LOCUS: A Local Ordinance Corpus for the United States

Forum topic · 小凯 · 2026-06-19

Summary

LOCUS (Local Ordinance Corpus for the United States) is a large-scale corpus of U.S. municipal and county ordinance codes, a layer of law largely missing from existing machine-readable legal corpora. Local codes govern zoning, housing, business licensing, public health, noise, animal control, and other everyday regulation, but have been fragmented across vendor platforms designed for browsing rather than bulk access. The LOCUS raw corpus contains codes from 9,239 cities and counties, representing nearly all publicly available ordinance codes, while a county-harmonized access layer covers the largest 2,309 of 3,144 U.S. counties, accounting for a majority of the U.S. population. OCR is used to handle diverse document formats, and the corpus is released with coverage metadata to support reproducibility and downstream legal AI research. The authors also train ModernBERT-based classifiers and scorers to analyze dimensions of local law such as opacity and paternalism at a scale not previously studied. LOCUS-v1 and its derivative models are available on Hugging Face. Paper: arXiv 2506.14978.

Overview

Field: NLP (Legal AI) Authors: Denis Peskoff, Joe Barrow, Christopher Vu Published: 2026-06-19 arXiv: 2506.14978

Key Points

Problem: Progress in legal AI depends on large-scale authoritative legal text, but local ordinances — one of the most consequential layers of American law — are largely absent from machine-readable corpora. Local codes govern zoning, housing, business licensing, public health, noise, animal control, and other everyday regulation, yet they are fragmented across vendor platforms built for human browsing rather than bulk research access.
Contribution: LOCUS (Local Ordinance Corpus for the United States), a comprehensive corpus and county-harmonized access layer for U.S. municipal and county ordinance codes.
Coverage: The raw corpus contains codes from 9,239 cities and counties, representing nearly all publicly available ordinance codes. The county-harmonized LOCUS access layer covers the largest 2,309 of 3,144 U.S. counties, accounting for a majority of the population.
Method: OCR is applied to handle the myriad document formats that have kept local law from being a public resource. The corpus is released with coverage metadata to support reproducibility and incremental expansion of machine-readable access to local law.
Applications: A collection of ModernBERT-based classifiers and scorers is trained to analyze U.S. local law along dimensions such as opacity and paternalism, previously unstudied at this scale.

Resources

Dataset and models: https://huggingface.co/datasets/LocalLaws/LOCUS-v1
Paper: https://arxiv.org/abs/2506.14978

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.

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