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City-Wide Perceptions of Neighbourhood Quality using Street View Images
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The interactions of individuals with city neighbourhoods is determined, in part, by the perceived quality of urban environments. Perceived neighbourhood quality is a core component of urban vitality, influencing social cohesion, sense of community, safety, activity and mental health of residents. Large-scale assessment of perceptions of neighbourhood quality was pioneered by the Place Pulse projects. Researchers demonstrated the efficacy of crowd-sourcing perception ratings of image pairs across 56 cities and training a model to predict perceptions from street-view images. Variation across cities may limit Place Pulse's usefulness for assessing within-city perceptions. In this paper, we set forth a protocol for city-specific dataset collection for the perception: 'On which street would you prefer to walk?'. This paper describes our methodology, based in London, including collection of images and ratings, web development, model training and mapping. Assessment of within-city perceptions of neighbourhoods can identify inequities, inform planning priorities, and identify temporal dynamics. Code available: https://emilymuller1991.github.io/urban-perceptions/.
Forward citations
Cited by 2 Pith papers
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Artifacts of Idiosyncracy in Global Street View Data
Street view coverage deviates from uniform road coverage in most of the 28 studied cities, and binary coverage percentages do not reveal these distributional biases.
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ZenSVI: An Open-Source Software for the Integrated Acquisition, Processing and Analysis of Street View Imagery Towards Scalable Urban Science
ZenSVI provides an integrated, documented Python pipeline for acquiring, cleaning, analyzing, and visualizing street view imagery for urban science.
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