tidycensus_idw_raster.R

library(mapboxapi)
library(tidycensus)
library(tidyverse)
library(sf)
library(ggspatial)
options(tigris_use_cache = TRUE)

# Get the data from the ACS
tarrant <- get_acs(
  geography = "tract",
  variables = "B25077_001",
  state = "TX",
  county = "Tarrant",
  geometry = TRUE
)

# Convert to centroids and omit NAs
tarrant_pts <- tarrant %>%
  st_transform(26914) %>%
  st_centroid() %>%
  na.omit()

# Make a raster template at 250m resolution
raster_template <- tarrant %>%
  st_transform(26914) %>%
  raster::raster(resolution = 250)

# Compute inverse distance weighted interpolation with an exponent of 2
# and a nearest-neighbor cutoff of 20
idw_formula <- gstat::gstat(
  formula = estimate ~ 1,
  locations = tarrant_pts,
  nmax = 20,
  set = list(idp = 2)
)

# Interpolate the IDW to the raster
idw_raster <- raster::interpolate(raster_template, idw_formula)

# Grab a custom basemap using the dev version of mapboxapi
# remotes::install_github("walkerke/mapboxapi")
base <- get_static_tiles(
  location = idw_raster,
  zoom = 10,
  style_id = "ckedp72zt059t19nssixpgapb",
  username = "kwalkertcu"
)

# Map it out with ggplot2 and ggspatial
ggplot() + 
  layer_spatial(base) + 
  layer_spatial(idw_raster, alpha = 0.5) + 
  scale_fill_viridis_c(labels = scales::dollar, na.value = "transparent") + 
  coord_sf(datum = NA) + 
  theme_bw(base_family = "Open Sans") + 
  scale_x_continuous(expand = c(0, 0)) + 
  scale_y_continuous(expand = c(0, 0)) + 
  labs(fill = "Median home value,\n2015-2019 ACS\n(interpolated)",
       caption = "Basemap © Mapbox, © OpenStreetMap")