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

rmapzen provides access to the following Mapzen API services:

  • Search: Structured and unstructured search and geocoding, reverse geocoding, and autocomplete.
  • Vector Tiles: Basemap vector data.
  • Isochrone: Calculation of areas reachable from a specified locations.

Installation and Set-up

rmapzen is available on CRAN. To install:

install.packages("rmapzen")

You’ll also need to set up options specific to the API provider you end up using. 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:

  • geocode.earth, for search services. Use the function mz_set_search_host_geocode.earth
  • Nextzen, for vector tiles. Use the function mz_set_tile_host_nextzen.
  • NYC GeoSearch, for search services using New York City’s Property Address Directory. Use mz_set_search_host_nyc_geosearch.

As of this writing, there are no public providers offering the Mapzen isochrone service, but mz_isochrone and related functions are available. If you know of a provider not listed here, use the more general mz_set_host function, and submit an issue so we can create a custom set up function for future users.

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. For instance, continuing the previous example:

library(tidyverse)
library(sf)

# mz_bbox is a generic that returns the bounding box of an object
oakland_tiles <- mz_vector_tiles(mz_bbox(oakland_public))

# vector tiles return all layers (roads, water, buildings, etc) in a list
roads <- as_sf(oakland_tiles$roads) %>% 
    filter(kind != "ferry")
water <- as_sf(oakland_tiles$water)

labels <- as.data.frame(oakland_public) %>% 
    mutate(name = str_replace_all(
        name, 
        "(Oakland Public Library)|(Branch)", ""))

# make a quick static map that includes roads and oceans as reference
ggplot() +
    geom_sf(data = water, 
            fill = "lightblue", colour = NA) + 
    geom_sf(data = roads, 
            size = .2, colour = "gray30") + 
    geom_sf(data = as_sf(oakland_public), 
            colour = "black", size = 1) + 
    ggrepel::geom_label_repel(
        data = labels,
        aes(x = lon, y = lat, label = name), size = 3,
        family = "Roboto Condensed", label.padding = unit(.1, "lines"),
        alpha = .7) +
    theme_void() + 
    theme(panel.grid.major = element_line(size = 0))

sf and Spatial*DataFrame conversion

As some of the above examples illustrate, 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:

require(dplyr)
as.data.frame(oakland_public) %>%
    select(name, confidence, region, locality, neighbourhood)
## # A tibble: 25 x 5
##    name                             confidence region    locality neighbourhood 
##    <chr>                                 <dbl> <chr>     <chr>    <chr>         
##  1 Oakland Public Library - Temesc…      0.926 Californ… Oakland  Shafter       
##  2 Oakland Public Library - Rockri…      0.926 Californ… Oakland  Rockridge     
##  3 Lakeview Branch Oakland Public …      0.664 Californ… Oakland  <NA>          
##  4 Golden Gate Branch Oakland Publ…      0.663 Californ… Oakland  Gaskill       
##  5 Brookfield Village Branch Oakla…      0.663 Californ… Oakland  South Stonehu…
##  6 West Oakland Branch Oakland Pub…      0.663 Californ… Oakland  Ralph Bunche  
##  7 Elmhurst Branch Oakland Public …      0.663 Californ… Oakland  Webster       
##  8 Montclair Branch Oakland Public…      0.663 Californ… Oakland  Montclair     
##  9 Main Branch Oakland Public Libr…      0.663 Californ… Oakland  Civic Center  
## 10 Latin American Branch Oakland P…      0.663 Californ… Oakland  St. Elizabeth 
## # … with 15 more rows

Accessor methods

Currently, 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_coordinates(oakland_public)
## # A tibble: 20 x 2
##      lon   lat
##    <dbl> <dbl>
##  1 -122.  37.8
##  2 -122.  37.8
##  3 -122.  37.8
##  4 -122.  37.8
##  5 -122.  37.8
##  6 -122.  37.8
##  7 -122.  37.8
##  8 -122.  37.8
##  9 -122.  37.8
## 10 -122.  37.8
## 11 -122.  37.8
## 12 -122.  37.8
## 13 -122.  37.8
## 14 -122.  37.8
## 15 -122.  37.8
## 16 -122.  37.8
## 17 -122.  37.7
## 18 -122.  37.8
## 19 -122.  37.8
## 20 -122.  37.8
mz_bbox(oakland_tiles)
## # A tibble: 1 x 4
##   min_lon min_lat max_lon max_lat
## *   <dbl>   <dbl>   <dbl>   <dbl>
## 1   -122.    37.7   -122.    37.9

Convenience features

Several of the search functions take, optionally, the arguments layers, sources, and boundary.country (the latter requires ISO-3166 codes). If you’re using an IDE with auto-complete, the objects mz_layers, mz_sources, and mz_countries should make it easier to get the correct codes.

Easy lookup for ISO-3166 codes

Similarly, argument constructors are available for easier specifications of isochrone and search requests.

  • mz_costing
  • mz_costing_options
  • mz_location
  • mz_contours
  • mz_date_time