that display attributes of a landscape like elevation and population density–to make predictions about how people might use an area. “We want to take large streams of data as input, observe the change as it happens, and make forward predictions,” he explains. Sledge and his colleagues developed a new temporal-spatial data model that extends the power and usefulness of GIS to a broad range of applications that seek to analyze complex environmental processes as they are happening.

Sledge designed a project to test his idea using a bicyclist as the source of data. He outfitted a bicycle with a GPS unit and special Cycleops Powertap wheels provided by Saris, which record a rider’s heart rate, the wattage and torque of the bike, the energy expended and the cadence of the wheel revolutions. Sledge, a triathlete and former racing cyclist, then pedaled his “rolling laboratory” for thousands of miles around south central Wisconsin.

The rides resulted in millions of pieces of data which when combined with existing information about the landscape of the route painted a detailed picture of Sledge’s performance over the ride-and showed why high-performance athletes might find such technology extremely useful.

“If you know the route, as in the case of a road race, and you know what finish time you’d like to have, you could calculate in advance how much energy you will need to expend,” Sledge explains. “As the race progresses, you can find out when you need to eat to replenish your energy, when you need to go faster to make your goal, and how much energy you have left to burn. You can even balance efforts among members of a race team.”

Beyond racing and athletic uses, Sledge predicts a broad range of applications for his research, which he hopes to complete within the next two years.

“Our next step is working with this technology to address the needs of underserved and at-risk populations, especially groups containing obese children or children who might ten