Figure 5.10 Simple regionalization of San Francisco Bay Area

figures

code

This page produces a coloured version of Figure 5.10 which is easier to read (but may not show the same clusters).

Code

library(tidyr)
library(dplyr)
library(sf)
library(ggplot2)
library(ggpattern)
library(cols4all)

Data

The data are as used in Luc Guillemot’s website example.

Code

sf_demog <- st_read("sf_clustering_data.gpkg")

Data cleanup

There are some NA data which we will show as hatched areas using the ggpattern package so some work is required on that.

Code

nas <- sf_demog |>
  st_union() |>
  st_difference(sf_demog |> 
  drop_na() |> 
  st_union())

Run the clustering

k-means clustering in R using the base kmeans function is straightforward, but requires a non-spatial version of the dataset. We also remove the ID columns and the cluster assignment from the data (since we don’t want to do clustering and include a variable that already designates a cluster).

Code

sf_demog <- sf_demog |>
  drop_na()

sf_demog.d <- sf_demog |>
  st_drop_geometry() |>
  select(-(1:2), -cluster)

km <- kmeans(sf_demog.d, centers = 9)

Next we append the cluster result to the original spatial dataset so we can map it.

Code

sf_demog$k9 <- km$cluster |> as.factor()

ggplot(nas) + 
  geom_sf_pattern(fill = "white", lwd = 0, 
                  pattern_fill = "black", pattern_density = 0.04, 
                  pattern_angle = 45, pattern_spacing = 0.012) + 
  geom_sf(data = sf_demog, aes(fill = k9)) +
  scale_fill_brewer(palette = "Paired") +
  guides(fill = "none") +
  coord_sf(xlim = c(1.82e6, 1.87e6), ylim = c(6.3e5, 6.6e5)) +
  theme_void() +
  theme(panel.background = element_rect(fill = "#bbddff"))

And for comparison, below is the regionalization from Luc Guillemot’s more complex clustering method. The colours don’t match up (they are just colours!) but you can see that the overall regionalisation result is broadly similar, even though we have requested only 9 clusters from the k-means analysis.

Code

ggplot(nas) + 
  geom_sf_pattern(fill = "white", lwd = 0, 
                  pattern_fill = "black", pattern_density = 0.04, 
                  pattern_angle = 45, pattern_spacing = 0.012) + 
  geom_sf(data = sf_demog, aes(fill = as.factor(cluster))) +
  scale_fill_discrete_c4a_cat(palette = "20") +
  guides(fill = "none") +
  coord_sf(xlim = c(1.82e6, 1.87e6), ylim = c(6.3e5, 6.6e5)) +
  theme_void() +
  theme(panel.background = element_rect(fill = "#bbddff"))

Code

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