Andrea Cuttone
ABSTRACT
Understanding both collective and personal human mobility is a central topic in Computational Social Science. Smartphone sensing data is emerging as a promising source for studying human mobility. However, most literature focuses on high-precision GPS positioning and high-frequency sampling, which is not always feasible in a longitudinal study or for everyday applications because location sensing has a high battery cost. In this paper we study the feasibility of inferring human mobility from sparse, low accuracy mobile sensing data. We validate our results using participants' location diaries, and analyze the inferred geographical networks, the time spent at different places, and the number of unique places over time. Our results suggest that low resolution data allows accurate inference of human mobility patterns.
- Aharony, N., Pan, W., Ip, C., Khayal, I., and Pentland, A. Social fmri: Investigating and shaping social mechanisms in the real world. Pervasive and Mobile Computing 7, 6 (2011), 643--659. Google Scholar
Digital Library
- Ashbrook, D., and Starner, T. Using GPS to learn significant locations and predict movement across multiple users. Personal and Ubiquitous Computing 7, 5 (2003), 275--286. Google ScholarDigital Library
- Bagrow, J. P., and Lin, Y.-R. Mesoscopic structure and social aspects of human mobility. PLoS One 7, 5 (2012).Google ScholarCross Ref
- Belik, V., Geisel, T., and Brockmann, D. Natural human mobility patterns and spatial spread of infectious diseases. Physical Review X 1, 1 (2011), 011001.Google ScholarCross Ref
- Boytsov, A., Zaslavsky, A., and Abdallah, Z. Where have you been? using location clustering and context awareness to understand places of interest. In Internet of Things, Smart Spaces, and Next Generation Networking. Springer, 2012, 51--62.Google Scholar
- Cao, X., Cong, G., and Jensen, C. S. Mining significant semantic locations from GPS data. Proc. of the VLDB Endowment 3, 1-2 (2010), 1009--1020. Google ScholarDigital Library
- Gonzalez, M. C., Hidalgo, C. A., and Barabási, A.-L. Understanding individual human mobility patterns. Nature 453, 7196 (2008), 779--782.Google ScholarCross Ref
- Krings, G., Calabrese, F., Ratti, C., and Blondel, V. D. Urban gravity: a model for inter-city telecommunication flows. Journal of Statistical Mechanics: Theory and Experiment 2009, 07 (2009), L07003.Google ScholarCross Ref
- Lu, H., Yang, J., Liu, Z., Lane, N. D., Choudhury, T., and Campbell, A. T. The Jigsaw continuous sensing engine for mobile phone applications. In Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems, ACM (2010), 71--84. Google ScholarDigital Library
- Marmasse, N., and Schmandt, C. Location-aware information delivery with ComMotion. In Handheld and Ubiquitous Computing, Springer (2000), 157--171. Google ScholarDigital Library
- Montoliu, R., and Gatica-Perez, D. Discovering human places of interest from multimodal mobile phone data. In Proc. of the 9th Int. Conf. on Mobile and Ubiquitous Multimedia, ACM (2010), 12. Google ScholarDigital Library
- Onnela, J.-P., Arbesman, S., González, M. C., Barabási, A.-L., and Christakis, N. A. Geographic constraints on social network groups. PLoS one 6, 4 (2011), e16939.Google Scholar
- Palma, A. T., Bogorny, V., Kuijpers, B., and Alvares, L. O. A clustering-based approach for discovering interesting places in trajectories. In Proc. of the 2008 ACM symposium on Applied computing, ACM (2008), 863--868. Google ScholarDigital Library
- Raento, M., Oulasvirta, A., and Eagle, N. Smartphones an emerging tool for social scientists. Sociological methods & research 37, 3 (2009), 426--454.Google Scholar
- Song, C., Qu, Z., Blumm, N., and Barabási, A.-L. Limits of predictability in human mobility. Science 327, 5968 (2010), 1018--1021.Google ScholarCross Ref
- Stopczynski, A., Sekara, V., Sapiezynski, P., Cuttone, A., Madsen, M. M., Larsen, J. E., and Lehmann, S. Measuring large-scale social networks with high resolution. PLoS One 9, 4 (04 2014), e95978.Google Scholar
- Yan, Z., Chakraborty, D., Parent, C., Spaccapietra, S., and Aberer, K. Semantic trajectories: Mobility data computation and annotation. ACM Transactions on Intelligent Systems and Technology (TIST) 4, 3 (2013), 49. Google ScholarDigital Library
- Zheng, V. W., Zheng, Y., Xie, X., and Yang, Q. Collaborative location and activity recommendations with GPS history data. In Proc. of the 19th Int. Conf. on World Wide Web, ACM (2010), 1029--1038. Google ScholarDigital Library
- Zheng, Y., Zhang, L., Xie, X., and Ma, W.-Y. Mining interesting locations and travel sequences from GPS trajectories. In Proc. of the 18th Int. Conf. on World Wide Web, ACM (2009), 791--800. Google ScholarDigital Library
- Zhou, C., Frankowski, D., Ludford, P., Shekhar, S., and Terveen, L. Discovering personal gazetteers: an interactive clustering approach. In Proc. of the 12th annual ACM Int. workshop on Geographic information systems, ACM (2004), 266--273. Google ScholarDigital Library
- Zhou, C., Frankowski, D., Ludford, P., Shekhar, S., and Terveen, L. Discovering personally meaningful places: An interactive clustering approach. ACM Transactions on Information Systems (TOIS) 25, 3 (2007), 12. Google ScholarDigital Library
Index Terms
- Inferring human mobility from sparse low accuracy mobile sensing data
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