摘要
中国风景园林行业的研究和实践对象正在由传统绿地向多元复合的城市公共空间系统转变,公共空间体系构建将成为新时代城市绿色发展的有效手段。在综合研判学科背景、研究内容及方法的基础上,从社会经济、规划与地理、景观生态、物质空间、量化分析维度对城市公共空间相关研究进行概述,进而提出基于多学科视角的公共空间研究谱系。通过系统梳理60年来国内外城市公共空间领域的学术成果与最新进展,有助于理解城市公共空间研究的脉络及价值,完善现有城市公共空间研究架构。最后指出当前研究中数字化技术应用成为热点,数据挖掘和人工智能算法等前沿领域值得关注;城市公共空间研究的学科壁垒需要突破,应基于城市语境审视公共空间,挖掘更多空间现象背后的动力机制。
The research and practice object of Chinese landscape architecture industry is changing from the traditional green space to a multi-composite urban public space system.The improvement of the public space system will become an effective means of urban green development in the new era.On the basis of comprehensive analysis of the discipline background, research contents and methods, this study summarizes the relevant researches of urban public space from the dimensions of social economy, planning & geography, landscape ecology, physical space and quantitative analysis, and further puts forward the pedigree of public space researches based on the multidisciplinary perspective.It is conducive to understand the context, value and significance of urban public space researches, enrich and expand the research framework of urban public space by systematically reviewing the academic achievements and the latest progress of urban public space researches at home and abroad over the past 60 years.Finally, the study points out that the application of digital technology has become a hot spot in the current researches, and the frontier areas such as data mining and artificial intelligence algorithm are worthy of attention;the discipline barriers of urban public space researches need to be broken, and the public space should be examined based on the urban context, to further explore the dynamic mechanism behind the space phenomenon.
作者
徐宁
XU Ning(Department of Landscape Architecture,School of Architecture,Southeast University)
出处
《风景园林》
2021年第4期52-57,共6页
Landscape Architecture
基金
国家重点研发计划课题(编号2019YFD1100405)
国家自然科学基金(编号51978147)。
关键词
城市公共空间
公园城市
公共性
可达公平
公共健康
环境行为
时空大数据
urban public space
park city
publicity
accessible equity
public health
environmental behavior
spatial-temporal big data