## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  eval = nzchar(Sys.getenv("CLOSECITY_KEY"))
)
library(closecity)
library(sf)
close <- closecity::close_client(api_key = Sys.getenv("CLOSECITY_KEY"))

## -----------------------------------------------------------------------------
# amenity_types <- close$destination_types()
# ids <- setNames(amenity_types$dest_type_id, amenity_types$label)
# 
# basket <- data.frame(
#   amenity = c("supermarket", "library", "park", "transit", "restaurant", "cafe"),
#   dest_type_id = unname(ids[c("grocery_stores", "libraries", "parks",
#                               "frequent_transit", "restaurants", "cafes")])
# )
# 
# city <- close$places(q = "Richmond")[1, ]
# city_boundary <- close$place_boundary(geoid = city$geoid)

## -----------------------------------------------------------------------------
# blocks <- close$blocks_query(
#   center = list(lon = city$lon, lat = city$lat),
#   radius_m = 2500,
#   mode = "walk",
#   type = basket$dest_type_id,
#   include_population = TRUE
# )

## -----------------------------------------------------------------------------
# one_per_block <- blocks[!duplicated(blocks$geoid), "geoid"]
# one_per_block$population <-
#   setNames(blocks$population, blocks$geoid)[one_per_block$geoid]
# for (i in seq_len(nrow(basket))) {
#   sub <- blocks[blocks$dest_type_id == basket$dest_type_id[i], ]
#   one_per_block[[paste0(basket$amenity[i], "_min")]] <-
#     setNames(sub$travel_time, sub$geoid)[one_per_block$geoid]
# }
# total_pop <- sum(one_per_block$population)
# 
# # The six walk-time columns, as a plain matrix, for the tallies below.
# mins <- as.matrix(sf::st_drop_geometry(one_per_block)[, paste0(basket$amenity, "_min")])
# within_15 <- mins <= 15
# within_15[is.na(within_15)] <- FALSE

## -----------------------------------------------------------------------------
# for (i in seq_len(nrow(basket))) {
#   pop <- sum(one_per_block$population[within_15[, i]])
#   cat(sprintf("%-11s %3.0f%%\n", basket$amenity[i], 100 * pop / total_pop))
# }

## -----------------------------------------------------------------------------
# one_per_block$has_library <- within_15[, basket$amenity == "library"]
# libraries <- close$pois_search(
#   lat = city$lat,
#   lon = city$lon,
#   radius_m = 2500,
#   type = basket$dest_type_id[basket$amenity == "library"]
# )
# closecity::close_map(
#   x = one_per_block,
#   highlight = "has_library",
#   color = "#058040",
#   points = libraries,
#   boundary = city_boundary
# )

## -----------------------------------------------------------------------------
# one_per_block$score <- rowSums(within_15)
# closecity::close_map(
#   x = one_per_block,
#   fill = "score",
#   reverse = TRUE,
#   boundary = city_boundary
# )

## -----------------------------------------------------------------------------
# one_per_block$full_basket <- one_per_block$score == 6
# basket_pop <- sum(one_per_block$population[one_per_block$full_basket])
# cat(sprintf("All six amenities: %.0f%% of residents\n", 100 * basket_pop / total_pop))
# 
# closecity::close_map(
#   x = one_per_block,
#   highlight = "full_basket",
#   color = "#f36e21",
#   boundary = city_boundary
# )

## -----------------------------------------------------------------------------
# for (i in seq_len(nrow(basket))) {
#   lacking <- !one_per_block$full_basket & !within_15[, i]
#   cat(sprintf("%-11s %6.0f residents would gain access\n", basket$amenity[i],
#               sum(one_per_block$population[lacking])))
# }

## -----------------------------------------------------------------------------
# n_missing <- 6 - one_per_block$score
# gap <- apply(within_15, 1, function(row) paste(basket$amenity[!row], collapse = " + "))
# 
# almost <- n_missing %in% c(1, 2)
# almost_pop <- sum(one_per_block$population[almost])
# cat(sprintf("%.0f%% of residents are one or two amenities short of the full basket.\n\n",
#             100 * almost_pop / total_pop))
# 
# pop_by_gap <- tapply(one_per_block$population[almost], gap[almost], sum)
# pop_by_gap <- sort(pop_by_gap, decreasing = TRUE)
# for (g in names(pop_by_gap)) {
#   cat(sprintf("  missing %-22s %3.0f%%\n", g, 100 * pop_by_gap[g] / almost_pop))
# }

## -----------------------------------------------------------------------------
# uncovered <- one_per_block[!within_15[, basket$amenity == "supermarket"] &
#                              one_per_block$population > 0, ]
# uncovered_points <- sf::st_point_on_surface(sf::st_geometry(uncovered))
# middle <- sf::st_centroid(sf::st_union(sf::st_geometry(one_per_block)))
# site <- sf::st_coordinates(
#   uncovered_points[which.min(sf::st_distance(uncovered_points, middle))]
# )
# 
# reachable <- close$isochrone(lon = site[1], lat = site[2], mode = "walk",
#                              direction = "to", minutes = 15, format = "blocks")
# near_supermarket <- one_per_block$geoid[within_15[, basket$amenity == "supermarket"]]
# newly_served <- setdiff(intersect(reachable$geoid, one_per_block$geoid), near_supermarket)
# cat(sprintf("A supermarket here would newly serve %.0f residents\n",
#             sum(one_per_block$population[one_per_block$geoid %in% newly_served])))

## -----------------------------------------------------------------------------
# one_per_block$newly_served <- one_per_block$geoid %in% newly_served
# closecity::close_map(
#   x = one_per_block,
#   highlight = "newly_served",
#   color = "#e8590c",
#   mark = c(site[1], site[2]),
#   boundary = city_boundary
# )