Data, Meet Human: Vis That Caresbookmark_border

In 3D visualizations of natural phenomena, improving aesthetics can provide measurable benefits, but often involves transformations that affect how the data is perceived. As a growing range of tools - including AI-based methods - make visual design and modification more accessible, it is increasingly important to understand trade offs and concerns when making these changes. We conducted an expert survey (N=95) with visualization researchers, practitioners, and domain scientists, investigating reactions to fifteen alterations spanning presentation-level adjustments (e.g., lighting, camera position) and data-level modifications (e.g., removing errors, filling gaps), applied by both humans and AI systems. Results show differences in perceived acceptability are driven by the transformation's meaning, regardless of whether it operates at the presentation or data level. Additionally, certain modifications were consistently judged as more permissible than others regardless of human or AI authorship. While this relative ordering remains largely stable, AI-generated transformations are consistently rated as less acceptable than identical human-produced changes. These results reveal a distinction between more permissible and more sensitive alterations, and suggest the need for both designers and AI-assisted visualization tools to incorporate constraints and guardrails that reflect these differences.

For people with colorblindness, the color palettes used in data visualizations can be illegible such that encoded data signals collapse. Although extensive prior research studies the perceptual mechanisms behind colorblindness and develops tools to help visualization designers choose colorblind-safe palettes, relatively little work empowers people with colorblindness to make found visualizations more legible for themselves. To explore the design of such assistive technologies, we contribute Visualization Color Repair (VCR), a browser plugin built to selectively recolor data-encoding elements in SVGs to avoid signal collapse under simulated colorblindness. We ground this signal preservation approach in a formative analysis of visualization examples, deriving perceptual invariants used for color linting in VCR and assembling a corpus of test cases to support validation. Our investigation highlights how signal-preserving palette repairs exhibit a ``curb cut effect’’ but also come into tension with original design choices about color semantics, surfacing value conflicts around color accessibility.

Studies have made steady progress identifying individual differences that predict how people use charts, such as graph literacy, spatial ability, and need for cognition. Yet, these measures rarely account for more than a fraction of the outcome variance, and their utility varies considerably across viewers and contexts in ways that are difficult to explain. A potential reason for this variability is that studies often examine these traits as independent or additive predictors, not accounting for how much they co-vary, interact, and suppress one another. We argue that a visualization interpretation is a configuration of how participants perceive, attend to, and recall visual information, and that it is not recoverable from any single dimension or their sum. To test this claim, we use latent profile analysis (LPA), developed in psychology for this purpose, on a dataset of 100 real-world communicative visualizations (n = 311) across measures of perceptual orientation, attentional engagement, and chart recall. The LPA revealed four visualization interpretation configurations, which we contextualize as viewer profiles: the Analytical Engager, Aesthetic Appreciator, Affective Integrator, and Detached Processor, which are substantially better predictors than individual differences. This work illustrates a more comprehensive approach that offers a richer account of the visualization user experience, with implications for how we design studies, evaluate visualizations, and build systems that adapt to viewers.

Although textual framing in data visualizations is known to influence comprehension, recall, and perceptions of bias, its effects on viewers' emotional responses remain underexplored. Drawing on two widely studied framing strategies in political communication, we examine how episodic framing (foregrounding a specific event) versus thematic framing (foregrounding broader trends) affects emotional and attitudinal responses to visualizations. We conducted a preregistered, between-subjects online experiment (N = 800) in which participants viewed identical visualizations of U.S. mass shooting data that varied only in textual framing: a thematic title, a thematic title with annotation, or an episodic title paired with the same annotation. Results show that episodic framing elicited significantly more negative emotional valence than both thematic conditions. In contrast, adding an annotation to a thematic title did not alter emotional impact. While framing did not significantly affect policy attitudes, mediation analysis revealed a significant indirect effect: increased negative emotion under episodic framing predicted greater support for gun control. These findings position emotion as a critical, yet underexamined, dimension of how textual framing shapes responses to data visualizations.

Affective visualization is increasingly studied in visualization research, yet how designers bring emotions into their visualization work remains unexplored. This paper addresses this gap through semi-structured interviews with 15 visualization practitioners. Using hybrid thematic analysis, we identify: (1) three functions that emotions can serve for viewers (entry, engagement, outcome); (2) three facets of how designers work with emotion (data, design, audience), along with design strategies; and (3) ethical considerations in the design process. We also observe that affective intent often emerges during the design process rather than being planned from the outset, and that emotional impact arises from accumulated design choices rather than isolated visual elements. Finally, we highlight evaluation as a key challenge identified by our participants.

Open online communities like Wikipedia offer a rich space for understanding the organic evolution of public-facing artifacts, but little is known about how these communities collectively edit chart images. We investigate the phenomenon of "community editing" of visualizations by exploring chart revision patterns on Wikimedia Commons. We introduce a novel dataset of 7,786 longitudinal chart revisions across 1,664 images, labeled with edit categories using an LLM pipeline. Initial data analysis uncovered several novel findings. Multi-contributor revision is associated with more edits to mark styling and text content compared to single-contributor revision. Temporally, mark and text edits occur in more "bursty" cadences than data edits. In addition, while SVG charts attract more unique contributors and revisions than raster charts, their edit histories involve more tightly scoped and less diverse changes. This dataset enables a wide range of analyses of chart change over time.

We investigate how input physicalizations can support discussions in citizen meetings. Input physicalizations are physical data representations that allow data collection and can enable participants to externalize and represent their stances and arguments during discussions. We conducted an exploratory workshop in a citizen meeting with 24 participants. Participants first used and critiqued two provotypes during discussions and then designed new physical discussion tools in groups. Our qualitative analysis shows the role of expressivity of input physicalization discussion tools to convey nuances of participants' viewpoints. Our findings highlight the tension between the expressivity of input physicalizations and the need for structured data input to enable comparable and interpretable data visualizations. Finally, we discuss trade-offs for expressivity and polysemy in visual representations.

Predictive urban models capture rich spatiotemporal dynamics of housing markets, but their outputs rarely reach the residents most affected by neighborhood change. We present What-If Demolition, an interactive gentrification explorer that couples an Attentive Neural Process for rent prediction with a narrative interface designed for non-expert civic audiences. We abstract a transferable pattern from the design process, which we call home-first narrative anchoring, that grounds exploration in the user's own address, progressively disclosing citywide patterns and "what-if" scenarios under uncertainty. We operationalize four principles of Data Humanism as design decisions and reflect on the deployed prototype through a study with eight residents of Zurich. Participants engaged with counterfactual scenarios anchored in their own homes, but also identified interpretive risks related to uncertainty and causality, which we discuss as lessons for public-facing predictive visualization. These findings suggest that home-first narrative anchoring can generalize beyond housing to other civic domains where spatiotemporal predictions must be made legible to the general users they affect.