The quickest way to feel what Close gives you: read the walk times from a starting point, map the supermarkets you can reach on foot, then draw how far a 30-minute walk takes you. The example city is Providence, Rhode Island.
Running this tutorial uses about 90 tokens.
Set up
Build a client, then read what you need from the free catalog.
library(closecity)
close <- closecity::close_client(api_key = "ck_live_your_key") # use your own key here
amenity_types <- close$destination_types()
supermarket_type <- amenity_types[amenity_types$label == "grocery_stores", ]$dest_type_id
providence_ri <- close$places(q = "Providence")[1, ]
start_lon <- providence_ri$lon
start_lat <- providence_ri$latRead travel times from a starting point
Pick a starting point (here the centre of Providence) and ask how
long it takes to walk to each kind of amenity.
$point_summary() takes a lat/lon
instead of a block GEOID. Join the catalog’s readable name
and sort by time, so the nearest things are on top.
walk_times <- close$point_summary(lat = start_lat, lon = start_lon, mode = "walk")
walk_times <- merge(
walk_times,
amenity_types[, c("dest_type_id", "name")],
by = "dest_type_id"
)
walk_times[order(walk_times$travel_time), c("name", "travel_time")]
#> name travel_time
#> 18 All transit stops 0
#> 27 Non-frequent transit stops 0
#> 28 Non-frequent other transit stops 0
#> 29 Other transit stops 0
#> 5 Restaurants 1
#> 9 Cafes and coffee shops 1
#> 19 Parks 2
#> 23 Parks (<0.5 acres) 2
#> 6 Bars 3
#> 17 Libraries 3
#> 32 Public libraries 3
#> 8 Grocery stores 6
#> 20 Parks (0.5–1 acres) 6
#> 21 Parks (1–10 acres) 6
#> 30 Parks (>0.5 acres) 6
#> 31 Parks (>1 acre) 6
#> 12 Pharmacies 7
#> 15 Bookstores 8
#> 16 Bike shops 8
#> 7 Convenience stores 9
#> 24 Playgrounds 9
#> 33 University/private libraries 9
#> 1 Public schools 11
#> 4 High schools 11
#> 22 Parks (>10 acres) 12
#> 13 Preschools 13
#> 25 Bakeries 13
#> 14 Community centers 14
#> 10 Dentists 15
#> 11 Gyms and exercise studios 18
#> 3 Middle schools 21
#> 2 Elementary schools 25
#> 26 Hardware stores 25Map the supermarkets within a 30-minute walk
A 30-minute walk is a travel-time question, not a distance one, so
let the routing answer it directly: $point_pois() returns
every POI reachable from the starting point within
max_minutes, each carrying its walk time, with no isochrone
to overlay. close_map() draws them in one line, shaded by
that walk time (blue = closest), with the starting point marked by an X
and the city boundary behind for context.
nearby_supermarkets <- close$point_pois(
lat = start_lat,
lon = start_lon,
mode = "walk",
type = supermarket_type,
max_minutes = 30
)
city_boundary <- close$place_boundary(geoid = providence_ri$geoid)
closecity::close_map(
x = nearby_supermarkets,
fill = "travel_time",
boundary = city_boundary,
label = "name",
mark = c(start_lon, start_lat)
)Draw how far you can walk
An isochrone is the headline map: the area you can reach on foot in
10, 20, and 30 minutes. Shade it by the contour minutes;
blue marks the nearest, most-reachable ring.
Walk versus transit
Comparing the same starting point and the same 30-minute budget, on foot and by bus, is the clearest way to see what transit buys you.
walk <- close$isochrone(
lon = start_lon,
lat = start_lat,
mode = "walk",
direction = "from",
minutes = 30,
format = "geojson"
)
transit <- close$isochrone(
lon = start_lon,
lat = start_lat,
mode = "transit",
direction = "from",
minutes = 30,
format = "geojson"
)
closecity::close_map(x = walk, color = "#058040")
closecity::close_map(x = transit, color = "#202a5b")```