Automating Analysis of Zoning Codes for the National Zoning Atlas

Cornell University, Ithaca NY

Investigators

Abstract

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.