ## 9 WARREN 13441 6878 NA NA 1913 ## 10 STOKES 31649 14444 2162 2 3648 ## geometry ## 1 MULTIPOLYGON (((-81.47276 3... ## 2 MULTIPOLYGON (((-81.23989 3... ## 3 MULTIPOLYGON (((-80.45634 3... ## 4 MULTIPOLYGON (((-76.00897 3... ## 5 MULTIPOLYGON (((-77.21767 3... ## 6 MULTIPOLYGON (((-76.74506 3... ## 7 MULTIPOLYGON (((-76.00897 3... ## 8 MULTIPOLYGON (((-76.56251 3... ## 9 MULTIPOLYGON (((-78.30876 3... ## 10 MULTIPOLYGON (((-80.02567 3... ``` ] --- ## Plotting with `ggplot()` ```r ggplot(nc) + geom_sf() + labs(title = "North Carolina counties") ``` <img src="spatial_files/figure-html/unnamed-chunk-10-1.png" style="display: block; margin: auto;" /> --- ## A look at some aesthetics ```r ggplot(nc) + * geom_sf(color = "green") + labs(title = "North Carolina counties with theme and aesthetics") ``` <img src="spatial_files/figure-html/unnamed-chunk-11-1.png" style="display: block; margin: auto;" /> --- ## A look at some aesthetics ```r ggplot(nc) + * geom_sf(color = "green", size = 1.5) + labs(title = "North Carolina counties with theme and aesthetics") ``` <img src="spatial_files/figure-html/unnamed-chunk-12-1.png" style="display: block; margin: auto;" /> --- ## A look at some aesthetics ```r ggplot(nc) + * geom_sf(color = "green", size = 1.5, fill = "purple") + labs(title = "North Carolina counties with theme and aesthetics") ``` <img src="spatial_files/figure-html/unnamed-chunk-13-1.png" style="display: block; margin: auto;" /> --- ## A look at some aesthetics ```r ggplot(nc) + * geom_sf(color = "green", size = 1.5, fill = "purple", alpha = 0.50) + labs(title = "North Carolina counties with theme and aesthetics") ``` <img src="spatial_files/figure-html/unnamed-chunk-14-1.png" style="display: block; margin: auto;" /> --- ## A look back at some of our data .small[ ``` ## Simple feature collection with 100 features and 6 fields ## Geometry type: MULTIPOLYGON ## Dimension: XY ## Bounding box: xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965 ## Geodetic CRS: NAD27 ## First 10 features: ## name regstrd voted mailed rejectd ml_rqst ## 1 ASHE 19414 8428 NA NA 2666 ## 2 ALLEGHANY 7556 4101 NA NA 971 ## 3 SURRY 46666 23660 4366 7 7088 ## 4 CURRITUCK 21803 7536 NA NA 2472 ## 5 NORTHAMPTON 13891 6196 828 2 1441 ## 6 HERTFORD 14945 6955 NA NA 1524 ## 7 CAMDEN 8128 3472 416 1 739 ## 8 GATES 8294 3105 NA NA 847 ## 9 WARREN 13441 6878 NA NA 1913 ## 10 STOKES 31649 14444 2162 2 3648 ## geometry ## 1 MULTIPOLYGON (((-81.47276 3... ## 2 MULTIPOLYGON (((-81.23989 3... ## 3 MULTIPOLYGON (((-80.45634 3... ## 4 MULTIPOLYGON (((-76.00897 3... ## 5 MULTIPOLYGON (((-77.21767 3... ## 6 MULTIPOLYGON (((-76.74506 3... ## 7 MULTIPOLYGON (((-76.00897 3... ## 8 MULTIPOLYGON (((-76.56251 3... ## 9 MULTIPOLYGON (((-78.30876 3... ## 10 MULTIPOLYGON (((-80.02567 3... ``` ] Let's incorporate these variables into our plot using `ggplot`. --- ## Choropleth map .panelset[ .panel[.panel-name[Plot] <img src="spatial_files/figure-html/unnamed-chunk-16-1.png" width="100%" style="display: block; margin: auto;" /> ] .panel[.panel-name[Code] ```r ggplot(nc) + * geom_sf(aes(fill = voted)) + labs(title = "Higher population counties have more votes cast", fill = "Total votes cast") ``` ] ] It is sometimes helpful to pick diverging colors, [colorbrewer2](http://colorbrewer2.org/#type=sequential&scheme=BuGn&n=3) can help. --- ## Choropleth map One way to set fill colors is with `scale_fill_gradient()`. .panelset[ .panel[.panel-name[Plot] <img src="spatial_files/figure-html/unnamed-chunk-17-1.png" width="100%" style="display: block; margin: auto;" /> ] .panel[.panel-name[Code] ```r ggplot(nc) + geom_sf(aes(fill = voted)) + * scale_fill_gradient(low = "#fee8c8", high = "#7f0000") + labs(title = "Higher population counties have more votes cast", fill = "Total votes cast") ``` ] ] --- ## "...it's just a population map!" <img src="spatial_files/figure-html/unnamed-chunk-18-1.png" style="display: block; margin: auto;" /> --- ## Let's make it more informative .panelset[ .panel[.panel-name[Plot] <img src="spatial_files/figure-html/unnamed-chunk-19-1.png" width="100%" style="display: block; margin: auto;" /> ] .panel[.panel-name[Code] ```r ggplot(nc) + * geom_sf(aes(fill = voted/regstrd)) + scale_fill_gradient(low = "#fff7f3", high = "#49006a") + labs(fill = "Votes cast per registered voter", title = "Early vote turnout varies by county") ``` ] ] --- class: center, middle # Map layers --- ## Game Lands data The North Carolina Department of Environment and Natural Resources, Wildlife Resources Commission and the NC Center for Geographic Information and Analysis has a [shapefile data set](https://digital.ncdcr.gov/digital/collection/p15012coll6/id/425) available on all public Game Lands in NC. ```r nc_game <- st_read("data/gamelands.shp", quiet = TRUE) ``` --- ## A closer look .small[ ```r nc_game ``` ``` ## Simple feature collection with 94 features and 6 fields ## Geometry type: MULTIPOLYGON ## Dimension: XY ## Bounding box: xmin: -84.29534 ymin: 33.98542 xmax: -75.54947 ymax: 36.58814 ## Geodetic CRS: NAD27 ## First 10 features: ## OBJECTID GML_HAB SUM_ACRES GameLandID Shape__Are ## 1 1 Alcoa 11395.9471 1 69931121 ## 2 2 Alligator River 24439.0891 2 151120825 ## 3 3 Angola Bay 34063.4468 3 204400526 ## 4 4 Bachelor Bay 2786.2577 4 17219484 ## 5 5 Bertie County 3883.7683 5 24044312 ## 6 6 Bladen Lakes State Forest 33671.8426 6 202085696 ## 7 7 Brinkleyville 1843.8439 92 11511489 ## 8 8 Buckhorn 491.3477 81 3046371 ## 9 9 Buckridge 17965.7187 10 110580903 ## 10 10 Buffalo Cove 6630.9453 11 41161465 ## Shape__Len geometry ## 1 549030.42 MULTIPOLYGON (((-80.07347 3... ## 2 186792.83 MULTIPOLYGON (((-76.11832 3... ## 3 105421.80 MULTIPOLYGON (((-77.86947 3... ## 4 32891.84 MULTIPOLYGON (((-76.73896 3... ## 5 83468.94 MULTIPOLYGON (((-76.9209 35... ## 6 255198.44 MULTIPOLYGON (((-78.46171 3... ## 7 46838.19 MULTIPOLYGON (((-77.90555 3... ## 8 13445.00 MULTIPOLYGON (((-79.22056 3... ## 9 142923.83 MULTIPOLYGON (((-76.10961 3... ## 10 98754.34 MULTIPOLYGON (((-81.53307 3... ``` ] --- ## Visualize `nc_game` ```r ggplot(nc_game) + geom_sf() + labs(title = "North Carolina gamelands") ``` <img src="spatial_files/figure-html/unnamed-chunk-22-1.png" style="display: block; margin: auto;" /> --- ## Visualize `nc_game` ```r ggplot(nc_game) + geom_sf(fill = "#ff6700") + labs(title = "North Carolina gamelands") ``` <img src="spatial_files/figure-html/unnamed-chunk-23-1.png" style="display: block; margin: auto;" /> --- ## Add layers .midi[ ```r ggplot(nc) + geom_sf() + geom_sf(data = nc_game, fill = "#ff6700", alpha = .5) + labs(title = "North Carolina gamelands and counties") ``` <img src="spatial_files/figure-html/unnamed-chunk-24-1.png" style="display: block; margin: auto;" /> ] --- ## Add layers and aesthetics .midi[ ```r ggplot(nc) + geom_sf() + geom_sf(data = nc_game, aes(alpha = SUM_ACRES), fill = "#ff6700") + labs(title = "North Carolina gamelands and counties") ``` <img src="spatial_files/figure-html/unnamed-chunk-25-1.png" style="display: block; margin: auto;" /> ] --- class: center, middle # Spatial challenges --- ## Challenge #1 Different types of data exist (raster and vector). --- ## Challenge #2 The coordinate reference system (CRS) matters. ```r Simple feature collection with 100 features and 1 field geometry type: MULTIPOLYGON dimension: XY bbox: xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965 *epsg (SRID): 4326 *proj4string: +proj=longlat +datum=WGS84 +no_defs # A tibble: 100 x 2 NAME geometry <chr> <MULTIPOLYGON [°]> 1 Ashe (((-81.47276 36.23436, -81.54084 36.27251, -... ``` --- ## Challenge #3 Manipulating spatial data objects is similar, but not identical to manipulating data frames. Note the core data-wrangling functions from `dplyr` do work. --- ## References - [North Carolina Early Voting Statistics](https://electproject.github.io/Early-Vote-2020G/NC.html) - [Simple Features for R vignette](https://r-spatial.github.io/sf/) - [mapview vignette](https://r-spatial.github.io/mapview/index.html) - [Coordinate Reference Systems in R](https://www.nceas.ucsb.edu/~frazier/RSpatialGuides/OverviewCoordinateReferenceSystems.pdf) - [Geocomputation with R](https://geocompr.robinlovelace.net/spatial-class.html)