Comparing the supported OSM providers
This vignette presents a simple comparison between the OSM providers
supported by osmextract, explaining their pros and cons. We
decided to write this vignette since, as you will see in the following
examples, even if you always start from the same pre-defined
place, you can get significantly different OSM extracts
according to the chosen provider. Hence, we want to help
you choose the best suitable provider for a given situation.
We assume that you are already familiar with the basic functions in
osmextract, otherwise please check the “Get Started”
vignette for a more detailed introduction. Now, let’s start with an
example, but, first of all, we have to load the package:
library(osmextract)
library(sf)
We geocode the coordinates of Lima, the Capital of Peru,
lima = tmaptools::geocode_OSM("Lima, Peru")$coords
and look for a match in the OSM extracts using
oe_match():
oe_match(lima, provider = "geofabrik")
#> $url
#> [1] "https://download.geofabrik.de/south-america/peru-latest.osm.pbf"
#>
#> $file_size
#> [1] 2.06e+08
oe_match(lima, provider = "bbbike")
#> $url
#> [1] "https://download.bbbike.org/osm/bbbike/Lima/Lima.osm.pbf"
#>
#> $file_size
#> [1] 17905003
oe_match(lima, provider = "openstreetmap_fr")
#> $url
#> [1] "http://download.openstreetmap.fr/extracts/south-america-latest.osm.pbf"
#>
#> $file_size
#> [1] 3889405217
We can see that:
- when we used
geofabrik provider (which is also the
default provider), then the input place was matched with an
OSM extract corresponding to Peru region;
- when we used the
bbbike provider, then the input
place was matched with an OSM extract corresponding to the
city of Lima;
- when we used
openstreetmap_fr provider, then the input
data was matched with an OSM extract covering the whole of South
America.
The reason behind these differences is that each OSM provider divides
the geographical space into different discrete chunks, and, in the
following paragraphs, we will show the tessellation used by each
provider.
Geofabrik
geofabrik is a society that provides map-based services
and free downloads of OSM extracts that are updated daily. These
extracts are based on a division of the world into different regions,
covering a whole continent (plus Russian Federation):
par(mar = rep(0, 4))
plot(geofabrik_zones[geofabrik_zones$level == 1, "name"], key.pos = NULL, main = NULL)
or several countries all around the world:
plot(geofabrik_zones[geofabrik_zones$level == 2, "name"], key.pos = NULL, main = NULL)
Geofabrik also defines several special zones, such as Alps, Britain
and Ireland, Germany, Austria and Switzerland, US Midwest, US Northeast,
US Pacific, US South and US West. Moreover, it contains extracts
relative to some administrative subregions, mainly in Europe, Russia,
Canada and South America:
plot(geofabrik_zones[geofabrik_zones$level == 3, "name"], key.pos = NULL, main = NULL)
Check ?geofabrik_zones and the provider’s webpage for more
details.
Openstreetmap.fr
openstreetmap_fr extracts are taken from http://download.openstreetmap.fr/, a web-service that
provides OSM data updated every few minutes. The extracts are based on
several regions, such as the continents:
# Russian federation is considered as a level 1 zone
plot(openstreetmap_fr_zones[openstreetmap_fr_zones$level == 1, "name"], key.pos = NULL, main = NULL)
or some countries around the world (less than
geofabrik):
plot(openstreetmap_fr_zones[openstreetmap_fr_zones$level == 2, "name"], key.pos = NULL, main = NULL)
It can be noticed that there are several holes (such as Peru, which
is the reason why, in the first example, Lima was matched with South
America data), implying that openstreetmap_fr cannot always
be used for geographical matching of a place. Nevertheless,
it provides extremely detailed extracts for some regions of the world,
like China,
plot(openstreetmap_fr_zones[openstreetmap_fr_zones$parent == "china", "name"], key.pos = NULL, main = NULL)
India,
plot(openstreetmap_fr_zones[openstreetmap_fr_zones$parent == "india", "name"], key.pos = NULL, main = NULL)
France,
ids_2 = openstreetmap_fr_zones$parent %in% "france"
ids_3 = openstreetmap_fr_zones$parent %in% openstreetmap_fr_zones$id[ids_2]
plot(openstreetmap_fr_zones[ids_2 | ids_3, "name"], key.pos = NULL, main = NULL)
and Brazil
ids_2 = openstreetmap_fr_zones$parent %in% "brazil"
ids_3 = openstreetmap_fr_zones$parent %in% openstreetmap_fr_zones$id[ids_2]
plot(openstreetmap_fr_zones[ids_2 | ids_3, "name"], key.pos = NULL, main = NULL)
BBBike
bbbike provider is based on https://download.bbbike.org/osm/bbbike/. It is quite
different from any other provider supported in osmextract
since it contains OSM data for more than 200 cities worldwide.
par(mar = rep(0, 4))
plot(sf::st_geometry(spData::world))
plot(sf::st_geometry(bbbike_zones), border = "darkred", add = TRUE, lwd = 3)
bbbike provider is the safest choice if you are looking
for OSM data relative to a particular city in the world.