Assessing a predictive model of land change using uncertain data
This paper presents a method to assess models that predict changes among land categories between two points in time. Cross-tabulation matrices show comparisons among three maps: 1) the reference calibration map of an initial time, 2) the reference validation map of a subsequent time, and 3) the model's predicted map of the same subsequent time. The proposed method analyzes these three maps to evaluate the ability of the model to predict land change vis-à-vis a null model, while accounting for the error in the reference maps. We illustrate this method with a prediction of land change from 1971 to 1999 in Central Massachusetts, USA. Results reveal that the land change model predicts a larger quantity of transition from forest to built than the reference maps indicate, and the model allocates the transition erroneously in space, thus causing substantial error where the model predicts built in 1999 but the reference map shows forest. If the accuracy of each category in the 1971 reference map is greater than 81 percent, then the predicted change is larger than the error in the 1971 reference map. If the accuracy of each category in the 1999 reference map is greater than 82 percent, then the model's prediction disagreement with respect to truth is larger than the error in the 1999 reference map. Partial information concerning the accuracy of the reference maps indicates that the maps are likely to be more accurate than the 82 percent threshold. The method is designed to analyze predictions for the common situation when the levels of accuracy in the reference maps are not known precisely. © 2009 Elsevier Ltd. All rights reserved.
Environmental Modelling and Software
Pontius, Robert Gilmore and Petrova, Silvia H., "Assessing a predictive model of land change using uncertain data" (2010). Geography. 764.