An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Why we twitter: understanding microblogging usage and communities
Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis
A data mining proxy approach for efficient frequent itemset mining
The VLDB Journal — The International Journal on Very Large Data Bases
Proceedings of the first workshop on Online social networks
How and why people Twitter: the role that micro-blogging plays in informal communication at work
Proceedings of the ACM 2009 international conference on Supporting group work
Detection of Unusually Crowded Places through Micro-Blogging Sites
WAINA '10 Proceedings of the 2010 IEEE 24th International Conference on Advanced Information Networking and Applications Workshops
Measuring geographical regularities of crowd behaviors for Twitter-based geo-social event detection
Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Location Based Social Networks
Characterizing Dense Urban Areas from Mobile Phone-Call Data: Discovery and Social Dynamics
SOCIALCOM '10 Proceedings of the 2010 IEEE Second International Conference on Social Computing
Discovery of user behavior patterns from geo-tagged micro-blogs
Proceedings of the 4th International Conference on Uniquitous Information Management and Communication
Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication
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It is essential to characterize geographic regions in order to make various geographic decisions. These regions can be characterized from various perspectives such as the physical appearance of a town. In this paper, as a novel approach to characterize geographic regions, we focus on the daily lifestyle patterns of crowds via location-based social networking sites in urban areas. For this purpose, we propose a novel method to characterize urban areas using Twitter, the most representative microblogging site. In order to grasp images of a city by social network based crowds, we define the geographic regularity of the region using daily crowd activity patterns; for instance, the number of tweets, through the number of users, and the movement of the crowds. We also analyze the changing patterns of geographic regularity with time and categorize clustered urban types by tracking common patterns among the regions. Finally, we present examples of several urban types through the observation of experimentally extracted patterns of crowd behavior in actual urban areas.