Spatiotemporal Data Visualization using Gravity Model

중력 모델을 이용한 시공간 데이터의 시각화

Kim, Seokyeon;Yeon, Hanbyul;Jang, Yun

  • Received : 2015.07.22
  • Accepted : 2015.11.02
  • Published : 2016.02.15


Visual analysis of spatiotemporal data has focused on a variety of techniques for analyzing and exploring the data. The goal of these techniques is to explore the spatiotemporal data using time information, discover patterns in the data, and analyze spatiotemporal data. The overall trend flow patterns help users analyze geo-referenced temporal events. However, it is difficult to extract and visualize overall trend flow patterns using data that has no trajectory information for movements. In order to visualize overall trend flow patterns, in this paper, we estimate continuous distributions of discrete events over time using KDE, and we extract vector fields from the continuous distributions using the gravity model. We then apply our technique on twitter data to validate techniques.


data visualization;spatiotemporal data;kernel density estimation;gravity model;twitter data


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Supported by : 한국연구재단, 정보통신기술진흥센터