Researchers at MIT and Harvard believe that their computer vision system (i.e., artificial intelligence) helps reveal fundamental characteristics of neighborhood change.
Larry Hardesty shares news of a new "computer vision system" from MIT's Media Lab, created with partners from Harvard University, that quantifies "the physical improvement or deterioration of neighborhoods in five American cities."
In work reported today in the Proceedings of the National Academy of Sciences, the system compared 1.6 million pairs of photos taken seven years apart. The researchers used the results of those comparisons to test several hypotheses popular in the social sciences about the causes of urban revitalization. They find that density of highly educated residents, proximity to central business districts and other physically attractive neighborhoods, and the initial safety score assigned by the system all correlate strongly with improvements in physical condition.
The new tool is built on a system created four years ago, which analyzes "street-level photos taken in urban neighborhoods in order to gauge how safe the neighborhoods would appear to human observers."
Hardesty goes into a lot more detail about the ideas that contributed to the creation of the computer vision system, and the findings that have been produced in its deployment.
Linda Poon offers additional insight into the use of artificial intelligence in the analysis of urban conditions.
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UCLA Lewis Center for Regional Policy Studies
Mpact (formerly Rail~Volution)
Chaddick Institute at DePaul University
City of Piedmont, CA
Great Falls Development Authority, Inc.
HUDs Office of Policy Development and Research