Geocode from R using {arcgisgeocode}

With the new package {arcgisgeocode}, which is part of the {arcgis} metapackage and the R-ArcGIS Bridge, you can now geocode directly from R! Geocoding is the process of identifying the location associated with an address. {arcgisgeocode} enables users to do this – and so much more – with functions for address candidate identification, batch geocoding, reverse geocoding, and autocomplete location suggestions. You can use the ArcGIS World Geocoding Service or even make use of custom locators created in ArcGIS Pro, ArcGIS Online, and ArcGIS Enterprise, as shown in the example below. Let’s take a quick look at how you can use {arcgisgeocode} to locate bike shops in Tacoma, Washington.

# install arcgisgeocode
install.packages("arcgisgeocode")

# load the package
library(arcgisgeocode)

# load arcgisutils
library(arcgisutils)

# set the token for authorization
set_arc_token(auth_user())

# load the bike shop addresses
csv <- "https://www.arcgis.com/sharing/rest/content/items/9a9b91179ac44db1b689b42017471ae6/data"
bikeshops <- readr::read_csv(csv) |>
  # add result_id field
  dplyr::mutate(
    result_id = dplyr::row_number()
  )
bikeshops
#> # A tibble: 10 × 3
#>    store_name                           original_address               result_id
#>    <chr>                                <chr>                              <int>
#>  1 Cascadia Wheel Co.                   3320 N Proctor St, Tacoma, WA…         1
#>  2 Puget Sound Bike and Ski Shop        between 3206 N. 15th and 1414…         2
#>  3 Takoma Bike & Ski                    3010 6th Ave, Tacoma, WA 98406         3
#>  4 Trek Bicycle Tacoma University Place 3550 Market Pl W Suite 102, U…         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 …         7
#>  8 Second Cycle                         1205 M.L.K. Jr Way, Tacoma, W…         8
#>  9 Penny bike co.                       6419 24th St NE, Tacoma, WA 9…         9
#> 10 Spider's Bike, Ski & Tennis Lab      3608 Grandview St, Gig Harbor…        10

# create a GeocodeServer object for the Tacoma custom locator
tacoma_gc <- geocode_server('https://gis.cityoftacoma.org/arcgis/rest/services/Locators/Tacoma_Geocoding_Service/GeocodeServer')
tacoma_gc
#> <GeocodeServer>
#> Version: 10.81
#> CRS: 2927

# geocode
candidates <- geocode_addresses(
   single_line = bikeshops$original_address,
   for_storage = TRUE,
   geocoder = tacoma_gc
   )

# reformat geocoded output
candidates <- candidates|>
  # select the relevant geocoding result columns
  dplyr::select(c(result_id:addr_type, geometry)) |>
  # merge back to bikeshops
  dplyr::left_join(bikeshops) |>
  # rearrange columns for readability
  dplyr::relocate(store_name:original_address, .after = result_id)

# view the results
dplyr::glimpse(candidates)
#> Rows: 10
#> Columns: 11
#> $ result_id        <int> 1, 3, 4, 5, 7, 6, 2, 8, 10, 9
#> $ store_name       <chr> "Cascadia Wheel Co.", "Takoma Bike & Ski", "Trek Bicy…
#> $ original_address <chr> "3320 N Proctor St, Tacoma, WA 98407", "3010 6th Ave,…
#> $ loc_name         <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA
#> $ status           <chr> "M", "M", "M", "M", "M", "U", "M", "M", "U", "M"
#> $ score            <dbl> 100.00, 100.00, 100.00, 100.00, 100.00, 0.00, 95.68, …
#> $ match_addr       <chr> "3320 N PROCTOR ST, Tacoma, WA, 98407", "3010 6TH AVE…
#> $ long_label       <chr> "3320 N PROCTOR ST, Tacoma, WA, 98407", "3010 6TH AVE…
#> $ short_label      <chr> "3320 N PROCTOR ST", "3010 6TH AVE", "3550 MARKET PL …
#> $ addr_type        <chr> "PointAddress", "PointAddress", "PointAddress", "Poin…
#> $ geometry         <POINT [US_survey_foot]> POINT (1146741 715552.6), POINT (1149922 706941.5), P…

# check for any status other than matched
candidates |>
   dplyr::filter(status != "M"]) |>
   dplyr::glimpse()
#> Rows: 2
#> Columns: 11
#> $ result_id        <int> 6, 10
#> $ store_name       <chr> "Sound Bikes", "Spider's Bike, Ski & Tennis Lab"
#> $ original_address <chr> "108 W Main, Puyallup, WA 98371", "3608 Grandview St,…
#> $ loc_name         <chr> NA, NA
#> $ status           <chr> "U", "U"
#> $ score            <dbl> 0, 0
#> $ match_addr       <chr> NA, NA
#> $ long_label       <chr> NA, NA
#> $ short_label      <chr> NA, NA
#> $ addr_type        <chr> NA, NA
#> $ geometry         <POINT [US_survey_foot]> POINT EMPTY, POINT EMPTY

Two bike shop addresses were not matched to a location in Tacoma. Upon reviewing the locations, these correspond to bike shops that are outside Tacoma city limits. You can remove these locations from your results. Then, view the score values assigned to all of the matching locations.

# only use the matched locations
candidates <- candidates[candidates$status == "M", ]

# view the geocoding scores
candidates$score
#> [1] 100.00 100.00 100.00 100.00 100.00  95.68 100.00 100.00

The scores look good – a score of 100 indicates a perfect match. You can move on to visualizing these results.

Learn more about geocoding results here.

# load arcgislayers
library(arcgislayers)

# publish the geocoding results as a hosted feature layer
res <- publish_layer(candidates, "Tacoma Bike Shops")
res$services$serviceurl
#> [1] "https://services1.arcgis.com/hLJbHVT9ZrDIzK0I/arcgis/rest/services/Tacoma Bike Shops/FeatureServer"

Now you can explore the published data in ArcGIS Online. From there, you can create and share web maps and apps, like this ArcGIS Dashboard: