- View, modify and group landcover transitions for sub-model.
- Transform variables for inclusion in sub-model. The transformations available include: natural log, exponential, logit, square root, power, and evidence likelihood.
- Test and select variables for their explanatory power. Both quantitative and qualitative variables can be tested. The driver variable test procedure is based on a contingency table analysis.
- Specify variables to be included in sub-model. Variables can be added to the model as either static or dynamic components. Static variables are unchanging over time and express aspects of basic suitability for the transition under consideration. Dynamic variables are time-dependent drivers such as proximity to existing development or infrastructure and are recalculated over time during the course of a prediction.
- Model transition sub-model using either Logistic Regression or our Multi-Layer Perceptron (MLP) neural network, extensively revised to offer an automatic mode requiring no user intervention. The result for either model is a potential map for each transition – an expression of time-specific potential for change.
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