Geoprivacy Open Data – A New FAIR Personal-Level Mobility Dataset for Research and Innovation

Abstract This paper presents Geoprivacy Open Data - an open personallevel movement dataset collected using the Geoprivacy platform. The Geoprivacy platform allows cyclists, runners and pedestrians to contribute GPS and other GNSS tracking data for scientific research and for free use in the open data repository. To ensure compliance with GDPR, Geoprivacy Open Data is sanitized using five privacy protection methods: regional and population density -based filtering, removal of explicit identifiers, sitedependent trajectory truncation, and time shifting. The paper provides details on the accumulation and spatial distribution of the data, and outlines potential uses given its spatio-temporal characteristics.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-07-04
DOI
https://doi.org/10.5281/zenodo.15352447
Primary Topic
Advanced Statistical Methods and Models
Type
article
Field-Weighted Citation Impact
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article

Geoprivacy Open Data – A New FAIR Personal-Level Mobility Dataset for Research and Innovation

Zenodo (CERN European Organization for Nuclear Research)
Advanced Statistical Methods and Models
article

Geoprivacy Open Data – A New FAIR Personal-Level Mobility Dataset for Research and Innovation

article en

Abstract

Abstract This paper presents Geoprivacy Open Data - an open personallevel movement dataset collected using the Geoprivacy platform. The Geoprivacy platform allows cyclists, runners and pedestrians to contribute GPS and other GNSS tracking data for scientific research and for free use in the open data repository. To ensure compliance with GDPR, Geoprivacy Open Data is sanitized using five privacy protection methods: regional and population density -based filtering, removal of explicit identifiers, sitedependent trajectory truncation, and time shifting. The paper provides details on the accumulation and spatial distribution of the data, and outlines potential uses given its spatio-temporal characteristics.

Zenodo (CERN European Organization for Nuclear Research)
Finnish Geospatial Research Institute (FI), Technische Universität Dresden (DE)
Industry, innovation and infrastructure
Openalex Percentile: Top 55%
Advanced Statistical Methods and Models
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