TY  - JOUR
T1  - Spatiotemporal Data Visualization using Gravity Model
AU  - Kim, Seokyeon 
AU  - Yeon, Hanbyul 
AU  - Jang, Yun 
JO  - Journal of KIISE, JOK
PY  - 2016
DA  - 2016/1/14
DO  - 
KW  - data visualization
KW  - spatiotemporal data
KW  - kernel density estimation
KW  - gravity model
KW  - twitter data
AB  - 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.