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.
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
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
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
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
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
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
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
Kamarianakis Y, Prastacos P (2005) Space–time modeling of traffic flow. Comput Geosci 31: 119–133
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
Kohonen T (2001) Self-organizing maps. Springer, Berlin
Kyriakidis PC, Journel AG (2001a) Stochastic modeling of atmospheric pollution: a spatial time-series framework. Part I: Methodology. Atmos Environ 35: 2331–2337
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
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
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
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
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
Sammon JW (1969) A nonlinear mapping for data structure analysis. IEEE Trans Comput 18: 401–409
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
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
Author information
Authors and Affiliations
Corresponding author
Additional information
Responsible editor: Barbara Hammer;Daniel Keim;Guy Lebanon;Neil Lawrence.
Rights 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
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10618-012-0285-7