Getting high resolution data can be immensely valuable- fueling tons of applications for analytics, data visualization, and also complex systems research (Pentland‘s work, ethnic violence research).  Mapping data has seen a recent growth with projects like Open Civic Data providing tools for mapping city data sets, and citizens creating useful mobile applications.

At the recent Health2Gov hackathon, I found myself in the open hardware room exploring ways to create sensors that could be mass deployed for high resolution data aggregation.  Some of the ideas involved people wearing these sensors.  Unless the device includes some benefit, adoption of such sensors are unrealistic- without an immediate individual benefit, they are too intrusive.

High resolution data can be time consuming to aggregate- using methods like surveying, site-visits by experts, or installation of expensive sensors.  For the case of energy efficiency, a group in Cambridge has devised a simple technique using thermal infrared pictures, and a Google street views setup.

That “non-invasive” aspect is a key difference from typical home energy audits, which often take a few hours and involve inspecting every part of the home, from the basement to the attic, and often require special equipment such as door blowers to measure air leakage. Even then, while such audits can determine where the energy losses are and suggest ways of reducing them, they do not provide quantitative estimates of the projected savings resulting from a given change (adding insulation, replacing windows, or installing a new heating system, for example).

This would have pretty cool implications for DIY scientists and groups like Project Laboratory.