Enrico Steiger
Alexander Zipf
ABSTRACT
In this paper, we propose a framework to detect human mobility transportation hubs and infer public transport flows from unstructured georeferenced social media data using semantic topic modeling and spatial clustering techniques. An infrastructure for receiving and storing large sets of social media data has been developed together with an ad hoc processing framework in order to consider the high uncertainty of our retrieved data. Given the detected and extracted social media signals indicating human mobility, we compared the results with the public transport network from OpenStreetMap and classified observed mobility patterns for an exemplary case study. To analyze collected datasets a web based visualization tool has been setup.
- Tapscott, D. 1996. The digital economy: Promise and peril in the age of networked intelligence. Educom Review 31.3. 52--57.Google Scholar
- o'Reilly, T. 2009. What is web 2.0.Google Scholar
- Resch, B. 2013. People as sensors and collective sensing-contextual observations complementing geo-sensor network measurements. Progress in Location-Based Services. (2013), 391--406.Google Scholar
- Goodchild, M. 2007. Citizens as sensors: the world of volunteered geography. Geo. Journal. 69, 4 (2007), 211--221.Google Scholar
- Boyd, D. M. and Ellison, N. B. 2007. Social Network Sites: Definition, History, and Scholarship. Journal of Computer-Mediated Communication. 13, 1 (Oct. 2007), 210--230.Google ScholarDigital Library
- Zheng, Y. 2011. Location-based social networks: Users. Computing with Spatial Trajectories. Springer New York. Google ScholarDigital Library
- Steiger, E., Lauer, J., Ellersiek, T., Zipf, A. 2014. Towards a framework for automatic geographic feature extraction from Twitter. Eighth International Conference on Geographic Information Science. Vienna.Google Scholar
- Cho, E., Myers, S. A. and Leskovec, J. 2011. Friendship and mobility. Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '11. ACM, New York, NY, Aug. 2011, 1082--1090. DOI= http://doi.acm.org/10.1145/2020408.2020579. Google ScholarDigital Library
- Noulas, A., Scellato, S., Mascolo, C. and Pontil, M. 2011. Exploiting Semantic Annotations for Clustering Geographic Areas and Users in Location-based Social Networks. The Social Mobile Web 11 (2011).Google Scholar
- Wakamiya, S., Lee, R. and Sumiya, K. 2011. Crowd-based Urban Characterization: Extracting Crowd Behavioral Patterns in Urban Areas from Twitter. Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks. ACM, New York, NY, 2011, 77--84. DOI= http://doi.acm.org/10.1145/2063212.2063225. Google ScholarDigital Library
- Wanichayapong, N., Pruthipunyaskul, W., Pattaraatikom, W. and Chaovalit, P. 2011. Social-based Traffic Information Extraction and Classification. ITS Telecommunications (ITST), 2011 11th International Conference on. IEEE (2011), 107--112.Google Scholar
- Sakaki, T., Okazaki, M. and Matsuo, Y. 2010. Earthquake shakes Twitter users: real-time event detection by social sensors. Proceedings of the 19th international conference on World wide web. ACM, New York, NY, 2010, 851--860. DOI= http://doi.acm.org/10.1145/1772690.1772777. Google ScholarDigital Library
- Gerais, M., Oliveira, D. R. R., Gonçalves, T. S., Jr, C. A. D. and Pappa, G. L. 2012. Traffic Observatory: a system to detect and locate traffic events and conditions using Twitter. Proceedings of the 5th International Workshop on Location-Based Social Networks. ACM, New York, NY, 2012, 5--11. DOI= http://doi.acm.org/10.1145/2442796.2442800. Google ScholarDigital Library
- Kosala, R. and Adi, E. 2012. Harvesting Real Time Traffic Information from Twitter. Procedia Engineering. 50, Icasce (Jan. 2012), 1--11.Google Scholar
- Kling, F., Kildare, C. and Pozdnoukhov, A. 2012. When a City Tells a Story: Urban Topic Analysis. Proceedings of the 20th International Conference on Advances in Geographic Information Systems. ACM, New York, NY, 2012, 482--485. DOI= http://doi.acm.org/10.1145/2424321.2424395. Google ScholarDigital Library
- Ferrari, L., Rosi, A., Mamei, M. and Zambonelli, F. 2011. Extracting urban patterns from location-based social networks. Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks - LBSN '11. ACM, New York, NY, 2011, 9--16. DOI= http://doi.acm.org/10.1145/2063212.2063226. Google ScholarDigital Library
- Cranshaw, J., Schwartz, R., Hong, J. and Sadeh, N. 2012. The Livehoods Project: Utilizing Social Media to Understand the Dynamics of a City. ICWSM (2012).Google Scholar
- Hasan, S., Zhan, X. and Ukkusuri, S. V. 2013. Understanding urban human activity and mobility patterns using large-scale location-based data from online social media. Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing - UrbComp '13. ACM, New York, NY, Aug. 2013. DOI= http://doi.acm.org/10.1145/2505821.2505823. Google ScholarDigital Library
- Coffey, C. and Pozdnoukhov, A. 2013. Temporal decomposition and semantic enrichment of mobility flows. Proceedings of the 6th ACM SIGSPATIAL International Workshop on Location-Based Social Networks - LBSN '13. ACM, New York, NY, Nov. 2013, 34--43. DOI= http://doi.acm.org/10.1145/2536689.2536806. Google ScholarDigital Library
- https://dev.twitter.com/docs/api/streaming, https://www.flickr.com/services/api/, https://api.instagram.com/v1/media/popular?client_id=C LIENT-IDGoogle Scholar
- Lewis, D. D., Yang, Y., Rose, T. G. and Li, F. 2004. RCV1: A New Benchmark Collection for Text Categorization Research. The Journal of Machine Learning Research. 5, (Dec. 2004), 361--397. Google ScholarDigital Library
- Blei, D., Ng, A. and Jordan, M. 2003. Latent dirichlet allocation. Journal of machine Learning research. (2003), 993--1022. Google ScholarDigital Library
- Griffiths, T. and Steyvers, M. 2004. Finding scientific topics. Proceedings of the National academy of Sciences of the United States of America (2004), 5228--5235.Google Scholar
- Ester, M., Kriegel, H. P., Sander, J., and Xu, X. 1996. A density-based algorithm for discovering clusters in large spatial databases with noise. In Kdd(Vol. 96, pp. 226--231).Google Scholar
- Zandbergen, P. a. and Barbeau, S. J. 2011. Positional Accuracy of Assisted GPS Data from High-Sensitivity GPS-enabled Mobile Phones. Journal of Navigation. 64, 03 (Jun. 2011), 381--399.Google ScholarCross Ref
- Mierswa, I., Wurst, M. and Klinkenberg, R. 2006. Yale: Rapid prototyping for complex data mining tasks. Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, New York, NY, 2006, 935--940. DOI= http://doi.acm.org/10.1145/1150402.1150531. Google ScholarDigital Library
- Ding, A., Zhao, X. and Jiao, L. 2002. Traffic flow time series prediction based on statistics learning theory. Proceedings. The IEEE 5th International Conference on Intelligent Transportation Systems (2002), 727--730.Google ScholarCross Ref
- Nagel, K. and Schreckenberg, M. 1992. A cellular automaton model for freeway traffic. Journal de physique I. (1992).Google Scholar
- Birant, D. and Kut, A. 2007. ST-DBSCAN: An algorithm for clustering spatial--temporal data. Data & Knowledge Engineering. (2007). Google ScholarDigital Library
- Hengl, T. 2006. Finding the right pixel size. Computers & Geosciences. 32, 9 (Nov. 2006), 1283--1298. Google ScholarDigital Library
Index Terms
- Explorative public transport flow analysis from uncertain social media data
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