Using Data from Location Based Social Networks for Urban Activity Clustering

  • Roberto RöslerEmail author
  • Thomas Liebig
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)


Understanding the spatial and temporal aspects of activities in urban regions is one of the key challenges for the emerging fields of urban computing and emergency management as it provides indispensable insights on the quality of services in urban environments and helps to describe the socio-dynamics of urban districts. This work presents a novel approach to obtain this highly valuable knowledge. We hereby propose a segmentation of a city into clusters based on activity profiles using data from a Location Based Social Network (LBSN). In our approach, a segment is represented by different locations sharing the same temporal distribution of check-ins. We reveal how to describe the topic of the determined segments by modelling the difference to the overall temporal distribution of check-ins of the region. Furthermore, a technique from multidimensional scaling is adopted to compute a classification of all segments and visualize the results. The proposed method was successfully applied to Foursquare data recorded from May to October 2012 in the region of Cologne (Germany) and returns clear patterns separating areas known for different activities like nightlife or daily work. Finally, we discuss different aspects related to the use of data from LBSNs.


Activity Profile Spectral Cluster Affinity Matrix Volunteer Geographic Information Location Base Social Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Andrienko N, Andrienko G, Stange H, Liebig T, Hecker D (2012) Visual analytics for understanding spatial situations from episodic movement data. KI—Künstliche Intelligenz 26:241–251 SpringerCrossRefGoogle Scholar
  2. Aubrecht C, Ungar J, Freire S (2011) Exploring the potential of volunteered geographic information for modeling spatio-temporal characteristics of urban population. In: Proceedings of the 7th international conference on virtual cities and territorie. 7VCT ’11, Lisbon, pp 57–60Google Scholar
  3. Bawa-Cavia A (2011) Sensing the urban: using location-based social network data in urban analysis. In: The 1st workshop on pervasive urban applications. PURBA ’11, San FranciscoGoogle Scholar
  4. Chen LJ, Li CW, Huang YT, Shih CS (2011) A rapid method for detecting geographically disconnected areas after disasters. In: IEEE international conference on technologies for homeland security. HST ’11, Greater Boston, pp 501–506Google Scholar
  5. Cheng Z, Caverlee J, Lee K, Sui DZ (2011) Exploring millions of footprints in location sharing services. In: The social mobile web. ICWSM ’11, BarcelonaGoogle Scholar
  6. Cho E, Myers SA, Leskovec J (2011) Friendship and mobility: user movement in location-based social networks. In: Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining. KDD ’11, New York, NY, USA, ACM (2011), pp 1082–1090Google Scholar
  7. Cranshaw J, Schwartz R, Hong JI, Sadeh N (2012) The livehoods project: utilizing social media to understand the dynamics of a city. In: To appear in the 6th international AAAI conference on weblogs and social media. Dublin, IrelandGoogle Scholar
  8. De Longueville B, Smith RS, Luraschi G (2009) 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. LBSN ’09, New York, NY, USA, ACM (2009), pp 73–80Google Scholar
  9. Fred ALN, Jain AK (2002) Evidence accumulation clustering based on the K-Means algorithm. In: Proceedings of the Joint IAPR international workshop on structural, syntactic, and statistical pattern recognition, London, UK, Springer, pp 442–451Google Scholar
  10. Gao H, Tang J, Liu H (2012) Exploring social-historical ties on location-based social networks. In: Breslin JG, Ellison NB, Shanahan JG, Tufekci Z (eds) ICWSM. The AAAI Press, CaliforniaGoogle Scholar
  11. Goodchild MF (2007) Citizens as sensors: the world of volunteered geography. GeoJournal 69(4):211–221 SpringerCrossRefGoogle Scholar
  12. Hong L, Ahmed A, Gurumurthy S, Smola AJ, Tsioutsiouliklis K (2012) Discovering geographical topics in the twitter stream. In: Proceedings of the 21st international conference on World Wide Web. WWW ’12, New York, NY, USA, ACM (2012), pp 769–778Google Scholar
  13. Jain AK, Murty MN, Flynn PJ (1999) Data clustering: a review. ACM Comput Surv 31(3):264–323CrossRefGoogle Scholar
  14. Jiang S, Ferreira Jr J, Gonzalez MC (2012) Discovering urban spatial-temporal structure from human activity patterns. In: Proceedings of the ACM SIGKDD international workshop on urban computing. UrbComp ’12, New York, NY, USA, ACM (2012), pp 95–102Google Scholar
  15. Jin L, Long X, Joshi JB (2012) Towards understanding residential privacy by analyzing users’ activities in foursquare. In: Proceedings of the 2012 ACM workshop on building analysis datasets and gathering experience returns for security. BADGERS ’12, New York, NY, USA, ACM (2012), pp 25–32 Google Scholar
  16. Joseph K, Tan CH, Carley KM (2012) Beyond “Local”, “Categories” and “Friends”: Clustering foursquare users with latent “Topics”. In: Proceedings of the 2012 ACM conference on ubiquitous computing. UbiComp ’12, New York, NY, USA, ACM (2012), pp 919–926Google Scholar
  17. Kindberg T, Chalmers M, Paulos E (2007) Guest editors’ introduction: urban computing. Pervasive Comput IEEE 6(3):18–20CrossRefGoogle Scholar
  18. Lindqvist J, Cranshaw J, Wiese J, Hong J, Zimmerman J (2011) I’m the mayor of my house: examining why people use foursquare—a social-driven location sharing application. In: Proceedings of the SIGCHI conference on human factors in computing systems. CHI ’11, New York, NY, USA, ACM (2011), pp 2409–2418Google Scholar
  19. Long X, Jin L, Joshi J (2012) Exploring trajectory-driven local geographic topics in Foursquare. In: Proceedings of the 2012 ACM conference on ubiquitous computing. UbiComp ’12, New York, NY, USA, ACM (2012), pp 927–934Google Scholar
  20. Ng AY, Jordan MI, Weiss Y (2001) On spectral clustering: analysis and an algorithm. Adv Neural Inf Process Syst 2:849–856 MIT pressGoogle Scholar
  21. Noulas A, Scellato S, Mascolo C, Pontil M (2011) An empirical study of geographic user activity patterns in Foursquare. In: Proceedings of the 5th International AAAI Conference on weblogs and social media. ICWSM ’11, Barcelona, pp 570–573Google Scholar
  22. Noulas A, Scellato S, Mascolo C, Pontil M (2011) Exploiting semantic annotations for clustering geographic areas and users in location-based social networks. In: The social mobile web. ICWSM ’11, BarcelonaGoogle Scholar
  23. Pontes T, Vasconcelos M, Almeida J, Kumaraguru P, Almeida V (2012) We know where you live: privacy characterization of Foursquare behavior. In: Proceedings of the 2012 ACM Conference on Ubiquitous Computing. UbiComp ’12, New York, NY, USA, ACM, pp 898–905Google Scholar
  24. Reades J, Calabrese F, Sevtsuk A, Ratti C (2007) Cellular census: Explorations in urban data collection. Pervasive Comput IEEE 6(3):30–38CrossRefGoogle Scholar
  25. Sammon JW (1969) A nonlinear mapping for data structure analysis. IEEE Trans Comput 18(5):401–409CrossRefGoogle Scholar
  26. Shimada K, Inoue S, Maeda H, Endo T (2011) Analyzing tourism information on twitter for a local city. In: 1st ACIS international symposium on software and network engineering. SSNE ’11, pp 61–66Google Scholar
  27. Thom D, Bosch H, Koch S, Worner M, Ertl T (2012) Spatiotemporal anomaly detection through visual analysis of geolocated twitter messages. In: Proceedings of the Pacific visualization symposium. PacificVis’12, IEEE Press, pp 41–48Google Scholar
  28. Todorovski L, Cestnik B, Kline M, Lavrac N, Dzeroski S (2002) Qualitative clustering of short time-series: a case study of firms reputation data. Helsinki University Printing House, Helsinki, pp 141–149Google Scholar
  29. Ye M, Janowicz K, Mülligann C, Lee WC (2011) What you are is when you are: the temporal dimension of feature types in location-based social networks. In: Proceedings of the 19th ACM SIGSPATIAL international conference on advances in geographic information systems. GIS ’11, New York, NY, USA, ACM (2011), pp 102–111Google Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  1. 1.Fraunhofer IAISSchloss BirlinghovenSankt AugustinGermany
  2. 2.Department of Computer Science LS8TU Dortmund UniversityDortmundGermany

Personalised recommendations