Chapter 11. Map algebra with NumPy and SciPy
This chapter covers
- Manipulating data with NumPy
- Using NumPy and SciPy for local, focal, and zonal map algebra calculations
- Using GDAL for global map algebra calculations
- Resampling data
You’ve seen how to read and write raster data, but you still don’t know how to manipulate pixel values to do any analysis. Aerial photos make nice basemaps, but many types of raster datasets are used for scientific data analysis. For example, you’ll see several examples of landcover classification in the next chapter. If you wanted to create your own landcover model, you might collect satellite imagery, elevation data, and climate data such as average precipitation or temperature, all of which are generally raster datasets. If you wanted to use vector data in the model, such as soil types, you’d convert it to raster first so that you could use it with your raster datasets. You could then use techniques from this chapter to derive slope and aspect from your elevation data and to combine all of your datasets to create a landcover model.