  The presence of people in an urban area throughout the day -- often called
'urban vitality' -- is one of the qualities world-class cities aspire to the
most, yet it is one of the hardest to achieve. Back in the 1970s, Jane Jacobs
theorized urban vitality and found that there are four conditions required for
the promotion of life in cities: diversity of land use, small block sizes, the
mix of economic activities, and concentration of people. To build proxies for
those four conditions and ultimately test Jane Jacobs's theory at scale,
researchers have had to collect both private and public data from a variety of
sources, and that took decades. Here we propose the use of one single source of
data, which happens to be publicly available: Sentinel-2 satellite imagery. In
particular, since the first two conditions (diversity of land use and small
block sizes) are visible to the naked eye from satellite imagery, we tested
whether we could automatically extract them with a state-of-the-art
deep-learning framework and whether, in the end, the extracted features could
predict vitality. In six Italian cities for which we had call data records, we
found that our framework is able to explain on average 55% of the variance in
urban vitality extracted from those records.
