{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/mining-spatio-temporal-data-on","title":"Mining Spatio-temporal Data on Industrialization from Historical Registries","arxiv_id":"1612.00992","date":"2016-12-03","proceeding":null,"authors":["David Berenbaum","Dwyer Deighan","Thomas Marlow","Ashley Lee","Scott Frickel","Mark Howison"],"abstract":"Despite the growing availability of big data in many fields, historical data\non socioevironmental phenomena are often not available due to a lack of\nautomated and scalable approaches for collecting, digitizing, and assembling\nthem. We have developed a data-mining method for extracting tabulated, geocoded\ndata from printed directories. While scanning and optical character recognition\n(OCR) can digitize printed text, these methods alone do not capture the\nstructure of the underlying data. Our pipeline integrates both page layout\nanalysis and OCR to extract tabular, geocoded data from structured text. We\ndemonstrate the utility of this method by applying it to scanned manufacturing\nregistries from Rhode Island that record 41 years of industrial land use. The\nresulting spatio-temporal data can be used for socioenvironmental analyses of\nindustrialization at a resolution that was not previously possible. In\nparticular, we find strong evidence for the dispersion of manufacturing from\nthe urban core of Providence, the state's capital, along the Interstate 95\ncorridor to the north and south.","url_abs":"http://arxiv.org/abs/1612.00992v1","url_pdf":"http://arxiv.org/pdf/1612.00992v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"mining-spatio-temporal-data-on","repo_url":"https://bitbucket.org/brown-data-science/georeg","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"optical-character-recognition","task_name":"Optical Character Recognition"},{"task_slug":"optical-character-recognition","task_name":"Optical Character Recognition (OCR)"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}