The Use and Uncertainties of Spatial Data for Landscape Models: An Overview with Examples from the Florida Everglades

Models are usually developed for one of two purposes: to better understand ecological systems or to evaluate the influence of an altered condition to make a decision or set policy. A spatial model can loosely be defined as one that has either one or more state variables that are a function of space or can be related to other space-dependent variables. The information-richness of spatial data and models comes from their simultaneous depiction of temporal and spatial patterns. This richness is easily grasped by only the most powerful of all computers, the mind. For example, the mind can immediately compare a temperature map of today with the map from the day before and quickly develop a spatial trend analysis for areas of change. Put three maps together and the mind computer may even venture to predict areas cooling down or warming up. It does this easily, with no thought of the spatial distribution of weather stations, kriging techniques, data collection errors, the influence of scale, or knowledge of meteorological models, yet these things and more are all factors that contribute to the spatial uncertainty of this mental model. These uncertainties become extremely important when one tries to formalize and quantify this spatial modeling process for prediction.

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