arcgisgeocode provides access to ArcGIS geocoding services from R. It supports address candidate identification, batch geocoding, reverse geocoding, and autocomplete suggestions.
Installation
Install the package from CRAN
# install from CRAN
install.packages("arcgisgeocode")You can also install the development version from r-universe as a binary for Mac, Windows, or Ubuntu from r-universe like so:
# install from R-universe
install.packages("arcgisgeocode", repos = "https://r-arcgis.r-universe.dev")Or you can install the package from source which requires Rust to be available. Follow the rustup instructions to install Rust and verify your installation is compatible using rextendr::rust_sitrep(). Then install the development version from GitHub:
# install pak if not available
if (!requireNamespace("pak")) install.packages("pak")
# install development version of {arcgisgeocode}
pak::pak("r-arcgis/arcgisgeocode")Usage
By default, the ArcGIS World Geocoder will be used. This geocoding server provides public access to the /findAddressCandidates, /reverseGeocode, and /suggest endpoints made available via the find_address_candidates(), reverse_geocode(), and suggest_places() functions respectively.
The batch geocoding endpoint /geocodeAddresses is available via geocode_addresses(). However, this requires the use of an authorization token and may consume credits.
Refer to the ArcGIS World Geocoder official documentation for additional information on use restrictions and licensing. For example, a valid token is required to store the results of geocoding transactions.
Reverse geocoding
Reverse geocoding takes a location and finds the associated address.
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A token is not required to use this function.
library(arcgisgeocode)
# Find addresses from locations
rev_res <- reverse_geocode(c(-117.172, 34.052))
# preview results
dplyr::glimpse(rev_res)
#> Rows: 1
#> Columns: 23
#> $ match_addr <chr> "600-620 Home Pl, Redlands, California, 92374"
#> $ long_label <chr> "600-620 Home Pl, Redlands, CA, 92374, USA"
#> $ short_label <chr> "600-620 Home Pl"
#> $ addr_type <chr> "StreetAddress"
#> $ type_field <chr> ""
#> $ place_name <chr> ""
#> $ add_num <chr> "608"
#> $ address <chr> "608 Home Pl"
#> $ block <chr> ""
#> $ sector <chr> ""
#> $ neighborhood <chr> "South Redlands"
#> $ district <chr> ""
#> $ city <chr> "Redlands"
#> $ metro_area <chr> ""
#> $ subregion <chr> "San Bernardino County"
#> $ region <chr> "California"
#> $ region_abbr <chr> "CA"
#> $ territory <chr> ""
#> $ postal <chr> "92374"
#> $ postal_ext <chr> ""
#> $ country_name <chr> "United States"
#> $ country_code <chr> "USA"
#> $ geometry <POINT [°]> POINT (-117.172 34.05204)Address search
The find_address_candidates() function returns geocoding candidate results. The function is vectorized over the input and will perform multiple requests in parallel. Each request geocodes one location at a time.
One or more candidates are returned from the endpoint. You can limit the number of candidates using the max_locations argument (with a maximum of 50).
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A token is not required to use this function.
# Find addresses from address search
candidates <- find_address_candidates(
address = "esri",
address2 = "380 new york street",
city = "redlands",
country_code = "usa",
max_locations = 2
)
dplyr::glimpse(candidates[, 1:10])
#> Rows: 2
#> Columns: 11
#> $ input_id <int> 1, 1
#> $ result_id <int> NA, NA
#> $ loc_name <chr> "World", "World"
#> $ status <chr> "M", "M"
#> $ score <dbl> 100.00, 98.57
#> $ match_addr <chr> "Esri", "380 New York St, Redlands, California, 92373"
#> $ long_label <chr> "Esri, 380 New York St, Redlands, CA, 92373, USA", "380 Ne…
#> $ short_label <chr> "Esri", "380 New York St"
#> $ addr_type <chr> "POI", "PointAddress"
#> $ type_field <chr> "Business Facility", NA
#> $ geometry <POINT [°]> POINT (-117.1957 34.05609), POINT (-117.1948 34.05726)…Suggest locations
Geocoding services can also provide a location suggestion based on a search term and, optionally, a location or extent. The suggest_places() function (/suggest endpoint) is intended to be used as part of a client-facing application that provides autocomplete suggestions.
In this example we create a search extent around a single point and find suggestions based on the search term "bellwood".
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A token is not required to use this function.
# identify a search point as a simple feature column
location <- sf::st_sfc(
sf::st_point(c(-84.34, 33.74)),
crs = 4326
)
# buffer and create a bbox object to search within the extent
search_extent <- sf::st_bbox(
sf::st_buffer(location, 10)
)
# find suggestions within the bounding box
suggestions <- suggest_places(
"bellwood",
location,
search_extent = search_extent
)
suggestions
#> # A data frame: 5 × 3
#> text magic_key is_collection
#> * <chr> <chr> <lgl>
#> 1 Bellwood Coffee, 1366 Glenwood Ave SE, Atlanta, GA, 3… dHA9MCN0… FALSE
#> 2 Bellwood, Atlanta, GA, USA dHA9MCN0… FALSE
#> 3 Bellwood Church, Atlanta, GA, USA dHA9MCN0… FALSE
#> 4 Bellwood Yard, Atlanta, GA, USA dHA9MCN0… FALSE
#> 5 Bellwood, IL, USA dHA9NCN0… FALSEThe result is intended to be provided to find_address_candidates() to complete the geocoding process. The column text contains the address to geocode. The column magic_key is a special identifier that makes it much faster to fetch results. Pass this into the argument magic_key.
# get address candidate information
# using the text and the magic key
res <- find_address_candidates(
suggestions$text,
magic_key = suggestions$magic_key
)
dplyr::glimpse(res[, 1:10])
#> Rows: 7
#> Columns: 11
#> $ input_id <int> 1, 2, 3, 4, 5, 5, 5
#> $ result_id <int> NA, NA, NA, NA, NA, NA, NA
#> $ loc_name <chr> NA, NA, NA, NA, NA, NA, NA
#> $ status <chr> "M", "M", "M", "M", "T", "T", "T"
#> $ score <dbl> 100, 100, 100, 100, 100, 100, 100
#> $ match_addr <chr> "Bellwood Coffee", "Bellwood, Atlanta, Georgia", "Bellwood…
#> $ long_label <chr> "Bellwood Coffee, 1366 Glenwood Ave SE, Atlanta, GA, 30316…
#> $ short_label <chr> "Bellwood Coffee", "Bellwood", "Bellwood Church", "Bellwoo…
#> $ addr_type <chr> "POI", "Locality", "POI", "POI", "Locality", "Locality", "…
#> $ type_field <chr> "Snacks", "City", "Church", "Building", "City", "City", "C…
#> $ geometry <POINT [°]> POINT (-84.34273 33.74034), POINT (-84.41243 33.77455), PO…Important: Storing results
By default, the argument for_storage = FALSE meaning that the results of the geocoding operation cannot be persisted. If you intend to persist the results of the geocoding operation, you must set for_storage = TRUE.
To learn more about free and paid geocoding operations refer to the storage parameter documentation.
Batch geocoding
Many addresses can be geocoded very quickly using the geocode_addresses() function which calls the /geocodeAddresses endpoint. Note that this function requires an authorization token. geocode_addresses() sends the input addresses in chunks as parallel requests.
Batch geocoding requires a signed in user. Load the {arcgisutils} to authorize and set your token. This example uses the Geocoding Test Dataset from the Urban Institute.
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A token is required to use this function with the World Geocoding Service. It may not be necessary if you are using a private ArcGIS Enterprise service.
library(arcgisutils)
library(arcgisgeocode)
set_arc_token(auth_user())
# Example dataset from the Urban Institute
fp <- "https://urban-data-catalog.s3.amazonaws.com/drupal-root-live/2020/02/25/geocoding_test_data.csv"
to_geocode <- readr::read_csv(fp, readr::locale(encoding = "latin1"))
geocoded <- to_geocode |>
dplyr::reframe(
geocode_addresses(
address = address,
city = city,
region = state,
postal = zip
)
)
dplyr::glimpse(res[, 1:10])Using other locators
arcgisgeocode can be used with other geocoding services, including custom locators hosted on ArcGIS Online or Enterprise. For example, we can use the AddressNC geocoding service available on ArcGIS Online.
Create a new GeocodeServer object using geocode_server(). This geocoder can be passed into the geocoder argument to any of the geocoding functions.
address_nc <- geocode_server(
"https://services.nconemap.gov/secure/rest/services/AddressNC/AddressNC_geocoder/GeocodeServer",
token = NULL
)
res <- find_address_candidates(
address = "rowan coffee",
city = "asheville",
geocoder = address_nc
)
dplyr::glimpse(res[, 1:10])
#> Rows: 2
#> Columns: 11
#> $ input_id <int> 1, 1
#> $ result_id <int> NA, NA
#> $ loc_name <chr> NA, NA
#> $ status <chr> "T", "T"
#> $ score <dbl> 78, 78
#> $ match_addr <chr> "ASHEVILLE", "ASHEVILLE"
#> $ long_label <chr> "ASHEVILLE", "ASHEVILLE"
#> $ short_label <chr> "ASHEVILLE", "ASHEVILLE"
#> $ addr_type <chr> "Locality", "Locality"
#> $ type_field <chr> "City", "City"
#> $ geometry <POINT [US_survey_foot]> POINT (943428.1 681596.4), POINT (948500.3 631973.4)…