22 Advanced Spatial Visualization

Today we will be focusing on the theory and practice of fancy geospatial data visualization.

22.1 Visual Categories and Encodings

Let’s go back to the beginning of this course. There are 3 categories of information that can be displayed.

Quantitative
Qualitative
Spatial

The three types of data can be encoded in:

Geometric primitives - points, lines, and areas
Visual channels - size, color, shape, position, angle, and texture

An advanced spatial visualization covering multiple layers of information needs to use multiple sets of encodings to convey information quickly and intuitively while not overwhelming the audience.

22.2 Circles, Lines, and Polygons - Oh My!

Fancy maps need distinct visual encodings, so the eye can be drawn the salient features.

One key way to do this is through ensuring different types/styles/aesthetics are displayed as unique fingerprints of visual encodings.

Another way to do this is through small multiples figures, as we showed in #fig-facetEJMap. However, that was a facet_wrap() done on a static map. It is also possible to do small multiples with leaflet maps.

First, get all the libraries we need loaded up.

library(tidyverse)
library(leaflet)
library(sf)

Now, we’ll need a new library called leafsync.

First, install it.

install.packages('leafsync')

Then load the library.

library(leafsync)

22.2.1 Example 1 - SoCalEJ multiples

Let’s test this out with just Orange County data from SoCalEJ - our robust and useful testing dataset.

First, we pull the data, if it isn’t already loaded.

URL.path <- 'https://raw.githubusercontent.com/RadicalResearchLLC/EDVcourse/main/CalEJ4/CalEJ.geoJSON'
OCEJ <- st_read(URL.path) |>  
  st_transform("+proj=longlat +ellps=WGS84 +datum=WGS84") |> 
  filter(County == 'Orange') |> 
  select(White, Hispanic, AAPI, geometry)

Reading layer `CalEJ' from data source 
  `https://raw.githubusercontent.com/RadicalResearchLLC/EDVcourse/main/CalEJ4/CalEJ.geoJSON' 
  using driver `GeoJSON'
Simple feature collection with 3747 features and 66 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 97418.38 ymin: -577885.1 xmax: 539719.6 ymax: -236300
Projected CRS: NAD83 / California Albers

Let’s create two figures and save them as individual maps before displaying them using leafsync. Let’s show White, Asian, and Hispanic populations for Orange County.

First, we create individual maps. The template is shown below for the White population in #fig-White

palW <- colorNumeric(palette = 'GnBu', 
                     domain = OCEJ$White)

leaflet() |> 
  addTiles() |> 
  addPolygons(data = OCEJ,
              color = ~palW(White),
              fillOpacity = 0.8,
              weight = 1) |> 
  addLegend(data = OCEJ,
            pal = palW,
            values = ~White,
            title = '% White')

Figure 22.1: Percentage of residents by census tract identified as White.

Ok, the map is straightforward. To make a synced multiples map, we have to do three steps.

Make a map of each type we want to show.
Assign the map to a named variable. In this case, we’ll call the maps white, hispanic, and aapi and assign the leaflet maps to those variables using the <- operator.
Use the sync() function from leafsync to show them.

The code for steps 1 and 2 are just slight variations on the white map we showed above, repeated three times.

Figure 22.2 shows the end result.

palW <- colorNumeric(palette = 'RdPu', 
                     domain = OCEJ$White)
palH <- colorNumeric(palette = 'RdPu',
                     domain = OCEJ$Hispanic)
palA <- colorNumeric(palette = 'RdPu',
                     domain = OCEJ$AAPI)
white <- 
  leaflet() |> 
  addTiles() |> 
  addPolygons(data = OCEJ,
              color = ~palW(White),
              fillOpacity = 0.8,
              weight = 1) |> 
  addLegend(data = OCEJ,
            pal = palW,
            values = ~White,
            title = '% White')

hispanic <- leaflet() |> 
  addTiles() |> 
  addPolygons(data = OCEJ,
              color = ~palH(Hispanic),
              fillOpacity = 0.8,
              weight = 1) |> 
  addLegend(data = OCEJ,
            pal = palH,
            values = ~Hispanic,
            title = '% Hispanic')

aapi <- 
  leaflet() |> 
  addTiles() |> 
  addPolygons(data = OCEJ,
              color = ~palA(AAPI),
              fillOpacity = 0.8,
              weight = 1) |> 
  addLegend(data = OCEJ,
            pal = palA,
            values = ~AAPI,
            title = '% AAPI')

## This shows all three together
sync(white, hispanic, aapi)

Figure 22.2: leaflet maps

The cool thing about sync is it zooms in and out as a combination, so if they are all on the same scale, we can really see some differences.