# 11 Raster-vector interactions

## Prerequisites

• This chapter requires the following packages:
library(sf)
library(raster)
library(tidyverse)
library(spData)
library(spDataLarge)

## 11.1 Introduction

This section focuses on interactions between raster and vector geographic data models, introduced in Chapter 2. It includes four main techniques: raster cropping and masking using vector objects (section 11.2); extracting raster values using different types of vector data (section 11.3); and raster-vector conversion (sections 11.4 and 11.5). The above concepts are demonstrated using data used in previous chapters to understand their potential real-world applications.

## 11.2 Raster cropping

Many geographic data projects involve integrating data from many different sources, such as remote sensing images (rasters) and administrative boundaries (vectors). Often the extent of input raster datasets is larger than the area of interest. In this case raster cropping and masking are useful for unifying the spatial extent of input data. Both operations reduce object memory use and associated computational resources for subsequent analysis steps, and may be a necessary preprocessing step before creating attractive maps involving raster data.

We will use two objects to illustrate raster cropping:

• A raster object srtm representing elevation (meters above sea level) in Southwestern Utah.
• A vector (sf) object zion representing Zion National Park.

Both target and cropping objects must have the same projection. The following code chunk therefore not only loads the datasets, from the spDataLarge package installed in Chapter 2. It also reprojects zion (see section 5.2 for more on reprojection):

srtm = raster(system.file("raster/srtm.tif", package = "spDataLarge"))
zion = read_sf(system.file("vector/zion.gpkg", package = "spDataLarge"))
zion = st_transform(zion, projection(srtm))

We will use crop() from the raster package to crop the srtm raster. crop() reduces the rectangular extent of the object passed to its first argument based on the extent of the object passed to its second argument, as demonstrated in the command below (which generates Figure 11.1:B — note the smaller extent of the raster background):

srtm_cropped = crop(srtm, as(zion, "Spatial"))

Related to crop() is the raster function mask(), which sets values outside of the bounds a the object passed to its second argument to NA. The following command therefore masks every cell outside of the the Zion National Park boundaries (Figure 11.1:C):

srtm_masked = mask(srtm, as(zion, "Spatial"))

Changing the settings of mask() yields in different results. Setting maskvalue = 0, for example, would set all pixels outside the national park to 0. Setting inverse = TRUE would mask everything inside the bounds of the park (see ?mask for details).

## 11.3 Raster extraction

Raster extraction is the process of identifying and returning the values associated with a ‘target’ raster at specific locations, based on a (typically vector) geographic ‘selector’ object. The results depend on the type of selector used (points, lines or polygons) and arguments passed to the raster::extract() function, which we use to demonstrate raster extraction. The reverse of raster extraction — assigning raster cell values based on vector objects — is rasterization, described in section 11.4.

The simplest example is extracting the value of a raster cell at specific points. For this purpose we will use zion_points, which contain a sample of 30 locations with the Zion National Park (Figure 11.2). The following command extracts elevation values from srtm and assigns the resulting vector to a new column (elevation) in the zion_points dataset:

zion_points$elevation = raster::extract(srtm, as(zion_points, "Spatial")) The buffer argument can be used to specify a buffer radius (in meters) around each point. The result of raster::extract(srtm, zion_points, buffer = 1000), for example, is a list of vectors, each of which representing the values of cells inside the buffer associated with each point. In practice this example is a special case of extraction with a polygon selector, described below. Raster extraction also works with line selectors. To demonstrate this, the code below creates zion_transect, a straight line going from northwest to southeast of the Zion National Park, illustrated in Figure 11.3:A (see section 2.1 for a recap on the vector data model): zion_transect = cbind(c(-113.2, -112.9), c(37.45, 37.2)) %>% st_linestring() %>% st_sfc() %>% st_sf() The utility of extracting heights from a linear selector is illustrated by imagining that you are planning a hike. The method demonstrated below provides an ‘elevation profile’ of the route (the line does not need to be straight), useful for estimating how long it will take due to long climbs: transect = raster::extract(srtm, as(zion_transect, "Spatial"), along = TRUE, cellnumbers = TRUE) Note the use of along = TRUE and cellnumbers = TRUE arguments to return cell IDs along the path. The result is a list containing a matrix of cell IDs in the first column and elevation values in the second. The subsequent code chunk first converts this tricky matrix-in-a-list object into a simple data frame, returns the coordinates associated with each extracted cell and finds the associated distances along the transect (see ?geosphere::distm() for details): transect_df = map_dfr(transect, as_data_frame, .id = "ID") transect_coords = xyFromCell(srtm, transect_df$cell)
transect_df$dist = c(0, cumsum(geosphere::distGeo(transect_coords)))  The resulting transect_df can be used to create elevation profiles, as illustrated in Figure 11.3:B. The final type of geographic vector object for raster extraction is polygons. Like lines and buffers, polygons tend to return many raster values per polygon. This is demonstrated in the command below, which results in a data frame with column names ID (the row number of the polygon) and srtm (associated elevation values): zion_srtm_values = raster::extract(x = srtm, y = as(zion, "Spatial"), df = TRUE) Such results can be used to generate summary statistics for raster values per polygon, for example to to characterize a single region or to compare many regions. The generation of summary statistics is demonstrated the code below, which creates the object zion_srtm_df containing summary statistics for elevation values in Zion National Park (see 11.4:A): group_by(zion_srtm_values, ID) %>% summarize_at(vars(srtm), funs(min, mean, max)) #> # A tibble: 1 x 4 #> ID min mean max #> <dbl> <dbl> <dbl> <dbl> #> 1 1. 1122. 1818. 2661. The preceding code chunk used the tidyverse to provide summary statistics for cell values per polygon ID, as described in Chapter 3. The results provide useful summaries, for example that the maximum height in the park is around 2,661 meters (other summary statistics such as standard deviation can also be calculated in this way). Because there is only one polygon in the example a data frame with a single row is returned, but the method works when multiple selector polygons are used. The same approach works for counting occurrences of categorical raster values within polygons. This is illustrated with a land cover dataset (nlcd) from the spDataLarge package in 11.4:B and demonstrated in the code below: zion_nlcd = raster::extract(nlcd, as(zion, "Spatial"), df = TRUE, factors = TRUE) dplyr::select(zion_nlcd, ID, levels) %>% gather(key, value, -ID) %>% group_by(ID, key, value) %>% tally() %>% spread(value, n, fill = 0) #> # A tibble: 1 x 9 #> # Groups: ID, key [1] #> ID key Barren Cultivated Developed Forest Herbaceous Shrubland #> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 1. levels 98285. 62. 4205. 298299. 235. 203701. #> # ... with 1 more variable: Wetlands <dbl> So far we have seen how raster::extract() is a flexible way of extracting raster cell values from a range of input geographic objects. An issue with the function, however, is that it is slow. If this is a problem it is useful to know about alternatives and work-arounds, three of which are presented below. • Parallelization: this approach works when using many geographic vector selector objects by splitting them into groups and extracting cell values independently for each group (see ?raster::clusterR() for details of this approach). • Use the velox package (Hunziker 2017), which provides a fast method for extracting raster data that fits in memory (see the packages extract vignette for details). • Using R-GIS bridges (see Chapter 10): efficient calculation of raster statistics from polygons can be found in the SAGA function saga:gridstatisticsforpolygons, for example, which can be accessed via RQGIS. ## 11.4 Rasterization Rasterization is the conversion of vector objects into their representation in raster objects. Usually, the output raster is used for quantitative analysis (e.g. analysis of terrain) or modeling. As we saw in Chapter 2 the raster data model has some characteristics that make it conducive to certain methods. Furthermore, the process of rasterization can help simplify datasets because the resulting values all have the same spatial resolution: rasterization can be seen as a special type of geographic data aggregation. The raster package contains the function rasterize() for doing this work. Its first two arguments are x, vector object to be rasterized and y, a ‘template raster’ object defining the extent, resolution and CRS of the output. The geographic resolution of the input raster has a major impact on the results: if it is too low (cell size is too large) the result may miss the full geographic variability of the vector data; if it is too high computational times may be excessive. There are no simple rules to follow when deciding an appropriate geographic resolution, which is heavily dependent on the intended use of the results. To demonstrate rasterization in action, we will use a template raster that has the same extent and CRS as the input vector data cycle_hire_osm_projected (a dataset on cycle hire points in London illustrated in Figure 11.5:A) and spatial resolution of 1000 meters: cycle_hire_osm_projected = st_transform(cycle_hire_osm, 27700) raster_template = raster(extent(cycle_hire_osm_projected), resolution = 1000, crs = st_crs(cycle_hire_osm_projected)$proj4string)

Rasterization is a very flexible operation: the results depend not only on the nature of the template raster, but also on the type of input vector (e.g. points, polygons) and a variety arguments taken by the rasterize() function.

To illustrate this flexibility we will try three different approaches rasterization. First we create a raster representing the presence or absence of cycle hire points (known as presence/absence rasters). In this case rasterize() requires only one argument in addition to x and y (the aformentioned vector and raster objects): a value to be transferred to all non-empty cells specified by field (results illustrated Figure 11.5:B).

ch_raster1 = rasterize(cycle_hire_osm_projected, raster_template, field = 1)

The fun argument specifies summary statistics used to covert multiple observations in close proximity into associate cells in the raster object. By default fun = 'last is used but other options such as fun = "count" can be used, in this case to count the number of cycle hire points in each grid cell (the results of this operation are illustrated in Figure 11.5:C).

ch_raster2 = rasterize(cycle_hire_osm_projected, raster_template,
field = 1, fun = "count")

The new output, ch_raster2, shows the number of cycle hire points in each grid cell. The cycle hire locations have different numbers of bicycles described by the capacity variable, raising the question, what’s the capacity in each grid cell? To calculate that we must sum the field ("capacity"), resulting in output illustrated in Figure 11.5:D, calculated with the following command (other summary functions such as mean could be used):

ch_raster3 = rasterize(cycle_hire_osm_projected, raster_template,
field = "capacity", fun = sum)

Another dataset based on California’s polygons and borders (created below) illustrates raterization of lines. After casting the polygon objects into a multilinestring, a template raster is created, with a resolution of a 0.5 degree:

california = dplyr::filter(us_states, NAME == "California")
california_borders = st_cast(california, "MULTILINESTRING")
raster_template2 = raster(extent(california), resolution = 0.5,
crs = st_crs(california)\$proj4string)

Line rasterization is demonstrated in the code below. In the resulting raster, all cells that are touched by a line get a value, as illustrated in Figure 11.6:A.

california_raster1 = rasterize(california_borders, raster_template2)

Polygon rasterization, by contrast, selects only cells whose centroids are inside the selector polygon, as illustrated in Figure 11.6:B.

california_raster2 = rasterize(california, raster_template2)

As with raster::extract(), raster::rasterize() works well for most cases but is not performance optimized. Fortunately, there are several alternatives, including the fasterize::fasterize() and gdalUtils::gdal_rasterize(). The former is much (100 times+) faster than rasterize() but is currently limited to polygon rasterization. The latter is part of GDAL and therefore requires a vector file (instead of an sf object) and rasterization parameters (instead of a Raster* template object) as inputs.65

## 11.5 Spatial vectorization

Spatial vectorization is the counterpart of rasterization 11.4, but in the opposite direction. It involves converting spatially continuous raster data into spatially discrete vector data such as points, lines or polygons.

Be careful with the wording! In R vectorization refers to the possibility of replacing for-loops and alike by doing things like 1:10 / 2 (see also Wickham (2014)).

The simplest form of vectorization is to convert the centroids of raster cells into points. rasterToPoints() does exactly this for all non-NA raster grid cells (Figure 11.7). Setting the spatial parameter to TRUE ensures the output is a spatial object, not a matrix.

elev_point = rasterToPoints(elev, spatial = TRUE) %>%
st_as_sf()

Another common type of spatial vectorization is the creation of contour lines representing lines of continuous height or temperatures (isotherms) for example. We will use a real-world digital elevation model (DEM) because the artificial raster elev produces parallel lines (task: verify this and explain why this happens). Contour lines can be created with the raster function rasterToContour(), which is itself a wrapper around contourLines(), as demonstrated below:

# not shown
data(dem, package = "RQGIS")
plot(dem, axes = FALSE)
contour(dem, add = TRUE)

Contours can be added to existing plots with functions such as contour(), rasterVis::contourplot() or tmap::tm_iso(). As illustrated in Figure 11.8 (which was created using the tmap package described in Chapter 9), isolines can be labelled.

The final type of vectorisation involves conversion of rasters to polygons. This can be done with raster::rasterToPolygons(), wich converts each raster cell into a polygon consisting of five coordinates, all of which are stored in memory (explaining why rasters are often fast compared with vectors!)

This is illustrated below by converting the grain object into polygons and subsequently dissolving borders between polygons with the same attribute values. Attributes in this case are stored in a collumn called layer (see section 5.3.6 and Figure 11.9). (Note: a convenient alternative for converting rasters into polygons is spex::polygonize() which by default returns an sf object.)

grain_poly = rasterToPolygons(grain) %>%
st_as_sf()
grain_poly2 = grain_poly %>%
group_by(layer) %>%
summarize()

## 11.6 Exercises

The next two exercises will use a vector (random_points) and raster dataset (ndvi) from the RQGIS package. It also uses a polygonal ‘convex hull’ derived from the vector dataset (ch) to represent the area of interest:

library(RQGIS)
data(random_points)
data(ndvi)
ch = st_combine(random_points) %>%
st_convex_hull()
1. Crop the ndvi raster using (1) the random_points dataset and (2) the ch dataset. Are there any difference in the output maps? Next, mask ndvi using these two datasets. Can you see any difference now? How can you explain that?

2. Firstly, extract values from ndvi at the points represented in random_points. Next, extract average values of ndvi using a 90 buffer around each point from random_points and compare these two sets of values. When would extracting values by buffers be more suitable than by points alone?

3. Subset points higher than 3100 meters in New Zealand (the nz_height object) and create a template raster with a resolution of 3km. Using these objects:
• Count numbers of the highest points in each grid cell.
• Find the maximum elevation in each grid cell.
4. Polygonize the grain dataset and filter all squares representing clay.
• Name two advantages and disadvantages of vector data over raster data.
• At which points would it be useful to convert rasters to vectors in your work?

### References

Hunziker, Philipp. 2017. Velox: Fast Raster Manipulation and Extraction.