Skip to main content
SpringerLink
Log in

Computer-gestützte Bewegungsanalyse

  • Chapter
  • First Online:
Geoinformatik

Part of the book series: Springer Reference Naturwissenschaften ((SRN))

  • 3503 Accesses

Zusammenfassung

Die jüngsten Fortschritte der Trackingtechnologie produzieren Geodaten, welche die Bewegung mobiler Objekte mit einer bisher unerreichten räumlichen und zeitlichen Auflösung erfassen. Diese neue, von Natur aus raumzeitliche Art geographischer Informationen ermöglicht neue Einsichten in dynamische geographische Prozesse, stellt aber auch die traditionell eher statischen Werkzeuge der Raumanalyse infrage. Dieses Kapitel gibt einen Überblick über Bewegungsdaten im Allgemeinen, die Theorie der Bewegungsmodellierung und -analyse sowie eine Reihe wichtiger Anwendungsfelder der computer-gestützten Bewegungsanalyse. Schließlich geht das Kapitel auf Überlegungen bezüglich der Privatsphäre ein, welche für die Analyse der Bewegung von Menschen sehr wichtig sind.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 119.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 129.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Literatur

  1. Gonzalez, M.C., Hidalgo, C.A., Barabasi, A.L.: Understanding individual human mobility patterns. Nature 453, 779–782 (2008)

    Article  Google Scholar 

  2. Kellerer, W., Bettstetter, C., Schwingenschlogl, C., Sties, P., Steinberg, K.E.: (Auto) mobile communication in a heterogeneous and converged world. IEEE Pers. Commun. 8, 41–47 (2001)

    Google Scholar 

  3. Gudmundsson, J., Wolle, T.: Football analysis using spatio-temporal tools. Comput. Environ. Urban Syst. 47, 16–27 (2014)

    Article  Google Scholar 

  4. Holyoak, M., Casagrandi, R., Nathan, R., Revilla, E., Spiegel, O.: Trends and missing parts in the study of movement ecology. Proc. Natl. Acad. Sci. USA 105, 19060–19065 (2008)

    Article  Google Scholar 

  5. Galton, A.: Dynamic collectives and their collective dynamics. In: Cohn, A.G., Mark, D.M. (Hrsg.) Spatial Information Theory, Proceedings. Lecture Notes in Computer Science, Bd. 3693, S. 300–315. Springer, Heidelberg (2005)

    Google Scholar 

  6. Claussen, D.L., Finkler, M.S., Smith, M.M.: Thread trailing of turtles: methods for evaluating spatial movements and pathway structure. Can. J. Zool. 75, 2120–2128 (1997)

    Article  Google Scholar 

  7. Tomkiewicz, S.M., Fuller, M.R., Kie, J.G., Bates, K.K.: Global positioning system and associated technologies in animal behaviour and ecological research. Philos. Trans. R. Soc. B 365(1550), 2163–2176 (2010)

    Article  Google Scholar 

  8. Miller, H.J., Goodchild, M.F.: Data-driven geography. GeoJournal 80(4), 449–461 (2015)

    Article  Google Scholar 

  9. Long, J.A., Nelson, T.A.: A review of quantitative methods for movement data. Int. J. Geogr. Inf. Sci. 27(2), 292–318 (2013)

    Article  Google Scholar 

  10. Demšar, U., Buchin, K., Cagnacci, F., Safi, K., Speckmann, B., Van de Weghe, N., Weiskopf, D., Weibel, R.: Analysis and visualisation of movement: an interdisciplinary review. Mov. Ecol. 3(1), 1–24 (2015)

    Article  Google Scholar 

  11. Laube, P.: Computational Movement Analysis, S. 1–87. Springer, Cham (2014)

    Google Scholar 

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

    Article  Google Scholar 

  13. Demšar, U., Buchin, K., van Loon, E.E., Shamoun-Baranes, J.: Stacked space-time densities: a geovisualisation approach to explore dynamics of space use over time. GeoInformatica 19(1), 85–115 (2015)

    Article  Google Scholar 

  14. Hägerstrand, T.: What about people in regional science. Pap. Reg. Sci. Assoc. 24, 7–21 (1970)

    Article  Google Scholar 

  15. Miller, H.J.: Modelling accessibility using space-time prism concepts within geographical information systems. Int. J. Geogr. Inf. Syst. 5, 287–301 (1991)

    Article  Google Scholar 

  16. Richter, K.F., Schmid, F., Laube, P.: Semantic trajectory compression: representing urban movement in a nutshell. J. Spat. Inf. Sci. 2012(4), 3–30 (2012)

    Google Scholar 

  17. Quddus, M.A., Ochieng, W.Y., Noland, R.B.: Current map-matching algorithms for transport applications: State-of-the art and future research directions. Transp. Res. C: Emerg. Technol. 15(5), 312–328 (2007)

    Article  Google Scholar 

  18. Du Mouza, C. Rigaux, P.: Mobility patterns. GeoInformatica 9, 297–319 (2005)

    Google Scholar 

  19. Järv, O., Ahas, R., Witlox, F.: Understanding monthly variability in human activity spaces: a twelve-month study using mobile phone call detail records. Transp. Res. C: Emerg. Technol. 38, 122–135 (2014)

    Article  Google Scholar 

  20. Lorentzos, N.A.: A formal extension of the relational model for the representation and manipulation of generic intervals. Dissertation, Birbeck College, Universität London (1988)

    Google Scholar 

  21. Langran, G.: Time in geographic information systems. Dissertation, Universität Washington (1999)

    Google Scholar 

  22. Sltenis, S., Jensen, C.S., Leutenegger, S.T., Lopez, M.A.: Indexing the positions of continuously moving objects. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, S. 331–342 (2000)

    Google Scholar 

  23. Hadjieleftheriou, M., Kollios, G., Tsotras, V.J., Gunopulos, D.: Indexing spatio-temporal archives. VLDB J. 15, 143–164 (2006)

    Article  Google Scholar 

  24. Buchin, M., Kruckenberg, H., Kölzsch, A.: Segmenting trajectories by movement states. In: Advances in Spatial Data Handling, S. 15–25. Springer, Berlin/Heidelberg (2013)

    Google Scholar 

  25. Buchin, M., Driemel, A., van Kreveld, M., Sacristán, V.: Segmenting trajectories: a framework and algorithms using spatiotemporal criteria. J. Spat. Inf. Sci. 2011(3), 33–63 (2011)

    Google Scholar 

  26. Anagnostopoulos, A., Vlachos, M., Hadjieleftheriou, M., Keogh, E., Yu, P.S.: Global distance-based segmentation of trajectories. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, S. 34–43 (2006)

    Google Scholar 

  27. Rasetic, S., Sander, J., Elding, J., Nascimento, M.A.: A trajectory splitting model for efficient spatio-temporal indexing. In: Proceedings of the 31st International Conference on Very Large Data Bases, S. 934–945 (2005)

    Google Scholar 

  28. Yoon, H., Shahabi, C.: Robust time-referenced segmentation of moving object trajectories. In: Proceedings of the IEEE international Conference on Data Mining, S. 1121–1126 (2008)

    Google Scholar 

  29. Douglas, D.H., Peucker, T.K.: Algorithms for the reduction of the number of points required to represent a digitized line or its caricature. Can. Cartogr. 10, 112–122 (1973)

    Article  Google Scholar 

  30. Cao, H., Wolfson, O., Trajcevski, G.: Spatio-temporal data reduction with deterministic error bounds. VLDB J. 15, 211–228 (2006)

    Article  Google Scholar 

  31. Gudmundsson, J., Katajainen, J., Merrick, D., Ong, C., Wolle, T.: Compressing spatiotemporal trajectories. Comput. Geom. Theory Appl. 42, 825–841 (2009)

    Article  Google Scholar 

  32. N. Meratnia, de By, R.A.: Spatiotemporal compression techniques for moving point objects. In: Proceedings of the 9th International Conference on Extending Database Technology, S. 765–782 (2004)

    Google Scholar 

  33. Toohey, K., Duckham, M.:. Trajectory similarity measures. SIGSPATIAL Spec. 7(1), 43–50 (2015)

    Article  Google Scholar 

  34. Agrawal, R. Faloutsos, C., Swami, A.N.: Efficient similarity search in sequence databases. In: Proceedings of the 4th International Conference on on Foundations of Data Organization and Algorithms, S. 69–84 (1993)

    Google Scholar 

  35. Chu, K., Wong, M.: Fast time-series searching with scaling and shifting. In: Proceedings of the 18th ACM Symposium on Principles of Database Systems, S. 237–248 (1999)

    Google Scholar 

  36. Rafiei, D., Mendelzon, A.O.: Querying time series data based on similarity. IEEE Trans. Knowl. Data Eng. 12, 675–693 (2000)

    Article  Google Scholar 

  37. Yi, B.-K., Faloutsos, C.: Fast time sequence indexing for arbitrary lp norms. In: Proceedings of the 26th International Conference on Very Large Data Bases, S. 385–394 (2000)

    Google Scholar 

  38. Chen, L., Özsu, M.T., Oria, V.: Robust and fast similarity search for moving object trajectories. In: Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data, S. 491–502. ACM, New York (2005)

    Google Scholar 

  39. Dodge, S., Laube, P., Weibel, R.: Movement similarity assessment using symbolic representation of trajectories. Int. J. Geogr. Inf. Sci. 26(9), 1563–1588 (2012)

    Article  Google Scholar 

  40. Berndt, D.J., Clifford, J.: Using dynamic time warping to find patterns in time series. In: Proceedings of the Knowledge Discovery in Databases Workshop, S. 359–370 (1994)

    Google Scholar 

  41. Keogh, E., Ratanamahatana, C.A.: Exact indexing of dynamic time warping. Knowl. Inf. Syst. 7, 358–386 (2005)

    Article  Google Scholar 

  42. Keogh, E.J., Pazzani, M.J.: Scaling up dynamic time warping for datamining applications. In: Proceedings of the 6th ACM International Conference on Knowledge Discovery and Data Mining, S. 285–289 (2000)

    Google Scholar 

  43. Sakurai, Y., Yoshikawa, M., Faloutsos, C.: Ftw: fast similarity search under the time warping distance. In: Proceedings of the 24th ACM Symposium on Principles of Database Systems, S. 326–337 (2005)

    Google Scholar 

  44. Yuan, Y.: Image-Based Gesture Recognition with Support Vector Machines. ProQuest (2008)

    Google Scholar 

  45. Agrawal, R., Lin, K.-I., Sawhney, H.S., Shim, K.: Fast similarity search in the presence of noise, scaling, and translation in time-series databases. In: Proceedings of the 21th International Conference on Very Large Data Bases, S. 490–501 (1995)

    Google Scholar 

  46. Das, G., Gunopulos, D., Mannila, H.: Finding similar time series. In: Proceedings of the 1st European Symposium on Principles of Data Mining and Knowledge Discovery, S. 88–100 (1997)

    Google Scholar 

  47. Vlachos, M., Gunopoulos, D., Kollios, G.: Discovering similar multidimensional trajectories. In: Proceedings of the 18th International Conference on Data Engineering, S. 673–682 (2002)

    Google Scholar 

  48. Buchin, K., Buchin, M., van Kreveld, M., Luo, J.: Finding long and similar parts of trajectories. In: Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, S. 296–305 (2009)

    Google Scholar 

  49. Sinha, G., Mark, D.M.: Measuring similarity between geospatial lifelines in studies of environmental health. J. Geogr. Syst. 7, 115–136 (2005)

    Article  Google Scholar 

  50. Trajcevski, G., Ding, H., Scheuermann, P., Tamassia, R., Vaccaro, D.: Dynamics-aware similarity of moving objects trajectories. In: Proceedings of the 15th Annual ACM International Symposium on Advances in Geographic Information Systems, S. 11:1–11:8 (2007)

    Google Scholar 

  51. Nanni, M., Pedreschi, D.: Time-focused clustering of trajectories of moving objects. J. Intell. Inf. Syst. 27, 267–289 (2006)

    Article  Google Scholar 

  52. van Kreveld, M., Luo, J.: The definition and computation of trajectory and subtrajectory similarity. In: Proceedings of the 15th Annual ACM International Symposium on Advances in Geographic Information Systems, S. 44:1–44:4 (2007)

    Google Scholar 

  53. Fréchet, M.: Sur quelques points du calcul fonctionnel. Rend. Circ. Math. Palermo 22, 1–74 (1906)

    Article  Google Scholar 

  54. Alt, H., Godau, M.: Computing the fréchet distance between two polygonal curves. Int. J. Comput. Geom. Appl. 5, 75–91 (1995)

    Article  Google Scholar 

  55. Buchin, K., Buchin, M., Gudmundsson, J.: Constrained free space diagrams: a tool for trajectory analysis. Int. J. Geogr. Inf. Sci. 24, 1101–1125 (2010)

    Article  Google Scholar 

  56. Maheshwari, A., Sack, J.-R., Shahbaz, K., Zarrabi-Zadeh, H.: Fréchet distance with speed limits. Comput. Geom. Theory Appl. 44, 110–120 (2011)

    Article  Google Scholar 

  57. Buchin, K., Buchin, M., Van Kreveld, M., Luo, J. (2011). Finding long and similar parts of trajectories. Comput. Geom. 44(9), 465–476

    Article  Google Scholar 

  58. Buchin, K., Buchin, M., Gudmundsson, J., Löffler, M., Luo, J.: Detecting commuting patterns by clustering subtrajectories. Int. J. Comput. Geom. Appl. 21, 253–282 (2011)

    Article  Google Scholar 

  59. Zhang, Z., Huang, K., Tan, T.: Comparison of similarity measures for trajectory clustering in outdoor surveillance scenes. In: Proceedings of the 18th International Conference on Pattern Recognition, S. 1135–1138 (2006)

    Google Scholar 

  60. Djordjevic, B., Gudmundsson, J., Pham, A., Wolle, T.: Detecting regular visit patterns. In: Proceedings of the 16th Annual European Symposium on Algorithms, S. 344–355 (2008)

    Google Scholar 

  61. Mamoulis, N., Cao, H., Kollios, G., Hadjieleftheriou, M., Tao, Y., Cheung, D.: Mining, indexing, and querying historical spatiotemporal data. In: Proceedings of the 10th ACM International Conference on Knowledge Discovery and Data Mining, S. 236–245 (2004)

    Google Scholar 

  62. Verhein, F., Chawla, S.: Mining spatio-temporal association rules, sources, sinks, stationary regions and thoroughfares in object mobility databases. In: Proceedings of the 11th International Conference on Database Systems for Advanced Applications, Lecture Notes in Computer Science Bd. 3882, S. 187–201. Springer, Berlin (2006)

    Google Scholar 

  63. Laube, P., van Kreveld, M., Imfeld, S.: Finding REMO – detecting relative motion patterns in geospatial lifelines. In: Fisher, P.F. (Hrsg.) Developments in Spatial Data Handling, Proceedings of the 11th International Symposium on Spatial Data Handling, S. 201–214. Springer, Berlin (2004)

    Google Scholar 

  64. Benkert, M., Gudmundsson, J., Hübner, F., Wolle, T.: Reporting flock patterns. Comput. Geom. Theory Appl. 41, 111–125 (2008)

    Article  Google Scholar 

  65. Gudmundsson, J., van Kreveld, M.: Computing longest duration flocks in trajectory data. In: Proceedings of the 14th Annual ACM Symposium on Advances in Geographic Information Systems, S. 35–42 (2006)

    Google Scholar 

  66. Kalnis, P., Mamoulis, N., Bakiras, S.: On discovering moving clusters in spatio-temporal data. In: Medeiros, C.B., Egenhofer, M.J., Bertino, E. (Hrsg.) Proceedings of the 9th International Symposium on Advances Spatial and Temporal Databases. Lecture Notes in Computer Science Bd. 3633, S. 364–381. Springer, Berlin (2005)

    Google Scholar 

  67. Andersson, M., Gudmundsson, J., Laube, P., Wolle, T.: Reporting leadership patterns among trajectories. In: Proceedings of the 22nd ACM Symposium on Applied Computing. ACM (2007)

    Google Scholar 

  68. Benkert, M., Djordjevic, B., Gudmundsson, J., Wolle, T.: Finding popular places. Int. J. Comput. Geom. Appl. 20, 19–42 (2010)

    Article  Google Scholar 

  69. Jeung, H., Shen, H.T., Zhou, X.: Convoy queries in spatio-temporal databases. In: Proceedings of the 24th International Conference on Data Engineering, S. 1457–1459 (2008)

    Google Scholar 

  70. Jeung, H., Yiu, M.L., Zhou, X., Jensen, C.S., Shen, H.T.: Discovery of convoys in trajectory databases. Proc. VLDB Endow. 1, 1068–1080 (2008)

    Article  Google Scholar 

  71. Brillinger, D.R., Preisler, H.K., Ager, A.A., Kie, J.G.: An exploratory data analysis (EDA) of the paths of moving animals. J. Stat. Plan. Inference 122, 43–63 (2004)

    Article  Google Scholar 

  72. Dykes, J.A., Mountain, D.M.: Seeking structure in records of spatio-temporal behavior: visualization issues, efforts and application. Comput. Stat. Data Anal. 43, 581–603 (2003)

    Article  Google Scholar 

  73. Andrienko, N.V., Andrienko, G.L.: Interactive maps for visual data exploration. Int. J. Geogr. Inf. Sci. 13, 355–374 (2003)

    Article  Google Scholar 

  74. Andrienko, G. , Andrienko, N., Demsar, U., Dransch, D., Dykes, J., Fabrikant, S.I., Jern, M., Kraak, M.J., Schumann, H., Tominski, C.: Space, time and visual analytics. Int. J. Geogr. Inf. Sci. 24, 1577–1600 (2010)

    Article  Google Scholar 

  75. Andrienko, G., Andrienko, N., Bak, P., Keim, D., Wrobel, S.: Visual Analytics of Movement. Springer Science & Business Media, Heidelberg (2013)

    Book  Google Scholar 

  76. Andrienko, N., Andrienko, G.: Designing visual analytics methods for massive collections of movement data. Cartographica 42, 117–138 (2007)

    Article  Google Scholar 

  77. Thomas, J.J., Cook, K.A.: A visual analytics agenda. IEEE Comput. Graph. Appl. 26, 10–13 (2006)

    Article  Google Scholar 

  78. Rinzivillo, S., Pedreschi, D., Nanni, M., Giannotti, F., Andrienko, N., Andrienko, G.: Visually driven analysis of movement data by progressive clustering. Inf. Vis. 7, 225–239 (2008)

    Article  Google Scholar 

  79. Kintisch, E.: Inching toward movement ecology. Science 313, 779–782 (2006)

    Article  Google Scholar 

  80. Nathan, R., Getz, W.M., Revilla, E., Holyoak, M., Kadmon, R., Saltz, D., Smouse, P.E.: A movement ecology paradigm for unifying organismal movement research. Proc. Nat. Acad. Sci. 105, 19052–19059 (2008)

    Article  Google Scholar 

  81. Nathan, R., Spiegel, O., Fortmann-Roe, S., Harel, R., Wikelski, M., Getz, W.M.: Using tri-axial acceleration data to identify behavioral modes of free-ranging animals: general concepts and tools illustrated for griffon vultures. J. Exp. Biol. 215(6), 986–996 (2012)

    Article  Google Scholar 

  82. Dodge, S., Bohrer, G., Weinzierl, R., Davidson, S.C., Kays, R., Douglas, D., … Wikelski, M.: The environmental-data automated track annotation (Env-DATA) system: linking animal tracks with environmental data. Mov. Ecol. 1(1), 3 (2013)

    Google Scholar 

  83. Horne, J.S., Garton, E.O., Krone, S.M., Lewis, J.S.: Analyzing animal movements using Brownian bridges. Ecology 88(9), 2354–2363 (2007)

    Article  Google Scholar 

  84. Güting, R.H., Schneider, M.: Moving Objects Databases. Elsevier Morgan Kaufmann, San Francisco, CA (2005)

    Google Scholar 

  85. Geers, G., Sester, M., Winter, S., Wolfson, O.E.: 10121 report – towards a computational transportation science. In: Geers, G., Sester, M., Winter, S., Wolfson, O.E. (Hrsg.) Computational Transportation Science. Leibniz-Zentrum für Informatik, Dagstuhl (2010)

    Google Scholar 

  86. Popoola, O.P., Wang, K. (2012). Video-based abnormal human behavior recognition—a review. IEEE Trans Syst. Man Cybern. C: Appl. Rev. 42(6), 865–878

    Google Scholar 

  87. Candamo, J., Shreve, M., Goldgof, D.B., Sapper, D.B., Kasturi, R.: Understanding transit scenes: a survey on human behavior-recognition algorithms. IEEE Trans. Intell. Transp. Syst., 11(1), 206–224 (2010)

    Article  Google Scholar 

  88. Blanke, U., Troster, G., Franke, T., Lukowicz, P.: Capturing crowd dynamics at large scale events using participatory gps-localization. In: 2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), S. 1–7. IEEE, Piscataway (2014)

    Google Scholar 

  89. Larson, J.S., Bradlow, E.T., Fader, P.S.: An exploratory look at supermarket shopping paths. Int. J. Res. Mark. 22, 395–414 (2005)

    Article  Google Scholar 

  90. Gudmundsson, J., Wolle, T.: Towards automated football analysis: algorithms and data structures. In: Proceedings of the 10th Australasian Conference on Mathematics and Computers in Sport (2010)

    Google Scholar 

  91. Kang, C.-H., Hwang, J.-R., Li, K.-J.: Trajectory analysis for soccer players. In: Proceedings of the 6th IEEE International Conference on Data Mining Workshop, S. 377–381 (2006)

    Google Scholar 

  92. Gudmundsson, J., Wolle, T.: Football analysis using spatio-temporal tools. Comput. Environ. Urban Syst. 47, 16–27 (2014)

    Article  Google Scholar 

  93. Fujimura, A., Sugihara, K.: Geometric analysis and quantitative evaluation of sport teamwork. Syst. Comput. Jpn. 36, 49–58 (2005)

    Article  Google Scholar 

  94. Horton, M., Gudmundsson, J., Chawla, S., Estephan, J.: Automated classification of passing in football. In: Advances in Knowledge Discovery and Data Mining, S. 319–330. Springer International Publishing, Berlin/Heidelberg (2015)

    Google Scholar 

  95. Memmert, D., Perl, J.: Game creativity analysis by means of neural networks. J. Sport Sci. 27, 139–149 (2009)

    Article  Google Scholar 

  96. Grunz, A., Memmert, D., Perl, J.: Analysis and simulation of actions in games by means of special self-organizing maps. Int. J. Comput. Sci. Sport 8, 22–36 (2009)

    Google Scholar 

  97. Laube, P., Imfeld, S., Weibel, R.: Discovering relative motion patterns in groups of moving point objects. Int. J. Geogr. Inf. Sci. 19, 639–668 (2005)

    Article  Google Scholar 

  98. Nittel, S., Stefanidis, A., Cruz, I., Egenhofer, M.J., Goldin, D., Howard, A., Labrinidis, A., Madden, S., Voisard, A. Worboys, M.: Report from the first workshop on geo sensor networks. ACM SIGMOD Rec. 33, 141–144 (2004)

    Article  Google Scholar 

  99. Lynch, N.: Distributed Algorithms. Morgan Kaufmann, San Mateo (1996)

    Google Scholar 

  100. Duckham, M.: Decentralized Spatial Computing: Foundations of Geosensor Networks. Springer Science & Business Media, Berlin/New York (2012)

    Google Scholar 

  101. Yu, X.: Approaches and principles of fall detection for elderly and patient. In: 2008 10th International Conference on e-Health Networking, Applications and Services (HealthCom 2008), S. 42–47. IEEE, Piscataway (2008)

    Google Scholar 

  102. Both, A., Duckham, M., Laube, P., Wark, T., Yeoman, J.: Decentralized monitoring of moving objects in a transportation network augmented with checkpoints. Comput. J. 56(12), 1432–1449 (2013)

    Article  Google Scholar 

  103. Laube, P., Duckham, M., Wolle, T.: Decentralized movement pattern detection amongst mobile geosensor nodes. In: Cova, T.J., Beard, K., Goodchild, M.F., Frank, A.U. (Hrsg.) GIScience 2008. LNCS, Bd. 5266, S. 199–216. Springer, Heidelberg (2008)

    Google Scholar 

  104. Dobson, J.E., Fisher, P.F.: Geoslavery. IEEE Technol. Soc. Mag. 22, 47–52 (2003)

    Article  Google Scholar 

  105. Bettini, C., Wang, X., Jajodia, S.: Protecting privacy against location-based personal identification. In: Jonker, W., Petkovic, M. (Hrsg.) Secure Data Management. Lecture Notes in Computer Science, Bd. 3674, S. 185–199. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  106. Duckham, M., Kulik, L.: Location privacy and location-aware computing. In: Drummond, J., Billen, R., Forrest, D., Joao, E. (Hrsg.) Dynamic and Mobile GIS. CRC Press, Boca Raton (2006)

    Google Scholar 

  107. U.S. Department of Justice, Office of Information and Privacy: overview of the privacy act of 1974 (2004)

    Google Scholar 

  108. Kaasinen, E.: User needs for location-aware mobile services. Pers. Ubiquitous Comput. 7, 70–79 (2003)

    Article  Google Scholar 

  109. Kido, H., Yanagisawa, Y., Satoh, T.: An anonymous communication technique using dummies for location-based services. In: International Conference on Pervasive Services (ICPS ’05), S. 88–97 (2005)

    Google Scholar 

  110. Duckham, M., Kulik, L.: Simulation of obfuscation and negotiation for location privacy. In: Spatial Information Theory (COSIT 2005). Lecture Notes in Computer Science, Bd. 3693, S. 31–48. Springer, Heidelberg (2005)

    Google Scholar 

  111. Duckham, M., Kulik, L.: A formal model of obfuscation and negotiation for location privacy. In: Gellersen, H.W., Want, R., Schmidt, A. (Hrsg.) Pervasive Computing, Proceedings. Lecture Notes in Computer Science, Bd. 3468, S. 152–170. Springer, Berlin (2005)

    Google Scholar 

  112. Giannotti, F. Pedreschi, D.: Mobility, data mining and privacy: a vision of convergence. In: Giannotti, F., Pedreschi, D. (Hrsg.) Mobility, Data Mining and Privacy, S. 1–11. Springer, Berlin (2008)

    Chapter  Google Scholar 

  113. de Montjoye, Y.A., Hidalgo, C.A., Verleysen, M., Blondel, V.D.: Unique in the crowd: the privacy bounds of human mobility. Sci. Rep. 3, 1–5 (2013)

    Article  Google Scholar 

  114. Uteck, A.: Ubiquitous computing and spatial privacy, anonymity, privacy and identity in a networked society. In: Kerr, I., Steeves, V., Lucock, C. (Hrsg.) Lessons from the Identity Trail, S. 83–102. Oxford University Press, Oxford (2009)

    Google Scholar 

  115. Nouwt, S.: Reasonable expectations of geo-privacy? SCRIPTed 5, 375–403 (2008)

    Article  Google Scholar 

  116. Peuquet, D.J.: It’s about time: a conceptual framework for the representation of temporal dynamics in geographic information systems. Ann. Assoc. Am. Geogr. 83, 441–461 (1994)

    Article  Google Scholar 

  117. Chrisman, N.R.: Beyond the snapshot: changing the approach to change, error, and process. In: Egenhofer, M.J., Golledge, R.G. (Hrsg.) Spatial and Temporal Reasoning in Geographic Information Systems, S. 85–93. Oxford University Press, Oxford (1998)

    Google Scholar 

  118. Laube, P.: The low hanging fruit is gone: achievements and challenges of computational movement analysis. SIGSPATIAL Spec. 7(1), 3–10 (2015)

    Article  Google Scholar 

  119. Dodge, S., Weibel, R., Lautenschutz, A.-K.: Towards a taxonomy of movement patterns. Inf. Vis. 7, 240–252 (2008)

    Article  Google Scholar 

  120. Wood, Z., Galton, A.: Classifying collective motion. In: Gottfried, B., Aghajan, H. (Hrsg.) Behaviour Monitoring and Interpretation – BMI – Smart Environments. Ambient Intelligence and Smart Environments, Bd. 3, S. 129–155. IOS Press, Amsterdam (2009)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Zürcher Hochschule für Angewandte Wissenschaften, ZHAW, Wädenswil, Schweiz

    Patrick Laube

  2. NICTA, Sydney, NSW, Australia

    Joachim Gudmundsson & Thomas Wolle

Authors
  1. Patrick Laube
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Joachim Gudmundsson
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Thomas Wolle
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Patrick Laube .

Editor information

Editors and Affiliations

  1. Hannover, Germany

    Monika Sester

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer-Verlag GmbH Deutschland, ein Teil von Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Laube, P., Gudmundsson, J., Wolle, T. (2019). Computer-gestützte Bewegungsanalyse. In: Sester, M. (eds) Geoinformatik. Springer Reference Naturwissenschaften . Springer Spektrum, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-47096-1_68

Download citation

Publish with us

Policies and ethics

Search

Navigation