Computer Science ›› 2019, Vol. 46 ›› Issue (8): 42-49.doi: 10.11896/j.issn.1002-137X.2019.08.007

• Big Data & Data Science • Previous Articles     Next Articles

Spatio-Temporal Evolution of Geographical Topics

SUN Guo-dao, ZHOU Zhi-xiu, LI Si, LIU Yi-peng, LIANG Rong-hua   

  1. (College of Information Engineering,Zhejiang University of Technology,Hangzhou 310023,China)
  • Received:2018-11-26 Online:2019-08-15 Published:2019-08-15

Abstract: The tweets posted by users in social media record a wide variety of user information.The text information includes various topics contained in the tweet.It is very important to analyze the temporal and spatial evolution of topics from these messages.Based on the tweet data,this paper designed a set of visual analysis process to mine the tweet information and display the spatiotemporal evolution process of the tweet topic through user interaction.Specifically,based on the partial historical tweet data,the global geographic space is divided by the DBSCAN clustering algorithm combined with the Tyson polygon.For the user to query the search topic of interest,the index finds all relevant tweet content and binds the information to the cluster center.Finally,the temporal and spatial evolution of the topic is demonstrated by the design of multiple combined time series clustering algorithms and visualization components of the adaptive algorithm.Through the experiment and analysis of the tweet data stored in the API provided by Twitter official website,the improved visual view adaptive layout algorithm effectively solves the problem of graphic occlusion and fully displays the temporal and spatial evolution mode of the tweet.The division of geographic regions and visualization components can effectively help researchers analyze the temporal and spatial evolution of tweets,as well as the distribution of hot topics of global concern

Key words: Tweet topic, Visual analysis process, Adaptive layout algorithm, Clustering, Spatio-Temporal evolution

CLC Number: 

  • TP391
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