Streetscore - Predicting the Perceived Safety of One Million Streetscapes

Nikhil Naik, Jade Philipoom, Ramesh Raskar, Cesar A. Hidalgo


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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