This tool performs a weighted overlay on multiple input images. It can be used to combine multiple factors with varying levels of weight or relative importance. The WeightedOverlay tool is similar to the WeightedSum tool but is more powerful because it automatically converts the input factors to a common user-defined scale and allows the user to specify benefit factors and cost factors. A benefit factor is a factor for which higher values are more suitable. A cost factor is a factor for which higher values are less suitable. By default, WeightedOverlay assumes that input images are benefit factors, unless a cost value of 'true' is entered in the cost array. Constraints are absolute restriction with values of 0 (unsuitable) and 1 (suitable). This tool is particularly useful for performing multi-criteria evaluations (MCE).

Notice that the algorithm will convert the user-defined factor weights internally such that the sum of the weights is always equal to one. As such, the user can specify the relative weights as decimals, percentages, or relative weightings (e.g. slope is 2 times more important than elevation, in which case the weights may not sum to 1 or 100).

NoData valued grid cells in any of the input images will be assigned NoData values in the output image. The output raster is of the float data type and continuous data scale.

## Warning

Each of the input rasters must have the same spatial extent and number of rows and columns.

## Function Signature

`def weighted_overlay(self, factors: List[Raster], weights: List[float], cost: List[Raster] = None, constraints: List[Raster] = None, scale_max: float = 1.0) -> Raster: ...`

## Project Links

WbW Homepage User Manual Support WbW