A SELF-MODIFYING CELLULAR-AUTOMATON MODEL OF HISTORICAL URBANIZATION IN THE SAN-FRANCISCO BAY AREA

In this paper we describe a cellular automaton (CA) simulation model d eveloped to predict urban growth as part of a project for estimating t he regional and broader impact of urbanization on the San Francisco Ba y area's climate. The rules of the model are more complex than those o f a typical CA and involve the use of multiple data sources, including topography, road networks, and existing settlement distributions, and their modification over time. In addition, the control parameters of the model are allowed to self-modify: that is, the CA adapts itself to the circumstances it generates, in particular, during periods of rapi d growth or stagnation. In addition, the model was written to allow th e accumulation of probabilistic estimates based on Monte Carlo methods . Calibration of the model has been accomplished by the use of histori cal maps to compare model predictions of urbanization, based solely up on the distribution in year 1900, with observed data for years 1940, 1 954, 1962, 1974, and 1990. The complexity of this model has made calib ration a particularly demanding step. Lessons learned about the method s, measures, and strategies developed to calibrate the model may be of use in other environmental modeling contexts. With the calibration co mplete, the model is being used to generate a set of future scenarios for the San Francisco Bay area along with their probabilities based on the Monte Carlo version of the model. Animated dynamic mapping of the simulations will be used to allow visualization of the impact of futu re urban growth.