建成环境对街道活力的非线性效应——基于XGBoost模型的多源大数据分析

Nonlinear Effect of Built Environment on Street Vitality:A Multi-source Big Data Analysis Based on XGBoost Model

深入认知建成环境对街道活力的影响效应,是街道环境优化与城市更新的基础。但多数研究忽略了二者之间的非线性关系,难以有效指导设计实践。基于多源大数据测度街道活力与建成环境,使用机器学习算法来分析其非线性效应,并针对不同类型街道进行探索。结果表明:1)提升开发强度是促进街道活力的最有效措施;2)建成环境要素对街道活力的影响表现出非线性特征,将其控制在合理范围内,街道活力才会有效提升;3)街道环境要素的综合设置应考虑其交互效应,一个要素的影响会随着另一个要素的变化被放大或缩小;4)老城区、商务片区、工业与区域交通设施周边区域及景观性街道的活力形成机制存在显著差异。相关规律可为街道的精细化设计提供人本尺度的理论参考。

Understanding the influence of built environment on street vitality is the basis of street environment optimization and urban renewal.However,most studies ignore the nonlinear relationship between them,which makes it difficult to effectively guide the design practice.Based on multi-source big data to measure street vitality and built environment,machine learning algorithm is used to analyze the nonlinear effect,and different types of streets are explored.The results show that:1)Enhancing development intensity is the most effective measures to promote street vitality;2)The influence of environmental factors on the street vitality shows nonlinear characteristics.Only by controlling them within reasonable ranges can the street vitality be effectively improved;3)The comprehensive setting of street environment elements should consider their interaction effects,and the influence of one element will be enlarged or reduced with the change of another element;4)There are significant differences in the vitality formation mechanisms among the streets in the old urban area,the business district,the surrounding areas of industrial lands and regional transportation facilities and the landscape streets.Relevant laws can provide a theoretical reference of people-oriented scale for the refined design of streets.