Modeling Urban Sprawl and Land Use Change in a Coastal Area-- A Neural Network Approach
AbstractComplexity of urban systems necessitates the consideration of interdependency among various factors for land use change modeling and prediction. The objective of this study is to explore the applicability of computational neural networks in modeling urban sprawl and land use change coupled with geographic information systems (GIS) in Hilton Head Island, South Carolina. We are particularly interested in the capabilities of neural networks to identify land use patterns, to model new development, and to predict future change. A binary logistic regression model is estimated comparison. The results indicate the neural network model is an improvement over the logistic regression model in terms of prediction accuracy.
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Bibliographic InfoPaper provided by American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association) in its series 2005 Annual meeting, July 24-27, Providence, RI with number 19364.
Date of creation: 2005
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- Hite, Diane & Sohngen, Brent & Templeton, Josh, 2003. "Zoning, Development Timing, and Agricultural Land Use at the Suburban Fringe: A Competing Risks Approach," Agricultural and Resource Economics Review, Northeastern Agricultural and Resource Economics Association, vol. 32(1), April.
- Carmen Carri�n-Flores & Elena G. Irwin, 2004. "Determinants of Residential Land-Use Conversion and Sprawl at the Rural-Urban Fringe," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 86(4), pages 889-904.
- Nancy E. Bockstael, 1996. "Modeling Economics and Ecology: The Importance of a Spatial Perspective," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 78(5), pages 1168-1180.
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