Freeing the Law with LOCUS: A Local Ordinance Corpus for the United States
2026-06-17 • Computation and Language
Computation and LanguageComputers and SocietyMachine Learning
AI summaryⓘ
The authors created LOCUS, a big collection of local laws from thousands of U.S. cities and counties that were hard to get before because they were spread out and in different formats. They used technology like OCR to digitize these local rules, making them easier for computers to read all at once. They also made special computer programs to help study these laws for things like how clear or strict they are. This resource helps researchers better analyze local laws across the country.
Local OrdinancesCorpusOCR (Optical Character Recognition)Legal AIMunicipal CodesCounty CodesMachine-readable TextBERT ClassifierLegal Text MiningData Harmonization
Authors
Denis Peskoff, Joe Barrow, Christopher Vu, Diag Davenport
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