Automating Analysis of Zoning Codes for the National Zoning Atlas

本项目研究自然语言处理技术，用于从分区代码文本中自动提取信息。这项研究的结果有助于更好的分区信息，以及数据收集，处理和映射工具，照亮分区，最重要的，但研究不足，政府权力影响我们的经济和社会之一。首先，该项目汇集了数百个分区代码，手动审查它们，并将具体的监管信息纳入公众可访问的国家分区地图集。然后，研究团队开发自然语言处理方法，将这些分区代码转换为结构化数据，并通过将自动化过程的输出与手动结果进行比较来确保数据准确性。汇编的数据和开发的工具可以促进具体的，可操作的见解，并解锁有关分区对住房可用性，交通系统，环境，经济机会，教育机会和我们的食品供应的影响的二次研究。具体而言，该研究考察了当代自然语言处理技术是否可以成功地破译复杂的本地分区代码。该团队收集有关分区代码的信息，并使用这些信息来训练算法，以提取分区代码和法规的关键要素。输出数据由分区变量组成，例如分区名称和每个地区的特定规则，可供分析和映射。该团队评估了该算法的准确性和精确度，以对抗人类对分区代码的审查。如果成功，所产生的数据工具可以为美国各地的当地政策制定者，研究人员和倡导者提供机会，以减少手动工作的方式比较他们所在州或全国范围内的分区代码和流程，并为改善决策，参与式规划和区域合作提供高质量的数据。此外，该方法可以指导未来有兴趣为其他类型的行政法律文本（包括公路安全手册、建筑规范、城市规划等）创建自然语言处理模型的研究人员。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

This project investigates natural language processing techniques for automating information extraction from zoning code text. The outcome of this research contributes better zoning information, as well as data collection, processing, and mapping tools, to illuminate zoning, one of the most important, yet understudied, governmental powers impacting our economy and society. First, the project assembles hundreds of zoning codes, manually reviews them, and incorporates specific regulatory information into the publicly-accessible National Zoning Atlas. Then, the research team develops natural language processing methods to transform these zoning codes to structured data and ensure data accuracy by comparing the outputs of the automated process to the manual results. The compiled data and developed tools can facilitate concrete, actionable insights and unlock secondary research about zoning’s impact on housing availability, transportation systems, the environment, economic opportunity, educational opportunity, and our food supply. Specifically, the research examines whether contemporary natural language processing techniques can successfully decipher complex local zoning codes. The team collects information about zoning codes and uses this information to train an algorithm for extracting key elements of zoning codes and regulations. The output data consist of zoning variables, such as zoning district names and specific rules for each district, ready for analysis and mapping. The team assesses the algorithm’s performance for accuracy and precision against human-performed reviews of zoning codes. If successful, the resulting data tool can offer local policymakers, researchers, and advocates across the U.S. the opportunity to compare zoning codes and processes in their state or nationwide with decreased manual effort, and to produce quality data for improved decision making, participatory planning, and regional collaboration. Moreover, the approach can guide future researchers interested in creating natural language processing models for other types of administrative law texts, including highway safety manuals, building codes, city plans, and more.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.