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Despite the growing availability of big data in many fields, historical dataon socioevironmental phenomena are often not available due to a lack ofautomated and scalable approaches for collecting, digitizing, and assemblingthem. We have developed a data-mining method for extracting tabulated, geocodeddata from printed directories. While scanning and optical character recognition(OCR) can digitize printed text, these methods alone do not capture thestructure of the underlying data. Our pipeline integrates both page layoutanalysis and OCR to extract tabular, geocoded data from structured text. Wedemonstrate the utility of this method by applying it to scanned manufacturingregistries from Rhode Island that record 41 years of industrial land use. Theresulting spatio-temporal data can be used for socioenvironmental analyses ofindustrialization at a resolution that was not previously possible. Inparticular, we find strong evidence for the dispersion of manufacturing fromthe urban core of Providence, the state's capital, along the Interstate 95corridor to the north and south.

Mining Spatio-temporal Data on Industrialization from Historical Registries