Discovering human places of interest from multimodal mobile phone data

Abstract

In this paper, a new framework to discover places-of-interest from multimodal mobile phone data is presented. Mobile phones have been used as sensors to obtain location information from users’ real lives. Two levels of clustering are used to obtain places of interest. First, user location points are grouped using a time-based clustering technique which discovers stay points while dealing with missing location data. The second level performs clustering on the stay points to obtain stay regions. A grid-based clustering algorithm has been used for this purpose. To obtain more user location points, a client-server system has been installed on the mobile phones, which is able to obtain location information by integrating GPS, Wifi, GSM and accelerometer sensors, among others. An extensive set of experiments have been performed to show the benefits of using the proposed framework, using data from the real life of 8 users over 5 continuous months of natural phone usage.