4  Introduction to Spatial Visualization 2

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

Once again, my preferred framework for the workflow of data visualization is shown in Figure 25.1

Figure 4.1: Tidyverse data visualization framework

4.1 Load and Install Packages

Load ggplot2 and sf packages. Today we will be making static maps in ggplot2 which is part of the tidyverse ecosystem. We need the sf package to load, transform, and display geospatial data.

Linking to GEOS 3.12.1, GDAL 3.8.4, PROJ 9.3.1; sf_use_s2() is TRUE

4.2 Acquire Data

Import the nc and SoCalEJ datasets again. Refer to Chapter 3 for details on functions.

nc <- st_read(system.file("shape/nc.shp", package="sf")) |> 
  st_transform("+proj=longlat +ellps=WGS84 +datum=WGS84")
Reading layer `nc' from data source 
  `C:\Users\MichaelMcCarthy\AppData\Local\R\cache\R\renv\cache\v5\R-4.4\x86_64-w64-mingw32\sf\1.0-16\ad57b543f7c3fca05213ba78ff63df9b\sf\shape\nc.shp' 
  using driver `ESRI Shapefile'
Simple feature collection with 100 features and 14 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965
Geodetic CRS:  NAD27
tail(nc)
Simple feature collection with 6 features and 14 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -79.07426 ymin: 33.88212 xmax: -76.28737 ymax: 35.01676
Geodetic CRS:  +proj=longlat +ellps=WGS84 +datum=WGS84
     AREA PERIMETER CNTY_ CNTY_ID        NAME  FIPS FIPSNO CRESS_ID BIR74 SID74
95  0.125     2.868  2156    2156    Carteret 37031  37031       16  2414     5
96  0.225     2.107  2162    2162      Bladen 37017  37017        9  1782     8
97  0.214     2.152  2185    2185      Pender 37141  37141       71  1228     4
98  0.240     2.365  2232    2232    Columbus 37047  37047       24  3350    15
99  0.042     0.999  2238    2238 New Hanover 37129  37129       65  5526    12
100 0.212     2.024  2241    2241   Brunswick 37019  37019       10  2181     5
    NWBIR74 BIR79 SID79 NWBIR79                       geometry
95      341  3339     4     487 MULTIPOLYGON (((-77.14865 3...
96      818  2052     5    1023 MULTIPOLYGON (((-78.26123 3...
97      580  1602     3     763 MULTIPOLYGON (((-78.02565 3...
98     1431  4144    17    1832 MULTIPOLYGON (((-78.65546 3...
99     1633  6917     9    2100 MULTIPOLYGON (((-77.96045 3...
100     659  2655     6     841 MULTIPOLYGON (((-78.65546 3...
URL.path <- 'https://raw.githubusercontent.com/RadicalResearchLLC/EDVcourse/main/CalEJ4/CalEJ.geoJSON'
SoCalEJ <- st_read(URL.path) |> 
  st_transform("+proj=longlat +ellps=WGS84 +datum=WGS84")
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
tail(SoCalEJ)
Simple feature collection with 6 features and 66 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -118.2062 ymin: 33.96317 xmax: -117.9014 ymax: 34.16102
Geodetic CRS:  +proj=longlat +ellps=WGS84 +datum=WGS84
          Tract   ZIP      County    ApproxLoc TotPop19  CIscore CIscoreP
3742 6037400602 91702 Los Angeles        Azusa     4250 32.63778 63.50227
3743 6037430302 91016 Los Angeles     Monrovia     5339 17.12483 30.61019
3744 6037430723 91007 Los Angeles      Arcadia     4365 13.84199 22.56682
3745 6037431100 91016 Los Angeles     Monrovia     6758 39.69785 74.50832
3746 6037533603 90201 Los Angeles         Bell     6986 62.93104 97.04992
3747 6037534101 90201 Los Angeles Bell Gardens     2358 63.31505 97.22642
          Ozone   OzoneP    PM2_5  PM2_5_P   DieselPM DieselPM_P Pesticide
3742 0.06236471 88.69944 11.32762 58.23273 0.25141035   70.16801   0.00000
3743 0.06236471 88.69944 11.87334 72.10952 0.03484052   12.43311   0.00000
3744 0.05938691 79.98755 11.81607 70.24269 0.05856793   21.30678   0.00000
3745 0.06133785 84.57996 11.89265 72.90604 0.52993389   90.91475   0.00000
3746 0.04632538 46.99440 12.01973 79.45240 0.11636699   42.26509   0.00000
3747 0.04716458 50.54138 12.02588 79.77598 0.89400714   97.51089  25.17289
     PesticideP  Tox_Rel Tox_Rel_P   Traffic TrafficP DrinkWat DrinkWatP
3742    0.00000 1128.208  66.72918  515.5913  22.0875 365.8082  37.91682
3743    0.00000 1235.359  69.21730  471.5739  18.7625 570.8214  66.86649
3744    0.00000 1324.937  70.43011 1003.0337  57.9875 667.6980  73.14850
3745    0.00000 1575.716  74.35609 1560.1642  79.2625 497.7552  60.33471
3746    0.00000 4435.870  91.04776 1071.6749  62.4000 639.0632  71.36256
3747   67.58621 4409.633  90.89772 3653.6009  98.0750 514.1607  61.83340
         Lead   Lead_P Cleanup  CleanupP GWThreat GWThreatP HazWaste HazWasteP
3742 59.07638 64.54946    3.00 31.243001    46.85  89.10842    0.460  66.60490
3743 71.04484 80.34026    1.40 19.914147     1.80  11.30666    0.320  52.64064
3744 50.87304 52.71582    0.40  5.636432     5.00  30.88162    0.150  26.67108
3745 54.59725 57.99622   43.50 94.438223    36.75  84.14411    1.885  92.15089
3746 87.11610 94.82042   14.65 74.300112    17.05  63.42354    0.375  58.80874
3747 90.85649 97.39130   20.35 82.232176    15.00  59.60485    1.110  86.60490
     ImpWatBod ImpWatBodP SolWaste SolWasteP PollBurd PolBurdSc PolBurdP Asthma
3742         0    0.00000     0.00  0.000000 46.49568  5.677114 60.95831  71.92
3743         3   33.15834     0.25 11.592211 42.60985  5.202654 50.36714  48.33
3744         0    0.00000     0.00  0.000000 39.69752  4.847059 42.02862  18.84
3745         2   23.87652     8.75 87.949599 68.86648  8.408583 97.88426  46.90
3746         7   66.73667     2.00 52.898053 61.77303  7.542474 93.03049  54.25
3747         7   66.73667     0.20  9.667812 73.95742  9.030186 99.39017  52.71
       AsthmaP LowBirtWt  LowBirWP Cardiovas CardiovasP Educatn  EducatP
3742 79.847956      5.09 54.849885     17.02   76.34596    14.1 53.40420
3743 53.950648      2.72  6.209905     11.22   39.70588     6.4 27.61326
3744  7.614656      3.48 15.871183      7.73   11.83948     4.5 18.80537
3745 51.856929      3.91 24.570182     10.87   36.88933    16.0 57.61832
3746 62.076271      5.87 72.478830     23.93   96.68495    42.6 91.88813
3747 59.895314      2.87  7.775212     23.15   95.45115    46.0 94.25462
     Ling_Isol Ling_IsolP Poverty PovertyP Unempl   UnemplP HousBurd HousBurdP
3742       5.6   40.89863    24.0 42.61307    2.9 15.838105     17.1  49.72117
3743       7.0   48.72107    18.9 31.73367    1.2  1.900052     15.4  40.89987
3744      13.6   73.09514    23.2 41.03015    4.0 30.882353     17.7  52.61090
3745      10.1   62.24117    32.2 57.29899    6.2 59.383134     13.3  29.72117
3746      21.7   88.69942    59.9 91.55779    9.1 82.326913     20.4  64.46134
3747      11.7   67.72229    56.1 88.31658    9.3 83.224883     22.0  70.53232
      PopChar PopCharSc PopCharP Child_10 Pop_10_64 Elderly65 Hispanic   White
3742 55.42148  5.749009 58.49723  13.3882   74.3294   12.2824  68.1412 20.7294
3743 31.73120  3.291557 22.33989   9.7584   72.7664   17.4752  28.7132 53.3995
3744 27.52994  2.855750 16.50277  12.0504   77.4570   10.4926  10.9507 26.3918
3745 45.51235  4.721110 43.58296  11.2607   81.4442    7.2951  58.2273 16.1438
3746 80.43337  8.343554 92.93999  18.4226   72.1586    9.4188  91.4114  6.9425
3747 67.59201  7.011489 76.72718  14.8431   75.0636   10.0933  91.0941  1.3147
     AfricanAm NativeAm OtherMult Shape_Leng Shape_Area    AAPI
3742    2.0941   0.3529    0.8941   6661.381  1537875.2  7.7882
3743    1.5733   0.0000    7.1549   7166.131  1938015.8  9.1590
3744    3.3677   0.0000    3.3677   3941.782   485563.0 55.9221
3745    8.9967   0.0000    1.1098   8020.091  3015660.7 15.5223
3746    0.6728   0.2577    0.7157   4949.117   811895.5  0.0000
3747    1.9084   0.0000    0.0000   4420.127   509871.8  5.6828
                           geometry
3742 MULTIPOLYGON (((-117.9024 3...
3743 MULTIPOLYGON (((-117.9917 3...
3744 MULTIPOLYGON (((-118.0496 3...
3745 MULTIPOLYGON (((-117.9987 3...
3746 MULTIPOLYGON (((-118.1874 3...
3747 MULTIPOLYGON (((-118.1636 3...

4.2.1 Create a Locations Table

I want to show the location of this classroom and my neighborhood. Let’s add locations again.

lat <- c(34.1100576, 33.8895145)
lng <- c(-117.710074, -117.319014)

locations <- data.frame(lat, lng) 
locations
       lat       lng
1 34.11006 -117.7101
2 33.88951 -117.3190

4.3 Visualize the Data - Geospatial ggplot Edition

While the tidyverse doesn’t have all the features of leaflet, it can be a quick way to visualize geospatial data for static maps and there are times when adding a static ggplot map is sufficient and actually preferred to the more detailed leaflet maps.

Let’s do a few example ggplot maps.

4.3.1 Visualization functions

4.3.2 Make a basic visualization

Start with the North Carolina data and make a basic ggplot and geom_sf map as shown in Figure 4.2.

ggplot(data = nc) + 
  geom_sf()
Figure 4.2: ggplot map for North Carolina counties

Note that ggplot uses + rather than the magrittr |> for connecting lines of code.

In contrast to the leaflet map, the ggplot defaults to showing the x- and y-axis coordinates (latitude and longitude), shows guidelines, and only draws the counties in the dataset, rather than defaulting to showing an interactive map.

Figure 4.3 shows the same style of map replacing nc with SoCalEJ.

ggplot() + 
  geom_sf(data = SoCalEJ)
Figure 4.3: ggplot map for census tracts in Inland SoCal counties

4.3.3 Improve the Visualization

We have many options to improve a ggplot visualization. Let’s start by cleaning up the background using theme_bw(). theme_bw() changes the background from gray to a cleaner black-white style as shown in Figure 4.4.

ggplot() + 
  geom_sf(data = nc) +
  theme_bw()
Figure 4.4: ggplot map for North Carolina counties using theme_bw()

We can apply a minimalist aesthetic by choosing theme_minimal() as shown in Figure 4.5

ggplot() + 
  geom_sf(data = nc) +
  theme_minimal()
Figure 4.5: ggplot map for North Carolina counties using theme_bw()

Let’s add colors in a ggplot way.

Use aes(fill = <VARIABLE NAME>) to assign a category to color the counties by. The color palette to fill with is selected in scale_fill_<TYPE> where TYPE can be any of the following categories

  • binned
  • brewer
  • continuous
  • date or datetime
  • discrete
  • fermenter
  • viridis

First let’s use the default palette for BIR79 to show the county birthrates in 1979. Adding fill = BIR79 to the aes() defaults to the Blues palette. Figure 4.6 shows the result of adding a fill color scale.

ggplot() + 
  geom_sf(data = nc, aes(fill = BIR79)) +
  theme_bw()
Figure 4.6: ggplot map for North Carolina counties using theme_bw() with fill

Let’s change that to a viridis color scale. The function scale_fill_viridis_c() adds a fancier color-blind viridis palette in a continuous scale. Figure 4.7 shows this color scale option.

ggplot() + 
  geom_sf(data = nc, aes(fill = BIR79)) +
  theme_bw() +
  scale_fill_viridis_c()
Figure 4.7: ggplot map for North Carolina counties using theme_bw() with viridis

We can also add other geoms, like points or labels to this map. Let’s try to label the counties.

The nc dataset has a variable called NAME for the county names. Figure 4.8 shows the figure when we add the county names using the function geom_sf_text().

ggplot(data = nc) + 
  geom_sf(aes(fill = BIR79)) +
  geom_sf_text(aes(label = NAME), size = 1.5, color = 'white') +
  theme_bw() +
  scale_fill_viridis_c()
Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
give correct results for longitude/latitude data
Figure 4.8: ggplot map for North Carolina counties with names overlaid

There is a lot going on in that function. I made the text white color = ‘white’, the size of the font 1.5 size = 1.5, and added the label aesthetic with aes(label = NAME). If you remove the size or the color, you can see why those alterations were made.

4.3.3.1 Exercise - Improve the SoCalEJ Visualization

  1. Add a different theme from one of the theme options
  2. Show a variable (categorical, continuous, or quantile) using a fill option.
  3. Add two or more SoCal locations to the map using geom_point and your locations table. If that is easy, try increasing the salience of the points through size, color, or shape modifications to that layer.

Figure 4.9 shows a potential example of what that might look like.

Figure 4.9: ggplot map for SoCal Diesel PM Percentile

It is really hard to see the details here. Let’s learn one last trick to zoom in on a ggplot to adjust the axes. The scale_x_continuous() and scale_y_continuous() functions allow us to set different axis limits. Figure 4.10 shows the

ggplot() + 
  geom_sf(data = SoCalEJ, aes(fill = DieselPM_P)) +
  geom_point(data = locations,  aes(x = lng, y = lat), color = 'orange') +
  theme_bw() +
  scale_fill_viridis_c(option = 'A', direction = -1) +
  scale_x_continuous(limits = c(-118, -117)) +
  scale_y_continuous(limits = c(33.7, 34.2))
Figure 4.10: ggplot map for SoCal Diesel PM Percentile zoomed into Claremont and Ontario.

4.3.3.2 Noodle Zone

Only includes nc data at this point. Can add SoCalEJ if needed.