A Space-Time GIS for Visualizing and Analyzing Clusters in Large Tracking Datasets

  • Hongbo Yu
Part of the Advances in Geographic Information Science book series (AGIS)


Emerging large individual-based tracking datasets have challenged the GIS community to develop effective research tools for analyzing such datasets and uncovering hidden information. Rooted in the time-geographic framework, a space-time GIS approach is proposed to facilitate the representation of the trajectories generated by the tracked moving objects and exploration of the spatiotemporal patterns of stations where the objects cluster. In particular, the space-time path concept is used to model trajectories, and the station concept is used to guide the aggregation of space-time paths to define the locations where the paths cluster in space and time. Since the spatial and temporal extent of a station may vary in different applications, several spatial and temporal methods are introduced and discussed in this study to aggregate the paths. A 3D (2D space + 1D time) space-time GIS environment is developed to support the implementation of these concepts and methods. Stations derived from aggregating space-time paths are represented and visualized as spatiotemporal cylinders in the space-time GIS environment. The space-time GIS, together with the aggregation methods supported by the station concept, offers a useful exploratory analysis environment to support the investigation of hidden spatiotemporal patterns in large trajectory datasets of moving objects.


Space-time GIS Tracking dataset Aggregation Space-time paths Station Spatiotemporal cluster 


  1. Andrienko, G., Andrienko, N., & Gritis, V. (2003). Interactive maps for visual exploration of grid and vector geodata. Photogrammetry and Remote Sensing, 9(2), 380–389.CrossRefGoogle Scholar
  2. Andrienko, G., Andrienko, N., & Wrobel, S. (2007). Visual analytics tools for analysis of movement data. ACM SIGKDD Explorations, 9(2), 38–46.CrossRefGoogle Scholar
  3. Brinkhoff, T. (2002). A framework for generating network-based moving objects. GeoInformatica, 6(2), 153–180.CrossRefGoogle Scholar
  4. Buliung, R. N., & Kanaroglou, P. S. (2006). A GIS toolkit for exploring geographies of household activity/travel behavior. Journal of Transport Geography, 14, 35–51.CrossRefGoogle Scholar
  5. Dodge, S., Weibel, R., & Forootan, E. (2009). Revealing the physics of movement: Comparing the similarity of movement characteristics of different types of moving objects. Computers, Environment and Urban Systems, 33(6), 419–434.CrossRefGoogle Scholar
  6. Dykes, J. A., & Mountain, D. M. (2003). Seeking structure in records of spatiotemporal behavior, visualization issues, efforts and applications. Computational Statistics & Data Analysis, 43, 581–603.CrossRefGoogle Scholar
  7. Erwig, M., Güting, R., Schneider, M., & Vazirgiannis, M. (1999). Spatio-temporal data types, an approach to modeling and querying moving objects in databases. GeoInformatica, 3(3), 269–296.CrossRefGoogle Scholar
  8. Gahegan, M. (2000). The case for inductive and visual techniques in the analysis of spatial data. Journal of Geographical Systems, 2, 77–83.CrossRefGoogle Scholar
  9. Golledge, R., & Stimson, R. (1997). Spatial Behavior: A Geographic Perspective. New York: The Guilford Press.Google Scholar
  10. Guo, D., Gahegan, M., MacEachren, A. M., & Zhou, B. (2005). Multivariate analysis and geovisualization with an integrated geographic knowledge discovery approach. Cartography and Geographic Information Science, 32, 113–132.CrossRefGoogle Scholar
  11. Güting, R., Böhlen, M., Erwig, M., Jensen, C., Lorentzos, N., Schneider, M., et al. (2000). A foundation for representing and querying moving objects. ACM Transactions on Database Systems, 25(1), 1–42.CrossRefGoogle Scholar
  12. Hägerstrand, T. (1970). What about people in regional science? Papers of the Regional Science Association, 24, 7–21.CrossRefGoogle Scholar
  13. Kulldoff, M. (2001). Prospective time periodic geographical disease surveillance using a scan statistic. Journal of the Royal Statistical Society Series A, 164, 61–72.CrossRefGoogle Scholar
  14. Kveladze, I., Kraak, M. J., & Van Elzakker, C. P. (2015). The space-time cube as part of a GeoVisual analytics environment to support the understanding of movement data. International Journal of Geographical Information Science, 29(11), 2001–2016.CrossRefGoogle Scholar
  15. Kwan, M.-P. (2000a). Human extensibility and individual hybrid-accessibility in space-time, a multi-scale representation using GIS. In D. Janelle & D. Hodge (Eds.), Information, place, and cyberspace, issues in accessibility (pp. 241–256). Berlin, Germany: Springer-Verlag.CrossRefGoogle Scholar
  16. Kwan, M.-P. (2000b). Interactive geovisualization of activity-travel patterns using three dimensional geographical information systems, A methodological exploration with a large data set. Transportation Research C, 8, 185–203.CrossRefGoogle Scholar
  17. Kwan, M.-P., & Hong, X. (1998). Network-based constraints-oriented choice set formation using GIS. Geographical Systems, 5, 139–162.Google Scholar
  18. Laube, P., Dennis, T., Forer, P., & Walker, M. (2007). Movement beyond the snapshot–dynamic analysis of geospatial lifelines. Computers, Environment and Urban Systems, 31(5), 481–501.CrossRefGoogle Scholar
  19. Laube, P., & Purves, R. S. (2006). An approach to evaluating motion pattern detection techniques in spatio-temporal data. Computers, Environment and Urban Systems, 30, 347–374.CrossRefGoogle Scholar
  20. Long, J. A., & Nelson, T. A. (2013). A review of quantitative methods for movement data. International Journal of Geographical Information Science, 27(2), 292–318.CrossRefGoogle Scholar
  21. Miller, H. (1991). Modeling accessibility using space-time prism concepts within geographical information systems. International Journal of Geographical Information Systems, 5, 287–301.CrossRefGoogle Scholar
  22. Miller, H. (2004). Activities in space and time. In D. Hensher, K. Button, K. Haynes, & P. Stopher (Eds.), Handbook of transport 5, transport geography and spatial systems (pp. 647–660). London, UK: Elsevier Science.CrossRefGoogle Scholar
  23. Miller, H. (2005). A measurement theory for time geography. Geographical Analysis, 37(1), 17–45.CrossRefGoogle Scholar
  24. Neutens, T., Van de Weghe, N., Witlox, F., & De Maeyer, P. (2008). A three-dimensional network-based space-time prism. Journal of Geographical Systems, 10(1), 89–107.CrossRefGoogle Scholar
  25. Onozuka, D., & Hagihara, A. (2007). Geographic prediction of tuberculosis clusters in Fukuoka, Japan, using the space-time scan statistic. BMC Infectious Diseases, 7, 26–34.CrossRefGoogle Scholar
  26. Parkes, D., & Thrift, N. (1980). Times, spaces, and places: A chronogeographic perspective. New York: Wiley.Google Scholar
  27. Porkaew, K., Lazaridis, I., & Mehrotra, S. (2001). Querying mobile objects in spatio-temporal databases. SSTD, 2001, 59–78.Google Scholar
  28. Postlethwaite, C. M., Brown, P., & Dennis, T. E. (2013). A new multi-scale measure for analysing animal movement data. Journal of Theoretical Biology, 317, 175–185.CrossRefGoogle Scholar
  29. Pred, A. (1977). The choreography of existence: Comments on Hägerstrand’s time-geography and its usefulness. Economic Geography, 53(2), 207–221.CrossRefGoogle Scholar
  30. Purves, R. S., Laube, P., Buchin, M., & Speckmann, B. (2014). Moving beyond the point: An agenda for research in movement analysis with real data. Computers, Environment and Urban Systems, 47, 1–4.CrossRefGoogle Scholar
  31. Rinner, C. (2004). Three-dimensional visualization of activity-travel patterns. In M. Raubal, A. Sliwinski & K. Kuhn (Eds.), Geoinformation und Mobilität [Geoinformation and Mobility], Proc. of the Münster GI Days, 1–2 July 2004, Münster, Germany, IfGIprints series No. 22. Verlag Natur and Wissenschaft, Solingen, Germany, pp. 231–237. Accessed on Jan 8, 2008.
  32. Shaw, S.-L., & Yu, H. (2009). A GIS-based time-geographic approach of studying individual activities and interactions in a hybrid physical-virtual space. Journal of Transport Geography, 17(2), 141–149.CrossRefGoogle Scholar
  33. Shaw, S.-L., Yu, H., & Bombom, L. S. (2008). A space-time GIS approach to exploring large individual-based spatiotemporal datasets. Transactions in GIS, 12(4), 425–441.CrossRefGoogle Scholar
  34. Vazirgiannis, M., & Wolfson, O. (2001). A spatiotemporal model and language for moving objects on road networks. SSTD, 2001, 20–35.Google Scholar
  35. Wolfson, O., Xu, B., Chamberlain, S., & Jiang, L. (1998). Moving objects databases, issues and solutions. Proceedings of SSDB Conference, 1998, 111–122.CrossRefGoogle Scholar
  36. Yu, H. (2006). Spatio-temporal GIS design for exploring interactions of human activities. Cartography and Geographic Information Science, 33(1), 3–19.CrossRefGoogle Scholar
  37. Yu, H., & Shaw, S.-L. (2008). Exploring potential human activities in physical and virtual spaces, a spatio-temporal GIS approach. International Journal of Geographical Information Science, 22(4), 409–430.CrossRefGoogle Scholar
  38. Yuan, M., Mark, D., Egenhofer, M., & Peuquet, D. (2004). Extensions to geographic representations. In R. McMaster & E. Usery (Eds.), A research agenda for geographic information science (pp. 129–156). Boca Raton, FL: CRC Press.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  1. 1.Department of GeographyOklahoma State UniversityStillwaterUSA

Personalised recommendations