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An AI-based analysis of zoning reforms in US cities

Nature Cities volume 2, pages 304–315 (2025)Cite this article

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Abstract

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 techniques, we analyzed zoning documents from over 2,000 United States 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 multifamily housing. Our findings highlight the utility of natural language processing for evaluating zoning codes and underscore the potential benefits of form-based zoning reforms for enhancing urban sustainability.

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Fig. 1: Thematic differences between FBCs and other zoning documents.
Fig. 2: Key themes in zoning codes by FBC alignment.
Fig. 3: Relationship between FBC similarity and regulatory stringency.
Fig. 4: FBC similarity in the USA.
Fig. 5: OLS estimates of high FBC and urban form, mobility, and housing measures.

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Data availability

We have provided information on all publicly available data used in our analysis in the Methods. The parcel data from Regrid by LOVELAND Technologies are confidential and cannot be shared publicly.

Code availability

The code to replicate the results in the paper is available via GitHub at https://github.com/ariannasm/Zoning.

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Acknowledgments

We are grateful to A. Saiz for helpful early discussions on the topic and to the Mansueto Institute for Urban Innovation at the University of Chicago for their support.

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Authors and Affiliations

  1. University of Chicago, Chicago, IL, USA

    Arianna Salazar-Miranda & Emily Talen

  2. School of the Environment, Yale, New Haven, CT, USA

    Arianna Salazar-Miranda

Authors
  1. Arianna Salazar-Miranda
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  2. Emily Talen
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A.S-M. designed research and performed data analysis. A.S-M. and E.T wrote the paper.

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Correspondence to Arianna Salazar-Miranda.

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Nature Cities thanks Manuel Bickel and Catherine Brinkley for their contribution to the peer review of this work.

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Salazar-Miranda, A., Talen, E. An AI-based analysis of zoning reforms in US cities. Nat Cities 2, 304–315 (2025). https://doi.org/10.1038/s44284-025-00214-0

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