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We introduce a trans-disciplinary collaboration between researchers, healthcare practitioners, and community health partners in the Southwestern U.S. to enable improved management, response, and recovery to our current pandemic and for future health emergencies. Our Center work enables effective and efficient decision-making through interactive, human-guided analytical environments. We discuss our PanViz 2.0 system, a visual analytics application for supporting pandemic preparedness through a tightly coupled epidemiological model and interactive interface. We discuss our framework, current work, and plans to extend the system with exploration of what-if scenarios, interactive machine learning for model parameter inference, and analysis of mitigation strategies to facilitate decision-making during public health crises.

Visualizations in organizational research have primarily been used in the context of traditional survey data, where individual data points (e.g., responses) can typically be plotted, and qualitative (e.g., language data) and quantitative (e.g., frequency data) information are not typically combined. Moreover, visualizations are typically used in a hypothetico-deductive fashion to showcase significant hypothesized results. With the advent of big data, which has been characterized as being particularly high in volume, variety, and velocity of collection, visualizations need to more explicitly and formally consider the issues of (a) identification (isolating or highlighting relevant data pertaining to the phenomena of interest), (b) integration (combining different modes of data to reveal insights about a phenomenon of interest), (c) immediacy (examining real-time data in a time-sensitive manner), and (d) interactivity (inductively uncovering and identifying new patterns). We discuss basic ideas for addressing these issues and provide illustrative examples of visualizations that incorporate and highlight ways of addressing these issues. Examples in our article include visualizing multiple performance criteria for police officers, publication network of organizational researchers, and social media language of Fortune 500 companies.

Our visual analytics system that we developed as part of the 2015 VAST Challenge provided us with insights to characterize the communication patterns of visitors.

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.

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.

The 2014 VAST Grand Challenge required us to find victims, suspects, and criminal motivations based on three separate dataseis. We developed three VA tools (AnnotatedTimeTree, Dodeca-Ring Map and SMART) to facilitate the understanding of heterogeneous multivariate dataseis. These tools were integrated to gain insights into the source data and find connections among complex information (Fig. 1). AnnotedTimeTree aims to identify the cause clues and timeline of kidnapping based on analysis of text and network data. Dodeca-Rings Map allows analysts to interact with geospatial, temporal, and card transaction data to find suspicious personal behaviors and social networks. SMART is a visual analytics tool that enables text stream analysis by dynamically visualizing microblog data on the map over time.

This article describes a real-time visual analytics process based on microblog and emergency call data to solve VAST 2014 Mini Challenge 3. We extended SMART system (Social Media Analytics and Reporting Toolkit), developed by the U.S. Department of Homeland Security's VACCINE Center. Our system consists of multiple linked views to allow the analyst monitor topic evolution, identify influential microblog users, observe geo-location patterns and examine correlations among different data sources. Extensions to our previous work include a time series view, a reply/retweet networks view, and integration of emergency call data.