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Forecasting Enrollment in Differential Assessment Programs Using Cellular Automata

Author

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  • Jeffrey A Onsted

    (Earth and Environment/Global Sociocultural Studies, Florida International University, ECS 332, 11200 SW 8th Street, Miami, FL 33199, USA)

  • Keith C Clarke

    (Department of Geography, UC Santa Barbara, EH 1720, 1832 Ellison Hall, Santa Barbara, CA 93106-4060, USA)

Abstract

Urban growth models have been used for decades to forecast urban development in metropolitan areas. Since the 1990s cellular automata, with simple computational rules and an explicitly spatial architecture, have been heavily utilized in this endeavor. One such cellular-automata-based model, SLEUTH, has been successfully applied around the world to better understand and forecast not only urban growth but also other forms of land-use and land-cover change, but like other models must be fed important information about which particular lands in the modeled area are available for development. Some of these lands are in categories for the purpose of excluding urban growth that are difficult to quantify since their function is dictated by policy. One such category includes voluntary differential assessment programs, whereby farmers agree not to develop their lands in exchange for significant tax breaks. Since they are voluntary, today's excluded lands may be available for development at some point in the future. Mapping the shifting mosaic of parcels that are enrolled in such programs allows this information to be used in modeling and forecasting. In this study, we added information about California's Williamson Act into SLEUTH's excluded layer for Tulare County. Assumptions about the voluntary differential assessments were used to create a sophisticated excluded layer that was fed into SLEUTH's urban growth forecasting routine. The results demonstrate not only a successful execution of this method but also yielded high goodness-of-fit metrics for both the calibration of enrollment termination as well as the urban growth modeling itself.

Suggested Citation

  • Jeffrey A Onsted & Keith C Clarke, 2011. "Forecasting Enrollment in Differential Assessment Programs Using Cellular Automata," Environment and Planning B, , vol. 38(5), pages 829-849, October.
    • Handle: RePEc:sae:envirb:v:38:y:2011:i:5:p:829-849
    DOI: 10.1068/b37010
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    References listed on IDEAS

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    1. Parks, Peter J. & Quimio, Wilma Rose H., 1996. "Preserving Agricultural Land with Farmland Assessment: New Jersey as a Case Study," Agricultural and Resource Economics Review, Cambridge University Press, vol. 25(1), pages 22-27, April.
    2. Parks, Peter J. & Quimio, Wilma Rose H., 1996. "Preserving Agricultural Land With Farmland Assessment: New Jersey As A Case Study," Agricultural and Resource Economics Review, Northeastern Agricultural and Resource Economics Association, vol. 25(1), pages 1-6, April.
    3. Robert A. Blewett & Julia I. Lane, 1988. "Development Rights and the Differential Assessment of Agricultural Land: Fractional Valuation of Farmland is Ineffective for Preserving Open Space and Subsidizes Speculation," American Journal of Economics and Sociology, Wiley Blackwell, vol. 47(2), pages 195-205, April.
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    1. Jing Yang & Feng Shi & Yizhong Sun & Jie Zhu, 2019. "A Cellular Automata Model Constrained by Spatiotemporal Heterogeneity of the Urban Development Strategy for Simulating Land-use Change: A Case Study in Nanjing City, China," Sustainability, MDPI, vol. 11(15), pages 1-19, July.

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