research-article

Temporal decomposition and semantic enrichment of mobility flows

Authors:

Alexei PozdnoukhovAuthors Info & Claims

LBSN '13: Proceedings of the 6th ACM SIGSPATIAL International Workshop on Location-Based Social Networks

Pages 34 - 43

https://doi.org/10.1145/2536689.2536806

Published: 05 November 2013 Publication History

Abstract

Mobility data has increasingly grown in volume over the past decade as localisation technologies for capturing mobility flows have become ubiquitous. Novel analytical approaches for understanding and structuring mobility data are now required to support the backend of a new generation of space-time GIS systems. It is increasingly important as GIS is becoming a decision support platform for operations in fleet management, urban data analysis and related applications. This paper applies the machine learning method of probabilistic topic modelling for semantic enrichment of mobility data recorded in terms of trip counts by using geo-referenced social media data. It further explores the questions of causality and correlation, as well as predictability of the obtained semantic decompositions of mobility flows on a real dataset from a bike sharing network.

References

[1]

G. L. Andrienko, N. V. Andrienko, C. Hurter, S. Rinzivillo, and S. Wrobel. From movement tracks through events to places: Extracting and characterizing significant places from mobility data. In IEEE VAST, pages 161--170. IEEE, 2011.

[2]

D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. J. Mach. Learn. Res., 3:993--1022, 2003.

[3]

P. Borgnat, P. Abry, P. Flandrin, C. Robardet, J.-B. Rouquier, and E. Fleury. Shared bicycles in a city: A signal processing and data analysis perspective. Advances in Complex Systems, 14(03):415--438, 2011.

[4]

L. Breiman. Statistical modeling: The two cultures. Statistical Science, 16:199--231, 2001.

[5]

Z. Cheng, J. Caverlee, K. Lee, and D. Z. Sui. Exploring millions of footprints in location sharing services. In Proceedings of the 5th International Conference on Weblogs and Social Media. AAAI, 2011.

[6]

Bikeshare and 4sq datasets. https://github.com/ccoffey/bikeshare-decomposition (last accessed 30.06.2013).

[7]

L. Ferrari and M. Mamei. Classification and prediction of whereabouts patterns from the reality mining dataset. Pervasive and Mobile Computing, 9(4):516--527, 2013.

[8]

Foursquare API. https://developer.foursquare.com/ (last accessed 25.06.2013).

[9]

C. W. J. Granger. Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 37(3):424--438, Aug. 1969.

[10]

F. Kling and A. Pozdnoukhov. When a city tells a story: urban topic analysis. In Proceedings of the 20th International Conference on Advances in Geographic Information Systems, SIGSPATIAL '12, pages 482--485, New York, NY, USA, 2012. ACM.

Digital Library

[11]

D. Lian and X. Xie. Learning location naming from user check-in histories. In Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, GIS '11, pages 112--121, New York, NY, USA, 2011. ACM.

Digital Library

[12]

Z. Liu, Y. Zhang, E. Y. Chang, and M. Sun. Plda+: Parallel latent dirichlet allocation with data placement and pipeline processing. ACM Transactions on Intelligent Systems and Technology, special issue on Large Scale Machine Learning, 2011. Software available at http://code.google.com/p/plda.

Digital Library

[13]

R. Montoliu. Discovering mobility patterns on bicycle-based public transportation system by using probabilistic topic models. In P. Novais, K. Hallenborg, D. I. Tapia, and J. M. C. Rodríguez, editors, Ambient Intelligence - Software and Applications, volume 153 of Advances in Intelligent and Soft Computing, pages 145--153. Springer Berlin Heidelberg, 2012.

[14]

M. Padgham. Human movement is both diffusive and directed. PLoS ONE, 7(5):e37754, 05 2012.

[15]

A. Pozdnoukhov and C. Kaiser. Space-time dynamics of topics in streaming text. In Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks, LBSN '11, pages 1--8, New York, NY, USA, 2011. ACM.

Digital Library

[16]

J. Reades, F. Calabrese, and C. Ratti. Eigenplaces: analysing cities using the space-time structure of the mobile phone network. Environment and Planning B: Planning and Design, 36(5):824--836, 2009.

[17]

G. Sugihara, R. May, H. Ye, C.-h. Hsieh, E. Deyle, M. Fogarty, and S. Munch. Detecting causality in complex ecosystems. Science, 338(6106):496--500, Oct. 2012.

[18]

J. Toole, M. Ulm, D. Bauer, and M. Gonzalez. Inferring land use from mobile phone activity. In Proceedings of the UrbComp'12. ACM, 2012.

Digital Library

[19]

Twitter streaming API. http://dev.twitter.com/docs/streaming-api (last accessed 25.06.2013).

[20]

M. Ye, K. Janowicz, C. Mülligann, and W.-C. Lee. What you are is when you are: the temporal dimension of feature types in location-based social networks. In Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, GIS '11, pages 102--111, New York, NY, USA, 2011. ACM.

Digital Library

[21]

J. Yuan, Y. Zheng, and X. Xie. Discovering regions of different functions in a city using human mobility and pois. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '12, pages 186--194, New York, NY, USA, 2012. ACM.

Digital Library

Cited By

Zia MFürle JLudwig CLautenbach SGumbrich SZipf A(2022)SocialMedia2Traffic: Derivation of Traffic Information from Social Media DataISPRS International Journal of Geo-Information10.3390/ijgi1109048211:9(482)Online publication date: 13-Sep-2022
https://doi.org/10.3390/ijgi11090482
Chen XHuang KJiang H(2022)Detecting Changes in the Spatiotemporal Pattern of Bike Sharing: A Change-Point Topic ModelIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2022.316162323:10(18361-18377)Online publication date: Oct-2022
https://doi.org/10.1109/TITS.2022.3161623
Tajaddini ARose GM. Kockelman KL. Vu H(2020)Recent Progress in Activity-Based Travel Demand Modeling: Rising Data and ApplicabilityTransportation Systems for Smart, Sustainable, Inclusive and Secure Cities [Working Title]10.5772/intechopen.93827Online publication date: 17-Sep-2020
https://doi.org/10.5772/intechopen.93827
Show More Cited By

Index Terms

Temporal decomposition and semantic enrichment of mobility flows
1. Human-centered computing
  1. Visualization
    1. Visualization application domains
      1. Geographic visualization
2. Information systems
  1. Information systems applications
    1. Data mining
    2. Spatial-temporal systems

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Published In

cover image ACM Conferences

LBSN '13: Proceedings of the 6th ACM SIGSPATIAL International Workshop on Location-Based Social Networks

November 2013

58 pages

ISBN:9781450325332

DOI:10.1145/2536689

Conference Chair:
Alexei Pozdnoukhov
University of California at Berkeley

Copyright © 2013 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]

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Publication History

Published: 05 November 2013

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Research-article

Funding Sources

Science Foundation Ireland
Stokes Lectureship award
International Business Machines Corporation

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SIGSPATIAL'13

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SIGSPATIAL'13: 21st SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 5, 2013

Florida, Orlando

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Overall Acceptance Rate 8 of 15 submissions, 53%

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Citations

Cited By

Zia MFürle JLudwig CLautenbach SGumbrich SZipf A(2022)SocialMedia2Traffic: Derivation of Traffic Information from Social Media DataISPRS International Journal of Geo-Information10.3390/ijgi1109048211:9(482)Online publication date: 13-Sep-2022
https://doi.org/10.3390/ijgi11090482
Chen XHuang KJiang H(2022)Detecting Changes in the Spatiotemporal Pattern of Bike Sharing: A Change-Point Topic ModelIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2022.316162323:10(18361-18377)Online publication date: Oct-2022
https://doi.org/10.1109/TITS.2022.3161623
Tajaddini ARose GM. Kockelman KL. Vu H(2020)Recent Progress in Activity-Based Travel Demand Modeling: Rising Data and ApplicabilityTransportation Systems for Smart, Sustainable, Inclusive and Secure Cities [Working Title]10.5772/intechopen.93827Online publication date: 17-Sep-2020
https://doi.org/10.5772/intechopen.93827
Wang SLiu YZhi WWen XZhou W(2020)Discovering Urban Functional Polycentricity: A Traffic Flow-Embedded and Topic Modeling-Based Methodology FrameworkSustainability10.3390/su1205189712:5(1897)Online publication date: 2-Mar-2020
https://doi.org/10.3390/su12051897
Rocha NDias ASantinha GRodrigues MQueirós ARodrigues C(2020)Prediction of Mobility Patterns in Smart Cities: A Systematic Review of the LiteratureTrends and Innovations in Information Systems and Technologies10.1007/978-3-030-45688-7_65(650-659)Online publication date: 18-May-2020
https://doi.org/10.1007/978-3-030-45688-7_65
Liao YYeh SJeuken G(2019)From individual to collective behaviours: exploring population heterogeneity of human mobility based on social media dataEPJ Data Science10.1140/epjds/s13688-019-0212-x8:1Online publication date: 14-Nov-2019
https://doi.org/10.1140/epjds/s13688-019-0212-x
Zhou YHuang Y(2019)Place Representation Based Bike Demand Prediction2019 IEEE International Conference on Big Data (Big Data)10.1109/BigData47090.2019.9006286(1577-1586)Online publication date: Dec-2019
https://doi.org/10.1109/BigData47090.2019.9006286
Shankari KBouzaghrane MMaurer SWaddell PCuller DKatz R(2018)e-mission: An Open-Source, Smartphone Platform for Collecting Human Travel DataTransportation Research Record: Journal of the Transportation Research Board10.1177/03611981187701672672:42(1-12)Online publication date: 19-Aug-2018
https://doi.org/10.1177/0361198118770167
Zhou YHuang Y(2018)Context Aware Flow Prediction of Bike Sharing Systems2018 IEEE International Conference on Big Data (Big Data)10.1109/BigData.2018.8621918(2393-2402)Online publication date: Dec-2018
https://doi.org/10.1109/BigData.2018.8621918
Chen XVo HWang YWang F(2018)A framework for annotating OpenStreetMap objects using geo-tagged tweetsGeoinformatica10.1007/s10707-018-0323-822:3(589-613)Online publication date: 1-Jul-2018
https://dl.acm.org/doi/10.1007/s10707-018-0323-8
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