The demand for precise data for analytical purposes grows rapidly among the research community and decision makers as more geographic information is being collected. Laws protecting data privacy are being enforced to prevent data disclosure. Statistical institutes and agencies need methods to preserve confidentiality while maintaining accuracy when disclosing geographic data. In this paper we present the AQuadtree package, a software intended to produce and deal with official spatial data making data privacy and accuracy compatible. The lack of specific methods in R to anonymize spatial data motivated the development of this package, providing an automatic aggregation tool to anonymize point data. We propose a methodology based on hierarchical geographic data structures to create a varying size grid adapted to local area population densities. This article gives insights and hints for implementation and usage. We hope this new tool may be helpful for statistical offices and users of official spatial data.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2021-013.zip
anonymizer, SciencePo, sdcMicro, AQuadtree, sp, dplyr, rgeos, rgdal
Spatial, Databases, ModelDeployment, SpatioTemporal
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For attribution, please cite this work as
Lagonigro, et al., "The R Journal: AQuadtree: an R Package for Quadtree Anonymization of Point Data", The R Journal, 2021
BibTeX citation
@article{RJ-2021-013,
author = {Lagonigro, Raymond and Oller, Ramon and Martori, Joan Carles},
title = {The R Journal: AQuadtree: an R Package for Quadtree Anonymization of Point Data},
journal = {The R Journal},
year = {2021},
note = {https://doi.org/10.32614/RJ-2021-013},
doi = {10.32614/RJ-2021-013},
volume = {12},
issue = {2},
issn = {2073-4859},
pages = {209-225}
}