Graphical Perception of Continuous Quantitative Maps: the Effects of Spatial Frequency and Colormap Design

Continuous 'pseudocolor' maps visualize how a quantitative attribute varies smoothly over space. These maps are widely used by experts and lay citizens alike for communicating scientific and geographical data. A critical challenge for designers of these maps is selecting a color scheme that is both effective and aesthetically pleasing. Although there exist empirically grounded guidelines for color choice in segmented maps (e.g., choropleths), continuous maps are significantly understudied, and their color-coding guidelines are largely based on expert opinion and design heuristics--many of these guidelines have yet to be verified experimentally. We conducted a series of crowdsourced experiments to investigate how the perception of continuous maps is affected by colormap characteristics and spatial frequency (a measure of data complexity). We find that spatial frequency significantly impacts the effectiveness of color encodes, but the precise effect is task-dependent. While rainbow schemes afforded the highest accuracy in quantity estimation irrespective of spatial complexity, divergent colormaps significantly outperformed other schemes in tasks requiring the perception of high-frequency patterns. We interpret these results in relation to current practices and devise new and more granular guidelines for color mapping in continuous maps

Marshalling the Many Facets of Diversity

International audienc

Integrating Human- and Computer-Based Approaches to Feature Extraction and Analysis

A major goal of imaging systems is to help doctors, scientists, engineers, and analysts identify patterns and features in complex data. There is a wide range of imaging, visualization, and graphics systems, ranging from fully automatic systems that extract features algorithmically to interactive systems that allow the analyst to manipulate visual representations directly to discover features. Although automatic feature-extraction algorithms are often directed by human observation, and human pattern recognition is often supported by algorithmic tools, very little work has been done to explore how to capitalize on the interaction between human and machine pattern recognition. This paper introduces a preliminary roadmap for guiding research in this space. One key concept is the explicit consideration of the task, which determines which methods and tools will be most effective. The second is the explicit inclusion of a "human-in-the-loop," who interacts with the data, the algorithms, and representations, to identify meaningful features. The third is the inclusion of a process for creating a mathematical representation of the features that have been "carved out" by the human analyst, for use in comparison, database query or analysis.Astronom