可比性
选择(遗传算法)
计算机科学
缓冲器(光纤)
选择偏差
风险分析(工程)
数据科学
缓冲区
管理科学
运筹学
质量(理念)
作者
Mohammad Javad Zare Sakhvidi,Matthew H. E. M. Browning,Karl Samuelsson,S.M. Labib,Achilleas Psyllidis,Adeladza Kofi Amegah,Thomas Astell‐Burt,Albert Bach,Michael Jerrett,Gregory N. Bratman,Matilda Annerstedt van den Bosch,Kees de Hoogh,Sjerp de Vries,Angel Mario Dzhambov,Rohollah Fallah Madvari,Xiaoqi Feng,Amanda Paula Fernandes,Elaine Fuertes,Vincenzo Giannico,Nélson Gouveia
标识
DOI:10.1016/j.lanplh.2025.101370
摘要
Greenspace can promote health via diverse pathways. A common approach to assessing greenspace exposure is to estimate vegetation availability within buffers surrounding locations where people reside or spend time. However, no clear framework for informed buffer selection exists, and choices made show considerable heterogeneity, impeding evidence synthesis and causal inference. In this Personal View conducted by an interdisciplinary panel of experts, we aimed to establish a framework for informed buffer selection for epidemiological studies on greenspace. We began by reviewing available approaches for the selection of buffer types, which range from single fixed-location approaches to high-resolution mobility-based activity-space approaches, as well as different buffer sizes. We then summarised the determinants of buffer type and size selection including health outcomes and underlying mechanisms, study population, contextual factors, and data characteristics. Finally, based on these determinants, we developed recommendations for future research. Buffer type and size selection should be hypothesis driven, reflecting presumed greenspace-health mechanisms. Buffer selection should target activity-based approaches where feasible, and multiple buffer sizes should be tested. Overall, the assessment of greenspace exposure should shift from ad-hoc approaches to personalised, multiscale, and context-specific methods. We call for standardising and reporting the rationale for buffer selection to minimise bias and enhance comparability and evidence synthesis across studies.
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