Spatio-Temporal-Network Visualization for Exploring Human Movements and Interactions in Physical and Virtual Spaces

  • Song GaoEmail author
  • Hanzhou Chen
  • Wei Luo
  • Yingjie Hu
  • Xinyue Ye
Part of the Human Dynamics in Smart Cities book series (HDSC)


In this research, we propose a conceptual framework for spatiotemporal and social network visualization in a three-dimensional context. Based on this framework, new spatio-temporal-network (STN) quantitative metrics (including STN-impact-extent, STN-impact-center, STN-distance, STN-efficiency, and STN-centrality) are introduced to measure the underlying dynamic interactions among entities. The proposed framework aims to help better understand spatiotemporal patterns of human dynamics and social interactions over both physical and virtual spaces simultaneously, as well as explore how emerging events trigger spatial-temporal-social interactions and information diffusion from a process perspective. As a proof of concept, we demonstrate the proposed framework with a case study using geotagged tweets and associated visualization in the ArcScene software. We hope that this research can stimulate new insights on integrating multidisciplinary knowledge to explore human dynamics in a broader way.


  1. Adrienko, N., & Adrienko, G. (2011). Spatial generalization and aggregation of massive movement data. IEEE Transactions on Visualization and Computer Graphics, 17(2), 205–219.CrossRefGoogle Scholar
  2. Amini, A., Kung, K., Kang, C., Sobolevsky, S., & Ratti, C. (2014). The impact of social segregation on human mobility in developing and industrialized regions. EPJ Data Science, 3(1), 6.CrossRefGoogle Scholar
  3. Andris, C. (2016). Integrating social network data into GISystem. International Journal of Geographical Information Science, 30(10), 2009–2031.Google Scholar
  4. Borgatti, S. P. (2005). Centrality and network flow. Social Networks, 27(1), 55–71.CrossRefGoogle Scholar
  5. Cao, N., Lin, Y. R., Sun, X., Lazer, D., Liu, S., & Qu, H. (2012). Whisper: Tracing the spatiotemporal process of information diffusion in real time. IEEE Transactions on Visualization and Computer Graphics, 18(12), 2649–2658.CrossRefGoogle Scholar
  6. Crucitti, P., Latora, V., & Porta, S. (2006). Centrality measures in spatial networks of urban streets. Physical Review E, 73(3), 036125.CrossRefGoogle Scholar
  7. Cui, W., Zhou, H., Qu, H., Wong, P. C., & Li, X. (2008). Geometry-based edge clustering for graph visualization. IEEE Transactions on Visualization and Computer Graphics, 14(6), 1277–1284.CrossRefGoogle Scholar
  8. Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry, 40, 35–41.Google Scholar
  9. Freeman, L. C., Borgatti, S. P., & White, D. R. (1991). Centrality in valued graphs: A measure of betweenness based on network flow. Social Networks, 13(2), 141–154.CrossRefGoogle Scholar
  10. Gao, S. (2015). Spatio-temporal analytics for exploring human mobility patterns and urban dynamics in the mobile age. Spatial Cognition & Computation, 15(2), 86–114.CrossRefGoogle Scholar
  11. Gao, S., Liu, Y., Wang, Y., & Ma, X. (2013a). Discovering spatial interaction communities from mobile phone data. Transactions in GIS, 17(3), 463–481.CrossRefGoogle Scholar
  12. Gao, S., Wang, Y., Gao, Y., & Liu, Y. (2013b). Understanding urban traffic-flow characteristics: a rethinking of betweenness centrality. Environment and Planning B: Planning and Design, 40(1), 135–153.CrossRefGoogle Scholar
  13. Gao, S., Yan, B., Gong, L., Regalia, B., Ju, Y., & Hu, Y. (2017). Uncovering the digital divide and the physical divide in senegal using mobile phone data. In Advances in geocomputation (pp. 143–151). Cham: Springer.Google Scholar
  14. Garcia, B. E., & Wimpy, C. (2016). Does information lead to emulation? Spatial dependence in anti-government violence. Political Science Research and Methods, 4(01), 27–46.CrossRefGoogle Scholar
  15. Gregory, D., & Urry, J. (1985). Suspended animation: The stasis of diffusion theory. In D. Gregory & J. Urry (Eds.), Social relations and spatial structures (pp. 296–336). New York: St. Martin’s Press.CrossRefGoogle Scholar
  16. Guare, J. (1990). Six degrees of separation: A play. New York: Vintage Books.Google Scholar
  17. Guo, D. (2009). Flow mapping and multivariate visualization of large spatial interaction data. IEEE Transactions on Visualization and Computer Graphics, 15, 1041–1048.CrossRefGoogle Scholar
  18. Hägerstrand, T. (1967). Aspects of the spatial structure of social communication and the diffusion of information. Papers in Regional Science, 16(1), 27–42.CrossRefGoogle Scholar
  19. He, J., & Chen, C. (2016, September). Spatiotemporal Analytics of Topic Trajectory. In Proceedings of the 9th International Symposium on Visual Information Communication and Interaction (pp. 112–116). ACM.Google Scholar
  20. Holme, P., & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.CrossRefGoogle Scholar
  21. Huang, Z., Das, A., Qiu, Y., & Tatem, A. J. (2012). Web-based GIS: The vector-borne disease airline importation risk (VBD-AIR) tool. International Journal of Health Geographics, 11, 1.CrossRefGoogle Scholar
  22. Hu, Y., Gao, S., Janowicz, K., Yu, B., Li, W., & Prasad, S. (2015). Extracting and understanding urban areas of interest using geotagged photos. Computers, Environment and Urban Systems, 54, 240–254.CrossRefGoogle Scholar
  23. Kempe, D., Kleinberg, J., & Kumar, A. (2000, May). Connectivity and inference problems for temporal networks. In Proceedings of the thirty-second annual ACM symposium on Theory of computing (pp. 504–513). USA: ACM.Google Scholar
  24. Kwan, M. P. (2004). GIS methods in time-geographic research: Geocomputation and geovisualization of human activity patterns. Geografiska Annaler: Series B, Human Geography, 86, 267–280.CrossRefGoogle Scholar
  25. Kwak, H., Lee, C., Park, H., & Moon, S. (2010). What is Twitter, a social network or a news media?. In Proceedings of the 19th international conference on World wide web, (pp. 591–600). ACM, April.Google Scholar
  26. Lee, J. Y. & M. P. Kwan (2011). Visualisation of socio‐spatial isolation based on human activity patterns and social networks in space‐time. Tijdschrift voor economische en sociale geografie, 102, 468–485.Google Scholar
  27. Liu, Y., Liu, X., Gao, S., Gong, L., Kang, C., Zhi, Y., et al. (2015). Social sensing: A new approach to understanding our socioeconomic environments. Annals of the Association of American Geographers, 105(3), 512–530.CrossRefGoogle Scholar
  28. Luo, W. (2016). Visual analytics of geo-social interaction patterns for epidemic control. International Journal of Health Geographics, 15, 28.CrossRefGoogle Scholar
  29. Luo, W., & MacEachren, A. M. (2014). Geo-social visual analytics. Journal of Spatial Information Science, 2014(8), 27–66.Google Scholar
  30. Luo, W., MacEachren, A. M., Yin, P., & Hardisty, F. (2011, November). Spatial-social network visualization for exploratory data analysis. In Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks (pp. 65–68). USA: ACM.Google Scholar
  31. Luo, W., Yin, P., Di, Q., Hardisty, F., & MacEachren, A. M. (2014). A geovisual analytic approach to understanding geo-social relationships in the international trade network. PLoS ONE, 9, e88666.CrossRefGoogle Scholar
  32. Morrill, R., Gaile, G. L., & Thrall, G. I. (1988). Spatial diffusion. SAGE Scientific Geography Series 10. Newbury Park, CA: SAGE Publications, Inc.Google Scholar
  33. Newman, M. E. (2005). A measure of betweenness centrality based on random walks. Social networks, 27(1), 39–54.CrossRefGoogle Scholar
  34. Peuquet, D. J., Robinson, A. C., Stehle, S., Hardisty, F. A., & Luo, W. (2015). A method for discovery and analysis of temporal patterns in complex event data. International Journal of Geographical Information Science, 29(9), 1588–1611.CrossRefGoogle Scholar
  35. Plane, D. A., & Rogerson, P. A. (2015). On tracking and disaggregating center points of population. Annals of the Association of American Geographers, 105(5), 968–986.CrossRefGoogle Scholar
  36. Shahaf, D., Guestrin, C., & Horvitz, E. (2012, April). Trains of thought: Generating information maps. In Proceedings of the 21st international conference on World Wide Web (pp. 899–908). USA: ACM.Google Scholar
  37. Shaw, S. L., Tsou, M. H., & Ye, X. (2016). Editorial: Human dynamics in the mobile and big data era. International Journal of Geographical Information Science, 30(9), 1687–1693.CrossRefGoogle Scholar
  38. 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
  39. Shi, L., Chi, G., Liu, X., & Liu, Y. (2015). Human mobility patterns in different communities: a mobile phone data-based social network approach. Annals of GIS, 21(1), 15–26.CrossRefGoogle Scholar
  40. Steiger, E, Westerholt, R & Zipf, A. (2016). Research on social media feeds—A GIScience perspective. In: Capineri, C, Haklay, M, Huang, H, Antoniou, V, Kettunen, J, Ostermann, F and Purves, R. (eds.) European handbook of crowd sourced geographic information (pp. 237–254). London: Ubiquity Press. License: CC-BY 4.0.
  41. Sui, D., & Goodchild, M. (2011). The convergence of GIS and social media: Challenges for GIScience. International Journal of Geographical Information Science, 25(11), 1737–1748.CrossRefGoogle Scholar
  42. Tobler, W. (1970). A computer movie simulating urban growth in the Detroit region. Economic Geography, 46, 234–240.CrossRefGoogle Scholar
  43. Tsou, M. H., & Leitner, M. (2013). Editorial: Visualization of social media: Seeing a mirage or a message? In special content issue: “Mapping cyberspace and social media”. Cartography and Geographic Information Science., 40(2), 55–60.CrossRefGoogle Scholar
  44. Tsou, M. H., Kim, I. H., Wandersee, S., Lusher, D., An, L., Spitzberg, B., Gupta, D., Gawron, J. M., Smith, J., Yang, J. A., & Han, S. (2013). Mapping Ideas from cyberspace to real space: Visualizing the spatial context of keywords from web page search results. International Journal of Digital Earth, 7(4), 316–335.Google Scholar
  45. Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications (Vol. 8). Cambridge: Cambridge university press.Google Scholar
  46. Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small-world’ networks. nature, 393(6684), 440–442.Google Scholar
  47. Ye, X., & He, C. (2016). The new data landscape for regional and urban analysis. GeoJournal. Scholar
  48. Ye, X., Huang, Q., & Li, W. (2016a). Integrating big social data, computing, and modeling for spatial social science, cartography and geographic information science. Science, 43(5), 377–378.Google Scholar
  49. Ye, X., & Lee, J. (2016). Integrating geographic activity space and social network space to promote healthy lifestyles. ACMSIGSPATIAL Health GIS, Newsletter, 8(1), 24–33.Google Scholar
  50. Ye, X., Li, S., Yang, X., & Qin, C. (2016b). Use of social media for detection and analysis of infectious disease in China. ISPRS International Journal of Geo-Information. Scholar
  51. Yin, L., & Shaw, S. L. (2015). Exploring space–time paths in physical and social closeness spaces: a space–time GIS approach. International Journal of Geographical Information Science, 29(5), 742–761.CrossRefGoogle Scholar
  52. 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
  53. Zhu, X., & Guo, D. (2014). Mapping large spatial flow data with hierarchical clustering. Transactions in GIS, 18(3), 421–435.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Song Gao
    • 1
    Email author
  • Hanzhou Chen
    • 2
  • Wei Luo
    • 3
  • Yingjie Hu
    • 4
  • Xinyue Ye
    • 5
  1. 1.Department of GeographyUniversity of WisconsinMadisonUSA
  2. 2.Department of GeographyPennsylvania State UniversityState CollegeUSA
  3. 3.School of Geographical Sciences and Urban PlanningArizona State UniversityTempeUSA
  4. 4.Department of GeographyUniversity of TennesseeKnoxvilleUSA
  5. 5.Department of GeographyKent State UniversityKentUSA

Personalised recommendations