arcgisplaces

arcgisplaces is an R package to interface with ArcGIS Places Service.

The places service is a ready-to-use location service that can search for businesses and geographic locations around the world. It allows you to find, locate, and discover detailed information about each place.

In order to use {arcgisplaces} you will need an ArcGIS Developers account. Get started here.

Installation

You can install a binary of the development version of arcgisplaces from r-universe with:

install.packages("arcgisplaces", repos = "https://r-arcgis.r-universe.dev")

You will also need the development version of {arcgisutils}

if (!requireNamespace("pak")) install.packages("pak")
pak::pak("r-arcgis/arcgisutils")

Building from source

Or, you can install the development version from GitHub. Note the development version requires an installation of Rust. See rustup for instructions to install Rust.

if (!requireNamespace("pak")) install.packages("pak")
pak::pak("r-arcgis/arcgisplaces")

Usage

Finding places:

• near_point(): search for places near a location.
• within_extent(): search for places within an extent.
• place_details(): get detailed information about the places returned from near_point() or within_extent().
  • Note: see fields for the possible attributes to return for place details.

Understanding categories:

• categories(): find categories by name or ID.

• category_details(): get detailed information about the categories returned from categories().

• Find place attributes such as name, address, description, opening hours, price ratings, user ratings, and social links.

Examples

{arcgisutils} is needed for authentication. The Places API supports either using an API key via auth_key() or one generated via OAuth2 using either auth_client() or auth_code(). See the Places API documentation for more.

library(arcgisutils)
library(arcgisplaces)

# Authenticate with a Developer Account API Key
token <- auth_key()
set_arc_token(token)

Place search

You can search for places near a location with near_point().

coffee <- near_point(x = -122.334, y = 47.655, search_text = "Coffee")
coffee
#> Simple feature collection with 8 features and 5 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: -122.3426 ymin: 47.65539 xmax: -122.3255 ymax: 47.66175
#> Geodetic CRS:  WGS 84
#> # A data frame: 8 × 6
#>   place_id             name  distance categories icon              geometry
#> * <chr>                <chr>    <dbl> <I<list>>  <chr>          <POINT [°]>
#> 1 f6059fc575735b5e3f5… Irwi…      97  <df>       <NA>  (-122.3328 47.65539)
#> 2 88a10ccf031f02ef269… Fuel…     724. <df>       <NA>  (-122.3369 47.66122)
#> 3 5cc2d40bf37bff28738… Youn…     728. <df>       <NA>  (-122.3331 47.66152)
#> 4 a8c6da1aa0d08fe96e5… Frid…     741. <df>       <NA>   (-122.342 47.65895)
#> 5 906da2fe5164619199a… Star…     767. <df>       <NA>  (-122.3361 47.66175)
#> 6 957c39de6e0a0eb8afe… Mosa…     774  <df>       <NA>  (-122.3276 47.66048)
#> 7 4bdfa82268e67a698d0… A Mu…     964. <df>       <NA>  (-122.3255 47.66149)
#> 8 090286b411e3337850e… The …     976. <df>       <NA>  (-122.3426 47.66162)

Locations are returned as an sf object with the place ID, the place name, distance from the search point, a character vector of categories.

Tip

arcgisplaces will return an sf object, but the sf package is not required to work with the package. The sf print method will not be used unless the package is loaded. If package size is a consideration—i.e. deploying an app in a Docker container—consider using wk or rsgeo.

Details for the places can be fetched using place_details(). The possible fields are documented online as well as contained in the exported vector fields. Because pricing is dependent upon which fields are requested, it is a required argument.

To get the add requested_fields = "hours". Note, that the other possible fields will still be present in the result, but completely empty.

details <- place_details(
  coffee$place_id,
  requested_fields = "rating",
  .progress = FALSE # remove progress bar
)

details[c("price", "user")]
#> Simple feature collection with 8 features and 2 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: Inf ymin: Inf xmax: -Inf ymax: -Inf
#> Geodetic CRS:  WGS 84
#> # A data frame: 8 × 3
#>   price     user    location
#> * <chr>    <dbl> <POINT [°]>
#> 1 Cheap      4.1       EMPTY
#> 2 Cheap      3.9       EMPTY
#> 3 <NA>      NA         EMPTY
#> 4 Moderate  NA         EMPTY
#> 5 Cheap      3.4       EMPTY
#> 6 Cheap      3         EMPTY
#> 7 Cheap      4         EMPTY
#> 8 <NA>      NA         EMPTY

Or, you can search for places within a bounding box using within_extent(). This could be quite handy for searching within current map bounds, for example.

bakeries <- within_extent(
  -70.356, 43.588, -70.176, 43.7182,
  category_id = "13002"
)

bakeries[c("name")]
#> Simple feature collection with 24 features and 1 field
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: -70.356 ymin: 43.588 xmax: -70.176 ymax: 43.7182
#> Geodetic CRS:  WGS 84
#> # A data frame: 24 × 2
#>    name                                geometry
#>  * <chr>                            <POINT [°]>
#>  1 Panera Bread            (-70.32966 43.67791)
#>  2 Crumbl Cookies          (-70.33067 43.67675)
#>  3 Electric Bike Cafe       (-70.2864 43.63655)
#>  4 BenReuben’s Knishery    (-70.25299 43.63748)
#>  5 Two Fat Cats Bakery      (-70.26101 43.6327)
#>  6 Auntie Anne's           (-70.33517 43.63372)
#>  7 Lolli and Pops          (-70.33512 43.63377)
#>  8 Panera Bread              (-70.3303 43.6367)
#>  9 Cookie Jar Pastry Shop  (-70.22644 43.63367)
#> 10 Bake Maine Pottery Cafe (-70.25334 43.66708)
#> # ℹ 14 more rows