So far, the kind of spatial data we have been working with is vector data, like points, lines and polygons. I have mentioned the other data type – raster data – in a previous blog, but I haven’t gone into detail about what it is.
Raster data functions like a digital image, where there are individual pixels that have a colour assigned to them. For raster spatial data, instead of being assigned a colour, each pixel (or cell) is assigned a value that corresponds with that location’s characteristics, for example, how many millimeters of precipitation fell in that location that year, or the elevation of that location. However, since each pixel covers an area, the assigned value must represent that entire area. The pixel size (or cell size) of a raster dataset may range widely, from 1 meter by 1 meter to tens of kilometers by tens of kilometers, or more. Therefore, the smaller the cell size, the more accurate the assigned value will be.
Raster data may be discrete, like land use (i.e., the cell represents agricultural land or forested area etc.), in which case, the value might be assigned based on which type of land use takes up the most space within the cell. Continuous raster data, such as elevation, may be averaged over the area of the cell, although the methods for calculating values may vary.
I enjoy working with raster data because it generally provides continuous detailed information over a large area, where point data may have large spaces between datapoints, for example. It also gives some freedom to view the data in a more abstract way. It is also possible to change raster data to suit your needs by reclassifying the data.
Raster reclassification is useful in a number of situations, particularly if you want to aggregate data into categories. For example, reclassification could be used if the raw data represents elevation, but you want to represent the data as areas above 1000 m elevation and areas below 1000 m elevation. You can use the Reclassification tool to assign all elevations above 1000 m a value of 1 (for example) and assign all elevations below 1000 m a value of 0. Reclassifications can become much more complex, such as the example in the image, and the way you can use reclassified rasters can vary widely as well.
Next time, I will share an example of how to use reclassification to represent your data in different ways, and how you can use the raster calculator to add rasters together (and more).