Actions for Mining Heterogeneous Data for Semantic Understanding of Mobility Data

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Mining Heterogeneous Data for Semantic Understanding of Mobility Data

Author
Wu, Fei
Published
[University Park, Pennsylvania] : Pennsylvania State University, 2018.
Physical Description
1 electronic document
Additional Creators
Li, Zhenhui
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Open Access.
Summary
With the prevalence of positioning technology, an increasing amount of human mobility data becomes available nowadays, including geotagged social media data, location records collected by mobile phone applications, and GPS traces collected by navigation services. There have been tremendous interests in mining different mobility patterns for understanding human activities and behaviors. While the mobility data are valuable, they are often numeric or categorical in nature, (e.g., a GPS point with timestamp and geo-coordinates). As a result, the subsequent mined patterns from the data often have limited semantics. At the same time, a massive volume of spatial contexts (e.g., venue databases and geotagged tweets) provides us with rich semantics about urban dynamics. By combining the mobility data with surrounding contexts, we are able to understand the semantics of human mobility.Semantics enriched mobility data can benefit various applications such as automatic human activity space inference and target advertisements.This dissertation describes several recent attempts in fusing external context data for understanding the human mobility data. I will motivate the problem by presenting one key limitation of conventional mobility pattern mining approaches.A fundamental step in understanding the semantics is the mobility record annotation problem, that is, to associate a raw mobility record with the relevant surrounding context. More specifically, I have proposed to use words from social media as a source of dynamic event information. I have also proposed a framework to model dependencies among annotations for individual and collective movements.As the context data could have duplicates, I have proposed a label propagation method for addressing the annotation problem under noisy contexts.Finally, I will present recent collaboration results on applying the proposed framework to a practical social science study.
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Dissertation Note
Ph.D. Pennsylvania State University 2018.
Reproduction Note
Microfilm (positive). 1 reel ; 35 mm. (University Microfilms 10-10903788)
Technical Details
The full text of the dissertation is available as an Adobe Acrobat .pdf file ; Adobe Acrobat Reader required to view the file.
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