ArticleLandscape and urban planning2021
Predicting Perceptions of the Built Environment using GIS, Satellite and Street View Image Approaches.
Article in Landscape and urban planning, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
11 citing papers in PubMed.
- Deep learning assessment of street spatial quality in old residential communities of Wuchang, Wuhan, China.Scientific reports · 2025Article
- Examining associations between street vegetation and child firearm violence at microgeographic locations in 3 US cities: a case-control study.BMC pediatrics · 2025Article
- Developing Nationwide Estimates of Built Environment Quality Characteristics Using Street-View Imagery and Computer Vision.Environmental science & technology · 2025Article
- Measurement of spatial heterogeneity in street restorative perceptions and street refinement design.Scientific reports · 2025Article
- Geospatial Modeling of Deep Neural Visual Features for Predicting Obesity Prevalence in Missouri: Quantitative Study.JMIR AI · 2024Article
- Assessing the built environment through photographs and its association with obesity in 21 countries: the PURE Study.The Lancet. Global health · 2024Article
- Article
- Measuring Greenspace in Rural Areas for Studies of Birth Outcomes: A Comparison of Street View Data and Satellite Data.GeoHealth · 2024Article
- Assessing the nonlinear impact of green space exposure on psychological stress perception using machine learning and street view images.Frontiers in public health · 2024Article
- Identifying children's environmental health risks, needs, misconceptions, and opportunities for research translation using social media.Exploration of digital health technologies · 2024Article
- Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
backgroundHigh quality built environments are important for human health and wellbeing. Numerous studies have characterized built environment physical features and environmental exposures, but few have examined urban perceptions at geographic scales needed for population-based research. The degree to which urban perceptions are associated with different environmental features, and traditional environmental exposures such as air pollution or urban green space, is largely unknown.
objectiveTo determine built environment factors associated with safety, lively and beauty perceptions across 56 cities.
methodsWe examined perceptions collected in the open source Place Pulse 2.0 dataset, which assigned safety, lively and beauty scores to street view images based on crowd-sourced labelling. We derived built environment measures for the locations of these images (110,000 locations across 56 global cities) using GIS and remote sensing datasets as well as street view imagery features (e.g. trees, cars) using deep learning image segmentation. Linear regression models were developed using Lasso penalized variable selection to predict perceptions based on visible (street level images) and GIS/remote sensing built environment variables.
resultsPopulation density, impervious surface area, major roads, traffic air pollution, tree cover and Normalized Difference Vegetation Index (NDVI) showed statistically significant differences between high and low safety, lively, and beauty perception locations. Visible street level features explained approximately 18% of the variation in safety, lively, and beauty perceptions, compared to 3-10% explained by GIS/remote sensing. Large differences in prediction were seen when modelling between city (R2 67-81%) versus within city (R2 11-13%) perceptions. Important predictor variables included visible accessibility features (e.g. streetlights, benches) and roads for safety, visible plants and buildings for lively, and visible green space and NDVI for beauty.
conclusionSubstantial within and between city differences in built environment perceptions exist, which visible street level features and GIS/remote sensing variables only partly explain. This offers a new research avenue to expand built environment measurement methods to include perceptions in addition to physical features.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.