Weighted overlays and landscape modeling

Weighted overlays allow you to prioritize several different types of information at once and visualize it so you can evaluate several different factors at once. A common scale is applied to the values to create an integrated analysis.

For landscape projects, it is common to consider several factors at once, such as fire risk, elevation, proximity to suburban development, and so forth. In this case, weighted overlays can be used to analyze all of these things at once and find the best site for new development. In these instances, the scale is applied and cell values are updated to indicate the best location.

There are several different ways to prioritize the factors to be considered. You can assign percentages to the layers, and you can assign additional weights to classes represented by the layers. As an example, we can see that the Critical Habitat layer accounts for only 10 percent of the model, and that weights can be assigned to Non Critical, Threatened, and Endangered classes. This allows you to determine what types of habitats will be given the most importance during the analysis. Generally, you would want the noncritical habitat to have the lowest weight, while the habitats for threatened and endangered species would have higher weights.

Once the weights are assigned to each category and the model is run, a common scale is applied to all the layers based on the percentages assigned to the layers and the weights assigned within each layer.

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 0 to 9, with 9 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 of 9 is twice as preferred as a preference of 4.

The preference values not only should be assigned relative to each other within the layer but should have the same meaning between the layers. For example, if a location for one criterion is assigned a preference of 4, it will have the same influence on the phenomenon as a 4 in a second criterion.

For example, in a simple housing suitability model, you may have three input criteria: slope, aspect, and distance to roads. The slopes are reclassed on a 0 to 9 scale 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 0. You do the same reclassification process to the 0 to 9 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 9. 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, because of long-term conservation purposes, that 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 than the slope and distance to roads criteria.