FCE Redlands Flood Zones, 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.

城市增长模型已日益被规划师与政策制定者广泛采用，用于可视化呈现、梳理分析、认知理解乃至预测城市增长态势。然而，此类模型对政策因素的关注程度存在显著差异：部分城市增长模型即便将具体政策效应（如作为模型变量）纳入考量也极为有限，而另一些模型仅整合了部分政策而非全部。鉴于土地分区政策是美国应用最广泛的土地用途管制手段，诸多城市增长模型竟未纳入此类政策，这一点着实令人费解。本研究以佛罗里达州南部某区域为研究对象，通过在两种场景下对元胞自动机（cellular automaton）城市增长模型SLEUTH进行校准与模拟，探究了土地分区政策对城市增长的影响：其中一种场景将限制性农业分区纳入SLEUTH模型，另一种场景则未使用分区数据。拟合优度指标显示，纳入农业分区数据可提升模型性能。研究结果进一步表明，农业分区政策在一定程度上有效抑制了佛罗里达州南部的城市扩张。忽视分区信息不仅会损害SLEUTH模型的表现，对城市增长建模领域整体而言亦是如此。