Mining Human Mobility Data to Discover Locations and Habits

TL;DR

This paper introduces a novel density-based clustering and Gaussian Mixture Model approach to automatically identify significant places and habitual movement patterns from mobile device spatio-temporal data, without prior knowledge.

Contribution

It presents a new method combining density-based clustering and GMM to discover user habits and meaningful locations from raw mobility data.

Findings

Successfully identified many unique user habits

Effective on both high-density GPS and coarse GSM datasets

Provides insights into human mobility patterns

Abstract

Many aspects of life are associated with places of human mobility patterns and nowadays we are facing an increase in the pervasiveness of mobile devices these individuals carry. 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. Therefore, detecting significant places and the frequency of movements between them is fundamental to understand human behavior. In this paper, we propose a method for discovering user habits without any a priori or external knowledge by introducing a density-based clustering for spatio-temporal data to identify meaningful places and by applying a Gaussian Mixture Model (GMM) over the set of meaningful places to identify the representations of individual habits. To evaluate the proposed method we use two real-world datasets. One dataset contains high-density GPS data and the other one contains GSM mobile phone data in a coarse representation. The results show that the proposed method is suitable for this task as many unique habits were identified. This can be used for understanding users' behavior and to draw their characterizing profiles having a panorama of the mobility patterns from the data.