公共空间品质的经济效益测度与城市设计导控:基于多源数据与机器学习

Economic Benefit Measurement for Public Space Quality and Urban Design Guidance:Based on Multi-Source Data and Machine Learning

在整体建成环境步入精细化发展的阶段,城市品质提升效益的量化需求日益显著。本文运用多源城市数据拓展经典的Hedonic特征价格模型,分析空间品质各要素对房屋价格的影响权重,并进一步通过机器学习模型明确各品质要素提升的有效范围,便于更精准的城市设计导控。本研究通过空间品质相关变量经济效益的研究,突破了传统城市设计理论偏重于空间美学与社会效益的评价标准,使经济效益成为城市设计评价的新方向,是面向设计科学新范式的有益尝试。

As the built environment moves towards a new refined stage of development,there is an increasing requirement to quantify the benefits of urban quality enhancement.This paper extends the Hedonic characteristic price model using multi-source urban data to analyse the influence of each spatial quality element on housing prices,and further calculates the effective range of each quality feature using a machine learning model to facilitate accurate urban design guidance.Through the quantification of the economic benefits of spatial quality-related variables,this research makes economic benefits a new direction for urban design evaluation,rather than the traditional evaluation criteria focused on spatial aesthetics and social benefits.It is a useful attempt toward a new paradigm in design science.