“Eyes on the Street”
Estimating Natural Surveillance Along Amsterdam’s City Streets Using Street-Level Imagery
T. van Asten (Dutch Ministry of the Interior and Kingdom Relations, The Hague)
V. Milias (TU Delft - Human-Centred Artificial Intelligence)
A. Bozzon (TU Delft - Human-Centred Artificial Intelligence)
A. Psyllidis (TU Delft - Internet of Things)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
Neighborhood safety and its perception are important determinants of citizens’ health and well-being. Contemporary urban design guidelines often advocate urban forms that encourage natural surveillance or “eyes on the street” to promote community safety. However, assessing a neighborhood’s level of natural surveillance is challenging due to its subjective nature and a lack of relevant data. We propose a method for measuring natural surveillance at scale by employing a combination of street-level imagery and computer vision techniques. We detect windows on building facades and calculate sightlines from the street level and surrounding buildings across forty neighborhoods in Amsterdam, the Netherlands. By correlating our measurements with the city’s Safety Index, we also validate how our method can be used as an estimator of neighborhood safety. We show how perceived safety varies with window level and building distance from the street, and we find a non-linear relationship between natural surveillance and (perceived) safety.