Journal of Computers, Vol 7, No 7 (2012), 1753-1760, Jul 2012
doi:10.4304/jcp.7.7.1753-1760

Detection and Analysis of Urban Area Hotspots Based on Cell Phone Traffic

Xiaoqing ZUO, Yongchuan ZHANG

Abstract


This paper is to explore new ways to better understand urban system emphasizing on detection and analysis of urban area hotspots through cell phone traffic data. Firstly, according to the characteristics of GSM network, Voronoi cellular network is introduced to determine the service area of the base station. Then two visualization methods are discussed through analyzing the distribution of cell phone traffic data. Next, activity patterns of residents in urban areas are discussed in macro-perspective. Additionally, an algorithm to detect hotspots of urban areas is proposed, and through the usage of the algorithm, many hotspots are found, and then, some typical hotspots are analyzed. Meanwhile, an experiment is conducted based on real cell phone traffic data during the month on February 2011, covering the entire area of Kunming, China.

 



Keywords


cell phone traffic; urban visualization; hotspot detection; urban dynamics; human activity

References


 

[1] B. Luca and D. Martin, “Mobility Environments and Network Cities,” Journal of Urban Design, Vol.8, No.1, pp.27-43, 2003.
http://dx.doi.org/10.1080/1357480032000064755

[2] Batty, “Thinking about cities as spatial events,” Environment and Planning B, 29, pp.1–2, 2003.

[3] J. Matthew and R. Carlo, “Urban Ritual in Rome: Characterizing the City with High-Resolution Cell Phone Data,” http://reality.media.mit.edu/publications.php, 2007. [accessed 13 September, 2011]

[4] R. Jonathan, C. Francesco, S. Andres, and R. Carlo, “Cellular Census: Explorations in Urban Data Collection,” MIT Senseable City Lab, 2007

[5] http://www.cnii.com.cn/yy/content/201106/22/content_887992.htm. [accessed 13 September, 2011]

[6] R. Francisca, et al, “Real Time Rome,” IEEE transactions on intelligent transportation systems, vol.12, NO.1, March 2011.

[7] N. Caceres, J. P. Wideberg, F. G. Benitez, “Review of traffic data estimations extracted from cellular networks,” Intelligent Transportation Engineering IET, 2008.

[8] Y. Zheng, L. Z. Zhang, X. Xie, W. Y. Ma, “Mining Correlation between Locations Using Human Location History,” In Proceedings of GIS, pp.472~475, 2009.
http://dx.doi.org/10.1145/1653771.1653847

[9] R. Carlo, M. P. Riccardo, W. Sarah, F. Dennis, “Mobile Landscapes: Using Location Data from Cell Phones for Urban Analysis,” Environment and Planning B: Planning and Design 33(5), pp.727 – 748, 2007.

[10] J. B. Sun, J. Yuan, Y. Wang, H. B. Si, X. M. Shan, “Exploring space–time structure of human mobility in urban space,” Physica A 390 (2011), pp.929–942.
http://dx.doi.org/10.1016/j.physa.2010.10.033

[11] M. P. Riccardo, R. Carlo, T. Enzo, “City out of Chaos: Social Patterns and Organization in Urban Systems,” International Journal of Ecodynamics. Vol. 1, No. 2 (2006), pp. 125–134.

[12] C. Francesco, R. Jon, R. Carlo, “Eigenplaces: analyzing cities using the space-time structure of the mobile phone network,” Environment and Planning B: Planning and Design 2009, volume 36, pp. 824 - 836.
http://dx.doi.org/10.1068/b34133t

[13] A. Baert, D. Sem, “Voronoi Mobile Cellular Networks: Topological Properties.” In: ISPDC 2004:3rd International Symposium on Parallel and Distributed Computing/ HeteroPar 04: 3rd International Workshop on Algorithms, Models and Tools for Parallel Computing on Heterogeneous Networks, proceeding, pp.29-35.


Full Text: PDF


Journal of Computers (JCP, ISSN 1796-203X)

Copyright @ 2006-2013 by ACADEMY PUBLISHER – All rights reserved.