@article{villanes_healey_2022, title={Domain-specific text dictionaries for text analytics}, ISSN={["2364-4168"]}, DOI={10.1007/s41060-022-00344-x}, journal={INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS}, author={Villanes, Andrea and Healey, Christopher G.}, year={2022}, month={Jul} }
 @article{healey_simmons_manivannan_ro_2022, title={Visual Analytics for the Coronavirus COVID-19 Pandemic}, ISSN={["2167-647X"]}, DOI={10.1089/big.2021.0023}, abstractNote={The coronavirus disease COVID-19 was first reported in Wuhan, China, on December 31, 2019. The disease has since spread throughout the world, affecting 227.2 million individuals and resulting in 4,672,629 deaths as of September 9, 2021, according to the Johns Hopkins University Center for Systems Science and Engineering. Numerous sources track and report information on the disease, including Johns Hopkins itself, with its well-known Novel Coronavirus Dashboard. We were also interested in providing information on the pandemic. However, rather than duplicating existing resources, we focused on integrating sophisticated data analytics and visualization for region-to-region comparison, trend prediction, and testing and vaccination analysis. Our high-level goal is to provide visualizations of predictive analytics that offer policymakers and the general public insight into the current pandemic state and how it may progress into the future. Data are visualized using a web-based jQuery+Tableau dashboard.† The dashboard allows both novice viewers and domain experts to gain useful insights into COVID-19's current and predicted future state for different countries and regions of interest throughout the world.}, journal={BIG DATA}, author={Healey, Christopher G. and Simmons, Susan J. and Manivannan, Chandra and Ro, Yoonchul}, year={2022}, month={Jan} }
 @article{padia_bandara_healey_2019, title={A system for generating storyline visualizations using hierarchical task network planning}, volume={78}, ISSN={["1873-7684"]}, DOI={10.1016/j.cag.2018.11.004}, abstractNote={Existing storyline visualization techniques present narratives as a node-link graph where a sequence of links shows the evolution of causal and temporal relationships between characters in the narrative. These techniques make a number of simplifying assumptions about the narrative structure, however. They assume that all narratives progress linearly in time, with a well-defined beginning, middle, and end. They assume that the narrative is complete prior to visualization. They also assume that at least two participants interact at every event. Finally, they assume that all events in the narrative occur along a single timeline. Thus, while existing techniques are suitable for visualizing linear narratives, they are not well suited for visualizing narratives with multiple timelines, non-linear narratives such as those with flashbacks, or for narratives that contain events with only one participant. In our previous work we presented Yarn, a system for automatic construction and visualization of narratives with multiple timelines. Yarn employs hierarchical task network planning to generate all possible narrative timelines and visualize them in a web-based interface. In this work, we extend Yarn to support non-linear narratives with flashbacks and flash-forwards, and non-linear point-of-view narratives. Our technique supports both single-participant as well as multi-participant events in the narrative, and constructs both linear as well as non-linear narratives. Additionally, it enables pairwise comparison within a group of multiple narrative timelines.}, journal={COMPUTERS & GRAPHICS-UK}, author={Padia, Kalpesh and Bandara, Kaveen Herath and Healey, Christopher G.}, year={2019}, month={Feb}, pages={64–75} }
 @article{kozik_tateosian_healey_enns_2019, title={Impressionism-Inspired Data Visualizations Are Both Functional and Liked}, volume={13}, ISSN={["1931-390X"]}, DOI={10.1037/aca0000175}, number={3}, journal={PSYCHOLOGY OF AESTHETICS CREATIVITY AND THE ARTS}, author={Kozik, Pavel and Tateosian, Laura G. and Healey, Christopher G. and Enns, James T.}, year={2019}, month={Aug}, pages={266–276} }
 @article{villanes_griffiths_rappa_healey_2018, title={Dengue Fever Surveillance in India Using Text Mining in Public Media}, volume={98}, ISSN={["1476-1645"]}, DOI={10.4269/ajtmh.17-0253}, abstractNote={Abstract. Despite the improvement in health conditions across the world, communicable diseases remain among the leading mortality causes in many countries. Combating communicable diseases depends on surveillance, preventive measures, outbreak investigation, and the establishment of control mechanisms. Delays in obtaining country-level data of confirmed communicable disease cases, such as dengue fever, are prompting new efforts for short- to medium-term data. News articles highlight dengue infections, and they can reveal how public health messages, expert findings, and uncertainties are communicated to the public. In this article, we analyze dengue news articles in Asian countries, with a focus in India, for each month in 2014. We investigate how the reports cluster together, and uncover how dengue cases, public health messages, and research findings are communicated in the press. Our main contributions are to 1) uncover underlying topics from news articles that discuss dengue in Asian countries in 2014; 2) construct topic evolution graphs through the year; and 3) analyze the life cycle of dengue news articles in India, then relate them to rainfall, monthly reported dengue cases, and the Breteau Index. We show that the five main topics discussed in the newspapers in Asia in 2014 correspond to 1) prevention; 2) reported dengue cases; 3) politics; 4) prevention relative to other diseases; and 5) emergency plans. We identify that rainfall has 0.92 correlation with the reported dengue cases extracted from news articles. Based on our findings, we conclude that the proposed method facilitates the effective discovery of evolutionary dengue themes and patterns.}, number={1}, journal={AMERICAN JOURNAL OF TROPICAL MEDICINE AND HYGIENE}, author={Villanes, Andrea and Griffiths, Emily and Rappa, Michael and Healey, Christopher G.}, year={2018}, pages={181–191} }
 @article{nie_healey_padia_leeman-munk_benson_caira_sethi_devarajan_2018, title={Visualizing Deep Neural Networks for Text Analytics}, ISSN={["2165-8765"]}, DOI={10.1109/pacificvis.2018.00031}, abstractNote={Deep neural networks (DNNs) have made tremendous progress in many different areas in recent years. How these networks function internally, however, is often not well understood. Advances in under-standing DNNs will benefit and accelerate the development of the field. We present TNNVis, a visualization system that supports un-derstanding of deep neural networks specifically designed to analyze text. TNNVis focuses on DNNs composed of fully connected and convolutional layers. It integrates visual encodings and interaction techniques chosen specifically for our tasks. The tool allows users to: (1) visually explore DNN models with arbitrary input using a combination of node–link diagrams and matrix representation; (2) quickly identify activation values, weights, and feature map patterns within a network; (3) flexibly focus on visual information of interest with threshold, inspection, insight query, and tooltip operations; (4) discover network activation and training patterns through animation; and (5) compare differences between internal activation patterns for different inputs to the DNN. These functions allow neural network researchers to examine their DNN models from new perspectives, producing insights on how these models function. Clustering and summarization techniques are employed to support large convolutional and fully connected layers. Based on several part of speech models with different structure and size, we present multiple use cases where visualization facilitates an understanding of the models.}, journal={2018 IEEE PACIFIC VISUALIZATION SYMPOSIUM (PACIFICVIS)}, author={Nie, Shaoliang and Healey, Christopher G. and Padia, Kalpesh and Leeman-Munk, Samuel and Benson, Jordan Riley and Caira, Dave and Sethi, Saratendu and Devarajan, Ravi}, year={2018}, pages={180–189} }
 @inbook{albanese_cooke_coty_hall_healey_jajodia_liu_mcneese_ning_reeves_et al._2017, title={Computer-Aided Human Centric Cyber Situation Awareness}, ISBN={9783319611518 9783319611525}, ISSN={0302-9743 1611-3349}, url={http://dx.doi.org/10.1007/978-3-319-61152-5_1}, DOI={10.1007/978-3-319-61152-5_1}, abstractNote={In this chapter, we provide an overview of Cyber Situational Awareness, an emerging research area in the broad field of cyber security, and discuss, at least at a high level, how to gain Cyber Situation Awareness. Our discussion focuses on answering the following questions: What is Cyber Situation Awareness? Why is research needed? What are the current research objectives and inspiring scientific principles? Why should one take a multidisciplinary approach? How could one take an end-to-end holistic approach? What are the future research directions?}, booktitle={Theory and Models for Cyber Situation Awareness}, publisher={Springer International Publishing}, author={Albanese, Massimiliano and Cooke, Nancy and Coty, González and Hall, David and Healey, Christopher and Jajodia, Sushil and Liu, Peng and McNeese, Michael D. and Ning, Peng and Reeves, Douglas and et al.}, year={2017}, pages={3–25} }
 @inbook{healey_hao_hutchinson_2017, title={Lessons Learned: Visualizing Cyber Situation Awareness in a Network Security Domain}, ISBN={9783319611518 9783319611525}, ISSN={0302-9743 1611-3349}, url={http://dx.doi.org/10.1007/978-3-319-61152-5_3}, DOI={10.1007/978-3-319-61152-5_3}, abstractNote={This chapter discusses lesson learned working with cyber situation awareness and network security domain experts to integrate visualizations into their current workflows. Working closely with network security experts, we discovered a critical set of requirements that a visualization must meet to be considered for use by the these domain experts. We next present two separate examples of visualizations that address these requirements: a flexible web-based application that visualizes network traffic and security data through analyst-driven correlated charts and graphs, and a set of ensemble-based extensions to visualize network traffic and security alerts using existing and future ensemble visualization algorithms.}, booktitle={Theory and Models for Cyber Situation Awareness}, publisher={Springer International Publishing}, author={Healey, Christopher G. and Hao, Lihua and Hutchinson, Steve E.}, year={2017}, pages={47–65} }
 @inbook{chen_healey_2016, title={Large Image Collection Visualization Using Perception-Based Similarity with Color Features}, ISBN={9783319508344 9783319508351}, ISSN={0302-9743 1611-3349}, url={http://dx.doi.org/10.1007/978-3-319-50835-1_35}, DOI={10.1007/978-3-319-50835-1_35}, abstractNote={This paper introduces the basic steps to build a similarity-based visualization tool for large image collections. We build the similarity metrics based on human perception. Psychophysical experiments have shown that human observers can recognize the gist of scenes within 100 milliseconds (ms) by comprehending the global properties of an image. Color also plays an important role in human rapid scene recognition. However, previous works often neglect color features. We propose new scene descriptors that preserve the information from coherent color regions, as well as the spatial layouts of scenes. Experiments show that our descriptors outperform existing state-of-the-art approaches. Given the similarity metrics, a hierarchical structure of an image collection can be built in a top-down manner. Representative images are chosen for image clusters and visualized using a force-directed graph.}, booktitle={Advances in Visual Computing}, publisher={Springer International Publishing}, author={Chen, Zeyuan and Healey, Christopher G.}, year={2016}, pages={379–390} }
 @inproceedings{hao_healey_hutchinson_2015, title={Ensemble visualization for cyber situation awareness of network security data}, DOI={10.1109/vizsec.2015.7312766}, abstractNote={Network security analysis and ensemble data visualization are two active research areas. Although they are treated as separate domains, they share many common challenges and characteristics. Both focus on scalability, time-dependent data analytics, and exploration of patterns and unusual behaviors in large datasets. These overlaps provide an opportunity to apply ensemble visualization research to improve network security analysis. To study this goal, we propose methods to interpret network security alerts and flow traffic as ensemble members. We can then apply ensemble visualization techniques in a network analysis environment to produce a network ensemble visualization system. Including ensemble representations provide new, in-depth insights into relationships between alerts and flow traffic. Analysts can cluster traffic with similar behavior and identify traffic with unusual patterns, something that is difficult to achieve with high-level overviews of large network datasets. Furthermore, our ensemble approach facilitates analysis of relationships between alerts and flow traffic, improves scalability, maintains accessibility and configurability, and is designed to fit our analysts' working environment, mental models, and problem solving strategies.}, booktitle={2015 IEEE Symposium on Visualization for Cyber Security (VIZSEC)}, author={Hao, L. H. and Healey, C. G. and Hutchinson, S. E.}, year={2015} }
 @article{canary_taylor_quammen_pratt_gomez_o'shea_healey_2014, title={Visualizing likelihood density functions via optimal region projection}, volume={41}, ISSN={["1873-7684"]}, DOI={10.1016/j.cag.2014.02.005}, abstractNote={Abstract Effective visualization of high-likelihood regions of parameter space is severely hampered by the large number of parameter dimensions that many models have. We present a novel technique, Optimal Percentile Region Projection, to visualize a high-dimensional likelihood density function that enables the viewer to understand the shape of the high-likelihood region. Optimal Percentile Region Projection has three novel components: first, we select the region of high likelihood in the high-dimensional space before projecting its shadow into a lower-dimensional projected space. Second, we analyze features on the surface of the region in the projected space to select the projection direction that shows the most interesting parameter dependencies. Finally, we use a three-dimensional projection space to show features that are not salient in only two dimensions. The viewer can also choose sets of axes to project along to explore subsets of the parameter space, using either the original parameter axes or principal-component axes. The technique was evaluated by our domain-science collaborators, who found it to be superior to their existing workflow both when there were interesting dependencies between parameters and when there were not.}, journal={COMPUTERS & GRAPHICS-UK}, author={Canary, Hal and Taylor, Russell M., II and Quammen, Cory and Pratt, Scott and Gomez, Facundo A. and O'Shea, Brian and Healey, Christopher G.}, year={2014}, month={Jun}, pages={62–71} }
 @article{healey_enns_2012, title={Attention and Visual Memory in Visualization and Computer Graphics}, volume={18}, ISSN={["1941-0506"]}, DOI={10.1109/tvcg.2011.127}, abstractNote={A fundamental goal of visualization is to produce images of data that support visual analysis, exploration, and discovery of novel insights. An important consideration during visualization design is the role of human visual perception. How we "see" details in an image can directly impact a viewer's efficiency and effectiveness. This paper surveys research on attention and visual perception, with a specific focus on results that have direct relevance to visualization and visual analytics. We discuss theories of low-level visual perception, then show how these findings form a foundation for more recent work on visual memory and visual attention. We conclude with a brief overview of how knowledge of visual attention and visual memory is being applied in visualization and graphics. We also discuss how challenges in visualization are motivating research in psychophysics.}, number={7}, journal={IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS}, author={Healey, Christopher G. and Enns, James T.}, year={2012}, month={Jul}, pages={1170–1188} }
 @article{phadke_pinto_alabi_harter_taylor_wu_petersen_bass_healey_2012, title={Exploring Ensemble Visualization}, volume={8294}, ISSN={["1996-756X"]}, DOI={10.1117/12.912419}, abstractNote={An ensemble is a collection of related datasets. Each dataset, or member, of an ensemble is normally large, multidimensional, and spatio-temporal. Ensembles are used extensively by scientists and mathematicians, for example, by executing a simulation repeatedly with slightly different input parameters and saving the results in an ensemble to see how parameter choices affect the simulation. To draw inferences from an ensemble, scientists need to compare data both within and between ensemble members. We propose two techniques to support ensemble exploration and comparison: a pairwise sequential animation method that visualizes locally neighboring members simultaneously, and a screen door tinting method that visualizes subsets of members using screen space subdivision. We demonstrate the capabilities of both techniques, first using synthetic data, then with simulation data of heavy ion collisions in high-energy physics. Results show that both techniques are capable of supporting meaningful comparisons of ensemble data.}, journal={VISUALIZATION AND DATA ANALYSIS 2012}, author={Phadke, Madhura N. and Pinto, Lifford and Alabi, Oluwafemi and Harter, Jonathan and Taylor, Russell M., II and Wu, Xunlei and Petersen, Hannah and Bass, Steffen A. and Healey, Christopher G.}, year={2012} }
 @article{healey_dennis_2012, title={Interest Driven Navigation in Visualization}, volume={18}, ISSN={["1941-0506"]}, DOI={10.1109/tvcg.2012.23}, abstractNote={This paper describes a new method to explore and discover within a large data set. We apply techniques from preference elicitation to automatically identify data elements that are of potential interest to the viewer. These "elements of interest (EOI)” are bundled into spatially local clusters, and connected together to form a graph. The graph is used to build camera paths that allow viewers to "tour” areas of interest (AOI) within their data. It is also visualized to provide wayfinding cues. Our preference model uses Bayesian classification to tag elements in a data set as interesting or not interesting to the viewer. The model responds in real time, updating the elements of interest based on a viewer's actions. This allows us to track a viewer's interests as they change during exploration and analysis. Viewers can also interact directly with interest rules the preference model defines. We demonstrate our theoretical results by visualizing historical climatology data collected at locations throughout the world.}, number={10}, journal={IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS}, author={Healey, Christopher G. and Dennis, Brent M.}, year={2012}, month={Oct}, pages={1744–1756} }
 @article{healey_sawant_2012, title={On the Limits of Resolution and Visual Angle in Visualization}, volume={9}, ISSN={["1544-3965"]}, DOI={10.1145/2355598.2355603}, abstractNote={
            This article describes a perceptual level-of-detail approach for visualizing data. Properties of a dataset that cannot be resolved in the current display environment need not be shown, for example, when too few pixels are used to render a data element, or when the element's subtended visual angle falls below the acuity limits of our visual system. To identify these situations, we asked: (1) What type of information can a human user perceive in a particular display environment? (2) Can we design visualizations that control what they represent relative to these limits? and (3) Is it possible to dynamically update a visualization as the display environment changes, to continue to effectively utilize our perceptual abilities? To answer these questions, we conducted controlled experiments that identified the pixel resolution and subtended visual angle needed to distinguish different values of luminance, hue, size, and orientation. This information is summarized in a perceptual display hierarchy, a formalization describing how many pixels—
            resolution
            —and how much physical area on a viewer's retina—
            visual angle
            —is required for an element's visual properties to be readily seen. We demonstrate our theoretical results by visualizing historical climatology data from the International Panel for Climate Change.
          }, number={4}, journal={ACM TRANSACTIONS ON APPLIED PERCEPTION}, author={Healey, Christopher G. and Sawant, Amit P.}, year={2012}, month={Oct} }
 @article{bass_petersen_quammen_canary_healey_taylor_2012, title={Probing the QCD critical point with relativistic heavy-ion collisions}, volume={10}, DOI={10.2478/s11534-012-0076-1}, abstractNote={Abstract}, number={6}, journal={Central European Journal of Physics}, author={Bass, S. A. and Petersen, H. and Quammen, C. and Canary, H. and Healey, C. G. and Taylor, R. M.}, year={2012}, pages={1278–1281} }
 @article{hsiao_healey_2011, title={Visualizing combinatorial auctions}, volume={27}, ISSN={["1432-2315"]}, DOI={10.1007/s00371-011-0576-9}, number={6-8}, journal={VISUAL COMPUTER}, author={Hsiao, Joe Ping-Lin and Healey, Christopher G.}, year={2011}, month={Jun}, pages={633–643} }
 @article{healey_kocherlakota_rao_mehta_amant_2008, title={Visual perception and mixed-initiative interaction for assisted visualization design}, volume={14}, ISSN={["1941-0506"]}, DOI={10.1109/TVCG.2007.70436}, abstractNote={This paper describes the integration of perceptual guidelines from human vision with an AI-based mixed-initiative search strategy. The result is a visualization assistant called ViA, a system that collaborates with its users to identify perceptually salient visualizations for large, multidimensional datasets. ViA applies knowledge of low-level human vision to: (1) evaluate the effectiveness of a particular visualization for a given dataset and analysis tasks; and (2) rapidly direct its search towards new visualizations that are most likely to offer improvements over those seen to date. Context, domain expertise, and a high-level understanding of a dataset are critical to identifying effective visualizations. We apply a mixed-initiative strategy that allows ViA and its users to share their different strengths and continually improve ViA's understanding of a user's preferences. We visualize historical weather conditions to compare ViA's search strategy to exhaustive analysis, simulated annealing, and reactive tabu search, and to measure the improvement provided by mixed-initiative interaction. We also visualize intelligent agents competing in a simulated online auction to evaluate ViA's perceptual guidelines. Results from each study are positive, suggesting that ViA can construct high-quality visualizations for a range of real-world datasets.}, number={2}, journal={IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS}, author={Healey, Christopher G. and Kocherlakota, Sarat and Rao, Vivek and Mehta, Reshma and Amant, Robert St.}, year={2008}, pages={396–411} }
 @inbook{sawant_raina_healey_2007, title={ChipViz: Visualizing Memory Chip Test Data}, ISBN={9783540768555 9783540768562}, ISSN={0302-9743 1611-3349}, url={http://dx.doi.org/10.1007/978-3-540-76856-2_70}, DOI={10.1007/978-3-540-76856-2_70}, abstractNote={This paper presents a technique that allows test engineers to visually analyze and explore within memory chip test data. We represent the test results from a generation of chips along a traditional 2D grid and a spiral. We also show correspondences in the test results across multiple generations of memory chips. We use simple geometric “glyphs” that vary their spatial placement, color, and texture properties to represent the critical attribute values of a test. When shown together, the glyphs form visual patterns that support exploration, facilitate discovery of data characteristics, relationships, and highlight trends and exceptions in the test data that are often difficult to identify with existing statistical tools.}, booktitle={Advances in Visual Computing}, publisher={Springer Berlin Heidelberg}, author={Sawant, Amit P. and Raina, Ravi and Healey, Christopher G.}, year={2007}, pages={711–720} }
 @article{hagh-shenas_kim_interrante_2007, title={Weaving versus blending: a quantitative assessment of the information carrying capacities of two alternative methods for conveying multivariate data with color}, volume={13}, ISSN={["1941-0506"]}, DOI={10.1109/TVCG.2007.70623}, abstractNote={In many applications, it is important to understand the individual values of, and relationships between, multiple related scalar variables defined across a common domain. Several approaches have been proposed for representing data in these situations. In this paper we focus on strategies for the visualization of multivariate data that rely on color mixing. In particular, through a series of controlled observer experiments, we seek to establish a fundamental understanding of the information-carrying capacities of two alternative methods for encoding multivariate information using color: color blending and color weaving. We begin with a baseline experiment in which we assess participants' abilities to accurately read numerical data encoded in six different basic color scales defined in the L*a*b* color space. We then assess participants' abilities to read combinations of 2, 3, 4 and 6 different data values represented in a common region of the domain, encoded using either color blending or color weaving. In color blending a single mixed color is formed via linear combination of the individual values in L*a*b* space, and in color weaving the original individual colors are displayed side-by-side in a high frequency texture that fills the region. A third experiment was conducted to clarify some of the trends regarding the color contrast and its effect on the magnitude of the error that was observed in the second experiment. The results indicate that when the component colors are represented side-by-side in a high frequency texture, most participants' abilities to infer the values of individual components are significantly improved, relative to when the colors are blended. Participants' performance was significantly better with color weaving particularly when more than 2 colors were used, and even when the individual colors subtended only 3 minutes of visual angle in the texture. However, the information-carrying capacity of the color weaving approach has its limits. We found that participants' abilities to accurately interpret each of the individual components in a high frequency color texture typically falls off as the number of components increases from 4 to 6. We found no significant advantages, in either color blending or color weaving, to using color scales based on component hues thatare more widely separated in the L*a*b* color space. Furthermore, we found some indications that extra difficulties may arise when opponent hues are employed.}, number={6}, journal={IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS}, author={Hagh-Shenas, Haleh and Kim, Sunghee and Interrante, Victoria}, year={2007}, pages={1270–1277} }
 @article{rhyne_dennis_kocherlakota_sawant_tateosian_healey_2005, title={Designing a visualization framework for multidimensional data}, volume={25}, number={6}, journal={IEEE Computer Graphics and Applications}, author={Rhyne, T. M. and Dennis, B. and Kocherlakota, S. and Sawant, A. and Tateosian, L. and Healey, C. G.}, year={2005}, pages={15-} }
 @article{liu_healey_enns_2003, title={Target detection and localization in visual search: A dual systems perspective}, volume={65}, DOI={10.3758/bf03194806}, abstractNote={The dual visual systems framework (Milner & Goodale, 1995) was used to explore target detection and localization in visual search. Observers searched for a small patch of tilted bars against a dense background of upright bars. Target detection was performed along with two different localization tasks: direct pointing, designed to engage the dorsal stream, and indirect pointing, designed to engage the ventral stream. The results indicated that (1) target detection was influenced more by orientation differences in 3-D space than by 2-D pictorial differences, (2) target localization was more accurate for direct than for indirect pointing, and (3) there were performance costs for indirect localization when it followed target detection, but not for direct localization. This is consistent with direct localization's having greater dependence on the dorsal visual system than either target detection or indirect localization.}, number={5}, journal={Perception & Psychophysics}, author={Liu, G. and Healey, C. G. and Enns, J. T.}, year={2003}, pages={678–694} }
 @article{kosara_healey_interrante_laidlaw_ware_2003, title={User studies: Why, how, and when?}, volume={23}, ISSN={["1558-1756"]}, DOI={10.1109/MCG.2003.1210860}, abstractNote={User studies offer a scientifically sound method to measure a visualization's performance. Reasons abound for pursuing user studies, particularly when evaluating the strengths and weaknesses of different visualization techniques. A good starting point in any study is the scientific or visual design question to be examined. This drives the process of experimental design. A poorly designed experiment will yield results of only limited value. Although a comprehensive discussion of experimental design is beyond the scope of the article, we offer suggestions and lessons learned. We also describe how we designed experiments to answer important questions from our own research.}, number={4}, journal={IEEE COMPUTER GRAPHICS AND APPLICATIONS}, author={Kosara, R and Healey, CG and Interrante, V and Laidlaw, DH and Ware, C}, year={2003}, pages={20–25} }
 @article{healey_enns_2002, title={Perception and painting: A search for effective, engaging visualizations}, volume={22}, ISSN={["0272-1716"]}, DOI={10.1109/38.988741}, abstractNote={Scientific visualization represents information as images that let us explore, discover, analyze and validate large collections of data. Much research in this area is dedicated to designing effective visualizations that support specific analysis needs. Recently, though, we've considered visualizations from another angle. We've started asking, "Are visualizations beautiful? Can we consider visualizations works of art?" You might expect answers to these questions to vary widely depending on an individual's interpretation what it means to be artistic. We believe that the issues of effectiveness and aesthetics may not be as independent as they seem initially. We can learn much from studying two related disciplines - human psychophysics and art theory and history. Human psychophysics teaches us how we see the world around us. Art history shows us how artistic masters capture our attention by designing works that evoke an emotional response. The common interest in visual attention provides an important bridge between these domains. We're using this bridge to produce effective and engaging visualizations, and in this article, we share some of the lessons we've learned along the way.}, number={2}, journal={IEEE COMPUTER GRAPHICS AND APPLICATIONS}, author={Healey, CG and Enns, JT}, year={2002}, pages={10–15} }
 @article{healey_wurman_2001, title={Visualizing market data}, volume={5}, number={2}, journal={IEEE Internet Computing}, author={Healey, C. G. and Wurman, P. R.}, year={2001}, pages={88} }
 @article{healey_2000, title={Building a perceptual visualization architecture}, volume={19}, ISSN={["1362-3001"]}, DOI={10.1080/014492900750000054}, abstractNote={Scientific datasets are often difficult to analyse or visualize, due to their large size and high dimensionality. A multistep approach to address this problem is proposed. Data management techniques are used to identify areas of interest within the dataset. This allows the reduction of a dataset's size and dimensionality, and the estimation of missing values or correction of erroneous entries. The results are displayed using visualization techniques based on perceptual rules. The visualization tools are designed to exploit the power of the low-level human visual system. The result is a set of displays that allow users to perform rapid and accurate exploratory data analysis. In order to demonstrate the techniques, an environmental dataset being used to model salmon growth and migration patterns was visualized. Data mining was used to identify significant attributes and to provide accurate estimates of plankton density. Colour and texture were used to visualize the significant attributes and estimated plankton densities for each month for the years 1956-1964. Experiments run in the laboratory showed that the chosen colours and textures support rapid and accurate element identification, boundary detection, region tracking and estimation. The result is a visualization tool that allows users to quickly locate specific plankton densities and the boundaries they form. Users can compare plankton densities to other environmental conditions like sea surface temperature and current strength. Finally, users can track changes in any of the dataset's attributes on a monthly or yearly basis.}, number={5}, journal={BEHAVIOUR & INFORMATION TECHNOLOGY}, author={Healey, CG}, year={2000}, pages={349–366} }
 @article{healey_enns_1999, title={Large datasets at a glance: Combining textures and colors in scientific visualization}, volume={5}, ISSN={["1941-0506"]}, DOI={10.1109/2945.773807}, abstractNote={We present a new method for using texture and color to visualize multivariate data elements arranged on an underlying height field. We combine simple texture patterns with perceptually uniform colors to increase the number of attribute values we can display simultaneously. Our technique builds multicolored perceptual texture elements (or pexels) to represent each data element. Attribute values encoded in an element are used to vary the appearance of its pexel. Texture and color patterns that form when the pexels are displayed can be used to rapidly and accurately explore the dataset. Our pexels are built by varying three separate texture dimensions: height, density, and regularity. Results from computer graphics, computer vision, and human visual psychophysics have identified these dimensions as important for the formation of perceptual texture patterns. The pexels are colored using a selection technique that controls color distance, linear separation, and color category. Proper use of these criteria guarantees colors that are equally distinguishable from one another. We describe a set of controlled experiments that demonstrate the effectiveness of our texture dimensions and color selection criteria. We then discuss new work that studies how texture and color can be used simultaneously in a single display.}, number={2}, journal={IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS}, author={Healey, CG and Enns, JT}, year={1999}, pages={145–167} }