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Profiling Spatial Collectives

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Research and Development in Intelligent Systems XXX (SGAI 2013)

Abstract

Much research has been undertaken in analysing an individual’s behaviour based on their movement patterns, but the behaviour of the collectives that the individuals may participate in, remains largely under-researched. The movement of a collective has similarities to that of an individual but also distinct features that must be accounted for. This research focuses on the development of a method that allows the motion of a class of collectives, known as spatial collectives, to be analysed. This method, referred to as the Three Level Analysis (TLA) method, uses string matching to produce a profile for a spatial collective which gives a detailed analysis of its movement patterns; such profiles could then be used to identify the type of spatial collective. A computer program has been developed that allows the method to be applied to a spatiotemporal dataset.

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References

  1. W. Ali and B. Moulin. 2D–3D multiagent geosimulation with knowledge-based agents of customers’ shopping behaviour in a shopping mall. In David M. Mark and Anthony G. Cohn, editors, Spatial Information Theory: Proceedings of International Conference COSIT 2005, Lecture Notes in Computer Science, pages 445–458, Ellicottville, NY, USA, 2005. Springer.

    Google Scholar 

  2. I. Alsmadi and M. Nuser. String matching evaluation methods for dna comparison. Internation Journal of Advanced Science and Technology, 47:13–32, 2012.

    Google Scholar 

  3. M. Andersson, J. Gudmundsson, P. Laube, and T. Wolle. Reporting leaders and followers among trajectories of moving point objects. Geoinformatica, 12(4):497–528, 2008.

    Article  Google Scholar 

  4. G. Andrienko and N. Andrienko. Extracting patterns of individual movement behaviour from a massive collection of tracked positions. In B. Gottfried, editor, Workshop on Behaviour Modelling and Interpretation., pages 1–16, Germany, 2007. Technologie-Zentrum Informatik.

    Google Scholar 

  5. Gennady Andrienko, Natalia Andrienko, Salvatore Rinzivillo, Mirco Nanni, Dino Pedreschi, and Fosca Giannotti. Interactive visual clustering of large collections of trajectories. In IEEE Visual Analytics Science and Technology (VAST 2009), pages 3–10, Atlantic City, New Jersey, USA, October 12–13 2009. IEEE Computer Society Press.

    Google Scholar 

  6. S. Dodge, R. Weibel, and E. Forootan. Revealing the physics of movement: Comparing the similarity of movement characteristics of different types of moving objects. Computers, Environment and Urban Systems, 33(6):419–434, November 2009.

    Google Scholar 

  7. S. Dodge, R. Weibel, and A.K. Lautenschütz. Towards a taxonomy of movement patterns. Information Visualization, 7:240–252, 2008.

    Article  Google Scholar 

  8. J. A. Dykes and D. M. Mountain. Seeking structure in records of spatiotmeporal behaviour: visualization issues, efforts and application. Computational Statistics and Data Analysis, 43 (Data Visualization II Special Edition)(4):581–603, 2003.

    Google Scholar 

  9. A. Galton and M. Duckham. What is the region occupied by a set of points? In M. Raubal, H. Miller, A. Frank, and M. Goodchild, editors, Geographic Information Science: Proceedings of the Fourth International Conferencem GIScience 2006, pages 81–98, Berlin, 2006. Springer.

    Google Scholar 

  10. J. Gudmundson, P. Laube, and T. Wolle. Computational movement analysis. In Wolfgang Kresse (1) W. Kresse and D. Danko, editors, Springer Handbook of Geographic Information, chapter C, pages 423–438. Springer, Berlin Heidelberg, 2012.

    Google Scholar 

  11. J. Gudmundsson, P. Laube, and T. Wolle. Movement patterns in spatio-temporal data. In Encyclopedia of GIS. Springer-verlag, 2007.

    Google Scholar 

  12. M. Hadjieleftheriouu and D. Srivastava. Weighted set-based string similarity. IEEE Data Engineering Bulletin, 33(1):25–36, 2010.

    Google Scholar 

  13. D. M. Mountain and J. F. Raper. Modelling human spatio-temporal behaviour: A challenge for location-based services. In Proceedings of the 6th International Conference on Geocomputation 2001, Brisbane, Australia, 24–26 September 2001.

    Google Scholar 

  14. G. Sinha and D. Mark. Measuring similarity between geospatial lifelines in studies of environmental health. Journal of Geographical Systems, 7(1):115–136, 2005.

    Article  Google Scholar 

  15. S. Spaccapietra and C. Parent. Adding meaning to your steps. In Conceptual Modeling ER 2011 Conference, 30th International Conference on Conceptual Modeling, volume 6998, Brussels, Belgium, October 31 - November 3 2011. Lecture Notes in Computer Science, Springer.

    Google Scholar 

  16. M. Thériault, C. Claramunt, and P.Y. Villeneuve. A spatio-temporal taxonomy for the representation of spatial set behaviours. In H.B. Böhlen, C.S. Jensen, and M. Scholl, editors, Spatio-Temporal Database Management, International Workshop STDBM’99, pages 1–18. Springer, 1999.

    Google Scholar 

  17. Z. Wood. Detecting and Identifying Collective Phenomena within Movement Data. PhD thesis, University of Exeter, 2011.

    Google Scholar 

  18. Z. Wood and A. Galton. Classifying collective motion. In B. Gottfried and H. Aghajan, editors, Behaviour Monitoring and Interpretation BMI: Smart Environments, pages 129–155. IOS Press, Amsterdam, The Netherlands, 2009.

    Google Scholar 

  19. Z. Wood and A. Galton. A taxonomy of collective phenomena. Applied Ontology, 4(3–4):267–292, 2009.

    Google Scholar 

  20. Z. Wood and A. Galton. Zooming in on collective motion. In Mehul Bhatt, Hans Guesgen, and Shyamanta Hazarika, editors, Proceedings of the International Workshop on Spatio-Temporal Dynamics, co-located with the European Conference on Artificial Intelligence (ECAI-10), Lisbon, Prtugal, 2010. ECAI Workshop Proceedings., and SFB/TR 8 Spatial Cognition Report Series.

    Google Scholar 

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Acknowledgments

Dr. Antony Galton is acknowledged for his insightful and useful comments.

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Authors and Affiliations

  1. University of Exeter, Exeter, UK

    Zena Wood

Authors
  1. Zena Wood
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Correspondence to Zena Wood .

Editor information

Editors and Affiliations

  1. School of Computing, University of Portsmouth, Portsmouth, United Kingdom

    Max Bramer

  2. School of Computing, Engineering and Mathematics, University of Brighton, Brighton, West Sussex, United Kingdom

    Miltos Petridis

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© 2013 Springer International Publishing Switzerland

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Wood, Z. (2013). Profiling Spatial Collectives. In: Bramer, M., Petridis, M. (eds) Research and Development in Intelligent Systems XXX. SGAI 2013. Springer, Cham. https://doi.org/10.1007/978-3-319-02621-3_7

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  • DOI: https://doi.org/10.1007/978-3-319-02621-3_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-02620-6

  • Online ISBN: 978-3-319-02621-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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