Siming Chen

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.

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.

Frontier of Information Visualization and Visual Analytics in 2016

Visualization has evolved into a flourishing research field in recent 30 years. There are substantial visualization methodologies and applications published every year. Most of literature surveys focus on reviewing the state-of-art techniques in a certain direction in-depth. In this work, we conduct a cross-section survey by taking all the latest literatures as a whole, to obtain insights into the ecology of Information Visualization and Visual Analytics field in 2016. Center around 70-related publications in the IEEE VIS, we perform a mixed quantitative and qualitative analysis to report the current research progress, including statistical overview as well as detailed research topics.Graphical Abstract

Visual data quality analysis for taxi GPS data

We present a novel visual analysis method to systematically discover data quality problems in raw taxi GPS data. It combines semi-supervised active learning and interactive visual exploration. It helps analysts interactively discover unknown data quality problems, and automatically extract known problems. We report analysis results on Beijing taxi GPS data.

MovementFinder: Visual analytics of origin-destination patterns from geo-tagged social media

Geo-tagged social media data can be viewed as sampling of peoples trajectories in daily life. It consists of peoples movements and embeds the semantics of movements. However, it is challenging to reveal patterns from the sparse and irregular sampling data. We proposed an interactive multi-filter visualization approach to analyze the spatial-temporal movement pattern in peoples daily life. Peoples trajectories are visualized on the map with multiple functional layers. With our visual analytics tools, users are able to drill down to details, with the awareness of the origin-destination flow patterns of spatial, temporal, and semantic meaning.

Interaction+: Interaction enhancement for web-based visualizations

In this work, we present Interaction+, a tool that enhances the interactive capability of existing web-based visualizations. Different from the toolkits for authoring interactions during the visualization construction, Interaction+ takes existing visualizations as input, analyzes the visual objects, and provides users with a suite of interactions to facilitate the visual exploration, including selection, aggregation, arrangement, comparison, filtering, and annotation. Without accessing the underlying data or process how the visualization is constructed, Interaction+ is application-independent and can be employed in various visualizations on the web. We demonstrate its usage in two scenarios and evaluate its effectiveness with a qualitative user study.

Behavior analysis through collaborative visual exploration on trajectory data

In VAST Challenge 2015, we proposed a collaborative visual exploration system for behavior analysis over trajectory records. We discuss technical details in this report, in order to deliberate how the system supports multiple users to collaboratively analyze the same data, assist in sharing their findings, and constructing an overall picture of their insights.

Uncertainty-aware visual analytics for exploring human behaviors from heterogeneous spatial temporal data

Abstract When analyzing human behaviors, we need to construct the human behaviors from multiple sources of data, e.g. trajectory data, transaction data, identity data, etc. The problems we’re facing are the data conflicts, different resolution, missing and conflicting data, which together lead to the uncertainty in the spatial temporal data. Such uncertainty in data leads to difficulties and even failure in the visual analytics task for analyzing people behavior, pattern and outliers. However, traditional automatic methods can not solve the problems in such complex scenario, where the uncertain and conflicting patterns are not well-defined. To solve the problems, we proposed a semi-automatic approach, for users to solve the conflicts and identify the uncertainties. To be general, we summarized five types of uncertainties and solutions to conduct the tasks of behavior analysis. Combined with the uncertainty-aware methods, we proposed a visual analytics system to analyze human behaviors, detect patterns and find outliers. Case studies from the IEEE VAST Challenge 2014 dataset confirm the effectiveness of our approach.

Visual exploration of ionosphere disturbances for earthquake research

In seismic research, a hypothesis is that ionosphere disturbances are related to lithosphere activities such as earthquakes. Domain scientists are urgent to discover disturbance patterns of electromagnetic attributes in ionosphere around earthquakes, and to propose related hypotheses. However, the workflow of seismic researchers usually only supports pattern extraction from a few earthquakes. To explore the pattern-based hypotheses on a large spatiotemporal scale meets challenges, due to the limitation of their analysis tools. To tackle the problem, we develop a visual analytics system which not only supports pattern extraction of the original workflow in a way of dynamic query, but also extends the work with hypotheses exploration on a global scale. Domain scientists can easily utilize our system to explore the heterogeneous dataset, and to extract patterns and explore related hypotheses visually and interactively. We conduct several case studies to demonstrate the usage and effectiveness of our system in the research of relationships between ionosphere disturbances and earthquakes.

A platform for collaborative visual analysis on streaming messages

We proposed a collaborative platform to analyze streaming microblog messages in real-time on the emergence of security events, for VAST 2014 Mini Challenge 3. Our team members monitored and analyzed the streaming data simultaneously on it, where we could flexibly distribute workloads by filtering the data streams as we liked, and share notable information such as suspects and locations. With the assistance of a keyword analyzer, we were able to stay vigilant for potential emergencies in the course of streaming and conduct postmortem analysis. We will describe the collaborative mechanisms, as well as the design principles and considerations in detail.