In the Land Change Modeler, change analysis and prediction are organized around a series of empirically evaluated transition sub-models. A transition sub-model can consist of a single landcover transition or a group of transitions that are thought to have the same underlying driver variables. All selected transition sub-models must be modeled before change prediction can be undertaken. The Transition Potentials tab allows one to group transitions between two landcover maps into a set of sub-models resulting in a transition potential map for each transition—an expression of time-specific potential for change. Transitions are modeled using either Logistic Regression or a Multi-Layer Perceptron (MLP) neural network.
Figure 2. Transition potential image derived from a set of driver variables such as slope, distance from roads, distance from towns, elevation, etc...
Driver variables can be added to the model either as static or dynamic components. Static variables express aspects of basic suitability for the transition under consideration, and are unchanging over time. Dynamic variables are time-dependent drivers such as proximity to existing development or infrastructure (roads) and are recalculated over time during the course of a prediction.
Once model variables have been selected here, each transition is modeled in the Run Transition Sub-Model panel using either Logistic Regression or the Multi-Layer Perceptron (MLP) neural network. The MLP neural network offers an automatic mode that requires no user intervention. At this stage samples are extracted from the two landcover maps of areas that underwent the transitions being modeled as well as the areas that were eligible to change, but did not. The result in either case is a transition potential map for each transition.
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