Shehzad Afzal

Mass media and the contagion of fear: The case of Ebola in America

Background In the weeks following the first imported case of Ebola in the U. S. on September 29, 2014, coverage of the very limited outbreak dominated the news media, in a manner quite disproportionate to the actual threat to national public health; by the end of October, 2014, there were only four laboratory confirmed cases of Ebola in the entire nation. Public interest in these events was high, as reflected in the millions of Ebola-related Internet searches and tweets performed in the month following the first confirmed case. Use of trending Internet searches and tweets has been proposed in the past for real-time prediction of outbreaks (a field referred to as “digital epidemiology”), but accounting for the biases of public panic has been problematic. In the case of the limited U. S. Ebola outbreak, we know that the Ebola-related searches and tweets originating the U. S. during the outbreak were due only to public interest or panic, providing an unprecedented means to determine how these dynamics affect such data, and how news media may be driving these trends. Methodology We examine daily Ebola-related Internet search and Twitter data in the U. S. during the six week period ending Oct 31, 2014. TV news coverage data were obtained from the daily number of Ebola-related news videos appearing on two major news networks. We fit the parameters of a mathematical contagion model to the data to determine if the news coverage was a significant factor in the temporal patterns in Ebola-related Internet and Twitter data. Conclusions We find significant evidence of contagion, with each Ebola-related news video inspiring tens of thousands of Ebola-related tweets and Internet searches. Between 65% to 76% of the variance in all samples is described by the news media contagion model.

Visual analytics decision support environment for epidemic modeling and response evaluation

In modeling infectious diseases, scientists are studying the mechanisms by which diseases spread, predicting the future course of the outbreak, and evaluating strategies applied to control an epidemic. While recent work has focused on accurately modeling disease spread, less work has been performed in developing interactive decision support tools for analyzing the future course of the outbreak and evaluating potential disease mitigation strategies. The absence of such tools makes it difficult for researchers, analysts and public health officials to evaluate response measures within outbreak scenarios. As such, our research focuses on the development of an interactive decision support environment in which users can explore epidemic models and their impact. This environment provides a spatiotemporal view where users can interactively utilize mitigative response measures and observe the impact of their decision over time. Our system also provides users with a linked decision history visualization and navigation tool that support the simultaneous comparison of mortality and infection rates corresponding to different response measures at different points in time.

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 Mobile Visual Analytics Approach for Law Enforcement Situation Awareness

The advent of modern smart phones and handheld devices has given analysts, decision-makers, and even the general public the ability to rapidly ingest data and translate it into actionable information on-the-go. In this paper, we explore the design and use of a mobile visual analytics toolkit for public safety data that equips law enforcement agencies with effective situation awareness and risk assessment tools. Our system provides users with a suite of interactive tools that allow them to perform analysis and detect trends, patterns and anomalies among criminal, traffic and civil (CTC) incidents. The system also provides interactive risk assessment tools that allow users to identify regions of potential high risk and determine the risk at any user-specified location and time. Our system has been designed for the iPhone/iPad environment and is currently being used and evaluated by a consortium of law enforcement agencies. We report their use of the system and some initial feedback.

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.

VASA: Interactive Computational Steering of Large Asynchronous Simulation Pipelines for Societal Infrastructure.

We present VASA, a visual analytics platform consisting of a desktop application, a component model, and a suite of distributed simulation components for modeling the impact of societal threats such as weather, food contamination, and traffic on critical infrastructure such as supply chains, road networks, and power grids. Each component encapsulates a high-fidelity simulation model that together form an asynchronous simulation pipeline: a system of systems of individual simulations with a common data and parameter exchange format. At the heart of VASA is the Workbench, a visual analytics application providing three distinct features: (1) low-fidelity approximations of the distributed simulation components using local simulation proxies to enable analysts to interactively configure a simulation run; (2) computational steering mechanisms to manage the execution of individual simulation components; and (3) spatiotemporal and interactive methods to explore the combined results of a simulation run. We showcase the utility of the platform using examples involving supply chains during a hurricane as well as food contamination in a fast food restaurant chain.

Safety in view: A public safety visual analytics tool based on CCTV camera angles of view

Campus security and police departments have implemented a multitude of safety precautions, including CCTV cameras. The efficiency and effectiveness of using CCTV camera resources for preventing crimes result in higher demand. We implemented a visual analytics tool to analyze the existing CCTV camera resources and suggest improved allocation schemas based on blind spots and crime data. Our tool provides the user with an interactive safe path calculation method for walking purpose on the basis of the maximum monitoring area. Additionally, avoiding buildings in the calculated path is an optional control factor. Our tool also provides functions for crime data analysis. The camera-alarming function highlights the cameras that a specific crime occurred in their visible range. The camera-ranking function highlights the camera that records the largest number of crime incidents. Based on the historical crime data, we suggest locations for future camera installation. We present two case studies to illustrate the usage and features of our tool on the campus of Purdue University.

VACCINATED — Visual analytics for characterizing a pandemic spread VAST 2010 Mini Challenge 2 award: Support for future detection

Given a set of hospital admittance and death records, the challenge was to characterize the spread of a pandemic in terms of the attack and mortality rates, spatiotemporal patterns of onset and the recovery time. We began the analysis by preprocessing the hospital admittance records using the University of Pittsburghs CoCo classifier [1]. CoCo is a text classification software that takes hospital admittance fields and classifies them into chief complaint categories (Botulinic, Constitutional, Gastrointestinal, Hemorrhagic, Neurological, Rash, Respiratory, and Other). The choice of the CoCo classifier was based on its online availability as well as its well documented classification performance metrics, see [1]. Once the data was classified, we utilized and extended work done by the Purdue University Visual Analytics Center on healthcare analysis [2]. Our system consists of a combination of linked views, showing time series views of syndromes and death rates through line graph views (Figure 1 — Top), stacked graph views showing deaths (Figure 1 — Bottom), geographical map views showing the impact by country (not illustrated in this paper), and summary windows providing statistical breakdowns of the data (not illustrated in this paper). All views are linked through an interactive time slider that allows users to explore the data over time. Extensions to our previous work [2] include the stacked graph view, summary windows, new control chart methods, and an interactive ‘tape measure’ tool.

Visual Analytics of User Influence and Location-Based Social Networks

Social media have evolved as an important source of information and situational awareness in crisis and emergency management. As the number of messages generated and diffused through social networks in time of crisis increase exponentially, locating reliable and critical information in a timely manner is crucial, especially for decision makers. In such scenarios, identifying influential users in social networks, detecting anomalous information diffusion patterns, and locating corresponding geographical coordinates are often instrumental in providing important information and helping analysts make decisions in a timely manner. We describe a visual analytics framework focusing on identifying influential users and anomalous information diffusion based on dynamic social networks using Twitter data. We also demonstrate a visual analytics approach that allows users to analyze a large volume of social media data to detect and examine abnormal events within Location-Based Social Network (LBSN). Our statistical models to extract user topics and evaluate their anomaly scores are applied to facilitate exploration and perception of Twitter semantics. The framework provides highly interactive filtering and geo-location mapping to help categorize different topics, detect influential users and anomalous information in specific events, and investigate the underlying spatiotemporal patterns.