Skip to main content
SpringerLink
Log in

A visual analytics framework for spatio-temporal analysis and modelling

  • Published:
Data Mining and Knowledge Discovery Aims and scope Submit manuscript

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.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • 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 799–806

  • Andrienko N, Andrienko G (2011) Spatial generalization and aggregation of massive movement data. IEEE Trans Vis Comput Graph 17(2): 205–219

    Article  Google Scholar 

  • 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 3–10

  • 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): 913–922

    Article  Google Scholar 

  • 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): 200–221

    Article  Google Scholar 

  • 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 83–90

  • 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: 181–197

    Article  Google Scholar 

  • 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 19–26

  • 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 67–74

  • Guo D (2009) Multivariate spatial clustering and geovisualization. In: Miller HJ, Han J (eds) Geographic data mining and knowledge discovery. Taylor & Francis, London, pp 325–345

    Chapter  Google Scholar 

  • 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 75–82

  • 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): 691–700

    Article  Google Scholar 

  • 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: 74–84

    Article  Google Scholar 

  • Kamarianakis Y, Prastacos P (2005) Space–time modeling of traffic flow. Comput Geosci 31: 119–133

    Article  Google Scholar 

  • 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

  • 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 154–175

    Google Scholar 

  • Kohonen T (2001) Self-organizing maps. Springer, Berlin

    Book  MATH  Google Scholar 

  • Kyriakidis PC, Journel AG (2001a) Stochastic modeling of atmospheric pollution: a spatial time-series framework. Part I: Methodology. Atmos Environ 35: 2331–2337

    Article  Google Scholar 

  • 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: 2339–2348

    Article  Google Scholar 

  • 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): 205–220

    Article  Google Scholar 

  • 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: 268–278

    Article  Google Scholar 

  • 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): 1449–1457

    Article  Google Scholar 

  • 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 45–48

  • 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 11–18

  • 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): 225–239

    Article  Google Scholar 

  • Sammon JW (1969) A nonlinear mapping for data structure analysis. IEEE Trans Comput 18: 401–409

    Article  Google Scholar 

  • Schreck T, Bernard J, von Landesberger T, Kohlhammer J (2009) Visual cluster analysis of trajectory data with interactive Kohonen maps. Inf Vis 8(1): 14–29

    Article  Google Scholar 

  • 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 2010

  • 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 191–198

  • 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 107–114

  • 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 83–90

Download references

Author information

Authors and Affiliations

  1. Fraunhofer Institute IAIS (Intelligent Analysis and Information Systems), Sankt Augustin, Germany

    Natalia Andrienko & Gennady Andrienko

Authors
  1. Natalia Andrienko
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gennady Andrienko
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gennady Andrienko.

Additional information

Responsible editor: Barbara Hammer;Daniel Keim;Guy Lebanon;Neil Lawrence.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Andrienko, N., Andrienko, G. A visual analytics framework for spatio-temporal analysis and modelling. Data Min Knowl Disc 27, 55–83 (2013). https://doi.org/10.1007/s10618-012-0285-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10618-012-0285-7

Keywords

Search

Navigation