Combining Raster and Vector Data

HES 505 Fall 2022: Session 13

Matt Williamson

Today’s Plan

Image Source: USGS

Objectives

  • By the end of today, you should be able to:

    • Clip, crop, or extend vector and raster data so that extents align

    • Convert between raster and vector datasets

    • Generate new rasters describing the spatial arrangement of vector data

    • Extract raster values as attributes of vector data

Modifying the Extent

Dealing with Different Extents

  • Raster extents often larger than our analysis

  • Reducing memory and computational resources

  • Making attractive maps

Using terra::crop()

  • Coordinate Reference System must be the same for both objects

  • Crop is based on the (converted) SpatExtent of the 2nd object

  • snap describes how y will be aligned to the raster

  • Returns all data within the extent

Using terra::crop()

library(sf)
library(terra)
library(spDataLarge)
srtm = rast(system.file("raster/srtm.tif", package = "spDataLarge"))
zion = read_sf(system.file("vector/zion.gpkg", package = "spDataLarge"))
zion = st_transform(zion, crs(srtm))

crs(srtm) == crs(zion)
[1] TRUE
srtm.crop <- crop(x=srtm, y=zion, snap="near")

Using mask()

  • Often want to get rid of all values outside of vector

  • Can set mask=TRUE in crop() (y must be SpatVector)

  • Or use mask()

srtm.crop.msk <- crop(x=srtm, y=vect(zion), snap="near", mask=TRUE)
plot(srtm.crop.msk)

srtm.msk <- mask(srtm.crop, vect(zion))
plot(srtm.msk)

Using mask()

  • Allows more control over what the mask does

  • Can set maskvalues and updatevalues to change the resulting raster

  • Can also use inverse to mask out the vector

srtm.msk <- mask(srtm.crop, vect(zion), updatevalue=-1000)
plot(srtm.msk)

srtm.msk <- mask(srtm.crop, vect(zion), inverse=TRUE, updatevalue=0)
plot(srtm.msk)

Extending boundaries

  • Vector slightly larger than raster

  • Especially when using buffered datasets

  • Can use extend

  • Not exact; depends on snap()

zion.buff <-  zion %>% 
  st_buffer(., 10000)
srtm.ext <- extend(srtm, vect(zion.buff))
ext(srtm.ext)
SpatExtent : -113.343749879444, -112.74791654615, 37.0479167631968, 37.5979167631601 (xmin, xmax, ymin, ymax)
ext(vect(zion.buff))
SpatExtent : -113.343652923976, -112.747986193365, 37.0477357596604, 37.5977812137969 (xmin, xmax, ymin, ymax)

Converting Between Formats

Converting Between Formats

  • Using coercion (as, rast, vect) can change class, but not data model

  • Sometimes we need to actually change the data model

Converting Vectors to Rasters Using rasterize

  • A special kind of data aggregation

  • x is your SpatVector object

  • y is a template raster with the appropriate CRS, resolution, and extent

  • fun allows you to specify the value of the resulting raster

Using rasterize

  • Presence/Absence
  • field specifies which value should be returned to non-empty cells
cycle_hire_osm = spData::cycle_hire_osm
cycle_hire_osm_projected = st_transform(cycle_hire_osm, "EPSG:27700")
raster_template = rast(ext(cycle_hire_osm_projected), resolution = 1000,
                       crs = st_crs(cycle_hire_osm_projected)$wkt)
ch_raster1 = rasterize(cycle_hire_osm_projected, raster_template,
                       field = 1)

Using rasterize

  • The fun argument specifies how we aggregate the data

  • Useful for counting occurrences (using length)

ch_raster2 = rasterize(cycle_hire_osm_projected, raster_template, 
                       fun = "length")

Using rasterize

  • The fun argument specifies how we aggregate the data

  • Can use a variety of functions

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

Lines and Polygons

  • Can use rasterize or stars::st_rasterize
  • Result depends on the touches argument

Converting rasters to vectors

  • Less common, but can convert to vector data

  • as.points, as.countour, and polygonize

dem = rast(system.file("raster/dem.tif", package = "spDataLarge"))
cl = as.contour(dem)

Generating New Data

Generating New Data

  • Sometimes we want a raster describing the spatial context of vector data

  • distance is a simple method

  • We’ll use interpolation in the next few weeks

Generating Distance Rasters

  • returns a distance matrix or SpatRaster
cycle_hire_osm = spData::cycle_hire_osm
cycle_hire_osm_projected = st_transform(cycle_hire_osm, "EPSG:27700")

cycle_dist <- distance(vect(cycle_hire_osm_projected))
head(as.matrix(cycle_dist))[1:5, 1:5]
          1         2        3         4         5
1    0.0000 2550.9619 1736.118  407.1558 1922.3728
2 2550.9619    0.0000 1072.401 2820.4804  725.2172
3 1736.1178 1072.4011    0.000 1908.1413  360.7490
4  407.1558 2820.4804 1908.141    0.0000 2148.0087
5 1922.3728  725.2172  360.749 2148.0087    0.0000

Generating Distance Rasters

  • returns a distance matrix or SpatRaster
raster_template = rast(ext(cycle_hire_osm_projected), resolution = 100,
                       crs = st_crs(cycle_hire_osm_projected)$wkt)
ch_raster1 = rasterize(cycle_hire_osm_projected, raster_template,
                       field = 1)

ch_dist_rast <- distance(ch_raster1)
plot(ch_dist_rast)

Creating Vector Data by Extraction

  • Sometimes we want to use rasters to create new attributes

  • fun controls how the cells are aggregated

cycle_hire_osm = spData::cycle_hire_osm
cycle_hire_osm_proj_buff <- st_transform(cycle_hire_osm, "EPSG:27700") %>% 
  st_buffer(., 5000) %>% 
  as(., "SpatVector")

cycle_ext <- extract(ch_dist_rast, cycle_hire_osm_proj_buff)
head(cycle_ext)
  ID    lyr.1
1  1 1360.147
2  1 1280.625
3  1 1204.159
4  1 1131.371
5  1 1063.015
6  1 1000.000