Visualization is often considered to consist of three phases: exploration, analysis, and presentation. While the former two topics are covered well in the literature, there has been very little work specifically on presentation. In an upcoming paper, Jock Mackinlay and I argue that presentation, and in particular storytelling and communication of data, are the logical […]
Visualization is largely defined as the transformation of data into images. Visualization tools don’t have a way of assessing their output, though: were there enough pixels to represent all the data? Are there too many overlapping lines? In a paper to be presented at EuroVis next week, Aritra Dasgupta, Min Chen, and I propose a taxonomy of the different sources of uncertainty when working with parallel coordinates.
The point of visualization is usually to reveal as much of the structure of a dataset as possible. But what if the data is sensitive or proprietary, and the person doing the analysis is not supposed to be able to know everything about it? In a paper to be presented next week at InfoVis, my Ph.D. student Aritra Dasgupta and I describe the issues involved in privacy-preserving visualization, and propose a variation of parallel coordinates that controls the amount of information shown to the user.
Parallel coordinates are a very popular visualization technique for multi-dimensional numerical data. In this paper, we propose a set of metrics to better understand the types of visual structures users commonly look for using this technique. Based on the metrics, we can optimize the display to make it more readable, and allow the user to select dimensions based on their visual structures, rather than their existing ideas about the data.
Visualization needs a new theory. Bertin’s ideas about marks and retinal variables have provided a great starting point, but we are now seeing their limitations. We need to turn a new page and move beyond those cosy, familiar ideas, into new territory. A recent paper by Caroline Ziemkiewicz and myself makes an argument why, and provides some possible directions.
Design is usually considered a minor point in visualization. Does it make a difference what color scheme you use (as long as it’s not an atrocious one), how thick your lines are, whether you put a background behind your chart, etc.? Caroline Ziemkiewicz and I presented a paper at Advanced Visual Interfaces (AVI) where we reported on a study we had performed to find out.
User studies are an important part of visualization, but they also require a considerable amount of effort and time. In a paper presented at the BELIV workshop (part of CHI 2010), we discussed our experiences with running a number of visualization studies using Amazon’s Mechanical Turk (MTurk) service. Using MTurk, we are able to run large studies in much less time than usual, and at very low cost. We also show how to avoid gaming the system, which had been reported in earlier work using MTurk.
In January, my Ph.D. student Caroline Ziemkiewicz told me about an interesting observation she had made: in different papers comparing tree visualizations, treemaps came out as best, worst, or somewhere in the middle. One difference she noticed was how the questions were worded: when a levels metaphor was used, treemaps did badly; a containment metaphor, on the other hand, seemed to favor treemaps. So we decided to investigate – the result will be presented at InfoVis on Monday, October 20.