mapchallenge_day8_osm.R

library(mapboxapi)
library(tidyverse)
library(sf)
library(patchwork)

city_halls <- c(
  "New York" = "City Hall Park, New York, NY 10007",
  "Los Angeles" = "200 N Spring St, Los Angeles, CA 90012",
  "Chicago" = "121 N La Salle St, Chicago, IL 60602",
  "Houston" = "901 Bagby St, Houston, TX 77002",
  "Phoenix" = "200 W Washington St, Phoenix, AZ 85003",
  "Philadelphia" = "1400 John F Kennedy Blvd, Philadelphia, PA 19107",
  "San Antonio" = "100 Military Plaza #4, San Antonio, TX 78205",
  "San Diego" = "Civic Ctr Plz, San Diego, CA 92101",
  "Dallas" = "1500 Marilla St, Dallas, TX 75201",
  "San Jose" = "200 E Santa Clara St, San Jose, CA 95113"
)

# Build out the mapping workflow
plots <- imap(city_halls, ~{
  
  print(glue::glue("Working on {.y}..."))
  
  # Geocode the hall
  hall_sf <- mb_geocode(.x, output = "sf")
  
  # 500m buffer for the hall - 
  # projected coordinates get smoother results, so 
  # use crsuggest for this
  my_crs <- crsuggest::suggest_top_crs(hall_sf)
  
  buf <- hall_sf %>%
    st_transform(my_crs) %>%
    st_buffer(500, nQuadSegs = 1000) %>%
    st_transform(4326)
  
  # Query the tiles
  vector_extract <- get_vector_tiles(
    tileset_id = "mapbox.mapbox-streets-v8",
    location = buf,
    zoom = 15
  )
  
  # Intersect the buildings with the buffer
  buildings_extract <- vector_extract$building %>%
    st_intersection(buf)
  
  # Plot the result
  ggplot() + 
    geom_sf(data = buf, color = "black", fill = "white") + 
    geom_sf(data = buildings_extract, fill = "navy", color = "navy") + 
    theme_void() + 
    labs(title = .y) + 
    theme(plot.title = element_text(hjust = 0.5))
  
})

wrap_plots(plots, ncol = 5) + 
  plot_annotation(caption = "Building footprints within approximately 500m of respective city halls.\nGraphic by @kyle_e_walker; data © Mapbox, OpenStreetMap contributors")