Data Mining and Knowledge Discovery

, Volume 27, Issue 1, pp 55–83 | Cite as

A visual analytics framework for spatio-temporal analysis and modelling

Article

Abstract

To support analysis and modelling of large amounts of spatio-temporal data having the form of spatially referenced time series (TS) of numeric values, we combine interactive visual techniques with computational methods from machine learning and statistics. Clustering methods and interactive techniques are used to group TS by similarity. Statistical methods for TS modelling are then applied to representative TS derived from the groups of similar TS. The framework includes interactive visual interfaces to a library of modelling methods supporting the selection of a suitable method, adjustment of model parameters, and evaluation of the models obtained. The models can be externally stored, communicated, and used for prediction and in further computational analyses. From the visual analytics perspective, the framework suggests a way to externalize spatio-temporal patterns emerging in the mind of the analyst as a result of interactive visual analysis: the patterns are represented in the form of computer-processable and reusable models. From the statistical analysis perspective, the framework demonstrates how TS analysis and modelling can be supported by interactive visual interfaces, particularly, in a case of numerous TS that are hard to analyse individually. From the application perspective, the framework suggests a way to analyse large numbers of spatial TS with the use of well-established statistical methods for TS analysis.

Keywords

Spatio-temporal data Interactive visual techniques Clustering Time series analysis Visual analytics 

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References

  1. Andrienko GL, Andrienko NV (2005) Visual exploration of the spatial distribution of temporal behaviours. In: 9th International conference on information visualisation IV2005, 6–8 July 2005, London, UK. IEEE Computer Society, pp 799–806Google Scholar
  2. Andrienko N, Andrienko G (2011) Spatial generalization and aggregation of massive movement data. IEEE Trans Vis Comput Graph 17(2): 205–219CrossRefGoogle Scholar
  3. Andrienko G, Andrienko N, Rinzivillo S, Nanni M, Pedreschi D, Giannotti F (2009) Interactive visual clustering of large collections of trajectories. In: Proceedings of the IEEE symposium on visual analytics science and technology VAST’09, pp 3–10Google Scholar
  4. Andrienko G, Andrienko N, Bremm S, Schreck T, von Landesberger T, Bak P, Keim D (2010a) Space-in-time and time-in-space self-organizing maps for exploring spatiotemporal patterns. Comput Graph Forum 29(3): 913–922CrossRefGoogle Scholar
  5. Andrienko G, Andrienko N, Bak P, Bremm S, Keim D, von Landesberger T, Pölitz C, Schreck T (2010b) A framework for using self-organizing maps to analyze spatio-temporal patterns, exemplified by analysis of mobile phone usage. J Locat Based Serv 4(3/4): 200–221CrossRefGoogle Scholar
  6. Crossno PJ, Dunlavy DM, Shead TM (2009) LSAView: a tool for visual exploration of latent semantic modelling. In: Proceedings of the IEEE symposium on visual analytics science and technology VAST’09, pp 83–90Google Scholar
  7. Demšar U, Fotheringham AS, Charlton M (2008) Exploring the spatio-temporal dynamics of geographical processes with geographically weighted regression and geovisual analytics. Inf Vis 7: 181–197CrossRefGoogle Scholar
  8. Garg S, Nam JE, Ramakrishnan IV, Mueller K (2008) Model-driven visual analytics. In: Proceedings of the IEEE symposium on visual analytics science and technology VAST’08, pp 19–26Google Scholar
  9. Garg S, Ramakrishnan IV, Mueller KA (2010) Visual analytics approach to model learning. In: Proceedings of the IEEE symposium on visual analytics science and technology VAST’10, pp 67–74Google Scholar
  10. Guo D (2009) Multivariate spatial clustering and geovisualization. In: Miller HJ, Han J (eds) Geographic data mining and knowledge discovery. Taylor & Francis, London, pp 325–345CrossRefGoogle Scholar
  11. Guo Z, Ward MO, Rundensteiner EA (2009) Model space visualization for multivariate linear trend discovery. In: Proceedings of the IEEE symposium on visual analytics science and technology VAST’09, pp 75–82Google Scholar
  12. Hao MC, Janetzko H, Mittelstädt S, Hill W, Dayal U, Keim DA, Marwah M, Sharma RK (2011) A visual analytics approach for peak-preserving prediction of large seasonal time series. Comput Graph Forum 30(3): 691–700CrossRefGoogle Scholar
  13. Kamarianakis Y, Prastacos P (2003) Forecasting traffic flow conditions in an urban network: comparison of multivariate and univariate approaches. Transp Res Rec J Transp Res Board 1857: 74–84CrossRefGoogle Scholar
  14. Kamarianakis Y, Prastacos P (2005) Space–time modeling of traffic flow. Comput Geosci 31: 119–133CrossRefGoogle Scholar
  15. Kamarianakis Y, Prastacos P (2006) Spatial time-series modeling: a review of the proposed methodologies. Working papers of the University of Crete, Department of Economics, No. 0604, http://ideas.repec.org/p/crt/wpaper/0604.html. Accessed September 19, 2011
  16. Keim D, Andrienko G, Fekete J-D, Görg C, Kohlhammer J, Melançon G (2008) Visual analytics: definition, process, and challenges. In: Kerren A, Stasko JT, Fekete J-D, North C (eds) Information visualization—human-centered issues and perspectives. Lecture notes in computer science. Springer, Berlin, pp 154–175Google Scholar
  17. Kohonen T (2001) Self-organizing maps. Springer, BerlinMATHCrossRefGoogle Scholar
  18. Kyriakidis PC, Journel AG (2001a) Stochastic modeling of atmospheric pollution: a spatial time-series framework. Part I: Methodology. Atmos Environ 35: 2331–2337CrossRefGoogle Scholar
  19. Kyriakidis PC, Journel AG (2001b) Stochastic modeling of atmospheric pollution: a spatial time-series framework. Part II: Application to monitoring monthly sulfate deposition over Europe. Atmos Environ 35: 2339–2348CrossRefGoogle Scholar
  20. Maciejewski R, Rudolph S, Hafen R, Abusalah A, Yakout M, Ouzzani M, Cleveland WS, Grannis SJ, Ebert DS (2010) A visual analytics approach to understanding spatiotemporal hotspots. IEEE Trans Vis Comput Graph 16(2): 205–220CrossRefGoogle Scholar
  21. Maciejewski R, Livengood P, Rudolph S, Collins TF, Ebert DS, Brigantic RT, Corley CD, Muller GA, Sanders SW (2011) A pandemic influenza modeling and visualization tool. J Vis Lang Comput 22: 268–278CrossRefGoogle Scholar
  22. Matković K, Gračanin D, Jelović M, Ammer A, Lež A, Hauser H (2010) Interactive visual analysis of multiple simulation runs using the simulation model view: understanding and tuning of an electronic unit injector. IEEE Trans Vis Comput Graph 16(6): 1449–1457CrossRefGoogle Scholar
  23. Matković K, Gračanin D, Jelović M, Cao Y (2011) Adaptive interactive multi-resolution computational steering for complex engineering systems. In: Proceedings of the EuroVA, Bergen, Norway, pp 45–48Google Scholar
  24. Migut M, Worring M (2010) Visual exploration of classification models for risk assessment. In: Proceedings of the IEEE symposium on visual analytics science and technology VAST’10, pp 11–18Google Scholar
  25. Rinzivillo S, Pedreschi D, Nanni M, Giannotti F, Andrienko N, Andrienko G (2008) Visually-driven analysis of movement data by progressive clustering. Inf Vis 7(3/4): 225–239CrossRefGoogle Scholar
  26. Sammon JW (1969) A nonlinear mapping for data structure analysis. IEEE Trans Comput 18: 401–409CrossRefGoogle Scholar
  27. Schreck T, Bernard J, von Landesberger T, Kohlhammer J (2009) Visual cluster analysis of trajectory data with interactive Kohonen maps. Inf Vis 8(1): 14–29CrossRefGoogle Scholar
  28. Slingsby A, Wood J, Dykes J, Clouston D, Foote M (2010) Visual analysis of sensitivity in CAT models: interactive visualisation for CAT model sensitivity analysis. In: Proceedings of accuracy 2010 conference, Leicester, UK, 20–23 July 2010Google Scholar
  29. Therón R, De Paz JF (2006) Visual sensitivity analysis for artificial neural networks. In: Lecture notes in computer science. IDEAL 2006, vol 4224. Springer, Berlin, pp 191–198Google Scholar
  30. Xiao L, Gerth J, Hanrahan P (2006) Enhancing visual analysis of network traffic using a knowledge representation. In: Proceedings of the IEEE symposium on visual analytics science and technology VAST’06, pp 107–114Google Scholar
  31. Ziegler H, Jenny M, Gruse T, Keim DA (2010) Visual market sector analysis for financial time series data. In: Proceedings of the IEEE symposium on visual analytics science and technology VAST’10, pp 83–90Google Scholar

Copyright information

© The Author(s) 2012

Authors and Affiliations

  1. 1.Fraunhofer Institute IAIS (Intelligent Analysis and Information Systems)Sankt AugustinGermany

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