Abstract
Accurate home location is increasingly important for urban computing. Existing methods either rely on continuous (and expensive) Global Positioning System (GPS) data or suffer from poor accuracy. In particular, the sparse and noisy nature of social media data poses serious challenges in pinpointing where people live at scale. We revisit this research topic and infer home location within 100 m×100 m squares at 70% accuracy for 76% and 71% of active users in New York City and the Bay Area, respectively. To the best of our knowledge, this is the first time home location has been detected at such a fine granularity using sparse and noisy data. Since people spend a large portion of their time at home, our model enables novel applications. As an example, we focus on modeling people’s health at scale by linking their home locations with publicly available statistics, such as education disparity. Results in multiple geographic regions demonstrate both the effectiveness and added value of our home localization method and reveal insights that eluded earlier studies. In addition, we are able to discover the real buzz in the communities where people live.
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Project supported by the Goergen Institute for Data Science, New York State and the Xerox Foundation
ORCID: Tian-ran HU, http://orcid.org/0000-0003-0086-2447
Dr. Jie-bo LUO, corresponding author of this invited research article, joined the University of Rochester in Fall 2011 after over 15 years at Kodak Research Laboratories, where he was a senior principal scientist leading research and advanced development. He has been involved in numerous technical conferences, including serving as the program co-chair of ACM Multimedia 2010 and IEEE CVPR 2012. He is the Editor-in-Chief of Journal of Multimedia, and has served on the editorial boards of IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Multimedia, IEEE Transactions on Circuits and Systems for Video Technology, Pattern Recognition, Machine Vision and Applications, and Journal of Electronic Imaging. He is a Fellow of the SPIE, IEEE, and IAPR.
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Hu, Tr., Luo, Jb., Kautz, H. et al. Home location inference from sparse and noisy data: models and applications. Frontiers Inf Technol Electronic Eng 17, 389–402 (2016). https://doi.org/10.1631/FITEE.1500385
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DOI: https://doi.org/10.1631/FITEE.1500385