README.md

osmdata

osmdata is an R package for accessing the data underlying OpenStreetMap (OSM), delivered via the Overpass API. (Other packages such as OpenStreetMap can be used to download raster tiles based on OSM data.) Overpass is a read-only API that extracts custom selected parts of OSM data. Data can be returned in a variety of formats, including as Simple Features (sf), Spatial (sp), or Silicate (sc) objects. The package is designed to allow access to small-to-medium-sized OSM datasets (see osmextract for an approach for reading-in bulk OSM data extracts).

Installation

To install latest CRAN version:

install.packages ("osmdata")

Alternatively, install the development version with any one of the following options:

# install.packages("remotes")
remotes::install_git ("https://git.sr.ht/~mpadge/osmdata")
remotes::install_bitbucket ("mpadge/osmdata")
remotes::install_gitlab ("mpadge/osmdata")
remotes::install_github ("ropensci/osmdata")

To load the package and check the version:

library (osmdata)
#> Data (c) OpenStreetMap contributors, ODbL 1.0. https://www.openstreetmap.org/copyright
packageVersion ("osmdata")
#> [1] '0.2.5.16'

Usage

Overpass API queries can be built from a base query constructed with opq followed by add_osm_feature. The corresponding OSM objects are then downloaded and converted to Simple Feature (sf) objects with osmdata_sf(), Spatial (sp) objects with osmdata_sp() or Silicate (sc) objects with osmdata_sc(). For example,

x <- opq (bbox = c (-0.27, 51.47, -0.20, 51.50)) %>% # Chiswick Eyot in London, U.K.
    add_osm_feature (key = "name", value = "Thames", value_exact = FALSE) %>%
    osmdata_sf ()
x

#> Object of class 'osmdata' with:
#>                  $bbox : 51.47,-0.27,51.5,-0.2
#>         $overpass_call : The call submitted to the overpass API
#>                  $meta : metadata including timestamp and version numbers
#>            $osm_points : 'sf' Simple Features Collection with 24548 points
#>             $osm_lines : 'sf' Simple Features Collection with 2219 linestrings
#>          $osm_polygons : 'sf' Simple Features Collection with 33 polygons
#>        $osm_multilines : 'sf' Simple Features Collection with 6 multilinestrings
#>     $osm_multipolygons : 'sf' Simple Features Collection with 3 multipolygons

OSM data can also be downloaded in OSM XML format with osmdata_xml() and saved for use with other software.

osmdata_xml(q1, "data.osm")

Bounding Boxes

All osmdata queries begin with a bounding box defining the area of the query. The getbb() function can be used to extract bounding boxes for specified place names.

getbb ("astana kazakhstan")
#>        min      max
#> x 71.21797 71.78519
#> y 50.85761 51.35111

The next step is to convert that to an overpass query object with the opq() function:

q <- opq (getbb ("astana kazakhstan"))
q <- opq ("astana kazakhstan") # identical result

It is also possible to use bounding polygons rather than rectangular boxes:

b <- getbb ("bangalore", format_out = "polygon")
class (b)
#> [1] "matrix" "array"
head (b [[1]])
#> [1] 77.46005

Features

The next step is to define features of interest using the add_osm_feature() function. This function accepts key and value parameters specifying desired features in the OSM key-vale schema. Multiple add_osm_feature() calls may be combined as illustrated below, with the result being a logical AND operation, thus returning all amenities that are labelled both as restaurants and also as pubs:

q <- opq ("portsmouth usa") %>%
    add_osm_feature (key = "amenity", value = "restaurant") %>%
    add_osm_feature (key = "amenity", value = "pub") # There are none of these

Features can also be requested by key only, in which case features with any values for the specified key will be returned:

q <- opq ("portsmouth usa") %>%
    add_osm_feature (key = "amenity")

Such key-only queries can, however, translate into requesting very large data sets, and should generally be avoided in favour of more precise key-value specifications.

Negation can also be specified by pre-pending an exclamation mark so that the following requests all amenities that are NOT labelled as restaurants and that are not labelled as pubs:

q <- opq ("portsmouth usa") %>%
    add_osm_feature (key = "amenity", value = "!restaurant") %>%
    add_osm_feature (key = "amenity", value = "!pub") # There are a lot of these

Additional arguments allow for more refined matching, such as the following request for all pubs with “irish” in the name:

q <- opq ("washington dc") %>%
    add_osm_feature (key = "amenity", value = "pub") %>%
    add_osm_feature (
        key = "name", value = "irish",
        value_exact = FALSE, match_case = FALSE
    )

Logical OR combinations can be constructed using the separate add_osm_features() function. The first of the above examples requests all features that are both restaurants AND pubs. The following query will request data on restaurants OR pubs:

q <- opq ("portsmouth usa") %>%
    add_osm_features (features = c (
        "\"amenity\"=\"restaurant\"",
        "\"amenity\"=\"pub\""
    ))

The vector of features contains key-value pairs separated by an overpass “filter” symbol such as =, !=, or ~. Each key and value must be enclosed in escape-delimited quotations as shown above.

Full lists of available features and corresponding tags are available in the functions ?available_features and ?available_tags.

Data Formats

An overpass query constructed with the opq() and add_osm_feature() functions is then sent to the overpass server to request data. These data may be returned in a variety of formats, currently including:

1. XML data (downloaded locally) via osmdata_xml();
2. Simple Features (sf) format via osmdata_sf();
3. R Spatial (sp) format via osmdata_sp();
4. Silicate (SC) format via osmdata_sc(); and
5. data.frame format via osmdata_data_frame().

Additional Functionality

Data may also be trimmed to within a defined polygonal shape with the trim_osmdata() function. Full package functionality is described on the website

Citation

citation ("osmdata")
#> To cite osmdata in publications use:
#> 
#>   Mark Padgham, Bob Rudis, Robin Lovelace, Maëlle Salmon (2017).
#>   "osmdata." _Journal of Open Source Software_, *2*(14), 305.
#>   doi:10.21105/joss.00305 <https://doi.org/10.21105/joss.00305>,
#>   <https://joss.theoj.org/papers/10.21105/joss.00305>.
#> 
#> A BibTeX entry for LaTeX users is
#> 
#>   @Article{,
#>     title = {osmdata},
#>     author = {{Mark Padgham} and {Bob Rudis} and {Robin Lovelace} and {Maëlle Salmon}},
#>     journal = {Journal of Open Source Software},
#>     year = {2017},
#>     volume = {2},
#>     number = {14},
#>     pages = {305},
#>     month = {jun},
#>     publisher = {The Open Journal},
#>     url = {https://joss.theoj.org/papers/10.21105/joss.00305},
#>     doi = {10.21105/joss.00305},
#>   }

Data licensing

All data that you access using osmdata is licensed under OpenStreetMap’s license, the Open Database Licence. Any derived data and products must also carry the same licence. You should make sure you understand that licence before publishing any derived datasets.

Other approaches

• osmextract is an R package for downloading and importing compressed ‘extracts’ of OSM data covering large areas (e.g. all roads in a country). The package represents data in sf format only, and only allows a single “layer” (such as points, lines, or polygons) to be read at one time. It is nevertheless recommended over osmdata for large queries of single layers, or where relationships between layers are not important.

• osmapiR is an R interface to the OpenStreetMap API v0.6 for fetching and saving raw geodata from/to the OpenStreetMap database. This package allows access to OSM maps data as well as map notes, GPS traces, changelogs, and users data. osmapiR enables editing or exploring the history of OSM objects, and is not intended to access OSM map data for other purposes (unlike the osmdata or osmextract packages).

Code of Conduct

Please note that this package is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

Contributors

All contributions to this project are gratefully acknowledged using the allcontributors package following the all-contributors specification. Contributions of any kind are welcome!

Code

mpadge	Robinlovelace	jmaspons	hrbrmstr	virgesmith	maelle	elipousson
espinielli	agila5	idshklein	anthonynorth	jeroen	neogeomat	ec-nebi
Tazinho	odeleongt	Mashin6	angela-li	rgzn	fzenoni	stragu
patperu	MHenderson	karthik	jlacko	JimShady	dpprdan	danstowell
ccamara	brry

Issue Authors

karpfen	sytpp	niklaas	RoyalTS	lrob	mem48	beingalink
yaakovfeldman	gregor-d	gregmacfarlane	legengliu	mtennekes	lbuk	prokulski
waholulu	ibarraespinosa	tbuckl	morellek	mdsumner	michielvandijk	loreabad6
slow-data	mroorda	MiKatt	alanlzl	PublicHealthDataGeek	mgageo	polettif
marcusyoung	barryrowlingson	ChrisWoodsSays	daluna1	khzannat26	gdkrmr	dipenpatel235
robitalec	nfruehADA	orlandombaa	changwoo-lee	maellecoursonnais	Suspicis	AlbertRapp
dmag-ir	FlxPo	vanhry	boiled-data	mlucassc	jedalong	mooibroekd
xiaofanliang	xtimbeau	joostschouppe	stalkerGH	RegularnaMatrica

Issue Contributors

sckott	nsfinkelstein	gawbul	edzer	MAnalytics	richardellison	cboettig
prise6	PaoloFrac	Dris101	TomBor	matkoniecz	urswilke	Robsteranium
assignUser	rsbivand