SmartCity 360 2016, SmartCity 360 2015: Smart City 360° pp 492-503 | Cite as

Visualizing a City Within a City – Mapping Mobility Within a University Campus

  • Dirk AhlersEmail author
  • Kristoffer Gebuhr Aulie
  • Jeppe Eriksen
  • John Krogstie
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 166)


Urban mobility analysis usually examines large cities or even regions. We take another angle and examine a university campus as a city within a city to focus on small-scale and hyperlocal characteristics. The campus mobility data exhibits a high spatial and temporal granularity that we use to drive analyses and visualizations towards the aim of campus analytics. We describe the abstraction approaches and visualizations used towards the development of our tool and share initial results of campus analytics.


Visualization Visual analytics Mobility WLAN localization Object traces Campus analytics 


  1. 1.
    Andresen, S.H., Krogstie, J., Jelle, T.: Lab and research activities at wireless trondheim. In: 4th ISWCS (2007)Google Scholar
  2. 2.
    Andrienko, G., Andrienko, N., Wrobel, S.: Visual analytics tools for analysis of movement data. SIGKDD Explor. Newsl. 9(2), 38–46 (2007)CrossRefGoogle Scholar
  3. 3.
    Aulie, K.G.: Human Mobility Patterns from Indoor Positioning Systems. Master’s thesis, Norwegian University of Science and Technology, Trondheim, Norway (2015)Google Scholar
  4. 4.
    Bak, P., Omer, I., Schreck, T.: Visual analytics of urban environments using high-resolution geographic data. In: Painho, M., Santos, M.Y., Pundt, H. (eds.) Geospatial Thinking. LNGC, vol. 0, pp. 25–42. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  5. 5.
    Becker, R., Cáceres, R., Hanson, K., Isaacman, S., Loh, J.M., Martonosi, M., Rowland, J., Urbanek, S., Varshavsky, A., Volinsky, C.: Human mobility characterization from cellular network data. Commun. ACM 56(1), 74–82 (2013)CrossRefGoogle Scholar
  6. 6.
    Biczok, G., Diez Martinez, S., Jelle, T., Krogstie, J.: Navigating MazeMap: indoor human mobility, spatio-logical ties and future potential. In: PerMoby 2014 (2014)Google Scholar
  7. 7.
    Eriksen, J.B.: Visualization of Crowds from Indoor Positioning Data. Master’s thesis, Norwegian University of Science and Technology, Trondheim, Norway (2015)Google Scholar
  8. 8.
    Gao, S., Krogstie, J., Thingstad, T., Tran, H.: A mobile service using anonymous location-based data: finding reading rooms. Int. J. Inf. Learn. Technol. 32(1), 32–44 (2015)CrossRefGoogle Scholar
  9. 9.
    Ghosh, J., Beal, M.J., Ngo, H.Q., Qiao, C.: On profiling mobility and predicting locations of wireless users. In: REALMAN 2006, pp. 55–62. ACM (2006)Google Scholar
  10. 10.
    Gonzalez, M.C., Hidalgo, C.A., Barabasi, A.L.: Understanding individual human mobility patterns. Nature 453(7196), 779–782 (2008)CrossRefGoogle Scholar
  11. 11.
    Little, J., O’Brien, B.: A Technical Review of Cisco’s Wi-Fi-Based Location Analytics. Technical report, Cisco (2013)Google Scholar
  12. 12.
    Ren, Y., Tomko, M., Ong, K., Bai, Y.B., Sanderson, M.: The influence of indoor spatial context on user information behaviours. In: Workshop on Information Access in Smart Cities. ECIR 2014 (2014)Google Scholar
  13. 13.
    Zheng, Y., Capra, L., Wolfson, O., Yang, H.: Urban computing: concepts, methodologies, and applications. ACM Trans. Intell. Syst. Technol. 5(3), 38:1–38:55 (2014)Google Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2016

Authors and Affiliations

  • Dirk Ahlers
    • 1
    Email author
  • Kristoffer Gebuhr Aulie
    • 1
  • Jeppe Eriksen
    • 1
  • John Krogstie
    • 1
  1. 1.NTNU – Norwegian University of Science and TechnologyTrondheimNorway

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