The R Journal: article published in 2020, volume 12:2

AQuadtree: an R Package for Quadtree Anonymization of Point Data PDF download
Raymond Lagonigro, Ramon Oller and Joan Carles Martori , The R Journal (2020) 12:2, pages 209-225.

Abstract 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.

Received: 2020-05-01; online 2021-01-14, supplementary material, (2.4 Kb)
CRAN packages: anonymizer, SciencePo, sdcMicro, AQuadtree, sp, dplyr, rgeos, rgdal
CRAN Task Views implied by cited CRAN packages: Spatial, Databases, ModelDeployment, SpatioTemporal


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This article and supplementary materials are licensed under a Creative Commons Attribution 4.0 International license.

@article{RJ-2021-013,
  author = {Raymond Lagonigro and Ramon Oller and Joan Carles Martori},
  title = {{AQuadtree: an R Package for Quadtree Anonymization of Point
          Data}},
  year = {2021},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2021-013},
  url = {https://doi.org/10.32614/RJ-2021-013},
  pages = {209--225},
  volume = {12},
  number = {2}
}