The goal of privlocR is to obtain meaningful tags for location data obtained in research, translating a list of longitudes and latitudes to a list of meaningful tags describing what is found near the various locations (e.g., shop, coastline, restaurant). Researchers fitting participants with wearables or collecting location data from smartphones can obtain coordinates of participant location over time1. However, given the coordinates of a participant’s location at a specific time, it would be interesting to know whether the participant is at a residential building, a shop, in a forest, or next to the sea. privlocR uses freely available, openly licensed data from the OpenStreetMap project2 to obtain that information easily, freely, privately, and reproducibly: Easily, as the package is hopefully easy to use. Freely, as data from OpenStreetMap is publicly available for free. Privately, as no data is not sent to any online service. Reproducibly, as running privlocR on the same downloaded map files will always return the same results, even as online map data changes. Additionally, privlocR code is available to understand how location data was analysed.
This is all in contrast to using commercial services which provide APIs that can obtain information about specific locations (see, for example, the function google_places from the googleway package). These APIs usually require setting up paid accounts with the various services, which adds complexity and cost; data is sent to the services, which could create privacy risks; analysis is not reproducible as online services update their maps, and their code is not available.
Installation
You can install the development version of privlocR from GitHub with:
# install.packages("pak")
pak::pak("haranse/privlocR")Example
Generating tags from a set of locations is as simple as placing an .osm.pbf map data file, which can be downloaded from Geofabrik, in a directory; selecting the distance around each location for which to search for tags; and running the get_close_tags function. The following example gets all location tags 100 meters or less from the two specified locations:
library(privlocR)
# Directory that contains pbf files (the R temporary dir in this case)
mydir <- tempdir()
# Populate the directory with map file(s) downloaded from OpenStreetMap
file.copy(system.file("extdata", "tokelau.osm.pbf", package = "privlocR"), mydir)
#> [1] TRUE
# The distance around each location in which we want to search for tags
mydst = units::set_units(100, m)
# Example longitude and latitude values
long = c(-9.1979860, -9.192079)
lat = c(-171.8501176, -171.856883)
get_close_tags(mydir, long, lat, dst = mydst)
#> Re-reading with feature count reset from 52 to 29
#> Re-reading with feature count reset from 4 to 2
#> [[1]]
#> [1] "landuse_residential" "amenity_restaurant" "natural_reef"
#> [4] "natural_coastline" "natural_scrub" "tourism_hotel"
#> [7] "leisure_park" "building_yes"
#>
#> [[2]]
#> [1] "natural_reef" "natural_coastline" "natural_water"
#> [4] "natural_wood" "building_yes"As we can see, the data tells us that while both locations are close to the ocean, the first is a more commercial area, around a hotel and a restauarant, while the second is only next to some unrecognized building.
Additional information including other ways to download map data, as well as advice regarding performance see the full tag workflow vignette.
Roadmap
There are additional ways to obtain meaning from such data, including clustering (i.e., identifying that participants were at the same “place” at time A and time B) or assessing mobility (e.g., by estimating the distance traveled by the participant every day). Future versions are planned to include additional analyses, providing a standard way to extract a variety of features from location data.