A group of scientists and researchers who met in Banff, Canada, in 2000 for the 4th International Conference on Integrating GIS and Environmental Modeling issued a brief but powerful statement regarding how to eliminate some of the major barriers to further progress in integrating modeling and GIS. Among the seven recommendations that appeared in "the Banff Statement" that summarized topics discussed at the meeting were: (3) Models should have metadata, and the propagation of error through the model should be assessed and communicated to the users of model predictions; and (6) "Honesty in Modeling" [should account] for uncertainty and error in predictions. This doctoral dissertation research project will seek to advance greater honesty in geographic modeling by investigating the sources of error propagation in a widely used urban cellular automata model, SLEUTH. The doctoral student argues that there are areas within the parameter space of SLEUTH and, by extension, all cellular automata models, where parameter interactions can create unusual and unstable behavior that propagates error into model outputs. Additionally the student expects to demonstrate that within the parameter space of a model, there are multiple sets of satisfactory urban system description parameters, no matter which measure of fit are used to compare the model's performance to control data. While prior work in urban automata modeling has been focused on the application of models, recent studies have begun to look at the model calibration as a critical stage in the modeling process. Prior research will be taken a step further by examining the "nuts and bolts" of one model and by determining if the parameter interactions can lead to the propagation of model error. The approach is a significant step forward in understanding how these widely used models internally function and behave. The parameter space of the SLEUTH urban growth model will be examined through a series of experiments with basic geometric, theoretical, and real-world urban data. The SLEUTH model will be exhaustively and repeatedly recalibrated by parsing the parameter space into blocks of 5 out of the range of 100 units, resulting in 4,084,101 initial parameter sets. These repeat calibrations will be used to find those that best fit the data using the fourteen metrics of fit that are currently used in the model. A new measure for the model, m, the cellular disorder of the parameter space will be added. This metric will calculate the stability throughout the parameter space and will allow for the recognition of areas that have inconsistent behavior. Metric results from the exhaustive calibration of the three datasets will then be analyzed and visualized using self-organizing maps. This will allow for the determination of links between the metrics used to measures of fit and model behavior. Using the three different datasets provides some degree of confidence that the results are not residuals of the data, but of the model's behavior.
The project will calibrate the model for an extremely large combination of the model parameter values. Such models have captured major interest in the past ten years in the hope that such spatial characteristics and land-use or land-cover change might be simulated and reality replicated with cellular automata models. Many scholars view these models with a great deal of skepticism, believing that any spatial pattern of change can be replicated given a model with a sufficient number of parameters whose values have been calibrated with the power of current geocomputational models. This project deals with these concerns by demonstrating whether the parameter space is reasonably stable and if the parameter values are reasonably consistent even given quite different initial state configurations. The broader impacts of this work include: the development of a new method for investigating the parameter space and interactions taking place within the framework of urban automata models. As a Doctoral Dissertation Research Improvement award, this award also will provide support to enable a promising student to establish a strong independent research career.
