Paper Overview
Field: NLP Authors: Denis Peskoff, Joe Barrow, Christopher Vu Published: 2026-06-19 arXiv: 2506.14978
Summary
Progress in legal AI increasingly depends on access to authoritative legal text at scale, yet one of the most consequential layers of American law—local ordinances—remains largely absent from existing machine-readable corpora. 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.
This paper introduces LOCUS (Local Ordinance Corpus for the United States), a comprehensive corpus and county-harmonized access layer for U.S. municipal and county ordinance codes. Key contributions include:
- Raw corpus: ordinance codes from 9,239 cities and counties, representing nearly all publicly available municipal and county codes.
- County-harmonized access layer: coverage of the largest 2,309 of 3,144 U.S. counties, accounting for a majority of the population.
- OCR pipeline: handles the myriad document formats that have kept local law from being a public resource.
- Coverage metadata: released with the corpus to support reproducibility, downstream legal AI research, and incremental expansion of machine-readable access to local law.
- Analysis models: a collection of ModernBERT-based classifiers and scorers facilitating analysis of U.S. local law along dimensions such as opacity and paternalism, which have not previously been studied at this scale.
Original 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: https://huggingface.co/datasets/LocalLaws/LOCUS-v1
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*Collected automatically on 2026-06-19*