Abstract not found

Fig. 1. In this chart of the European Union’s budget by the BBC [7] the original design (1) forces viewers to mentally project a line to the y-axis to extract values. (2) A gridline overlay provides visual anchors, which can simplify the process of extracting values. (3) A line overlay encodes the data redundantly but better illustrates the trends in the data across time. (4) Finally, a statistical summary overlay depicts the mean value of the data so that viewers can easily compare each year’s budget to the average budget across the years. All of these overlays were generated by our system without access to the underlying data, based on automatic extraction of the chart’s mark and axis properties. Abstract—Reading a visualization can involve a number of tasks such as extracting, comparing or aggregating numerical values. Yet, most of the charts that are published in newspapers, reports, books, and on the Web only support a subset of these tasks. In this paper we introduce graphical overlays—visual elements that are layered onto charts to facilitate a larger set of chart reading tasks. These overlays directly support the lower-level perceptual and cognitive processes that viewers must perform to read a chart. We identify five main types of overlays that support these processes; the overlays can provide (1) reference structures such as gridlines, (2) highlights such as outlines around important marks, (3) redundant encodings such as numerical data labels, (4) summary statistics such as the mean or max and (5) annotations such as descriptive text for context. We then present an automated system that applies user-chosen graphical overlays to existing chart bitmaps. Our approach is based on the insight that generating most of these graphical overlays only requires knowing the properties of the visual marks and axes that encode the data, but does not require access to the

How things work visualizations use a variety of visual techniques to depict the operation of complex mechanical assemblies. We present an automated approach for generating such visualizations. Starting with a 3D CAD model of an assembly, we first infer the motions of individual parts and the interactions between parts based on their geometry and a few user specified constraints. We then use this information to generate visualizations that incorporate motion arrows, frame sequences and animation to convey the causal chain of motions and mechanical interactions between parts. We present results for a wide variety of assemblies.