  Policy makers, urban planners, architects, sociologists, and economists are
interested in creating urban areas that are both lively and safe. But are the
safety and liveliness of neighborhoods independent characteristics? Or are they
just two sides of the same coin? In a world where people avoid unsafe looking
places, neighborhoods that look unsafe will be less lively, and will fail to
harness the natural surveillance of human activity. But in a world where the
preference for safe looking neighborhoods is small, the connection between the
perception of safety and liveliness will be either weak or nonexistent. In this
paper we explore the connection between the levels of activity and the
perception of safety of neighborhoods in two major Italian cities by combining
mobile phone data (as a proxy for activity or liveliness) with scores of
perceived safety estimated using a Convolutional Neural Network trained on a
dataset of Google Street View images scored using a crowdsourced visual
perception survey. We find that: (i) safer looking neighborhoods are more
active than what is expected from their population density, employee density,
and distance to the city centre; and (ii) that the correlation between
appearance of safety and activity is positive, strong, and significant, for
females and people over 50, but negative for people under 30, suggesting that
the behavioral impact of perception depends on the demographic of the
population. Finally, we use occlusion techniques to identify the urban features
that contribute to the appearance of safety, finding that greenery and street
facing windows contribute to a positive appearance of safety (in agreement with
Oscar Newman's defensible space theory). These results suggest that urban
appearance modulates levels of human activity and, consequently, a
neighborhood's rate of natural surveillance.
