用AI分析2000多个城市分区条例，发现新式规划能提升步行性与住房密度。
Zoning in American Cities: Are Reforms Making a Difference? An AI-based Analysis
- 通过NLP识别分区法规中的形式化设计特征
- 采用新规划的城市步行性更高、通勤更短、多户住宅更多
- 适合城市规划、政策研究者参考
城市在应对全球可持续性挑战中处于前沿，尤其是气候变暖加剧的问题。传统分区制度常导致功能分离，引发车辆依赖、城市蔓延与社会割裂，不利于社会与环境可持续目标。本研究考察了形式化分区代码（FBCs）的采纳及其影响，旨在推动紧凑、混合用途的城市形态。我们运用自然语言处理（NLP）技术分析了超过2000个美国人口普查指定区域的分区文件，识别出体现FBC原则的语言模式。结果表明，全国范围内广泛采纳了FBCs，区域间存在显著差异。采用FBCs的城市具有更高的容积率、更窄且一致的街道退线和更小地块。同时，这些地区步行性改善、通勤时间缩短、多户住宅占比更高。研究凸显了NLP在评估分区法规中的作用，并证实形式化分区改革对提升城市可持续性的潜力。
原文摘要 · Abstract (English)
Cities are at the forefront of addressing global sustainability challenges, particularly those exacerbated by climate change. Traditional zoning codes, which often segregate land uses, have been linked to increased vehicular dependence, urban sprawl, and social disconnection, undermining broader social and environmental sustainability objectives. This study investigates the adoption and impact of form-based codes (FBCs), which aim to promote sustainable, compact, and mixed-use urban forms as a solution to these issues. Using Natural Language Processing (NLP) techniques, we analyzed zoning documents from over 2000 U.S. census-designated places to identify linguistic patterns indicative of FBC principles. Our findings reveal widespread adoption of FBCs across the country, with notable variations within regions. FBCs are associated with higher floor-to-area ratios, narrower and more consistent street setbacks, and smaller plots. We also find that places with FBCs have improved walkability, shorter commutes, and a higher share of multi-family housing. Our findings highlight the utility of NLP for evaluating zoning codes and underscore the potential benefits of form-based zoning reforms for enhancing urban sustainability.
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