ArcGIS REST API category database

Database of available categories used to filter results provided by arc_geo(), arc_geo_multi() and arc_geo_categories() in tibble format.

Format

A tibble with 383 rows and fields:

level_1: Top-level category.
level_2: Second-level category.
level_3: Child-level category.

Source

ArcGIS REST Category filtering.

Details

See ArcGIS REST Category filtering for details and examples.

The geocoding service allows users to search for and geocode many types of addresses and places around the world. This simplifies application development because developers do not need to know what types of places their users are searching for; the service can decipher that. However, due to this flexibility, it is possible for ambiguous searches to match to many different places, and users may sometimes receive unexpected results. For example, a search for a city may match to a street name, or a search for an airport code may match to a country abbreviation.

For such cases, the service provides the ability to filter out unwanted geocode results with the category argument. The category argument limits the types of places for which the service searches, thus eliminating false positive matches and potentially speeding up the search process.

The results show a list of categories with three different hierarchy levels (level_1, level_2, level_3). If a level_1 category is requested (i.e. POI), the child categories may also be included in the results.

Note

Data extracted on 15 January 2026.

Examples

# \donttest{
# Get all possible values.
data("arc_categories")
arc_categories
#> # A tibble: 383 × 3
#>    level_1 level_2         level_3
#>    <chr>   <chr>           <chr>  
#>  1 Address Subaddress      NA     
#>  2 Address Point Address   NA     
#>  3 Address Street Address  NA     
#>  4 Address Distance Marker NA     
#>  5 Address Intersection    NA     
#>  6 Address Street Midblock NA     
#>  7 Address Street Between  NA     
#>  8 Address Street Name     NA     
#>  9 Postal  Primary Postal  NA     
#> 10 Postal  Postal Locality NA     
#> # ℹ 373 more rows

# Use categories.

sea_1 <- arc_geo("sea",
  custom_query = list(outFields = c("LongLabel", "Type")),
  limit = 2
)

dplyr::glimpse(sea_1)
#> Rows: 2
#> Columns: 15
#> $ query      <chr> "sea", "sea"
#> $ lat        <dbl> 47.44362, 47.44899
#> $ lon        <dbl> -122.3029, -122.3093
#> $ address    <chr> "SEA", "SEA"
#> $ score      <int> 100, 100
#> $ x          <dbl> -122.3029, -122.3093
#> $ y          <dbl> 47.44362, 47.44899
#> $ LongLabel  <chr> "SEA, 17801 International Blvd, Seatac, WA, 98158, USA", "S…
#> $ Type       <chr> "Airport", "Airport"
#> $ xmin       <dbl> -122.3189, -122.3393
#> $ ymin       <dbl> 47.42762, 47.41899
#> $ xmax       <dbl> -122.2869, -122.2793
#> $ ymax       <dbl> 47.45962, 47.47899
#> $ wkid       <int> 4326, 4326
#> $ latestWkid <int> 4326, 4326

# An airport, but if we use categories...

sea_2 <- arc_geo("sea",
  custom_query = list(outFields = c("LongLabel", "Type")),
  limit = 2, category = "Food"
)

dplyr::glimpse(sea_2)
#> Rows: 2
#> Columns: 15
#> $ query      <chr> "sea", "sea"
#> $ lat        <dbl> 40.71816, 47.86564
#> $ lon        <dbl> -73.959946, -4.221409
#> $ address    <chr> "Sea", "Sea"
#> $ score      <int> 100, 100
#> $ x          <dbl> -73.959946, -4.221409
#> $ y          <dbl> 40.71816, 47.86564
#> $ LongLabel  <chr> "Sea, 114 N 6th St, Brooklyn, NY, 11249, USA", "Sea, 6 Rue …
#> $ Type       <chr> "Restaurant", "Restaurant"
#> $ xmin       <dbl> -73.960946, -4.226409
#> $ ymin       <dbl> 40.71716, 47.86064
#> $ xmax       <dbl> -73.958946, -4.216409
#> $ ymax       <dbl> 40.71916, 47.87064
#> $ wkid       <int> 4326, 4326
#> $ latestWkid <int> 4326, 4326

# Use a list of categories.
sea_3 <- arc_geo("sea",
  custom_query = list(outFields = c("LongLabel", "Type")),
  sourcecountry = "UK", limit = 5,
  category = c("Amusement Park", "Aquarium")
)

dplyr::glimpse(sea_3)
#> Rows: 5
#> Columns: 15
#> $ query      <chr> "sea", "sea", "sea", "sea", "sea"
#> $ lat        <dbl> 50.81961, 53.81288, 53.46756, 52.93494, 54.30120
#> $ lon        <dbl> -0.1357540, -3.0548750, -2.3409094, 0.4834270, -0.4097964
#> $ address    <chr> "Sea Life Brighton", "Sea Life Blackpool", "Sea Life Manche…
#> $ score      <dbl> 82.00, 81.88, 81.76, 81.76, 81.67
#> $ x          <dbl> -0.1357540, -3.0548750, -2.3409094, 0.4834270, -0.4097964
#> $ y          <dbl> 50.81961, 53.81288, 53.46756, 52.93494, 54.30120
#> $ LongLabel  <chr> "Sea Life Brighton, 1 Marine Parade, Queen's Park, Brighton…
#> $ Type       <chr> "Aquarium", "Aquarium", "Aquarium", "Aquarium", "Aquarium"
#> $ xmin       <dbl> -0.1407540, -3.0598750, -2.3459094, 0.4784270, -0.4147964
#> $ ymin       <dbl> 50.81461, 53.80788, 53.46256, 52.92994, 54.29620
#> $ xmax       <dbl> -0.1307540, -3.0498750, -2.3359094, 0.4884270, -0.4047964
#> $ ymax       <dbl> 50.82461, 53.81788, 53.47256, 52.93994, 54.30620
#> $ wkid       <int> 4326, 4326, 4326, 4326, 4326
#> $ latestWkid <int> 4326, 4326, 4326, 4326, 4326
# }

Format

Source

Details

Note

See also

Examples