Skip to main content
Top

2014 | OriginalPaper | Chapter

3. Movement Mining

Author : Patrick Laube

Published in: Computational Movement Analysis

Publisher: Springer International Publishing

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

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

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Footnotes
1
Whereas this chapter discusses movement mining in conventional omniscient and centralized information systems or databases, the following Chap. 4 discusses the rather peculiar case where data mining is performed in decentralized systems such as geosensor networks. Even though most of the work summarized in Chap. 4 nominally also proposes data mining techniques, its theoretical underpinning in decentralized spatial computing justifies a separate chapter focusing on decentralized movement analysis alone.
 
2
Note, the research on flocking featured in this book combines data mining concepts with decentralized spatial computing principles. This chapter focuses on the data general data mining aspects, Chap. 4 on the specifics of mining movement patterns in a decentralized setting.
 
Literature
go back to reference Andersson, M., Gudmundsson, J., Laube, P., & Wolle, T. (2008). Reporting leaders and followers among trajectories of moving point objects. GeoInformatica, 12(4), 497–528.CrossRef Andersson, M., Gudmundsson, J., Laube, P., & Wolle, T. (2008). Reporting leaders and followers among trajectories of moving point objects. GeoInformatica, 12(4), 497–528.CrossRef
go back to reference 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.CrossRef 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.CrossRef
go back to reference Andrienko, N., & Andrienko, G. (2007). Designing visual analytics methods for massive collections of movement data. Cartographica, 42(2), 117–138.CrossRef Andrienko, N., & Andrienko, G. (2007). Designing visual analytics methods for massive collections of movement data. Cartographica, 42(2), 117–138.CrossRef
go back to reference Andrienko, N., & Andrienko, G. (2011). Spatial generalization and aggregation of massive movement data. IEEE Transactions on Visualization and Computer Graphics, 17(2), 205–219.CrossRef Andrienko, N., & Andrienko, G. (2011). Spatial generalization and aggregation of massive movement data. IEEE Transactions on Visualization and Computer Graphics, 17(2), 205–219.CrossRef
go back to reference Anselin, L. (1995). Local indicators of spatial association—LISA. Geographical Analysis, 27(2), 93–115.CrossRef Anselin, L. (1995). Local indicators of spatial association—LISA. Geographical Analysis, 27(2), 93–115.CrossRef
go back to reference 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.CrossRef 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.CrossRef
go back to reference Bertin, J., Berg, W., and Scott, P. (1981). Graphics and graphic information processing. De Gruyter. Bertin, J., Berg, W., and Scott, P. (1981). Graphics and graphic information processing. De Gruyter.
go back to reference 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.CrossRef 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.CrossRef
go back to reference 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.CrossRef 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.CrossRef
go back to reference 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.CrossRef 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.CrossRef
go back to reference 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. 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.
go back to reference Buchin, K., Buchin, M., van Kreveld, M., & Luo, J. (2011a). Finding long and similar parts of trajectories. Computational Geometry, 44(9), 465–476.MATHMathSciNetCrossRef Buchin, K., Buchin, M., van Kreveld, M., & Luo, J. (2011a). Finding long and similar parts of trajectories. Computational Geometry, 44(9), 465–476.MATHMathSciNetCrossRef
go back to reference 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. 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.
go back to reference 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. 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.
go back to reference Buchin, M., Driemel, A., van Kreveld, M., & Sacristan, V. (2011b). Segmenting trajectories: A framework and algorithms using spatiotemporal criteria. JOSIS, 3, 33–63. Buchin, M., Driemel, A., van Kreveld, M., & Sacristan, V. (2011b). Segmenting trajectories: A framework and algorithms using spatiotemporal criteria. JOSIS, 3, 33–63.
go back to reference 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. 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.
go back to reference 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.CrossRef 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.CrossRef
go back to reference 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.CrossRef 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.CrossRef
go back to reference Dodge, S., Weibel, R., & Lautenschütz, A.-K. (2008). Towards a taxonomy of movement patterns. Information Visualization, 7(3–4), 240–252.CrossRef Dodge, S., Weibel, R., & Lautenschütz, A.-K. (2008). Towards a taxonomy of movement patterns. Information Visualization, 7(3–4), 240–252.CrossRef
go back to reference 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.CrossRef 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.CrossRef
go back to reference 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. 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.
go back to reference 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.CrossRef 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.CrossRef
go back to reference 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.CrossRef 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.CrossRef
go back to reference Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI Magazine, 17(3), 37–54. Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI Magazine, 17(3), 37–54.
go back to reference 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. 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.
go back to reference Geng, L., & Hamilton, H. J. (2006). Interestingness measures for data mining: A survey. ACM Computing Surveys, 38(3), 9.CrossRef Geng, L., & Hamilton, H. J. (2006). Interestingness measures for data mining: A survey. ACM Computing Surveys, 38(3), 9.CrossRef
go back to reference Gonzalez, M. C., Hidalgo, C. A., & Barabasi, A. L. (2008). Understanding individual human mobility patterns. Nature, 453(7196), 779–782.CrossRef Gonzalez, M. C., Hidalgo, C. A., & Barabasi, A. L. (2008). Understanding individual human mobility patterns. Nature, 453(7196), 779–782.CrossRef
go back to reference Gottfried, B. (2011). Interpreting motion events of pairs of moving objects. GeoInformatica, 15(2), 247–271.CrossRef Gottfried, B. (2011). Interpreting motion events of pairs of moving objects. GeoInformatica, 15(2), 247–271.CrossRef
go back to reference 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.CrossRef 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.CrossRef
go back to reference Han, J., & Kamber, M. (2006). Data mining: Concepts and techniques. Amsterdam: Morgan Kaufmann Publishers. Han, J., & Kamber, M. (2006). Data mining: Concepts and techniques. Amsterdam: Morgan Kaufmann Publishers.
go back to reference Hand, D. J., Manilla, H., & Smyth, P. (2001). Principles of data mining. Cambridge, MA: MIT Press. Hand, D. J., Manilla, H., & Smyth, P. (2001). Principles of data mining. Cambridge, MA: MIT Press.
go back to reference Huang, Y., Chen, C. & Dong, P. (2008). Modeling herds and their evolvements from trajectory data. Proceedings of Fifth International Conference on Geographic Information Science. Huang, Y., Chen, C. & Dong, P. (2008). Modeling herds and their evolvements from trajectory data. Proceedings of Fifth International Conference on Geographic Information Science.
go back to reference 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. 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.
go back to reference 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.CrossRef 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.CrossRef
go back to reference 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. 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.
go back to reference 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. 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.
go back to reference 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. 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.
go back to reference 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.CrossRef 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.CrossRef
go back to reference 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.CrossRef 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.CrossRef
go back to reference 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. 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.
go back to reference 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. 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.
go back to reference 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.CrossRef 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.CrossRef
go back to reference Laube, P., & Purves, R. S. (2011). How fast is a cow? Cross-scale analysis of movement data. Transactions in GIS, 15(3), 401–418.CrossRef Laube, P., & Purves, R. S. (2011). How fast is a cow? Cross-scale analysis of movement data. Transactions in GIS, 15(3), 401–418.CrossRef
go back to reference 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. 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.
go back to reference 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. 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.
go back to reference Miller, H., & Han, J. (2009). Geographic data mining and knowledge discovery. Boca Raton: CRC Press. Miller, H., & Han, J. (2009). Geographic data mining and knowledge discovery. Boca Raton: CRC Press.
go back to reference 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. 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.
go back to reference Nagy, M., Akos, Z., Biro, D., & Vicsek, T. (2010). Hierarchical group dynamics in pigeon flocks. Nature, 464(7290), 890–893.CrossRef Nagy, M., Akos, Z., Biro, D., & Vicsek, T. (2010). Hierarchical group dynamics in pigeon flocks. Nature, 464(7290), 890–893.CrossRef
go back to reference Orellana, D. (2012). Exploring Pedestrian Movement Patterns (PhD thesis, Wageningen University). Orellana, D. (2012). Exploring Pedestrian Movement Patterns (PhD thesis, Wageningen University).
go back to reference Orellana, D., Bregt, A. K., Ligtenberg, A., & Wachowicz, M. (2012). Exploring visitor movement patterns in natural recreational areas. Tourism Management, 33(3), 672–682.CrossRef Orellana, D., Bregt, A. K., Ligtenberg, A., & Wachowicz, M. (2012). Exploring visitor movement patterns in natural recreational areas. Tourism Management, 33(3), 672–682.CrossRef
go back to reference 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. 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.
go back to reference Orellana, D., & Wachowicz, M. (2011). Exploring patterns of movement suspension in pedestrian mobility. Geographical Analysis, 43(3), 241–260.CrossRef Orellana, D., & Wachowicz, M. (2011). Exploring patterns of movement suspension in pedestrian mobility. Geographical Analysis, 43(3), 241–260.CrossRef
go back to reference 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.CrossRef 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.CrossRef
go back to reference 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.CrossRef 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.CrossRef
go back to reference Randell, D. A., Cui, Z., & Cohn, A. G. (1992). A spatial logic based on regions and connection. KR, 92, 165–176. Randell, D. A., Cui, Z., & Cohn, A. G. (1992). A spatial logic based on regions and connection. KR, 92, 165–176.
go back to reference Richter, K.-F., Schmid, F., & Laube, P. (2012). Semantic trajectory compression: Representing urban movement in a nutshell. JOSIS, 4, 3–30. Richter, K.-F., Schmid, F., & Laube, P. (2012). Semantic trajectory compression: Representing urban movement in a nutshell. JOSIS, 4, 3–30.
go back to reference 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.CrossRef 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.CrossRef
go back to reference Rykiel, E. J. J. (1996). Testing ecological models: The meaning of validation. Ecological Modelling, 90(3), 229–244.CrossRef Rykiel, E. J. J. (1996). Testing ecological models: The meaning of validation. Ecological Modelling, 90(3), 229–244.CrossRef
go back to reference 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.CrossRef 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.CrossRef
go back to reference Sester, M., Feuerhake, U., Kuntzsch, C., & Zhang, L. (2012). Revealing underlying structure and behaviour from movement data. KI, 26(3), 223–231. Sester, M., Feuerhake, U., Kuntzsch, C., & Zhang, L. (2012). Revealing underlying structure and behaviour from movement data. KI, 26(3), 223–231.
go back to reference 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.CrossRef 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.CrossRef
go back to reference 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.CrossRef 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.CrossRef
go back to reference Shapiro, L. G., & Stockman, G. C. (2001). Computer vision. New Jersey: Prentice-Hall. Shapiro, L. G., & Stockman, G. C. (2001). Computer vision. New Jersey: Prentice-Hall.
go back to reference Silberschatz, A., & Tuzhilin, A. (1996). What makes patterns interesting in knowledge discovery systems. IEEE Transactions on Knowledge and Data Engineering, 8(6), 970–974.CrossRef Silberschatz, A., & Tuzhilin, A. (1996). What makes patterns interesting in knowledge discovery systems. IEEE Transactions on Knowledge and Data Engineering, 8(6), 970–974.CrossRef
go back to reference 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.CrossRef 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.CrossRef
go back to reference Thomas, J. J., & Cook, K. A. (2006). A visual analytics agenda. IEEE Computer Graphics and Applications, 26(1), 10–13.CrossRef Thomas, J. J., & Cook, K. A. (2006). A visual analytics agenda. IEEE Computer Graphics and Applications, 26(1), 10–13.CrossRef
go back to reference Tufte, E., & Graves-Morris, P. (1983). The visual display of quantitative information (Vol. 31). Cheshire, CT: Graphics Press. Tufte, E., & Graves-Morris, P. (1983). The visual display of quantitative information (Vol. 31). Cheshire, CT: Graphics Press.
go back to reference 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. 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.
go back to reference 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. 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.
go back to reference 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. 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.
go back to reference Vlachos, M., Kollios, G., & Gunopulos, D. (2002b). Discovering similar multidimensional trajectories. In Proceedings of 18th International Converence on Data Engineering (ICDE’02). Vlachos, M., Kollios, G., & Gunopulos, D. (2002b). Discovering similar multidimensional trajectories. In Proceedings of 18th International Converence on Data Engineering (ICDE’02).
go back to reference 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.CrossRef 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.CrossRef
go back to reference 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. 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.
go back to reference 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. 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.
go back to reference Wood, Z., & Galton, A. (2009b). A taxonomy of collective phenomena. Applied Ontology, 4(3), 267–292. Wood, Z., & Galton, A. (2009b). A taxonomy of collective phenomena. Applied Ontology, 4(3), 267–292.
go back to reference 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). 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).
go back to reference 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). 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).
Metadata
Title
Movement Mining
Author
Patrick Laube
Copyright Year
2014
DOI
https://doi.org/10.1007/978-3-319-10268-9_3

Premium Partner