PDF(Pubmed)
Abstract：
The formation of urban districts and the appeal of densely populated areas reflect a spatial equilibrium in which workers migrate to locations with greater urban vitality but diminished environmental qualities. However, the pandemic and associated health concerns have accelerated remote and hybrid work modes, altered people\'s sense of place and appreciation of urban density, and transformed perceptions of desirable places to live and work. This study presents a systematic method for evaluating the trade-offs between perceived urban environmental qualities and urban amenities by analysing post-pandemic urban residence preferences. By evaluating neighbourhood Street View Imagery (SVI) and urban amenity data, such as park sizes, the study collects subjective opinions from surveys on two working conditions (work-from-office or from-home). On this basis, several Machine Learning (ML) models were trained to predict the preference scores for both work modes. In light of the complexity of work-from-home preferences, the results demonstrate that the method predicts work-from-office scores with greater precision. In the post-pandemic era, the research aims to shed light on the development of a valuable instrument for driving and evaluating urban design strategies based on the potential self-organisation of work-life patterns and social profiles in designated neighbourhoods.
摘要：
城市地区的形成和人口稠密地区的吸引力反映了一种空间平衡，即工人迁移到城市活力更强但环境质量下降的地方。然而,大流行和相关的健康问题加速了远程和混合工作模式，改变了人们的位置感和对城市密度的欣赏，并改变了人们对理想生活和工作场所的看法。本研究提供了一种系统的方法，用于通过分析大流行后的城市居住偏好来评估感知的城市环境质量与城市便利设施之间的权衡。通过评估邻里街景图像(SVI)和城市舒适度数据，比如公园的大小，该研究从对两种工作条件（办公室工作或在家工作）的调查中收集主观意见。在此基础上,训练了几个机器学习(ML)模型来预测两种工作模式的偏好得分。鉴于在家工作偏好的复杂性，结果表明，该方法预测办公室工作分数具有更高的精度。在后大流行时代，这项研究旨在阐明开发一种有价值的工具，用于根据指定社区工作生活模式和社会概况的潜在自我组织来驱动和评估城市设计策略。
