Balancing geo-privacy and spatial patterns in epidemiological studies

DOI https://doi.org/10.4081/gh.2017.573

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Published: Nov 8, 2017
Original Articles

Issue
Vol 12, No 2 (2017)
Keywords
Geo-privacy, Geo-masking, Spatial epidemiology, D statistics
Statistics
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Chen, C.-C., Chuang, J.-H., Wang, D.-W., Wang, C.-M., Lin, B.-C., & Chan, T.-C. (2017). Balancing geo-privacy and spatial patterns in epidemiological studies. Geospatial Health, 12(2). https://doi.org/10.4081/gh.2017.573
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Chien-Chou Chen
Center for Geographic Information Science, Research Center for Humanities and Social Sciences, Academia Sinica, Taipei, Taiwan, Province of China.
Jen-Hsiang Chuang
Centers for Disease Control, Taipei, Taiwan, Province of China.
Da-Wei Wang
Institute of Information Science, Academia Sinica, Taipei, Taiwan, Province of China.
Chien-Min Wang
Center for Geographic Information Science, Research Center for Humanities and Social Sciences, Academia Sinica, Taipei; Department of Geography, National Taiwan University, Taipei, Taiwan, Province of China.
Bo-Cheng Lin
Center for Geographic Information Science, Research Center for Humanities and Social Sciences, Academia Sinica, Taipei, Taiwan, Province of China.
Ta-Chien Chan *
Center for Geographic Information Science, Research Center for Humanities and Social Sciences, Academia Sinica, Taipei, Taiwan, Province of China.
(*) Corresponding Author:
Ta-Chien Chan | dachianpig@gmail.com

Abstract

To balance the protection of geo-privacy and the accuracy of spatial patterns, we developed a geo-spatial tool (GeoMasker) intended to mask the residential locations of patients or cases in a geographic information system (GIS). To elucidate the effects of geo-masking parameters, we applied 2010 dengue epidemic data from Taiwan testing the tool’s performance in an empirical situation. The similarity of pre- and post-spatial patterns was measured by D statistics under a 95% confidence interval. In the empirical study, different magnitudes of anonymisation (estimated Kanonymity ≥10 and 100) were achieved and different degrees of agreement on the pre- and post-patterns were evaluated. The application is beneficial for public health workers and researchers when processing data with individuals’ spatial information.

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