Measuring Natural Surveillance at Scale

Measuring Natural Surveillance at Scale: An Automated Method for Investigating the Relation Between the 'Eyes on the Street' and Urban Safety

van Asten, Timo (TU Delft Electrical Engineering, Mathematics and Computer Science)

Psyllidis, A. (mentor)
Bozzon, A. (graduation committee)

Delft University of Technology

2021-12-08

To create safe urban areas, it is important to gain insight into what influences the (perceived) safety of our cities and human settlements. One of the factors that can contribute to safety is the way urban spaces are designed. Previous work has highlighted the importance of natural surveillance: a type of surveillance that is a byproduct of how citizens normally and routinely use the environment. However, studying this concept is not a trivial task. Manual approaches such as observation studies are costly and time consuming and have therefore often limited themselves to smaller geographical areas.

In this work, we present a methodology that can automatically provide an estimate of natural surveillance by detecting building openings (i.e. windows and doors) in street level imagery and localizing them in 3 dimensions. The proposed method is able to estimate natural surveillance at the street segment level, while simultaneously being able to gather data on a whole city in a matter of hours. We then apply our method to the city of Amsterdam to analyze the relationship between natural surveillance and urban safety using the Amsterdam Safety Index.

We conclude that our chosen operationalization of natural surveillance (road surveillability and occupant surveillability) is correlated with decreases in high impact crime and nuisance as well as increases in perceived safety. Furthermore we provide evidence for the existence of a threshold after which extra natural surveillance is no longer associated with higher degrees of safety.

Urban safety
Natural surveillance

http://resolver.tudelft.nl/uuid:7741ffe9-84f1-4652-8a7b-a26451ddb8db

https://github.com/timovanasten/natural-surveillance Github repository

Student theses

master thesis

© 2021 Timo van Asten