Mining candidate causal relationships in movement patterns

References

• Agrawal, R., Imieliski, T., and Swami, A., 1993. Mining association rules between sets of items in large databases. In: P. Buneman and S. Jajodia, eds. Proceedings of the ACM SIGMOD international conference on management of data. Washington, D.C.: ACM, 207–216.
   Google Scholar
• Allen, E., Edwards, G., and Bédard, Y., 1995. Qualitative causal modeling in temporal GIS. In: A. Frank and W. Kuhn, eds. Spatial information theory (COSIT), Vol. 988 of Lecture notes in computer science. Berlin: Springer, 397–412.
   Google Scholar
• Andersson, M., et al., 2008. Reporting leaders and followers among trajectories of moving point objects. GeoInformatica, 12 (4), 497–528.
   Web of Science ®Google Scholar
• Andrienko, G., Andrienko, N., and Heurich, M., 2011. An event-based conceptual model for context-aware movement analysis. International Journal of Geographical Information Science, 25 (9), 1347–1370.
   Web of Science ®Google Scholar
• Beebee, H., Hitchcock, C., and Menzies, P., eds., 2009. The Oxford handbook of causation. Oxford: Oxford University Press.
   Google Scholar
• Both, A., et al., 2012. Decentralized monitoring of moving objects in a transportation network augmented with checkpoints. The Computer Journal [Accessed September 2012].
   Google Scholar
• Buchin, K., et al., 2011a. Finding long and similar parts of trajectories. Computational Geometry, 44 (9), 465–476.
   Web of Science ®Google Scholar
• Buchin, M., et al., 2011b. Segmenting Trajectories: a framework and algorithms using spatiotemporal criteria. Journal of Spatial Information Science, 3, 33–63.
   Google Scholar
• Buchin, M., Kruckenberg, H., and Klzsch, A., 2012. Segmenting trajectories by movement states. Advances in geographic information science. In: S. Timpf and P. Laube, eds. Advances in spatial data handling. Berlin Heidelberg: Springer, 15–25.
   Google Scholar
• Bunge, M., 1966. Causality. New York, NY: Dover.
   Google Scholar
• Claramunt, C. and Thériault, M., 1996. Toward semantics for modelling spatio-temporal processes within GIS. In: M.J. Kraak and M. Molenaar, eds. Advances in GIS II. Deflt, The Netherlands: Taylor and Francis, 47–64.
   Google Scholar
• Claramunt, C. and Thériault, M., 1995. Managing time in GIS: an event-oriented approach. In: J. Clifford and A. Tuzhilin, eds. Recent advances on temporal databases. Zürich, Switzerland: Springer-Verlag, 23–42.
   Google Scholar
• Das, G., et al., 1998. Rule discovery from time series. In: R. Agrawal and P. Stolorz, eds. Proceedings knowledge discovery and data mining (KDD). Palo Alto, CA: AAAI Press, 16–22.
   Google Scholar
• Duckham, M., 2012. Decentralized spatial computing, foundations of geosensor networks. Heidelberg: Springer.
   Google Scholar
• El-Geresy, B.A., Abdelmoty, A.I., and Jones, C.B., 2002. Spatio-temporal geographic information systems: a causal perspective. In: Y. Manolopoulos and P. Návrat, eds. Proceedings of the 6th East European conference on advances in databases and information systems (ADBIS). Berlin: Springer, 191–203.
   Google Scholar
• Gabadinho, A., et al., 2011. Analyzing and visualizing state sequences in R with TraMineR. Journal of Statistical Software, 40 (4), 1–37.
   Web of Science ®Google Scholar
• Galton, A., 2012. States, processes and events, and the ontology of causal relations. In: M. Donnelly and G. Guizzardi, eds. Proceedings of the 7th international conference on formal ontology in information systems (FOIS), Vol. 239 of Frontiers in artificial intelligence and applications. Amsterdam, NL: IOS Press, 279–292.
   Google Scholar
• Galton, A. and Worboys, M., 2005. Processes and events in dynamic geo-networks. In: M.A. Rodriguez, I.F. Cruz, S. Levashkin, and M.J. Egenhofer, eds. Proceedings of the first international conference on geospatial semantics. Berlin Heidelberg: Springer, 45–59.
   Google Scholar
• Gonzalez, M.C., Hidalgo, C.A., and Barabasi, A.L., 2008. Understanding individual human mobility patterns. Nature, 453 (7196), 779–782.
   PubMed Web of Science ®Google Scholar
• Gudmundsson, J., Laube, P., and Wolle, T., 2012. Computational movement analysis. In: W. Kresse and D.M. Danko, eds. Springer handbook of geographic information. Berlin Heidelberg: Springer, 423–438.
   Google Scholar
• Gudmundsson, J., van Kreveld, M., and Speckmann, B., 2007. Efficient detection of patterns in 2D trajectories of moving points. GeoInformatica, 11 (2), 195–215.
   Web of Science ®Google Scholar
• Huang, Y., Chen, C., and Dong, P., 2008. Modeling herds and their evolvements from trajectory data. In: T.J. Cova, H. Miller, K. Beard, A.U. Frank, and M.F. Goodchild, eds. GIScience 2008, LNCS5266, Berlin Heidelberg: Springer, 90–105.
   Google Scholar
• Hume, D., 1739. A treatise of human nature [2003]. New York, NY: Dover.
   Google Scholar
• Jeung, H., et al., 2008. Discovery of convoys in trajectory databases. Proceedings of the VLDB Endowment, 1 (1), 1068–1080.
   Google Scholar
• Johnston, P. and Bergeron, N., 2010. Variation of juvenile Atlantic salmon (Salmo salar) body composition along sedimentary links. Ecology of Freshwater Fish, 19 (2), 187–196.
   Web of Science ®Google Scholar
• Laube, P., et al., 2011. Report on the first workshop on Movement Pattern Analysis MPA10. Journal of Spatial Information Science, 1 (2), 127–133.
   Google Scholar
• Laube, P., van Kreveld, M., and Imfeld, S., 2005. Finding REMO – detecting relative motion patterns in geospatial lifelines. In: P.F. Fisher, ed. Developments in spatial data handling. Proceedings of the 11th international symposium on spatial data handling. Berlin Heidelberg: Springer, 201–214.
   Google Scholar
• Lyon, J.P., 2012. Snags underpin Murray River restoration plan. ECOS, 2012 (177).
   Google Scholar
• Mohammad, Y. and Nishida, T., 2010. Mining causal relationships in multidimensional time series. In: E. Szczerbicki and N. Nguyen, eds. Smart information and knowledge management, Vol. 260 of Studies in computational intelligence. Berlin Heidelberg: Springer, 309–338.
   Google Scholar
• Nagy, M., et al., 2010. Hierarchical group dynamics in pigeon flocks. Nature, 464 (7290), 890–893.
   PubMed Web of Science ®Google Scholar
• Nathan, R., et al., 2008. A movement ecology paradigm for unifying organismal movement research. Proceedings of the National Academy of Sciences, 105 (49), 19052–19059.
   PubMed Web of Science ®Google Scholar
• Pelekis, N., et al., 2012. Visually exploring movement data via similarity-based analysis. Journal of Intelligent Information Systems, 38 (2), 343–391.
   Web of Science ®Google Scholar
• Schneider, C.M., et al., 2013. Unravelling daily human mobility motifs. Journal of the Royal Society: Interface, 10 (84), 246–253.
   Web of Science ®Google Scholar
• Wood, Z. and Galton, A., 2009a. Classifying collective motion. In: B. Gottfried and H. Aghajan, eds. Behaviour monitoring and interpretation – BMI – smart environments, Vol. 3 of Ambient intelligence and smart environments. Amsterdam, NL: IOS Press, 129–155.
   Google Scholar
• Wood, Z. and Galton, A., 2009b. A taxonomy of collective phenomena. Applied Ontology, 4 (3), 267–292.
   Google Scholar
• Yuan, M., 2007. GIS Approaches for geographic dynamics understanding and event prediction. In: R. Suresh, ed. Proceedings of SPIE, Vol. 6578, 65781A. Defense Transformation and Net-Centric Systems 2007, Orlando, FL: The International Society for Optical Engineering.
   Google Scholar
• Yuan, M. and Hornsby, K.S., 2008. Computation and visualisation for understanding dynamics in geographic domains: a research agenda. New York, NY: Taylor and Francis (CRC Press).
   Google Scholar
• Zaki, M.J., 2001. SPADE: an efficient algorithm for mining frequent sequences. Machine Learning, 42, 31–60.
   Web of Science ®Google Scholar