Form Follows Functionbookmark_border

Multivariate geospatial data visualization often relies on multiple coordinated views, where color can be used to either link views or encode data attributes. Encoding spatial locations through color can reveal patterns in non-spatial visualizations, yet most applications of colormaps focus on high-dimensional attribute encodings instead. While 2D colormaps have been studied extensively, color encodings designed for spherical geospatial data have received much less attention, even though all locations lie on a sphere. To address this, we propose SpheriColor, a colormap generation approach for mapping geospatial references guided by the design goals of distance preservation and colorspace exploitation. We project a geospatial distance function into a perceptually linear colorspace, followed by two different gamut-constrained optimization strategies. We perform a quantitative evaluation using both real-world and synthetic datasets, demonstrating superior performance over 2D colormaps and HSLuv double cone encodings. The simplex-based optimization excels at distance preservation, whereas the ray-based approach provides better colorspace exploitation.

There has been limited clarity and consistency regarding what the term infographics, or information graphics, refers to in visualization research and practice. In particular, little is understood about where people’s conceptualizations of infographics converge or diverge. To address this gap, we conducted a systematic literature review and a practitioner survey to identify and contrast different perspectives on infographics. We performed inductive coding on 487 sentences from 111 visualization and HCI papers, and analyzed questionnaire responses from 44 domain practitioners. Our findings reveal recurring dimensions of conceptual disagreement on infographics: role of text, relationship with data visualization, and relationships with statistical charts and data comics. Based on the results, we recommend future work to explicitly report any assumptions made along these diverging conceptualizations, to investigate the cognitive origins of these disagreements, and to develop more holistic design component frameworks for infographics. The supplementary materials are available via OSF and as an interactive web application.

We introduce mental imagery, or seeing images in the “mind’s eye,” as a cognitive process that can be shaped by data visualization design and in turn impact decisions. We found in a preregistered study (n = 400) that abstract geometric icons visualizing fire risk data evoked more mental images than concrete house-on-fire icons, and also produced more diverse and personalized mental images. Mediation analysis showed that increased mental imagery subsequently led to risk-averse decisions through evoking negative affect. These findings reveal a nuanced mechanism through which visualization concreteness influences decisions: concrete designs may actually suppress affect-driven behavior by restricting mental imagery.

Learning gross anatomy generally requires more than passive observation. Cadaver dissection, considered by many anatomists to be the gold-standard for anatomy education, involves sensorimotor engagement which grounds knowledge in action. However, cadaver dissection is resource intensive and expensive. In this paper, we describe two techniques for interactive visualization of virtual dissectible models for anatomy education: rest-pose clipping and incision retraction/reflection. These interactions are designed to allow users to explore the 3-dimensional structure of cryosection data to discover the spatial relationships between internal anatomical structures. Although our model is not a physically accurate simulation, it is capable of stable and plausible reaction to user interaction. We propose to use these dissectible models in later studies to explore the role of embodied learning in anatomy education.

Visualising all dimensions of multi-dimensional real data while providing semantics is a challenging problem in data analysis. In this paper, we present the visualisation of multi-dimensional real data using our tool, 360° RAYS. RAYS plots all features and points of the dataset and provides information such as the geometric positioning of points or classes in the orthants of space, thereby aiding understanding of the closeness of points, classification of points, and ordering of subspaces. This visualisation technique is built after exploring viable options to plot multi-dimensional data, which included parallel coordinates, concentric coordinates, and circle segments, among others. The key idea of our approach is a hierarchical concentric ring structure, where each successive ring incrementally adds a dimension to the existing subspace, ordered by the increasing or decreasing coefficient of variation of data in the added dimension. Normalised data points are mapped to ring sectors based on a bitwise encoding of their coordinate values (non-negative or negative). The advantage of RAYS is that it presents the complete data with all dimensions and provides a clear visual grouping of points by subspace concentration. The visualisation also shows pure and overlapping orthants within the data in a flattened 2D radial layout.

Money laundering is not only about moving illicit funds, but about hiding the money’s origin and traces to complicate detection. Financial criminals resort to many methods to avoid regulators and legal thresholds. But analysts investigating alerts, dedicated to pin mule accounts and track suspicious transactions daily, also have theirs. Network visualizations can be key in countering adversarial money laundering activities, especially if they provide a clear overview of the money flows and a seamless analysis experience, but they are often not structured for this type of task. That is why we propose a tabular sequential graph visualization tailored to money laundering analysis — following transactions (edges) from the victim account that triggered an alert through multiple accounts (nodes) and banks (rows). To reduce the number of nodes and edges, we propose three methods for grouping these tabular sequential graphs: an amount-based approach, a time-based approach, and a combined solution that considers both the transaction amount and its order. A qualitative user study with experts suggests that the most effective method in node reduction was not necessarily the most interesting for analysis and that there is a trade-off between manual work and time for interpretation in more granular graphs.

Encoding theory provides principled guidance for mapping data types to visual channels, but it operates at the data level. Whether a representation’s structural form corresponds to the structure of the phenomenon it depicts remains a separate question; one the field currently lacks a tool to assess. We name structural correspondence as critical theory for visualization design, define fidelity as the degree of structural correspondence between a representation’s parameters and the structural properties of the phenomenon it depicts, and propose parameter mapping — systematic assessment of each parameter of a representation against the structural properties of its phenomenon — as the analytical tool for assessing it. For each parameter, the method asks: what structural property of the phenomenon does it correspond to (fidelity gained)? What structural dimensions are absent or collapsed (fidelity lost)? What relationships does the representation introduce that the phenomenon does not have (false structure)? Applied to six existing representations spanning visualization, sonification, and physicalization, the framework surfaces patterns of structural and arbitrary coupling, deliberate abstraction, and false structure that current methods do not detect.

Cumulative-link ordinal regression models are an alternative approach for analysing ordinal data such as Likert items, which are widely used in Visualization (and other related fields like HCI, psychology etc.). There are many researchers who are strong proponents of this approach, as it makes less stringent assumptions about the data, compared to the more commonly used linear model or ANOVA. Yet, ordinal regression models have seen limited adoption. I posit that one possible reason for this might be due to the difficulty in visually representing the results from such models, and in communicating the key takeaways in an intuitive manner. I propose the use of (modified) Complementary Cumulative Distribution Function (mCCDF) plots to visualize the results of ordinal regression models, and demonstrate how the same takeaways that researchers present from analyses which treat ordinal data as metric can be easily communicated using mCCDFs.