Streetscore -- Predicting the Perceived Safety of One Million Streetscapes

Nikhil Naik · Jade Philipoom · Ramesh Raskar · César A. Hidalgo

511 citations0/0 files storedIEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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Urban Design and Spatial Analysis Crime Patterns and Interventions Image Retrieval and Classification Techniques

Abstract

Social science literature has shown a strong connection between the visual appearance of a city's neighborhoods and the behavior and health of its citizens. Yet, this research is limited by the lack of methods that can be used to quantify the appearance of streetscapes across cities or at high enough spatial resolutions. In this paper, we describe 'Streetscore', a scene understanding algorithm that predicts the perceived safety of a streetscape, using training data from an online survey with contributions from more than 7000 participants. We first study the predictive power of commonly used image features using support vector regression, finding that Geometric Texton and Color Histograms along with GIST are the best performers when it comes to predict the perceived safety of a streetscape. Using Streetscore, we create high resolution maps of perceived safety for 21 cities in the Northeast and Midwest of the United States at a resolution of 200 images/square mile, scoring ~1 million images from Google Streetview. These datasets should be useful for urban planners, economists and social scientists looking to explain the social and economic consequences of urban perception.

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References

2008The Spreading of Disorder 2013The Collaborative Image of The City: Mapping the Inequality of Urban Perception 1990The Evaluative Image of the City 2003Neighborhood Physical Conditions and Health 2013Developing and testing a street audit tool using Google Street View to measure environmental supportiveness for physical activity

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2018Measuring daily accessed street greenery: A human-scale approach for informing better urban planning practices 2018Measuring visual quality of street space and its temporal variation: Methodology and its application in the Hutong area in Beijing 2021Measuring human perceptions of streetscapes to better inform urban renewal: A perspective of scene semantic parsing 2020A review of urban physical environment sensing using street view imagery in public health studies

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