Seeing our signals: combining location traces and web-based models for personal discovery

ABSTRACT

Each of us has a complex and reciprocal relationship with our environment. Based on limited knowledge of this interwoven set of influences and consequences, we constantly make choices: where to live, how to go to work, what brands to buy, what to do with our leisure time. These choices evolve into patterns, and these patterns become driving functions of our relationship with the world around us. With increasing ease, devices we carry can sense, process, and transmit data on these patterns for our own use or to share, carefully, with others. In particular, here we will focus on location time series, gathered from GPS-enabled personal mobile devices. From this capacity emerges a new class of hybrid mobile-web applications that, first, enable personal exploration of our own patterns and, second, use the same data to index our life into other available datasets about the world around us. Such applications, revealing the previously unobservable about our own lives, offer an opportunity to employ mobile technology to illuminate the ramifications of our choices on others and the effects of the "microenvironments" we move through on us [1, 10].

References

1. Abdelzaher, T., et al. (2007). Mobiscopes for Human Spaces, Pervasive Computing, 2007
2. Ashbrook, D. and Starner, T. (2002). "Learning significant locations and predicting user movement with GPS". In Proceedings of ISWC. 101--108.
3. Austion, S. B. et. al. (2005). "Clustering of Fast-Food Restaurants Around Schools: A Novel 3. Application of Spatial Statistics to the Study of Food Environments". American Journal of Public Health. 95 (9). September 2005.
4. Axhausen, K. W. and T. Garling (1992). "Activity-Based Approaches to Travel Analysis, Conceptual Frameworks, Models, and Research Problems". Transport Reviews 12(4): 323--341.
5. Crane, R.,. R. Crepeau. (1998) "Does Neighborhood Design Influence Travel?: Behavioral Analysis of Travel Diary and GIS Data." Working Paper. University of California Transportation Center, UCTC No 374. January 1998.
6. Golob, T. F., H. Meurs. (1986) "Biases in response over time in a seven-day travel diary". Transportation 13: 163--181 (1986)
7. Gruteser, M., D. Grunwald, (2003). "Anonymous Usage of Location-Based Services through Spatial and Temporal Cloaking," Proceedings of First ACM/USENIX International Conference on Mobile Systems, Applications, and Services (MobiSys), San Francisco, CA, May
8. Gulliver, J. and D. J. Briggs. (2005) "Time-space modeling of journey-time exposure to traffic related air pollution using GIS". Environmental Research 97: 10--25.
9. Hoh, B., Gruteser, M., Xiong, H., Alrabady, A. (2007). "Preserving Privacy in GPS Traces via Density-Aware Path Cloaking.", ACM Conference on Computer and Communications Security (CCS), 2007
10. Hull, B., et al (2006). "CarTel: A Distributed Mobile Sensor Computing System". Proceedings of ACM Conference on Embedded Networked Sensor Systems (SenSys), 2006.
11. Kang, J. H., Welbourne, W., Stewart, B., and Borriello, G. (2004). Extracting places from traces of locations. In Proceedings of WMASH. 110--118.
12. Krumm, J. (2007). "Inference Attacks on Location Tracks", Fifth International Conference on Pervasive Computing (Pervasive 2007), Toronto, Ontario, Canada
13. Krumm, J. J. Letchner, E. Horvitz. (2007). "Map Matching with Travel Time Constratins,", SAE 2007 World Congress, April 16--19, Detroit, MI
14. Liao, L., Fox, D., and Kautz, H. (2005). "Location-based Activity Recognition, Advances in Neural Information Processing Systems"
15. Liao, L. D. Fox, and H. Kautz. (2007). "Extracting Places and Activities from GPS Traces Using Hierarchical Conditional Random Fields". International Journal of Robotics Research
16. Miller, H. (2007). "Place-Based versus People-Based Geographic Information Science." Geography Compass 1(3): 503--535
17. Midden, C., F. G. Kaiser, L. T. McCalley. (2007) "Technology's Four Roles in Understanding Individuals' Conservation of Natural Resources". J. of Social Issues 63(1): 155--174.
18. Najjar, E. L., M. E., Bonnifait, P. (2005) "A Road Matching Method for Precise Vehicle Localization using Kalman Filtering and Belief Theory". Autonomous Robots 19 (2): 173--191.
19. Quddus, M. A., W. Y. Ochieng, R. B. Noland. (2007) "Current Map Matching Algorithm for Transport Applications: State of the art and Future Research Directions," Transportation Research Part C.
20. Chen, M., et al (2006), "Practical Metropolitan-Scale Positioning for GSM Phones", In Proceedings of Eighth International Conference on Ubiquitous Computing, Ubicomp 2006.
21. Sultana, S., J. Weber (2007). "Journey-to-work patterns in the age of sprawl: Evidence from two midsize southern metropolitan areas." Professional Geographer 59(2): 193--208.
22. Weiser, M. (1991) The Computer for the 21^st Century, Scientific American Special Issue on Communications, Computers, and Networks, September 1991
23. Wilson, E. J., R. Wilson, K. J. Krizek. (2007) "The implication of school choice on travel behavior and environmental emissions." Transportation Research Part D. 12: 506--518.

Index Terms

• Applied computing

• Information systems

  • Information systems applications