rmapzen is a client for any implementation of the Mapzen API. Though Mapzen itself has gone out of business, rmapzen can be set up to work with any provider who hosts Mapzen’s open-source software, including geocode.earth, Nextzen, and NYC GeoSearch from NYC Planning Labs. For more information, see https://www.mapzen.com/documentation/. The project is available on github as well as CRAN.
rmapzen provides access to the following Mapzen API services:
rmapzen works with API providers who implement the Mapzen API. In order to specify provider information (such as URL and API key), use mz_set_host. There are custom set-up functions for the following providers:
mz_set_search_host_geocode.earthmz_set_tile_host_nextzen.mz_set_search_host_nyc_geosearch.As of this writing, there are no public providers offering the Mapzen isochrone service.
All of the services in Mapzen search have been implemented. Search functions:
mz_searchmz_reverse_geocodemz_autocompletemz_placemz_structured_search (what’s this?)Each of those functions returns a mapzen_geo_list. The sample dataset oakland_public contains the results of mz_search("Oakland public library branch") on January 8, 2017:
#> GeoJSON response from Mapzen
#> Attribution info: https://search.mapzen.com/v1/attribution
#> Bounds (lon/lat): (-122.29, 37.74) - (-122.17, 37.85)
#> 25 locations:
#> Oakland Public Library - Temescal Branch (-122.26, 37.84)
#> Oakland Public Library - Rockridge Branch (-122.25, 37.84)
#> Lakeview Branch Oakland Public Library (-122.25, 37.81)
#> Golden Gate Branch Oakland Public Library (-122.28, 37.84)
#> Brookfield Village Branch Oakland Public Library (-122.19, 37.74)
#> ...mz_bbox(oakland_public)
#> # A tibble: 1 x 4
#> min_lon min_lat max_lon max_lat
#> <dbl> <dbl> <dbl> <dbl>
#> 1 -122. 37.7 -122. 37.8
as.data.frame(oakland_public)
#> # A tibble: 25 x 26
#> id gid layer source source_id name housenumber confidence accuracy
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <chr>
#> 1 way:1… openst… venue opens… way:1256… Oaklan… 5205 0.926 point
#> 2 way:4… openst… venue opens… way:4325… Oaklan… <NA> 0.926 point
#> 3 way:3… openst… venue opens… way:3697… Lakevi… <NA> 0.664 point
#> 4 53528… geonam… venue geona… 5352843 Golden… <NA> 0.663 point
#> 5 node:… openst… venue opens… node:368… Brookf… <NA> 0.663 point
#> 6 way:4… openst… venue opens… way:4391… West O… 1801 0.663 point
#> 7 node:… openst… venue opens… node:368… Elmhur… <NA> 0.663 point
#> 8 node:… openst… venue opens… node:368… Montcl… <NA> 0.663 point
#> 9 way:2… openst… venue opens… way:2837… Main B… 125 0.663 point
#> 10 node:… openst… venue opens… node:368… Latin … <NA> 0.663 point
#> # … with 15 more rows, and 17 more variables: country <chr>, country_gid <chr>,
#> # country_a <chr>, region <chr>, region_gid <chr>, region_a <chr>,
#> # county <chr>, county_gid <chr>, locality <chr>, locality_gid <chr>,
#> # neighbourhood <chr>, neighbourhood_gid <chr>, label <chr>, street <chr>,
#> # postalcode <chr>, lon <dbl>, lat <dbl>Search can, optionally, be constrained to a particular country, data layer, boundary rectangle, or boundary circle. Furthermore, search can prioritize results near a given “focus” point. See ?mz_search.
rmapzen provides an interface to Mapzen’s vector tiles service. Tile requests can be specified using the x, y, zoom coordinates of the tile service, as well as with a lat/long bounding box. Multiple tiles are stitched together and returned as an object of class mz_vector_tiles. See ?mz_vector_tiles. The sample data set ca_tiles contains zoomed out vector tile data for all of California as well as parts of neighboring states.
ca_tiles
#> Mapzen vector tile data
#> Layers: (count of features in parentheses)
#> water (144)
#> buildings (0)
#> places (28)
#> transit (10)
#> pois (30)
#> boundaries (22)
#> roads (308)
#> earth (4)
#> landuse (176)Each element of a vector tile response includes point, line, and/or polygon data for an individual map layer, and has class mapzen_vector_layer. Like other response types, the mapzen_vector_layer can be converted to sf and sp objects for further processing, using the generic functions as_sf and as_sp.
# points of interest
as_sf(ca_tiles$pois)
#> Registered S3 method overwritten by 'geojsonsf':
#> method from
#> print.geojson geojson
#> Simple feature collection with 30 features and 11 fields
#> Geometry type: POINT
#> Dimension: XY
#> Bounding box: xmin: -123.536 ymin: 32.009 xmax: -112.58 ymax: 48.808
#> Geodetic CRS: WGS 84
#> # A tibble: 30 x 12
#> kind protect_class area operator source min_zoom tier osm_relation name
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 natio… 2 13775… United … opens… 5.58 1 TRUE Crat…
#> 2 natio… 2 20353… United … opens… 5.29 1 TRUE Moun…
#> 3 natio… 2 21324… United … opens… 3.6 1 TRUE Deat…
#> 4 natio… 2 25430… United … opens… 5.13 1 TRUE Crat…
#> 5 natio… 2 25524… United … opens… 5.13 1 TRUE Sequ…
#> 6 natio… 2 27404… United … opens… 5.08 1 TRUE Nort…
#> 7 natio… 2 28128… United … opens… 5.06 1 TRUE King…
#> 8 natio… 2 46710… United … opens… 4.7 1 TRUE Josh…
#> 9 natio… 2 48587… United … opens… 4.67 1 TRUE Yose…
#> 10 natio… 2 77901… United … opens… 4.33 1 TRUE Olym…
#> # … with 20 more rows, and 3 more variables: id <chr>, name.de <chr>,
#> # geometry <POINT [°]>sf and Spatial*DataFrame conversionAny object returned by a Mapzen service can be converted to the appropriate Spatial*DataFrame or sf object using the generics as_sp and as_sf, for easy interoperability with other packages. You can also convert most objects directly to data frames, allowing for use within tidy pipelines:
library(dplyr)
library(sf)
as_sf(oakland_public) %>%
select(name, confidence, region, locality, neighbourhood)
#> Simple feature collection with 25 features and 5 fields
#> Geometry type: POINT
#> Dimension: XY
#> Bounding box: xmin: -122.2854 ymin: 37.73742 xmax: -122.1749 ymax: 37.84632
#> Geodetic CRS: WGS 84
#> # A tibble: 25 x 6
#> name confidence region locality neighbourhood geometry
#> <chr> <dbl> <chr> <chr> <chr> <POINT [°]>
#> 1 Oakland P… 0.926 Califo… Oakland Shafter (-122.2625 37.83824)
#> 2 Oakland P… 0.926 Califo… Oakland Rockridge (-122.2511 37.84)
#> 3 Lakeview … 0.664 Califo… Oakland <NA> (-122.249 37.80919)
#> 4 Golden Ga… 0.663 Califo… Oakland Gaskill (-122.2822 37.83937)
#> 5 Brookfiel… 0.663 Califo… Oakland South Stoneh… (-122.1886 37.73742)
#> 6 West Oakl… 0.663 Califo… Oakland Ralph Bunche (-122.2854 37.81296)
#> 7 Elmhurst … 0.663 Califo… Oakland Webster (-122.1749 37.75154)
#> 8 Montclair… 0.663 Califo… Oakland Montclair (-122.2141 37.83204)
#> 9 Main Bran… 0.663 Califo… Oakland Civic Center (-122.2638 37.80101)
#> 10 Latin Ame… 0.663 Califo… Oakland St. Elizabeth (-122.2225 37.78354)
#> # … with 15 more rowsCurrently, the following methods are available to pull out commonly used pieces of a response:
mz_coordinates (only available for search results): extracts lat/lon coordinates from search results, and returns them as a data.frame.mz_bbox: returns the bounding box of an object as a data.frame with columns min_lon, min_lat, max_lon, and max_lat.mz_bbox(ca_tiles)
#> # A tibble: 1 x 4
#> min_lon min_lat max_lon max_lat
#> * <dbl> <dbl> <dbl> <dbl>
#> 1 -135 32.0 -112. 48.9