{"slug": "the-largest-open-database-of-local-laws-in-the-us", "title": "The largest open database of local laws in the US", "summary": "Researchers released LOCUS, the largest open database of U.S. local laws, containing codes from 9,239 cities and counties. The corpus aims to fill a gap in machine-readable legal text for AI research, covering zoning, housing, and other regulations. It includes OCR-processed documents and classifiers for analyzing legal dimensions like opacity and paternalism.", "body_md": "# Computer Science > Computation and Language\n\n[Submitted on 17 Jun 2026]\n\n# Title:Freeing the Law with LOCUS: A Local Ordinance Corpus for the United States\n\n[View PDF](/pdf/2606.19334)\n\n[HTML (experimental)](https://arxiv.org/html/2606.19334v1)\n\nAbstract: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]\n\n### Current browse context:\n\ncs.CL\n\n### References & Citations\n\nLoading...\n\n# Bibliographic and Citation Tools\n\nBibliographic Explorer\n\n*(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))\nConnected Papers\n\n*(*[What is Connected Papers?](https://www.connectedpapers.com/about))\nLitmaps\n\n*(*[What is Litmaps?](https://www.litmaps.co/))\nscite Smart Citations\n\n*(*[What are Smart Citations?](https://www.scite.ai/))# Code, Data and Media Associated with this Article\n\nalphaXiv\n\n*(*[What is alphaXiv?](https://alphaxiv.org/))\nCatalyzeX Code Finder for Papers\n\n*(*[What is CatalyzeX?](https://www.catalyzex.com))\nDagsHub\n\n*(*[What is DagsHub?](https://dagshub.com/))\nGotit.pub\n\n*(*[What is GotitPub?](http://gotit.pub/faq))\nHugging Face\n\n*(*[What is Huggingface?](https://huggingface.co/huggingface))\nScienceCast\n\n*(*[What is ScienceCast?](https://sciencecast.org/welcome))# Demos\n\n# Recommenders and Search Tools\n\nInfluence Flower\n\n*(*[What are Influence Flowers?](https://influencemap.cmlab.dev/))\nCORE Recommender\n\n*(*[What is CORE?](https://core.ac.uk/services/recommender))# arXivLabs: experimental projects with community collaborators\n\narXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.\n\nBoth individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.\n\nHave an idea for a project that will add value for arXiv's community? [ Learn more about arXivLabs](https://info.arxiv.org/labs/index.html).", "url": "https://wpnews.pro/news/the-largest-open-database-of-local-laws-in-the-us", "canonical_source": "https://arxiv.org/abs/2606.19334", "published_at": "2026-06-21 02:48:39+00:00", "updated_at": "2026-06-21 03:07:21.181870+00:00", "lang": "en", "topics": ["natural-language-processing", "large-language-models", "ai-research", "ai-tools"], "entities": ["LOCUS", "ModernBERT", "U.S. counties", "U.S. municipal codes", "arXiv"], "alternates": {"html": "https://wpnews.pro/news/the-largest-open-database-of-local-laws-in-the-us", "markdown": "https://wpnews.pro/news/the-largest-open-database-of-local-laws-in-the-us.md", "text": "https://wpnews.pro/news/the-largest-open-database-of-local-laws-in-the-us.txt", "jsonld": "https://wpnews.pro/news/the-largest-open-database-of-local-laws-in-the-us.jsonld"}}