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Estimating the Biasing Effect of Behavioural Patterns on Mobile Fitness App Data by Density-Based Clustering

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Geospatial Data in a Changing World

Part of the book series: Lecture Notes in Geoinformation and Cartography ((LNGC))

Abstract

Crowd-sourced data of high spatial and temporal resolution can provide a new basis for mobility analyses given that its various types of biases distorting the results are identified and adequately handled. In this paper, trajectory patterns that can affect the validity of mobile fitness app data are examined by means of cycling trajectories (n = 50,524) from the Helsinki Metropolitan Area, in Finland. In addition to mass events and group journeys, we evaluated the biasing effect of routes that have been repeatedly recorded by the same application user. Based on the results, repeatedly recorded commuting routes may skew fitness application data more than group patterns. Many of the changes in the frequencies and length distributions at different temporal granularities before and after extracting the ‘bias patterns’ were statistically significant. Also the skewed distribution of tracks among users (i.e. contribution inequality) became more even. The biases induced by behavioural patterns ought to be considered when evaluating the validity of fitness app data in analyses of general mobility behaviour and when designing value-added applications based on the data. Considering the trade-off between privacy and data accuracy regarding dissemination of sensitive crowd-sourced movement data, the findings emphasise the importance of preserving the possibility to detect individual-level phenomena in order to produce valid analysis results.

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Acknowledgments

We gratefully thank Sports Tracking Technologies Ltd. (currently Amer Sports Digital Services Ltd.) for providing us the workout tracking data. This work was carried out as a part of the projects MyGeoTrust and SUPRA (Revolution of Location-Based Services: Embedded data refinement in Service Processes from Massive Geospatial Datasets) funded by Tekes, the Finnish Funding Agency for Technology and Innovation (grants 40302/14 and 40261/12).

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Authors and Affiliations

  1. Department of Geoinformatics and Cartography, Finnish Geospatial Research Institute, National Land Survey of Finland, Kirkkonummi, Finland

    Cecilia Bergman & Juha Oksanen

Authors
  1. Cecilia Bergman
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  2. Juha Oksanen
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Corresponding author

Correspondence to Cecilia Bergman .

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Editors and Affiliations

  1. NLS, Finnish Geospatial Research Institute, Masala, Finland

    Tapani Sarjakoski

  2. Department of Information Systems, Universidade do Minho, Guimaraes, Portugal

    Maribel Yasmina Santos

  3. Finnish Geospatial Research Institute, National Land Survey of Finland, Helsinki, Finland

    L. Tiina Sarjakoski

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Bergman, C., Oksanen, J. (2016). Estimating the Biasing Effect of Behavioural Patterns on Mobile Fitness App Data by Density-Based Clustering. In: Sarjakoski, T., Santos, M., Sarjakoski, L. (eds) Geospatial Data in a Changing World. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-319-33783-8_12

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