摘要
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.