Novel modelling to improve decision-making for neighbourhood design to reduce chronic disease risk

可能支持宜居性和健康的城市设计研究（20分钟社区概念）使用的分析方法没有考虑到城市环境的复杂性。本研究创新性地使用了一种灵活且适用的方法（贝叶斯网络）来显示哪些邻域特征运行独特或不独特，哪些特征可以优先考虑，哪些特征具有成本效益，以及每个特征需要多少才能实现降低慢性病风险的改进。

Research on urban design that might support liveability and health (the 20-minute neighbourhood concept) has used analytic methods that do not account for the complexity of urban environments. This study innovatively uses a flexible and applicable approach (Bayesian Networks) to show where neighbourhood features operate uniquely or not, which features can be prioritised, which are cost effective, and how much of each feature is needed to achieve improvement in reducing risk of chronic disease.