Balancing geo-privacy and spatial patterns in epidemiological studies

https://doi.org/10.4081/gh.2017.573
  • 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 | dachianpig@gmail.com Center for Geographic Information Science, Research Center for Humanities and Social Sciences, Academia Sinica, Taipei, Taiwan, Province of China.

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.

Downloads

Download data is not yet available.
Published
2017-11-08
Issue
Vol 12, No 2 (2017)
Section
Original Articles
Keywords:
Geo-privacy, Geo-masking, Spatial epidemiology, D statistics
Statistics
Abstract views: 1622

PDF: 237
APPENDIX: 152
HTML: 863
Share it

PlumX Metrics

PlumX Metrics provide insights into the ways people interact with individual pieces of research output (articles, conference proceedings, book chapters, and many more) in the online environment. Examples include, when research is mentioned in the news or is tweeted about. Collectively known as PlumX Metrics, these metrics are divided into five categories to help make sense of the huge amounts of data involved and to enable analysis by comparing like with like.

How to Cite
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

Copyright (c) 2017 Chien-Chou Chen, Jen-Hsiang Chuang, Da-Wei Wang, Chien-Min Wang, Bo-Cheng Lin, Ta-Chien Chan

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.