Estimating the biasing effect of behavioural patterns on mobile fitness app data by density-based clustering

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.