Database of available categories that can be used for filtering results
provided by arc_geo(), arc_geo_multi() and arc_geo_categories() in
tibble format.
Format
A tibble with
376 rows and
fields:
- level_1
Top-level category
- level_2
Second-level category
- level_3
Child-level category
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 the application building process, as developers don't need to know what types of places their users are searching for, because 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 parameter. The category parameter
limits the types of places for which the service searches, thus eliminating
false positive matches and potentially speeding up the search process.
The results shows 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 be included also in the results.
See also
arc_geo_categories(), arc_geo(), arc_geo_multi()
Other datasets:
arc_spatial_references
Examples
# \donttest{
# Get all possible values
data("arc_categories")
arc_categories
#> # A tibble: 376 × 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 Name NA
#> 8 Postal Primary Postal NA
#> 9 Postal Postal Locality NA
#> 10 Postal Postal Extension NA
#> # ℹ 366 more rows
# Using 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, 40.74846
#> $ lon <dbl> -73.95995, -73.99177
#> $ address <chr> "Sea", "Sea"
#> $ score <int> 100, 100
#> $ x <dbl> -73.95995, -73.99177
#> $ y <dbl> 40.71816, 40.74846
#> $ LongLabel <chr> "Sea, 114 N 6th St, Brooklyn, NY, 11249, USA", "Sea, 151 W …
#> $ Type <chr> "Restaurant", "Restaurant"
#> $ xmin <dbl> -73.96095, -73.99277
#> $ ymin <dbl> 40.71716, 40.74746
#> $ xmax <dbl> -73.95895, -73.99077
#> $ ymax <dbl> 40.71916, 40.74946
#> $ wkid <int> 4326, 4326
#> $ latestWkid <int> 4326, 4326
# We can 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")
)
#>
#> No results for query sea
dplyr::glimpse(sea_3)
#> Rows: 1
#> Columns: 3
#> $ query <chr> "sea"
#> $ lat <dbl> NA
#> $ lon <dbl> NA
# }