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Spatiotemporal Data Visualization using Gravity Model
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  • Journal title : Journal of KIISE
  • Volume 43, Issue 2,  2016, pp.135-142
  • Publisher : Korean Institute of Information Scientists and Engineers
  • DOI : 10.5626/JOK.2016.43.2.135
 Title & Authors
Spatiotemporal Data Visualization using Gravity Model
Kim, Seokyeon; Yeon, Hanbyul; Jang, Yun;
 
 Abstract
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.
 Keywords
data visualization;spatiotemporal data;kernel density estimation;gravity model;twitter data;
 Language
Korean
 Cited by
1.
시각적 분석을 위한 응용 소프트웨어,유상봉;연한별;김석연;정성민;피민규;정대교;백희원;장윤;

정보과학회지, 2016. vol.34. 11, pp.8-21
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