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

Hugging Face Daily Papers Papers

Summary

Introduces LOCUS, a comprehensive corpus of U.S. local ordinance codes designed to enable machine-readable legal AI research, covering codes from 9,239 cities and counties with ModernBERT-based classifiers for analysis.

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: https://huggingface.co/datasets/LocalLaws/LOCUS-v1
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Source: https://huggingface.co/papers/2606.19334

Abstract

A comprehensive corpus and access layer for U.S. local ordinance codes has been developed to enable machine-readable legal AI research, addressing the lack of authoritative legal text at scale for local regulations.

Progress inlegal AIincreasingly 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 OrdinanceCorpusfor the United States - a comprehensivecorpusand county-harmonized access layer for U.S. municipal and county ordinance codes. The rawcorpus, available for release to researchers, represents nearly all publicly available municipal and county ordinance codes. The resulting rawcorpuscontains 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 useOCRto handle the myriad of document formats that have kept the law from being a public resource. We release thecorpuswith coverage metadata to supportreproducibility, downstreamlegal AIresearch, and the incremental expansion ofmachine-readable accessto local law. We train a collection ofModernBERT-based classifiersandscorersto 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: https://huggingface.co/datasets/LocalLaws/LOCUS-v1

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