Modeling Urban Sprawl and Land Use Change in a Coastal Area-- A Neural Network Approach
Complexity 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.
|Date of creation:||2005|
|Contact details of provider:|| Postal: 555 East Wells Street, Suite 1100, Milwaukee, Wisconsin 53202|
Phone: (414) 918-3190
Fax: (414) 276-3349
Web page: http://www.aaea.org
More information through EDIRC
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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.
- 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 Carrión-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.
When requesting a correction, please mention this item's handle: RePEc:ags:aaea05:19364. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (AgEcon Search)
If references are entirely missing, you can add them using this form.