Data Visualization.

March 27, 2013 § 1 Comment

The greatest value of a picture is when it forces us to notice what we never expected to see.

— John W. Tukey. Exploratory Data Analysis. 1977.

Yes, indeed, there is an immense power in the ability to visualize data. It is the ability to make the unseen, the oft missed, the too small, the too big, the too boring, the utterly disperse, and the ‘only in comparison’ appear in ways they never have before. It can excite the general public to topics otherwise confined to texts they’ll never read; and it can excite such texts to consider questions they’d otherwise never ask. Data visualization is an art, a science,  a skill, and an instrument.  It can be found, now, in almost every avenue of our lives: our ideas, our politics, our religion, our health care, our browsingour sports, our music, our social networking…I certainly encourage everyone who is interested to learn more about data visualization–whether by way of design, statistics, programming, or some other route.

Of course, as with all things good, data visualizations (e.g. infographics) must not go unquestioned. They too can go the way of all bad science–leading people astray when the data, design, or computations are poor or fallaciously manipulated.

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So be cautious and curious when consuming data visualizations–but also be grateful for them!

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University College London’s Center for Advanced Spatial Analysis and its SpaceTimeLab joined forces to produce this impressive, interactive infographic of New York’s multilingual twitter activity (London version also available).  If you like this, you may also find this Microsoft tool enviable, and this popular NYT tool (Cascade) exciting.

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“Designed by the team at MIT SENSEable City Lab, Health InfoScape is a disease network that combines 7.2 million patient records from General Electric’s proprietary database in an effort to illustrate relationships between various conditions that commonly affect Americans today” (visualcomplexity.com). Visualizing otherwise disperse or siloed relationship may help lead to innovation, discovery, and cure.

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Along similar lines, this interactive infographic from the Institute for Health Metrics and Evaluation allows you to “compare how a given set of 21 cause groups affects specific age groups or regions in terms of death and disability. You can change age group or region, year, and metric to view results for absolute numbers, rates, and percentages. You also have the option to further explore each cause group and view specific diseases, injuries, or risk factors.” This  institute has a smattering of other data visualizations for researchers as well. In this case, the ability to visualize inequality may assist in the obtainment of a more egalitarian future.

If you are interested in infographics too, you’ll like to follow Jer Thorp’s blog (former Data Artist in Residence at the New York Times, his TED talk is here, and an interesting article written by him about Big Data is here). You also might like datavisualization.ch, which has put together a nice list of fun data visualization tools to play around with (along with some fun datasets provided by informationisbeautiful.net)

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