Raster data lends itself towards several types of common spatial analysis.
Spatial coincidence involves overlaying different raster datasets in order to create a results layer that weighs the coinciding factors from each of the datasets. For example, finding the optimal location for a specie’s habitat based on the type of land cover and location of water.
How close to other geographic phenomena is a feature. The distance can be shown as a straight distance or based on a factor like travel time.
Surface show the measurements of a continuous layer such as elevation, rainfall, or temperature. More: Statistical surfaces.
Dispersion models the movement of a geographic phenomenon such as an oil spill or a wildfire.
Least-cost Path Analysis
This surface shows the costs of traveling from one point to the next. Cost can be a function of time, distance or other criteria that is defined by the user. More: Overview of Least Cost Path Analysis.