ohsome: An R package to interact with the ohsome API for OpenStreetMap history data aggregation and extraction
This ohsome R package grants access to the power of the ohsome API from R. ohsome lets you analyze the rich data source of the OpenStreetMap (OSM) history. It aims to leverage the tools of the OpenStreetMap History Database (OSHDB).
With ohsome, you can …
Get aggregated statistics on the evolution of OpenStreetMap elements and specify your own temporal, spatial and/or thematic filters. The data aggregation endpoint allows you to access functions, e.g., to calculate the area of buildings or the length of streets at any given timestamp.
Retrieve the geometry of the historical OpenStreetMap data, e.g., to visualize the evolution of certain OpenStreetMap elements over time. You can get the geometries for specific points in time or all changes within a timespan (full-history).
install.packages("ohsome") remotes::install_github("GIScience/ohsome-r", ref = "main")
Upon attaching the ohsome package, a metadata request is sent to the ohsome API. The package message provides some essential metadata information, such as the current temporal extent of the underlying OSHDB:
library(ohsome) #> Data: © OpenStreetMap contributors https://ohsome.org/copyrights #> ohsome API version: 1.9.1 #> Temporal extent: 2007-10-08T00:00:00Z to 2023-08-10T20:00Z
The metadata is stored in
ohsome_metadata. You can print it to the
console to get more details.
You can create any ohsome API query using the generic
function. It takes the endpoint path and any query parameters as inputs.
For information on all available endpoints with their parameters,
ohsome API documentation or print
to the console.
However, this ohsome R package provides specific wrapper functions for queries to all available endpoints.
The elements aggregation endpoints of the ohsome API allow querying for the aggregated amount, length, area or perimeter of OpenStreetMap elements with given properties, within given boundaries and at given points in time.
Let us create a query for the total amount of breweries on OSM in the
region of Franconia. The first argument to
franconia that is included in the
mapview package and contains boundary polygons of the
37 districts of the region:
library(mapview) q <- ohsome_elements_count(franconia, filter = "craft=brewery")
ohsome_query object can be sent to the ohsome API with
ohsome_post(). By default,
ohsome_post() returns the parsed API
response. In this case, this is a simple
data.frame of only one row.
ohsome_post(q, strict = FALSE) #> Warning: The time parameter is not defined and defaults to the latest available timestamp within the underlying OSHDB. #> You can use set_time() to set the time parameter. #> timestamp value #> 1 2023-08-10 20:00:00 180
ohsome_query object was created without an explicit
parameter. When using functions to create element aggregation queries,
time defaults to the most recent available timestamp in the underlying
time parameter unlocks the full power of ohsome API by
giving access to the OSM history. It requires one or more
ISO-8601 conform timestring(s). Here is how to create
a query for the number of breweries at the first of each month between
2010 and 2020:
ohsome_elements_count(franconia, filter = "craft=brewery", time = "2010/2020/P1M")
Alternatively, we can update the existing
set_time() function, pipe 2 the modified query directly into
ohsome_post() and make a quick visualization with
library(ggplot2) q |> set_time("2010/2020/P1M") |> ohsome_post() |> ggplot(aes(x = timestamp, y = value)) + geom_line()
This is how to query the total number of breweries in all of Franconia. But what if we want to aggregate the amount per district? The ohsome API provides specific endpoints for different grouped calculations, such as aggregation grouped by bounding geometry.
There are several ways to define a query for an aggregation grouped by boundary:
set_endpointfunction is used to change or append the endpoint path
of an API request. In this case, we could append
the existing query to the
elements/count endpoint. The endpoint path
can either be given as a single string (
/groupBy/boundary) or as a
set_endpoint(q, c("groupBy", "boundary"), append = TRUE) 3.
More comfortable, however, is the use of either the grouping argument
with an elements aggregation function (e.g.
ohsome_elements_count(grouping = "boundary)) or of the
set_grouping() function to modify an existing query object:
library(dplyr) franconia |> mutate(id = NAME_ASCI) |> ohsome_elements_count(filter = "craft=brewery", time = "2021-06-01") |> set_grouping("boundary") |> ohsome_post() #> Simple feature collection with 37 features and 3 fields #> Geometry type: MULTIPOLYGON #> Dimension: XY #> Bounding box: xmin: 8.975926 ymin: 48.8625 xmax: 12.27535 ymax: 50.56422 #> Geodetic CRS: WGS 84 #> First 10 features: #> groupByBoundaryId value timestamp geometry #> 1 Bamberg, Kreisfreie Stadt 6 2021-06-01 MULTIPOLYGON (((10.92581 49... #> 2 Bayreuth, Kreisfreie Stadt 6 2021-06-01 MULTIPOLYGON (((11.58157 49... #> 3 Coburg, Kreisfreie Stadt 0 2021-06-01 MULTIPOLYGON (((10.95355 50... #> 4 Hof, Kreisfreie Stadt 1 2021-06-01 MULTIPOLYGON (((11.93067 50... #> 5 Bamberg, Landkreis 13 2021-06-01 MULTIPOLYGON (((10.87615 50... #> 6 Bayreuth, Landkreis 13 2021-06-01 MULTIPOLYGON (((11.70656 50... #> 7 Coburg, Landkreis 6 2021-06-01 MULTIPOLYGON (((10.88654 50... #> 8 Forchheim 8 2021-06-01 MULTIPOLYGON (((11.26376 49... #> 9 Hof, Landkreis 4 2021-06-01 MULTIPOLYGON (((11.91989 50... #> 10 Kronach 1 2021-06-01 MULTIPOLYGON (((11.36979 50...
If you want your own identifiers for the geometries returned by ohsome,
sf object needs a column explicitly named
id. You can use
rename() from the
to create such a column as in the example below.
ohsome_post() returns an
sf object whenever the ohsome
API is capable of delivering GeoJSON data. This is the case for elements
extraction queries as well as for aggregations grouped by boundaries.
Thus, you can easily create a choropleth map from the query results. In
addition, you can set the argument
will modify the endpoint path of the query so that ohsome returns the
number of breweries per area instead of the absolute value:
franconia |> mutate(id = NAME_ASCI) |> ohsome_elements_count(filter = "craft=brewery", return_value = "density") |> set_time("2021-06-01") |> set_grouping("boundary") |> ohsome_post() |> mapview(zcol = "value", layer.name = "Breweries per sqkm")
The elements extraction endpoints of the ohsome API allow obtaining geometries, bounding boxes or centroids of OSM elements with given properties, within given boundaries and at given points in time. Together with the elements, you can choose to query for their tags and/or their metadata such as the changeset ID, the time of the last edit or the version number.
The following query extracts the geometries of buildings within 1000 m
of Heidelberg main station with their tags. The response is used to
visualize the buildings and the values of their
hd_station_1km <- ohsome_boundary("8.67542,49.40347,1000") ohsome_elements_geometry( boundary = hd_station_1km, filter = "building=* and type:way", time = "2021-12-01", properties = "tags", clipGeometry = FALSE ) |> ohsome_post() |> transmute(level = factor(`building:levels`)) |> mapview(zcol = "level", lwd = 0, layer.name = "Building level")
Similarly, you can use
ohsome_elements_centroid() to extract centroids
of OSM elements and
ohsome_elements_bbox() for their bounding boxes.
Note that OSM node elements (with point geometries) are omitted from the
results if querying for bounding boxes.
While the elements extraction endpoints provide geometries and
properties of OSM elements at specific timestamps, results of queries to
the full history endpoints will include all changes to
matching OSM features with corresponding
Here, we request the full history of OSM buildings within 1000 m of Heidelberg main station, filter for features that still exist and visualize all building features with their year of creation:
meta <- ohsome_get_metadata() start <- meta$extractRegion$temporalExtent end <- "2023-01-01" ohsome_elementsFullHistory_geometry( boundary = hd_station_1km, time = c(start, end), filter = "building=* and geometry:polygon", clipGeometry = FALSE, properties = "metadata" ) |> ohsome_post() |> janitor::clean_names() |> group_by(osm_id) |> mutate(year = min(format(valid_from, "%Y"))) |> filter(valid_to == lubridate::ymd(end)) |> mapview(zcol = "year", lwd = 0, layer.name = "Year of Feature Creation")
You may find using
clean_names() from the
package helpful in order to remove special characters from column names
in the parsed ohsome API response – just as in the example above.
With queries to the ohsome API’s contributions aggregation endpoints, you can get counts of the contributions provided by users to OSM. The following code requests the number of deletions of man-made objects at the location of the hypothetical Null Island per year between 2010 and 2020:
ohsome_contributions_count( boundary = "0,0,10", filter = "man_made=*", time = "2010/2020/P1Y", contributionType = "deletion" ) |> ohsome_post() #> fromTimestamp toTimestamp value #> 1 2010-01-01 2011-01-01 0 #> 2 2011-01-01 2012-01-01 1 #> 3 2012-01-01 2013-01-01 0 #> 4 2013-01-01 2014-01-01 0 #> 5 2014-01-01 2015-01-01 0 #> 6 2015-01-01 2016-01-01 3 #> 7 2016-01-01 2017-01-01 1 #> 8 2017-01-01 2018-01-01 8 #> 9 2018-01-01 2019-01-01 4 #> 10 2019-01-01 2020-01-01 4
contributionType parameter is used to filter for specific types of
contributions (in this case: deletions). If it is not set, any
contribution is counted. Note that the resulting values apply to time
intervals defined by a
fromTimestamp and a
The contributions extraction endpoints of the ohsome API can be used to extract feature geometries of contributions.
In the following example, we extract the centroids of all amenities in the Berlin city district of Neukölln that have had contributions in March 2020. Consequently, we filter for features that have had tags changed and visualize their locations:
nominatimlite::geo_lite_sf("Berlin Neukoelln", points_only = FALSE) |> ohsome_contributions_centroid() |> set_filter("amenity=*") |> set_time("2020-03,2020-04") |> set_properties("contributionTypes") |> ohsome_post() |> filter(`@tagChange`) |> mapview(layer.name = "Amenities with Tag Changes") #> Warning: 60 element(s) with empty geometries omitted.
You can get statistics on the number of users editing specific features through the users aggregation endpoints of the ohsome API.
Here, we show the number of users editing buildings before, during and after the Nepal earthquake 2015:
ohsome_users_count( boundary = "82.3055,6.7576,87.4663,28.7025", filter = "building=* and geometry:polygon", time = "2015-03-01/2015-08-01/P1M" ) |> ohsome_post() #> fromTimestamp toTimestamp value #> 1 2015-03-01 2015-04-01 97 #> 2 2015-04-01 2015-05-01 3489 #> 3 2015-05-01 2015-06-01 3103 #> 4 2015-06-01 2015-07-01 478 #> 5 2015-07-01 2015-08-01 185
The ohsome API requires bounding geometries either as bounding polygons
bpolys), bounding boxes (
bboxes) or bounding circles (
parameters to the query in a textual form (see
ohsome API documentation). The ohsome R package uses
the generic function
ohsome_boundary() under the hood to make your
life easier. It accepts a wider range of input geometry formats, while
guessing the right type of bounding geometry.
As seen above,
sf objects can be passed into the
ohsome_query() and any of its wrapper functions. You can also
update queries with
sf object will be converted
into GeoJSON and passed into the
bpolys parameter of the query.
If you wish to aggregate or extract OSM elements on administrative
boundaries in the
sf format, you might want to check out packages such
(in particular its
nominatimlite for the acquisition of boundary data
that can be used with
There are also the following methods of
ohsome_boundary() for other
classes of input geometry objects:
bboxobjects created with
st_bboxare converted into a textual
bboxesparameter to the query:
q <- ohsome_query("users/count") |> set_boundary(sf::st_bbox(franconia)) q$body$bboxes #>  "8.97592600000002,48.862505,12.2753535,50.5642245"
matrixobjects created with
terra::bbox()are also converted into a textual
bboxesparameter. This even applies for matrices created with
tmaptools::bb(), so that you can comfortably acquire bounding boxes from the Nominatim API:
osmdata::getbb("Kigali") |> ohsome_elements_length(time = "2018/2018-12/P1M", filter = "route=bus") |> ohsome_post() #> timestamp value #> 1 2018-01-01 28251.24 #> 2 2018-02-01 28251.24 #> 3 2018-03-01 29103.11 #> 4 2018-04-01 186645.71 #> 5 2018-05-01 378185.54 #> 6 2018-06-01 473065.23 #> 7 2018-07-01 615801.98 #> 8 2018-08-01 648385.19 #> 9 2018-09-01 753618.88 #> 10 2018-10-01 771239.17 #> 11 2018-11-01 847337.73 #> 12 2018-12-01 858886.63
- You can pass any
characterobject with text in the format allowed by the ohsome API to
ohsome_boundary()– even GeoJSON FeatureCollections. It will automatically detect whether you have passed the definition of
bcircles. It is possible to use
charactervectors where each element represents one geometry:
c("Circle 1:8.6528,49.3683,1000", "Circle 2:8.7294,49.4376,1000") |> ohsome_elements_count(filter = "amenity=*", grouping = "boundary", time = 2021) |> ohsome_post() #> Simple feature collection with 2 features and 3 fields #> Geometry type: POLYGON #> Dimension: XY #> Bounding box: xmin: 8.639026 ymin: 49.35931 xmax: 8.743193 ymax: 49.44659 #> Geodetic CRS: WGS 84 #> groupByBoundaryId value timestamp geometry #> 1 Circle 1 16 2021-01-01 POLYGON ((8.666574 49.36834... #> 2 Circle 2 20 2021-01-01 POLYGON ((8.743193 49.43763...
bbox objects will be automatically transformed to
WGS 84 if in a different coordinate reference system, coordinates in
matrix objects always need to be provided as WGS 84.
As seen above, existing
ohsome_query objects can be modified by
The latter and other functions such as
set_filter() are just wrappers
around the more generic
set_parameters(). This can be used to modify
the parameters of a query in any possible way:
q <- ohsome_elements_count("8.5992,49.3567,8.7499,49.4371") q |> set_endpoint("ratio", append = TRUE) |> set_parameters( filter = "building=*", filter2 = "building=* and building:levels=*", time = "2010/2020/P2Y" ) |> ohsome_post() #> timestamp value value2 ratio #> 1 2010-01-01 554 3 0.005415 #> 2 2012-01-01 10600 7 0.000660 #> 3 2014-01-01 21005 77 0.003666 #> 4 2016-01-01 25849 798 0.030872 #> 5 2018-01-01 29397 1223 0.041603 #> 6 2020-01-01 31495 1456 0.046230
Grouping endpoints are available for aggregation resources and can be used to compute the aggregated results grouped by:
- tag, and
In many cases, a grouping by
boundary can be combined with a grouping
tag. Some of the grouping endpoints require additional query
tag groupings require a
groupByKey parameter. Not
all grouping endpoints are available for all aggregation resources –
please consult the
ohsome API documentation for details.
You can set the
grouping argument to any aggregation endpoint wrapper
ohsome_elements_count(grouping = c("boundary", "tag")))
set_grouping() to modify existing query objects.
Many aggregation resources have endpoints for requesting
density (i.e. count, length, perimeter or area of features per area)
or ratios (of OSM elements satisfying a
filter2 to elements satisfying
filter) instead of or in addition to absolute values.
You can request density or ratio values by setting the
argument to aggregation endpoint wrapper functions (e.g.
ohsome_elements_count(return_value = "density")). Mind that ratio
endpoints require an additional
filter2 query parameter. Please
ohsome API documentation or print
names(ohsome_endpoints) to the console in order to check for the
availability of specific density and ratio endpoints.
The ohsome API allows grouping aggregate values for various timestamps by boundary and tag at the same time. The parsed content of the response can be rather complex. In the following case, building feature counts for the districts of Franconia at two different timestamps are requested – additionally grouped by the building:levels tag. To avoid lots of redundant geometries, comma-separated values (instead of GeoJSON) are explicitly requested as the response format:
building_levels <- franconia |> mutate(id = NUTS_ID) |> ohsome_elements_count(grouping = c("boundary", "tag"), format = "csv") |> set_filter("building=* and geometry:polygon") |> set_time("2015/2020") |> set_groupByKey("building:levels") |> ohsome_post() dim(building_levels) #>  2 1999
The query results in a confusing data.frame with 1999 columns and 2 rows. This happens because there is a building count column for each combination of boundary polygon and number of levels, while the two requested timestamps are in the rows. Fortunately, there is the tidyr package to do its magic and pivot this table into a long format with one value per row:
library(tidyr) building_levels |> pivot_longer(-timestamp, names_to = c("id", "levels"), names_sep = "_") #> # A tibble: 3,996 × 4 #> timestamp id levels value #> <dttm> <chr> <chr> <dbl> #> 1 2015-01-01 00:00:00 DE241 remainder 4307 #> 2 2015-01-01 00:00:00 DE241 building.levels.1 6347 #> 3 2015-01-01 00:00:00 DE241 building.levels.2 6727 #> 4 2015-01-01 00:00:00 DE241 building.levels.3 2787 #> 5 2015-01-01 00:00:00 DE241 building.levels.4 745 #> 6 2015-01-01 00:00:00 DE241 building.levels.5 96 #> 7 2015-01-01 00:00:00 DE241 building.levels.6 38 #> 8 2015-01-01 00:00:00 DE241 building.levels.9 7 #> 9 2015-01-01 00:00:00 DE241 building.levels.0 0 #> 10 2015-01-01 00:00:00 DE241 building.levels.7 32 #> # ℹ 3,986 more rows
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