arXiv subjects

Keke Wu

Publications and source records attributed to Keke Wu.

20 records · Page 2Linked to original sources

arXiv2019

Designing for Mobile and Immersive Visual Analytics in the Field

Data collection and analysis in the field is critical for operations in domains such as environmental science and public safety. However, field workers currently face data- and platform-oriented issues in efficient data collection and analysis in the field, such as limited connectivity, screen space, and attentional resources. In this paper, we explore how visual analytics tools might transform field practices by more deeply integrating data into these operations. We use a design probe coupling mobile, cloud and immersive analytics components to guide interviews with ten experts from five domains to explore how visual analytics could support data collection and analysis needs in the field. The results identify shortcomings of current approaches and target scenarios and design considerations for future field analysis systems. We embody these findings in FieldView, an extensible, open-source prototype designed to support critical use cases for situated field analysis. Our findings suggest the potential for integrating mobile and immersive technologies to enhance data's utility for various field operations and new directions for visual analytics tools to transform fieldwork.

cs.HC↗

arXiv2019

Color Crafting: Automating the Construction of Designer Quality Color Ramps

Visualizations often encode numeric data using sequential and diverging color ramps. Effective ramps use colors that are sufficiently discriminable, align well with the data, and are aesthetically pleasing. Designers rely on years of experience to create high-quality color ramps. However, it is challenging for novice visualization developers that lack this experience to craft effective ramps as most guidelines for constructing ramps are loosely defined qualitative heuristics that are often difficult to apply. Our goal is to enable visualization developers to readily create effective color encodings using a single seed color. We do this using an algorithmic approach that models designer practices by analyzing patterns in the structure of designer-crafted color ramps. We construct these models from a corpus of 222 expert-designed color ramps, and use the results to automatically generate ramps that mimic designer practices. We evaluate our approach through an empirical study comparing the outputs of our approach with designer-crafted color ramps. Our models produce ramps that support accurate and aesthetically pleasing visualizations at least as well as designer ramps and that outperform conventional mathematical approaches.

cs.HC↗