Areal Data: Rasters

HES 505 Fall 2023: Session 9

Matt Williamson

Today’s Plan

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

  • Access the elements that define a raster

  • Build rasters from scratch using matrix operations and terra

  • Evaluate logical conditions with raster data

  • Calculate different measures of raster data

Revisiting the Raster Data Model

  • Vector data describe the “exact” locations of features on a landscape (including a Cartesian landscape)

  • Raster data represent spatially continuous phenomena (NA is possible)

  • Depict the alignment of data on a regular lattice (often a square)

    • Operations mimic those for matrix objects in R

  • Geometry is implicit; the spatial extent and number of rows and columns define the cell size

Rasters with terra

  • syntax is different for terra compared to sf

  • Representation in Environment is also different

  • Can break pipes, Be Explicit

Rasters by Construction

Rasters by Construction

mtx <- matrix(1:16, nrow=4)
mtx
     [,1] [,2] [,3] [,4]
[1,]    1    5    9   13
[2,]    2    6   10   14
[3,]    3    7   11   15
[4,]    4    8   12   16
rstr <- terra::rast(mtx)
rstr
class       : SpatRaster 
dimensions  : 4, 4, 1  (nrow, ncol, nlyr)
resolution  : 1, 1  (x, y)
extent      : 0, 4, 0, 4  (xmin, xmax, ymin, ymax)
coord. ref. :  
source(s)   : memory
name        : lyr.1 
min value   :     1 
max value   :    16

Note: you must have raster or terra loaded for plot() to work on Rast* objects; otherwise you get Error in as.double(y) : cannot coerce type 'S4' to vector of type 'double'

Rasters by Construction: Origin

  • Origin defines the location of the intersection of the x and y axes
r <- rast(xmin=-4, xmax = 9.5, ncols=10)
r[] <- runif(ncell(r))
origin(r)
[1] 0.05 0.00
r2 <- r
origin(r2) <- c(2,2)

Rasters by Construction: Resolution

  • Geometry is implicit; the spatial extent and number of rows and columns define the cell size
  • Resolution (res) defines the length and width of an individual pixel
r <- rast(xmin=-4, xmax = 9.5, 
          ncols=10)
res(r)
[1] 1.35 1.00
r2 <- rast(xmin=-4, xmax = 5, 
           ncols=10)
res(r2)
[1] 0.9 1.0
r <- rast(xmin=-4, xmax = 9.5, 
          res=c(0.5,0.5))
ncol(r)
[1] 27
r2 <- rast(xmin=-4, xmax = 9.5, 
           res=c(5,5))
ncol(r2)
[1] 3

Predicates and measures in terra

Extending predicates

  • Predicates: evaluate a logical statement asserting that a property is TRUE

  • terra does not follow the same hierarchy as sf so a little trickier

Unary predicates in terra

  • Can tell us qualities of a raster dataset

  • Many similar operations for SpatVector class (note use of .)

predicate asks…
is.lonlat Does the object have a longitude/latitude CRS?
inMemory is the object stored in memory?
is.factor Are there categorical layers?
hasValues Do the cells have values?

Unary predicates in terra

  • global: tests if the raster covers all longitudes (from -180 to 180 degrees) such that the extreme columns are in fact adjacent
r <- rast()
is.lonlat(r)
[1] TRUE
is.lonlat(r, global=TRUE)
[1] TRUE
  • perhaps: If TRUE and the crs is unknown, the method returns TRUE if the coordinates are plausible for longitude/latitude
crs(r) <- ""
is.lonlat(r)
[1] NA
is.lonlat(r, perhaps=TRUE, warn=FALSE)
[1] TRUE
crs(r) <- "+proj=lcc +lat_1=48 +lat_2=33 +lon_0=-100 +ellps=WGS84"
is.lonlat(r)
[1] FALSE

Binary predicates in terra

  • Take exactly 2 inputs, return 1 matrix of cell locs where value is TRUE

  • adjacent: identifies cells adajcent to a set of raster cells

Unary measures in terra

  • Slightly more flexible than sf

  • One result for each layer in a stack

measure returns
cellSize area of individual cells
expanse summed area of all cells
values returns all cell values
ncol number of columns
nrow number of rows
ncell number of cells
res resolution
ext minimum and maximum of x and y coords
origin the orgin of a SpatRaster
crs the coordinate reference system
cats categories of a categorical raster

Binary measures in terra

  • Returns a matrix or SpatRaster describing the measure
measure returns
distance shortest distance to non-NA or vector object
gridDistance shortest distance through adjacent grid cells
costDistance Shortest distance considering cell-varying friction
direction azimuth to cells that are not NA