Data visualization

In this work, we develop a novel flow analysis technique to extract, represent, and analyze flow maps of non-directional spatiotemporal data unaccompanied by trajectory information. We estimate a continuous distribution of these events over space and time, and extract flow fields for spatial and temporal changes utilizing a gravity model. Then, we visualize the spatiotemporal patterns in the data by employing flow visualization techniques. Overall spatiotemporal data flow patterns help users analyze geo-referenced temporal events, such as disease outbreaks, crime patterns, etc. 

Paper : 

Kim, S., Yeon, H., Jang, Y. (2016). Spatiotemporal Data Visualization using Gravity Model. Journal of KIISE, 43(2), 135-142.

Kim, S., Jeong, S., Woo, I., Jang, Y., Maciejewski, R., & Ebert, D. S. (2017). Data flow analysis and visualization for spatiotemporal statistical data without trajectory information. IEEE transactions on visualization and computer graphics, 24(3), 1287-1300. 

Patent :Jang, Y., Kim, S., Jeong, S., APPARATUS AND METHOD OF VISUALIZATION FOR SPATIOTEMPORAL DATA, 1018241940000 (2018.01.25)

We are developing a visual analytics system to analyze the causes of traffic congestion. It is important to know the cause of the traffic congestion in order to solve the traffic congestion because the solution is different according to the cause of traffic congestion.

We propose a novel visualization applying the smudge technique to the attention map. The proposed visualization intuitively shows the gaze flow and AoIs (Area of Interests) of an observer. Besides, it provides fixation, saccade, and micro-movement information, which allows us to respond to various analytical goals within a single visualization. Finally, we provide two case studies to show the effectiveness of our technique.

Paper: Yoo, S., Jeong, S., Kim, S., & Jang, Y. (2019). Gaze Attention and Flow Visualization using the Smudge Effect. The 27th International Conference on Computer Graphics and Applications (Pacific Graphics 2019), Short paper.

Patent: Jang Y., Yoo S., Kim, S. Y., Jeong D. K., "Method and apparatus for analyzing saliency-based visual stimulus and gaze data", Korean Patent 10-1987229, June 3, 2019 

Finding major flows and trends is an important factor in analyzing movement data. However, it is difficult to define the trend flow and the major flow when the movement data has multiple directions rather than one direction. In this paper, we explore how to effectively visualize trend flows and major flows in multi-directional data fields to illustrate multi-directional movement data. We visualize the multi-directional New York taxi data in four different styles, explaining the advantages and disadvantages of each visualization technique.

Paper : Jeong, S., Kim, S., Jang, Y.(2017). Multidirectional Flow Visualization Methodology. 2017 IEEE Pacific Visualization Symposium(PacificVis), short paper.

Patent : Jang, Y., Jeong, S., Kim, S., METHOD AND APPARATUS FOR VISUALIZING MOVEMENT DATA IN MULTI-DIRECTIONAL VECTOR FIELD ,1020170173311 (2017.12.15)