Implementation of cellular automata models in a raster GIS dynamic modeling environment: an example using the Clarke Urban Growth Model

Author:

Matthew J. Ungerer

Abstract

This paper discusses some of the issues involved in implementing a cellular automaton (CA) model of urban growth in a fully-integrated raster Geographic Information System (GIS) dynamic modeling environment. CA models are dynamic spatial models in which the basic unit is the cell, situated in a two dimensional plane. CA models are thus very similar to raster GIS in many respects. Work by Batty, White and others has shown that cellular automata (CA) models are simple and effective means for creating portable models of urban growth. The Clarke Urban Growth model is a CA model which has been calibrated and used to predict the urban growth in several urban areas. Using only the built-in functions of the PCRaster GIS package, an approximation of the Clarke Urban Growth Model can be implemented using only about 200 lines of code. The GIS model has the same input maps and growth parameters, very similar growth rules and produces the same output files as the original C-code version. Thus the output files from the two models can be compared to examine if the models are functionally equivalent. The C-code version and the PCRaster version of the Clarke Urban Growth model were each run ten times using the same input maps and model parameters, for a period of 20 years. The last output file (i.e. year 20) from each of the ten runs of the C-code version was compared to the last output file from each of the ten runs of the PCRaster version. The results of the comparison indicate that the output files from the two versions of the model do differ statistically. However, it is also apparent from a visual comparison of the output files in animation format that the output files are remarkably similar. Modeling using a dynamic modeling language in a GIS does not give the modeler the same degree of control as an ordinary computer language, but has several advantages including a reduced development time, enhanced ease of use, and access to the functionality of the GIS for display and file management. A dynamic modeling language tightly coupled with a raster GIS offers an integrated environment that makes it relatively simple to implement cellular automata models, as well as other models of continuous phenomena which vary in time as well as space.

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