Citation

Chen CC, Chuang JH, Wang DW, Wang CM, Lin BC, Chan TC. Geospat. Health 2017; 12(2): e573.

Affiliation

Center for Geographic Information Science, Research Center for Humanities and Social Sciences, Academia Sinica, Taipei. tojoechen@gmail.com.

Copyright

(Copyright © 2017, Global Network for Geospatial Health, Publisher University of Naples)

DOI

10.4081/gh.2017.573

PMID

29239562

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.

Language: en

Keywords

D statistics; Geo-masking; Geo-privacy; Spatial epidemiology