Background

High 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.

Objective

To determine built environment factors associated with safety, lively and beauty perceptions across 56 cities.

Methods

We 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.

Results

Population 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.

Conclusion

Substantial 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.

中文翻译：

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使用 GIS、卫星和街景图像方法预测建筑环境的感知

背景

高质量的建筑环境对人类健康和福祉非常重要。许多研究已经对建筑环境的物理特征和环境暴露进行了表征，但很少有研究在基于人口的研究所需的地理尺度上检查城市感知。城市感知与不同环境特征以及空气污染或城市绿地等传统环境暴露的关联程度在很大程度上是未知的。

客观的

确定与 56 个城市的安全、活力和美感相关的建筑环境因素。

方法

我们检查了在开源 Place Pulse 2.0 数据集中收集的看法，该数据集根据众包标签为街景图像分配了安全、生动和美丽的分数。我们使用 GIS 和遥感数据集以及使用深度学习图像分割的街景图像特征（例如树木、汽车）为这些图像的位置（全球 56 个城市的 110,000 个位置）导出了建筑环境测量值。使用 Lasso 惩罚变量选择开发线性回归模型，以预测基于可见（街道图像）和 GIS/遥感构建环境变量的感知。

结果

人口密度、不透水面积、主要道路、交通空气污染、树木覆盖率和归一化差异植被指数 (NDVI) 在安全性、热闹性和美感感知位置之间的高低差异具有统计学意义。可见的街道特征解释了大约 18% 的安全、热闹和美感变化，而 GIS/遥感解释了 3–10%。在对城市 (R2 67–81%) 与城市内部 (R2 11–13%) 的认知进行建模时，可以看到预测存在很大差异。重要的预测变量包括可见的可达性特征（例如路灯、长椅）和安全道路、可见的植物和建筑物以生动、可见的绿地和美丽的 NDVI。

结论

城市内部和城市之间在建筑环境感知方面存在显着差异，可见的街道特征和 GIS/遥感变量只能部分解释。这提供了一个新的研究途径来扩展建筑环境测量方法，以包括除了物理特征之外的感知。