A context sensitive approach to anonymizing public participation GIS data : From development to the assessment of anonymization effects on data quality

Kamyar Hasanzadeh*, Anna Kajosaari, Dan Häggman, Marketta Kyttä

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)
24 Downloads (Pure)

Abstract

Use of Public Participation Geographic Information System (PPGIS) for data collection has been significantly growing over the past few years in different areas of research and practice. With the growing amount of data, there is little doubt that a potentially wider community can benefit from open access to them. Additionally, open data add to the transparency of research and can be considered as an essential feature of science. However, data anonymization is a complex task and the unique characteristics of PPGIS add to this complexity. PPGIS data often include personal spatial and non-spatial information, which essentially require different approaches for anonymization. In this study, we first identify different privacy concerns and then develop a PPGIS data anonymization strategy to overcome them for an open PPGIS data. Specifically, this article introduces a context-sensitive spatial anonymization method to protect individual home locations while maintaining their spatial resolution for mapping purposes. Furthermore, this study empirically evaluates the effects of data anonymization on PPGIS data quality. The results indicate that a satisfactory level of anonymization can be reached using this approach. Moreover, the assessment results indicate that the environmental and home range measurements as well as their intercorrelations are not significantly biased by the anonymization. However, necessary analytical measures such as use of larger spatial units is recommendable when anonymized data is used. In this study, European data protection regulations were used as the legal guidelines. However, adaptation of methods employed in this study may be also relevant to other countries where comparable regulations exist. Although specifically targeted at PPGIS data, what is discussed in this paper can be applicable to other similar spatial datasets as well.

Original languageEnglish
Article number101513
Number of pages12
JournalComputers, Environment and Urban Systems
Volume83
DOIs
Publication statusPublished - Sep 2020
MoE publication typeA1 Journal article-refereed

Keywords

  • Anonymization
  • GDPR
  • GIS
  • Open data
  • PPGIS
  • Spatial

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