Visualization of Life Patterns through Deformation of Maps Based on Users’ Movement Data

  • Hayato Yokoi
  • Kohei Matsumura
  • Yasuyuki Sumi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8210)


This paper proposes a system for visualizing individual and collective movement within dense geographical contexts, such as cities and urban neighborhoods. Specifically, we describe a method for creating “spatiotemporal maps” deformed according to personal movement and stasis. We implement and apply a prototype of our system to demonstrate its effectiveness in revealing patterns of spatiotemporal behavior, and in composing maps that more closely correspond to the node-oriented “mental maps” traditionally used by individuals in the act of navigation.


Movement Data Image Deformation Urban Neighborhood Stay Time Spatial Mesh 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Ashbrook, D., Starner, T.: Learning Significant Locations and Predicting User Movement with GPS. In: Proceedings of the 6th IEEE International Symposium on Wearable Computers, pp. 101–108 (2002)Google Scholar
  2. 2.
    Agrawala, M., Stolte, C.: Rendering Effective Route Maps: Improving Usability Through Generalization. In: Proceedings of ACM SIGGRAPH 2001, pp. 241–250 (2001)Google Scholar
  3. 3.
    Patterson, D.J., Liao, L., Fox, D., Kautz, H.: Inferring High-Level Behavior from Low-Level Sensors. In: Dey, A.K., Schmidt, A., McCarthy, J.F. (eds.) UbiComp 2003. LNCS, vol. 2864, pp. 73–89. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  4. 4.
    Schoning, J., Cheverst, K., Lochtefeld, M., Kruger, A., Rohs, M., Taher, F.: PhotoMap: Using Spontaneously taken Images of Public Maps for Pedestrian Navigation Tasks on Mobile Devices. In: Proceedings of MobileHCI 2009 (2009)Google Scholar
  5. 5.
    Shen, Z., Ma, K.-L.: MobiVis: A Visualization System for Exploring Mobile Data. In: Proceedings of Pacific Visualisation Symposium 2008 (2008)Google Scholar
  6. 6.
    Shimizu, E., Inoue, R.: A Generalized Solution of Time-Distance Mapping. In: Proceedings of the 8th International Conference on Computers in Urban Planning and Urban Management (2003)Google Scholar
  7. 7.
    Schaefer, S., McPhail, T., Warren, J.: Image Deformation Using Moving Least Squares. In: Proceedings of ACM SIGGRAPH 2006, pp. 533–540 (2006)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Hayato Yokoi
    • 1
  • Kohei Matsumura
    • 1
  • Yasuyuki Sumi
    • 1
  1. 1.Future University HakodateHakodateJapan

Personalised recommendations