Disclosure risk in a geo-spatial setting

2026-06-11

AI Summary

This study addresses the challenge of balancing disclosure risk and data utility in the release of geospatial statistical maps, where existing risk measures are often unstable due to the modifiable areal unit problem (MAUP). To overcome this limitation, the authors propose a novel disclosure risk metric that explicitly incorporates local population density and multi-unit spatial dependencies into its formulation. The resulting framework adaptively assesses disclosure risk across varying map resolutions and zoom levels, effectively mitigating MAUP-induced instability. Empirical validation on simulated datasets mimicking real-world business locations demonstrates that the proposed method consistently and accurately reflects disclosure risk under diverse spatial partitioning and scaling scenarios, thereby substantially enhancing the robustness and practical applicability of risk assessment in geospatial data dissemination.

Abstract

Using thematic maps to publish statistical information has become a popular visualization. As is the case with all statistical publications, thematic maps also have to deal with the balance between disclosure risk and utility. However, most risk and utility measures do not take into account the spatial character of a map. Some of the proposed spatial risk measures suffer from the Modifiable Areal Unit Problem (MAUP): slightly changing regional classifications may influence the risk. Indeed, even a small translation of for example a grid may influence that risk. We propose a new risk measure that does not suffer from MAUP. Moreover, our risk is directly related to the local density of the (target) population and takes into account that often multiple units may be connected to a single location. We show the behavior of our risk measure using an example dataset of fake but realistic locations of enterprises. Our risk measure can be adapted to take into account the effect on the (perceived) risk of zooming in or out and the effect of the used resolution.

Problem

• disclosure risk
• thematic maps
• modifiable areal unit problem
• spatial data
• geospatial privacy

Innovation

• disclosure risk
• spatial data
• modifiable areal unit problem
• thematic maps
• privacy-preserving visualization