A city planner wants every resident to be able to walk to a basket of six everyday amenities: a supermarket, a library, a park, a frequent-transit stop, a restaurant, and a cafe. This tutorial measures how many residents already have that, and shows where the gaps are. The idea follows this analysis, here applied to Richmond, Virginia.
Running this tutorial uses about 3,600 tokens.
Set up
Turn the city name into a centre point and a boundary, and build the basket as a small table: each amenity paired with its destination-type id.
library(closecity)
library(sf)
close <- closecity::close_client(api_key = "ck_live_your_key") # use your own key here
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)Pull the blocks, with population
$blocks_query() reads every page: the walk time from
every block to each of the six categories, plus each block’s population.
To keep this tutorial cheap we take the central blocks within a radius;
$place_blocks(geoid = city$geoid) pulls
every block in the city the same way (at a higher token
cost).
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
)Reshape to one row per block: keep just the GEOID and geometry, add
the population, and add a walk-time column for each amenity
(NA when it is more than 30 minutes away). The result is
one clean table, with no leftover census-block metadata.
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$amenity, "_min")])
within_15 <- mins <= 15
within_15[is.na(within_15)] <- FALSECoverage, one amenity at a time
For each amenity, a block counts as covered when it is within a 15-minute walk. Add up the population of the covered blocks.
for (i in seq_len(nrow(basket))) {
pop <- sum(one_per_block$population[within_15[, i]])
cat(sprintf("%-11s %3.0f%%\n", basket$amenity[i], 100 * pop / total_pop))
}
#> supermarket 13%
#> library 38%
#> park 89%
#> transit 5%
#> restaurant 71%
#> cafe 64%Parks and restaurants tend to be everywhere; supermarkets and libraries are usually the hardest to reach. Map the library coverage: every block shown, the covered ones highlighted, the library locations as points, and the city boundary behind.
one_per_block$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
)The 15-minute-city score
Count, for each block, how many of the six amenities are within a 15-minute walk. That score, from 0 to 6, is the map planners reach for; blue marks the best-served blocks. It reuses the data you already pulled, so it costs nothing more.
Who can reach all six
A block is fully covered only when all six amenities are within 15 minutes.
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))
#> All six amenities: 4% of residents
closecity::close_map(
x = one_per_block,
highlight = "full_basket",
color = "#f36e21",
boundary = city_boundary
)Which amenity to add first
Look at the residents who are not yet fully covered, and count how many of them are missing each amenity. The amenity that the most people lack is the one to add first.
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$population[lacking])))
}
#> supermarket 26219 residents would gain access
#> library 18720 residents would gain access
#> park 3373 residents would gain access
#> transit 28424 residents would gain access
#> restaurant 8732 residents would gain access
#> cafe 10798 residents would gain accessWho is one or two amenities away
The residents worth targeting first are the ones almost there: a block that has five of the six is a much easier win than one that has none. Count how many amenities each block is missing, and for the blocks short by just one or two, break down which amenities are the gap.
n_missing <- 6 - one_per_block$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))
#> 31% of residents are one or two amenities short of the full basket.
pop_by_gap <- tapply(one_per_block$population[almost], gap[almost], sum)
pop_by_gap <- sort(pop_by_gap, decreasing = TRUE)
for (g in names(pop_by_gap)) {
cat(sprintf(" missing %-22s %3.0f%%\n", g, 100 * pop_by_gap[g] / almost_pop))
}
#> missing supermarket + transit 78%
#> missing transit 10%
#> missing library + transit 8%
#> missing supermarket 3%
#> missing library 1%Site a new supermarket
The counts above say which amenity to add; the next question
is where. Pick the candidate site automatically: the uncovered
block (no supermarket within a 15-minute walk) nearest the middle of the
study area. A direction = "to" isochrone then gives the
blocks that could reach the site on foot in 15 minutes, and the ones not
already served are the population this site would newly reach.
uncovered <- one_per_block[!within_15[, basket$amenity == "supermarket"] &
one_per_block$population > 0, ]
uncovered_points <- sf::st_point_on_surface(sf::st_geometry(uncovered))
#> Warning in st_point_on_surface.sfc(sf::st_geometry(uncovered)):
#> st_point_on_surface may not give correct results for longitude/latitude data
middle <- sf::st_centroid(sf::st_union(sf::st_geometry(one_per_block)))
site <- sf::st_coordinates(
uncovered_points[which.min(sf::st_distance(uncovered_points, middle))]
)
reachable <- close$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])))
#> A supermarket here would newly serve 643 residentsMap the whole city and highlight the blocks that would newly gain access, with the candidate site marked by an X.