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Movement Spaces and Movement Traces

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Computational Movement Analysis

Part of the book series: SpringerBriefs in Computer Science ((BRIEFSCOMPUTER))

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Abstract

The analysis of the observed movement by means of computers requires abstraction, conceptual modeling, and formalization of the moving entities and the spaces embedding that movement. This preliminary but crucial stage of Computational Movement Analysis (CMA) requires modeling choices but is also constrained by the data sources at hand. This chapter investigates how movement can be modeled from the various data sources contributing to CMA, and discusses implications of the characteristics of models and sources on how movement can be captured and characterized, structured and analyzed.

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Notes

  1. 1.

    Note that this section is focused on how movement traces can be abstracted and represented in spatial information systems. Other authors have put forward conceptual models for movement in different contexts, such as, for example, for explaining organismal movement in movement ecology (Nathan et al. 2008), discussed in the related work Sect. 2.4.

References

  • Ahas, R., Silm, S., Järv, O., Saluveer, E., & Tiru, M. (2010). Using mobile positioning data to model locations meaningful to users of mobile phones. Journal of Urban Technology, 17(1), 3–27.

    Article  Google Scholar 

  • Ahas, R., Silm, S., Saluveer, E., & Järv, O. (2009). Modelling home and work locations of populations using passive mobile positioning data. In G. Gartner & K. Rehrl (Eds.), Location based services and telecartography II (pp. 301–315)., Lecture Notes in Geoinformation and Cartography Berlin: Springer.

    Chapter  Google Scholar 

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

    Article  Google Scholar 

  • Andrienko, N., Andrienko, G., Pelekis, N., & Spaccapietra, S. (2008). Basic concepts of movement data. In F. Giannotti & D. Pedreschi (Eds.), Mobility, data mining and privacy (pp. 15–38). Berlin: Springer.

    Chapter  Google Scholar 

  • Benkert, M., Gudmundsson, J., Hübner, F., & Wolle, T. (2008). Reporting flock patterns. Computational Geometry, 41(3), 111–125.

    Article  MATH  MathSciNet  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Borger, L., Franconi, N., De Michele, G., Gantz, A., Meschi, F., Manica, A., et al. (2006). Effects of sampling regime on the mean and variance of home range size estimates. Journal of Animal Ecology, 75(6), 1393–1405.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Cao, H., & Wolfson, O. (2005). Nonmaterialized motion information in transport networks. In T. Eiter & L. Libkin (Eds.), Database theory—ICDT 2005, proceedings (Vol. 3363, pp. 173–188)., Lecture Notes in Computer Science Berlin: Springer.

    Chapter  Google Scholar 

  • Delafontaine, M., Versichele, M., Neutens, T., & Van de Weghe, N. (2012). Analysing spatiotemporal sequences in bluetooth tracking data. Applied Geography, 34, 659–668.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Dodge, S., Weibel, R., & Lautenschütz, A.-K. (2008). Towards a taxonomy of movement patterns. Information Visualization, 7(3–4), 240–252.

    Article  Google Scholar 

  • Du Mouza, C., & Rigaux, P. (2005). Mobility patterns. GeoInformatica, 9(4), 297–319.

    Article  Google Scholar 

  • Fisher, P., Wood, J., & Cheng, T. (2004). Where is Helvellyn? Fuzziness of multi-scale landscape morphometry. Transactions of the Institute of British Geographers, 29(1), 106–128.

    Article  Google Scholar 

  • Fryxell, J. M., Hazell, M., Börger, L., Dalziel, B. D., Haydon, D. T., Morales, J. M., et al. (2008). Multiple movement modes by large herbivores at multiple spatiotemporal scales. Proceedings of the National Academy of Sciences, 105(49), 19114–19119.

    Article  Google Scholar 

  • Giannotti, F., & Pedreschi, D. (2008). Mobility, data mining and privacy: A vision of convergence. In F. Giannotti & D. Pedreschi (Eds.), Mobility, data mining and privacy (pp. 1–11). Berlin: Springer.

    Chapter  Google Scholar 

  • Gong, H., Chen, C., Bialostozky, E., & Lawson, C. T. (2012). A GPS/GIS method for travel mode detection in New York City. Computers, Environment and Urban Systems, 36(2), 131–139.

    Article  Google Scholar 

  • Gudmundsson, J., Katajainen, J., Merrick, D., Ong, C., & Wolle, T. (2009). Compressing spatio-temporal trajectories. Computational Geometry, 42(9), 825–841.

    Article  MATH  MathSciNet  Google Scholar 

  • Gudmundsson, J., van Kreveld, M., & Speckmann, B. (2007). Efficient detection of patterns in 2D trajectories of moving points. GeoInformatica, 11(2), 195–215.

    Article  Google Scholar 

  • Hägerstrand, T. (1970). What about people in regional science. Papers of the Regional Science Association, 24, 7–21.

    Article  Google Scholar 

  • Hurford, A. (2009). GPS measurement error gives rise to spurious 180\(^{\circ }\)-turning angles and strong directional biases in animal movement data. PLoS ONE, 4(5), e5632.

    Article  Google Scholar 

  • Imfeld, S., Haller, R., & Laube, P. (2006). Positional accuracy of biological research data in GIS—A case study in the swiss national park. In M. Caetano & M. Painho (Eds.), 7th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences (pp. 275–280). Portugal: Lisbon.

    Google Scholar 

  • Jerde, C. L., & Visscher, D. R. (2005). GPS measurement error influences on movement model parameterization. Ecological Applications, 15(3), 806–810.

    Article  Google Scholar 

  • Kuijpers, B., Miller, H. J., Neutens, T., & Othman, W. (2010). Anchor uncertainty and space-time prisms on road networks. International Journal of Geographical Information Science, 24(8), 1223–1248.

    Article  Google Scholar 

  • Laube, P. (2009). Progress in movement pattern analysis. In B. Gottfried & H. Aghajan (Eds.), Behaviour Monitoring and Interpretation, BMI, Smart Environments (Vol. 3, pp. 43–71)., Ambient Intelligence and Smart Environments Amsterdam, NL: IOS Press.

    Google Scholar 

  • Laube, P., & Dennis, T. (2006). Exploratory analysis of movement trajectories. In GeoCart,. (2006). National Cartographic Conference. Auckland, NZ.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Laube, P., Duckham, M., & Palaniswami, M. (2011). Deferred decentralized movement pattern mining for geosensor networks. International Journal of Geographical Information Science, 25(2), 273–292.

    Article  Google Scholar 

  • Laube, P., Duckham, M., & Wolle, T. (2008). Decentralized movement pattern detection amongst mobile geosensor nodes. In T. J. Cova, K. Beard, M. F. Goodchild, & A. U. Frank (Eds.), GIScience 2008 (Vol. 5266, pp. 199–216)., Lecture Notes in Computer Science, Springer: Berlin Heidelberg.

    Google Scholar 

  • Laube, P., Duckham, M., Worboys, M., & Joyce, T. (2010). Decentralized spatial computing in urban environments. In B. Jiang & X. Yao (Eds.), Geospatial analysis and modelling of urban structure and dynamics, geojournal library (pp. 53–74). Berlin: Springer.

    Chapter  Google Scholar 

  • Laube, P., & Purves, R. S. (2011). How fast is a cow? Cross-scale analysis of movement data. Transactions in GIS, 15(3), 401–418.

    Article  Google Scholar 

  • 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 

  • Montello, D. (2001). Scale in geography. In N. J. Smelser & P. B. Baltes (Eds.), International Encyclopedia of the social and behavioral sciences (pp. 3501–13504). Oxford: Pergamon Press.

    Google Scholar 

  • Moreira, A., Santos, M. Y., Wachowicz, M., & Orellana, D. (2010). The impact of data quality in the context of pedestrian movement analysis. In M. Painho, M. Y. Santos, & H. Pundt (Eds.), Geospatial thinking (pp. 61–78)., Lecture Notes in Geoinformation and Cartography Berlin: Springer.

    Chapter  Google Scholar 

  • Nams, V. O. (2005). Using animal movement paths to measure response to spatial scale. Oecologia, 143(2), 179–188.

    Article  Google Scholar 

  • Nathan, R., Getz, W. M., Revilla, E., Holyoak, M., Kadmon, R., Saltz, D., et al. (2008). A movement ecology paradigm for unifying organismal movement research. Proceedings of the National Academy of Sciences, 105(49), 19052–19059.

    Article  Google Scholar 

  • Noyon, V., Devogele, T., & Claramunt, C. (2005). A formal model for representing point trajectories in two-dimensional spaces. In J. Akoka, S. Liddle, I.-Y. Song, M. Bertolotto, I. Comyn-Wattiau, W.-J. Heuvel, M. Kolp, J. Trujillo, C. Kop, & H. Mayr (Eds.), Perspectives in conceptual modeling (Vol. 3770, pp. 208–217)., Lecture Notes in Computer Science Berlin: Springer.

    Chapter  Google Scholar 

  • Peuquet, D. J. (2002). Representation of space and time. London, UK: The Guilford Press.

    Google Scholar 

  • Postlethwaite, C. M., Brown, P., & Dennis, T. E. (2013). A new multi-scale measure for analysing animal movement data. Journal of Theoretical Biology, 317, 175–185.

    Article  MathSciNet  Google Scholar 

  • Richter, K.-F., Schmid, F., & Laube, P. (2012). Semantic trajectory compression: Representing urban movement in a nutshell. JOSIS, 4, 3–30.

    Google Scholar 

  • Shamoun-Baranes, J., van Loon, E. E., Purves, R. S., Speckmann, B., Weiskopf, D., & Camphuysen, C. J. (2012). Analysis and visualization of animal movement. Biology Letters, 8(1), 6–9.

    Article  Google Scholar 

  • Silm, S., & Ahas, R. (2010). The seasonal variability of population in estonian municipalities. Environment and Planning A, 42(10), 2527–2546.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Thériault, M., Claramunt, C., Séguin, A. M., & Villeneuve, P. (2002). Temporal GIS and statistical modelling of personal lifelines. In D. E. Richardson & P. van Oosterom (Eds.), Advances in Geographic Information Systems Research II: Proceedings ot the Interantional Symposium on Spatial Data Handling, Delft (pp. 433–449). Berlin: Springer.

    Google Scholar 

  • Tomlin, C. D. (1990). Geographic information systems and cartographic modeling. Englewood Cliffs: Prentice Hall.

    Google Scholar 

  • Trajcevski, G., Wolfson, O., Hinrichs, K., & Chamberlain, S. (2004). Managing uncertainty in moving objects databases. ACM Transactions on Database Systems (TODS), 29(3), 463–507.

    Article  Google Scholar 

  • Versichele, M., Neutens, T., Delafontaine, M., & Van de Weghe, N. (2012). The use of bluetooth for analysing spatiotemporal dynamics of human movement at mass events: A case study of the ghent festivities. Applied Geography, 32(2), 208–220.

    Article  Google Scholar 

  • Worboys, M., & Duckham, M. (2004). GIS—A computing perspective (2nd ed.). New York: CRC Press.

    Google Scholar 

  • Yan, Z., Macedo, J., Parent, C., & Spaccapietra, S. (2008). Trajectory ontologies and queries. Transactions in GIS, 12, 75–91.

    Article  Google Scholar 

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Laube, P. (2014). Movement Spaces and Movement Traces. In: Computational Movement Analysis. SpringerBriefs in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-319-10268-9_2

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  • DOI: https://doi.org/10.1007/978-3-319-10268-9_2

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