FCE Redlands 2006 Land Use, Miami-Dade County, South Florida

Urban growth models have increasingly been used by planners and policy makers to visualize, organize, understand, and predict urban growth. However, these models reveal a wide disparity in their attention to policy factors. Some urban growth models capture few if any specific policy effects (e.g.,as model variables), while others integrate certain policies but not others. Since zoning policies are the most widely used form of land use control in the United States, their conspicuous absence from so many urban growth models is surprising. This research investigated the impacts of zoning on urban growth by calibrating and simulating a cellular automaton urban growth model, SLEUTH, under two conditions in a South Florida location. The first condition integrated restrictive agricultural zoning into SLEUTH, while the other ignored zoning data. Goodness of fit metrics indicate that including the agricultural zoning data improved model performance. The results further suggest that agricultural zoning has been somewhat successful in retarding urban growth in South Florida. Ignoring zoning information is detrimental to SLEUTH performance in particular, and urban growth modeling in general.城市增长模型已愈发被规划人员与政策制定者用于可视化呈现、梳理整合、理解剖析以及预测城市增长进程。然而，这类模型在对政策因素的关注度上存在显著差异。部分城市增长模型几乎未纳入任何具体政策效应（如将其作为模型变量），其余模型虽整合了部分政策，但并未涵盖所有政策类型。鉴于区划政策（zoning policies）是美国应用最广泛的土地用途管制手段，如此多的城市增长模型却明显未纳入该类政策，这着实令人意外。本研究以佛罗里达州南部某区域为研究对象，通过在两种情景下对元胞自动机（cellular automaton）城市增长模型SLEUTH进行校准与模拟，探究了区划政策对城市增长的影响。第一种情景将限制性农业区划政策纳入SLEUTH模型，另一种情景则未使用区划数据。拟合优度指标显示，纳入农业区划数据可提升模型的整体性能。研究结果进一步表明，农业区划政策在抑制佛罗里达州南部城市增长方面已取得一定成效。忽略区划信息不仅对SLEUTH模型的性能存在不利影响，且总体上有损城市增长建模的整体效果。