Available with Spatial Analyst license.
Overlay analysis geoprocessing functions allow you to apply weights to several input layers, combine them into a single output, and subject to specifications of distribution and shape, identify preferred locations within that result. These geoprocessing functions are commonly used for suitability modeling.
There are several approaches for performing overlay analysis. Although the methods differ, they all follow the same general steps for solving a multi criteria problem. There is a general sequence of steps to follow when performing this type of analysis, using geoprocessing functions from this geoprocessing function category as well as others available in the Spatial Analyst toolbox. Since each approach is based on different assumptions, the meaning of the numbers and the analysis techniques are specific to the approach. Which one to select will depend on the problem you are addressing.
Use the Weighted Overlay and Weighted Sum geoprocessing functions to perform the most general approach to reclassifying and weighting multiple input rasters in Overlay analysis. The Fuzzy Overlay and Fuzzy Membership geoprocessing functions employ fuzzy logic as a mechanism to address inherent inaccuracies in attributes and in the geometry of spatial datasets.
The Locate Regions geoprocessing function allows you to identify the best locations or regions within the combined surface that meet your specific needs. You can control the total area desired, the number of regions that area should be distributed between, the shape of the regions, and how close or how far the regions can be from each other.
The following topics provide background information on the theoretical aspects of these geoprocessing functions as well as some examples of their implementation.
The following table lists the available geoprocessing functions and provides a brief description of each.
Transforms the input raster into a 0 to 1 scale, indicating the strength of a membership in a set, based on a specified fuzzification algorithm.
Combine fuzzy membership rasters data together, based on selected overlay type.
Identifies the best regions, or groups of contiguous cells, from an input utility (suitability) raster that satisfy a specified evaluation criterion and that meet identified shape, size, number, and interregion distance constraints.
Overlays several rasters using a common measurement scale and weights each according to its importance.
Overlays several rasters, multiplying each by their given weight and summing them together.