Jie Liang

Interactive Visual Discovering of Movement Patterns from Sparsely Sampled Geo-tagged Social Media Data

Social media data with geotags can be used to track peoples movements in their daily lives. By providing both rich text and movement information, visual analysis on social media data can be both interesting and challenging. In contrast to traditional movement data, the sparseness and irregularity of social media data increase the difficulty of extracting movement patterns. To facilitate the understanding of peoples movements, we present an interactive visual analytics system to support the exploration of sparsely sampled trajectory data from social media. We propose a heuristic model to reduce the uncertainty caused by the nature of social media data. In the proposed system, users can filter and select reliable data from each derived movement category, based on the guidance of uncertainty model and interactive selection tools. By iteratively analyzing filtered movements, users can explore the semantics of movements, including the transportation methods, frequent visiting sequences and keyword descriptions. We provide two cases to demonstrate how our system can help users to explore the movement patterns.

A Visualization Approach for Frauds Detection in Financial Market

The traditional solutions to the stock market security are not sufficient in identifying attackers and further attack plans from the analysis of existing events.Therefore, it is difficult for analysts to prevent future unexpected events or frauds by only monitoring the realtime trading information. The event-driven fraud detection in financial market could not help analysts to find attack plans and the further intention of attackers. This paper proposed a new framework of visual analytics for stock market security. The proposed solution consists of two stages: 1) Visual Surveillance of Market Performance, and 2) Behavior-Driven Visual Analysis of Trading Networks.In the first stage, we use a 3D treemaps to monitor the real-time stock market performance and to identify a particular stock that produced an unusual trading pattern. We then move to the next stage: social network visualization to conduct behavior-driven visual analysis of suspected pattern. Through the visual analysis of social (or trading)network, analysts may finally identify the attackers (the sources of the fraud), and further attack plans.

Highlighting in Information Visualization: A Survey

Highlighting was the basic viewing control mechanism in computer graphics and visualization to guide users’ attention in reading diagrams, images, graphs and digital texts. As the rapid growth of theory and practice in information visualization, highlighting has extended its role that acts as not only a viewing control, but also an interaction control and a graphic recommendation mechanism in knowledge visualization and visual analytics. In this work, we attempt to give a formal summarization and classification of the existing highlighting methods and techniques that can be applied in Information Visualization, Visual Analytics and Knowledge Visualization. We propose a new three-layer model of highlighting. We discuss the responsibilities of each layer in the different stage of the visual information processing.

Visualizing large trees with divide & conquer partition

While prior works on enclosure approach, guarantees the space utilization of a single geometrical area, mostly rectangle, this paper proposes a flexible enclosure tree layout method for partitioning various polygonal shapes that break through the limitation of rectangular constraint. Similar to Treemap techniques, it uses enclosure to divide display space into smaller areas for its sub-hierarchies. The algorithm can partition a polygonal shape or even an arbitrary shape into smaller polygons, rotated rectangles or vertical-horizontal rectangles. The proposed method and implementation algorithms provide an effective interactive visualization tool for partitioning large hierarchical structures within a confined display area with different shapes for real-time applications. We demonstrated the effective of the new method with a case study, an automated evaluation and a usability study.

Exploring OD patterns of interested region based on taxi trajectories

Traffics of different regions in a city have different Origin-Destination (OD) patterns, which potentially reveal the surrounding traffic context and social functions. In this work, we present a visual analysis system to explore OD patterns of interested regions based on taxi trajectories. The system integrates interactive trajectory filtering with visual OD patterns exploration. Trajectories related to interested region are selected by a suite of graphical filtering tools, from which OD clusters are detected automatically. OD traffic patterns can be explored at two levels: overview of OD and detailed exploration on dynamic OD patterns, including information of dynamic traffic volume and travel time. By testing on real taxi trajectory data sets, we demonstrate the effectiveness of our system.Graphical Abstract

Angular Treemaps - A New Technique for Visualizing and Emphasizing Hierarchical Structures

Space-filling visualization techniques have proved their capability in visualizing large hierarchical structured data. However, most existing techniques restrict their partitioning process in vertical and horizontal direction only, which cause problem with identifying hierarchical structures. This paper presents a new space-filling method named Angular Treemaps that relax the constraint of the rectangular subdivision. The approach of Angular Treemaps utilizes divide and conquer paradigm to visualize and emphasize large hierarchical structures within a compact and limited display area with better interpretability. Angular Treemaps generate various layouts to highlight hierarchical sub-structure based on users preferences or system recommendations. It offers flexibility to be adopted into a wider range of applications, regarding different enclosing shapes. Preliminary usability results suggest users performance by using this technique is improved in locating and identifying categorized analysis tasks.

Sunburst with ordered nodes based on hierarchical clustering: a visual analyzing method for associated hierarchical pesticide residue data

According to the characteristics of pesticide residue data and analyzing requirements in food safety fields, we presented a visual analyzing method for associated hierarchical data, called sunburst with ordered nodes based on hierarchical clustering (SONHC). SONHC arranged the leaf nodes in sunburst in order using hierarchical clustering algorithm, put the associated dataset as a node in center of the sunburst, and connected it with the associated leaf nodes in sunburst using colored lines. So, it can present not only two hierarchical structures but also the relationships between them. Based on SONHC and some interaction techniques (clicking, contraction and expansion, etc) we developed an associated visual analyzing system (AVAS) for pesticide residues detection results data, which can help users to inspect the hierarchical structure of pesticide and agricultural products and to explore the associations between pesticides and agricultural products, and associations between different pesticides. The results of user experience test showed that SONHC algorithm overperforms than SA and SR algorithm in ULE and ULE’s variance. AVAS system is effective in helping users to analyze the pesticide residues data. Furthermore, SONHC algorithm can also be adopted to analyze associated hierarchical data in other fields, such as finance, insurance and e-commerce.Graphical Abstract

D-Map: Visual analysis of ego-centric information diffusion patterns in social media

Popular social media platforms could rapidly propagate vital information over social networks among a significant number of people. In this work we present D-Map (Diffusion Map), a novel visualization method to support exploration and analysis of social behaviors during such information diffusion and propagation on typical social media through a map metaphor. In D-Map, users who participated in reposting (i.e., resending a message initially posted by others) one central users posts (i.e., a series of original tweets) are collected and mapped to a hexagonal grid based on their behavior similarities and in chronological order of the repostings. With additional interaction and linking, D-Map is capable of providing visual portraits of the influential users and describing their social behaviors. A comprehensive visual analysis system is developed to support interactive exploration with D-Map. We evaluate our work with real world social media data and find interesting patterns among users. Key players, important information diffusion paths, and interactions among social communities can be identified.

Synthesis and characterization of biocompatible antimicrobial N-halamine-functionalized titanium dioxide core-shell nanoparticles

As one of the most powerful biocides, N-halamine based antimicrobial materials have attracted much interest due to their non-toxicity, rechargeability, and rapid inactivation against a broad range of microorganisms. In this study, novel titanium dioxide-ADMH core-shell nanoparticles [TiO2@poly (ADMH-co-MMA) NPs] were prepared via miniemulsion polymerization using 3-allyl-5,5-dimethylhydantoin (ADMH) and methyl methacrylate (MMA) with nano-TiO2. The produced nanoparticles were characterized by FT-IR, TEM, TGA, and XPS. The UV stability of N-halamine nanoparticles has been improved with the addition of titanium dioxide. After chlorination treatment by sodium hypochlorite, biocidal efficacies of the chlorinated nanoparticles against S. aureus (ATCC 6538) and E. coli O157:H7 (ATCC 43895) were determined. The nanoparticles showed excellent antimicrobial properties against bacteria within brief contact time. In addition, in vitro cell cytocompatibility tests showed that the antibacterial nanoparticles had good biocompatibility.

OD-Wheel: Visual design to explore OD patterns of a central region

Understanding the Origin-Destination (OD) patterns between different regions of a city is important in urban planning. In this work, based on taxi GPS data, we propose OD-Wheel, a novel visual design and associated analysis tool, to explore OD patterns. Once users define a region, all taxi trips starting from or ending to that region are selected and grouped into OD clusters. With a hybrid circular-linear visual design, OD-Wheel allows users to explore the dynamic patterns of each OD cluster, including the variation of traffic flow volume and traveling time. The proposed tool supports convenient interactions and allows users to compare and correlate the patterns between different OD clusters. A use study with real data sets demonstrates the effectiveness of the proposed OD-Wheel.