Yun Jang

Spatiotemporal social media analytics for abnormal event detection and examination using seasonal-trend decomposition

Recent advances in technology have enabled social media services to support space-time indexed data, and internet users from all over the world have created a large volume of time-stamped, geo-located data. Such spatiotemporal data has immense value for increasing situational awareness of local events, providing insights for investigations and understanding the extent of incidents, their severity, and consequences, as well as their time-evolving nature. In analyzing social media data, researchers have mainly focused on finding temporal trends according to volume-based importance. Hence, a relatively small volume of relevant messages may easily be obscured by a huge data set indicating normal situations. In this paper, we present a visual analytics approach that provides users with scalable and interactive social media data analysis and visualization including the exploration and examination of abnormal topics and events within various social media data sources, such as Twitter, Flickr and YouTube. In order to find and understand abnormal events, the analyst can first extract major topics from a set of selected messages and rank them probabilistically using Latent Dirichlet Allocation. He can then apply seasonal trend decomposition together with traditional control chart methods to find unusual peaks and outliers within topic time series. Our case studies show that situational awareness can be improved by incorporating the anomaly and trend examination techniques into a highly interactive visual analysis process.

Special Section on Visual Analytics: Public behavior response analysis in disaster events utilizing visual analytics of microblog data

Analysis of public behavior plays an important role in crisis management, disaster response, and evacuation planning. Unfortunately, collecting relevant data can be costly and finding meaningful information for analysis is challenging. A growing number of Location-based Social Network services provides time-stamped, geo-located data that opens new opportunities and solutions to a wide range of challenges. Such spatiotemporal data has substantial potential to increase situational awareness of local events and improve both planning and investigation. However, the large volume of unstructured social media data hinders exploration and examination. To analyze such social media data, our system provides the analysts with an interactive visual spatiotemporal analysis and spatial decision support environment that assists in evacuation planning and disaster management. We demonstrate how to improve investigation by analyzing the extracted public behavior responses from social media before, during and after natural disasters, such as hurricanes and tornadoes.

Illustration and photography inspired visualization of flows and volumes

Understanding and analyzing complex volumetrically varying data is a difficult problem. Many computational visualization techniques have had only limited success in succinctly portraying the structure of three-dimensional turbulent flow. Motivated by both the extensive history and success of illustration and photographic flow visualization techniques, we have developed a new interactive volume rendering and visualization system for flows and volumes that simulates and enhances traditional illustration, experimental advection, and photographic flow visualization techniques. Our system uses a combination of varying focal and contextual illustrative styles, new advanced two-dimensional transfer functions, enhanced Schlieren and shadowgraphy shaders, and novel oriented structure enhancement techniques to allow interactive visualization, exploration, and comparative analysis of scalar, vector, and time-varying volume datasets. Both traditional illustration techniques and photographic flow visualization techniques effectively reduce visual clutter by using compact oriented structure information to convey three-dimensional structures. Therefore, a key to the effectiveness of our system is using one-dimensional (Schlieren and shadowgraphy) and two-dimensional (silhouette) oriented structural information to reduce visual clutter, while still providing enough three-dimensional structural information for the users visual system to understand complex three-dimensional flow data. By combining these oriented feature visualization techniques with flexible transfer function controls, we can visualize scalar and vector data, allow comparative visualization of flow properties in a succinct, informative manner, and provide continuity for visualizing time-varying datasets.

Visual Analytics on Mobile Devices for Emergency Response

Using mobile devices for visualization provides a ubiquitous environment for accessing information and effective decision making. These visualizations are critical in satisfying the knowledge needs of operators in areas as diverse as education, business, law enforcement, protective services, medical services, scientific discovery, and homeland security. In this paper, we present an efficient and interactive mobile visual analytic system for increased situational awareness and decision making in emergency response and training situations. Our system provides visual analytics with locational scene data within a simple interface tailored to mobile device capabilities. In particular, we focus on processing and displaying sensor network data for first responders. To verify our system, we have used simulated data of The Station nightclub fire evacuation.

Enhancing the Interactive Visualization of Procedurally Encoded Multifield Data with Ellipsoidal Basis Functions

Functional approximation of scattered data is a popular technique for compactly representing various types of datasets in computer graphics, including surface, volume, and vector datasets. Typically, sums of Gaussians or similar radial basis functions are used in the functional approximation and PC graphics hardware is used to quickly evaluate and render these datasets. Previously, researchers presented techniques for spatially‐limited spherical Gaussian radial basis function encoding and visualization of volumetric scalar, vector, and multifield datasets. While truncated radially symmetric basis functions are quick to evaluate and simple for encoding optimization, they are not the most appropriate choice for data that is not radially symmetric and are especially problematic for representing linear, planar, and many non‐spherical structures. Therefore, we have developed a volumetric approximation and visualization system using ellipsoidal Gaussian functions which provides greater compression, and visually more accurate encodings of volumetric scattered datasets. In this paper, we extend previous work to use ellipsoidal Gaussians as basis functions, create a rendering system to adapt these basis functions to graphics hardware rendering, and evaluate the encoding effectiveness and performance for both spherical Gaussians and ellipsoidal Gaussians.

Spatial Text Visualization Using Automatic Typographic Maps

We present a method for automatically building typographic maps that merge text and spatial data into a visual representation where text alone forms the graphical features. We further show how to use this approach to visualize spatial data such as traffic density, crime rate, or demographic data. The technique accepts a vector representation of a geographic map and spatializes the textual labels in the space onto polylines and polygons based on user-defined visual attributes and constraints. Our sample implementation runs as a Web service, spatializing shape files from the OpenStreetMap project into typographic maps for any region.

A correlative analysis process in a visual analytics environment

Finding patterns and trends in spatial and temporal datasets has been a long studied problem in statistics and different domains of science. This paper presents a visual analytics approach for the interactive exploration and analysis of spatiotemporal correlations among multivariate datasets. Our approach enables users to discover correlations and explore potentially causal or predictive links at different spatiotemporal aggregation levels among the datasets, and allows them to understand the underlying statistical foundations that precede the analysis. Our technique utilizes the Pearsons product-moment correlation coefficient and factors in the lead or lag between different datasets to detect trends and periodic patterns amongst them.

Hardware-assisted feature analysis and visualization of procedurally encoded multifield volumetric data

We take a new approach to interactive visualization and feature detection of large scalar, vector, and multifield computational fluid dynamics data sets that is also well suited for meshless CFD methods. Radial basis functions (RBFs) are used to procedurally encode both scattered and irregular gridded scalar data sets. The RBF encoding creates a complete, unified, functional representation of the scalar field throughout 3D space, independent of the underlying data topology, and eliminates the need for the original data grid during visualization. The capability of commodity PC graphics hardware to accelerate the reconstruction and rendering and to perform feature detection from this functional representation is a powerful tool for visualizing procedurally encoded volumes. Our RBF encoding and GPU-accelerated reconstruction, feature detection, and visualization tool provides a flexible system for visually exploring and analyzing large, structured, scattered, and unstructured scalar, vector, and multifield data sets at interactive rates on desktop PCs.

Smart Transparency for Illustrative Visualization of Complex Flow Surfaces

The perception of transparency and the underlying neural mechanisms have been subject to extensive research in the cognitive sciences. However, we have yet to develop visualization techniques that optimally convey the inner structure of complex transparent shapes. In this paper, we apply the findings of perception research to develop a novel illustrative rendering method that enhances surface transparency nonlocally. Rendering of transparent geometry is computationally expensive since many optimizations, such as visibility culling, are not applicable and fragments have to be sorted by depth for correct blending. In order to overcome these difficulties efficiently, we propose the illustration buffer. This novel data structure combines the ideas of the A and G-buffers to store a list of all surface layers for each pixel. A set of local and nonlocal operators is then used to process these depth-lists to generate the final image. Our technique is interactive on current graphics hardware and is only limited by the available graphics memory. Based on this framework, we present an efficient algorithm for a nonlocal transparency enhancement that creates expressive renderings of transparent surfaces. A controlled quantitative double blind user study shows that the presented approach improves the understanding of complex transparent surfaces significantly.

Interactive Visualization and Analysis of Network and Sensor Data on Mobile Devices

Mobile devices are rapidly gaining popularity due to their small size and their wide range of functionality. With the constant improvement in wireless network access, they are an attractive option not only for day to day use. but also for in-field analytics by first responders in widespread areas. However, their limited processing, display, graphics and power resources pose a major challenge in developing effective applications. Nevertheless, they are vital for rapid decision making in emergencies when combined with appropriate analysis tools. In this paper, we present an efficient, interactive visual analytic system using a PDA to visualize network information from Purdues Ross-Ade Stadium during football games as an example of in-held data analytics combined with text and video analysis. With our system, we can monitor the distribution of attendees with mobile devices throughout the stadium through their access of information and association/disassociation from wireless access points, enabling the detection of crowd movement and event activity. Through correlative visualization and analysis of synchronized video (instant replay video) and text information (play statistics) with the network activity, we can provide insightful information to network monitoring personnel, safety personnel and analysts. This work provides a demonstration and testbed for mobile sensor analytics that will help to improve network performance and provide safety personnel with information for better emergency planning and guidance