Creating Rasters in R and Python

Having the ability to create rasters yourself is useful, especially when working with raw sensor data that hasn’t been conveniently pre-packaged.

Basic Raster Concepts

Rasters are 2 dimensional data structures in which data is contained in a regularly spaced grid. Each cell in the grid contains a single data value which can be retrieved by specifying either the exact grid address (row and column), or the region (group of grid cells) in which the value is stored. Usually the spacing of the grid cells is uniform in both the X and Y directions, but this is not always the case. The formal definition for this type of data structure is a 2-dimensional array, or matrix.

Rasters are referenced from an origin that is either at the top or bottom left corner of the grid. The row and column numbers of each grid cell (or pixel) increase in count from the origin, but the Y position of the data may either go up or down as the count increases. When a raster is georeferenced, the origin corresponds to a geographic location expressed as a point in whatever coordinate system the raster has been defined in.

Fig 1. Raster grid with origin at top-left

Various types of data can be stored in rasters but commonly they are used to store images, in which the cell values are representations of RGD color values (in the case of color images), or light reflectance or brightness (in the case of B&W images). Another common use for rasters is to store scientific data such as land elevation, air temperature, smoke density or water depth. These are examples of continuous data, where each pixel can represent a different and continuously variable value. Rasters can also be used to store discrete values, where a group of pixels represent a discrete class of data, such as a land-use category, where each pixel in a specific class will have the same value. Finally, rasters can also be used to aggregate data from various point locations, like from a sensor network, where each sensor location is represented by a pixel in the raster. This type of point location data is often converted into a continuous data set later by extrapolating pixel values between the point locations.

Fig. 2 Continuous data in raster as a hillshade

Elements Required to Create a Raster

Very little information is needed to create a basic raster. All that is really needed is some data arranged in a matrix. (Note that in Python the term “array” is used instead of “matrix”.)

R

library(raster)

simple_matrix <- matrix(seq(1,9),
                        nrow = 3,
                        ncol = 3,
                        byrow = TRUE)

simple_raster <- raster(simple_matrix)

Fig 3. Simple 3x3 raster in R

Python

from PIL import Image

simple_array = np.asarray(range(1,10)).reshape(3,3)

im = Image.fromarray(np.uint8(simple_array))
pyplot.imshow(im)

Fig. 4 Simple 3x3 raster in Python

In both examples above, a simple 3x3 matrix was created from a sequence of numbers that range from [1, 9]. That matrix was then passed into an image creation library and the resulting image was then rendered. The important concept to take away from this is that we can control the content of a raster simply by controlling the content of the numeric matrix that is used to create it. Here are a few examples.

Creating a Raster with a User-Defined Matrix

R

simple_grid <- c(1,0,1, 0,1,0, 1,0,1)

simple_matrix <- matrix(simple_grid,
                 nrow = 3,
                 ncol = 3,
                 byrow = TRUE)

simple_raster <- raster(simple_matrix)

# plot(simple_raster, legend = FALSE, axes = FALSE)

plot(simple_raster)

Fig 5. User defined 3 x 3 raster in R

Python

user_array = np.array([[1,0,1],
                       [0,1,0],
                       [1,0,1]])
user_im = Image.fromarray(np.uint8(user_array))
pyplot.imshow(user_im)

Fig. 6 User defined 3x3 raster in Python

In these examples above, a user-defined 3 x 3 matrix is converted into a raster. Again, the important thing to remember is that we can control the raster content by modifying the numeric matrix that it’s created from. Below we can see how a single pixel value can be modified.

Modifying a Raster via Matrix Address

R

simple_matrix[2,2] <- 0
user_raster <- raster(simple_matrix)
plot(user_raster)

Fig. 7 Modifying a single pixel by matrix address in R

Python

user_array[1,1] = 0

user_im = Image.fromarray(np.uint8(user_array))
pyplot.imshow(user_im)

Fig. 8 Modifying a single pixel by matrix address in Python

Next

In my next post, I’ll cover how rasters are georeferenced and some of the ways in which having a spatial context allows us to use rasters to do interesting things.