osmdata is an R package for downloading and using data from OpenStreetMap (OSM). OSM is a global open access mapping project, which is free and open under the ODbL licence (OpenStreetMap contributors 2017). This has many benefits, ensuring transparent data provenance and ownership, enabling real-time evolution of the database and, by allowing anyone to contribute, encouraging democratic decision making and citizen science (Johnson 2017). See the OSM wiki to find out how to contribute to the world’s open geographical data commons.
OpenStreetMap package, which facilitates the download of raster tiles,
osmdata provides access to the vector data underlying OSM.
osmdata can be installed from CRAN with
and then loaded in the usual way:
## Warning in system("timedatectl", intern = TRUE): running command 'timedatectl' ## had status 1
## Data (c) OpenStreetMap contributors, ODbL 1.0. https://www.openstreetmap.org/copyright
The development version of
osmdata can be installed with the
remotes package using the following command:
osmdata uses the
overpass API to download
OpenStreetMap (OSM) data and can convert the results to a variety of formats, including both Simple Features (typically of class
sf) and Spatial objects (e.g.
SpatialPointsDataFrame), as defined by the packages
sp packages respectively.
overpass is a C++ library that serves OSM data over the web. All
overpass queries begin with a bounding box, defined in
osmdata with the function
The following sub-section provides more detail on bounding boxes. Following the initial
osmdata queries are built by adding one or more ‘features’, which are specified in terms of
key-value pairs. For example, all paths, ways, and roads are designated in OSM with
key=highway, so that a query all motorways in greater London (UK) can be constructed as follows:
##  "4wd only" "abandoned" "abutters" "access" "addr" "addr:city"
There are two primary
osmdata functions for obtaining data from a query:
osmdata_sp(), which return data in Simple Features (
sf) and Spatial (
sp) formats, respectively. The typical workflow for extracting OSM data with
osmdata thus consists of the three lines:
x <- opq(bbox = 'greater london uk') %>% add_osm_feature(key = 'highway', value = 'motorway') %>% osmdata_sf ()
The return object (
x) is described in the third section below.
While bounding boxes may be explicitly specified for the
opq() function, they are more commonly obtained from the
getbb() function, which accepts character strings. As illustrated in the above example, the
opq() function also accepts character strings, which are simply passed directly to
getbb() to convert them to rectangular bounding boxes.
Note that the text string is not case sensitive, as illustrated in the following code:
Note also that
getbb() can return a data frame reporting multiple matches or matrices representing bounding polygons of matches:
overpass API only accepts simple rectangular bounding boxes, and so data requested with a bounding polygon will actually be all data within the corresponding rectangular bounding box, but such data may be subsequently trimmed to within the polygon with the
trim_osmdata() function, demonstrated in the code immediately below.
All highways from within the polygonal boundary of Greater London can be extracted with,
bb <- getbb ('london uk', format_out = 'polygon') x <- opq(bbox = bb) %>% add_osm_feature(key = 'highway', value = 'motorway') %>% osmdata_sf () %>% trim_osmdata (bb)
?trim_osmdata() for further ways to obtain polygonally bounded sets of OSM data.
getbb() function also allows specification of an explicit
featuretype, such as street, city, county, state, or country. The default value of
settlement combines all results below country and above streets. See
?getbb for more details.
a read-only API that serves up custom selected parts of the OSM map data.
The syntax of
overpass queries is powerful yet hard to learn. This section briefly introduces the structure of
overpass queries in order to help construct more efficient and powerful queries. Those wanting to skip straight onto query construction in
osmdata may safely jump ahead to the query example below.
osmdata simplifies queries so that OSM data can be extracted with very little understanding of the
overpass query syntax, although it is still possible to submit arbitrarily complex
overpass queries via
osmdata. An excellent place to explore
overpass queries specifically and OSM data in general is the online interactive query builder at overpass-turbo, which includes a helpful corrector function for incorrectly formatted queries. Examples of its functionality in action can be found on the OpenStreetMap wiki, with full details of the
overpass query language given in the Query Language Guide as well as the overpass API Language Guide.
osmdata sends queries to one of the four main
overpass server instances, such as
https://overpass-api.de/api/interpreter but other servers listed on the page linked to above can be used, thanks to functions that get and set the base url:
##  "https://overpass.kumi.systems/api/interpreter"
new_url <- "https://overpass.openstreetmap.ie/api/interpreter"
set_overpass_url(new_url) # reset the base url (not run)
osmdata queries are lists of class
overpass_query. The actual query passed to the
overpass API with a query can be obtained with the function
opq_string(). Applied to the preceding query, this function gives:
opq_string(q) ## [out:xml][timeout:25]; ## ( ## node ## ["highway"="motorway"] ## (51.2867602,-0.510375,51.6918741,0.3340155); ## way ## ["highway"="motorway"] ## (51.2867602,-0.510375,51.6918741,0.3340155); ## relation ## ["highway"="motorway"] ## (51.2867602,-0.510375,51.6918741,0.3340155); ## ); ## (._;>);out body;
The resultant output may be pasted directly into the overpass-turbo online interactive query builder. (The output of
opq_string has been somewhat reformatted here to reflect the format typically used in
This query will request all natural water water bodies in Kunming, China. A particular water body may be requested through appending a further call to
q <- opq(bbox = 'Kunming, China') %>% add_osm_feature(key = 'natural', value = 'water') %>% add_osm_feature(key = 'name:en', value = 'Dian', value_exact = FALSE)
Each successive call to
add_osm_feature() adds features to a query. This query is thus a request for all bodies of natural water and those with English names that include ‘Dian’. The requested data may be extracted through calling one of the
Single queries are always constructed through adding features, and therefore correspond to logical AND operations: natural water bodies AND those whose names include ‘Dian’. The equivalent OR combination can be extracted with the
add_osm_features() function. The following query represents the OR-equivalent of the above query, requesting data on both all natural features with the value of
"water" OR all features whose English name is
q <- opq(bbox = 'Kunming, China') %>% add_osm_features(features = c ("\"natural\"=\"water\"", "\"name:en\"=\"Dian\""))
Note that the
"=" symbols here requests features whose values exactly match the given values. Other “filter” symbols are possible, as described in the overpass query language definition, including symbols for negation (
!=), or approximate matching (
Passing this query to
osmdata_sf() will return identical data to the following way to explicitly construct an OR query through using the inbuilt
c operator of
dat1 <- opq(bbox = 'Kunming, China') %>% add_osm_feature(key = 'natural', value = 'water') %>% osmdata_sf () dat2 <- opq(bbox = 'Kunming, China') %>% add_osm_feature(key = 'name:en', value = 'Dian', value_exact = FALSE) %>% osmdata_sf () dat <- c (dat1, dat2)
While the “filter” symbols may be explicitly specified in the
add_osm_features() function, the single-feature version of
add_osm_feature() function has several logical parameters to control matching without needing to remember precise overpass syntax:
key_exactcan be set to
FALSEto approximately match given keys;
value_exactcan be set to
FALSEto approximately match given values; and
match_casecan be set to
FALSEto match keys and values in both lower and upper case forms.
The previous query with
key = 'name:end' and
value = 'Dian' could thus be replaced by the following:
add_osm_feature(key = 'name', value = 'dian', key_exact = FALSE, value_exact = FALSE, match_case = FALSE)
osmdata_sp() pass these queries to
overpass and return OSM data in corresponding
sp format, respectively. Both of these functions also accept direct
overpass queries, such as those produced by the
opq_string(), or copied directly from the
overpass-turbo query builder.
osmdata_sf(opq_string(q)) ## Object of class 'osmdata' with: ## $bbox : ## $overpass_call : The call submitted to the overpass API ## $timestamp : [ Thurs 5 May 2017 14:33:54 ] ## $osm_points : 'sf' Simple Features Collection with 360582 points ## ...
Note that the result contains no value for
bbox, because that information is lost when the full
q, is converted to a string. Nevertheless, the results of the two calls
osmdata_sf (opq_string (q)) and
osmdata_sf (q) differ only in the values of
timestamp, while returning otherwise identical data.
osmdata queries are generally simplified versions of potentially more complex
overpass queries, although arbitrarily complex
overpass queries may be passed directly to the primary
osmdata functions. As illustrated above,
osmdata queries are generally constructed through initiating a query with
opq(), and then specifying OSM features in terms of
key-value pairs with
add_osm_feature(), along with judicious usage of the
The simplest way to use
osmdata is to simply request all data within a given bounding box (warning - not intended to run):
Queries are, however, usually more useful when refined through using
add_osm_feature(), which minimally requires a single
key and returns all objects specifying any value for that
not_so_much_data <- opq(bbox = 'city of london uk') %>% add_osm_feature(key = 'highway') %>% add_osm_feature(key = 'name') %>% osmdata_sf()
osmdata will use that query to return all named highways within the requested bounding box. Note that
key specifications are requests for features which must include those keys, yet most features will also include many other keys, and thus
osmdata objects generally list a large number of distinct keys, as demonstrated below.
To appreciate query building in more concrete terms, let’s imagine that we wanted to find all cycle paths in Seville, Spain:
q1 <- opq('Sevilla') %>% add_osm_feature(key = 'highway', value = 'cycleway') cway_sev <- osmdata_sp(q1) sp::plot(cway_sev$osm_lines)
Now imagine we want to make a more specific query that only extracts designated cycleways or those which are bridges. Combining these into one query will return only those that are designated cycleways AND that are bridges:
des_bike <- osmdata_sf(q1) q2 <- add_osm_feature(q1, key = 'bridge', value = 'yes') des_bike_and_bridge <- osmdata_sf(q2) nrow(des_bike_and_bridge$osm_points); nrow(des_bike_and_bridge$osm_lines) ##  99 ##  32
That query returns only 99 points and 32 lines. Designed cycleways OR bridges can be obtained through simply combining multiple
osmdata objects with the
q2 <- opq('Sevilla') %>% add_osm_feature(key = 'bridge', value = 'yes') bridge <- osmdata_sf(q2) des_bike_or_bridge <- c(des_bike, bridge) nrow(des_bike_or_bridge$osm_points); nrow(des_bike_or_bridge$osm_lines) ##  9757 ##  1061
And as expected, the
OR operation produces more data than the equivalent
AND, showing the utility of combining
osmdata objects with the generic function
osmdata extraction functions (
osmdata_sp()), both return objects of class
osmdata. The structure of
osmdata objects are clear from their default print method, illustrated using the
bridge example from the previous section:
bridge ## Object of class 'osmdata' with: ## $bbox : 37.3002036,-6.0329182,37.4529579,-5.819157 ## $overpass_call : The call submitted to the overpass API ## $timestamp : [ Thurs 5 May 2017 14:41:19 ] ## $osm_points : 'sf' Simple Features Collection with 69 points ## $osm_lines : 'sf' Simple Features Collection with 25 linestrings ## $osm_polygons : 'sf' Simple Features Collection with 0 polygons ## $osm_multilines : 'sf' Simple Features Collection with 0 multilinestrings ## $osm_multipolygons : 'sf' Simple Features Collection with 0 multipolygons
As the results show, all
osmdata objects should contain:
bridge$timestamp, useful for checking data is up-to-date)
Some or all of these can be empty: the example printed above contains only points and lines. The more complex features of
osm_multipolygons refer to OSM relations than contain multiple lines and polygons.
In addition to these two functions,
osmdata provides a third function,
osmdata_xml(), which allows raw OSM data to be returned and optionally saved to disk in XML format. The following code demonstrates this function, beginning with a new query.
dat <- opq(bbox = c(-0.12, 51.51, -0.11, 51.52)) %>% add_osm_feature(key = 'building') %>% osmdata_xml(file = 'buildings.osm') class(dat) ##  "xml_document" "xml_node"
This call both returns the same data as the object
dat and saves them to the file
buildings.osm. Downloaded XML data can be converted to
sp formats by simply passing the data to the respective
osmdata functions, either as the name of a file or an XML object:
q <- opq(bbox = c(-0.12, 51.51, -0.11, 51.52)) %>% add_osm_feature(key = 'building') doc <- osmdata_xml(q, 'buildings.osm') dat1 <- osmdata_sf(q, doc) dat2 <- osmdata_sf(q, 'buildings.osm') identical(dat1, dat2) ##  TRUE
The following sub-sections now explore these three functions in more detail, beginning with
osmdata_xml() returns OSM data in native XML format, and also allows these data to be saved directly to disk (conventionally using the file suffix
.osm, although any suffix may be used). The
XML data are formatting using the
xml2, and may be processed within
R using any methods compatible with such data, or may be processed by any other software able to load the
XML data directly from disk.
The first few lines of the XML data downloaded above look like this:
readLines('buildings.osm')[1:6] ##  "<?xml version=\"1.0\" encoding=\"UTF-8\"?>" ##  "<osm version=\"0.6\" generator=\"Overpass API\">" ##  " <note>The data included in this document is from www.openstreetmap.org. The data is made available under ODbL.</note>" ##  " <meta osm_base=\"2017-03-07T09:28:03Z\"/>" ##  " <node id=\"21593231\" lat=\"51.5149566\" lon=\"-0.1134203\"/>" ##  " <node id=\"25378129\" lat=\"51.5135870\" lon=\"-0.1115193\"/>"
These data can be used in any other programs able to read and process XML data, such as the open source GIS QGIS or the OSM data editor JOSM. The remainder of this vignette assumes that not only do you want to get OSM data using R, you also want to import and eventually process it, using R. For that you’ll need to import the data into a native R class.
As demonstrated above, downloaded data can be directly processed by passing either filenames or the
R objects containing those data to the
osmdata_sf() returns OSM data in Simple Features (SF) format, defined by the Open Geospatial Consortium, and implemented in the
sf. This package provides a direct interface to the
C++ Graphical Data Abstraction Library (GDAL) which also includes a so-called ‘driver’ for OSM data. This means that OSM data may also be read directly with
sf, rather than using
osmdata. In this case, data must first be saved to disk, which can be readily achieved using
osmdata_xml() described above, or through downloading directly from the overpass interactive query builder.
The following example is based on this query:
opq(bbox = 'Trentham, Australia') %>% add_osm_feature(key = 'name') %>% osmdata_xml(filename = 'trentham.osm')
sf can then read such data independent of
sf::st_read('trentham.osm', layer = 'points') ## Reading layer `points' from data source `trentham.osm' using driver `OSM' ## Simple feature collection with 38 features and 10 fields ## geometry type: POINT ## dimension: XY ## bbox: xmin: 144.2894 ymin: -37.4846 xmax: 144.3893 ymax: -37.36012 ## epsg (SRID): 4326 ## proj4string: +proj=longlat +datum=WGS84 +no_defs
GDAL drivers used by
sf can only load single ‘layers’ of features, for example,
polygons. In contrast,
osmdata loads all features simultaneously:
osmdata_sf(q, 'trentham.osm') ## Object of class 'osmdata' with: ## $bbox : -37.4300874,144.2863388,-37.3500874,144.3663388 ## $overpass_call : The call submitted to the overpass API ## $timestamp : [ Thus 5 May 2017 14:42:19 ] ## $osm_points : 'sf' Simple Features Collection with 7106 points ## $osm_lines : 'sf' Simple Features Collection with 263 linestrings ## $osm_polygons : 'sf' Simple Features Collection with 38 polygons ## $osm_multilines : 'sf' Simple Features Collection with 1 multilinestrings ## $osm_multipolygons : 'sf' Simple Features Collection with 6 multipolygons
Even for spatial objects of the same type (the same ‘layers’ in
osmdata returns considerably more objects–7,166 points compared .with just 38. The raw sizes of data returned can be compared with:
s1 <- object.size(osmdata_sf(q, 'trentham.osm')$osm_points) s2 <- object.size(sf::st_read('trentham.osm', layer = 'points', quiet = TRUE)) as.numeric(s1 / s2) ##  511.4193
osmdata points contain over 500 times as much data. The primary difference between
osmdata is that the former returns only those objects unique to each category of spatial object. Thus OSM nodes (
sf/osmdata representations) include, in
sf/GDAL representation, only those points which are not part of any other objects (such as lines or polygons). In contrast, the
osm_points object returned by
osmdata includes all points regardless of whether or not these are represented in other spatial objects. Similarly,
line objects in
sf/GDAL exclude any lines that are part of other objects such as
This processing of data by
sf/GDAL has two important implications:
An implicit hierarchy of spatial objects is enforced through including elements of objects only at their ‘highest’ level of representation, where
multiline objects are assumed to be at ‘higher’ levels than
line objects, and these in turn are at ‘higher’ levels than
osmdata makes no such hierarchical assumptions.
All OSM are structured by giving each object a unique identifier so that the components of any given object (the nodes of a line, for example, or the lines of a multipolygon) can be described simply by giving these identifiers. This enables the components of any OSM object to be examined in detail. The
sf/GDAL representation obviates this ability through removing these IDs and reducing everything to geometries alone (which is, after all, why it is called ‘Simple Features’). This means, for example, that the
key-value pairs of the
polygon components of
multipolygon can never be extracted from an
sf/GDAL representation. In contrast,
osmdata retains all unique identifiers for all OSM objects, and so readily enables, for example, the properties of all
point objects of a
line to be extracted.
Another reason why
osmdata returns more data than
GDAL/sf is that the latter extracts only a restricted list of OSM
osmdata returns all
key fields present in the requested data:
names(osmdata_sf(q, 'trentham.osm')$osm_points) ##  "osm_id" "name" "X_description_" "X_waypoint_" ##  "addr.city" "addr.housenumber" "addr.postcode" "addr.street" ##  "amenity" "barrier" "denomination" "foot" ##  "ford" "highway" "leisure" "note_1" ##  "phone" "place" "railway" "railway.historic" ##  "ref" "religion" "shop" "source" ##  "tourism" "waterway" "geometry"
key fields which are not specified in a given set of OSM data are not returned by
GDAL/sf returns the same
key fields regardless of whether any values are specified.
key=address contains no data yet is still returned by
Finally, note that
osmdata will generally extract OSM data considerably faster than equivalent
sf/GDAL routines (as detailed here).
osmdata_sf() described above, OSM data may be converted to
sp format without using
osmdata via the
sf functions demonstrated below:
These data are extracted using the GDAL, and so suffer all of the same shortcomings mentioned above. Note differences in the amount of data returned:
dim(dat_sp) ##  560 25
As described above,
osmdata returns all data of each type and so allows the components of any given spatial object to be examined in their own right. This ability to extract, for example, all points of a line, or all polygons which include a given set of points, is referred to as recursive searching.
Recursive searching is not possible with
GDAL/sf, because OSM identifiers are removed, and only the unique data of each type of object are retained. To understand both recursive searching and why it is useful, note that OSM data are structured in three hierarchical levels:
nodes representing spatial points
ways representing lines, both as
polygons (with connected ends) and non-polygonal
relations representing more complex objects generally comprising collections of
nodes. Examples include
multipolygon relations comprising an outer polygon (which may itself be made of several distinct
ways which ultimately connect to form a single circle), and several inner polygons.
Recursive searching allows for objects within any one of these hierarchical levels to be extracted based on components in any other level. Recursive searching is performed in
osmdata with the following functions:
osm_points(), which extracts all
osm_lines(), which extracts all
way objects that are
lines (that are, that are not
osm_polygons(), which extracts all
osm_multilines(), which extracts all
multiline objects; and
osm_multipolygons(), which extracts all
Each of these functions accepts as an argument a vector of OSM identifiers. To demonstrate these functions, we first re-create the example above of named objects from Trentham, Australia:
Then imagine we are interested in the
osm_line object describing the ‘Coliban River’:
i <- which(tr$osm_lines$name == 'Coliban River') coliban <- tr$osm_lines[i, ] coliban[which(!is.na(coliban))] ## Simple feature collection with 1 feature and 3 fields ## geometry type: LINESTRING ## dimension: XY ## bbox: xmin: 144.3235 ymin: -37.37162 xmax: 144.3335 ymax: 37.36366 ## epsg (SRID): 4326 ## proj4string: +proj=longlat +datum=WGS84 +no_defs ## osm_id name waterway geometry ## 87104907 87104907 Coliban River river LINESTRING(144.323471069336...
The locations of the points of this line can be extracted directly from the
sf object with:
coliban$geometry[] ## LINESTRING(144.323471069336 -37.3716201782227, 144.323944091797 -37.3714790344238, 144.324356079102 -37.3709754943848, 144.324493408203 -37.3704833984375, 144.324600219727 -37.370174407959, 144.324981689453 -37.3697204589844, 144.325149536133 -37.369441986084, 144.325393676758 -37.3690567016602, 144.325714111328 -37.3686943054199, 144.326080322266 -37.3682441711426)
The output contains nothing other than geometries (because, to reiterate, these are ‘Simple Features’), and no further information regarding those points can be extracted. The Coliban River has a waterfall in Trentham, and one of the
osm_points objects describes this waterfall. The information necessary to locate this waterfall is removed from the
GDAL/sf representation, but can be extracted with
osmdata with the following lines, noting that the OSM ID of the line
coliban is given by
pts <- osm_points(tr, rownames(coliban)) wf <- pts[which(pts$waterway == 'waterfall'), ] wf[which(!is.na(wf))] ## Simple feature collection with 1 feature and 4 fields ## geometry type: POINT ## dimension: XY ## bbox: xmin: 144.3246 ymin: -37.37017 xmax: 144.3246 ymax: -37.37017 ## epsg (SRID): 4326 ## proj4string: +proj=longlat +datum=WGS84 +no_defs ## osm_id name tourism waterway ## 1013064837 1013064837 Trentham Falls attraction waterfall ## geometry ## 1013064837 POINT(144.324600219727 -37....
This point could be used as the basis for further recursive searches. For example, all
multipolygon objects which include Trentham Falls could be extracted with:
Although this returns no data in this case, it nevertheless demonstrates the usefulness and ease of recursive searching with
A special type of OSM object is a relation. These can be defined by their name, which can join many divers features into a single object. The following example extracts the London Route Network Route 9, which is composed of many (over 100) separate lines:
lcnr9 <- opq ('greater london uk') %>% add_osm_feature (key = "name", value = "LCN 9", value_exact = FALSE) %>% osmdata_sp() sp::plot(lcnr9$osm_lines)
This section briefly describes a few of additional functions, with additional detail provided in the help files for each of these function.
trim_osmdata()function, as described above in the sub-section on bounding boxes, trims an
osmdataobject to within a defined bounding polygon, rather than bounding box.
opq_osm_id()function allows queries for particular OSM objects by their OSM-allocated ID values.
osm_poly2line()function converts all
$osm_polygonsitems of an
$osm_lines. These objects remain polygonal in form, through sharing identical start and end points, but can then be treated as simple lines. This is important for polygonal highways, which are automatically classified as
$osm_polygonssimply because they form closed loops. The function enables all highways to be grouped together (as
$osm_lines) regardless of the form.
unique_osmdata()function removes redundant items from the different components of an
osmdataobject. A multilinestring, for example, is composed of multiple lines, and each line is composed of multiple points. For a multilinestring, an
osmdataobject will thus contain several
$osm_lines, and for each of these several
$osm_points. This function removes all of these redundant objects, so that
$osm_linesonly contains lines which are not part of any higher-level objects, and
$osm_pointsonly contains points which are not part of any higher-level objects.
A further additional function is the ability to extract data as represented in the OSM database prior to a specified date, or within a specified range of dates. This is achieved by passing one or both values to the
opq() function of
datetime2. The resultant data extracted with one or more
add_osm_feature() calls and an extraction function (
osmdata_sf/sp/sc/xml) will then contain only those data present prior to the specified date (when
datetime only given), or between the two specified dates (when both
Eugster, Manuel J a, and Thomas Schlesinger. 2012. “Osmar: OpenStreetMap and R.” The R Journal 5 (1): 53–64.
Johnson, Peter A. 2017. “Models of Direct Editing of Government Spatial Data: Challenges and Constraints to the Acceptance of Contributed Data.” Cartography and Geographic Information Science 44 (2): 128–38. https://doi.org/10.1080/15230406.2016.1176536.
Lovelace, Robin. 2014. “Harnessing Open Street Map Data with R and QGIS.” EloGeo.
OpenStreetMap contributors. 2017. “Planet dump retrieved from https://planet.osm.org.” https://www.openstreetmap.org.
Padgham, Mark. 2016. Osmplotr: Customisable Images of Openstreetmap Data. https://cran.r-project.org/package=osmplotr.