This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Eigenplaces: Segmenting Space through Digital Signatures
January-March 2010 (vol. 9 no. 1)
pp. 78-84
Francesco Calabrese, Massachusetts Institute of Technology , Cambridge
Jonathan Reades, University College London, London
Carlo Ratti, Massachusetts Institute of Technology, Cambridge
Unlike radio and television's unidirectional broadcast, wireless data network transceivers can act as probes to propagate environmental data back to a network observer. This fundamental difference lets researchers use the volume, timing, and distribution of packets passing across communications networks of varying scales to study the "bricks and mortar" of physical space. The authors use the Massachusetts Institute of Technology's Wi-Fi (IEEE 802.11) network to collect data generated as a by-product of network activity and correlate it with the physical environment using eigendecomposition.

1. T. Henderson, D. Kotzand, and I. Abyzov, The Changing Usage of a Mature Campus-wide Wireless Network, tech. report TR2004-496, Computer Science Dept., Dartmouth College, 2004, pp. 1–19.
2. M. Kim and D. Kotz, "Classifying the Mobility of Users and the Popularity of Access Points," Proc. Int'l Workshop Location- and Content-Awareness, LNCS 3479, Springer, 2005, pp. 198–210.
3. M. Kim and D. Kotz, "Modeling Users' Mobility among WiFi Access Points," Proc. Int'l Workshop Wireless Traffic Measurements and Modeling, Usenix Assoc., 2005, pp. 19–24.
4. J.H. Kang et al., "Extracting Places from Traces of Locations," Proc. 2nd ACM Int'l Workshop Wireless Mobile Applications and Services on WLAN, ACM Press, 2004, pp. 110–118.
5. F. Dal Fiore, E. Beinat, and C. Ratti, "Do Mobile Users Move Differently? Exploring the Spatial Implications of Ubiquitous Connectivity at MIT Campus," Proc. Geoinformatics Forum Salzburg, A. Car, G. Griesebner, and J. Strobl eds., 2008.
6. A. Sevtsuk et al., "Mapping the MIT Campus in Real Time Using WiFi," , Handbook of Research on Urban Informatics: The Practice and Promise of the Real-Time City, M. Foth ed., IGI Global, 2008, pp. 326–338.
7. A. Sevtsuk, and C. Ratti, "Urban Activity Dynamics," working paper, SENSEable City Laboratory, Massachusetts Inst. of Technology, 2008; http://senseable.mit.edu/papers/pdf/SevtsukRatti-ActivityDynamics-2008.pdf.
8. J. Reades et al., "Cellular Census: Explorations in Urban Data Collection," IEEE Pervasive Computing, vol. 6, no. 3, 2007, pp. 30–38.
9. I.T. Jolliffe, Principal Component Analysis, Springer, 2002.
10. P. Rousseuw, "Silhouettes: A Graphical Aid to the Interpretation and Validation of Cluster Analysis," Computational and Applied Mathematics, vol. 20, no. 1, 1987, pp. 53–65.
11. N. Eagle and A. Pentland, "Eigenbehaviors: Identifying Structure in Routine," Behavioral Ecology and Sociobiology, vol. 63, no. 7, 2009, pp. 1057–1066.
12. N. Eagle and A. Pentland, "Reality Mining: Sensing Complex Social Systems," Personal and Ubiquitous Computing, vol. 10, no. 4, 2006, pp. 255–268.

Index Terms:
campus, Wi-Fi, mobile environments, eigenvalues, eigenplaces, spectral signatures, architectural space, wireless communications, pervasive computing, mobile/wireless, ubiquitous computing
Citation:
Francesco Calabrese, Jonathan Reades, Carlo Ratti, "Eigenplaces: Segmenting Space through Digital Signatures," IEEE Pervasive Computing, vol. 9, no. 1, pp. 78-84, Jan.-March 2010, doi:10.1109/MPRV.2009.62
Usage of this product signifies your acceptance of the Terms of Use.