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The objective of this paper is to simulate the location of land-use change, specifically forest disturbance, in Costa Rica over several decades. This paper presents a GIS-based model, GEOMOD2, which quantifies factors associated with land-use, and simulates the spatial pattern of land-use forward and backward in time. GEOMOD2 reads rasterized maps of land-use and other biogeophysical attributes to determine empirically the attributes of land that humans tend to use. Then GEOMOD2 uses the patterns of those biogeophysical attributes to simulate the spatial pattern of land-use change. GEOMOD2 can select locations for land-use change according to any of three decision rules based on (1) nearest neighbors, (2) stratification by political sub-region, and/or (3) the pattern of biogeophysical attributes. GEOMOD2 simulates the progressive loss of closed-canopy forest in Costa Rica for 1940, 1961 and 1983, which are the years for which maps of land-use are available. Also, GEOMOD2 extrapolates the pattern of land-use to the year 2010. When GEOMOD2 extrapolates land-use change over several decades, it is able to classify correctly between 74 and 88% of the grid cells, for two categories: forest versus non-forest. Over various simulation runs, Kappa ranges from 0.31 to 0.53. The model's ability to predict the location of disturbance is best when the model is driven by the location of biogeophysical characteristics, most importantly lifezones.

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This paper describes a methodology by which modelers, ecologists and planners can quantify the certainty in predicting the location of change for a given quantity of change. The specification of the quantity of a land cover category and the specification of the location of a land cover category are two distinct fundamental concepts in geographical analysis. It is crucial that scientists have appropriate quantitative tools to analyze each of these two concepts independently of one another. This paper gives methods whereby a scientist can convert a map of relative propensity for disturbance to a map of probability of future disturbance, based on a quantifiable validation of a map's predictive ability. The required inputs are: (1) maps that show a Boolean categorical variable at times 0, 1 and 2, (2) a technique to create a map that shows the relative propensity for membership in the Boolean category, and (3) a predicted proportion of the category at time 3.