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多源数据与深度学习支持下的人本城市设计:以上海苏州河两岸城市绿道规划研究为例
叶宇, 黄鎔, 张灵珠
同济大学
摘要:
随着城市化进程步入后半程,高品质人居环境的营造日益受到关注,城市绿道也在此背景下作为空间品质提升的有效途径受到重视。但当前绿道规划更多从自上而下的宏观视角出发,在分析框架中未有效纳入人本尺度的各类空间与行为要素。针对这一情况,整合经典设计理论、多源城市数据与深度学习算法,以苏州河两岸片区为案例探索新数据环境下的精准分析框架,为高密度建成环境下绿道“在哪做”和“怎么做”提供科学化的分析路径。通过街景数据、位置服务数据、兴趣点数据、结构化网页数据、精细化建成环境数据等多源数据的整合,实现了高品质建成环境五要素理论(5D)中划定的密度、多样性、设计、目的地可达性与交通设施距离这5个人本尺度关键要素的系统性测度,结合现有街道的可建设性,进一步基于层次分析法开展选线潜力评估,并绘制各路段特征画像,为绿道选线和断面设计提供精准支持。这一新分析框架是将人本尺度的诸多要素与城市尺度分析相结合的有益尝试,也是对绿道规划设计在分析技术上的有效拓展。经典理论与新数据新技术的深度整合,可为人本导向的城市设计实践提供有力支持。
关键词:  多源城市数据  深度学习  城市绿道  城市设计  上海
DOI:10.14085/j.fjyl.2021.01.0039.07
分类号:TU985.1;TU984.2
基金项目:国家自然科学基金(编号 52078343,52008297,51708410);上海市自然科学基金面上项目(编号 20ZR1462200);高密度人居环境生态与节能教育部重点实验室(同济大学)开放课题(编号2020010102,2020010205)
Human-oriented Urban Design with Support of Multi-source Data and Deep Learning: A Case Study on Urban Greenway Planning of Suzhou Creek, Shanghai
YE Yu, HUANG Rong, ZHANG Lingzhu
Tongji University
Abstract:
As China’s urbanization drive enters the “second half”, increasing attention has been paid to the construction of high-quality living environment. Under this backdrop, urban greenways, an effective means to improve the spatial quality, have received high attention. However, the current greenway planning is mainly organized in a top-down macro perspective, which fails to effectively integrate human-based spatial and behavioral elements into the analysis framework. In this context, this study proposes a data-informed analytical approach combining classical urban design theories, multi-sourced urban data, and deep learning algorithm to compute the “where” and “how” questions of urban greenway planning, with the Suzhou Creek area of Shanghai as an example. According to the classical urban design theories, Cervero and Ewing’s 5Ds, i.e., density, diversity, design, dimensions of destination accessibility, and distance to transit, are selected as key factors. A series of new urban data, including street view images, points of interest (POI), location-based services (LBS) positioning data, structured web data and built environment data, are applied together with deep learning algorithms and geographical information system (GIS) tools to measure these key factors within the human-oriented resolution. Combining the constructability of existing streets, the suitability analysis of urban greenways is achieved via analytic hierarchy process and the street portraits are generated to assist detailed design decisions. This analytical approach is an attempt to add human-oriented concerns within a city-wide scale into the greenway planning. It also contributes to pushing the methodological boundaries of greenway planning by combining classical urban design thinking with new urban data and new techniques, which helps to assist human-oriented urban design practices.
Key words:  multi-source urban data  deep learning  urban greenway  urban design  Shanghai
引用本文:叶宇,黄鎔,张灵珠.多源数据与深度学习支持下的人本城市设计:以上海苏州河两岸城市绿道规划研究为例[J].风景园林,2021,28(1):39-45.
YE Yu,HUANG Rong,ZHANG Lingzhu.Human-oriented Urban Design with Support of Multi-source Data and Deep Learning: A Case Study on Urban Greenway Planning of Suzhou Creek, Shanghai[J].Landscape Architecture Journal, 2021, 28(1):39-45.