IEEE VIS 2019 Paper : A Natural-language-based Visual Query Approach of Uncertain Human Trajectories

Vancouver, Canada, Friday, 9:30am, Oct, 25, 2019
Zhaosong Huang, Ye Zhao, Wei Chen, Shengjie Gao, Kejie Yu, Weixia Xu, Mingjie Tang, Minfeng Zhu, and Mingliang Xu.
IEEE transactions on visualization and computer graphics (VIS 2019 VAST TVCG track).

In this paper, we propose a visual analytics approach that can extract spatial-temporal constraints from a textual sentence and support an effective query method over uncertain mobile trajectory data. It is built up on encoding massive, spatially uncertain trajectories by the semantic information of the POIs and regions covered by them, and then storing the trajectory documents in text database with an effective indexing scheme. Usage scenarios on real-world human mobility datasets demonstrate the effectiveness of our approach.

IEEE TVCG Paper on VIS2019 : Exploring the Sensitivity of Choropleths under Attribute Uncertainty.

Vancouver, Canada, Thursday, 3:30pm, Oct, 24, 2019
Zhaosong Huang, Yafeng Lu, Elizabeth Mack, Wei Chen, and Ross Maciejewski.
IEEE transactions on visualization and computer graphics (TVCG 2019).

In this paper, we present a visual analytics system that enhances our understanding of the impact of attribute uncertainty on data visualization and statistical analyses of these data. Our system consists of a parallel coordinates-based uncertainty specification view, an impact river and impact matrix visualization for region-based and simulation-based analysis, and a dual-choropleth map and t-SNE plot for visualizing the changes in classification and spatial autocorrelation over the range of uncertainty in the attribute values. We demonstrate our system through three use cases illustrating the impact of attribute uncertainty in geographic analysis.

IEEE VIS 2018: Urban Trajectory Data Visualization

Berlin, Germany, Monday, 9:00am-12:40pm, Oct, 22, 2018

Ye Zhao, Kent State University, Kent, Ohio, United States
Jing Yang, UNCC, Charlotte, North Carolina, United States
Wei Chen, Zhaosong Huang, Zhejiang University, Hangzhou, China
Shamal AL-Dohuki, Kent State University, Kent, Ohio, United States

In this tutorial, our major goal is to help visualization researchers and practitioners in the development of visualization systems of big trajectory datasets. Our tutorial contents will focus on important and practical topics people usually face when developing a visualization system of urban trajectories including:
1.Trajectory data representation, processing, indexing, and data queries
2.Trajectory data visualization tasks, challenges, and techniques. PDF
3.Developing web-based interactive visualization system
4.Case studies of urban visual analytics with shared source codes and examples We expect the audience of the tutorial not only gain knowledge about the visualization of urban trajectory data but also achieve experiences in implementing real-world visualization systems.

The 9th Visual Summer School of Zhejiang University (2017):
Data Visualization in Immersive System (Big screen)

Hangzhou, Zhejiang, Jul, 13, 2017. Learn more

The immersive system based on large-screen environment enables analysts to efficiently understand the data. We introduce the technics to develop a visual analytics system in an immersive environment. This tutorial mainly includes:
1. Data Processing: Data processing leverages data cleansing and efficient storage technologies to provide the foundation for an interactive system.
2. Socket-based communication technology.
3. Data analysis methods: Data mining algorithms and rendering methods.
4. Data visualization: Visualization design and implement.

The 8th Visual Summer School of Zhejiang University (2016):
Data Visualization of Urban Data

Hangzhou, Zhejiang, Jul, 31, 2016. Learn more

This tutorial introduces the visual analysis technology of urban data, mainly includes:
1. The introduction and the applications of urban data. This tutorial introduces three major challenges of urban data including (a) a large amount of data volume, (b) multi-source and heterogeneity, and (c) difficult to understand.
2. Related works of urban data mining, urban data management, and urban data visualization.
3. A case study to show how to implement a visual analytics system. The mainly technochs Includes data cleaning, data storage, data processing, and data visualization.