David S. Ebert

Purdue University | Purdue · School of Electrical and Computer Engineering

Public health officials dealing with pandemics like COVID-19 have to evaluate and prepare response plans. This planning phase requires not only looking into the spatiotemporal dynamics and impact of the pandemic using simulation models, but they also need to plan and ensure the availability of resources under different spread scenarios. To this end...

Online sampling-supported visual analytics is increasingly important, as it allows users to explore large datasets with acceptable approximate answers at interactive rates. However, existing online spatiotemporal sampling techniques are often biased, as most researchers have primarily focused on reducing computational latency. Biased sampling appro...

In order to contain the COVID-19 pandemic, countries around the world have introduced social distancing guidelines as public health interventions to reduce the spread of the disease. However, monitoring the efficacy of these guidelines at a large scale (nationwide or worldwide) is difficult. To make matters worse, traditional observational methods...

Computer‐based technology has played a significant role in crime prevention over the past 30 years, especially with the popularization of spatial databases and crime mapping systems. Police departments frequently use hotspot analysis to identify regions that should be a priority in receiving preventive resources. Practitioners and researchers agree...

We present route packing, a novel (geo)visualization technique for displaying several routes simultaneously on a geographic map while preserving the geospatial layout, identity, directionality, and volume of individual routes. The technique collects variable-width route lines side by side while minimizing crossings, encodes them with categorical co...

Information, not just data, is key to today's global challenges. To solve these challenges requires not only advancing geospatial and big data analytics but requires new analysis and decision-making environments that enable reliable decisions from trustable, understandable information that go beyond current approaches to machine learning and artifi...

Real-time tweets can provide useful information on evolving events and situations. Geotagged tweets are especially useful, as they indicate the location of origin and provide geographic context. However, only a small portion of tweets are geotagged, limiting their use for situational awareness. In this paper, we adapt, improve, and evaluate a state...

The first responder community has traditionally relied on calls from the public, officially-provided geographic information and maps for coordinating actions on the ground. The ubiquity of social media platforms created an opportunity for near real-time sensing of the situation (e.g. unfolding weather events or crises) through volunteered geographi...

Real-time tweets can provide useful information on evolving events and situations. Geotagged tweets are especially useful, as they indicate the location of origin and provide geographic context. However, only a small portion of tweets are geotagged, limiting their use for situational awareness. In this paper, we adapt, improve, and evaluate a state...

We present route packing, a novel (geo)visualization technique for displaying several routes simultaneously on a geographic map while preserving the geospatial layout, identity, directionality, and volume of individual routes. The technique collects variable-width route lines side by side while minimizing crossings, encodes them with categorical co...

Social media data has been increasingly used to facilitate situational awareness during events and emergencies such as natural disasters. While researchers have investigated several methods to summarize, visualize or mine the data for analysis, first responders have not been able to fully leverage research advancements largely due to the gap betwee...

Various domain users are increasingly leveraging real-time social media data to gain rapid situational awareness. However, due to the high noise in the deluge of data, effectively determining semantically relevant information can be difficult, further complicated by the changing definition of relevancy by each end user for different events. The maj...

Evaluating employee performance in organizations with varying workloads and tasks is challenging. Specifically, it is important to understand how quantitative measurements of employee achievements relate to supervisor expectations, what the main drivers of good performance are, and how to combine these complex and flexible performance evaluation me...

Many evaluation methods have been used to assess the usefulness of Visual Analytics (VA) solutions. These methods stem from a variety of origins with different assumptions and goals, which cause confusion about their proofing capabilities. Moreover, the lack of discussion about the evaluation processes may limit our potential to develop new evaluat...

Social media platforms are filled with social spambots. Detecting these malicious accounts is essential, yet challenging, as they continually evolve to evade detection techniques. In this article, we present VASSL, a visual analytics system that assists in the process of detecting and labeling spambots. Our tool enhances the performance and scalabi...

Feature selection is used in machine learning to improve predictions, decrease computation time, reduce noise, and tune models based on limited sample data. In this article, we present FeatureExplorer, a visual analytics system that supports the dynamic evaluation of regression models and importance of feature subsets through the interactive select...

Various domain users are increasingly leveraging real-time social media data to gain rapid situational awareness. However, due to the high noise in the deluge of data, effectively determining semantically relevant information can be difficult, further complicated by the changing definition of relevancy by each end user for different events. The maj...

Many evaluation methods have been used to assess the usefulness of Visual Analytics (VA) solutions. These methods stem from a variety of origins with different assumptions and goals, which cause confusion about their proofing capabilities. Moreover, the lack of discussion about the evaluation processes may limit our potential to develop new evaluat...

Social media platforms such as Twitter are filled with social spambots. Detecting these malicious accounts is essential, yet challenging, as they continually evolve and evade traditional detection techniques. In this work, we propose VASSL, a visual analytics system that assists in the process of detecting and labeling spambots. Our tool enhances t...

Evaluating employee performance in organizations with varying workloads and tasks is challenging. Specifically, it is important to understand how quantitative measurements of employee achievements relate to supervisor expectations, what the main drivers of good performance are, and how to combine these complex and flexible performance evaluation me...

We present an interactive visual analytics system that enables traffic congestion exploration, surveillance, and forecasting based on vehicle detector data. Through domain expert collaboration, we have extracted task requirements, incorporated the Long Short-Term Memory (LSTM) model for congestion forecasting, and designed a weighting method for de...

Designing, evaluating, and improving visual analytics (VA) systems is a primary area of activities in our discipline. In this paper, we present an ontological framework for recording and categorizing technical shortcomings to be addressed in a VA workflow, reasoning about the causes of such problems, identifying technical solutions, and anticipatin...

Communication‐minded visualizations are designed to provide their audience—managers, decision‐makers, and the public—with new knowledge. Authoring such visualizations effectively is challenging because the audience often lacks the expertise, context, and time that professional analysts have at their disposal to explore and understand datasets. We p...

This paper explores the meaning of the term "skill" in the context of information (data) visualization and its place in the labor market. It examines the visualization skills and software competencies that are in high demand in industry today, and the ramifications for teaching Data Visualization for professional students in higher education.

Physical media (like surveillance cameras) and social media (like Instagram and Twitter) may both be useful in attaining on-the-ground information during an emergency or disaster situation. However, the intersection and reliability of both surveillance cameras and social media during a natural disaster are not fully understood. To address this gap,...

Background Improving the accuracy and precision of predictive analytics for temporal trends in crime necessitates a good understanding of the how exogenous variables, such as weather and holidays, impact crime. Methods We examine 5.7 million reported incidents of crime that occurred in the City of Chicago between 2001 to 2014. Using linear regress...

Interpretation and diagnosis of machine learning models have gained renewed interest in recent years with breakthroughs in new approaches. We present Manifold, a framework that utilizes visual analysis techniques to support interpretation, debugging, and comparison of machine learning models in a more transparent and interactive manner. Conventiona...

Interpretation and diagnosis of machine learning models have gained renewed interest in recent years with breakthroughs in new approaches. We present Manifold, a framework that utilizes visual analysis techniques to support interpretation, debugging, and comparison of machine learning models in a more transparent and interactive manner. Conventiona...

Technological advances in communication and the ubiquity of mobile devices have changed the role of emergency information during disasters. Information can now be easily shared between disaster managers, first responders, government agencies, and the public via websites and social media. However, is it unclear how the emergency information is acces...

Evaluating policing strategies for effectiveness and community acceptance is a difficult task due to the lack of effective tools. To address these problems, we have developed the Visual Analytics Law Enforcement Toolkit (VALET) software that uses historical crime incident data to capture spatial and temporal trends while providing insight into depa...

TopoText is a context-preserving technique for visualizing text data for multi-scale spatial aggregates to gain insight into spatial phenomena. Conventional exploration requires users to navigate across multiple scales but only presents the information related to the current scale. This limitation potentially adds more steps of interaction and cogn...

In order to better understand the struggles and practices of growers in improving sustainability, two one-day workshops were conducted in California in April, 2018. The workshops brought together local growers, academic research groups, local, regional, and state agencies, and sustainability groups to discuss practical ways to increase sustainabili...

A primary aim of visual analytics is to provide end-users interactive and scalable environments to facilitate their decision making tasks. Researchers have often utilized several server-client solutions to support interactive data exploration (e.g., data cubes and parallel computing). However, these solutions can suffer from scalability issues esp...

Identification of crime patterns and trends can help law enforcement agencies to conduct proactive resource allocation and predictive policing practice. To satisfy analytical scenarios across different spa- tial aggregates (e.g., neighborhoods or census tracts) and temporal localities (e.g., weeks or months), a system, Visual Analytics Law Enforcem...

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...

Spatial datasets, such as tweets in a geographic area, often exhibit different distribution patterns at multiple levels of scale, such as live updates about events occurring in very specific locations on the map. Navigating in such multi-scale data-rich spaces is often inefficient, requires users to choose between overview or detail information, an...

Application-oriented papers provide an important way to invigorate and cross-pollinate the visualization field, but the exact criteria for judging an application paper's merit remain an open question. This article builds on a panel at the 2016 IEEE Visualization Conference entitled "Application Papers: What Are They, and How Should They Be Evaluate...

Visual clutter is a common challenge when visualizing large rank time series data. WikiTopReader, a reader of Wikipedia page rank, lets users explore connections among top-viewed pages by connecting page-rank behaviors with page-link relations. Such a combination enhances the unweighted Wikipedia page-link network and focuses attention on the page...

Geographic visualization research has focused on a variety of techniques to represent and explore spatiotemporal data. The goal of those techniques is to enable users to explore events and interactions over space and time in order to facilitate the discovery of patterns, anomalies and relationships within the data. However, it is difficult to extra...

The 2016 Visualization Technical Achievement Award goes to David Ebert in recognition of foundational work in visual analytics, both through development of fundamental predictive techniques and as Director of the Purdue/DHS Visual Analytics Center of Excellence.

Martin William Ribarsky, a leading scientist and inspiring leader, died on 23 February 2017 in Charlotte, North Carolina, after a serious bicycling accident. He is survived by his wife, Barbara Schreiber, and children. Ribarsky helped shape the fields of scientific visualization, virtual reality, and visual analytics. His work, leadership, and stud...

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 an...

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...

We present TimeFork, an interactive prediction technique to support users predicting the future of time-series data, such as in financial, scientific, or medical domains. TimeFork combines visual representations of multiple time series with prediction information generated by computational models. Using this method, analysts engage in a back-and-fo...

Surveillance cameras, also called CCTV (closed-circuit television), are widely deployed as one of the solutions to improve public safety. The visual data from these cameras are usually unavailable to the public. In recent years, many organizations have deployed network cameras with diverse purposes such as monitoring traffic congestion and observin...

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. Pub...

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...

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 logisti...

Dodeca-Rings Map is the visual analytics system we designed to analyze geo-temporal traffic problems. The system is organized by three kinds of visualizations: dodecagons that show events on the map (Fig. 1 A & B), activity temporal charts (Fig. 1 C), and a social relationship matrix (Fig. 1D). We used it to solve the VAST 2014 Mini-Challenge 2 and...

We present TimeFork, an analytics technique for predicting the behavior of multivariate time-series data originating from modern disciplines such as economics (stock market) and meteorology (climate), with human-in-the-loop. We identify two types of machine-generated predictions for such datasets: temporal prediction that predicts the future of an...

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...

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 encapsulat...

In this paper, we present a visual analytics approach that provides decision makers with a proactive and predictive environment in order to assist them in making effective resource allocation and deployment decisions. The challenges involved with such predictive analytics processes include end-users' understanding, and the application of the underl...

A method for forecasting hotspots is provided. The method may include the steps of receiving input data at an input of the computational device, generating a temporal prediction based on the input data, generating a geospatial prediction based on the input data, and generating output data based on the time series and geospatial predictions. The out...