城市绿地定量研究的八个思考:制图-监测-建模-管理(4M)视角

IF 0.6 0 ARCHITECTURE
Chen Bin, C. Webster
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引用次数: 0

摘要

绿地是城市环境的重要组成部分,为我们的社会经济文化活动提供了可观的生态系统服务。旨在捕捉绿色空间供应、供应和需求、衡量可用性、可达性和可见性的指标已被广泛采用,以衡量从地方到区域范围内实现可持续发展目标的进展情况。在这篇文章中,我们对城市绿地的定量研究提出了八项思考,以供景观设计和规划中的制图、监测、建模和管理(4M)实践之用。本文的目的是在将数据转化为干预措施的过程中激发新鲜和创新的思维。提出八个观点:1)绿地映射应具有数量、质量、类型和结构等多属性概念模型;2)绿地制图的来源、方法和用途因定义、途径和尺度而异;物候改变了城市绿地的季节性质量和数量;4)时空绿地数据立方体有助于实现城市绿地变化近实时监测的目标;5)绿地覆盖度揭示了绿地供给,而绿地暴露度通过构建人-绿地供需关系模型可以捕捉有效需求;6)绿地暴露措施应考虑到空间、时间和社会差异;7)景观设计师和规划师的绿化优化应考虑生物物理、生物多样性和健康效益;城市绿地的管理应着眼长远。最后,我们提倡数据科学决策支持系统,可以帮助指导和促进城市绿地的4M实践。这些反思对城市绿色景观设计、规划和管理的研究、实践和理论具有广泛的意义,并共同构成了一套原则,可以指导科学家、政策制定者和实践者制定城市绿地最佳4M策略
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Eight Reflections on Quantitative Studies of Urban Green Space: A Mapping-Monitoring-Modeling-Management (4M) Perspective
Green space is an important component in urban environment, providing considerable ecosystem services to our socio-economic-cultural activities. Metrics designed to capture green space provision, supply and demand, measuring availability, accessibility, and visibility have been widely adopted to gauge progress toward achieving sustainable development goals from local to regional scales. In this article, we offer eight reflections on quantitative studies of urban green space for mapping, monitoring, modeling, and management (4M) practices in landscape design and planning. The article’s objective is to stimulate fresh and innovative thinking in the conversion of data to interventions. Eight points are made: 1) Green space mapping should be characterized in a multi-attribute conceptual model, including quantity, quality, type, and structure; 2) green space mapping sources, methods, and uses vary by definitions, approaches, and scales; 3) phenology modifies seasonal quality and quantity of urban green space; 4) spatial and temporal green space data cubes will help realize the goal of near real-time monitoring of urban green space change; 5) green space coverage reveals green space supply, but green space exposure can capture effective demand via modeling the supply–demand relationships of human–green space; 6) green space exposure measures should account for spatial, temporal, and social differences; 7) greening optimization by landscape architects and planners should consider both biophysical, biodiversity, and health benefits; and 8) urban green space management should be strategized with a long-term view. Finally, we advocate data–science–decision support systems that can help guide and promote 4M practices of urban green space. These points of reflection have broad implications for research, practice, and theory of urban green landscape design, planning, and management, and altogether constitute a set of principles that can guide scientists, policy makers, and practitioners toward strategizing optimal 4M of urban green space
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