Integrating GPS trajectory and topics from Twitter stream for human mobility estimation
作者机构：Department of Socio-Cultural Environmental Studies Graduate School of Frontier Sciences The University of Tokyo Chiba 277-8563 Japan Center for Spatial Information Science The University of Tokyo Kashiwa 277-8568 Japan Zenrin DataCom Co. Ltd Tokyo 108-6206 Japan
出 版 物：《Frontiers of Computer Science》 (中国计算机科学前沿（英文版）)
年 卷 期：2019年第13卷第3期
页 面：460-470页
核心收录：
学科分类：0810[工学-信息与通信工程] 12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0808[工学-电气工程] 08[工学] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：supported by JST, Strategic International Collaborative Research Program (SICORP) Grant in-Aid for Scientific Research B Grant in-Aid for Young Scientists of Japan’s Ministry of Education, Culture, Sports, Science, and Technology (MEXT)
主 题：GPS trajectory human mobility SNS locationbased social network (LBSN) topic modeling data mining spatiotemporal topic
摘 要：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 topic modeling 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.
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