Chapter 5 Spatial Data and Maps
The Spatial section of this book adds the spatial dimension and geographic data science. Most environmental systems are significantly affected by location, so the geographic perspective is highly informative. In this chapter, we’ll explore the basics of
- building and using feature-based (vector) data using the
sf(simple features) package
- raster data using the
- some common mapping methods in
Especially for the feature data which is built on a database model (with geometry as one variable), the tools we’ve been learning in
tidyr will also be useful to working with attributes.
Caveat: Spatial data and analysis is very useful for environmental data analysis, and we’ll only scratch the surface. Readers are encouraged to explore other resources on this important topic, such as Geocomputation with R at https://geocompr.robinlovelace.net/ or https://r-spatial.github.io/sf/ .
5.1 Spatial Data (and Geospatial Data)
Spatial data are data using a Cartesian coordinate system with x, y, z, and maybe more dimensions. Geospatial data are spatial data that we can map on our planet and relate to other geospatial data based on geographic coordinate systems (GCS) of longitude and latitude or known projections to planar coordinates like Mercator, Albers, or many others. You can also use local coordinate systems with the methods we’ll learn, to describe geometric objects or a local coordinate system to “map out” an archaeological dig or describe the movement of ants on an anthill, but we’ll primarily use geospatial data.
To work with spatial data requires extending R to deal with it using packages. Many have been developed, but the field is starting to mature using international open GIS standards.
sp (until recently, the dominant library of spatial tools)
- Includes functions for working with spatial data
spplotto create maps
- Also needs
readOGR– reads spatial data frames
- Earliest archived version on CRAN is 0.7-3, 2005-04-28, with 1.0 released 2012-09-30. Authors: Edzer Pebesma & Roger Bivand
sf (Simple Features)
- Feature-based (vector) methods
- ISO 19125 standard for GIS geometries
- Also has functions for working with spatial data, but clearer to use.
- Doesn’t need many additional packages, though you may still need
rgdalinstalled for some tools you want to use.
spplotthough you’ll still find them in code. We’ll give it a try…
- Works with ggplot2 and tmap for nice looking maps.
- Cheat sheet: https://github.com/rstudio/cheatsheets/raw/master/sf.pdf
- Earliest CRAN archive is 0.2 on 2016-10-26, 1.0 not until 2021-06-29.
- Author: Edzer Pebesma
- The established raster package for R, with version 1.0 from 2010-03-20, Author: Robert J. Hijmans (Professor of Environmental Science and Policy at UC Davis)
- Depends on
- CRAN: “Reading, writing, manipulating, analyzing and modeling of spatial data. The package implements basic and high-level functions for raster data and for vector data operations such as intersections. See the manual and tutorials on https://rspatial.org/ to get started.”
- Intended to eventually replace
raster, by the same author (Hijmans), with version 0.5-2 2020-03-20, and 1.0 2021-01-04. Does not depend on
sp, but includes Bivand and Pebesma as contributors.
- CRAN: “Methods for spatial data analysis with raster and vector data. Raster methods allow for low-level data manipulation as well as high-level global, local, zonal, and focal computation. The predict and interpolate methods facilitate the use of regression type (interpolation, machine learning) models for spatial prediction, including with satellite remote sensing data. Processing of very large files is supported. See the manual and tutorials on https://rspatial.org/terra/ to get started.”
5.1.1 Simple geometry building in sf
Simple Features (
sf) includes a set of simple geometry building tools that use matrices or lists of matrices of XYZ (or just XY) coordinates (or lists of lists for multipolygon) as input, and each basic geometry type having single and multiple versions (Table 5.1).
|st_point()||numeric vector of length 2, 3, or 4, represent a single point|
|st_multipoint()||numeric matrix with points in rows|
|st_linestring()||numeric matrix with points in rows|
|st_multilinestring()||list of numeric matrices with points in rows|
|st_polygon()||list of numeric matrices with points in rows|
|st_multipolygon()||list of lists with numeric matrices|
sf functions have the pattern
st_* (st means “space and time”)
See Geocomputation with R at https://geocompr.robinlovelace.net/ or https://r-spatial.github.io/sf/ for more details, but here’s an example of manual feature creation of sf geometries (sfg) followed by building a simple feature geometry list column from the collection with
library(tidyverse) library(sf) library(igisci)
184.108.40.206 Building simple sf geometries
We’ll build some simple geometries and plot them, just to illustrate the types of geometries. Try the following code.
library(sf); library(tidyverse) <- st_multipoint(rbind(c(1,5), c(3,5))) eyes <- st_point(c(2,4)) nose <- st_linestring(rbind(c(1,3),c(3, 3))) mouth <- st_polygon(list(rbind(c(0,5), c(1,2), c(2,1), c(3,2), border c(4,5), c(3,7), c(1,7), c(0,5)))) plot(st_sfc(eyes, nose, mouth, border))
220.127.116.11 Building geospatial points as sf columns (
The face-building example above is really intended for helping to understand the concept and isn’t really how we’d typically build data. For one thing, it doesn’t have a real coordinate system that can be reprojected into locations anywhere on the planet. And most commonly we’ll be accessing data that have been created by government agencies such as those from the USGS in The National Map, or NGO, university or corporate sources who have an interest in collecting and providing data.
However, let’s take the
sfc building a bit further to better understand it, and build mappable sf columns (sfc) from scratch.
We’ll build a generalized map of California and Nevada boundaries (and then later in the exercises, you’ll expand this to a map of the west.) We’ll use a shortcut to the coordinate referencing system (crs) by using a short numeric EPSG code (EPSG Geodetic Parameter Dataset (n.d.)), 4326, for the common geographic coordinate system (GCS) of latitude and longitude.
library(sf) <- rbind(c(-124,42),c(-120,42),c(-120,39),c(-114.5,35), CA_matrix c(-114.1,34.3),c(-114.6,32.7),c(-117,32.5),c(-118.5,34),c(-120.5,34.5), c(-122,36.5),c(-121.8,36.8),c(-122,37),c(-122.4,37.3),c(-122.5,37.8), c(-123,38),c(-123.7,39),c(-124,40),c(-124.4,40.5),c(-124,41),c(-124,42)) <- rbind(c(-120,42),c(-114,42),c(-114,36),c(-114.5,36), NV_matrix c(-114.5,35),c(-120,39),c(-120,42)) <- list(CA_matrix); NV_list <- list(NV_matrix) CA_list <- st_polygon(CA_list); NV_poly <- st_polygon(NV_list) CA_poly <- st_sfc(CA_poly,NV_poly,crs=4326) # crs=4326 specifies GCS sfc_2states st_geometry_type(sfc_2states)
##  POLYGON POLYGON ## 18 Levels: GEOMETRY POINT LINESTRING POLYGON MULTIPOINT ... TRIANGLE
Then we’ll use the
geom_sf() function in ggplot2 to create a map (Figure 5.1).
library(tidyverse) ggplot() + geom_sf(data = sfc_2states)
18.104.22.168 Building an sf class from sf columns.
So far, we’ve built an
sfg (sf geometry) and then a collection of matrices in an
sfc (sf column), and we were even able to make a map of it. But to be truly useful, we’ll want other attributes other than just where the feature is, so we’ll build an
sf that has attributes, and that can also be used like a data frame – similar to a shapefile, since a data frame is like a database table
.dbf. We’ll build one with the
st_sf() function from attributes and geometry (Figure 5.2).
<- bind_rows(c(abb="CA", area=423970, pop=39.56e6), attributes c(abb="NV", area=286382, pop=3.03e6)) <- st_sf(attributes, geometry = sfc_2states) twostates ggplot(twostates) + geom_sf() + geom_sf_text(aes(label = abb))
Or we could let the plot system try to figure out what to do with it. To help it out a bit, we can select just one variable, for which we need to use the
 accessor (Figure 5.3). Try the
$ accessor instead to see what you get, and consider why this might be.
5.1.2 Building points from a data frame
We’re going to take a different approach with points, since if we’re building them from scratch they commonly come in the form of a data frame with x and y columns, so the
st_as_sf is much simpler than what we just did with
sf::st_as_sf function is useful for a lot of things, and follows the convention of R’s many “as” functions: if it can understand the input as having “space and time” (st) data, it can convert it to an sf.
We’ll start by creating a data frame representing 12 Sierra Nevada (and some adjacent) weather stations, with variables entered as vectors, including attributes. The order of vectors needs to be the same for all, where the first point has index 1, etc.
<- c("OROVILLE","AUBURN","PLACERVILLE","COLFAX","NEVADA CITY","QUINCY", sta "YOSEMITE","PORTOLA","TRUCKEE","BRIDGEPORT","LEE VINING","BODIE") <- c(-121.55,-121.08,-120.82,-120.95,-121.00,-120.95, long -119.59,-120.47,-120.17,-119.23,-119.12,-119.01) <- c(39.52,38.91,38.70,39.09,39.25,39.94, lat 37.75,39.81,39.33,38.26,37.96,38.21) <- c(52,394,564,725,848,1042, elev 1225,1478,1775,1972,2072,2551) <- c(10.7,9.7,9.2,7.3,6.7,4.0, temp 5.0,0.5,-1.1,-2.2,0.4,-4.4) <- c(124,160,171,207,268,182, prec 169,98,126,41,72,40)
To build the sf data, we start by building the data frame…
… then use the very handy
sf::st_as_sf function to make an sf out of it, using the long and lat as a source of coordinates (Figure 5.4).
<- st_as_sf(df, coords=c("long","lat"), crs=4326) wst ggplot(data=wst) + geom_sf(mapping = aes(col=elev))
5.1.3 SpatVectors in
We’ll briefly look at another feature-based object format, the
terra, which uses a formal S4 object definition which will provide some advantages over S3 objects such as what’s in most of R, at the expense of being a bit less flexible and not compatible with some packages, at least at the time of writing; that will probably change. See Hijmans (n.d.) for more about SpatVectors. We’ll use terra’s raster capabilities a lot more, but we’ll take a brief look at building and using a
We’ll start with climate data from a selection of Sierra Nevada (and some adjacent) weather stations, with variables entered as vectors:
Then we’ll build a
SpatVector we’ll call
station_pts from just the longitude and latitude vectors we just created:
library(terra) <- cbind(long,lat) longlat <- vect(longlat)station_pts
Unlike other data sets which use a simple object model called S3,
SpatVector data use the more formal S4 definition. SpatVectors have a rich array of properties and associated methods, which you can see in the Environment tab of RStudio, or access with
@ptr. This is a good thing, but the
str() function we’ve been using for S3 objects presents us with arguably too much information for easy understanding. But just by entering
station_pts on the command line we get something similar to what
str() does with S3 data, so this might be better for seeing a few key properties:
## class : SpatVector ## geometry : points ## dimensions : 12, 0 (geometries, attributes) ## extent : -121.55, -119.01, 37.75, 39.94 (xmin, xmax, ymin, ymax) ## coord. ref. :
Note that there’s nothing listed for
coord. ref. meaning the coordinate referencing system (CRS). We’ll look at this more below, but for now let’s change the code a bit to add this information using the PROJ-string method:
<- vect(longlat, crs = "+proj=longlat +datum=WGS84")station_pts
Of the many methods associated with these objects, one example is getting with geom():
## geom part x y hole ## [1,] 1 1 -121.55 39.52 0 ## [2,] 2 1 -121.08 38.91 0 ## [3,] 3 1 -120.82 38.70 0 ## [4,] 4 1 -120.95 39.09 0 ## [5,] 5 1 -121.00 39.25 0 ## [6,] 6 1 -120.95 39.94 0 ## [7,] 7 1 -119.59 37.75 0 ## [8,] 8 1 -120.47 39.81 0 ## [9,] 9 1 -120.17 39.33 0 ## [10,] 10 1 -119.23 38.26 0 ## [11,] 11 1 -119.12 37.96 0 ## [12,] 12 1 -119.01 38.21 0
Let’s add the attributes to the original
longlat matrix to build a SpatVect with attributes we can see in a basic plot (Figure 5.5).
<- data.frame(sta,elev,temp,prec) df <- vect(longlat, atts=df, crs="+proj=longlat +datum=WGS84") clim clim
## class : SpatVector ## geometry : points ## dimensions : 12, 4 (geometries, attributes) ## extent : -121.55, -119.01, 37.75, 39.94 (xmin, xmax, ymin, ymax) ## coord. ref. : +proj=longlat +datum=WGS84 +no_defs ## names : sta elev temp prec ## type : <chr> <num> <num> <num> ## values : OROVILLE 52 10.7 124 ## AUBURN 394 9.7 160 ## PLACERVILLE 564 9.2 171
plot(clim) text(clim, labels="sta", pos=4)
The SpatVector format looks promising, but sometimes you may want an sf instead. Fortunately, you can use the
sf::sf_as_sf function to convert it, just as you can create a
SpatVector from an
sf with the
terra::vect function. The following code demonstrates both, with a ggplot2 map of sf data (Figure 5.6) and then a base R plot map with SpatVector data (Figure 5.7).
library(sf); library(ggplot2) <- st_as_sf(clim) climsf str(climsf)
## Classes 'sf' and 'data.frame': 12 obs. of 5 variables: ## $ sta : chr "OROVILLE" "AUBURN" "PLACERVILLE" "COLFAX" ... ## $ elev : num 52 394 564 725 848 ... ## $ temp : num 10.7 9.7 9.2 7.3 6.7 4 5 0.5 -1.1 -2.2 ... ## $ prec : num 124 160 171 207 268 182 169 98 126 41 ... ## $ geometry:sfc_POINT of length 12; first list element: 'XY' num -121.5 39.5 ## - attr(*, "sf_column")= chr "geometry" ## - attr(*, "agr")= Factor w/ 3 levels "constant","aggregate",..: NA NA NA NA ## ..- attr(*, "names")= chr [1:4] "sta" "elev" "temp" "prec"
ggplot() + geom_sf(data=twostates) + geom_sf(data=climsf)
<- vect(twostates) twostatesV plot(twostatesV) plot(clim, add = T)
## class : SpatVector ## geometry : polygons ## dimensions : 2, 3 (geometries, attributes) ## extent : -124.4, -114, 32.5, 42 (xmin, xmax, ymin, ymax) ## coord. ref. : lon/lat WGS 84 (EPSG:4326) ## names : abb area pop ## type : <chr> <chr> <chr> ## values : CA 423970 39560000 ## NV 286382 3030000
5.1.4 Creating features from shapefiles
st_read and terra’s
vect read the open-GIS shapefile format developed by Esri for points, polylines and polygons. You would normally have shapefiles (and all the files that go with them – .shx, etc.) stored on your computer, but we’ll access one from the
igisci external data folder, and use that
ex() function we used earlier with CSVs. Remember that we could just include that and the library calls just once at the top of our code like this…
If we just send a spatial dataset like an
sf spatial data frame to the plot system, it will plot all of the variables by default (Figure 5.8).
But with just one variable, of course, it just produces a single map (Figure 5.9).
Notice that in the above map, we used the
 accessor. Why didn’t we use the simple
$ accessor? Remember that plot() figures out what to do based on what you provide it. And there’s an important difference in what you get with the two accessors, which we can check with class():
##  "sf" "data.frame"
##  "numeric"
You might see what you get with
plot(BayAreaCounties$POPULATION), which is not very informative, since the object is just a numeric vector, while using
 accessor returns a spatial dataframe.
There’s a lot more we could do with the base R plot system, so we’ll learn some of these before exploring what we can do
leaflet. But first we need to learn more about building geospatial data.
st_as_sf() for that, but we’ll need to specify the coordinate referencing system (CRS), in this case GCS.We’ll only briefly explore how to specify the CRS here. For a thorough coverage, please see Lovelace, Nowosad, and Muenchow (2019) .
5.1.5 Coordinate Referencing System
Before we try the next method for bringing in spatial data – converting data frames – we need to look at coordinate referencing systems (CRS). First, there are quite a few, with some spherical like the geographic coordinate system (GCS) of longitude and latitude, and others planar projections of GCS using mathematically defined projections such as Mercator, Albers Conformal Conic, Azimuthal, etc., and including widely used government-developed systems such as UTM (universal transverse mercator) or state plane. Even for GCS, there are many varieties since geodetic datums can be chosen, and for very fine resolution work where centimetres or even millimetres matter, this decision can be important (and tectonic plate movement can play havoc with tying it down.)
There are also multiple ways of expressing the CRS, either to read it or to set it. The full specification of a CRS can be displayed for data already in a CRS, with either
## Coordinate Reference System: ## User input: WGS 84 ## wkt: ## GEOGCRS["WGS 84", ## DATUM["World Geodetic System 1984", ## ELLIPSOID["WGS 84",6378137,298.257223563, ## LENGTHUNIT["metre",1]]], ## PRIMEM["Greenwich",0, ## ANGLEUNIT["degree",0.0174532925199433]], ## CS[ellipsoidal,2], ## AXIS["latitude",north, ## ORDER, ## ANGLEUNIT["degree",0.0174532925199433]], ## AXIS["longitude",east, ## ORDER, ## ANGLEUNIT["degree",0.0174532925199433]], ## ID["EPSG",4326]]
… though there are other ways to see the crs in shorter forms, or its individual properties :
##  "+proj=longlat +datum=WGS84 +no_defs"
##  "degree"
##  4326
st_crs()$Wktwhich you should try, but it creates a long continuous string that goes off the page, so doesn’t work for the book formatting.
st_crs(CA_counties)$Wkt # [Creates too long of a string for this book]
So, to convert the sierra data into geospatial data with
st_as_sf, we might either do it with the reasonably short PROJ format …
GCS <- "+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0" wsta = st_as_sf(sierraFeb, coords = c("LONGITUDE","LATITUDE"), crs=GCS)
… or with the even shorter EPSG code (which we can find by Googling), which can either be provided as text
"epsg:4326" or often just the number, which we’ll use next.
5.1.6 Creating sf data from data frames
As we saw earlier, if your data frame has geospatial coordinates like
##  "STATION_NAME" "COUNTY" "ELEVATION" "LATITUDE" ##  "LONGITUDE" "PRECIPITATION" "TEMPERATURE"
… we have what we need to create geospatial data from it. Earlier we read in a series of vectors built in code with
c() functions from 12 selected weather stations; this time we’ll use a data frame that has all of the Sierra Nevada weather stations (Figure 5.10).
<- st_as_sf(sierraFeb, coords = c("LONGITUDE","LATITUDE"), crs=4326) wsta ggplot(data=wsta) + geom_sf(aes(col=ELEVATION))
5.1.7 Removing Geometry
There are many instances where you want to remove geometry from a sf data frame
Some R functions run into problems with geometry and produce confusing error messages, like “non-numeric argument”
You’re wanting to work with an sf data frame in a non-spatial way
One way to remove geometry :
myNonSFdf <- mySFdf %>% st_set_geometry(NULL)
5.2 Base R’s
As we’ve seen in the examples above, the venerable plot system in base R can often do a reasonable job of displaying a map. The terra package extends this a bit by providing tools for overlaying features on other features (or rasters using plotRGB), so it’s often easy to use (Figure 5.11).
library(terra) plot(vect(wsta)) lines(vect(CA_counties), col="gray")
Note that we converted the sf data to
SpatVector data with
vect(), but the base R plot system can work with either
sf data or
SpatVector data from
polys functions (to name a few – see
?terra and look at the Plotting section) will add to an existing plot. Alternatively, we could use plot’s
add=TRUE parameter to add onto a plot that we’ve previously scaled with a plot. In this case, we’ll cheat and just use the first plot to set the scaling but then cover over that plot with other plots (Figure 5.12).
library(terra) plot(vect(wsta)) # simple way of establishing the coordinates # Then will be covered by the following: plot(vect(CA_counties), col="lightgray", add=TRUE) plot(vect(wsta), col="red", add=TRUE)
5.2.1 Using maptiles to create a basemap
For vector data, it’s often nice to display over a basemap by accessing raster tiles that are served on the internet by various providers. We’ll use the
maptiles package to try displaying the
NV boundaries we created earlier. The
maptiles package supports a variety of basemap providers, and I’ve gotten the following to work:
"Wikimedia", however they don’t work at all scales and locations – you’ll often see an Error in grDevices, if so then try another provider – the default
"OpenStreetMap" seems to work the most reliably (Figure 5.13).
library(terra); library(maptiles) # Get the raster that covers the extent of CANV: <- get_tiles(twostates, provider="OpenTopoMap") calnevaBase st_crs(twostates)$epsg
##  4326
plotRGB(calnevaBase) # starts plot with a raster basemap lines(vect(twostates), col="black", lwd=2)
In the code above, we can also see the use of
terra functions and parameters. Learn more about these by reviewing the code and considering:
terra functions: The
terra::vect() function creates a
SpatVector that works with
terra::lines to display the boundary lines in
plot(). (More on
terra later, in the Raster section.)
parameters: Note the use of the parameter
lwd (line width) from the plot system. This is one of many parameter settings described in
?par. It defaults to 1, so 2 makes it twice as thick. You could also use lty (line type) to change it to a dashed line with
And for the
sierraFeb data, we’ll start with
st_as_sf and the coordinate system (4326 for GCS), then use a
maptiles basemap again, and the
terra::points method to add the points (Figure 5.14).
library(terra); library(maptiles) <- st_as_sf(sierraFeb, coords = c("LONGITUDE", "LATITUDE"), crs=4326) sierraFebpts <- get_tiles(sierraFebpts) sierraBase st_crs(sierraFebpts)$epsg
##  4326
5.3 Raster data
Simple Features are feature-based, of course, so it’s not surprising that
sf doesn’t have support for rasters. So we’ll want to either use the
raster package or its imminent replacement,
terra (which also has vector methods – see Hijmans (n.d.) and Lovelace, Nowosad, and Muenchow (2019)), and I’m increasingly using
terra since it has some improvements that I find useful.
5.3.1 Building rasters
We can start by building one from scratch. The
terra package has a
rast() function for this, and creates an S4 (as opposed to the simpler S3) object type called a
library(terra) <- rast(ncol = 12, nrow = 6, new_ras xmin = -180, xmax = 180, ymin = -90, ymax = 90, vals = 1:72)
Formal S4 objects like
SpatRasters have many associated properties and methods. To get a sense, type into the console
new_ras@ptr$ and see what suggestions you get from the autocomplete system. To learn more about them, see Hijmans (n.d.) but we’ll look at a few of the key properties by simply entering the
SpatRaster name in the console.
## class : SpatRaster ## dimensions : 6, 12, 1 (nrow, ncol, nlyr) ## resolution : 30, 30 (x, y) ## extent : -180, 180, -90, 90 (xmin, xmax, ymin, ymax) ## coord. ref. : lon/lat WGS 84 ## source : memory ## name : lyr.1 ## min value : 1 ## max value : 72
ncoldimensions should be familiar, and the
nlyrtells us that this raster has a single layer (or band as they’re referred to with imagery), and it gives it the name
- The resolution is what you get when you divide 360 (degrees of longitude) by 12 and 180 (degrees of latitude) by 6, and the extent is what we entered to create it.
- But how does it know that these are degrees of longitude and latitude, as it appears to from the
coord. ref.property? The author of this tool seems to let it assume GCS if it doesn’t exceed the limits of the planet: -180 to +180 longitude and -90 to +90 latitude.
- The source is in memory, since we didn’t read it from a file, but entered it directly. New rasters we might create from raster operations will also be in memory.
- The minimum and maximum values are what we used to create it.
lyr.1 isn’t very useful, so let’s change the name with the
names function, and then access the name with a
@ptr property (you could also use
names(new_ras) to access it, but I thought it might be useful to see the
@ptr in action.)
names(new_ras) <- "world30deg" new_ras
## class : SpatRaster ## dimensions : 6, 12, 1 (nrow, ncol, nlyr) ## resolution : 30, 30 (x, y) ## extent : -180, 180, -90, 90 (xmin, xmax, ymin, ymax) ## coord. ref. : lon/lat WGS 84 ## source : memory ## name : world30deg ## min value : 1 ## max value : 72
Simple feature data or
SpatVectors can be plotted along with rasters using terra’s lines, polys, or points functions. Here we’ll use the
sf we created earlier (make sure to run that code again if you need it). Look closely at the map for our two states (Figure 5.15).
plot(new_ras, main=paste("CANV on",new_ras@ptr$names)) <- vect(twostates) CANV lines(CANV)
Let’s plot some Shuttle Radar Topography Mission (SRTM) elevation data for the Virgin River Canyon at Zion National Park (Figure 5.16). First, the following required this method of installation:
install.packages("spDataLarge", repos = "https://nowosad.github.io/drat/", type = "source")
library(terra) plot(rast(system.file("raster/srtm.tif", package="spDataLarge")))
5.4 ggplot2 for maps
The Grammar of Graphics is the gg of ggplot.
- Key concept is separating aesthetics from data
- Aesthetics can come from variables (using aes()setting) or be constant for the graph
Mapping tools that follow this lead
- ggplot, as we have seen, and it continues to be enhanced (Figure 5.17)
- tmap (Thematic Maps) https://github.com/mtennekes/tmap Tennekes, M., 2018, tmap: Thematic Maps in R, Journal of Statistical Software 84(6), 1-39
ggplot(CA_counties) + geom_sf()
?geom_sf and you’ll find that its first parameters is mapping with
aes() by default. The data property is inherited from the ggplot call, but commonly you’ll want to specify data=something in your geom_sf call.
Another simple ggplot, with labels
Adding labels is also pretty easy using
aes() (Figure 5.18).
ggplot(CA_counties) + geom_sf() + geom_sf_text(aes(label = NAME), size = 1.5)
And now with fill color, repositioned legend, and no “x” or “y” labels
The x and y labels are unnecessary since the graticule is provided, and for many maps there’s a better place to put the legend than what happens by default – for California’s shape, the legend goes best in Nevada (Figure 5.19).
ggplot(CA_counties) + geom_sf(aes(fill=MED_AGE)) + geom_sf_text(aes(label = NAME), col="white", size=1.5) + theme(legend.position = c(0.8, 0.8)) + labs(x="",y="")
Map in ggplot2, zoomed into two counties
We can zoom into two counties by accessing the extent of an existing spatial dataset using
st_bbox() (Figure 5.20).
library(tidyverse); library(sf); library(igisci) <- st_make_valid(BayAreaTracts) %>% census filter(CNTY_FIPS %in% c("013", "095")) <- read_csv(ex("TRI/TRI_2017_CA.csv")) %>% TRI st_as_sf(coords = c("LONGITUDE", "LATITUDE"), crs=4326) %>% st_join(census) %>% filter(CNTY_FIPS %in% c("013", "095"), `5.1_FUGITIVE_AIR` + `5.2_STACK_AIR`) > 0) (= st_bbox(census) bnd ggplot() + geom_sf(data = BayAreaCounties, aes(fill = NAME)) + geom_sf(data = census, color="grey40", fill = NA) + geom_sf(data = TRI) + coord_sf(xlim = c(bnd, bnd), ylim = c(bnd, bnd)) + labs(title="Census Tracts and TRI air-release sites") + theme(legend.position = "none")
5.4.1 Rasters in ggplot2
Raster display in
ggplot2 is currently a little awkward, as are rasters in general in the world of database-centric GIS where rasters don’t comfortably sit.
We can use a trick: converting rasters to a grid of points, to then be able to create Figure 5.21.
library(igisci) library(tidyverse) library(sf) library(terra) <- rast(ex("marbles/elev.tif")) elevation <- st_read(ex("marbles/trails.shp")) trails <- st_as_sf(as.points(elevation))elevpts
ggplot() + geom_sf(data = elevpts, aes(col=elev)) + geom_sf(data = trails)
Note that the raster name stored in the
elevation@ptr$names property is derived from the of the original source raster
elev.tif, not from the object name we gave it, so we needed to specify
elev for the variable to color from the resulting point features.
##  "elev"
The tmap package provides some nice cartographic capabilities. We’ll use some of its capabilities, but for more thorough coverage, see Geocomputation with R Chapter 8 “Making Maps with R”: https://geocompr.robinlovelace.net/adv-map.html
The basic building block is
) followed by various layer elements such as tm_fill(). The tm_shape function can work with either features (Figure 5.22) or rasters.
library(spData) library(tmap) <- tm_shape(world) + tm_fill() + tm_borders() m m
Color by variable
As with plot and ggplot2, we can reference a variable to provide a range of colors for features we’re plotting, such as coloring polygon fills to create a choropleth map (Figure 5.23).
library(sf) library(tmap) tm_shape(st_make_valid(BayAreaTracts)) + tm_fill(col = "MED_AGE")
tmap of sierraFeb with hillshade and point symbols
We’ll use a raster hillshade as a basemap using
tm_raster, zoom into an area with a bounding box (from
st_bbox), include county boundaries with
tm_borders, and color station points with temperatures with
tm_symbols (Figure 5.24).
library(terra) # alt: library(raster) tmap_mode("plot") tmap_options(max.categories = 8) <- st_as_sf(sierraFeb, coords = c("LONGITUDE", "LATITUDE"), crs = 4326) sierra <- rast(ex("CA/ca_hillsh_WGS84.tif")) # alt: hillsh <- raster(...) hillsh <- st_bbox(sierra) bounds tm_shape(hillsh,bbox=bounds)+ tm_raster(palette="-Greys",legend.show=FALSE,n=10) + tm_shape(sierra) + tm_symbols(col="TEMPERATURE", palette=c("blue","red"), style="cont",n=8) + tm_shape(st_make_valid(CA_counties)) + tm_borders() + tm_legend() + tm_layout(legend.position=c("RIGHT","TOP"))
Color (rgb) basemap
Earlier we looked at creating a basemap using
maptiles. These are also supported in tmap using the
tm_rgb (Figure 5.25).
library(tmap); library(maptiles) # The following gets the raster that covers the extent of CA & NV [twostates] <- get_tiles(twostates, provider="OpenTopoMap")# g calnevaBase tm_shape(calnevaBase) + tm_rgb() + tm_shape(twostates) + tm_borders(lwd=3)
5.6 Interactive Maps
The word “static” in “static maps” isn’t something you would have heard in a cartography class 30 years ago, since essentially all maps then were static. Very important in designing maps was, and still is, considering your audience and their perception of symbology.
- Figure-to-ground relationships assume “ground” is a white piece of paper (or possibly a standard white background in a pdf), so good cartographic color schemes tend to range from light for low values to dark for high values.
- Scale is fixed and there are no “tools” for changing scale, so a lot of attention must be paid to providing scale information.
- Similarly, without the ability to see the map at different scales, inset maps are often needed to provide context.
Interactive maps change the game in having tools for changing scale, and always being “printed” on a computer or device where the color of the background isn’t necessarily white. We are increasingly used to using interactive maps on our phones or other devices, and often get frustrated not being able to zoom into a static map. However, as we’ll see there are trade-offs in that interactive maps don’t provide the same level of control on symbology for what we want to put on the map, but instead depend a lot on basemaps for much of the cartographic design, generally limiting the symbology of data being mapped on top of it.
Interactive maps tend to focus on using basemaps for most of the cartographic design work, and quite a few are available. The only code required to display the basemap is
addTiles() which will display the default
OpenStreetMap in the area where you provide any features, typically with
addMarkers(). This default basemap is pretty good to use for general purposes, especially in urban areas where OpenStreetMap contributors (including a lot of former students I think) have provided a lot of data.
You can specify additional choices using
addProviderTiles(), and use a layers control with the choices provided as
baseGroups. You have access to all of the basemap provider names with the vector
providers, and to see what they look like in your area, explore http://leaflet-extras.github.io/leaflet-providers/preview/index.html where you can zoom into an area of interest and select the base map to see how it would look in a given zoom (Figure ??).
library(leaflet) leaflet() %>% addTiles(group="OpenStreetMap") %>% addProviderTiles("OpenTopoMap",group="OpenTopoMap") %>% addProviderTiles("Esri.NatGeoWorldMap",group="Esri.NatGeoWorldMap") %>% addProviderTiles("Esri.WorldImagery",group="Esri.WorldImagery") %>% addMarkers(lng=-122.4756, lat=37.72222, popup="Institute for Geographic Information Science, SFSU") %>% addLayersControl( baseGroups = c("OpenStreetMap","OpenTopoMap", "Esri.WorldImagery","Esri.NatGeoWorldMap"))
With an interactive map, we do have the advantage of a good choice of base maps and the ability to resize and explore the map, but symbology is more limited, mostly just color and size, with only one variable in a legend. Note that interactive maps display in the Viewer window of RStudio or in R Markdown code output as you see in the html version of this book.
For a lot more information on the leaflet package in R, see:
In the mapview package, either
mapView produces perhaps the simplest interactive map display where the view provides the option of changing basemaps, and just a couple of options make it a pretty useful system for visualizing point data, so for environmental work involving samples, is a pretty useful exploratory tool.
Try the following code. Mapview doesn’t create figure headings (though leaflet does), so we won’t include the output here, but as you can see the mapview code is very simple.
library(sf); library(mapview) <- st_as_sf(sierraFeb, coords = c("LONGITUDE", "LATITUDE"), crs = 4326) sierra mapview(sierra, zcol="TEMPERATURE", popup = TRUE)
5.6.3 tmap (view mode)
You can change to an interactive mode with tmap by using
tmap_mode("view") so you might think that you can do all the great things that
tmap does in normal plot mode here, but as we’ve seen, interactive maps don’t provide the same level of symbology. The view mode of tmap, like mapview, is just a wrapper around leaflet, so we’ll focus on the latter. The key parameter needed is
tmap_mode which must be set to
"view" to create an interactive map.
library(tmap); library(sf) tmap_mode("view") tmap_options(max.categories = 8) <- st_as_sf(sierraFeb, coords = c("LONGITUDE", "LATITUDE"), crs = 4326) sierra <- st_bbox(sierra) bounds #sierraMap <- tm_basemap(leaflet::providers$Esri.NatGeoWorldMap) + tm_shape(sierra) + tm_symbols(col="TEMPERATURE", palette=c("blue","red"), style="cont",n=8,size=0.2) + tm_legend() + tm_layout(legend.position=c("RIGHT","TOP"))
One nice feature of tmap view mode (and mapview) is the ability to select the basemap interactively using the layers symbol. There are lots of basemap available with leaflet (and thus with tmap view). To explore them, see http://leaflet-extras.github.io/leaflet-providers/preview/index.html but recognize that not all of these will work everywhere; many are highly localized or may only work at certain scales.
The following map using tmap could presumably be coded in leaflet, but tmap makes it a lot easier. We’ll use it to demonstrate picking the basemap, which really can help in communicating our data with context (Figure ??).
library(sf); library(tmap); library(igisci) = "view" tmap_mode tmap_options(basemaps=c(Topo="OpenTopoMap", Imagery = "Esri.WorldImagery", NatGeo="Esri.NatGeoWorldMap")) <- st_read(ex("marbles/co2july95.shp")) soilCO2 <- st_read(ex("marbles/geology.shp"))geology
<- tm_basemap() + mblCO2map tm_shape(geology) + tm_fill(col="CLASS", alpha=0.5) + tm_shape(soilCO2) + tm_symbols(col="CO2_", palette="viridis", style="cont",n=8,size=0.6) + tm_legend() + tm_layout(legend.position=c("RIGHT","TOP")) mblCO2map
5.6.4 Interactive mapping of individual penguins abstracted from a big dataset
In a study of spatial foraging patterns by Adélie penguins (Pygoscelis adeliae), Ballard et al. (Ballard et al. (2019)) collected data over five austral summer seasons (parts of December and January in 2005-06, 2006-07, 2007-08, 2008-09, 2012-13) period using SPLASH tags that combine Argos satellite tracking with a time-depth recorder, with 11,192 observations. The 24 SPLASH tags were re-used over the period of the project with a total of 162 individual breeding penguins, each active over a single season.
Our code takes advantage of the abstraction capabilities of base R and
dplyr to select an individual penguin from the large data set and prepare its variables for useful display. Then the interactive mode of tmap works well for visualizing the penguin data – both the cloud of all all observations and the focused individual – since we can zoom in to see the locations in context, and basemaps can be chosen. The
tmap code colors the individual penguin’s locations based on a decimal day derived from day and time. While interactive view is more limited in options, it does at least support a legend and title (Figure ??).
library(sf); library(tmap); library(tidyverse); library(igisci) <- read_csv(ex("penguins/sat_table_splash.csv")) sat_table <- st_as_sf(filter(sat_table,include==1), coords=c("lon","lat"), crs=4326) obs <- unique(obs$uniq_id) # uniq_id identifies an individual penguin uniq_ids <- obs %>% onebird filter(uniq_id==uniq_ids[sample.int(length(uniq_ids), size=1)]) %>% mutate(decimalDay = as.numeric(day) + as.numeric(hour)/24)
= "view" tmap_mode tmap_options(basemaps=c(Terrain = "Esri.WorldTerrain", Imagery = "Esri.WorldImagery", OceanBasemap = "Esri.OceanBasemap", Topo="OpenTopoMap", Ortho="GeoportailFrance.orthos")) <- tm_basemap() + penguinMap tm_shape(obs) + tm_symbols(col="gray", size=0.01, alpha=0.5) + tm_shape(onebird) + tm_symbols(col="decimalDay", palette="viridis", style="cont",n=8,size=0.6) + tm_legend() + tm_layout() tmap_leaflet(penguinMap)