research-article

MoveSense: spatio-temporal Clustering Technique for Discovering Residence Change in Mobile Phone Data

Authors:

Dunstan Matekenya,

Ryosuke Shibasaki,

Kaoru SezakiAuthors Info & Claims

IWGS '15: Proceedings of the 6th ACM SIGSPATIAL International Workshop on GeoStreaming

Pages 59 - 68

https://doi.org/10.1145/2833165.2833175

Published: 03 November 2015 Publication History

Abstract

The ability to detect when a person change their place of residence in a city or country is vitally important not just for urban planning but also for business intelligence. Although there are traditional approaches such as population census to collect this type of data, they have serious drawbacks. Thanks to the ubiquity of mobile phones, researchers have demonstrated that data generated from cellular network such as Call Detailed Records(CDR) can provide similar information at a relatively lower cost and higher temporal resolution.

In this paper, we investigate two research questions: first, whether we can reliably discover a person's residence change from unlabeled CDR data. Second, if we can develop an algorithm that can autamatically carry out this task. To this end, we first formulate the residence change discovery problem by learning from population census approach and then propose a sequential spatio-temporal clustering technique-MoveSense to solve this problem. We use a large scale CDR dataset with over 3.5 billion call records and 16 million unique users to conduct experiments to validate our technique. We find that across the three categories of test datasets, the technique performed well with average detection rate of 71 percent, 68 percent and 72 percent.

References

[1]

R. Becker, R. Cáceres, K. Hanson, S. Isaacman, J. M. Loh, M. Martonosi, J. Rowland, S. Urbanek, A. Varshavsky, and C. Volinsky. Human mobility characterization from cellular network data. Communications of the ACM, 56(1):74--82, 2013.

Digital Library

[2]

J. E. Blumenstock. Inferring patterns of internal migration from mobile phone call records: evidence from rwanda. Information Technology for Development, 18(2):107--125, 2012.

[3]

F. Calabrese, G. Di Lorenzo, L. Liu, and C. Ratti. Estimating origin-destination flows using mobile phone location data. IEEE Pervasive Computing, pages 36--44, 2011.

Digital Library

[4]

F. Calabrese, G. Di Lorenzo, and C. Ratti. Human mobility prediction based on individual and collective geographical preferences. In Proceedings of the IEEE International Conference on Intelligent Transportation Systems, pages 312--317, 2010.

[5]

X. Cao, G. Cong, and C. S. Jensen. Mining significant semantic locations from gps data. Proceedings of the VLDB Endowment, 3(1-2):1009--1020, 2010.

Digital Library

[6]

M. L. Damiani, H. Issa, and F. Cagnacci. Extracting stay regions with uncertain boundaries from gps trajectories: a case study in animal ecology. In 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2014). ACM, 2014.

Digital Library

[7]

P. Deville, C. Linard, S. Martin, M. Gilbert, F. R. Stevens, A. E. Gaughan, V. D. Blondel, and A. J. Tatem. Dynamic population mapping using mobile phone data. Proceedings of the National Academy of Sciences, 111(45):15888--15893, 2014.

[8]

U. N. S. Division. Principles and recommendations for population and housing censuses. United Nations Publications, 2008.

[9]

E. Eskin, A. Arnold, M. Prerau, L. Portnoy, and S. Stolfo. A geometric framework for unsupervised anomaly detection. In Applications of data mining in computer security, pages 77--101. Springer, 2002.

[10]

M. C. Gonzalez, C. A. Hidalgo, and A.-L. Barabasi. Understanding individual human mobility patterns. Nature, 453(7196):779--782, 2008.

[11]

J. A. Hartigan. Clustering Algorithms. John Wiley & Sons, Inc., New York, NY, USA, 99th edition, 1975.

Digital Library

[12]

S. Isaacman, R. Becker, R. Cáceres, S. Kobourov, M. Martonosi, J. Rowland, and A. Varshavsky. Identifying important places in people's lives from cellular network data. In Pervasive Computing, pages 133--151. Springer, 2011.

Digital Library

[13]

W. LJXu-DOEVE. Measurement of internal and international migration.

[14]

V. Manual. Methods of measuring internal migration. Population Studies, (47), 1970.

[15]

D. Matekenya, I. Masaki, T. Yoshito, H. Teerayut, S. Ryosuke, and S. Kaoru. Event detection in individuals using mobile phone traces. In Proceedings of the 2014 IEICE Communications Society Conference,vol. BS-6, 2014.

[16]

A. T. Palma, V. Bogorny, B. Kuijpers, and L. O. Alvares. A clustering-based approach for discovering interesting places in trajectories. In Proceedings of the 2008 ACM symposium on Applied computing, pages 863--868. ACM, 2008.

Digital Library

[17]

L. Portnoy. Intrusion detection with unlabeled data using clustering. 2000.

[18]

F. J. WILLEKENS. Probability models of migration: complete and incomplete data. Southern African Journal of Demography, pages 31--43, 1997.

[19]

Y. Zheng, L. Zhang, X. Xie, and W.-Y. Ma. Mining interesting locations and travel sequences from gps trajectories. In Proceedings of the 18th international conference on World wide web, pages 791--800. ACM, 2009.

Digital Library

Cited By

Allahviranloo MDe Castaing LRehmann J(2017)Mobility knowledge discovery to generate activity pattern trajectories2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC)10.1109/ITSC.2017.8317737(1-8)Online publication date: Oct-2017
https://doi.org/10.1109/ITSC.2017.8317737

Index Terms

MoveSense: spatio-temporal Clustering Technique for Discovering Residence Change in Mobile Phone Data
1. Information systems
  1. Information systems applications

Recommendations

Urban Computing: Concepts, Methodologies, and Applications
Special Section on Urban Computing

Urbanization's rapid progress has modernized many people's lives but also engendered big issues, such as traffic congestion, energy consumption, and pollution. Urban computing aims to tackle these issues by using the data that has been generated in ...
Human Mobility Modeling on Metropolitan Scale Based on Multiple Cellphone Networks: Poster Abstract
IoTDI '17: Proceedings of the Second International Conference on Internet-of-Things Design and Implementation

Modeling human mobility patterns from CDR(Call Detail Record) data is an efficient way to understand the effects of human movements on transportation, society and the environment. Previous human mobility models are focused on single cellphone network ...
Network Analysis of Internal Migration in Austria
Regular Papers

Human migration, and urbanization as its direct consequence, are among the crucial topics in regional and national governance. People’s migration and mobility flows make a network structure, with large cities acting as hubs and smaller settlements as ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

IWGS '15: Proceedings of the 6th ACM SIGSPATIAL International Workshop on GeoStreaming

November 2015

102 pages

ISBN:9781450339711

DOI:10.1145/2833165

Copyright © 2015 ACM.

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]

Sponsors

SIGSPATIAL: ACM Special Interest Group on Spatial Information

In-Cooperation

SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 November 2015

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

SIGSPATIAL'15

Sponsor:

SIGSPATIAL

SIGSPATIAL'15: 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 3 - 6, 2015

WA, Bellevue, USA

Acceptance Rates

Overall Acceptance Rate 7 of 9 submissions, 78%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
104
Total Downloads

Downloads (Last 12 months)1
Downloads (Last 6 weeks)0

Reflects downloads up to 26 Jul 2024

Other Metrics

View Author Metrics

Citations

Cited By

Allahviranloo MDe Castaing LRehmann J(2017)Mobility knowledge discovery to generate activity pattern trajectories2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC)10.1109/ITSC.2017.8317737(1-8)Online publication date: Oct-2017
https://doi.org/10.1109/ITSC.2017.8317737

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents