摘要
本文在数字化和城市大数据的时代背景下,以城市规划与设计的角度探讨适宜步行的城市。我们首先简要回顾历史上的可步行城市设计,然后讨论什么是可步行城市以及可步行城市的空间要素是什么,接着对新兴的主题及其实证方法进行了探讨,以衡量可步行城市的空间和城市设计特征。本文的第一部分着眼于城市设计的关键命题,以及它们是如何被提出以促进步行性。第二部分论述了步行性的概念,这是设计步行城市的基础。我们强调可步行城市的物理元素(走道、邻近用途、空间)和感知元素(安全、舒适、享受),然后我们研究构成可步行城市的各种空间元素。第三部分着眼于可步行城市和社区设计的新兴主题。我们讨论了由不断增长的计算能力以及相关的经验方法和跨学科的合作(包括空间规划、城市设计、城市生态和公共卫生)带来的城市大数据的应用前景。本文旨在为理解城市设计和可步行性提供一个整体的方法,重新评估构建可步行城市的空间要素,并讨论未来的政策干预。
In this paper,we discuss walkable cities from the perspective of urban planning and design in the era of digitalization and urban big data.We start with a brief review on historical walkable cities schemes;followed by a deliberation on what a walkable city is and what the spatial elements of a walkable city are;and a discussion on the emerging themes and empirical methods to measure the spatial and urban design features of a walkable city.The first part of this paper looks at key urban design propositions and how they were proposed to promote walkability.The second part of this paper discusses the concept of walkability,which is fundamental to designing a walkable city.We emphasize both the physical(walkways,adjacent uses,space)and the perceived aspects(safety,comfort,enjoyment),and then we look at the variety of spatial elements constituting a walkable city.The third part of this paper looks at the emerging themes for designing walkable cities and neighborhoods.We discuss the application of urban big data enabled by growing computational powers and related empirical methods and interdisciplinary approaches including spatial planning,urban design,urban ecology,and public health.This paper aims to provide a holistic approach toward understanding of urban design and walkability,re-evaluate the spatial elements to build walkable cities,and discuss future policy interventions.
出处
《国际城市规划》
CSSCI
北大核心
2019年第5期9-15,共7页
Urban Planning International
基金
英国研究与创新部门资助的PEAK城市项目(ES/P01105 5/1)
哈佛全球研究所资助的项目“中国2030/2050:未来的能源和环境挑战”
关键词
可步行城市
步行社区
时空尺度和指标
计算能力
城市大数据
Walkable Cities
Walkable Neighborhoods
Spatial Temporal Scale and Indicators
Computational Power
Urban Big Data