Advertisement

Grand Challenges in Computational Movement Analysis

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

Abstract

This final chapter addresses the prospect of Computational Movement Analysis (CMA) as a relatively young research field. The first decade of CMA was shaped by significant technological developments resulting in much increased availability of fine-grained movement data, an innocent and somewhat naïve enthusiasm over moving points resulting in a wide but fragmented variety of methods for movement analysis, and finally due to this lack of a unifying theory of CMA only moderate success in overcoming GIS’ and GIScience’ legacy of static cartography. The final chapter concludes this book by proposing a set of grand challenges of CMA.

Keywords

Movement Data Grand Challenge Final Chapter Social Media Data Movement Ecology 
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.

References

  1. Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI Magazine, 17(3), 37–54.Google Scholar
  2. Galton, A. (2005). Dynamic collectives and their collective dynamics. In A. Cohn & D. M. Mark (Eds.), Spatial information theory, proceedings, (Vol. 3693, pp. 300–315)., Lecture Notes in Computer Science Berlin: Springer.CrossRefGoogle Scholar
  3. Graham, M., & Shelton, T. (2013). Geography and the future of big data, big data and the future of geography. Dialogues in Human Geography, 3(3), 255–261.CrossRefGoogle Scholar
  4. Kitchin, R. (2013). Big data and human geography: Opportunities, challenges and risks. Dialogues in Human Geography, 3(3), 262–267.CrossRefGoogle Scholar
  5. Krumm, J. (2009). A survey of computational location privacy. Personal and Ubiquitous Computing, 13(6), 391–399.CrossRefGoogle Scholar
  6. 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
  7. 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.CrossRefGoogle Scholar
  8. Mayer-Schönberger, V., & Cukier, K. (2013). Big data: A revolution that will transform how we live, work, and think. Boston, MA: Houghton Mifflin Harcourt.Google Scholar
  9. Sui, D., & Goodchild, M. (2011). The convergence of gis and social media: challenges for giscience. International Journal of Geographical Information Science, 25(11), 1737–1748.CrossRefGoogle Scholar
  10. Wirz, M., Franke, T., Roggen, D., Mitleton-Kelly, E., Lukowicz, P., & Troster, G. (2012). Inferring crowd conditions from pedestrians’ location traces for real-time crowd monitoring during city-scale mass gatherings. IEEE 21st International Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE), pp. 367–372.Google Scholar

Copyright information

© The Author(s) 2014

Authors and Affiliations

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

Personalised recommendations