Data Visualization Lab
Research area


Spatiotemporal visualization


Introduction

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.

Publications

Spatiotemporal Data Visualization using Gravity Model
Seokyeon Kim, Hanbyul Yeon, Jang Y
Journal of KIISE : Computer Systems and Theory 2016

Spatiotemporal Data Visualization using Twitter Data
Seokyeon Kim, Hanbyul Yeon, Jang Y
Korea Computer Congress 2015

Twitter DataMigration Analysis within Korea using Spatiotemporal Data Visualization
Seokyeon Kim, Hanbyul Yeon, Jang Y
Korea Computer Congress 2014