Estimating Color-Concept Associations from Image Statistics

To interpret the meanings of colors in visualizations of categorical information, people must determine how distinct colors correspond to different concepts. This process is easier when assignments between colors and concepts in visualizations match people's expectations, making color palettes semantically interpretable. Efforts have been underway to optimize color palette design for semantic interpretablity, but this requires having good estimates of human color-concept associations. Obtaining these data from humans is costly, which motivates the need for automated methods. We developed and evaluated a new method for automatically estimating color-concept associations in a way that strongly correlates with human ratings. Building on prior studies using Google Images, our approach operates directly on Google Image search results without the need for humans in the loop. Specifically, we evaluated several methods for extracting raw pixel content of the images in order to best estimate color-concept associations obtained from human ratings. The most effective method extracted colors using a combination of cylindrical sectors and color categories in color space. We demonstrate that our approach can accurately estimate average human color-concept associations for different fruits using only a small set of images. The approach also generalizes moderately well to more complicated recycling-related concepts of objects that can appear in any color.Comment: IEEE VIS InfoVis 2019 ACM 2012 CSS: 1) Human-centered computing, Human computer interaction (HCI), Empirical studies in HCI 2) Human-centered computing, Human computer interaction (HCI), HCI design and evaluation methods, Laboratory experiments 3) Human-centered computing, Visualization, Empirical studies in visualizatio

OSCAR: A Semantic-based Data Binning Approach

Binning is applied to categorize data values or to see distributions of data. Existing binning algorithms often rely on statistical properties of data. However, there are semantic considerations for selecting appropriate binning schemes. Surveys, for instance, gather respondent data for demographic-related questions such as age, salary, number of employees, etc., that are bucketed into defined semantic categories. In this paper, we leverage common semantic categories from survey data and Tableau Public visualizations to identify a set of semantic binning categories. We employ these semantic binning categories in OSCAR: a method for automatically selecting bins based on the inferred semantic type of the field. We conducted a crowdsourced study with 120 participants to better understand user preferences for bins generated by OSCAR vs. binning provided in Tableau. We find that maps and histograms using binned values generated by OSCAR are preferred by users as compared to binning schemes based purely on the statistical properties of the data.Comment: 5 pages (4 pages text + 1 page references), 3 figure

A Visual Exploration of Bias in Covid-19 Coverage

During the Covid-19 pandemic, news outlets used information visualizations to convey noteworthy data about different facets of the crisis in a short period of time. Despite claims of neutrality, an information visualization also conveys bias. Exploring bias in visualizations allows us to understand the bias that some news outlets hold. I chose to explore how news outlets conveyed political bias in a visualization. In this study, using the AllSides scale, I first identified ten news outlets of varying political bias. I then collected five Covid-19 visualizations from each news outlet. I analyzed each visualization’s use of information visualization techniques and topics in order to explore the ways political bias manifests visually. It is unsurprising that I found that news outlets were concerned about Covid-19, discussing the spread and number of Covid-19 cases. News outlets were also similar in the types of colors and graphs they used. The news outlets explored the pandemic on both a national and international level. We see that the bias manifests into either accurately exploring the severity of the pandemic or downplaying the severity of the pandemic. No news outlet overstates the concern of Covid-19. By understanding how media bias manifests in information visualizations, we can further understand how to decrease these biases and truly understand what a visualization is trying to convey. Information literacy is one underused method that can help us understand bias in information visualizations. Specifically, visual literacy is essential to determining which visualizations to believe