A coffee shop wants to understand the competition inside its own catchment. Its walkshed is every residential block that can walk to it in 10 minutes. This tutorial asks, block by block, how many other cafes those residents can also reach on foot, and which one is closest. The example city is Providence, Rhode Island.
Running this tutorial uses about 450 tokens.
Set up
Read the cafe category id from the free catalog and find the city.
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$label)
cafe <- ids[["cafes"]]
city <- close$places(q = "Providence")[1, ]
city_boundary <- close$place_boundary(geoid = city$geoid)Find the shops and our walkshed
$place_pois() returns every cafe within the city’s
boundary. Pick one as the subject, then pull its walkshed: every block
that can walk to it in 10 minutes. Draw the walkshed with the cafes on
top, our shop in orange.
cafes <- close$place_pois(geoid = city$geoid, type = cafe)
ours <- cafes[1, ]
ours$name
#> [1] "Little Sister"
our_shed <- close$poi_catchment(dest_id = ours$dest_id, mode = "walk",
max_minutes = 10)
closecity::close_map(
x = cafes,
color = ifelse(cafes$dest_id == ours$dest_id, "#f36e21", "#202a5b"),
label = "name",
background = sf::st_union(our_shed),
background_color = "#74b9ff",
boundary = city_boundary
)What each block can reach
Now split the walkshed by block. A single $block_pois()
call takes the whole vector of walkshed blocks and returns, for every
block, each cafe its residents can walk to within 10 minutes: the real
routed answer, not a straight-line guess, and one request rather than
one per block. Passing a vector of GEOIDs tags every row with its origin
geoid, so grouping by it reads two things per block: how
many cafes are in reach, and which one is closest by walk time.
reach <- close$block_pois(
our_shed$geoid,
mode = "walk", type = cafe, max_minutes = 10, output = "tabular"
)
per_block <- split(reach, reach$geoid)
counts <- vapply(per_block, nrow, integer(1))
closest <- vapply(
per_block, function(r) r$dest_id[which.min(r$travel_time)], numeric(1)
)
our_shed$n_cafes <- unname(counts[as.character(our_shed$geoid)])
our_shed$n_cafes[is.na(our_shed$n_cafes)] <- 0L
our_shed$closest_cafe <- unname(closest[as.character(our_shed$geoid)])How many cafes each block can reach
Shade every block in the walkshed by the number of cafes within a 10-minute walk; blue marks the blocks with the most choice, and the cafes those blocks can reach are drawn on top as points.
Which cafe is closest
Give each cafe that wins at least one block a colour, then paint every block with the colour of its closest cafe. The cafe points share those colours. The result is the contested ground: where our shop’s catchment gives way to a competitor’s.
closest <- unique(stats::na.omit(our_shed$closest_cafe))
palette <- setNames(grDevices::hcl.colors(length(closest), "Dark 3"), closest)
block_color <- palette[as.character(our_shed$closest_cafe)]
block_color[is.na(block_color)] <- "#dddddd"
winning_cafes <- cafes[cafes$dest_id %in% closest, ]
closecity::close_map(
x = our_shed,
color = block_color,
points = winning_cafes,
points_color = palette[as.character(winning_cafes$dest_id)],
boundary = city_boundary
)The same recipe scales up: raise max_minutes, or compare
whole cities by pulling each one’s cafes with
$place_pois().