In tamilyn/dhomer: DHOME Data

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(dhomer)
library(tigris)
library(sf)
library(tmap)
library(tmaptools)

Loading the data

The datastes are not saved as spatial objects, rather with latitude and longitude columns, so we must first convert all of the point data into spatial objects with st_as_sf

sc_tracts <- tracts(state = 45)

data("superfund")
data("brownfields")
data("tri")
data("contaminants")
data("free_clinics")
data("health_facilities")
data("hrsa")
data("markets")
data("libraries")
data("markets")
data("pharmacies")
data("public_schools")
data("recreation")
data("transit_terminals")

superfund_map <- superfund %>%
  st_as_sf(coords = c('lon', 'lat'), crs = st_crs(sc_tracts))

brownfields_map <- brownfields %>%
  st_as_sf(coords = c('lon', 'lat'), crs = st_crs(sc_tracts))

tri_map <- tri %>%
  st_as_sf(coords = c('lon', 'lat'), crs = st_crs(sc_tracts))

contaminants_map <- contaminants %>%
  st_as_sf(coords = c('lon', 'lat'), crs = st_crs(sc_tracts))

clinics_map <- free_clinics %>%
  st_as_sf(coords = c('lon', 'lat'), crs = st_crs(sc_tracts))

facilities_map <- health_facilities %>%
  st_as_sf(coords = c('lon', 'lat'), crs = st_crs(sc_tracts))

hrsa_map <- hrsa %>%
  st_as_sf(coords = c('lon', 'lat'), crs = st_crs(sc_tracts))

libraries_map <- libraries %>%
  st_as_sf(coords = c('lon', 'lat'), crs = st_crs(sc_tracts))

markets_map <- markets %>%
  st_as_sf(coords = c('lon', 'lat'), crs = st_crs(sc_tracts))

pharmacies_map <- pharmacies %>%
  st_as_sf(coords = c('lon', 'lat'), crs = st_crs(sc_tracts))

schools_map <- public_schools %>%
  st_as_sf(coords = c('lon', 'lat'), crs = st_crs(sc_tracts))

rec_map <- recreation %>%
  st_as_sf(coords = c('lon', 'lat'), crs = st_crs(sc_tracts))

transit_map <- transit_terminals %>%
  st_as_sf(coords = c('lon', 'lat'), crs = st_crs(sc_tracts))

Mapping the data {.tabset}

Environmental Hazards

plot1 <- tm_shape(sc_tracts) + 
  tm_polygons(col = '#CCCCCC') +
  tm_layout(main.title = 'Environmental Hazards', 
            main.title.position = c('center','top')) + 
  # tm_shape(tri_map) +
  # tm_dots(size = 0.1, col = '#700074') +
  tm_shape(brownfields_map) +
  tm_dots(size = 0.1, col = '#E62B8B') +
  # tm_shape(superfund_map) +
  # tm_dots(size = 0.1, col = '#4AB9C2') +
  # tm_shape(contaminants_map) +
  # tm_dots(size = 0.1, col = '#6BAB34') +
  tm_add_legend(title = 'Point Labels', type = 'symbol', 
                labels = c('Toxic Release Inventory Sites', 'Brownfields',
                           'Superfund Sites','EPA Contaminants of Concern'),
                col = c('#700074','#E62B8B','#4AB9C2','#6BAB34'))

plot1

Healthcare Resources

plot2 <- tm_shape(sc_tracts) + 
  tm_polygons(col = '#CCCCCC') +
  tm_layout(main.title = 'Healthcare Resources', 
            main.title.position = c('center','top')) + 
  tm_shape(facilities_map) +
  tm_dots(size = 0.1, col = '#E41A1C') +
  tm_shape(pharmacies_map) +
  tm_dots(size = 0.1, col = '#1F9E89FF') +
  tm_shape(hrsa_map) +
  tm_dots(size = 0.1, col = '#FFD92F') +
  tm_shape(clinics_map) +
  tm_dots(size = 0.1, col = '#0D0887FF') +
  tm_add_legend(title = 'Point Labels', type = 'symbol', 
                labels = c('Pharmacies', 'Licensed Health Facilities',
                           'HRSA Health Facilities','Free Clinics'),
                col = c('#E41A1C','#1F9E89FF','#FFD92F','#0D0887FF'))

plot2

Social, Education, and Community Resources

plot3 <- tm_shape(sc_tracts) + 
  tm_polygons(col = '#CCCCCC') +
  tm_layout(main.title = 'Social, Education, & Community Resources', 
            main.title.position = c('center','top')) + 
  tm_shape(rec_map) +
  tm_dots(size = 0.1, col = '#E75A47') +
  tm_shape(markets_map) +
  tm_dots(size = 0.1, col = '#00336FFF') +
  tm_shape(schools_map) +
  tm_dots(size = 0.1, col = '#33A02C') +
  tm_shape(libraries_map) +
  tm_dots(size = 0.1, col = '#FFD92F') +
  tm_shape(transit_map) +
  tm_dots(size = 0.1, col = '#8C0C25') +
  tm_add_legend(title = 'Point Labels', type = 'symbol', 
                labels = c('Recreation Areas', "Farmer's and Roadside Markets",
                           'Public Schools','Public Libraries','Intermodal Passenger Transit Terminals'),
                col = c('#E75A47','#00336FFF','#33A02C','#FFD92F','#8C0C25'))

plot3