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Modeling Urban Sprawl and Land Use Change in a Coastal Area-- A Neural Network Approach

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  • Lin, Huiyan
  • Lu, Kang Shou
  • Espey, Molly
  • Allen, Jeffery

Abstract

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.

Suggested Citation

  • Lin, Huiyan & Lu, Kang Shou & Espey, Molly & Allen, Jeffery, 2005. "Modeling Urban Sprawl and Land Use Change in a Coastal Area-- A Neural Network Approach," 2005 Annual meeting, July 24-27, Providence, RI 19364, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    • Handle: RePEc:ags:aaea05:19364
    DOI: 10.22004/ag.econ.19364
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    Cited by:

    1. Cong Cao & Suzana Dragićević & Songnian Li, 2019. "Short-Term Forecasting of Land Use Change Using Recurrent Neural Network Models," Sustainability, MDPI, vol. 11(19), pages 1-18, September.
    2. OMRANI Hichem & CHARIF Omar & GERBER Philippe & BÓDIS Katalin & BASSE Reine Maria, 2012. "Simulation of land use changes using cellular automata and artificial neural network," LISER Working Paper Series 2012-01, Luxembourg Institute of Socio-Economic Research (LISER).
    3. Hashem Dadashpoor & Fardis Salarian, 2020. "Urban sprawl on natural lands: analyzing and predicting the trend of land use changes and sprawl in Mazandaran city region, Iran," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(2), pages 593-614, February.

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