Scalable and privacy-respectful interactive discovery of place semantics from human mobility traces

Abstract

Mobility diaries of a large number of people are needed for assessing transportation infrastructure and spatial development planning. Acquisition of personal mobility diaries through population surveys is a costly and error-prone endeavour. We examine an alternative approach to obtaining similar information from episodic digital traces of people’s presence in various locations, which appear when people use their mobile devices for making phone calls, accessing the Internet or posting georeferenced contents (texts, photos or videos) in social media. Having episodic traces of a person over a long time period, it is possible to detect significant (repeatedly visited) personal places and identify them as home, work or place of social activities based on temporal patterns of a person’s presence in these places. Such analysis, however, can lead to compromising personal privacy. We have investigated the feasibility of deriving place meanings and reconstructing personal mobility diaries while preserving the privacy of individuals whose data are analysed. We have devised a visual analytics approach and a set of supporting tools making such privacy-preserving analysis possible. The approach was tested in two case studies with publicly available data: simulated tracks from the VAST Challenge 2014 and real traces built from georeferenced Twitter posts.