1. Introduction

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.

Unlike the 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

install.packages("osmdata")

and then loaded in the usual way:

library(osmdata)
## 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:

remotes::install_github('ropensci/osmdata')

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 sf and 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 opq():

q <- opq(bbox = c(51.1, 0.1, 51.2, 0.2))

The following sub-section provides more detail on bounding boxes. Following the initial opq() call, 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:

q <- opq(bbox = 'greater london uk') %>%
    add_osm_feature(key = 'highway', value = 'motorway')

A detailed description of features is provided at the OSM wiki, or the osmdata function available_features() can be used to retrive the comprehensive list of feature keys currently used in OSM.

head (available_features ())
## [1] "4wd only"  "abandoned" "abutters"  "access"    "addr"      "addr:city"

There are two primary osmdata functions for obtaining data from a query: osmdata_sf() and 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.

1.1 Bounding boxes: the getbb() function

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.

bb <- getbb('Greater London, U.K.')
q <- opq(bbox = bb)

Note that the text string is not case sensitive, as illustrated in the following code:

identical(q, opq(bbox = 'greater london uk'))
## TRUE

Note also that getbb() can return a data frame reporting multiple matches or matrices representing bounding polygons of matches:

bb_df <- getbb(place_name = "london", format_out = "data.frame")
bb_poly <- getbb(place_name = "london", format_out = "polygon")

The 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)

See ?trim_osmdata() for further ways to obtain polygonally bounded sets of OSM data.

The 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.

2. The overpass API

As mentioned, osmdata obtains OSM data from the overpass API, which is

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.

By default, 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:

get_overpass_url()
## [1] "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 overpass-turbo.)

2.1. osmdata queries

As demonstrated above, an osmdata query begins by specifying a bounding box with the function opq(), followed by specifying desired OSM features with add_osm_feature().

q <- opq(bbox = 'Kunming, China') %>%
    add_osm_feature(key = 'natural', value = 'water')

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 add_osm_feature():

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 osmdata_xml/sp/sf() functions.

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 requires extracting separate data sets and combining then with the inbuilt c operator of osmdata. The following code demonstrates this with osmdata_sf():

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)

The following code extracted numbers of objects returned in each case (where the fourth-to-eighth items of an osmdata object are those containing the actual spatial data)

unlist (lapply (dat1, nrow) [4:8])
unlist (lapply (dat2, nrow) [4:8])
unlist (lapply (dat, nrow) [4:8])
##        osm_points         osm_lines      osm_polygons osm_multipolygons 
##             51399               186               905                47
##        osm_points         osm_lines      osm_polygons    osm_multilines 
##              4473                55                 4                 1 
## osm_multipolygons 
##                 3
##        osm_points         osm_lines      osm_polygons    osm_multilines 
##             53777               238               908                 1 
## osm_multipolygons 
##                49

The number of resultant objects is less than the sum of the individual components because the c operator only combines unique objects. There are thus, for example, 53,777 points corresponding to objects in Kunming, China that are either natural waterbodies or that include ‘Dian’ in their English name. Compare this with the result of the equivalent AND operation

unlist (lapply (osmdata_sf (q), nrow) [4:8])
##        osm_points         osm_lines      osm_polygons    osm_multilines 
##              2087                 3                 1                 0 
## osm_multipolygons 
##                 1

And there are, for example, the considerably lower number of 2,087 points describing objects in Kunming that are both natural waterbodies and that include ‘Dian’ in their English name.

Both key and value fields may be matched either exactly or approximately. The previous query is repeated here for convenience:

q <- opq(bbox = 'Kunming, China') %>%
    add_osm_feature(key = 'natural', value = 'water') %>%
    add_osm_feature(key = 'name:en', value = 'Dian', value_exact = FALSE)

In addition to value_exact, add_osm_feature also offers the additional two options of key_exact and match_case. The former functions just like value_exact, but is applied to the specified key parameter, while match_case (default TRUE) specifies whether case should also be matched. These options allow the above query to be rewritten,

q <- opq(bbox = 'Kunming, China') %>%
    add_osm_feature(key = 'natural', value = 'water') %>%
    add_osm_feature(key = 'name', value = 'dian', key_exact = FALSE,
                value_exact = FALSE, match_case = FALSE)

2.2 Extracting OSM data from a query

The primary osmdata functions osmdata_sf() or osmdata_sp() pass these queries to overpass and return OSM data in corresponding sf or sp format, respectively. Both of these functions also accept direct overpass queries, such as those produced by the osmdata function 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 osmdata_query, 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 bbox and timestamp, while returning otherwise identical data.

In summary, 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 key_exact, value_exact, and match_case parameters.

The simplest way to use osmdata is to simply request all data within a given bounding box (warning - not intended to run):

q <- opq(bbox = 'London City, U.K.')
lots_of_data <- osmdata_sf(q)

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 key:

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.

2.3. Query example

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)
## [1] 99
## [1] 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 c operator:

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)
## [1] 9757
## [1] 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 c().

3. The osmdata object

The osmdata extraction functions (osmdata_sf() and 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:

  • A bounding box (which can be accessed with bridge$bbox)
  • A time-stamp of the query (bridge$timestamp, useful for checking data is up-to-date)
  • The spatial data, consisting of osm_points, osm_lines, osm_polygons, osm_multilines and osm_multipolygons.

Some or all of these can be empty: the example printed above contains only points and lines. The more complex features of osm_multilines and osm_multipolygons refer to OSM relations than contain multiple lines and polygons.

The actual spatial data contained in an osmdata object are of either sp format when extracted with osmdata_sp() or sf format when extracted with osmdata_sf().

class(osmdata_sf(q)$osm_lines)
## [1] "sf"         "data.frame"
class(osmdata_sp(q)$osm_lines)
## [1] "SpatialLinesDataFrame"
## attr(,"package")
## [1] "sp"

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)
## [1] "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 sf or 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)
## [1] TRUE

The following sub-sections now explore these three functions in more detail, beginning with osmdata_xml().

3.1. The osmdata_xml() function

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 R package 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]
## [1] "<?xml version=\"1.0\" encoding=\"UTF-8\"?>"
## [2] "<osm version=\"0.6\" generator=\"Overpass API\">"
## [3] "  <note>The data included in this document is from www.openstreetmap.org. The data is made available under ODbL.</note>"
## [4] "  <meta osm_base=\"2017-03-07T09:28:03Z\"/>"
## [5] "  <node id=\"21593231\" lat=\"51.5149566\" lon=\"-0.1134203\"/>"
## [6] "  <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/sp() functions:

dat_sp <- osmdata_sp(q, 'buildings.osm')
dat_sf <- osmdata_sf(q, 'buildings.osm')

3.2. The osmdata_sf() function

osmdata_sf() returns OSM data in Simple Features (SF) format, defined by the Open Geospatial Consortium, and implemented in the R package 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 osmdata though:

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

The GDAL drivers used by sf can only load single ‘layers’ of features, for example, points, lines, or 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 sf terminology), 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)
## [1] 511.4193

And the osmdata points contain over 500 times as much data. The primary difference between sf/GDAL and osmdata is that the former returns only those objects unique to each category of spatial object. Thus OSM nodes (points in 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 multipolygon or multiline objects.

This processing of data by sf/GDAL has two important implications:

  1. An implicit hierarchy of spatial objects is enforced through including elements of objects only at their ‘highest’ level of representation, where multipolygon and multiline objects are assumed to be at ‘higher’ levels than polyon or line objects, and these in turn are at ‘higher’ levels than point objects. osmdata makes no such hierarchical assumptions.

  2. 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 line or 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 keys, whereas osmdata returns all key fields present in the requested data:

names(sf::st_read('trentham.osm', layer = 'points', quiet = TRUE)) # the keys
## [1] "osm_id"     "name"       "barrier"    "highway"
## [5] "ref"        "address"    "is_in"      "place"
## [9] "man_made"   "other_tags" "geometry"
names(osmdata_sf(q, 'trentham.osm')$osm_points)
## [1] "osm_id"           "name"             "X_description_"   "X_waypoint_"
## [5] "addr.city"        "addr.housenumber" "addr.postcode"    "addr.street"
## [9] "amenity"          "barrier"          "denomination"     "foot"
## [13] "ford"             "highway"          "leisure"          "note_1"
## [17] "phone"            "place"            "railway"          "railway.historic"
## [21] "ref"              "religion"         "shop"             "source"
## [25] "tourism"          "waterway"         "geometry"

key fields which are not specified in a given set of OSM data are not returned by osmdata, while GDAL/sf returns the same key fields regardless of whether any values are specified.

addr <- sf::st_read('trentham.osm', layer = 'points', quiet = TRUE)$address
all(is.na(addr))
## TRUE

and key=address contains no data yet is still returned by GDAL/sf.

Finally, note that osmdata will generally extract OSM data considerably faster than equivalent sf/GDAL routines (as detailed here).

3.3. The osmdata_sp() function

As with osmdata_sf() described above, OSM data may be converted to sp format without using osmdata via the sf functions demonstrated below:

dat <- sf::st_read('buildings.osm', layer = 'multipolygons', quiet = TRUE)
dat_sp <- as(dat, 'Spatial')
class(dat_sp)
## [1] "SpatialPolygonsDataFrame"\nattr(,"package")\n[1] "sp"

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)
## [1] 560  25
dim(osmdata_sp(q, doc = 'buildings.osm')$osm_polygons)
## [1] 566 114
dim(osmdata_sp(q, doc = 'buildings.osm')$osm_multipolygons)
## [1] 15 52

4. Recursive searching

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:

  1. nodes representing spatial points

  2. ways representing lines, both as polygons (with connected ends) and non-polygonal lines

  3. relations representing more complex objects generally comprising collections of ways and/or 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:

  1. osm_points(), which extracts all point or node objects

  2. osm_lines(), which extracts all way objects that are lines (that are, that are not polygons)

  3. osm_polygons(), which extracts all polygon objects

  4. osm_multilines(), which extracts all multiline objects; and

  5. osm_multipolygons(), which extracts all multipolygon objects.

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:

tr <- opq(bbox = 'Trentham, Australia') %>%
    add_osm_feature(key = 'name') %>%
    osmdata_sf()

4.1. Example

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[[1]]
## 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 rownames(coliban).

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:

mp <- osm_multipolygons(tr, rownames(wf))

Although this returns no data in this case, it nevertheless demonstrates the usefulness and ease of recursive searching with osmdata.

4.2 Relation example

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)

5. Additional Functionality

This section briefly describes a few of additional functions, with additional detail provided in the help files for each of these function.

  1. The trim_osmdata() function, as described above in the sub-section on bounding boxes, trims an osmdata object to within a defined bounding polygon, rather than bounding box.
  2. The opq_osm_id() function allows queries for particular OSM objects by their OSM-allocated ID values.
  3. The osm_poly2line() function converts all $osm_polygons items of an osmdata object to $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_polygons simply because they form closed loops. The function enables all highways to be grouped together (as $osm_lines) regardless of the form.
  4. The unique_osmdata() function removes redundant items from the different components of an osmdata object. A multilinestring, for example, is composed of multiple lines, and each line is composed of multiple points. For a multilinestring, an osmdata object 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_lines only contains lines which are not part of any higher-level objects, and $osm_points only contains points which are not part of any higher-level objects.

7. References

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 http://planet.osm.org.” https://www.openstreetmap.org.

Padgham, Mark. 2016. Osmplotr: Customisable Images of Openstreetmap Data. https://cran.r-project.org/package=osmplotr.