Space-Time Mapping of Mass Event Data

Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)


In conventional thematic cartography the visualisation techniques to symbolise spatio-temporal phenomena are limited. On a two dimensional map temporal changes can only be visualised adequately as time series or by animation. To simultaneously visualise thematic data in space and time a third dimension must be added. In this work conventional cartographic symbolization meets the space-time cube to create a holistic 3D spatio-temporal visualisation model. The two dimensional proportional symbol mapping technique is adopted and extruded into the third dimension to model the temporal factor. Kernel density estimation is performed on the time line to create a temporal continuous model from discrete points in time. The resulting visualisation model is implemented into an earth viewer to enable the user to freely navigate the phenomenon and visually detect anomalies without losing the overall view. This tool is evaluated by visualizing the events of a mobile phone location dataset over space and time in one single model.


Visual analytics Geostatistics Thematic cartography 3D visualisation Mobile phone location data 


  1. Ahas R, Silm S et al (2010) Using mobile positioning data to model locations meaningful to users of mobile phones. J urban technol 17:25CrossRefGoogle Scholar
  2. Aigner W, Miksch S et al (2007) Visualizing time-oriented data: a systematic view. Comput Graphics 31(3):17CrossRefGoogle Scholar
  3. Andrienko N, Andrienko G (2006) Exploratory analysis of spatial and temporal data: a systematic approach. Springer, BerlinGoogle Scholar
  4. Bertin J (1967/1984) Semiology of graphics: diagrams, networks, maps. University of Wisconsin Press, MadisonGoogle Scholar
  5. Forlines C, Wittenburg K (2010) Wakame: sense making of multi-dimensional spatial-temporal data. International conference on advanced visual interfaces, ACM Press, RomaGoogle Scholar
  6. Green M (1998) Toward a perceptual science of multidimensional data visualization: bertin and beyond., Retrieved Access 6 May 2011
  7. Hägerstrand T (1970) What about People in Regional Science? Pap Reg Sci Assoc 24:15CrossRefGoogle Scholar
  8. Haggett P (1990) The Geographer‘s Art. Blackwell, MassachusettsGoogle Scholar
  9. Keim D, Kohlhammer J et al (eds) (2010) Mastering the information age—solving problems with visual analytics. Druckhaus “Thomas Müntzer” GmbH, Bad Langensalza, Eurographics AssociationGoogle Scholar
  10. Müller W, Schumann H (2003) Visualization methods for time-dependent data: an overview. In: Simulation Conference 2003. Proceedings of the 2003 WinterGoogle Scholar
  11. Norman JF, Todd JT et al (1995) The perception of surface orientation from multiple sources of optical information. Percept Psychophys 57(5):8Google Scholar
  12. O′Sullivan D, Unwin DJ (2003) Geographic Information Analysis. Wiley, HobokenGoogle Scholar
  13. Scott DW (1992) Multivariate density estimation: theory, practice, and visualization. Wiley, CanadaCrossRefGoogle Scholar
  14. Thakur S, Rhyne TM (2009) Data vases: 2D and 3D plots for visualizing multiple time series. In: 5th international symposium on advances in visual computing: part II Las Vegas, Springer, BerlinGoogle Scholar
  15. Thomas JJ, Cook KA eds (2005) Illuminating the path: the research and development agenda for visual analytics, IEEEGoogle Scholar
  16. Tominski C, Schulze-Wollgast P et al (2005) 3D Information visualization for time dependent data on maps. In: 9th international conference on information visualisation, London, UKGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of CartographyTechnische Universität MünchenMunichGermany

Personalised recommendations