Introduction to rmapzen

Tarak Shah

2018-10-07

Introduction

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://mapzen.com/documentation/. The project is available on github as well as CRAN.

rmapzen provides access to the following Mapzen API services:

Set-up

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:

As of this writing, there are no public providers offering the Mapzen isochrone service.

Vector tile service

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)
#> Simple feature collection with 30 features and 11 fields
#> geometry type:  POINT
#> dimension:      XY
#> bbox:           xmin: -123.536 ymin: 32.009 xmax: -112.58 ymax: 48.808
#> epsg (SRID):    4326
#> proj4string:    +proj=longlat +datum=WGS84 +no_defs
#> # A tibble: 30 x 12
#>    kind  protect_class area  operator source min_zoom tier  osm_relation
#>    <chr> <chr>         <chr> <chr>    <chr>  <chr>    <chr> <chr>       
#>  1 nati… 2             1377… United … opens… 5.58     1     TRUE        
#>  2 nati… 2             2035… United … opens… 5.29     1     TRUE        
#>  3 nati… 2             2132… United … opens… 3.6      1     TRUE        
#>  4 nati… 2             2543… United … opens… 5.13     1     TRUE        
#>  5 nati… 2             2552… United … opens… 5.13     1     TRUE        
#>  6 nati… 2             2740… United … opens… 5.08     1     TRUE        
#>  7 nati… 2             2812… United … opens… 5.06     1     TRUE        
#>  8 nati… 2             4671… United … opens… 4.7      1     TRUE        
#>  9 nati… 2             4858… United … opens… 4.67     1     TRUE        
#> 10 nati… 2             7790… United … opens… 4.33     1     TRUE        
#> # ... with 20 more rows, and 4 more variables: name <chr>, id <chr>,
#> #   name.de <chr>, geometry <POINT [°]>

sf and Spatial*DataFrame conversion

Any 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
#> bbox:           xmin: -122.2854 ymin: 37.73742 xmax: -122.1749 ymax: 37.84632
#> epsg (SRID):    4326
#> proj4string:    +proj=longlat +datum=WGS84 +no_defs
#> # A tibble: 25 x 6
#>    name  confidence region locality neighbourho…                  geometry
#>    <chr>      <dbl> <chr>  <chr>    <chr>                      <POINT [°]>
#>  1 Oakl…      0.926 Calif… Oakland  Shafter           (-122.2625 37.83824)
#>  2 Oakl…      0.926 Calif… Oakland  Rockridge            (-122.2511 37.84)
#>  3 Lake…      0.664 Calif… Oakland  <NA>               (-122.249 37.80919)
#>  4 Gold…      0.663 Calif… Oakland  Gaskill           (-122.2822 37.83937)
#>  5 Broo…      0.663 Calif… Oakland  South Stone…      (-122.1886 37.73742)
#>  6 West…      0.663 Calif… Oakland  Ralph Bunche      (-122.2854 37.81296)
#>  7 Elmh…      0.663 Calif… Oakland  Webster           (-122.1749 37.75154)
#>  8 Mont…      0.663 Calif… Oakland  Montclair         (-122.2141 37.83204)
#>  9 Main…      0.663 Calif… Oakland  Civic Center      (-122.2638 37.80101)
#> 10 Lati…      0.663 Calif… Oakland  St. Elizabe…      (-122.2225 37.78354)
#> # ... with 15 more rows

Accessor methods

Currently, the following methods are available to pull out commonly used pieces of a response:

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