1 Introduction
Visualizing spatial data in R can be a challenging task. Fortunately the task is made a good deal easier by the data structures and plot methods of sp, RgoogleMaps, and related packages (Pebesma and Bivand 2006; Bivand et al. 2008; Loecher and Berlin School of Economics and Law 2013). Using those methods, one can plot the basic geographic information of (for instance) a shape file containing polygons for areal data or points for point referenced data. However, compared to specialized geographic information systems (GISs) such as ESRI’s ArcGIS, which can plot points, polygons, etc. on top of maps and satellite imagery with drag-down menus, these visualizations can be pretty disappointing. This article details some new methods for the visualization of spatial data in R using the layered grammar of graphics implementation of ggplot2 in conjunction with the contextual information of static maps from Google Maps, OpenStreetMap, Stamen Maps or CloudMade Maps (Wickham 2009, 2010). The result is an easy to use R package named ggmap. After describing the nuts and bolts of ggmap, we showcase some of its capabilities in a simple case study concerning violent crimes in downtown Houston, Texas and present an overview of a few utility functions.
2 Plotting spatial data in R
Areal data is data which corresponds to geographical extents with polygonal boundaries. A typical example is the number of residents per zip code. Considering only the boundaries of the areal units, we are used to seeing areal plots in R which resemble those in Figure 1 (left).
While these kinds of plots are useful, they are not as informative as we would like in many situations. For instance, when plotting zip codes it is helpful to also see major roads and other landmarks which form the boundaries of areal units.
The situation for point referenced spatial data is often much worse. Since we can’t easily contextualize a scatterplot of points without any background information at all, it is common to add points as an overlay of some areal data—whatever areal data is available. The resulting plot looks like Figure 1 (right).
In most cases the plot is understandable to the researcher who has worked on the problem for some time but is of hardly any use to his audience, who must work to associate the data of interest with their location. Moreover, it leaves out many practical details—are most of the events to the east or west of landmark \(x\)? Are they clustered around more well-to-do parts of town, or do they tend to occur in disadvantaged areas? Questions like these can’t really be answered using these kinds of graphics because we don’t think in terms of small scale areal boundaries (e.g. zip codes or census tracts).
With a little effort better plots can be made, and tools such as maps, maptools, sp, or RgoogleMaps make the process much easier; in fact, RgoogleMaps was the inspiration for ggmap (Becker et al. 2013; Bivand and Lewin-Koh 2013).
Moreover, there has recently been a deluge of interest in the subject of mapmaking in R—Ian Fellows’ excellent interactive GUI-driven DeducerSpatial package based on Bing Maps comes to mind (Fellows et al. 2013). ggmap takes another step in this direction by situating the contextual information of various kinds of static maps in the ggplot2 plotting framework. The result is an easy, consistent way of specifying plots which are readily interpretable by both expert and audience and safeguarded from graphical inconsistencies by the layered grammar of graphics framework. The result is a spatial plot resembling Figure 2. Note that map images and information in this work may appear slightly different due to map provider changes over time.
murder <- subset(crime, offense == "murder")
qmplot(lon, lat, data = murder, colour = I('red'), size = I(3), darken = .3)
3 The layered grammar of graphics
One advantage of making the plots with ggplot2 is the layered grammar of graphics on which ggplot2 is based (Wilkinson 2005; Wickham 2010). By definition, the layered grammar demands that every plot consist of five components :
a default dataset with aesthetic mappings,
one or more layers, each with a geometric object (“geom”), a statistical transformation (“stat”), and a dataset with aesthetic mappings (possibly defaulted),
a scale for each aesthetic mapping (which can be automatically generated),
a coordinate system, and
a facet specification.
Since ggplot2 is an implementation of the layered grammar of graphics, every plot made with ggplot2 has each of the above elements. Consequently, ggmap plots also have these elements, but certain elements are fixed to map components : the \(x\) aesthetic is fixed to longitude, the \(y\) aesthetic is fixed to latitude, and the coordinate system is fixed to the Mercator projection.1
The major theoretical advantage of using the layered grammar in plotting maps is that aesthetic scales are kept consistent. In the typical situation where the map covers the extent of the data, in ggmap the latitude and longitude scales key off the map (by default) and one scale is used for those axes. The same is true of colors, fills, alpha blendings, and other aesthetics which are built on top of the map when other layers are presented—each is allotted one scale which is kept consistent across each layer of the plot. This aspect of the grammar is particularly important for faceted plots in order to make a proper comparison across several plots. Of course, the scales can still be tricked if the user improperly specifies the spatial data, e.g. using more than one projection in the same map, but fixing such errors is beyond any framework.
The practical advantage of using the grammar is even better. Since the
graphics are done in ggplot2 the user can draw from the full range of
ggplot2’s capabilities to layer elegant visual content—geoms, stats,
scales, etc.—using the usual ggplot2 coding conventions. This was
already seen briefly in Figure 2 where the
arguments of qmplot are identical to that of ggplot2’s qplot; much
more will be seen shortly.
4 How ggmap works
The basic idea driving ggmap is to take a downloaded map image, plot
it as a context layer using ggplot2, and then plot additional content
layers of data, statistics, or models on top of the map. In ggmap this
process is broken into two pieces – (1) downloading the images and
formatting them for plotting, done with get_map, and (2) making the
plot, done with ggmap. qmap marries these two functions for quick
map plotting (c.f. ggplot2’s ggplot), and qmplot attempts to wrap
up the entire plotting process into one simple command (c.f. ggplot2’s
qplot).
The get_map function
In ggmap, downloading a map as an image and formatting the image for
plotting is done with the get_map function. More specifically,
get_map is a wrapper function for the underlying functions
get_googlemap, get_openstreetmap, get_stamenmap, and
get_cloudmademap which accepts a wide array of arguments and returns a
classed raster object for plotting with ggmap.
As the most important characteristic of any map is location, the most
important argument of get_map is the location argument. Ideally,
location is a longitude/latitude pair specifying the center of the map
and accompanied by a zoom argument, an integer from 3 to 20 specifying
how large the spatial extent should be around the center, with 3 being
the continent level and 20 being roughly the single building level.
location is defaulted to downtown Houston, Texas, and zoom to 10,
roughly a city-scale.
While longitude/latitude pairs are ideal for specifying a location, they
are somewhat inconvenient on a practical level. For this reason,
location also accepts a character string. The string, whether
containing an address, zip code, or proper name, is then passed to the
geocode function which then determines the appropriate
longitude/latitude coordinate for the center. In other words, there is
no need to know the exact longitude/latitude coordinates of the center
of the map—get_map can determine them from more colloquial (“lazy”)
specifications so that they can be specified very loosely. For example,
since
> geocode("the white house")
lon lat
-77.03676 38.89784 works, "the white house" is a viable location argument. More details
on geocode and other utility functions are discussed at the end of
this article.
In lieu of a center/zoom specification, some users find a bounding box
specification more convenient. To accommodate this form of
specification, location also accepts numeric vectors of length four
following the left/bottom/right/top convention. This option is not
currently available for Google Maps.
While each map source has its own web application programming interface
(API), specification of location/zoom in get_map works for each by
computing the appropriate parameters (if necessary) and passing them to
each of the API specific get_* functions. To ensure that the resulting
maps are the same across the various sources for the same
location/zoom specification, get_map first grabs the appropriate
Google Map, determines its bounding box, and then downloads the other
map as needed. In the case of Stamen Maps and CloudMade Maps, this
involves a stitching process of combining several tiles (small map
images) and then cropping the result to the appropriate bounding box.
The result is a single, consistent specification syntax across the four
map sources as seen for Google Maps and OpenStreetMap in Figure
3.
baylor <- "baylor university"
qmap(baylor, zoom = 14)
qmap(baylor, zoom = 14, source = "osm")
Before moving into the source and maptype arguments, it is important
to note that the underlying API specific get_* functions for which
get_map is a wrapper provide more extensive mechanisms for downloading
from their respective sources. For example, get_googlemap can access
almost the full range of the Google Static Maps API as seen in Figure
4.
set.seed(500)
df <- round(data.frame(
x = jitter(rep(-95.36, 50), amount = .3),
y = jitter(rep( 29.76, 50), amount = .3)
), digits = 2)
map <- get_googlemap('houston', markers = df, path = df, scale = 2)
ggmap(map, extent = 'device')
Tile style – the source and maptype arguments of get_map
The most attractive aspect of using different map sources (Google Maps,
OpenStreetMap, Stamen Maps, and CloudMade Maps) is the different map
styles provided by the producer. These are specified with the maptype
argument of get_map and must agree with the source argument. Some
styles emphasize large roadways, others bodies of water, and still
others political boundaries. Some are better for plotting in a
black-and-white medium; others are simply nice to look at. This section
gives a run down of the various map styles available in ggmap.
Google provides four different familiar types—terrain (default), satellite (e.g. Figure 13), roadmap, and hybrid (e.g. Figure 12). OpenStreetMap, on the other hand, only provides the default style shown in Figure 3.
Style is where Stamen Maps and CloudMade Maps really shine. Stamen Maps has three available tile sets—terrain (e.g. Figures 2 or 13), watercolor, and toner (for the latter two see Figure 5).
qmap(baylor, zoom = 14, source = "stamen", maptype = "watercolor")
qmap(baylor, zoom = 14, source = "stamen", maptype = "toner")
Stamen’s terrain tile set is quite similar to Google’s, but obviously the watercolor and toner tile sets are substantially different than any of the four Google tile sets. The latter, for example, is ideal for black-and-white plotting.
CloudMade Maps takes the tile styling even further by allowing the user
to either (1) select among thousands of user-made sets or (2) create
an entirely new style with a simple online editor where the user can
specify colors, lines, and so forth for various types of roads,
waterways, landmarks, etc., all of which are generated by CloudMade and
accessible in ggmap. ggmap, through get_map (or
get_cloudmademap) allows for both options. This is a unique feature of
CloudMade Maps which really boosts their applicability and expands the
possibilities with ggmap. The one minor drawback to using CloudMade
Maps is that the user must register with CloudMade to obtain an API key
and then pass the API key into get_map with the api_key argument.
API keys are free of charge and can be obtained in a matter of minutes.
Two low-light CloudMade map styles are seen in Figure
6. Note that map styles are only available to the
user that owns them.
Both Stamen Maps and CloudMade Maps are built using OpenStreetMap data. These data are contributed by an open community of online users in much the same way Wikipedia is—both are free, both are user-contributed, and both are easily edited. Moreover, OpenStreetMap has data not only on roadways and bodies of water but also individual buildings, fountains, stop signs and other apparent minutiae. The drawback is that (like Google Maps) not all locations are mapped with the same degree of precision, and imperfections may be observed in small-scale out of the way features.2
qmap(baylor, zoom = 14, maptype = 53428, api_key = api_key,
source = "cloudmade")
qmap("houston", zoom = 10, maptype = 58916, api_key = api_key,
source = "cloudmade")
The ggmap function
Once get_map has grabbed the map of interest, ggmap is ready to plot
it. The result of get_map is a specially classed raster object (a
matrix of colors as hexadecimal character strings) –
> paris <- get_map(location = "paris")
> str(paris)
chr [1:1280, 1:1280] "#C6DAB6" "#C2D6B3" "#C2D6B3" ...
- attr(*, "class")= chr [1:2] "ggmap" "raster"
- attr(*, "bb")='data.frame': 1 obs. of 4 variables:
..$ ll.lat: num 48.6
..$ ll.lon: num 1.91
..$ ur.lat: num 49.1
..$ ur.lon: num 2.79The purpose of ggmap is to take the map from the raster object to the
screen, and it fulfills this purpose by creating a ggplot object which,
when printed, draws the desired map in the graphics device. This is
illustrated in Figure 7.
While ggmap requires a ggmap object, it accepts a handful of other
arguments as well—extent, base_layer, maprange, legend,
padding, and darken. With none of these additional arguments,
ggmap effectively returns the following ggplot object
ggplot(aes(x = lon, y = lat), data = fourCorners) +
geom_blank() + coord_map("mercator") +
annotation_raster(ggmap, xmin, xmax, ymin, ymax)where fourCorners is the data frame resulting from applying
expand.grid to the longitude and latitude ranges specified in the bb
attribute of the ggmap object. Thus, the default base layer of the
ggplot2 object created by ggmap is
ggplot(aes(x = lon, y = lat), data = fourCorners), and the default \(x\)
and \(y\) aesthetic scales are calculated based on the longitude and
latitude ranges of the map.
ggmap(paris, extent = "normal")
The extent argument dictates how much of the graphics device is
covered by the map. It accepts three possible strings: "normal" shown
in Figure 7, "panel" shown in Figures
10 and 12, and "device" shown in every
other figure. "normal" situates the map with the usual axis padding
provided by ggplot2 and, consequently, one can see the panel behind
it. "panel" eliminates this, setting the limits of the plot panel to
be the longitude and latitude extents of the map with
scale_[x,y]_continuous(expand = c(0,0)). "device" takes this to the
extreme by eliminating the axes themselves with the new exported
theme_nothing.
base_layer is a call which substitutes the default base layer to the
user’s specification. Thus, in the above code the user can change
ggplot(aes(x = lon, y = lat), data = fourCorners) to a different call.
This is essential for faceting plots since the referent of ggplot2
functions facet_wrap and facet_grid is the base layer. Since
changing the base layer changes the base scales and therefore limits of
the plot, it is possible that when the base layer is changed only part
of the map is visible. Setting the maprange argument to TRUE (it
defaults to FALSE) ensures that the map determines the \(x\) and \(y\)
axis limits (longitude and latitude) via the bb attribute of the ggmap
object itself, and not the base_layer argument.
The legend-related arguments of ggmap are legend and padding, and
they are only applicable when extent = "device". The legend argument
determines where a legend should be drawn on the map if one should be
drawn. Its options are "left", "right" (default), "bottom",
"top", "topleft", "bottomleft", "topright", "bottomright" and
"none". The first four draw the legend according to ggplot2’s normal
specifications (without any axes); the next four draw the legend on top
of the map itself similar to ArcGIS; and the last eliminates the legend
altogether. padding governs how far from the corner the legend should
be drawn.
The darken argument, a suggestion by Jean-Olivier Irisson, tints the
map image. The default, c(0, "black"), indicates a fully translucent
black layer, i.e. no tint at all. Generally, the first argument
corresponds to an alpha blending (0 = invisible, 1 = opaque) and the
second argument the color of the tint. If only a number is supplied to
the darken argument ggmap assumes a black tint. The tint itself is
made by adding a geom_rect layer on top of the map. An example is
provided by Figure 2, where a black tint was
added to the map to enhance the visibility of the points.
Since ggmap returns a ggplot object, the product of ggmap can itself
act as a base layer in the ggplot2 framework. This is an incredibly
important realization which allows for the full range of ggplot2
capabilities. We now illustrate many of the ways in which this can be
done effectively through a case study of violent crime in downtown
Houston, Texas.
5 ggmap in action
Data
Crime data were compiled from the Houston Police Department’s
website over the period
of January 2010–August 2010. The data were lightly cleaned and
aggregated using plyr
(Wickham 2011) and geocoded using Google Maps (to the center of the
block, e.g., 6150 Main St.); the full data set is available in ggmap
as the data set crime.
> str(crime)
'data.frame': 86314 obs. of 17 variables:
$ time : POSIXt, format: "2010-01-01 0...
$ date : chr "1/1/2010" "1/1/2010" "1...
$ hour : int 0 0 0 0 0 0 0 0 0 0 ...
$ premise : chr "18A" "13R" "20R" "20R" ...
$ offense : chr "murder" "robbery" "aggr...
$ beat : chr "15E30" "13D10" "16E20" ...
$ block : chr "9600-9699" "4700-4799" ...
$ street : chr "marlive" "telephone" "w...
$ type : chr "ln" "rd" "ln" "st" ...
$ suffix : chr "-" "-" "-" "-" ...
$ number : int 1 1 1 1 1 1 1 1 1 1 ...
$ month : Factor w/ 12 levels "january"...
$ day : Factor w/ 7 levels "monday" ...
$ location: chr "apartment parking lot" ...
$ address : chr "9650 marlive ln" "4750 ...
$ lon : num -95.4 -95.3 -95.5 -95.4 ...
$ lat : num 29.7 29.7 29.6 29.8 29.7...Since we are only interested in violent crimes which take place
downtown, we restrict the data set to those qualifiers. To determine a
bounding box, we first use gglocator, a ggplot2 analogue of base’s
locator function exported from ggmap. gglocator works not only for
ggmap plots, but ggplot2 graphics in general.
> # find a reasonable spatial extent
> qmap('houston', zoom = 13)
> gglocator(2)
> lon lat
> 1 -95.39681 29.78400
> 2 -95.34188 29.73631
>
> # only violent crimes
> violent_crimes <- subset(crime,
+ offense != "auto theft" & offense != "theft" & offense != "burglary")
>
> # order violent crimes
> violent_crimes$offense <- factor(violent_crimes$offense,
+ levels = c("robbery", "aggravated assault", "rape", "murder"))
>
> # restrict to downtown
> violent_crimes <- subset(violent_crimes,
+ -95.39681 <= lon & lon <= -95.34188 &
+ 29.73631 <= lat & lat <= 29.78400)The analysis performed only concerns data on the violent crimes of aggravated assault, robbery, rape and murder. Note that while some effort was made to ensure the quality of the data, the data were only leisurely cleaned and the data set may still contain errors.
Analysis
The first step we might want to take is to look at where the individual
crimes took place. Modulo some simple ggplot2 styling changes
(primarily in the fonts and key-styles of the legends via ggplot2’s
guide function), Figure 8 contains the code to
produce the spatial bubble chart shown on the left.
One of the problems with the bubble chart is overplotting and point size—we can’t really get a feel for what crimes are taking place and where. One way around this is to bin the points and drop the bins which don’t have any samples in them. The result (Figure 8 right) shows us where the crimes are happening at the expense of knowing their frequency.
The binned plot is the first time we really begin to see the power of
having the maps in the ggplot2 framework. While it is actually not a
very good plot, it illustrates the practical advantage of the ggplot2
framework with the contextual information of the map—the process of
splitting the data frame violent_crimes into chunks based on the
offense variable, binning the points of each, and aggregating back into
one data set to plot is all done entirely behind the scenes by
ggplot2.
theme_set(theme_bw(16))
HoustonMap <- qmap("houston", zoom = 14, color = "bw", legend = "topleft")
HoustonMap +
geom_point(aes(x = lon, y = lat, colour = offense, size = offense),
data = violent_crimes)
HoustonMap +
stat_bin2d(
aes(x = lon, y = lat, colour = offense, fill = offense),
size = .5, bins = 30, alpha = 1/2,
data = violent_crimes
)
What about violent crimes in general? If we neglect the type of offense,
we can get a good idea of the spatial distribution of violent crimes by
using a contour plot. Since the map image itself is based on ggplot2’s
annotation_raster, which doesn’t have a fill aesthetic, we can access
the fill aesthetic to make a filled contour plot. This is seen in Figure
9 (left).
houston <- get_map("houston", zoom = 14)
HoustonMap <- ggmap("houston", extent = "device", legend = "topleft")
HoustonMap +
stat_density2d(
aes(x = lon, y = lat, fill = ..level.., alpha = ..level..),
size = 2, bins = 4, data = violent_crimes,
geom = "polygon"
)
overlay <- stat_density2d(
aes(x = lon, y = lat, fill = ..level.., alpha = ..level..),
bins = 4, geom = "polygon",
data = violent_crimes
)
HoustonMap + overlay + inset(
grob = ggplotGrob(ggplot() + overlay + theme_inset()),
xmin = -95.35836, xmax = Inf, ymin = -Inf, ymax = 29.75062
)
These kinds of overlays can be incredibly effective; however, their
ability to communicate information can be hindered by the fact that the
map overlay can be visually confused with the map itself. This is
particularly common when using colored maps. To get around this problem
the inset function can be used to insert map insets containing the
overlay on a white background with major and minor axes lines for visual
guides made possible by the exported function theme_inset; this is
seen in Figure 9 (right).
The image indicates that there are three main hotspots of activity. Each of these three corresponds to locations commonly held by Houstonians to be particularly dangerous locations. From east to west, the hotspots are caused by (1) a county jail which releases its inmates twice daily, who tend to loiter in the area indicated, (2) a commercial bus station in an area of substantial homelessness and destitution, and (3) a prostitution hotspot in a very diverse and pedestrian part of town.
In addition to single plots, the base_layer argument to ggmap or
qmap allows for faceted plots (see Figure 10). This is
particularly useful for spatiotemporal data with discrete temporal
components (day, month, season, year, etc.).
This last plot displays one of the known issues with contour plots in ggplot2—a “clipping” or “tearing” of the contours. Aside from that fact (which will likely be fixed in subsequent ggplot2 versions), we can see that in fact most violent crimes happen on Monday, with a distant second being Friday. Friday’s pattern is easily recognizable—a small elevated dot in the downtown bar district and an expanded region to the southwest in the district known as midtown, which has an active nightlife. Monday’s pattern is not as easily explainable.
houston <- get_map(location = "houston", zoom = 14, color = "bw",
source = "osm")
HoustonMap <- ggmap(houston, base_layer = ggplot(aes(x = lon, y = lat),
data = violent_crimes))
HoustonMap +
stat_density2d(aes(x = lon, y = lat, fill = ..level.., alpha = ..level..),
bins = 5, geom = "polygon",
data = violent_crimes) +
scale_fill_gradient(low = "black", high = "red") +
facet_wrap(~ day)
6 ggmap’s utility functions
ggmap has several utility functions which aid in spatial exploratory data analysis.
The geocode function
The ability to move from an address to a longitude/latitude coordinate
is virtually a must for visualizing spatial data. Unfortunately however,
the process is almost always done outside R by using a proper geographic
information system (GIS), saving the results, and importing them into R.
The geocode function simplifies this process to a single line in R.
geocode is a vectorized function which accepts character strings and
returns a data frame of geographic information. In the default case of
output = "simple", only longitudes and latitudes are returned. These
are actually Mercator projections of the ubiquitous unprojected 1984
world geodetic system (WGS84), a spheroidal earth model used by Google
Maps. When output is set to "more", a larger data frame is returned
which provides much more Google Geocoding information on the query:
> geocode("baylor university", output = "more")
lon lat type loctype address north south east
1 -97.11441 31.54872 university approximate [long address] 31.55823 31.53921 -97.0984
west postal_code country administrative_area_level_2
1 -97.13042 76706 united states mclennan
administrative_area_level_1 locality street streetNo point_of_interest
1 texas waco s 5th st 1311 <NA> In particular, administrative bodies at various levels are reported.
Going further, setting output = "all" returns the entire JavaScript
Object Notation (JSON) object given by the Google Geocoding API parsed
by rjson (Couture-Beil 2013).
The Geocoding API has a number of request limitations in place to
prevent abuse. An unspecified short-term rate limit is in place (see
mapdist below) as well as a 24-hour limit of 2,500 requests. These are
monitored to some extent by the hidden global variable
.GoogleGeocodeQueryCount and exported function geocodeQueryCheck.
geocode uses these to monitor its own progress and will either (1)
slow its rate depending on usage or (2) throw an error if the query
limit is exceeded. Note that revgeocode shares the same request pool
and is monitored by the same variable and function. To learn more about
the Google Geocoding, Distance Matrix, and Directions API usage
regulations, see the websites listed in the bibliography.
The revgeocode function
In some instances it is useful to convert longitude/latitude coordinates
into a physical address. This is made possible (to the extent to which
it is possible) with the revgeocode function which also relies on the
Google Geocoding API.
> gc <- geocode("baylor university")
> (gc <- as.numeric(gc))
[1] -97.11441 31.54872
> revgeocode(gc)
[1] "S 1st St, Baylor University, Waco, TX 76706, USA"Like geocode, more output can be provided as well –
> revgeocode(gc, output = "more")
address route establishment neighborhood locality
1 [long address] S 1st St Baylor University Baylor Waco
administrative_area_level_2 administrative_area_level_1 country postal_code
1 McLennan Texas United States 76706 Thus, in addition to the physical where of a reverse geocode (i.e.,
the address), revgeocode can report the what at various levels of
granularity. Finally, an output = "all" option is available which
returns the entire JSON object reported by Google.
The mapdist function
The ability to compute colloquial distances in a spatial setting is another invaluable commodity which typically sends analysts to a GIS. Using the Google Distance Matrix API, ggmap is able to provide distances for Google-determined routes for driving, bicycling, or walking. In addition to the distances, Google reports estimated travel durations as well. The full output is placed in an easy-to-use data frame. For example,
> from <- c("houston", "houston", "dallas")
> to <- c("waco, texas", "san antonio", "houston")
> mapdist(from, to)
from to m km miles seconds minutes hours
1 houston waco, texas 298004 298.004 185.1797 11907 198.45 3.307500
2 houston san antonio 320764 320.764 199.3227 11997 199.95 3.332500
3 dallas houston 387389 387.389 240.7235 14592 243.20 4.053333 The default mode of transportation is driving; however, the other modes
are also available. The input forms of from and to can be either
physical addresses (ideal), lazy ("the white house"), or geographic
coordinates (which are reverse geocoded). While the output defaults to
the data frame format seen above, setting output = "all" provides the
full JSON object from Google.
The Distance Matrix API limits users to 100 requests per query, 100
requests per 10 seconds, and 2500 requests per 24 hours. To the extent
to which these can be easily monitored, the exported function
distQueryCheck helps the user keep track of their remaining balance of
queries. It relies on the hidden global variable .GoogleDistQueryCount
–
> distQueryCheck()
2495 distance queries remaining.
> .GoogleDistQueryCount
time url elements
1 2012-03-16 00:12:11 [url used] 1
2 2012-03-16 00:16:10 [url used] 2If the user exceeds the limitations, mapdist either (1) pauses until
the short-term request limit has lapsed or (2) errors if no queries are
remaining. Thus, it is almost identical to the mechanism in place for
geocoding. If the user believes this to be incorrect, an override is
available with the mapdist specification override_limit = TRUE.
The data frame output of mapdist is very convenient for use with
ggplot2. An example is provided by Figure 11, where
travel times from one location ("My Office") to several nearby
locations are (1) determined using mapdist, (2) binned into categories
using cut, and then (3) plotted using a combination of qmap,
geom_text, and geom_rect with the fill aesthetic set to the category
of travel time. The full code is in the examples section of the
documentation of ggmap.
The route function
The route function provides the map distances for the sequence of
“legs” which constitute a route between two locations. Each leg has a
beginning and ending longitude/latitude coordinate along with a distance
and duration in the same units as reported by mapdist. The collection
of legs in sequence constitutes a single route (path) most easily
plotted with geom_leg, a new exported ggplot2 geom which is simply
geom_segment with rounded ends.3
A nice illustration of plotting routes with geom_leg can be seen in
Figure 12 where three routes are plotted between the
same two locations. These can be obtained using the
alternatives = TRUE specification in route. alternatives requests
more than one route from the origin to the destination; the returned
value is again a data frame with an additional variable which serves as
a route identifier (A, B, C, etc.).
legs_df <- route(
'marrs mclean science, baylor university',
'220 south 3rd street, waco, tx 76701',
alternatives = TRUE
)
qmap('424 clay avenue, waco, tx', zoom = 15, maptype = 'hybrid',
base_layer = ggplot(aes(x = startLon, y = startLat), data = legs_df)) +
geom_leg(
aes(x = startLon, y = startLat, xend = endLon, yend = endLat,
colour = route),
alpha = 3/4, size = 2, data = legs_df
) +
labs(x = 'Longitude', y = 'Latitude', colour = 'Route') +
facet_wrap(~ route, ncol = 3) + theme(legend.position = 'top') 
Like map distances, there are restrictions on the limit of routes which
can be requested in a given period of time as well. The number of
queries left is monitored by the .GoogleRouteQueryCount variable with
the routeQueryCheck function.
7 Plotting shape files
As a final example which is perhaps too common to ignore, plotting shape
files with ggmap is a breeze and can be done in several ways. The
easiest way is to convert the shape file to a data frame with fortify
(ggplot2) and then add a point/path/polygon layer to the map,
depending on what the contents of the shape file are (note that
fortify does not yet have methods for all shape files). Additional
layers can be added by simply layering on more geom layers. Figure
13 shows a basic example of plotting the U.S. Census
2000 census tracts along with complete code (U.S. Census Bureau, Geography Division, Cartographic Products Management Branch 2001).
# get an example shape file
download.file('http://www.census.gov/geo/cob/bdy/tr/tr00shp/tr48_d00_shp.zip',
destfile = 'census.zip')
# unzip, and load tools
unzip('census.zip'); library(maptools); library(gpclib); library(sp);
gpclibPermit()
# read data into R
shapefile <- readShapeSpatial('tr48_d00.shp',
proj4string = CRS("+proj=longlat +datum=WGS84"))
# convert to a data.frame for use with ggplot2/ggmap and plot
data <- fortify(shapefile)
qmap('texas', zoom = 6, maptype = 'satellite') +
geom_polygon(aes(x = long, y = lat, group = group), data = data,
colour = 'white', fill = 'black', alpha = .4, size = .3)
8 Conclusion and future directions
Building on top of ggplot2, ggmap provides several new useful tools for visualizing spatial data. Theoretically speaking, the layered grammar of graphics attempts to enforce plotting consistency and therefore good plotting practice. Practically speaking, building ggmap on ggplot2 makes the result even better as the full range of ggplot2 capabilities can be brought to bear.
There are a number of future directions in store for ggmap. The new osmar package integrates R and the OpenStreetMap data structures with which OpenStreetMap maps, Stamen Maps, and CloudMade Maps are rendered, thereby opening a floodgate of possibilities for plotting geographic objects on top of maps or satellite imagery all within R using ggmap (Eugster and T. Schlesinger 2013). Alternatively, integration with other spatial packages in R could provide several incredibly useful practical tools for spatial data analysis. Finally, the Google Elevation API and Places API provide additional interesting frontiers which can be incorporated into the ggmap framework just like the other Google APIs to give users additional capabilities though freely available geographical data.
9 Acknowledgment
The authors would like to thank Jean-Olivier Irisson for his comments.
CRAN packages used
sp, RgoogleMaps, ggplot2, ggmap, maps, maptools, DeducerSpatial, plyr, rjson, osmar
CRAN Task Views implied by cited packages
Phylogenetics, Spatial, SpatioTemporal, TeachingStatistics, WebTechnologies
Note
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