- Specify the end year of the prediction and the prediction procedure. After specifying the end date, the quantity of change in each transition can either be modeled through a Markov Chain analysis or by providing a transition probability matrix from an external (e.g., econometric) model.
- Two basic models of change are provided. The hard prediction model is based on a multi-objective land competition model. The soft prediction model yields a map of vulnerability to change for the selected set of transitions. The soft prediction model is generally preferred for habitat and biodiversity assessment since it provides a comprehensive assessment of change potential. The hard prediction yields only a single realization.
- Set parameters for the change prediction and run model. During set-up of the change prediction analysis, the user can specify the number of dynamic reassessment stages during which dynamic variables are updated. This also includes the optional dynamic growth (intensification) of the road network. At each stage, the system also checks for the presence of planning interventions (see below), including incentives and constraints and major infrastructure improvements.
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