How Digital and Computational Demography Make Hard-to-Reach Population Visible: Evidence from a Two-Decade Research on the Population of Taiwan Indigenous Peoples

Ji-Ping Lin, Academia Sinica Taiwan

This research focuses on determinants of digital and computational demography, i.e., the role of scientific computing, data science, and open science in the context of extracting valuable information embedded in source individual data, enriching the extracted information through the processes of cleaning, cleansing, crunching, reorganizing, and reshaping the source data. The new research methods and techniques enable us to build a number of big open population data that help overcome long-term research data shortages in hard-to-reach population and ethical/legal issues. In addition, the research has developed a simple but fast geocoding method and has successfully built longitudinally linked register big data of population dynamics, with individual-level spatial information (point) and temporal information (monthly) being integrated. The research aims are as follows: (1) to demonstrate techniques in record linkage and highly precise address-matching geocoding that allow us to enrich temporal and spatial information in big data; (2) to highlight how data engineering and data sharing enable us to build and integrate open data repositories systematically and automatically; (3) To demonstrate the role of building open data in promoting online crowd collaboration and making hard-to-reach population visible to the real world, using the research on Taiwan indigenous peoples as an example.

Keywords: Digital and computational demography, Linked data sets , Data and Methods, The Demography of Indigenous Populations

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