Junghoon Chae

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.

Volume Estimation Using Food Specific Shape Templates in Mobile Image-Based Dietary Assessment.

As obesity concerns mount, dietary assessment methods for prevention and intervention are being developed. These methods include recording, cataloging and analyzing daily dietary records to monitor energy and nutrient intakes. Given the ubiquity of mobile devices with built-in cameras, one possible means of improving dietary assessment is through photographing foods and inputting these images into a system that can determine the nutrient content of foods in the images. One of the critical issues in such the image-based dietary assessment tool is the accurate and consistent estimation of food portion sizes. The objective of our study is to automatically estimate food volumes through the use of food specific shape templates. In our system, users capture food images using a mobile phone camera. Based on information (i.e., food name and code) determined through food segmentation and classification of the food images, our system choose a particular food template shape corresponding to each segmented food. Finally, our system reconstructs the three-dimensional properties of the food shape from a single image by extracting feature points in order to size the food shape template. By employing this template-based approach, our system automatically estimates food portion size, providing a consistent method for estimation food volume.

Comparison of Known Food Weights with Image-Based Portion-Size Automated Estimation and Adolescents' Self-Reported Portion Size

Background: Diet is a critical element of diabetes self-management. An emerging area of research is the use of images for dietary records using mobile telephones with embedded cameras. These tools are being designed to reduce user burden and to improve accuracy of portion-size estimation through automation. The objectives of this study were to (1) assess the error of automatically determined portion weights compared to known portion weights of foods and (2) to compare the error between automation and human. Methods: Adolescents (n = 15) captured images of their eating occasions over a 24 h period. All foods and beverages served were weighed. Adolescents self-reported portion sizes for one meal. Image analysis was used to estimate portion weights. Data analysis compared known weights, automated weights, and self-reported portions. Results: For the 19 foods, the mean ratio of automated weight estimate to known weight ranged from 0.89 to 4.61, and 9 foods were within 0.80 to 1.20. The largest error was for lettuce and the most accurate was strawberry jam. The children were fairly accurate with portion estimates for two foods (sausage links, toast) using one type of estimation aid and two foods (sausage links, scrambled eggs) using another aid. The automated method was fairly accurate for two foods (sausage links, jam); however, the 95% confidence intervals for the automated estimates were consistently narrower than human estimates. Conclusions: The ability of humans to estimate portion sizes of foods remains a problem and a perceived burden. Errors in automated portion-size estimation can be systematically addressed while minimizing the burden on people. Future applications that take over the burden of these processes may translate to better diabetes self-management.

Analyzing high-dimensional multivaríate network links with integrated anomaly detection, highlighting and exploration

This paper focuses on the integration of a family of visual analytics techniques for analyzing high-dimensional, multivariate network data that features spatial and temporal information, network connections, and a variety of other categorical and numerical data types. Such data types are commonly encountered in transportation, shipping, and logistics industries. Due to the scale and complexity of the data, it is essential to integrate techniques for data analysis, visualization, and exploration. We present new visual representations, Petal and Thread, to effectively present many-to-many network data including multi-attribute vectors. In addition, we deploy an information-theoretic model for anomaly detection across varying dimensions, displaying highlighted anomalies in a visually consistent manner, as well as supporting a managed process of exploration. Lastly, we evaluate the proposed methodology through data exploration and an empirical study.

A survey on visual analysis approaches for financial data

Market participants and businesses have made tremendous efforts to make the best decisions in a timely manner under varying economic and business circumstances. As such, decision‐making processes based on Financial data have been a popular topic in industries. However, analyzing Financial data is a non‐trivial task due to large volume, diversity and complexity, and this has led to rapid research and development of visualizations and visual analytics systems for Financial data exploration. Often, the development of such systems requires researchers to collaborate with Financial domain experts to better extract requirements and challenges in their tasks. Work to systematically study and gather the task requirements and to acquire an overview of existing visualizations and visual analytics systems that have been applied in Financial domains with respect to real‐world data sets has not been completed. To this end, we perform a comprehensive survey of visualizations and visual analytics. In this work, we categorize Financial systems in terms of data sources, applied automated techniques, visualization techniques, interaction, and evaluation methods. For the categorization and characterization, we utilize existing taxonomies of visualization and interaction. In addition, we present task requirements extracted from interviews with domain experts in order to help researchers design better systems with detailed goals.

A Visual Analytics Framework for Microblog Data Analysis at Multiple Scales of Aggregation

Real‐time microblogs can be utilized to provide situational awareness during emergency and disaster events. However, the utilization of these datasets requires the decision makers to perform their exploration and analysis across a range of data scales from local to global, while maintaining a cohesive thematic context of the transition between the different granularity levels. The exploration of different information dimensions at the varied data and human scales remains to be a non‐trivial task. To this end, we present a visual analytics situational awareness environment that supports the real‐time exploration of microblog data across multiple scales of analysis. We classify microblogs based on a fine‐grained, crisis‐related categorization approach, and visualize the spatiotemporal evolution of multiple categories by coupling a spatial lens with a glyph‐based visual design. We propose a transparency‐based spatial context preserving technique that maintains a smooth transition between different spatial scales. To evaluate our system, we conduct user studies and provide domain expert feedback.

ParkAnalyzer: Characterizing the movement patterns of visitors VAST 2015 Mini-Challenge 1

The 2015 VAST challenge features movement tracking (Mini- Challenge 1 (MC1)) and communication information (Mini- Challenge 2 (MC2)) datasets of all visitors in an amusement park over a three-day weekend. The data includes around 25 million individual movement records, along with 4 million communication records. Analyzing and exploring such large-scale datasets require intelligent data mining methods that characterize the overall trends and anomalies, as well as interactive visual interfaces to support investigation at different spatiotemporal granularities. The objective of MC1 was to characterize the behavior of different groups of visitors, compare different activity patterns over the three days, and discover anomalies or unusual behavior patterns that relate to the crime that occurred during the weekend. We utilized both movement data provided in MC1 and communication data provided in MC2 to answer the questions asked in MC1.

Visual analytics for investigative analysis of hoax distress calls using social media

A hoax distress call is a serious concern for the U.S. Coast Guard. Hoax calls not only put the Coast Guard rescue personnel in potentially dangerous situations, but also waste valuable assets that should be used for real emergency situations. However, conventional approaches do not provide enough information for investigating hoax calls and callers. As social media has played a pervasive role in the way people communicate, such data opens new opportunities and solutions to a wide range of challenges. In this paper, we present social media visual analytics solutions for supporting the investigation for hoax distress calls. We not only provide a set of comprehensive keyword collections, but also resolve the lack of social media data for the investigation. Our framework allows investigators to identify suspicious Twitter users and provide a visual analytics environment designed to examine geo-tagged tweets and Instagram messages in the context of hoax distress calls.

Visual analytics of heterogeneous data for criminal event analysis VAST challenge 2015: Grand challenge

We developed a visual analytics system to analyze the provided heterogeneous 2015 VAST Challenge data. This system utilized several analytic models and visualization techniques. Currently, the underlying data models and clustering techniques have limitations in processing the large volume of data in real time. Therefore, for future work, we will improve the scalability of our system to support real time interactivity and analysis.