Raul Montoliu
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.
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Index Terms
- Discovering human places of interest from multimodal mobile phone data
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Reviews
Goran Trajkovski
Using mobile phone global positioning systems (GPSs), location data for individuals can be gathered and analyzed for various purposes. Using two-level clustering, Montoliu and Gatica-Perez gather this data for the purpose of investigating individuals' places of interest. Data for eight people over a period of five months revealed their places of interest. About 63 percent of their daily locations were captured. In this setting, the subjects used phones with GPSs-there was no need for a beacon location database. The authors introduce an algorithm that clusters the GPS data into stay points and stay regions. The geographical and temporal data was analyzed in a client-server mobile application. Places can be discovered, remembered, and forgotten-the algorithm takes care of that. Also, parameters can be defined in the grid-based clustering technique to make the algorithm perform on other data points. The places of interest discovered were juxtaposed against user statements. The so-called experiments are testimonies of tasks completed within the data collection and data crunching process. Compared to two similar methods, this approach seems to perform the best. The paper is easy to read. The idea and method are easy to understand and follow. Perhaps the next step is implementation in a sensory system context or a geo-interest discovery social application. Online Computing Reviews Service
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