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7 Future Work

There are several avenues for future work. We plan to conduct a comparative study with existing urban flow analysis techniques in order to evaluate and better understand the added value and potential pitfalls that may occur when using geolocated social media data to inform spatial planning. To optimise our system, we will experiment with visualisation techniques such as interactive clustering [44-46] to address challenges with visual clutter. Finally, feedback from spatial planners suggests that contextual data such keywords could be useful for characterising flow patterns. The occurrence of special events such as festivals or strikes can be better understood by combining what people say with what they do.


1. Asakura, Y., Hato, E.: Tracking survey for individual travel behaviour using mobile communication instruments. Transportation Research Part C: Emerging Technologies 12, 273–291 (2004)

2. Witlox, F.: Evaluating the reliability of reported distance data in urban travel behaviour analysis. J. Transp. Geogr. 15, 172–183 (2007)

3. O'Neill, E., Kostakos, V., Kindberg, T., Schiek, A., Penn, A., Fraser, D.S., Jones, T.: Instrumenting the city: Developing methods for observing and understanding the digital cityscape. In: Dourish, P., Friday, A. (eds.) UbiComp 2006. LNCS, vol. 4206, pp. 315–332. Springer, Heidelberg (2006)

4. Servillo, L., Atkinson, R., Smith, I., Russo, A., Sýkora, L.k., Demazière, C., Hamdouche, A.-I.: TOWN, small and medium sized towns in their functional territorial context, Final Report, Espon (2014)

5. Slocum, T.A.: Flow mapping. In: Thematic cartography and visualization, pp. 360–370. Prentice hall, Upper Saddle River (1999)

6. Demirbas, M., Bayir, M.A., Akcora, C.G., Yilmaz, Y.S., Ferhatosmanoglu, H.: Crowdsourced sensing and collaboration using twitter. In: IEEE International Symposium on a World of Wireless Mobile and Multimedia Networks (WoWMoM) 2010, pp. 1–9. IEEE (2010)

7. Silva, T.H., Vaz de Melo, P.O.S., Almeida, J.M.d., Loureiro, A.A.F.: Uncovering properties in participatory sensor networks. In: Proceedings of the 4th ACM International Workshop on Hot Topics in Planet-Scale Measurement, pp. 33–38. ACM (2012)

8. Burke, J.A., Estrin, D., Hansen, M., Parker, A., Ramanathan, N., Reddy, S., Srivastava, M.B.: Participatory sensing. Center for Embedded Network Sensing (2006)

9. Foth, M., Choi, J.H.-j., Satchell, C.: Urban informatics. In: Proceedings of the ACM 2011 Conference on Computer Supported Cooperative Work, pp. 1–8. ACM (2011)

10. Twitter.

11. Java, A., Song, X., Finin, T., Tseng, B.: Why we twitter: understanding microblogging usage and communities. In: Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 Workshop on Web Mining and Social Network Analysis, pp. 56–65. ACM (2007)

12. Cranshaw, J., Schwartz, R., Hong, J.I., Sadeh, N.M.: The livehoods project: utilizing social media to understand the dynamics of a city. In: ICWSM (2012)

13. Kling, F., Pozdnoukhov, A.: When a city tells a story: urban topic analysis. In: Proceedings of the 20th International Conference on Advances in Geographic Information Systems, pp. 482–485. ACM (2012)

14. Gonzalez, M.C., Hidalgo, C.A., Barabasi, A.-L.: Understanding individual human mobility patterns. Nature 453, 779–782 (2008)

15. Song, C., Qu, Z., Blumm, N., Barabási, A.-L.: Limits of predictability in human mobility. Science 327, 1018–1021 (2010)

16. Sevtsuk, A., Ratti, C.: Does urban mobility have a daily routine? Explorations using aggregate mobile network data. In: Proceedings of the 11th International Conference on Computers in Urban Planning and Urban Management (2009)

17. Doyle, J., Hung, P., Farrell, R., McLoone, S.: Population Mobility Dynamics Estimated from Mobile Telephony Data. Journal of Urban Technology 21, 109–132 (2014)

18. Pousman, Z., Stasko, J.T., Mateas, M.: Casual information visualization: Depictions of data in everyday life. IEEE Trans. Visual Comput. Graphics 13, 1145–1152 (2007)

19. Grammel, L., Tory, M., Storey, M.: How information visualization novices construct visualizations. IEEE Trans. Visual Comput. Graphics 16, 943–952 (2010)

20. Citylab. city/1131/

21. Twitter Tongues.

22. Twitter NYC.

23. Business Insider.

24. Frias-Martinez, V., Soto, V., Hohwald, H., Frias-Martinez, E.: Characterizing urban landscapes using geolocated tweets. In: Privacy, Security, Risk and Trust (PASSAT), 2012 International Conference on Social Computing (SocialCom), pp. 239–248. IEEE (2012)

25. De Longueville, B., Smith, R.S., Luraschi, G.: OMG, from here, I can see the flames!: a use case of mining location based social networks to acquire spatio-temporal data on forest fires. In: Proceedings of the 2009 International Workshop on Location Based Social Networks, pp. 73–80. ACM (2009)

26. Prasetyo, P.K., Gao, M., Lim, E.-P., Scollon, C.N.: Social sensing for urban crisis management: The case of singapore haze. In: Jatowt, A., Lim, E.-P., Ding, Y., Miura, A., Tezuka, T., Dias, G., Tanaka, K., Flanagin, A., Dai, B.T. (eds.) SocInfo 2013. LNCS, vol. 8238, pp. 478–491. Springer, Heidelberg (2013)

27. Wei, L.Y., Zheng, Y., Peng, W.C.: Constructing popular routes from uncertain trajectories. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 195–203. ACM (2012)

28. Pan, B., Zheng, Y., Wilkie, D., Shahabi, C.: Crowd sensing of traffic anomalies based on human mobility and social media. In: Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 334–343. ACM (2013)

29. Fuchs, G., Andrienko, G., Andrienko, N., Jankowski, P.: Extracting personal behavioral patterns from geo-referenced tweets. AGILE (2013)

30. Andrienko, G., Andrienko, N., Fuchs, G., Raimond, A.-M.O., Symanzik, J., Ziemlicki, C.: Extracting semantics of individual places from movement data by analyzing temporal patterns of visits. In: Proceedings of The First ACM SIGSPATIAL International Workshop on Computational Models of Place (COMP 2013) (2013)

31. Gabrielli, L., Rinzivillo, S., Ronzano, F., Villatoro, D.: From tweets to semantic trajectories: Mining anomalous urban mobility patterns. In: Nin, J., Villatoro, D. (eds.) CitiSens 2013. LNCS, vol. 8313, pp. 25–34. Springer, Heidelberg (2014)

32. Sedlmair, M., Meyer, M., Munzner, T.: Design study methodology: Reflections from the trenches and the stacks. IEEE Trans. Visual Comput. Graphics 18, 2431–2440 (2012)

33. Thomas, J.J., Cook, K.A.: A visual analytics agenda. IEEE Comput. Graphics Appl. 26, 10–13 (2006)

34. DiBiase, D.: Visualization in the earth sciences. Earth and Mineral Sciences 59, 13–18 (1990)

35. Robinson, A.H.: The thematic maps of Charles Joseph Minard (1967)

36. Tobler, W.R.: Experiments in migration mapping by computer. The American Cartogra-

pher 14, 155–163 (1987)

37. Phan, D., Xiao, L., Yeh, R., Hanrahan, P.: Flow map layout. In: IEEE Symposium on Information Visualization 2005, pp. 219–224. IEEE (2005)

38. Wood, J., Dykes, J., Slingsby, A.: Visualisation of origins, destinations and flows with OD maps. The Cartographic Journal 47, 117–129 (2010)

39. Ghoniem, M., Fekete, J., Castagliola, P.: A comparison of the readability of graphs using node-link and matrix-based representations. In: IEEE Symposium on Information Visualization, pp. 17–24. IEEE (2004)

40. Guo, D.: Flow mapping and multivariate visualization of large spatial interaction data. IEEE Trans. Visual Comput. Graphics 15, 1041–1048 (2009)

41. Shneiderman, B.: The eyes have it: A task by data type taxonomy for information visualizations. In: Proceedings: IEEE Symposium on Visual Languages, pp. 336–343. IEEE (1996)

42. Holten, D., van Wijk, J.J.: A user study on visualizing directed edges in graphs. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 2299–2308. ACM (2009)

43. Duggan, M., Smith, A.: Social media update 2013. Pew Internet and American Life Project (2013)

44. Rinzivillo, S., Pedreschi, D., Nanni, M., Giannotti, F., Andrienko, N., Andrienko, G.: Visually driven analysis of movement data by progressive clustering. Information Visualization 7, 225–239 (2008)

45. Andrienko, G., Andrienko, N., Rinzivillo, S., Nanni, M., Pedreschi, D., Giannotti, F.: Interactive visual clustering of large collections of trajectories. In: IEEE Symposium on Visual Analytics Science and Technology, pp. 3–10. IEEE (2009)

46. Adrienko, N., Adrienko, G.: Spatial generalization and aggregation of massive movement data. IEEE Trans. Visual Comput. Graphics 17, 205–219 (2011)

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