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1. Vis Ex Machina: An Analysis of Trust in Human versus Algorithmically Generated Visualization Recommendations

   https://doi.org/10.1145/3411764.3445195  

   Zehrung, Rachael; Singhal, Astha; Correll, Michael; Battle, Leilani (May 2021, CHI '21: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems)

   null (Ed.)

   More visualization systems are simplifying the data analysis process by automatically suggesting relevant visualizations. However, little work has been done to understand if users trust these automated recommendations. In this paper, we present the results of a crowd-sourced study exploring preferences and perceived quality of recommendations that have been positioned as either human-curated or algorithmically generated. We observe that while participants initially prefer human recommenders, their actions suggest an indifference for recommendation source when evaluating visualization recommendations. The relevance of presented information (e.g., the presence of certain data fields) was the most critical factor, followed by a belief in the recommender’s ability to create accurate visualizations. Our findings suggest a general indifference towards the provenance of recommendations, and point to idiosyncratic definitions of visualization quality and trustworthiness that may not be captured by simple measures. We suggest that recommendation systems should be tailored to the information-foraging strategies of specific users. 

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2. Artifact-Based Rendering: Harnessing Natural and Traditional Visual Media for More Expressive and Engaging 3D Visualizations

   https://doi.org/10.1109/TVCG.2019.2934260  

   Johnson, Seth; Samsel, Francesca; Abram, Gregory; Olson, Daniel; Solis, Andrew J.; Herman, Bridger; Wolfram, Phillip J.; Lenglet, Christophe; Keefe, Daniel F. (August 2019, IEEE Transactions on Visualization and Computer Graphics)

   We introduce Artifact-Based Rendering (ABR), a framework of tools, algorithms, and processes that makes it possible to produce real, data-driven 3D scientific visualizations with a visual language derived entirely from colors, lines, textures, and forms created using traditional physical media or found in nature. A theory and process for ABR is presented to address three current needs: (i) designing better visualizations by making it possible for non-programmers to rapidly design and critique many alternative data-to-visual mappings; (ii) expanding the visual vocabulary used in scientific visualizations to depict increasingly complex multivariate data; (iii) bringing a more engaging, natural, and human-relatable handcrafted aesthetic to data visualization. New tools and algorithms to support ABR include front-end applets for constructing artifact-based colormaps, optimizing 3D scanned meshes for use in data visualization, and synthesizing textures from artifacts. These are complemented by an interactive rendering engine with custom algorithms and interfaces that demonstrate multiple new visual styles for depicting point, line, surface, and volume data. A within-the-research-team design study provides early evidence of the shift in visualization design processes that ABR is believed to enable when compared to traditional scientific visualization systems. Qualitative user feedback on applications to climate science and brain imaging support the utility of ABR for scientific discovery and public communication. 

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3. An Evaluation-Focused Framework for Visualization Recommendation Algorithms

   https://doi.org/10.1109/TVCG.2021.3114814  

   Zeng, Zehua; Moh, Phoebe; Du, Fan; Hoffswell, Jane; Lee, Tak Yeon; Malik, Sana; Koh, Eunyee; Battle, Leilani (September 2021, IEEE Transactions on Visualization and Computer Graphics)

   null (Ed.)

   Although we have seen a proliferation of algorithms for recommending visualizations, these algorithms are rarely compared with one another, making it difficult to ascertain which algorithm is best for a given visual analysis scenario. Though several formal frameworks have been proposed in response, we believe this issue persists because visualization recommendation algorithms are inadequately specified from an evaluation perspective. In this paper, we propose an evaluation-focused framework to contextualize and compare a broad range of visualization recommendation algorithms. We present the structure of our framework, where algorithms are specified using three components: (1) a graph representing the full space of possible visualization designs, (2) the method used to traverse the graph for potential candidates for recommendation, and (3) an oracle used to rank candidate designs. To demonstrate how our framework guides the formal comparison of algorithmic performance, we not only theoretically compare five existing representative recommendation algorithms, but also empirically compare four new algorithms generated based on our findings from the theoretical comparison. Our results show that these algorithms behave similarly in terms of user performance, highlighting the need for more rigorous formal comparisons of recommendation algorithms to further clarify their benefits in various analysis scenarios. 

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4. Designing an Interactive Visual Analytics System for Precipitation Data Analysis

   https://doi.org/10.3390/app15105467  

   Jeong, Dong Hyun; Behera, Pradeep; Jeong, Bong Keun; Luna_Sangama, Carlos David; Higgs, Bryan; Ji, Soo-Yeon (May 2025, Applied Sciences)

   As precipitation analysis reveals critical statistical characteristics, temporal patterns, and spatial distributions of rainfall and snowfall events, it plays an important role in planning urban drainage systems, flood forecasting, hydrological modeling, and climate studies. It helps engineers design climate-resilient infrastructure capable of withstanding extreme weather events, which is becoming increasingly important as precipitation patterns change over time. With precipitation analysis, multiple valuable information can be determined, such as storm intensity, duration, and frequency. To enhance understanding of precipitation data and analysis results, researchers often use graphical representation methods to show the data in visual formats. Although existing precipitation analysis and basic visual representations are helpful, it is critical to have a comprehensive analysis and visualization system to detect significant patterns and anomalies in high-resolution temporal precipitation data more effectively. This study presents a visual analytics system enabling interactive analysis of hourly precipitation data across all U.S. states. Multiple coordinated visualizations are designed to support both single and multiple-station analysis. These visualizations allow users to examine temporal patterns, spatial distributions, and statistical characteristics of precipitation events directly within visualizations. Case studies demonstrate the usefulness of the designed system by evaluating various historical storm events. 

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5. Visualization model validation via inline replication

   https://doi.org/10.1177/1473871618821747  

   Gotz, David; Wang, Wenyuan; Chen, Annie_T; Borland, David (January 2019, Information Visualization)

   Data visualizations typically show a representation of a data set with little to no focus on the repeatability or generalizability of the displayed trends and patterns. However, insights gleaned from these visualizations are often used as the basis for decisions about future events. Visualizations of retrospective data therefore often serve as “visual predictive models.” However, this visual predictive model approach can lead to invalid inferences. In this article, we describe an approach to visual model validation called Inline Replication. Inline Replication is closely related to the statistical techniques of bootstrap sampling and cross-validation and, like those methods, provides a non-parametric and broadly applicable technique for assessing the variance of findings from visualizations. This article describes the overall Inline Replication process and outlines how it can be integrated into both traditional and emerging “big data” visualization pipelines. It also provides examples of how Inline Replication can be integrated into common visualization techniques such as bar charts and linear regression lines. Results from an empirical evaluation of the technique and two prototype Inline Replication–based visual analysis systems are also described. The empirical evaluation demonstrates the impact of Inline Replication under different conditions, showing that both (1) the level of partitioning and (2) the approach to aggregation have a major influence over its behavior. The results highlight the trade-offs in choosing Inline Replication parameters but suggest that using [Formula: see text] partitions is a reasonable default. 

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