Enhancing Exploratory Analysis by Summarizing Spatiotemporal Events Across Multiple Levels of Detail

  • Ricardo Almeida Silva
  • João Moura Pires
  • Maribel Yasmina Santos
  • Nuno Datia
Conference paper
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)


There are many spatiotemporal events with high levels of detail (LoDs) being collected in many phenomena. The LoD of analysis plays a crucial role in the user’s perception of phenomena. From one LoD to another, some patterns can be easily perceived or different patterns may be detected. Standard practices work on a single LoD driven by the user in spite of the fact that there is no exclusive LoD to study a phenomenon. Our proposal aims to support users in carrying the inspection and comparison tasks of a phenomenon across multiple LoDs, without having to look at raw data, and to handle the spatiotemporal complexity. This paper presents a framework to build abstracts at different LoDs where five types of abstracts are proposed. The framework makes no assumption about the phenomenon, the analytical task and the phenomenon’s LoDs. The SUITE’s prototype implements the proposed framework allowing users to inspect abstracts across multiple LoDs simultaneously, helping to understand in what LoDs the phenomenon perception distinguishes itself or in what LoDs “interesting patterns” emerge.


Multiple levels of detail Spatiotemporal data Visual analytics 



This work has been supported by FCT—Fundação para a Ciência e Tecnologia MCTES, UID/CEC/04516/2013 (NOVA LINCS) and UID/CEC/00319/2013 (ALGORITMI), and COMPETE: POCI-01-0145-FEDER-007043 (ALGORITMI).


  1. Andrienko G et al (2010) Space, time and visual analytics. Int J Geogr Inf Sci 24(10):1577–1600CrossRefGoogle Scholar
  2. Andrienko G et al (2011) A conceptual framework and taxonomy of techniques for analyzing movement. J Vis Lang Comput 22(3):213–232CrossRefGoogle Scholar
  3. Bettini C, Jajodia S, Wang S (2000) Time granularities in databases, data mining, and temporal reasoning. SpringerGoogle Scholar
  4. Camossi E, Bertolotto M, Bertino E (2006) A multigranular object-oriented framework supporting spatio-temporal granularity conversions. Int J Geogr Inf Sci 20(5):511–534CrossRefGoogle Scholar
  5. Camossi E, Bertolotto M, Kechadi T (2008) Mining spatio-temporal data at different levels of detail. In: The european information society. Springer, pp. 225–240Google Scholar
  6. Ferreira N et al (2013) Visual exploration of big spatio-temporal urban data: a study of new york city taxi trips. IEEE Trans Vis Comput Graph 19(12):2149–2158CrossRefGoogle Scholar
  7. Goodwin S et al (2016) Visualizing Multiple Variables Across Scale and Geography. IEEE Trans Vis Comput Graph 22(1):599–608CrossRefGoogle Scholar
  8. Keim D et al (2008) Visual analytics: definition, process, and challenges. In: Kerren A et al (eds) Information visualization, Lecture notes in computer science. Springer, Berlin Heidelberg, pp 154–175Google Scholar
  9. Keogh E, Lin J, Fu A (2005) Hot sax: Efficiently finding the most unusual time series subsequence. In fifth IEEE international conference on data mining, 8 ppGoogle Scholar
  10. Laube P, Purves RS (2011) How fast is a cow? Cross-Scale analysis of movement data. Trans GIS 15(3):401–418CrossRefGoogle Scholar
  11. Laurini R (2014) A conceptual framework for geographic knowledge engineering. J Vis Lang Comput 25(1):2–19CrossRefGoogle Scholar
  12. Maciejewski R et al (2010) A visual analytics approach to understanding spatiotemporal hotspots. IEEE Trans Vis Comput Graph 16(2):205–220CrossRefGoogle Scholar
  13. Malizia N, Mack EA (2012) Enhancing the Jacquez k nearest neighbor test for space–time interaction. Stat Med 31(21):2318–2334CrossRefGoogle Scholar
  14. Moran PAP (1950) Notes on continuous stochastic phenomena. Biometrika, pp. 17–23Google Scholar
  15. Oliveira R, Santos MY, Moura Pires J (2013) 4D + SNN: A Spatio-Temporal Density-Based Clustering Approach with 4D Similarity. In: 2013 IEEE 13th international conference on data mining workshops (ICDMW), pp 1045–1052Google Scholar
  16. Openshaw S, Openshaw S (1984) The modifiable areal unit problemGoogle Scholar
  17. Parent C et al (2009) Multiple Representation Modeling. In: Liu L, Özsu MT (eds) Encyclopedia of Database Systems. Springer, US, pp 1844–1849Google Scholar
  18. Pires JM, Silva RA, Santos MY (2014) Reasoning about space and time: moving towards a theory of granularities. In: Computational science and its applications–ICCSA 2014. Springer, pp 328–343Google Scholar
  19. Pozzani G, Zimányi E (2012) Defining spatio-temporal granularities for raster data. In: Data security and security data. Springer, pp 96–107Google Scholar
  20. Silva RA, Pires JM, Santos MY (2015a) A granularity theory for modelling spatio-temporal phenomena at multiple levels of detail. Int J Bus Intell Data Min 10(1):33Google Scholar
  21. Silva RA, Pires JM et al (2015b) aggregating spatio-temporal phenomena at multiple levels of detail. In: AGILE 2015. Springer Science Business Media, pp 291–308Google Scholar
  22. Sips M et al (2012) A visual analytics approach to multiscale exploration of environmental time series. IEEE Trans Vis Comput Graph 18(12):2899–2907CrossRefGoogle Scholar
  23. Stell J, Worboys M (1998) Stratified map spaces: a formal basis for multi-resolution spatial databases. In: Proceedings 8th international symposium on spatial data handling. Department of Computer Science, Keele University, Staffordshire, UK ST5 5BG, pp 180–189Google Scholar
  24. Weibel R, Dutton G (1999) Generalising spatial data and dealing with multiple representations. Geograph Inform Syst 1:125–155Google Scholar
  25. Zhou X et al (2004) Multiresolution spatial databases: making web-based spatial applications faster. In: Yu J et al (eds) advanced web technologies and applications SE – 5, Lecture notes in computer science. Springer, Berlin Heidelberg, pp 36–47CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Ricardo Almeida Silva
    • 1
  • João Moura Pires
    • 1
  • Maribel Yasmina Santos
    • 2
  • Nuno Datia
    • 3
  1. 1.NOVA LINCS, DI, FCT, Universidade NOVA de LisboaCaparicaPortugal
  2. 2.ALGORITMI Research CentreUniversity of MinhoBragaPortugal
  3. 3.ISEL, Instituto Superior de Engenharia de Lisboa, Instituto Politécnico de LisboaLisboaPortugal

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