research-article

Discovering human places of interest from multimodal mobile phone data

Authors:

Raul Montoliu,

Daniel Gatica-PerezAuthors Info & Claims

MUM '10: Proceedings of the 9th International Conference on Mobile and Ubiquitous Multimedia

Article No.: 12, Pages 1 - 10

https://doi.org/10.1145/1899475.1899487

Published: 01 December 2010 Publication History

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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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Cited By

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Vallejos SBerdun LArmentano MSchiaffino SGodoy D(2023)A Lightweight Approach for Building User Mobility ProfilesISPRS International Journal of Geo-Information10.3390/ijgi1301001113:1(11)Online publication date: 27-Dec-2023
https://doi.org/10.3390/ijgi13010011
Ibrahim AZhang HClinch SHarper S(2021)From GPS to semantic data: how and why—a framework for enriching smartphone trajectoriesComputing10.1007/s00607-021-00993-zOnline publication date: 23-Aug-2021
https://doi.org/10.1007/s00607-021-00993-z
Lutu APerino DBagnulo MFrias-Martinez EKhangosstar J(2020)A Characterization of the COVID-19 Pandemic Impact on a Mobile Network Operator TrafficProceedings of the ACM Internet Measurement Conference10.1145/3419394.3423655(19-33)Online publication date: 27-Oct-2020
https://dl.acm.org/doi/10.1145/3419394.3423655
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Index Terms

Discovering human places of interest from multimodal mobile phone data
1. Applied computing
  1. Law, social and behavioral sciences
    1. Sociology

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Reviews

Reviewer: 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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Information & Contributors

Information

Published In

MUM '10: Proceedings of the 9th International Conference on Mobile and Ubiquitous Multimedia

December 2010

239 pages

ISBN:9781450304245

DOI:10.1145/1899475

General Chairs:
Marios Angelides,
Lambros Lambrinos,
Program Chairs:
Michael Rohs,
Enrico Rukzio

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGMM: ACM Special Interest Group on Multimedia

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 December 2010

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Research-article

Funding Sources

Ministerio de Ciencia e Innovación

Conference

MUM '10

Sponsor:

FRED-Cyprus

MUM '10: 9th International Conference on Mobile and Ubiquitous Multimedia

December 1 - 3, 2010

Limassol, Cyprus

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Overall Acceptance Rate 190 of 465 submissions, 41%

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Cited By

View all

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https://doi.org/10.3390/ijgi13010011
Ibrahim AZhang HClinch SHarper S(2021)From GPS to semantic data: how and why—a framework for enriching smartphone trajectoriesComputing10.1007/s00607-021-00993-zOnline publication date: 23-Aug-2021
https://doi.org/10.1007/s00607-021-00993-z
Lutu APerino DBagnulo MFrias-Martinez EKhangosstar J(2020)A Characterization of the COVID-19 Pandemic Impact on a Mobile Network Operator TrafficProceedings of the ACM Internet Measurement Conference10.1145/3419394.3423655(19-33)Online publication date: 27-Oct-2020
https://dl.acm.org/doi/10.1145/3419394.3423655
Zhang RStanley KFuller DBell S(2020)Differentiating Population Spatial Behavior Using Representative Features of Geospatial Mobility (ReFGeM)ACM Transactions on Spatial Algorithms and Systems10.1145/33620636:1(1-25)Online publication date: 6-Feb-2020
https://dl.acm.org/doi/10.1145/3362063
Tran CVu DShin W(2020)An improved approach for estimating social POI boundaries with textual attributes on social mediaKnowledge-Based Systems10.1016/j.knosys.2020.106710(106710)Online publication date: Dec-2020
https://doi.org/10.1016/j.knosys.2020.106710
Guinea ABoytsov AMouline LTraon YPoor VHan Z(2019)Smart discovery of periodic-frequent human routines for home automationProceedings of the 16th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services10.1145/3360774.3360784(268-277)Online publication date: 12-Nov-2019
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Hong LWu JFrias-Martinez EVillarreal AFrias-Martinez VChandwani RSingh PKumar NVeeraraghavan R(2019)Characterization of internal migrant behavior in the immediate post-migration period using cell phone tracesProceedings of the Tenth International Conference on Information and Communication Technologies and Development10.1145/3287098.3287119(1-12)Online publication date: 4-Jan-2019
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Praet SMartens D(2019)Efficient Parcel Delivery by Predicting Customers’ Locations*Decision Sciences10.1111/deci.1237651:5(1202-1231)Online publication date: 23-Apr-2019
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García-Albertos PPicornell MSalas-Olmedo MGutiérrez J(2019)Exploring the potential of mobile phone records and online route planners for dynamic accessibility analysisTransportation Research Part A: Policy and Practice10.1016/j.tra.2018.02.008125(294-307)Online publication date: Jul-2019
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Mulders Dde Bodt CBjelland JPentland AVerleysen Mde Montjoye Y(2019)Inference of node attributes from social network assortativityNeural Computing and Applications10.1007/s00521-018-03967-zOnline publication date: 7-Jan-2019
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