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]. 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_models_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. http://www.openstreetmap.org/copyright
The development version of osmdata can be installed with the devtools
package using the following command:
devtools::install_github('osmdatar/osmdata')
This vignette also uses the magrittr pipe, %>%, for 'pipy' workflows.
osmdata uses the overpass API to download
OpenStreetMap (OSM) data and can convert the results to either Simple Features
(typically of class sf) or 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))
Bounding boxes may also be defined by simply passing the name of a desired area
bbox is a text string opq() uses the helper function getbb() to
convert it into a bounding box:bb = getbb('Greater London, U.K.')
q <- opq(bbox = bb)
Note that the text string is not case sensitive, and that you can avoid the
intermediate step of using getbb() by placing a text string directly into the
bbox argument of opq():
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 bounding box, but such data may be
subsequently trimmed to within the polygon with the trim_osmdata() function,
demonstrated in the code immediately below.
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')
All highways from within the polygonal boundary of Greater London can be extracted with,
q <- opq(bbox = 'greater london uk', format_out = 'polygon') %>%
add_osm_feature(key = 'highway', value = 'motorway') %>%
trim_osmdata ()
See ?time_osmdata() for further ways to obtain polygonally bounded sets of OSM
data.
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.
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 http://overpass-api.de/api/interpreter
but other servers can be used, thanks to functions that get and set the base
url:
get_overpass_url()
## [1] "http://overpass-api.de/api/interpreter"
new_url <- "http://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.)
As demonstrated above, an osmdata query begins by specifying a bounding box
with the function opq(), followed by specifying desired OSM features with
add_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_multilines
## 7373 31 118 0
## osm_multipolygons
## 10
## osm_points osm_lines osm_polygons osm_multilines
## 2254 30 2 0
## osm_multipolygons
## 1
## osm_points osm_lines osm_polygons osm_multipolygons
## 7590 46 119 10
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, 7,590 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
## 2029 15 1 0
## osm_multipolygons
## 1
And there are, for example, the considerably lower number of 2,029 points describing objects in Kunming that are both natural waterbodies and that include 'Dian' in their English name.
Each successive feature added with add_feature() is added to previous
features, so, for example, extending the previous query by an additional
key-value pair to:
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 addtion 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)
OSM data from a queryThe 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 ]
## $osm_points : 'sf' Simple Features Collection with 360582 points
## ...
Note that the result contains no value for bbox, because that information is
lost when the full osmdata_query, q, is converted to a string.
Nevertheless, the results of the two calls osmdata_sf (opq_string (q)) and
osmdata_sf (q) differ only in the values of bbox and timestamp, while
returning otherwise identical data.
In summary, osmdata queries are generally simplified versions of potentially
more complex overpass queries, although arbitrarily complex overpass queries
may be passed directly to the primary osmdata functions. As illustrated
above, osmdata queries are generally constructed through initiating a query
with opq(), and then specifying OSM features in terms of key-value pairs
with add_osm_feature(), along with judicious usage of the key_exact,
value_exact, and match_case parameters.
The simplest way to use osmdata is to simply request all data within a given
bounding box (warning - not intended to run):
q <- opq(bbox = 'London City, U.K.')
lots_of_data <- osmdata_sf(q)
Queries are, however, usually more useful when refined through using
add_feature(), which minimally requires a single key and returns all objects
specifying any value for that key:
not_so_much_data <- opq(bbox = 'city of london uk') %>%
add_osm_feature(key = 'highway') %>%
add_osm_feature(key = 'name') %>%
osmdata_sf()
osmdata will use that query to return all named highways within the requested
bounding box. Note that key specifications are requests for features which
must include those keys, yet most features will also include many other keys,
and thus osmdata objects generally list a large number of distinct keys, as
demonstrated below.
To appreciate query building in more concrete terms, let's imagine that we wanted to find all cycle paths in Seville, Spain:
q1 <- opq('Sevilla') %>%
add_osm_feature(key = 'highway', value = 'cycleway')
cway_sev <- osmdata_sp(q1)
sp::plot(cway_sev$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:
q2 <- add_feature(q1, key = 'bicycle', value = 'designated')
des_bike <- osmdata_sf(q2)
q3 <- add_feature(q2, key = 'bridge', value = 'yes')
des_bike_and_bridge <- osmdata_sf(q3)
nrow(des_bike_and_bridge$osm_points); nrow(des_bike_and_bridge$osm_lines)
## [1] 7
## [1] 3
That query returns only 7 points and 3 lines. Designed cycleways OR bridges
can be obtained through simply combining multiple osmdata objects with the c
operator:
q4 <- add_feature(q1, key = 'bridge', value = 'yes')
bridge <- osmdata_sf(q4)
des_bike_or_bridge <- c(des_bike, bridge)
nrow(des_bike_or_bridge$osm_points); nrow(des_bike_or_bridge$osm_lines)
## [1] 208
## [1] 40
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().
osmdata objectThe 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
## $osm_multipolygons : 'sf' Simple Features Collection with 0 multipolygons
As the results show, all osmdata objects should contain:
bridge$bbox)bridge$timestamp, useful for checking data is
up-to-date)The spatial data, consisting of osm_points, osm_lines, osm_polygons,
osm_multilines and osm_multipolygons.
hese some or all of these can be empty: the example printed above contains only
points and linesThe more complex features of osm_multilines and
osm_multipolygons refer to OSM relations than contain multiple lines and
polygons.
The actual spatial data contained in an osmdata object are of either sp
format when extracted with osmdata_sp() or sf format when extracted with
osmdata_sf().
class(osmdata_sf(q)$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().
osmdata_xml() functionosmdata_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')
osmdata_sf() functionosmdata_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:
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.
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).
osmdata_sp() functionAs 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
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 lines
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:
osm_points(), which extracts all point or node objects
osm_lines(), which extracts all way objects that are lines (that are, that are
not polygons)
osm_polygons(), which extracts all polygon objects
osm_multilines(), which extracts all multiline objects; and
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()
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$waterway == 'waterfall'), ]
wf[which(!is.na(wf))]
## Simple feature collection with 1 feature and 4 fields
## geometry type: POINT
## dimension: XY
## bbox: xmin: 144.3246 ymin: -37.37017 xmax: 144.3246 ymax: -37.37017
## epsg (SRID): 4326
## proj4string: +proj=longlat +datum=WGS84 +no_defs
## osm_id name tourism waterway
## 1013064837 1013064837 Trentham Falls attraction waterfall
## geometry
## 1013064837 POINT(144.324600219727 -37....
This point could be used as the basis for further recursive searches. For
example, all multipolygon objects which include Trentham Falls could be
extracted with:
mp <- osm_multipolygons(tr, rownames(wf))
Although this returns no data in this case, it nevertheless demonstrates the
usefulness and ease of recursive searching with osmdata.
A special type of OSM object is a relation. These can be defined by their name, which can join many divers features into a single object. The following example extracts the London Route Network Route 9, which is composed of many (over 100) separate lines:
lcnr9 <- opq ('greater london uk') %>%
add_osm_feature (key = "name", value = "LCN 9",
value_exact = FALSE) %>%
osmdata_sp()
sp::plot(lcnr9$osm_lines)
@eugster_osmar:_2012 describe osmar, an R package for handling OSM data
that enables visualisation, search and even rudimentary routing operations.
osmar is not user friendly or able to download OSM data flexibly,
as reported in an early tutorial
comparing R and QGIS for handling OSM data [@lovelace_harnessing_2014].
osmdata builds on two previous R packages:
osmplotr, a package available from CRAN
for accessing and plotting OSM data [@osmplotr]
and overpass, a GitHub package
by Bob Rudis that provides an R interface to the
overpass API.