Computational Movement Analysis by Patrick Laube

By Patrick Laube

This SpringerBrief discusses the features of spatiotemporal circulation facts, together with uncertainty and scale. It investigates 3 center points of Computational flow research: Conceptual modeling of move and move areas, spatiotemporal research equipment aiming at a greater knowing of circulate strategies (with a spotlight on info mining for circulate patterns), and utilizing decentralized spatial computing equipment in move research. the writer offers Computational stream research as an interdisciplinary umbrella for interpreting move tactics with tools from various fields together with GIScience, spatiotemporal databases and knowledge mining. Key demanding situations in Computational circulate research comprise bridging the semantic hole, privateness concerns while flow info contains humans, incorporating huge and open information, and possibilities for decentralized circulation research coming up from the net of items. The interdisciplinary strategies of Computational stream research make this an incredible booklet for pros and scholars in machine technological know-how, geographic info technological know-how and its software components, in particular move ecology and transportation research.

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Multiple movement modes by large herbivores at multiple spatiotemporal scales. Proceedings of the National Academy of Sciences, 105(49), 19114–19119. , & Pedreschi, D. (2008). Mobility, data mining and privacy: A vision of convergence. In F. Giannotti & D. ), Mobility, data mining and privacy (pp. 1–11). Berlin: Springer. , & 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. , & Wolle, T. (2009). Compressing spatiotemporal trajectories.

This methodology—another GIScience classic—revealed for the example studied in the paper, that for an uncertainty of 1 m speed is reliably computed for temporal scales of 60 s and greater. Imfeld et al. (P1. 2006) illustrate the importance of quality control of raw data in a field experiment. The study goes at great length to get an idea of the positional accuracy of movement and movement context data. The field experiment also showed that specifically point in polygon tests for linking fixes to the environment are sensitive to both inaccuracies in the location data but also the context information.

In J. Gensel, D. Josselin, & D. ), AGILE’2012 International Conference on Geographic Information Science. FR: Avignon. Montello, D. (2001). Scale in geography. In N. J. Smelser & P. B. ), International Encyclopedia of the social and behavioral sciences (pp. 3501–13504). Oxford: Pergamon Press. , Santos, M. , & Orellana, D. (2010). The impact of data quality in the context of pedestrian movement analysis. In M. Painho, M. Y. Santos, & H. ), Geospatial thinking (pp. , Lecture Notes in Geoinformation and Cartography Berlin: Springer.

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