Modeling and Prediction of Future Urban Growth in the Charleston Region of South Carolina: a GIS-based Integrated Approach
Jeffery Allen, Clemson University
Kang Lu, Clemson University
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The complexity of urban systems makes it difficult to adequately address their changes using a model based on a single approach. In this research, we developed a GIS-based integrated approach to modeling and prediction of urban growth in terms of land use change. The model was built upon a binomial logistic framework, coupled with a rule-based suitability module and focus group involvement, and is designed to predict land transition probabilities and simulate urban growth under different scenarios. The model was calibrated in the Charleston region of South Carolina through a GIS-facilitated participatory process involving both statistical assessment and human evaluation. The model achieved high overall success rates, although its predictive power varied spatially and temporally with different types of land use. The model was used to predict future urban growth in the region through the year 2030.
Charleston, focus group, geographic information system, land use prediction, logistic regression model, rule-based model, urban growth modeling
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