[2606.19334] Freeing the Law with LOCUS: A Local Ordinance Corpus for the United States

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Computer Science > Computation and Language

arXiv:2606.19334 (cs)

[Submitted on 17 Jun 2026]

Title:Freeing the Law with LOCUS: A Local Ordinance Corpus for the United States

Authors:Denis Peskoff, Joe Barrow, Christopher Vu, Diag Davenport<br>View a PDF of the paper titled Freeing the Law with LOCUS: A Local Ordinance Corpus for the United States, by Denis Peskoff and 3 other authors

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Abstract:Progress in legal AI increasingly depends on access to authoritative legal text at scale. Yet one of the most consequential layers of American law remains largely absent from existing machine-readable corpora: local ordinances. Local codes govern zoning, housing, business licensing, public health, noise, animal control, and many other domains of everyday regulation, but they are fragmented across vendor platforms designed for human browsing rather than bulk research access. We introduce LOCUS - the Local Ordinance Corpus for the United States - a comprehensive corpus and county-harmonized access layer for U.S. municipal and county ordinance codes. The raw corpus, available for release to researchers, represents nearly all publicly available municipal and county ordinance codes. The resulting raw corpus contains codes from 9,239 cities and counties. A smaller county-harmonized LOCUS access layer provides coverage for the largest 2,309 of 3,144 U.S. counties, accounting for a majority of the population. We use OCR to handle the myriad of document formats that have kept the law from being a public resource. We release the corpus with coverage metadata to support reproducibility, downstream legal AI research, and the incremental expansion of machine-readable access to local law. We train a collection of ModernBERT-based classifiers and scorers to facilitate analyzing U.S. local law among several dimensions, such as opacity and paternalism, that have not previously been studied at this scale. LOCUS-v1 and its derivative models are available at: this https URL

Comments:<br>14 pages, 6 figures

Subjects:

Computation and Language (cs.CL); Computers and Society (cs.CY); Machine Learning (cs.LG)

Cite as:<br>arXiv:2606.19334 [cs.CL]

(or<br>arXiv:2606.19334v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2606.19334

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arXiv-issued DOI via DataCite

Submission history<br>From: Denis Peskoff [view email]<br>[v1]<br>Wed, 17 Jun 2026 17:58:22 UTC (3,199 KB)

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