Discovering locations and habits from human mobility data
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Discovering locations and habits from human mobility data Thiago Andrade1,2
˜ Gama1,3 · Brais Cancela1,2 · Joao
Received: 1 November 2019 / Accepted: 21 August 2020 © Institut Mines-T´el´ecom and Springer Nature Switzerland AG 2020
Abstract Human mobility patterns are associated with many aspects of our life. With the increase of the popularity and pervasiveness of smartphones and portable devices, the Internet of Things (IoT) is turning into a permanent part of our daily routines. Positioning technologies that serve these devices such as the cellular antenna (GSM networks), global navigation satellite systems (GPS), and more recently the WiFi positioning system (WPS) provide large amounts of spatio-temporal data in a continuous way (data streams). In order to understand human behavior, the detection of important places and the movements between these places is a fundamental task. That said, the proposal of this work is a method for discovering user habits over mobility data without any a priori or external knowledge. Our approach extends a density-based clustering method for spatio-temporal data to identify meaningful places the individuals’ visit. On top of that, a Gaussian mixture model (GMM) is employed over movements between the visits to automatically separate the trajectories accordingly to their key identifiers that may help describe a habit. By regrouping trajectories that look alike by day of the week, length, and starting hour, we discover the individual’s habits. The evaluation of the proposed method is made over three real-world datasets. One dataset contains high-density GPS data and the others use GSM mobile phone data with 15-min sampling rate and Google Location History data with a variable sampling rate. The results show that the proposed pipeline is suitable for this task as other habits rather than just going from home to work and vice versa were found. This method can be used for understanding person behavior and creating their profiles revealing a panorama of human mobility patterns from raw mobility data. Keywords Habits · Meaningful places · Gaussian mixture model · Pattern · Mobility · Spatio-Temporal clustering
1 Introduction Many aspects of life are related to human mobility patterns and understanding these patterns can be helpful in the exploration of the driving factors of the society. Classic social sciences are associated with the vanguard efforts to learn human mobility patterns. Sociologists have been measuring the time people spend doing different Thiago Andrade
[email protected] Brais Cancela [email protected] Jo˜ao Gama [email protected] 1
INESC TEC, Porto, Portugal
2
Universidade da Coru˜na, Coru˜na, Spain
3
University of Porto, Porto, Portugal
activities throughout the day in studies called time-use or time-budget since the nineteenth century [1]. However, with the pervasiveness of mobile devices all over the world, the methods for human mobility data collection have shifted [2, 3]. According to [4], positioning technologies that serve these devices
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