The R package osmplotr
uses OpenStreetMap (OSM) data to produce highly
customisable maps. Data are downloaded via the
osmdata
package, and different
aspects of map data - such as roads, buildings, parks, or water bodies - are
able to be visually customised. This vignette demonstrates both data
downloading and the creation of simple maps. The subsequent vignette
('data-maps')
demonstrates how osmplotr
enables user-defined data to be visualised using OSM
data. The maps in this vignette represent a small portion of central London,
U.K.
A map can be generated using the following simple steps:
library (osmplotr) map_dpi <- 72 # dpi res for all maps
dat_B <- rbind (london$dat_BR, london$dat_BNR) dat_H <- rbind (london$dat_H, london$dat_HP) dat_T <- london$dat_T
bbox <- get_bbox (c (-0.13, 51.51, -0.11, 51.52))
dat_B <- extract_osm_objects (key = "building", bbox = bbox)
osm_basemap
with desired background (bg
) colourmap <- osm_basemap (bbox = bbox, bg = "gray20")
map <- add_osm_objects (map, dat_B, col = "gray40")
print_osm_map (map)
print_osm_map (map, filename = "map_a1.png", width = 600, units = "px", dpi = map_dpi )
The function print_osm_map
creates a graphics device that is scaled to the
bounding box of the map. Note also that osmplotr
maps contain no margins and
fill the entire plot area, reflecting the general layout of most printed maps.
Additional capabilities of osmplotr
are described in the following sections,
beginning with downloading and extraction of data.
The package osmdata
is used to
download data from 'OpenStreetMap' using the 'overpass' API overpass
API. Data may be returned in either
'Simple Features' (sf
) or
'R Spatial' (sp
) form. osmplotr
has
a convenience function, extract_osm_objects
, to allow direct import, or the
functions of osmdata
can also be
used directly.
Data of a particular type can be extracted by specifying the appropriate OSM
key
, as in the above example:
bbox <- get_bbox (c (-0.13, 51.51, -0.11, 51.52)) dat_B <- extract_osm_objects (key = "building", bbox = bbox) dat_H <- extract_osm_objects (key = "highway", bbox = bbox)
These objects are of appropriate Spatial
classes:
class (dat_B) class (dat_H) class (dat_B$geometry) class (dat_H$geometry)
Spatial
(sp
) objects may be
returned with,
dat_B <- extract_osm_objects (key = "building", bbox = bbox, sf = FALSE)
otherwise sf
is used as the default format. The Simple Features (sf
)
objects with polygons of London buildings and linestrings of highways
respectively contain
nrow (dat_B) nrow (dat_H)
... 1,759 building polygons and 1,133 highway lines. extract_osm_objects
also
accepts key-value
pairs which are passed to the
overpass API :
dat_T <- extract_osm_objects (key = "natural", value = "tree", bbox = bbox)
Trees are located by single coordinates and are thus point objects:
class (dat_T$geometry) nrow (dat_T)
osmdata
The osmdata
package provides a
more powerful interface for downloading OSM data, and may be used directly with
osmplotr
. The osmplotr
function extract_osm_objects
is effectively just a
convenience wrapper around omsdata
functionality. The primary differences
between the two are:
osmdata
returns all spatial data for a given query; that is, all
points, lines, polygons, multilines, and multipolygons, while osmplotr
returns a single specified geometric type.osmplotr
accepts multiple key-value
pairs in a single call to
extract_osm_objects
, which the equivalent osmdata
function,
add_feature
, accepts only a single key-value
pair, with queries
successively build through multiple calls to add_feature
.These differences are illustrated in the following code which generates identical results in both cases (with namespaces explicitly given to aid clarity),
dat1 <- osmplotr::extract_osm_objects ( key = "highway", value = "!primary", bbox = bbox ) dat2 <- osmdata::opq (bbox = bbox) %>% add_feature (key = "highway") %>% add_feature (key = "highway", value = "!primary") %>% osmdata_sf () dat2 <- dat2$osm_lines
The osmdata
function opq()
constructs an overpass query, with successive
calls to add_feature
extending the query until it is finally submitted to
overpass by osmdata_sf()
(or the sp
version osmdata_sp()
).
Note that add_feature()
has to be called twice in this case, because a single
call to add_feature (key = 'highway", value = "!primary")
would request all
features that are not primary highways. The initial query for key = "highway"
ensures that only npn-primary highways are returned.
As demonstrated above, negation can be specified by pre-pending !
to the
value
argument so that, for example, all natural
objects that are not
trees can be extracted with
dat_NT <- extract_osm_objects (bbox = bbox, key = "natural", value = "!tree")
message ("Cannot determine return type; maybe specify explicitly?")
The message is generated because of course a request for anything that is not a
tree could be for any kind of spatial object. osmplotr
makes several educated
guesses in the absence of specified return types, but these can always be forced
with the return_type
parameter:
pts_NT <- extract_osm_objects ( bbox = bbox, key = "natural", value = "!tree", return_type = "points" )
london$dat_H
contains all non-primary highways, and was extracted with the
call demonstrated above, while london$dat_HP
contains the corresponding set of
exclusively primary highways. An osmplotr
request for key = "highway"
automatically returns line objects (although, again, other kinds of objects may
be forced through specifying return_type
).
key-value
pairsAny number of key-value
pairs may be passed to extract_osm_objects
. For
example, a named building can be extracted with
bbox <- get_bbox (c (-0.13, 51.50, -0.11, 51.52)) extra_pairs <- c ("name", "Royal.Festival.Hall") dat <- extract_osm_objects ( key = "building", extra_pairs = extra_pairs, bbox = bbox )
These data are stored in london$dat_RFH
. Note that periods or dots are used for
white space, and in fact symbolise (in grep
terms) any character whatsoever.
The polygon of a building at a particular street address can be extracted with
extra_pairs <- list ( c ("addr:street", "Stamford.St"), c ("addr:housenumber", "150") ) dat <- extract_osm_objects ( key = "building", extra_pairs = extra_pairs, bbox = bbox )
These data are stored as london$dat_ST
. Note that addresses generally require
combining both addr:street
with addr:housenumber
.
osm_structures
and make_osm_map
The functions osm_structures
and make_osm_map
aid both downloading multiple
OSM data types and plotting (with the latter described below). osm_structures
returns a data.frame
of OSM structure types, associated key-value
pairs,
unique suffices which may be appended to data structures for storage purposes,
and suggested colours. Passing this list to make_osm_map
will return a list of
the requested OSM data items, named through combining the dat_prefix
specified
in make_osm_map
and the suffices specified in osm_structures
.
osm_structures ()
Many structures are identified by keys only, in which cases the values are empty strings.
osm_structures ()$value [1:4]
The last row of osm_structures
exists only to define the background colour of
the map, as explained below
(4.3 Automating map production).
The suffices include as many letters as are necessary to represent all unique
structure names. make_osm_map
returns a list of two components:
osm_data
containing the data objects passed in the osm_structures
argument. Any existing osm_data
may also be submitted to make_osm_map
, in
which case any objects not present in the submitted data will be
appended to the returned version. If osm_data
is not submitted, all objects in
osm_structures
will be downloaded and returned.map
containing the ggplot2
map objects with layers overlaid according to
the sequence and colour schemes specified in osm_structures
The data specified in osm_structures
can then be downloaded simply by calling:
dat <- make_osm_map (structures = osm_structures (), bbox = bbox)
dat1 <- list ( dat_BU = NULL, dat_A = NULL, dat_W = NULL, dat_G = NULL, dat_N = NULL, dat_P = NULL, dat_H = NULL, dat_BO = NULL, dat_T = NULL ) dat <- list (osm_data = dat1, map = ggplot2::ggplot ())
names (dat) sapply (dat, class) names (dat$osm_data)
The requested data are contained in dat$osm_data
. A list of desired structures
can also be passed to this function, for example,
osm_structures (structures = c ("building", "highway"))
Passing this to make_osm_map
will download only these two structures.
Finally, note that the example of,
osm_structures (structures = "grass")
demonstrates that osm_structures
converts a number of common keys
to
OSM-appropriate key-value
pairs.
london
data of osmplotr
To illustrate the use of osm_structures
to download data, this section
reproduces the code that was used to generate the london
data object which
forms part of the osmplotr
package.
structures <- c ( "highway", "highway", "building", "building", "building", "amenity", "park", "natural", "tree" ) structs <- osm_structures (structures = structures, col_scheme = "dark") structs$value [1] <- "!primary" structs$value [2] <- "primary" structs$suffix [2] <- "HP" structs$value [3] <- "!residential" structs$value [4] <- "residential" structs$value [5] <- "commercial" structs$suffix [3] <- "BNR" structs$suffix [4] <- "BR" structs$suffix [5] <- "BC"
Suffices are generated automatically from structure names only, not values, and
the suffices for negated forms must therefore be specified manually. The
london
data can then be downloaded by simply calling make_osm_map
:
london <- make_osm_map (structures = structs, bbox = bbox)$osm_data
The requested data are contained in the $osm_data
list item. make_osm_map
also returns a $map
item which is described below
(see 4.3 Automating map production).
The visualisation functions described in the second osmplotr
vignette
(Data maps)
enable particular regions of maps
to be highlighted. While it may often be desirable to highlight regions
according to a user's own data, osmplotr
also enables regions to be defined by
providing a list of the names of encircling highways. The function which
achieves this is connect_highways
, which returns a sequential matrix of
coordinates from those segments of the named highways which connected
continuously and sequentially to form a single enclosed space. An example is,
load (system.file ("extdata", "hwys.rda", package = "osmplotr")) highways1 <- hwys [[1]] highways2 <- hwys [[2]] highways3 <- hwys [[3]]
highways <- c ( "Monmouth.St", "Short.?s.Gardens", "Endell.St", "Long.Acre", "Upper.Saint.Martin" ) highways1 <- connect_highways (highways = highways, bbox = bbox)
Note the use of the regex
character ?
which declares that the previous character is optional. This
matches both "Shorts Gardens" and "Short's Gardens", both of which appear in OSM
data.
class (highways1) length (highways1) highways1 [[1]] [[1]]
The extraction of bounding polygons from named highways is not fail-safe, and may
generate various warning messages. To understand the kinds of conditions under
which it may not work, it is useful to examine connect_highways
in more
detail.
connect_highways
in detailconnect_highways
takes a list of OpenStreetMap highways and sequentially
connects closest nodes of adjacent highways until the set of named highways
connects to form a cycle. Cases where no circular connection is possible
generate an error message. The routine proceeds through the three stages of,
Adding intersection nodes to junctions of ways where these don't already exist
Filling a connectivity matrix between the listed highways and extracting the longest cycle connecting all of them
Inserting extra connections between highways until the length of the longest
cycle is equal to length (highways)
.
This procedure can not be guaranteed fail-safe owing both to the inherently
unpredictable nature of OpenStreetMap, as well as to the unknown relationships
between named highways. To enable problematic cases to be examined and hopefully
resolved, connect_highways
has a plot
option:
bbox_big <- get_bbox (c (-0.15, 51.5, -0.10, 51.52)) highways <- c ( "Kingsway", "Holborn", "Farringdon.St", "Strand", "Fleet.St", "Aldwych" ) highway_list <- connect_highways ( highways = highways, bbox = bbox_big, plot = TRUE )
load (system.file ("extdata", "hwys.rda", package = "osmplotr")) ways <- hwys$highways4 osmplotr:::plot_highways (ways) ways <- osmplotr:::connect_single_ways (ways) ways <- osmplotr:::get_highway_cycle (ways) conmat <- osmplotr:::get_conmat (ways) cycles <- try (ggm::fundCycles (conmat), TRUE) cyc <- cycles [[which.max (sapply (cycles, nrow))]] path <- osmplotr:::sps_through_cycle (ways, cyc) lines (path [, 1], path [, 2], lwd = 2, lty = 2)
The plot depicts each highway in a different colour, along with numbers at start
and end points of each segment. This plot reveals in this case that highway#6
("Aldwych") is actually nested within two components of highway#4 ("Strand").
connect_highways
searches for the shortest path connecting all named highways,
and since "Strand" connects to both highways#1 and #5, the shortest path
excludes #6. This exclusion of one of the named components generates the
warning message.
These connected polygons returned from connect_highways
can then be used to
highlight the enclosed regions within maps, as demonstrated in the second
vignette,
'Data Maps'.
Maps will generally contain multiple kinds of OSM data, for example,
dat_B <- extract_osm_objects (key = "building", bbox = bbox) dat_H <- extract_osm_objects (key = "highway", bbox = bbox) dat_T <- extract_osm_objects (key = "natural", value = "tree", bbox = bbox)
As illustrated above, plotting maps requires first making a basemap with a
specified background colour. Portions of maps can also be plotted by creating a
basemap
with a smaller bounding box.
bbox_small <- get_bbox (c (-0.13, 51.51, -0.11, 51.52)) map <- osm_basemap (bbox = bbox_small, bg = "gray20") map <- add_osm_objects (map, dat_H, col = "gray70") map <- add_osm_objects (map, dat_B, col = "gray40")
map
is then a ggplot2
which may be viewed simply by passing it to
print_osm_map
:
print_osm_map (map)
print_osm_map (map, filename = "map_a2.png", width = 600, units = "px", dpi = map_dpi )
Other graphical parameters can also be passed to add_osm_objects
, such as
border colours or line widths and types. For example,
map <- osm_basemap (bbox = bbox_small, bg = "gray20") map <- add_osm_objects (map, dat_B, col = "gray40", border = "orange", size = 0.2 ) print_osm_map (map)
map <- osm_basemap (bbox = bbox_small, bg = "gray20") map <- add_osm_objects (map, dat_B, col = "gray40", border = "orange", size = 0.2 ) print_osm_map (map, filename = "map_a3.png", width = 600, units = "px", dpi = map_dpi )
The size
argument is passed to the corresponding ggplot2
routine for
plotting polygons, lines, or points, and respectively determines widths of lines
(for polygon outlines and for lines), and sizes of points. The col
argument
determines the fill colour of polygons, or the colour of lines or points.
map <- add_osm_objects (map, dat_H, col = "gray70", size = 0.7) map <- add_osm_objects (map, dat_T, col = "green", size = 2, shape = 1) print_osm_map (map)
map <- add_osm_objects (map, dat_H, col = "gray70", size = 0.7) map <- add_osm_objects (map, dat_T, col = "green", size = 2, shape = 1) print_osm_map (map, filename = "map_a4.png", width = 600, units = "px", dpi = map_dpi )
Note also that the shape
parameter determines the point shape, for details of
which see ?ggplot2::shape
. Also note that plot order affects the final
outcome, because components are sequentially overlaid and thus the same map
components plotted in a different order will generally produce a different
result.
The function print_osm_map()
can be used to print either to on-screen
graphical devices or to graphics files (see, for example, ?png
for a list of
possible graphics devices). Sizes and resolutions of devices may be
specified with the appropriate parameters. Device dimensions are scaled by
default to the proportions of the bounding box (although this can be
over-ridden).
A screen-based device simply requires
print_osm_map (map)
while examples of writing higher resolution versions to files include:
print_osm_map (map, filename = "map.png", width = 10, units = "in", dpi = map_dpi ) print_osm_map (map, filename = "map.eps", width = 1000, units = "px", dpi = map_dpi ) print_osm_map (map, filename = "map", device = "jpeg", width = 10, units = "cm")
The ability demonstrated above to use negation in extract-osm-objects
allows
different kinds of the same object to be visually contrasted, for example
primary and non-primary highways:
dat_HP <- extract_osm_objects (key = "highway", value = "primary", bbox = bbox) dat_H <- extract_osm_objects (key = "highway", value = "!primary", bbox = bbox)
dat_HP <- london$dat_HP dat_H <- london$dat_H
map <- osm_basemap (bbox = bbox_small, bg = "gray20") map <- add_osm_objects (map, dat_H, col = "gray50") map <- add_osm_objects (map, dat_HP, col = "gray80", size = 2) print_osm_map (map)
map <- osm_basemap (bbox = bbox_small, bg = "gray20") map <- add_osm_objects (map, dat_H, col = "gray50") map <- add_osm_objects (map, dat_HP, col = "gray80", size = 2) print_osm_map (map, filename = "map_a5.png", width = 600, units = "px", dpi = map_dpi )
The additional key-value
pairs demonstrated above (for Royal Festival Hall,
dat_RFH
and 150 Stamford Street, dat_ST
) also demonstrated above allow for
highly customised maps in which distinct objects are plotting with different
colour schemes.
dat_RFH <- london$dat_RFH dat_ST <- london$dat_ST
bbox_small2 <- get_bbox (c (-0.118, 51.504, -0.110, 51.507)) map <- osm_basemap (bbox = bbox_small2, bg = "gray95") map <- add_osm_objects (map, dat_H, col = "gray80") map <- add_osm_objects (map, dat_HP, col = "gray20", size = 2) map <- add_osm_objects (map, dat_RFH, col = "orange", border = "red", size = 2) map <- add_osm_objects (map, dat_ST, col = "skyblue", border = "blue", size = 2) print_osm_map (map)
bbox_small2 <- get_bbox (c (-0.118, 51.504, -0.110, 51.507)) map <- osm_basemap (bbox = bbox_small2, bg = "gray95") map <- add_osm_objects (map, dat_H, col = "gray80") map <- add_osm_objects (map, dat_HP, col = "gray60", size = 2) map <- add_osm_objects (map, dat_RFH, col = "orange", border = "red", size = 2) map <- add_osm_objects (map, dat_ST, col = "skyblue", border = "blue", size = 2) print_osm_map (map, filename = "map_a7.png", width = 600, units = "px", dpi = map_dpi )
Different portions of a map may sometimes be delineated by lines, for example
with coastlines which are always represented in OpenStreetMap as lines. Plotting
the water or land either side of a coastline in a single block of colour
requires the regions to be polygons, not lines. osmplotr
has a function
osm_line2poly()
which converts boundary lines extending beyond a given
bounding box into polygons encircling the perimeter of the bounding box. An
example is given in ?osm_line2poly
, using both the osmdata
package to obtain
the bounding box of a named region, and the magrittr
pipe operator.
library (osmdata) bb <- osmdata::getbb ("melbourne, australia") coast <- extract_osm_objects ( bbox = bb, key = "natural", value = "coastline", return_type = "line" ) coast <- osm_line2poly (coast, bbox = bb) map <- osm_basemap (bbox = bb) %>% add_osm_objects (coast [[1]], col = "lightsteelblue") %>% print_osm_map ()
The osm_line2poly()
function returns a list of two sf
polygons. For
coastline, one of these will correspond to water, one to land. In the preceding
example, the first polygon is the ocean, which is coloured in
"lightsteelblue"
. Users must determine for themselves which polygon is to be
plotted in which colour. Note that osm_line2poly()
only accepts sf
-formatted
data, and not sp
.
As indicated above
(2.4 Downloading with osm_structures
and make_osm_map
),
the production of maps overlaying various type of OSM objects is facilitated
with make_osm_map
. The structure of a map is defined by osm_structures
as
described above.
Producing a map with customised data is as simple as,
structs <- c ("highway", "building", "park", "tree") structures <- osm_structures (structures = structs, col_scheme = "light") dat <- make_osm_map (structures = structures, bbox = bbox_small) print_osm_map (dat$map)
structs <- c ("highway", "building", "park", "tree") structures <- osm_structures (structures = structs, col_scheme = "light") osm_dat <- list ( dat_B = dat_B, dat_H = dat_H, dat_P = london$dat_P, dat_A = london$dat_A, dat_P = london$dat_P, dat_T = london$dat_T ) dat <- make_osm_map ( structures = structures, osm_data = osm_dat, bbox = bbox ) print_osm_map (dat$map, filename = "map_a8.png", width = 600, units = "px", dpi = map_dpi )
Calling make_osm_map()
downloads the requested structures within the given
bbox
and returns a list of two components, the first of which contains the
downloaded data:
names (dat) names (dat$osm_data)
Pre-downloaded data may also be passed to make_osm_map()
dat <- make_osm_map ( structures = structures, osm_data = dat$osm_data, bbox = bbox ) print_osm_map (dat$map)
dat <- make_osm_map (structures = structures, osm_data = osm_dat, bbox = bbox) print_osm_map (dat$map, filename = "map_a9.png", width = 600, units = "px", dpi = map_dpi )
Note that omitting the bounding box argument (bbox
) produces a map with a bounding
box is extracted as the largest box spanning all objects in osm_data
. This
may be considerably larger than the desired boundaries, particularly because
highways are returned by overpass
in their entirety, and will generally extend
well beyond the specified bounding box.
Finally, objects in maps are overlaid on the plot according to the order of rows
in osm_structures
, with the single exception that background
is plotted
first. This order can be readily changed or restricted simply by submitting
structures in a desired order.
structs <- c ("amenity", "building", "highway", "park") osm_structures (structs, col_scheme = "light")
Axes may be added to maps using the add_axes
function. In contrast to many R
packages for producing maps, maps in osmplotr
fill the entire plotting space,
and axes are added internal to this space. The separate function for adding
axes allows them to be overlaid on top of all previous layers.
Axes added to a dark version of the previous map look like this:
structures <- osm_structures (structures = structs, col_scheme = "dark") dat <- make_osm_map ( structures = structures, osm_data = dat$osm_dat, bbox = bbox_small ) map <- add_axes (dat$map, colour = "black")
Note that, as described above, make_osm_map
returns a list of two items: (i)
potentially modified data (in $osm_data
) and (ii) the map object (in $map
).
All other add_
functions take a map object as one argument and return the
single value of the modified map object.
print_osm_map (map)
print_osm_map (map, filename = "map_a10.png", width = 600, units = "px", dpi = map_dpi )
This map reveals that the axes and labels are printed above semi-transparent
background rectangles, with transparency controlled by the alpha
parameter.
Axes are always plotted on the left and lower side, but positions can be
adjusted with the pos
parameter which specifies the positions of axes and
labels relative to entire plot device
map <- add_axes (map, colour = "blue", pos = c (0.1, 0.2), fontsize = 5, fontface = 3, fontfamily = "Times" ) print_osm_map (map)
map <- add_axes (map, colour = "blue", pos = c (0.1, 0.2), fontsize = 5, fontface = 3, fontfamily = "Times" ) print_osm_map (map, filename = "map_a11.png", width = 600, units = "px", dpi = map_dpi )
The second call to add_axes
overlaid additional axes on a map that already had
axes from the previous call. This call also demonstrates how sizes and other
font characteristics of text labels can be specified.
Finally, the current version of osmplotr
does not allow text labels of axes to
be rotated. (This is because the semi-transparent underlays are generated with
ggplot2::geom_label
which currently prevents rotation.)
Click on the following link to proceed to the second osmplotr
vignette:
Data maps
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