Available with Spatial Analyst license.
This function allows you to overlay several rasters using a common measurement scale and weight each according to its importance.
All input rasters must be integer. A floating-point raster must first be converted to an integer raster before it can be used.
Each value class in an input raster is assigned a new value based on an evaluation scale. These new values are reclassifications of the original input raster values. A restricted value is used for areas you want to exclude from the analysis.
Each input raster is weighted according to its importance or its percent influence. The weight is a relative percentage, and the sum of the percent influence weights must equal 100. Influences are specified by integer values only. Decimal values are rounded down to the nearest integer.
Changing the evaluation scales or the percent influence can change the results of the weighted overlay analysis.
Weighted Overlay Table
The weighted overlay table consists of four parts:
Choose which extent should be used in the output raster:
Choose which cell size to use in the output raster. If all the input cell sizes are the same, all the options will yield the same results.
Learn more about weighted overlays
The Weighted Overlay function applies one of the most used approaches for overlay analysis to solve multicriteria problems such as site selection and suitability models. In a weighted overlay analysis, each of the general overlay analysis steps is followed. As with all overlay analysis, in weighted overlay analysis, you must define the problem, break the model into submodels, and identify the input layers.
Since the input criteria layers will be in different numbering systems with different ranges, to combine them in a single analysis, each cell for each criterion must be reclassified into a common preference scale such as 1 to 10, with 10 being the most favorable. An assigned preference on the common scale implies the phenomenon's preference for the criterion. The preference values are on a relative scale. That is, a preference value of 10 is twice as preferred as a preference value of 5.
The preference values should not only be assigned relative to each other within the layer but also have the same meaning between the layers. For example, if a location for one criterion is assigned a preference of 5, it will have the same influence on the phenomenon as a 5 in a second criterion.
In a simple housing suitability model, you may have three input criteria: slope, aspect, and distance to roads. The slopes are reclassed on a scale of 1 to 10, with the flatter being less costly; therefore, they are the most favorable and are assigned the higher values. As the slopes become steeper, they are assigned decreasing values, with the steepest slopes being assigned a 1. You do the same reclassification process to the 1-to-10 scale for aspect, with the more favorable aspects—in this case, the more southerly—being assigned the higher values. The same reclassification process is applied to the distance to roads criterion. The locations closer to the roads are more favorable since they are less costly to build on, because they have easier access to power and require shorter driveways. A location assigned a suitability value of 5 on the reclassed slope layer will be twice as costly to build on as a slope assigned a value of 10. A location assigned a suitability of 5 on the reclassed slope layer will have the same cost as a 5 assigned on the reclassed distance to roads layer.
Each of the criteria in the weighted overlay analysis may not be equal in importance. You can weight the important criteria more than the other criteria. For instance, in our sample housing suitability model, you might decide that because of long-term conservation purposes, the better aspects are more important than the short-term costs associated with the slope and distance to roads criteria. Therefore, you may weight the aspect values as twice as important as the slope and distance to roads criteria.
The input criteria are multiplied by the weights and then added together. For example, in the housing suitability model, aspect is multiplied by 2 and the three criteria are added together, or represented another way, (2 * aspect) + slope + distance to roads.
The final step of the overlay analysis process is to validate the model to make sure that what the model indicates is at a site is actually there. Once the model is validated, a site is selected and the house is built.
Using Restricted and NoData for the scale value
Setting a scale value to Restricted assigns a value to that cell in the output weighted overlay result that is the minimum value of the evaluation scale set, minus 1. If there are no inputs to Weighted Overlay with cells of NoData, you could use NoData as the scale value to exclude certain values. However, it is safest, and essential if you have NoData cells in any of your inputs, to use Restricted instead. Potentially, you could have a result from Weighted Overlay that contains cells of NoData that have come from one or more of the inputs (NoData on any input equals NoData in the result) and restricted areas that you purposely excluded. NoData and Restricted values should not be confused. Each serves a specific purpose. There may be areas of NoData where you don't know the value that are actually suitable areas. If you use NoData instead of Restricted to exclude certain cell values, and there is NoData in one or more inputs, you will not know if a cell of NoData means the area is restricted from use or whether there was no input data available in that location.
Take care using Restricted for the scale value when creating a cost surface. Since using Restricted gives a value to the cell that is the minimum value of the evaluation scale, minus 1, your restricted areas will appear to be given the lowest cost, when they are actually excluded from the analysis. Instead, you should assign a high cost or set the scale value to NoData for areas you want excluded from the analysis.