According to a paper published in the Proceedings of the National Acadamy of Sciences, researchers used the system to analyze over a million pairs of photos taken seven years apart. These results were then used to test popular theories about the causes of urban revitalization.

    Contrary to popular belief, raw income levels and housing prices do not predict change in a neighborhood. Instead, it had more to do with other factors. The researchers found that the density of highly educated residents, proximity to central business districts or other physically attractive neighborhoods, and the initial safety score assigned by the computer vision system all lead to improvements in the physical condition.

veen - the article itself is Journalism Lite™ but the paper might be interesting.

veen:

    The finding that variables that predict the level of Streetscore in 2007 also predict the change in Streetscore between 2007 and 2014 seems to support a positive feedback loop—the essence of tipping models.

In the article I'm writing, I make a very similar argument in the realm of transportation: that better-off places are often improved more than worse-off places, making inequality worse. There, I trace the existence of the feedback loop to problematic assumptions in cost-benefit analysis continually tipping the balance in favor of the well-off.

I wonder what keeps the feedback loop going in this case? My guess is that property value and taxes lead to a virtuous cycle, but I don't know enough about that to say for sure.


posted 2475 days ago