Empirical Study of Routine Structure in University Campus
This paper presents the use of wireless usage data as a research tool for analyzing the routine structure of people. The patterns of wireless usage can infer the routine of student life in campus. In our experiments, we discover the student routine structure from the volume and time of the wireless usage. Without following an individual trace for any particular person, we use the volume and time of the whole accesses for particular time and location in a university campus. The analysis is based on the large wireless LANs, one-year log data of the city campus of Bangkok University (August 2011 - July 2012), and the experiment is focused on the wireless access points provided in important places of student activity such as canteens, classrooms, libraries. The resulting outputs are the location preference vectors and a new calendar based on student routine structure. The results can support the computational and comparative analysis of space through the lens of service management and enhance user-driven facilitates of the university campus.
KeywordsEigen-decomposition Eigenplaces Eigenbehaviors Eigenvectors Principal component analysis (PCA) Behavior research Wireless networks Segmentation Classification
Unable to display preview. Download preview PDF.
- 2.Sevtsuk, A., Huang, S., Calabrese, F., Ratti, C.: Mapping the mit campus in real time using wifi. In: Handbook of Research on Urban Informatics: The Practice and Promise of the Real-Time City, pp. 326–338 (2008)Google Scholar
- 3.Sevtsuk, A., Ratti, C.: iSpots. how wireless technology is changing life on the mit campus. In: Proceedings of the 9th International Conference on Computers in Urban Planning and Urban Management, CUPUM (2005)Google Scholar
- 4.Huang, S., Proulx, F., Ratti, C.: iFind: a peer-to-peer application for real-time location monitoring on the mit campus (2007)Google Scholar
- 5.Hsu, W.J., Dutta, D., Helmy, A.: Mining behavioral groups in large wireless lans. In: Proceedings of the 13th Annual ACM International Conference on Mobile Computing and Networking, pp. 338–341. ACM (2007)Google Scholar
- 6.Sevtsuk, A., Ratti, C.: Urban activity dynamics. Economist (2007)Google Scholar
- 7.Kang, J.H., Welbourne, W., Stewart, B., Borriello, G.: Extracting places from traces of locations. In: Proceedings of the 2nd ACM International Workshop on Wireless Mobile Applications and Services on WLAN Hotspots, pp. 110–118. ACM (2004)Google Scholar
- 9.Magazine, E.: Important facts about college students and technology (retrieved on February 26, 2012)Google Scholar
- 10.Sookhanaphibarn, K., Thawonmas, R., Rinaldo, F., Chen, K.T.: Spatiotemporal analysis in virtual environments using eigenbehaviors. In: Proceedings of the 7th International Conference on Advances in Computer Entertainment Technology, pp. 62–65. ACM (2010)Google Scholar
- 11.Sookhanaphibarn, K., Thawonmas, R., Rinaldo, F.: Eigenplaces for segmenting exhibition space. In: Proc. of the 4th Annual Asian GAME-ON Conference on Simulation and AI in Computer Games (GAMEON ASIA 2012), Kyoto, February 24-25 (2012)Google Scholar
- 12.Sookhanaphibarn, K., Thawonmas, R.: Visualization and analysis of visiting styles in 3d virtual museums. In: Digital Humanities 2010, Conference Abstracts Book, London UK (2010)Google Scholar