Streetscore -- Predicting the Perceived Safety of One Million Streetscapes

By Nikhil Naik, Jade Philipoom, Ramesh Raskar, César A. Hidalgo. 2014

450 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition, Epidemiology, Media Technology, Social Sciences & Humanities

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Citations in the corpus

Closed-Form Factorization of Latent Semantics in GANs (2020, background): “objects (LSUN [28]), streetscapes [20], and ImageNet [6].”
Subjective and Objective Measures of Streetscape Perceptions: Relationships with Property Value in Shanghai (2021, methodology): “The global importance of individual variables were tested with Scikit-learn library in Python.”
Deep Learning the City: Quantifying Urban Perception at a Global Scale (2016, methodology): “The Streetscore [11] algorithm was able to successfully generalize to the Northeast and Midwest of the U.S., based on training data from just two cities, New York and Boston.”
Analyzing the correlation between visual space and residents' psychology in Wuhan, China using street-view images and deep-learning technique (2021, unknown): “Naik et al. [75] also found that well-closed streets give people the impression of high safety, thus providing residents with opportunities to interact with the society.”
Using deep learning and Google Street View to estimate the demographic makeup of neighborhoods across the United States (2017, background): “(7) used publicly available imagery to quantify people’s subjective perceptions of a neighborhood’s physical appearance.”
Street view imagery in urban analytics and GIS: A review (2021, background): “Thanks to the wide coverage and fine spatial sampling of various SVI providers, comparative studies among cities around the world and the creation of indicators and indexes to rank them have also emerged (Naik, Philipoom, Raskar, & Hidalgo, 2014; Li et al., 2015; Long & Liu, 2017).”
Fine-Grained Car Detection for Visual Census Estimation (2017, methodology): “Similarly, works from Zhou et al, Ordonez et al, Naik et al and Arietta et al use global image features from Google Street View images to learn various neighborhood characteristics (Zhou et al. 2014; Ordonez and Berg 2014; Naik et al. 2014; Arietta et al. 2014).”