Forward geocoding is the process of taking an address or place information and identifying its location on the globe.
To geocode addresses, the {arcgisgeocode} package provides the function find_address_candidates(). This function geocodes a single address at a time and returns up to 50 address candidates (ranked by a score).
There are two ways in which you can provide address information:
single_line argumentaddress, city, region, postal etc.It can be tough to parse out addresses into their components. Using the single_line argument is a very flexible way of geocoding addresses. Doing utilizes the ArcGIS World Geocoder’s address parsing capabilities.
For example, we can geocode the same location using 3 decreasingly specific addresses.
library(arcgisgeocode)
addresses <- c(
"380 New York Street Redlands, California, 92373, USA",
"Esri Redlands",
"ESRI CA"
)
locs <- find_address_candidates(
addresses,
max_locations = 1L
)
locs$geometryGeometry set for 3 features
Geometry type: POINT
Dimension: XY
Bounding box: xmin: -117.1948 ymin: 34.05726 xmax: -117.1948 ymax: 34.05726
Geodetic CRS: WGS 84POINT (-117.1948 34.05726)POINT (-117.1957 34.05609)
POINT (-117.1957 34.05609)In each case, it finds the correct address!
Most commonly, you will need to geocode addresses from a column in a data.frame. It is important to note that the find_address_candidates() function does not work well in a dplyr::mutate() function call. Particularly because it is possible to return more than 1 address at a time.
Let’s read in a csv of bike stores in Tacoma, WA. To use find_address_candidates() with a data.frame, it is recommended to create a unique identifier of the row positions.
library(dplyr)
fp <- "https://www.arcgis.com/sharing/rest/content/items/9a9b91179ac44db1b689b42017471ae6/data"
bike_stores <- readr::read_csv(fp) |>
mutate(id = row_number())
bike_stores# A tibble: 10 × 3
store_name original_address id
<chr> <chr> <int>
1 Cascadia Wheel Co. 3320 N Proctor St, Tacoma, WA 984… 1
2 Puget Sound Bike and Ski Shop between 3206 N. 15th and 1414, N … 2
3 Takoma Bike & Ski 3010 6th Ave, Tacoma, WA 98406 3
4 Trek Bicycle Tacoma University Place 3550 Market Pl W Suite 102, Unive… 4
5 Opalescent Cyclery 814 6th Ave, Tacoma, WA 98405 5
6 Sound Bikes 108 W Main, Puyallup, WA 98371 6
7 Trek Bicycle Tacoma North End 3009 McCarver St, Tacoma, WA 98403 7
8 Second Cycle 1205 M.L.K. Jr Way, Tacoma, WA 98… 8
9 Penny bike co. 6419 24th St NE, Tacoma, WA 98422 9
10 Spider's Bike, Ski & Tennis Lab 3608 Grandview St, Gig Harbor, WA… 10To geocode addresses from a data.frame, you can use dplyr::reframe().
bike_stores |>
reframe(
find_address_candidates(original_address)
)# A tibble: 13 × 62
input_id result_id loc_name status score match_addr long_label short_label
<int> <int> <chr> <chr> <dbl> <chr> <chr> <chr>
1 1 NA World M 100 3320 N Proct… 3320 N Pr… 3320 N Pro…
2 2 NA World M 97.6 N 15th St & … N 15th St… N 15th St …
3 2 NA World M 97.3 1414 N Alder… 1414 N Al… 1414 N Ald…
4 2 NA World M 94.7 S 15th St & … S 15th St… S 15th St …
5 2 NA World M 84.4 3206 N 15th … 3206 N 15… 3206 N 15t…
6 3 NA World M 100 3010 6th Ave… 3010 6th … 3010 6th A…
7 4 NA World M 100 3550 Market … 3550 Mark… 3550 Marke…
8 5 NA World M 100 814 6th Ave,… 814 6th A… 814 6th Ave
9 6 NA World M 100 108 W Main, … 108 W Mai… 108 W Main
10 7 NA World M 100 3009 McCarve… 3009 McCa… 3009 McCar…
11 8 NA World M 100 1205 Martin … 1205 Mart… 1205 Marti…
12 9 NA World M 97.9 6419 24th St… 6419 24th… 6419 24th …
13 10 NA World M 100 3608 Grandvi… 3608 Gran… 3608 Grand…
# ℹ 54 more variables: addr_type <chr>, type_field <chr>, place_name <chr>,
# place_addr <chr>, phone <chr>, url <chr>, rank <dbl>, add_bldg <chr>,
# add_num <chr>, add_num_from <chr>, add_num_to <chr>, add_range <chr>,
# side <chr>, st_pre_dir <chr>, st_pre_type <chr>, st_name <chr>,
# st_type <chr>, st_dir <chr>, bldg_type <chr>, bldg_name <chr>,
# level_type <chr>, level_name <chr>, unit_type <chr>, unit_name <chr>,
# sub_addr <chr>, st_addr <chr>, block <chr>, sector <chr>, nbrhd <chr>, …Notice how there are multiple results for each input_id. This is because the max_locations argument was not specified. To ensure only the best match is returned set max_locations = 1
geocoded <- bike_stores |>
reframe(
find_address_candidates(original_address, max_locations = 1)
) |>
# reframe drops the sf class, must be added
sf::st_as_sf()
geocodedSimple feature collection with 10 features and 61 fields
Geometry type: POINT
Dimension: XY
Bounding box: xmin: -122.5871 ymin: 47.19164 xmax: -122.294 ymax: 47.32301
Geodetic CRS: WGS 84
# A tibble: 10 × 62
input_id result_id loc_name status score match_addr long_label short_label
<int> <int> <chr> <chr> <dbl> <chr> <chr> <chr>
1 1 NA World M 100 3320 N Proct… 3320 N Pr… 3320 N Pro…
2 2 NA World M 97.6 N 15th St & … N 15th St… N 15th St …
3 3 NA World M 100 3010 6th Ave… 3010 6th … 3010 6th A…
4 4 NA World M 100 3550 Market … 3550 Mark… 3550 Marke…
5 5 NA World M 100 814 6th Ave,… 814 6th A… 814 6th Ave
6 6 NA World M 100 108 W Main, … 108 W Mai… 108 W Main
7 7 NA World M 100 3009 McCarve… 3009 McCa… 3009 McCar…
8 8 NA World M 100 1205 Martin … 1205 Mart… 1205 Marti…
9 9 NA World M 97.9 6419 24th St… 6419 24th… 6419 24th …
10 10 NA World M 100 3608 Grandvi… 3608 Gran… 3608 Grand…
# ℹ 54 more variables: addr_type <chr>, type_field <chr>, place_name <chr>,
# place_addr <chr>, phone <chr>, url <chr>, rank <dbl>, add_bldg <chr>,
# add_num <chr>, add_num_from <chr>, add_num_to <chr>, add_range <chr>,
# side <chr>, st_pre_dir <chr>, st_pre_type <chr>, st_name <chr>,
# st_type <chr>, st_dir <chr>, bldg_type <chr>, bldg_name <chr>,
# level_type <chr>, level_name <chr>, unit_type <chr>, unit_name <chr>,
# sub_addr <chr>, st_addr <chr>, block <chr>, sector <chr>, nbrhd <chr>, …With this result, you can now join the address fields back onto the bike_stores data.frame using a left_join().
left_join(
bike_stores,
geocoded,
by = c("id" = "input_id")
) |>
# left_join keeps the class of the first table
# must add sf class back on
sf::st_as_sf()Simple feature collection with 10 features and 63 fields
Geometry type: POINT
Dimension: XY
Bounding box: xmin: -122.5871 ymin: 47.19164 xmax: -122.294 ymax: 47.32301
Geodetic CRS: WGS 84
# A tibble: 10 × 64
store_name original_address id result_id loc_name status score match_addr
<chr> <chr> <int> <int> <chr> <chr> <dbl> <chr>
1 Cascadia W… 3320 N Proctor … 1 NA World M 100 3320 N Pr…
2 Puget Soun… between 3206 N.… 2 NA World M 97.6 N 15th St…
3 Takoma Bik… 3010 6th Ave, T… 3 NA World M 100 3010 6th …
4 Trek Bicyc… 3550 Market Pl … 4 NA World M 100 3550 Mark…
5 Opalescent… 814 6th Ave, Ta… 5 NA World M 100 814 6th A…
6 Sound Bikes 108 W Main, Puy… 6 NA World M 100 108 W Mai…
7 Trek Bicyc… 3009 McCarver S… 7 NA World M 100 3009 McCa…
8 Second Cyc… 1205 M.L.K. Jr … 8 NA World M 100 1205 Mart…
9 Penny bike… 6419 24th St NE… 9 NA World M 97.9 6419 24th…
10 Spider's B… 3608 Grandview … 10 NA World M 100 3608 Gran…
# ℹ 56 more variables: long_label <chr>, short_label <chr>, addr_type <chr>,
# type_field <chr>, place_name <chr>, place_addr <chr>, phone <chr>,
# url <chr>, rank <dbl>, add_bldg <chr>, add_num <chr>, add_num_from <chr>,
# add_num_to <chr>, add_range <chr>, side <chr>, st_pre_dir <chr>,
# st_pre_type <chr>, st_name <chr>, st_type <chr>, st_dir <chr>,
# bldg_type <chr>, bldg_name <chr>, level_type <chr>, level_name <chr>,
# unit_type <chr>, unit_name <chr>, sub_addr <chr>, st_addr <chr>, …