7/12/2017

Data Driven Guides

 

Design expressive information graphics.

By Nam Wook Kim, DDG

What is Data Driven Guides (DDG)?

DDG is a technique for designing expressive data driven graphics. Instead of being confined by predefined templates, designers can generate guides from their data and use the guides to accurately place and measure custom shapes. 

Image: Length and area guides. They can be used as a position guide as well.

We provide guides to encode three main visual channels: length, area, and position, each following the principles of information encoding. Users can combine more than one guide to construct a variety of visual structures that represent data.

Why we developed Data Driven Guides

Although there are many visualization construction tools are available (take Tableau, for example), a lot of infographic designers still rely on freeform illustration tools such as Adobe Illustrator to create custom visual representations of data, which currently do not provide data driven abstractions. This results in time-consuming and error-prone manual visual encoding that prevents designers from exploring diverse design variations.

Image: Excerpt from an online tutorial explaining how to measure a custom visual mark using a hand-crafted scale.

Use Case #1 data driven drawing

We draw inspiration from existing design practices in areas such as architectural or user interface design, where a guide, such as a rule or grid, is used as a reference for precise drawing or alignment. 

Interacting with guides

DDG is designed to be fluidly integrated into a flexible graphic design environment. To provide familiar interactions used in graphic design tools, it offers free manipulation (move, rotate, scale) to create a custom layout. The relative sizes of data guides are preserved to maintain data integrity.

Image: Free manipulation with proportional lengths to preserve data integrity.

A data guide serves as a ruler backed up by data to minimize the designer’s effort to manually place and measure graphics; its size and shape indicate where a data value lies on the canvas. Users can draw custom shapes from scratch directly on top of data guides. The overall drawing experience is closer to drawing with a pen and ruler (that is, it uses a bottom-up design process).

Image: Data sketching with the help of DDG.

Users can combine multiple data guides in order to construct more expressive structures. This has the same effect as combining different visual variables, in our case length, area, and position. The repeat feature in DDG allows an associated shape to be repeated over sibling guides in the same group. If more than one guide encodes a single shape, we only repeat the shape once as shown below.

Image:  Composing multiple guides to construct a more complex visual structure.

Here is a flower chart example created using DDG. Each flower represents the wellbeing index of each country while the stem is its GDP. Here, the guide of the stem is used as a position guide for the flower. Other examples can be found on the project website.

Image: A flower chart created using DDG.

Use Case #2 retargeting existing artworks

Users can use data guides to repurpose existing artworks by matching the artworks to the size of the guides. The example below is inspired by drawing an inspiration from Nigel Holmes’ Monstrous Costs chart.

Image: Nigel Holmes’ Monstrous Costs chart drawn by hand in 1982.

Instead of drawing it from scratch, we imported a monster graphic into the tool and repurposed it with data guides by adjusting the teeth to match the size of the guides. This workflow is the top-down, graphical process of placing data on existing graphics. We skip the chart title and description for brevity.

Image: Retargeting the size of the teeth with data using DDG.

Labels are generated using the context menu and are automatically linked to data guides when created. The sizes of the guides constrain the positions of the labels.

Image: Generating data labels.

Taking advantage of the data binding capability of DDG, a duplicate is easily created by copying the chart, pasting it, and changing the data for the cloned chart. This increases the reusability of custom charts. DDG is basically an intermediate layer for associating data with any objects including shapes, texts, or guides.

Image: Reusing the monster chart through a simple copy and paste.

Use Case #3 Proofreading Existing Infographics

DDG can also be used to proofread existing infographics. For example, when we juxtaposed data guides on top of the original image we found that the factory worker chart by Nigel Holmes may have an incorrect representation of the data. The lengths of three lava marks representing France, Japan, and Britain do not match the size of data guides; the baseline is not clear, however.

Image: Original factory worker chart (left) and superimposed guides created using the same data (right).

We also found a similar case in the balloon chart below; that is, the radius of the balloon instead of the area was used to represent the data value. Readers perceive the differences in the areas not radii of the balloons. This case is actually a commonly found mistake in existing infographic design practice.

Image: Original balloon chart (left) and both length and area guides superimposed (right).

A Step Forward Towards New Visualization Design Tools

DDG only scratches the surface of the broad, unexplored design space of new visualization tools. There is still a much unexplored gap in how designers create innovative visualizations and how currently available tools mandate the process of generating visualizations.

Most existing visualization creation tools are based on formal specifications for rapidly generating traditional statistical graphics. However, designers still engage in manual encoding in order to design unique visual representations of data that are often found to be more attractive, engaging and easier to remember. 

For example, how can we design tools to support freeform data sketching like Dear Data

Image: Dear Data by Giorgia Lupi and Stefanie Posavec.

DDG was developed by Nam Wook Kim, Eston Schweickart, Zhicheng Liu, Mira Dontcheva, Wilmot Li, Jovan Popovic, and Hanspeter Pfister. It was originally published in IEEE Transactions on Visualization and Computer Graphics (InfoVis’16), 2017.

About the author

Nam Wook Kim is a PhD candidate in the Computer Science department at Harvard. Prior to Harvard, he worked as a research engineer at Samsung and LG. He received his MSc in Computer Science from Stanford. His research in human-computer interaction and information visualization focuses on making complex data approachable to the public by designing interactive tools for creating communicative visualizations and building data-driven applications in various domains.

You can find more information about DDG on the project website, and the drawing tool

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