Integrating GPS trajectory and topics from Twitter stream for human mobility estimation

doi:10.1007/s11704-017-6464-3

Front. Comput. Sci.

2019, Vol. 13

Issue (3) : 460-470 https://doi.org/10.1007/s11704-017-6464-3

RESEARCH ARTICLE

Integrating GPS trajectory and topics from Twitter stream for human mobility estimation

Satoshi MIYAZAWA¹(

), Xuan SONG², Tianqi XIA¹, Ryosuke SHIBASAKI², Hodaka KANEDA³

¹. Department of Socio-Cultural Environmental Studies, Graduate School of Frontier Sciences, The University of Tokyo, Chiba 277-8563, Japan
². Center for Spatial Information Science, The University of Tokyo, Kashiwa 277-8568, Japan
³. Zenrin DataCom Co.,Ltd, Tokyo 108-6206, Japan

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Abstract

Understanding urban dynamics and large-scale human mobility will play a vital role in building smart cities and sustainable urbanization. Existing research in this domain mainly focuses on a single data source (e.g., GPS data, CDR data, etc.). In this study, we collect big and heterogeneous data and aim to investigate and discover the relationship between spatiotemporal topics found in geo-tagged tweets and GPS traces from smartphones. We employ Latent Dirichlet Allocation-based topicmodeling on geo-tagged tweets to extract and classify the topics. Then the extracted topics from tweets and temporal population distribution from GPS traces are jointly used to model urban dynamics and human crowd flow. The experimental results and validations demonstrate the efficiency of our approach and suggest that the fusion of cross-domain data for urban dynamics modeling is more practical than previously thought.

Keywords GPS trajectory human mobility SNS locationbasedsocial network (LBSN) topic modeling data mining spatiotemporal topic

Corresponding Author(s): Satoshi MIYAZAWA

Just Accepted Date: 16 May 2017 Online First Date: 25 May 2018 Issue Date: 24 April 2019

Cite this article:

Satoshi MIYAZAWA,Xuan SONG,Tianqi XIA, et al. Integrating GPS trajectory and topics from Twitter stream for human mobility estimation[J]. Front. Comput. Sci., 2019, 13(3): 460-470.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-017-6464-3
https://academic.hep.com.cn/fcs/EN/Y2019/V13/I3/460

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