Libraries

Data

List of schools and libraries from OpenStreetMap. Includes all schools (no easy way to filter for just primary schools).

# load useful libraries
if (!require("osmdata")) install.packages("osmdata")
if (!require("tidyverse")) install.packages("tidyverse")
if (!require("sf")) install.packages("sf")
if (!require("leaflet")) install.packages("leaflet")
if (!require("revgeo")) install.packages("revgeo")
if (!require("DT")) install.packages("DT")

# Query openstreemap (overpass api) ----------------------------------------------------

# set area value for map e.g. "Glasgow UK"
area <- "Herefordshire UK" 

# query libraries
library_query <- getbb(area) %>%
  opq() %>%
  add_osm_feature("amenity", "library")
# query schools
school_query <- getbb(area) %>%
  opq() %>%
  add_osm_feature("amenity", "school")

# merge schools and libraries in to single object
libraries <- c(osmdata_sf(library_query), osmdata_sf(school_query)) 
# return unqiue objects (in case featues mapped at points and polygons)
libraries <- unique_osmdata(libraries)

# get centroids of polygons because want to display as points
lib_centroids <- st_sf(st_centroid(libraries$osm_polygon))

# get points so can merge with centroid
lib_point <- libraries$osm_points
# merge centroid points and points
all_points <- bind_rows(data.frame(lib_point), data.frame(lib_centroids))
# add geometry to this new merged dataset
all_points$geometry <- c(lib_point$geometry, lib_centroids$geometry)
# convert to 'sf' object
all_points <- st_as_sf(all_points)
# add object back to osmdata object
libraries$osm_points <- all_points

# filter only schools and libraries (appears to 'empty' things)
libraries$osm_points <- filter(libraries$osm_points, amenity %in% c("school", "library"))

Map

Click points to see name and address (if available)

# Map -----------------------------------------------------------------------------------

# Colour for icons
libs <- mutate(libs, colour = ifelse(amenity == "school","blue","green"))
# Make icons
icons <- awesomeIcons(
  icon = "ios-close",
  iconColor = "black",
  library = "ion",
  markerColor = libs$colour
)

# make web 'slippy' map 
m <- leaflet(libs) %>%
  addTiles() %>%
  addAwesomeMarkers(data = libs, popup = paste(libs$name, libs$street, libs$city, sep = "<br/>"), icon = icons)
m

Nearest library

Distance to library (as the crow flies).

all_lib <- filter(libs, amenity == "library")
all_schools <- filter(libs, amenity == "school")

all_schools$nearest <- st_nearest_feature(all_schools, all_lib)
all_lib$nearest <- as.integer(row.names(all_lib))

df <- as_tibble(select(
all_schools, name,
street,
city,
nearest
))

df_lib <- as_tibble(
  select(
  all_lib, name,
  street,
  city,
  nearest))

df <- right_join(df, df_lib, by = c("nearest" = "nearest"))

df$distance <- st_distance(df$geometry.x, df$geometry.y, by_element = TRUE) / 1000
units(df$distance) = NULL
df$distance <- round(df$distance, 1)

table_df <- as_tibble(transmute(df,
                             "School" = name.x,
                             "School Address" = paste(df$street.x, df$city.x, sep = ", "),
                             "Nearest Library" = name.y,
                             "Library Address" = paste(df$street.y, df$city.y, sep = ", "),
                             "Distance in km" = distance))

datatable(table_df)

NA