Methods for deriving and calibrating privacy-preserving heat maps from mobile sports tracking application data. (October 2015)

• Record Type: Journal Article
• Authors: Oksanen, Juha; Bergman, Cecilia; Sainio, Jani; Westerholm, Jan
• Is Part Of: Journal of transport geography. Volume 48(2015)
• Page Start: 135
• Page End: 144
• Publication Date: 2015-10
• Subjects: Cycling -- Location-based services (LBSs) -- Urban planning -- Privacy -- Big data -- GIS; Transportation -- Periodicals; Telecommunication -- Periodicals; Transport -- Périodiques; Télécommunications -- Périodiques; Telecommunication; Transportation; Periodicals; 388
• DOI: 10.1016/j.jtrangeo.2015.09.001
• Languages: English
• ISSNs: 0966-6923

Abstract

Abstract: Utilization of movement data from mobile sports tracking applications is affected by its inherent biases and sensitivity, which need to be understood when developing value-added services for, e.g., application users and city planners. We have developed a method for generating a privacy-preserving heat map with user diversity (ppDIV), in which the density of trajectories, as well as the diversity of users, is taken into account, thus preventing the bias effects caused by participation inequality. The method is applied to public cycling workouts and compared with privacy-preserving kernel density estimation (ppKDE) focusing only on the density of the recorded trajectories and privacy-preserving user count calculation (ppUCC), which is similar to the quadrat-count of individual application users. An awareness of privacy was introduced to all methods as a data pre-processing step following the principle of k-Anonymity. Calibration results for our heat maps using bicycle counting data gathered by the city of Helsinki are good (R 2 > 0.7) and raise high expectations for utilizing heat maps in a city planning context. This is further supported by the diurnal distribution of the workouts indicating that, in addition to sports-oriented cyclists, many utilitarian cyclists are tracking their commutes. However, sports tracking data can only enrich official in-situ counts with its high spatio-temporal resolution and coverage, not replace them. Highlights: We developed a method for deriving privacy-preserving heat-maps from mobility data. In addition, the method resolves the bias issues related to participation inequality. We demonstrate how heat maps can be calibrated with in-situ counting data. For planning, sports tracking data can enrich official in-situ counts, but not replace them.