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CLC number: TP391

On-line Access: 2016-05-04

Received: 2015-11-07

Revision Accepted: 2016-02-19

Crosschecked: 2016-04-11

Cited: 1

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Citations:  Bibtex RefMan EndNote GB/T7714


Tian-ran Hu


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Frontiers of Information Technology & Electronic Engineering  2016 Vol.17 No.5 P.389-402


Home location inference from sparse and noisy data: models and applications

Author(s):  Tian-ran Hu, Jie-bo Luo, Henry Kautz, Adam Sadilek

Affiliation(s):  Computer Science Department, University of Rochester, NY 14623, USA

Corresponding email(s):   thu@cs.rochester.edu, jluo@cs.rochester.edu, kautz@cs.rochester.edu, sadilek@cs.rochester.edu

Key Words:  Home location, Mobility patterns, Healthcare

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Tian-ran Hu, Jie-bo Luo, Henry Kautz, Adam Sadilek. Home location inference from sparse and noisy data: models and applications[J]. Frontiers of Information Technology & Electronic Engineering, 2016, 17(5): 389-402.

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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.

This is an interesting paper with an important contribution to the literature. In this paper, the authors have proposed a method to detect users’ homes from geo-located tweets. The authors have shown a number of applications of identifying the home locations including analyzing mobility patterns, topics of Twitter conversation and health states.




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