Abstract
The massive growth of GPS equipped smartphones coupled with the increasing importance of Social Media has led to the emergence of new location-based services over LBSNs (Location-based Social Networks) which allow citizens to act as social sensors reporting about their locations. This proactive social reporting might be beneficial for researchers in a wide number of scenarios like the one addressed in this paper: monitoring crowds in the city involving an assembly of individuals in term of size, duration, motivation, cohesion and proximity. We introduce a methodology for crowd-detection that combines social data mining, density-based clustering and outlier detection into a solution that can operate on-the-fly to predict public crowds, i.e. to foresee, in short term, the formation of potential multitudes based on the prior analysis of the region. Twitter is mined to analyze geo-tagged data in New York at New Year’s Eve, so that those predictable public crowds are discovered.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Cheng, Z., Caverlee, J., Lee, K., Sui, D.: Exploring Millions of Footprints in Location Sharing Services. In: ICWSM 2011, pp. 81–88 (2011)
Zheng, Y., Xie, X.: Ma GeoLife: A Collaborative Social Networking Service among User, Location and Trajectory. IEEE Data Eng. Bull. 33, 32–39 (2010)
Gao, H., Liu, H.: Data Analysis on Location-Based Social Networks. In: Mob. Soc. Netw., pp. 165–194. Springer, New York (2014)
Forsyth, D.: Group dynamics, 5th edn., vol. 40, p. 9823 (2009)
Reicher, S.: The Psychology of Crowd Dynamics. Psychol. Soc. 44, 113–128 (2012)
Le Bon, G.: The Crowd. Transaction Publishers (1994)
Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes Twitter users: real-time event detection by social sensors. In: Proc. 19th Int. Conf. World Wide Web, pp. 851–860 (2010)
De Longueville, B., Smith, R., 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: Proc. 2009 Int. Work. Locat. Based Soc. Networks, pp. 73–80 (2009)
Kumar, S., Zafarani, R., Liu, H.: Understanding User Migration Patterns in Social Media. In: AAAI 2011 (2011)
Lee, R., Sumiya, K.: Measuring geographical regularities of crowd behaviors for Twitter-based geo-social event detection. In: Proc. 2nd ACM SIGSPATIAL Int. Work. LBSNs, pp. 1–10 (2010)
Ester, M., Kriegel, H., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD 1996, pp. 226–231 (1996)
Beniger, J.R., Barnett, V., Lewis, T.: Outliers in Statistical Data. Contemp. Sociol. 9, 560 (1980)
White, D.J., Chang, H.G., Benach, J.L., et al.: The geographic spread and temporal increase of the Lyme disease epidemic. JAMA 266, 1230–1236 (1991)
Ankerst, M., Breunig, M.M.M., Kriegel, H.H., Sander, J.: Optics: Ordering points to identify the clustering structure. ACM SIGMOD Rec., 49–60 (1999)
Hinneburg, A., Gabriel, H.H.: Denclue 2.0: Fast clustering based on kernel density estimation. In: Berthold, M., Shawe-Taylor, J., Lavrač, N. (eds.) IDA 2007. LNCS, vol. 4723, pp. 70–80. Springer, Heidelberg (2007)
Zhou, H., Wang, P., Li, H.: Research on Adaptive Parameters Determination in DBSCAN Algorithm. J. Inf. Comput. Sci. 9, 1967–1973 (2012)
Morstatter, F., Pfeffer, J., Liu, H., Carley, K.: Is the Sample Good Enough? Comparing Data from Twitter’s Streaming API with Twitter’s Firehose. In: ICWSM (2013)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Ben Kalifa, M., Redondo, R.P.D., Fernández Vilas, A., López Serrano, R., Servia Rodríguez, S. (2014). Is There a Crowd? Experiences in Using Density-Based Clustering and Outlier Detection. In: Prasath, R., O’Reilly, P., Kathirvalavakumar, T. (eds) Mining Intelligence and Knowledge Exploration. Lecture Notes in Computer Science(), vol 8891. Springer, Cham. https://doi.org/10.1007/978-3-319-13817-6_16
Download citation
DOI: https://doi.org/10.1007/978-3-319-13817-6_16
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13816-9
Online ISBN: 978-3-319-13817-6
eBook Packages: Computer ScienceComputer Science (R0)