Movement Mining

  • Patrick LaubeEmail author
Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)


With ever increasing volumes and complexity of spatio-temporal information, knowledge discovery in databases and its best known step data mining, have rapidly gained importance within Geography and GIScience. Analyzing spatio-temporal data first of all means structuring data, then extracting relevant spatial patterns and rules and providing decision makers with enriched information and condensed knowledge rather than flooding them with raw data. Movement patterns, for example, represent such sought-for high-level process knowledge derived from low-level trajectory data. This second chapter introducing the research field of Computational Movement Analysis (CMA) reviews research on several aspects of mining movement data, including the conceptualization and formalization of movement patterns and the development of algorithms for their detection, the computing of trajectory similarity, and methods for visualization-based exploratory analysis of movement data


Data Mining Movement Pattern Movement Data Data Mining Technique Movement Mining 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Andersson, M., Gudmundsson, J., Laube, P., & Wolle, T. (2008). Reporting leaders and followers among trajectories of moving point objects. GeoInformatica, 12(4), 497–528.CrossRefGoogle Scholar
  2. Andrienko, G., Andrienko, N., Demsar, U., Dransch, D., Dykes, J., Fabrikant, S. I., et al. (2010). Space, time and visual analytics. International Journal of Geographical Information Science, 24(10), 1577–1600.CrossRefGoogle Scholar
  3. Andrienko, N., & Andrienko, G. (2007). Designing visual analytics methods for massive collections of movement data. Cartographica, 42(2), 117–138.CrossRefGoogle Scholar
  4. Andrienko, N., & Andrienko, G. (2011). Spatial generalization and aggregation of massive movement data. IEEE Transactions on Visualization and Computer Graphics, 17(2), 205–219.CrossRefGoogle Scholar
  5. Anselin, L. (1995). Local indicators of spatial association—LISA. Geographical Analysis, 27(2), 93–115.CrossRefGoogle Scholar
  6. Baglioni, M., & Fernandes de Macedo, J. A. (2009). Towards semantic interpretation of movement behavior advances in giscience. In M. Sester (Ed.), Advances in GIScience (pp. 271–288)., Lecture Notes in Geoinformation and Cartography Berlin: Springer.CrossRefGoogle Scholar
  7. Benkert, M., Gudmundsson, J., Hübner, F., & Wolle, T. (2008). Reporting flock patterns. Computational Geometry, 41(3), 111–125.zbMATHMathSciNetCrossRefGoogle Scholar
  8. Bertin, J., Berg, W., and Scott, P. (1981). Graphics and graphic information processing. De Gruyter.Google Scholar
  9. Bleisch, S., Duckham, M., Galton, A., Laube, P., & Lyon, J. (2014). Mining candidate causal relationships in movement patterns. International Journal of Geographical Information Science, 28(2), 363–382.CrossRefGoogle Scholar
  10. Bogaert, P., Van De Weghe, N., Cohn, A. G., Witlox, F., & De Maeyer, P. (2007). The qualitative trajectory calculus on networks. Spatial cognition V reasoning, action, interaction (Vol. 4387, pp. 20–38)., Lecture Notes in Computer Science, LNAI Berlin: Springer.CrossRefGoogle Scholar
  11. Both, A., Duckham, M., Laube, P., Wark, T., & Yeoman, J. (2013). Decentralized monitoring of moving objects in a transportation network augmented with checkpoints. The Computer Journal, 56(12), 1432–1449.CrossRefGoogle Scholar
  12. Buchin, K., Buchin, M., & Gudmundsson, J. (2010a). Constrained free space diagrams: A tool for trajectory analysis. International Journal of Geographical Information Science, 24(7), 1101–1125.Google Scholar
  13. Buchin, K., Buchin, M., van Kreveld, M., & Luo, J. (2011a). Finding long and similar parts of trajectories. Computational Geometry, 44(9), 465–476.zbMATHMathSciNetCrossRefGoogle Scholar
  14. Buchin, M., Dodge, S., Speckmann, B., et al. (2012). Context-aware similarity of trajectories. In N. Xiao, M. -P. Kwan, M. Goodchild, & S. Shekhar (Eds.), Geographic information science. Lecture Notes in Computer Science (Vol. 7478, pp. 43–56). Berlin: Springer.Google Scholar
  15. Buchin, M., Driemel, A., van Kreveld, M., & Sacristan, V. (2010b). An algorithmic framework for segmenting trajectories based on spatio-temporal criteria. In 18th International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL GIS. (2010). San Jose. California: ACM.Google Scholar
  16. Buchin, M., Driemel, A., van Kreveld, M., & Sacristan, V. (2011b). Segmenting trajectories: A framework and algorithms using spatiotemporal criteria. JOSIS, 3, 33–63.Google Scholar
  17. Chakrabarti, S., Ester, M., Fayyad, U., Gehrke, J., Han, J., Morishita, S., & et al. (2006). Data mining curriculum: A proposal. Intensive Working Group of ACM SIGKDD Curriculum Committee: Technical report.Google Scholar
  18. Demsar, U., & Virrantaus, K. (2010). Space-time density of trajectories: Exploring spatio-temporal patterns in movement data. International Journal of Geographical Information Science, 24(10), 1527–1542.CrossRefGoogle Scholar
  19. Dennis, T. E., Chen, W. C., Koefoed, I. M., Lacoursiere, C. J., Walker, M. M., Laube, P., et al. (2010). Performance characteristics of small global-positioning-system tracking collars for terrestrial animals. Wildlife Biology in Practice, 6(1), 14–31.CrossRefGoogle Scholar
  20. Dodge, S., Weibel, R., & Lautenschütz, A.-K. (2008). Towards a taxonomy of movement patterns. Information Visualization, 7(3–4), 240–252.CrossRefGoogle Scholar
  21. Dodge, S., Laube, P., & Weibel, R. (2012). Movement similarity assessment using symbolic representation of trajectories. International Journal of Geographical Information Science, 26(9), 1563–1588.CrossRefGoogle Scholar
  22. Downs, J. A., & Horner, M. W. (2010). In S. Fabrikant, T. Reichenbacher, M. Kreveld, & C. Schlieder (Eds.), Geographic information science. Lecture Notes in Computer Science (Vol. 6292, pp. 16–26). Berlin: Springer.Google Scholar
  23. Downs, J. A., & Horner, M. W. (2012). Analysing infrequently sampled animal tracking data by incorporating generalized movement trajectories with kernel density estimation. Computers, Environment and Urban Systems, 36(4), 302–310.CrossRefGoogle Scholar
  24. Dumont, B., Boissy, A., Achard, C., Sibbald, A. M., & Erhard, H. W. (2005). Consistency of animal order in spontaneous group movements allows the measurement of leadership in a group of grazing heifers. Applied Animal Behaviour Science, 95(1–2), 55–66.CrossRefGoogle Scholar
  25. Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI Magazine, 17(3), 37–54.Google Scholar
  26. Galton, A. (2005). Dynamic collectives and their collective dynamics. In A. Cohn & D. M. Mark (Eds.), Spatial Information Theory, Proceedings. Lecture Notes in Computer Science (Vol. 3693, pp. 300–315). Heidelberg: Springer.Google Scholar
  27. Geng, L., & Hamilton, H. J. (2006). Interestingness measures for data mining: A survey. ACM Computing Surveys, 38(3), 9.CrossRefGoogle Scholar
  28. Gonzalez, M. C., Hidalgo, C. A., & Barabasi, A. L. (2008). Understanding individual human mobility patterns. Nature, 453(7196), 779–782.CrossRefGoogle Scholar
  29. Gottfried, B. (2011). Interpreting motion events of pairs of moving objects. GeoInformatica, 15(2), 247–271.CrossRefGoogle Scholar
  30. Guilford, T., Meade, J., Willis, J., Phillips, R., Boyle, D., Roberts, S., et al. (2009). Migration and stopover in a small pelagic seabird, the manx shearwater puffinus puffinus: Insights from machine learning. Proceedings of the Royal Society B: Biological Sciences, 276(1660), 1215–1223.CrossRefGoogle Scholar
  31. Han, J., & Kamber, M. (2006). Data mining: Concepts and techniques. Amsterdam: Morgan Kaufmann Publishers.Google Scholar
  32. Hand, D. J., Manilla, H., & Smyth, P. (2001). Principles of data mining. Cambridge, MA: MIT Press.Google Scholar
  33. Huang, Y., Chen, C. & Dong, P. (2008). Modeling herds and their evolvements from trajectory data. Proceedings of Fifth International Conference on Geographic Information Science.Google Scholar
  34. Jeung, H., Shen, H. T., & Zhou, X. (2008a). Convoy queries in spatio-temporal databases. In 2008 IEEE 24th International Conference on Data Engineering (pp. 1457–1459), Cancun, Mexico.Google Scholar
  35. Jeung, H., Yiu, M. L., Zhou, X., Jensen, C. S., & Shen, H. T. (2008b). Discovery of convoys in trajectory databases. Proceedings of the VLDB Endowment, 1(1), 1068–1080.CrossRefGoogle Scholar
  36. Keim, D., Andrienko, G., Fekete, J.-D., Görg, C., Kohlhammer, J. & Melançon, G. (2008). Visual analytics: Definition, process, and challenges. In A. Kerren, J. Stasko, J.-D. Fekete, C. North (Eds.), Information visualization. Lecture Notes in Computer Science (Vol. 4950, pp. 154–175). Berlin: Springer.Google Scholar
  37. Laube, P. (2009) Progress in movement pattern analysis. In B. Gottfried & H. Aghajan (Eds.), Behaviour monitoring and interpretation, BMI, smart environments. Ambient Intelligence and Smart Environments (Vol. 3, pp. 43–71). Amsterdam, NL: IOS Press.Google Scholar
  38. Laube, P., Berg, M., Kreveld, M., et al. (2008a). Spatial support and spatial confidence for spatial association rules. In A. Ruas & C. Gold (Eds.), Headway in spatial data handling. Berlin: Springer.Google Scholar
  39. Laube, P., Dennis, T., Walker, M., & Forer, P. (2007). Movement beyond the snapshot–dynamic analysis of geospatial lifelines. Computers, Environment and Urban Systems, 31(5), 481–501.CrossRefGoogle Scholar
  40. Laube, P., Duckham, M., & Palaniswami, M. (2011a). Deferred decentralized movement pattern mining for geosensor networks. International Journal of Geographical Information Science, 25(2), 273–292.CrossRefGoogle Scholar
  41. Laube, P., Duckham, M., & Wolle, T. (2008b). Decentralized movement pattern detection amongst mobile geosensor nodes. In T. J. Cova, K. Beard, M. F. Goodchild, & A. U. Frank (Eds.), GIScience 2008. Lecture Notes in Computer Science (Vol. 5266, pp. 199–216). Berlin: Springer.Google Scholar
  42. Laube, P., Gottfried, B., Klippel, A., Billen, R., & van de Weghe, N. (2011b). Report on the first workshop on movement pattern analysis MPA10. JOSIS, 1(2), 127–133.Google Scholar
  43. Laube, P., & Purves, R. (2006). An approach to evaluating motion pattern detection techniques in spatio-temporal data. Computers, Environment and Urban Systems, 30(3), 347–374.CrossRefGoogle Scholar
  44. Laube, P., & Purves, R. S. (2011). How fast is a cow? Cross-scale analysis of movement data. Transactions in GIS, 15(3), 401–418.CrossRefGoogle Scholar
  45. Laube, P., van Kreveld, M., & 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 (pp. 201–214). Berlin, DE: Springer.Google Scholar
  46. Merki, M., & Laube, P. (2012). Detecting reaction movement patterns in trajectory data. In J. Gensel, D. Josselin, & D. Vandenbroucke (Eds.), AGILE’2012 International Conference on Geographic Information Science. FR: Avignon.Google Scholar
  47. Miller, H., & Han, J. (2009). Geographic data mining and knowledge discovery. Boca Raton: CRC Press.Google Scholar
  48. Mohammad, Y., & Nishida, T. (2010). Mining causal relationships in multidimensional time series. In E. Szczerbicki & N. Nguyen (Eds.), Smart information and knowledge management. Studies in Computational Intelligence (Vol. 260, pp. 309–338). Berlin: Springer.Google Scholar
  49. Nagy, M., Akos, Z., Biro, D., & Vicsek, T. (2010). Hierarchical group dynamics in pigeon flocks. Nature, 464(7290), 890–893.CrossRefGoogle Scholar
  50. Orellana, D. (2012). Exploring Pedestrian Movement Patterns (PhD thesis, Wageningen University).Google Scholar
  51. Orellana, D., Bregt, A. K., Ligtenberg, A., & Wachowicz, M. (2012). Exploring visitor movement patterns in natural recreational areas. Tourism Management, 33(3), 672–682.CrossRefGoogle Scholar
  52. Orellana, D. & Renso, C. (2010). Developing an interactions ontology for characterising pedestrian movement behaviour. In Movement-aware applications for sustainable mobility: Technologies and approaches (pp. 62–86). IGI Global.Google Scholar
  53. Orellana, D., & Wachowicz, M. (2011). Exploring patterns of movement suspension in pedestrian mobility. Geographical Analysis, 43(3), 241–260.CrossRefGoogle Scholar
  54. Pelekis, N., Andrienko, G., Andrienko, N., Kopanakis, I., Marketos, G., & Theodoridis, Y. (2012). Visually exploring movement data via similarity-based analysis. Journal of Intelligent Information Systems, 38(2), 343–391.CrossRefGoogle Scholar
  55. Peterson, R. O., Jacobs, A. K., Drummer, T. D., Mech, L. D., & Smith, D. W. (2002). Leadership behavior in relation to dominance and reproductive status in gray wolves. Canis lupus. Canadian Journal of Zoology, 80(8), 1405–1412.CrossRefGoogle Scholar
  56. Randell, D. A., Cui, Z., & Cohn, A. G. (1992). A spatial logic based on regions and connection. KR, 92, 165–176.Google Scholar
  57. Richter, K.-F., Schmid, F., & Laube, P. (2012). Semantic trajectory compression: Representing urban movement in a nutshell. JOSIS, 4, 3–30.Google Scholar
  58. Rinzivillo, S., Pedreschi, D., Nanni, M., Giannotti, F., Andrienko, N., & Andrienko, G. (2008). Visually driven analysis of movement data by progressive clustering. Information Visualization, 7(3–4), 225–239.CrossRefGoogle Scholar
  59. Rykiel, E. J. J. (1996). Testing ecological models: The meaning of validation. Ecological Modelling, 90(3), 229–244.CrossRefGoogle Scholar
  60. Schreck, T., Bernard, J., von Landesberger, T., & Kohlhammer, J. (2009). Visual cluster analysis of trajectory data with interactive Kohonen maps. Information Visualization, 8(1), 14–29.CrossRefGoogle Scholar
  61. Sester, M., Feuerhake, U., Kuntzsch, C., & Zhang, L. (2012). Revealing underlying structure and behaviour from movement data. KI, 26(3), 223–231.Google Scholar
  62. Shamoun-Baranes, J., Bom, R., van Loon, E. E., Ens, B. J., Oosterbeek, K., & Bouten, W. (2012a). From sensor data to animal behaviour: An oystercatcher example. PLoS ONE, 7(5), e37997.CrossRefGoogle Scholar
  63. Shamoun-Baranes, J., van Loon, E. E., Purves, R. S., Speckmann, B., Weiskopf, D., & Camphuysen, C. J. (2012b). Analysis and visualization of animal movement. Biology Letters, 8(1), 6–9.CrossRefGoogle Scholar
  64. Shapiro, L. G., & Stockman, G. C. (2001). Computer vision. New Jersey: Prentice-Hall.Google Scholar
  65. Silberschatz, A., & Tuzhilin, A. (1996). What makes patterns interesting in knowledge discovery systems. IEEE Transactions on Knowledge and Data Engineering, 8(6), 970–974.CrossRefGoogle Scholar
  66. Spaccapietra, S., Parent, C., Damiani, M. L., de Macedo, J. A., Portoa, F., & Vangenot, C. (2008). A conceptual view on trajectories. Data and Knowledge Engineering, 65(1), 126–146.CrossRefGoogle Scholar
  67. Thomas, J. J., & Cook, K. A. (2006). A visual analytics agenda. IEEE Computer Graphics and Applications, 26(1), 10–13.CrossRefGoogle Scholar
  68. Tufte, E., & Graves-Morris, P. (1983). The visual display of quantitative information (Vol. 31). Cheshire, CT: Graphics Press.Google Scholar
  69. Van de Weghe, N., Cohn, A. G., Bogaert, P., & De Maeyer, P. (2004). Representation of moving objects along a road network. In Proceedings of the 12th International Conference on Geoinformatics, Citeseer.Google Scholar
  70. Vlachos, M., Gunopulos, D., & Das, G. (2004). Rotation invariant distance measures for trajectories. In Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 707–712). Seattle, WA. ACM.Google Scholar
  71. Vlachos, M., Gunopulos, D., & Kollios, G. (2002a). Robust similarity measures for mobile object trajectories. In Preceedings of 13th International Workshop on Database and Expert Systems Applications (pp. 721–728). IEEE Computer Society.Google Scholar
  72. Vlachos, M., Kollios, G., & Gunopulos, D. (2002b). Discovering similar multidimensional trajectories. In Proceedings of 18th International Converence on Data Engineering (ICDE’02).Google Scholar
  73. Wachowicz, M., Ong, R., Renso, C., & Nanni, M. (2011). Finding moving flock patterns among pedestrians through collective coherence. International Journal of Geographical Information Science, 25(11), 1849–1864.CrossRefGoogle Scholar
  74. Van de Weghe, N., Cohn, A. G., De Tré, G., & De Maeyer, P. (2006). A qualitative trajectory calculus as a basis for representing moving objects in geographical information systems. Control and Cybernetics, 35(1), 97–119.Google Scholar
  75. Wood, Z., & Galton, A. (2009a). Classifying collective motion. In B. Gottfried & H. Aghajan (Eds.), Behaviour monitoring and interpretation–BMI–smart environments. Ambient Intelligence and Smart Environments (Vol. 3, pp. 129–155). Amsterdam, NL: IOS Press.Google Scholar
  76. Wood, Z., & Galton, A. (2009b). A taxonomy of collective phenomena. Applied Ontology, 4(3), 267–292.Google Scholar
  77. Yoon, H. & Shahabi, C. (2008). Robust time-referenced segmentation of moving object trajectories. In 8th IEEE International Conference on Data Mining (ICDM ’08) (pp. 1121–1126).Google Scholar
  78. Zhang, Q., Slingsby, A., Dykes, J., Wood, J., Kraak, M.-J., Blok, C. A., & Ahas, R. (2013). Visual analysis design to support research into movement and use of space in tallinn: A case study. Information Visualization. (In Press).Google Scholar

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© The Author(s) 2014

Authors and Affiliations

  1. 1.Institute of Natural Resource SciencesZurich University of Applied SciencesWädenswilSwitzerland

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