The goal of osmenrich is to easily enrich geocoded data
(latitude/longitude) with geographic features from OpenStreetMap
(OSM). The main language of the package is R and this package is
designed to work with the sf and
osmdata
packages for collecting and manipulating geodata.
To install the package, you first need to have the remotes package
installed. If you do not have this package yet, please install it first
with:
install.packages("remotes")If you do have this package, due to recent changes in GitHub’s naming of
branches, please make sure you have the latest version of remotes or
at least version 2.2.
Once you did this, to continue the installation of the osmenrich
package, run:
remotes::install_github("sodascience/osmenrich@main")or, for the development version, run:
remotes::install_github("sodascience/osmenrich@develop")This will use the default public APIs for OSM data and routing (for
computing driving/walking distances and durations). Do not use
osmenrich with default APIs for large datasets! If you want to
learn how to use osmenrich for large queries follow the instructions
in section Local API Setup below.
Let’s enrich a spatial (sf) dataset (sf_example) with the number of
waste baskets in a radius of 500 meters from each of the point specified
in a dataset:
# Import libraries
library(tidyverse)
library(sf)
library(osmenrich)
# Create an example dataset to enrich
sf_example <-
tribble(
~person, ~lat, ~lon,
"Alice", 52.12, 5.09,
"Bob", 52.13, 5.08,
) %>%
sf::st_as_sf(
coords = c("lon", "lat"),
crs = 4326
)
# Print it
sf_example
#> Simple feature collection with 2 features and 1 field
#> geometry type: POINT
#> dimension: XY
#> bbox: xmin: 5.08 ymin: 52.12 xmax: 5.09 ymax: 52.13
#> CRS: EPSG:4326
#> # A tibble: 2 x 2
#> person geometry
#> * <chr> <POINT [°]>
#> 1 Alice (5.09 52.12)
#> 2 Bob (5.08 52.13)To enrich the sf_example dataset with “waste baskets” in a 500m
radius, you can create a query using the enrich_osm() function. This
function uses the bounding box created by the points present in the
example dataset and searches for the specified key = "amenity" and
value = "waste_basket. You can also add a custom name for the newly
created column and specify the radius (r) used in the search. See Map
Features on the website of
OSM for a complete
list of key and value combinations.
# Simple OSMEnrich query
sf_example_enriched <- sf_example %>%
enrich_osm(
name = "n_waste_baskets",
key = "amenity",
value = "waste_basket",
r = 500
)
#> Downloading data for waste_baskets... Done.
#> Downloaded 147 points, 0 lines, 0 polygons, 0 mlines, 0 mpolygons.
#> Computing distance matrix for n_waste_baskets...Done.The resulting enriched dataset sf_example_enriched is a sf object
and can be printed as usual to inspect the newly added column
n_waste_baskets.
sf_example_enriched
#> Simple feature collection with 2 features and 2 fields
#> geometry type: POINT
#> dimension: XY
#> bbox: xmin: 5.08 ymin: 52.12 xmax: 5.09 ymax: 52.13
#> geographic CRS: WGS 84
#> # A tibble: 2 x 3
#> person geometry n_waste_baskets
#> * <chr> <POINT [°]> <int>
#> 1 Alice (5.09 52.12) 75
#> 2 Bob (5.08 52.13) 1The waste baskets column is now the result of summing all the wastebaskets in a 500 meter radius for Alice and Bob:
OSM enrichment can ask for a lot of data, which can overload public APIs. If you intend to enrich large amounts of data or compute routing distances (e.g., driving duration) between many points, you should set up a local API endpoint.
Multiple docker-compose workflows for doing this are avaialble in the
separate osmenrich_docker
repository. Use the
README in the repository to select the workflow that fits your desired
outcome.
Contributions are what make the open source community an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.
Please refer to the CONTRIBUTING file for more information on issues and pull requests.
The osmenrich package is published under the MIT license. When using
osmenrich for academic work, please cite:
van Kesteren, Erik-Jan, Vida, Leonardo, de Bruin, Jonathan, & Oberski, Daniel. (2021, February 11).
Enrich sf Data with Geographic Features from OpenStreetMaps (Version v1.0). Zenodo. http://doi.org/10.5281/zenodo.4534188
This package is developed and maintained by the ODISSEI Social Data Science (SoDa) team.
Do you have questions, suggestions, or remarks? File an issue in the issue tracker or feel free to contact Erik-Jan van Kesteren ([@ejvankesteren](https://twitter.com/ejvankesteren)) or Leonardo Vida ([@leonardojvida](https://twitter.com/leonardojvida))