Tuesday, April 29, 2008

Taking the Heat Out of a Hot Kitchen

(Long-time fans of the Pittsburgh hockey team will understand the title of this post. Go Pens!)

We’ve all seen ‘heat maps’ used as visualization tools. A heat map is a graphical representation of data where the values taken by the variables are represented as colors. Often, heat maps are used in conjunction with an actual map – like the weather map on the back page of USAToday, or the real-time traffic display at traffic.com. And while the information from these maps is useful - “It’s cold and rainy in Boston in April, and the traffic on the Mass Pike is really bad at 5:00pm” - it’s not particularly insightful.

Here’s an interesting application of heat map visualization. It’s from Purdue University’s Project Vulcan, which is quantifying North American fossil fuel carbon dioxide (CO2) emissions at space and time scales much finer than have been achieved in the past. This 5-minute video provides an overview and shows several fascinating examples of the heat map visualizations used in representing the underlying data:

Again, some of the results are expected – "carbon dioxide emissions are high where there are lots of people spending lots of time in their cars" – but not overly insightful. More interesting, however, are the discoveries that researchers have made from analyzing the data in graphical form. There’s an excellent summary in the April 27, 2008 issue of the Boston Globe and two results stand out:

“When you rank America’s counties by their carbon emissions, San Juan County, NM – a mostly empty stretch of desert with just 100,000 people – comes in sixth, above heavily populated places like Boston and even New York City. It turns out that San Juan County hosts two generating plants fired by coal, the dirtiest form of electrical production in use today.”

And the heat maps shows a small, bright-red area (high carbon emissions) in the northwest corner of New Mexico surrounded by wide expanses colored green.

“Purdue researchers discovered higher-than-expected emissions levels in the Southeast, likely due to the increasing population of the Sun Belt, long commutes, and the region’s heavy use of air conditioning. According to Kevin Gurney, assistant professor of atmospheric science at Purdue and the project leader, this part of the map also overturns the prevailing assumption that industry follows population centers: In the Southeast, smaller factories and plants are distributed more evenly across the landscape. Cities, meanwhile, prove less damaging than their large populations might suggest, partly thanks to shorter commutes and efficient mass transit.”
Work is underway to add Canadian and Mexican data to the Project Vulcan inventories. It will be interesting to see what other non-intuitive conclusions will be reached with these analytical and visualization techniques.

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