Inproceedings,
Methods and techniques for rigorous calibration of a cellular automaton model of urban growth
Third International Conference/Workshop on Integrating GIS and Environmental Modeling, Santa Fe, New Mexico, (1996)
Abstract
Several lessons about the process of calibration were learned during development of a self-modifying
cellular automaton model to predict urban growth. This model, part of a global change research project
on human-induced land transformations, was used to predict the spatial extent of urban growth 100 years
into the future. The context of the prediction was to evaluate urban environmental disturbances such as
land use conversion, urban heat island intensification, and greenhouse gas generation. Using data for the
San Francisco Bay area as a test case, methods were developed, including interactive and statistical
versions of the model, animation and visualization tools, automated testing methods, and Monte Carlo
simulations. This presentation will enumerate, analyze, and discuss the lessons learned during the
extensive process of model calibration. Experience with the methods developed may have broader use in
assisting the rigorous calibration for other CA models, and perhaps those coupled environmental models
with an extensive spatial data component. These methods are now under test as the project moves to a
new data set for the Washington, D.C.-Baltimore area.
