In an experiment in social data gathering, Slate's Michael Agger suggests that there is a host of ways we could be contributing our personal data to help make better policy decisions.
Agger offers the example of the daily commute:
"How long did it take you to get to work today? How long did it take you to get to work on this day last week? How long, on average, did it take you to get to work this month? The Swiss economists Bruno Frey and Alois Stutzer argue that people do not appreciate the real cost of a long commute. And especially when that commute is unpredictable, it takes a toll on our daily well-being.
Now, imagine if we shared our commuting information so that we could calculate the average commute from various locations around a city. When the growing family of four pulls up to a house for sale for in New Jersey, the listing would indicate not only the price and the number of bathrooms but also the rush-hour commute time to Midtown Manhattan. That would be valuable information to have, since buyers could realistically factor the tradeoffs of remaining in a smaller space closer to work against moving to a larger space and taking on a longer commute."
After citing some other examples of social data aggregation such as Quanitified Self and Cure Together, Agger opens the forum to readers to comment on and submit their own ideas for data collection, while promising additional columns on data in transportation, economic productivity, and health care.
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