2007 volume 34(5) pages 858 – 883
doi:10.1068/b32132

Cite as:
Fragkias M, Seto K C, 2007, "Modeling urban growth in data-sparse environments: a new approach" Environment and Planning B: Planning and Design 34(5) 858 – 883

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Modeling urban growth in data-sparse environments: a new approach

Michail Fragkias, Karen C Seto

Received 2 September 2005; in revised form 24 February 2006; published online 3 July 2007

Abstract. Although there exist numerous urban growth models, most have significant data input requirements, limiting their utility in a developing-world context. Yet, it is precisely in the developing world where there is an urgent need for urban growth models and scenarios since most expected urban growth in the next two decades will occur in such countries. This paper describes a physical urban growth model that requires few, but widely available, spatially explicit data. Utilizing binary urban/nonurban maps generated by satellite imaging, our model can inform urban planners and policy makers about the most probable locations and periods of future urban land-use change. Using a discrete choice framework, the model employs a spatially explicit logistic regression analysis to evaluate probabilities of urban growth for a baseline period. It calibrates parameters, validates results, predicts urban land-use change and examines future growth scenarios. Future growth scenarios can be generated through the inclusion of land prohibited from development, transportation routes, or new planned urban developments. A novel and important element of the model is the incorporation of an explicit policy-making framework that captures and reduces model uncertainty (theory and specification uncertainties), effectively addressing problems of predictive bias; this framework also allows the user or policy maker to associate predictions with a loss function. The model is applied to three cities in southern China that have experienced dramatic urban land growth in the last two decades. From 1988 to 1999, urban land in the region increased by 451.6% or at an annual rate of approximately 16.5%. Results show that the model achieves 73% – 77% accuracy for different cities at 30 m and 60 m resolutions. Aggregating the predictions to the county/administrative district shows that prediction through thresholding underperforms in comparison to the technique of sample enumeration.

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