摘 要以上海衡复历史街区为例, 探讨基于数据自适应的慢行系统研究与设计模式. 衡复历史街区面临着转型更新的需求, 本研究以慢行系统为切入点, 首先结合多维的数据对基地现状问题进行挖掘并从上位提出规划引导; 其次结合大数据进行街道慢行指数评分, 依据评估结果分析各类街道特征, 概括空间组织模式, 将现状街道划分为维持并优化现有特征的 A 类街道及根据数据测度动态调整的 B 类街道; 再者基于街道特征, 搭建测度空间数据的动态反馈平台; 最后对 A/B 类不同空间模式的街道提出有针对性的空间设计导则. Abstract

This paper discusses the paradigm for data adaptive urban design of pedestrian walking system in Hengfu historic district of Shanghai. Hengfu historical district is in urgent trend for functional promotion. Firstly, focused on slow traffic system, the quantitative evaluation is conducted based on big data analysis, integrated with traditional qualitative analysis. And the planning guidelines are proposed accordingly. Secondly, a walk score system is formulated and the spatial prototype features of different kinds of streets are generalized. The existing streets are divided into basically two categories: type A which would strengthen existing historic features in the future, and type B which would dynamically adapt the features according to big data feedbacks. Thirdly, design guidelines are proposed for spatial quality enhancement. The big data infrastructure is planted and integrated with street context, with clear sets of spatial data collection methodology. And the cloud platform including big data analysis, APP development, and spatial sensors for data-adaptive regeneration of historic district is established both for dynamical spatial evaluation and information guidance for government, planners, citizens and enterprises. 关键词