间隙之间:基于三维可达性的“纳凉网络”设计研究

Between the In-between:A Design Study of“Cool Network”Based on 3D Accessibility

当前一些大城市针对城市热岛效应制定的相关政策与规划策略对城市中公共空间的访问行为、市民步行行为、高密度城市的立体化特征等尚欠考虑。以香港中西区和湾仔区的小型公共空间作为立体化城市设计的极端案例,基于认知寻路机制、结合三维空间设计网络分析(sDNA)技术对公共空间的可达性与流量潜力进行测算,并利用无监督机器学习,探索公共空间的大小、地理位置可见性和设计品质之间的关系,发现其设计品质随着面积增加与可见性的提高而提升。结合小型空间分布、城市通风及行人使用最频繁的日常路线,为湾仔分区勾勒出一幅“纳凉网络”设计图,使其可以服务于不同年龄层、不同职业的各个群体。对公共空间进行定量和定性分析,探讨步行可达的复杂性,提出具有逻辑的空间设计干预方法,从而增加公共空间的使用频率与使用群体,同时为高密度城市公共空间规划与设计实践提供有力支持。

Some major cities have formulated relevant policies and planning strategies targeting at urban heat island effect.However,such policies and strategies seldom consider public space visiting behavior,citizen walking behavior and three-dimensional(3D)characteristic of high-density cities.Taking the small public spaces in the Central and Western District and Wan Chai District in Hong Kong as an extreme example of volumetric urban design,this research uses 3D spatial design network analysis(3D sDNA)to measure the accessibility and traffic potential of public spaces based on a cognitive pathfinding mechanism.Additionally,by virtue of unsupervised machine learning,the research investigates the relationships between such factors as size,location visibility and design quality of the public spaces in Hong Kong,finding that the design quality of such public spaces improves with the increase of their area and visibility.In combination with small public spaces,urban ventilation and daily routes most frequently used by pedestrians,the research outlines a design sketch for a proposed“cool network”in Wan Chai,with a view to enabling the“cool network”to serve all age groups with different occupations therein.Through both quantitative and qualitative discussions,the research explores the complexity of accessibility for pedestrians based on which a logical space design intervention method was proposed,to increase both the usage frequency and the coverage of user groups of public spaces while providing a powerful support for planning and design practice regarding public spaces in high-density cities.