Although many studies have explored the correlations between mobility intervention policies and park use during COVID-19, only a few have used causal inference approaches to assessing the policy’s treatment effects and how such effects vary across park features and surrounding built environments. In this study, we develop an interrupted time-series quasi-experimental design based on three-month mobile phone big data to infer the causal effects of mobility intervention policies on park visits in Shenzhen, including the first-level response (FLR) and return-to-work (RTW) order. The results show that the FLR caused an abrupt decline of 2.21 daily visits per park, with a gradual reduction rate of 0.54 per day, whereas the RTW order helped recover park visits with an immediate increase of 2.20 daily visits and a gradual growth rate of 0.94 visits per day. The results also show that the impact of COVID-19 on park visits exhibited social and spatial heterogeneities: the mobility-reduction effect was smaller in low-level parks (e.g., community-level parks) with small sizes but without sports facilities and water scenes, whereas parks surrounded by compact neighborhoods and land use were more impacted by the pandemic. These findings provide planners with important insights into resilient green space and sustainable neighborhood planning for the post-COVID era.

尽管许多研究探讨了 COVID-19 期间出行干预政策与公园使用之间的相关性，但只有少数研究使用因果推理方法来评估政策的治疗效果以及这些效果如何因公园特征和周围建筑环境而异。在这项研究中，我们开发了一个基于三个月手机大数据的中断时间序列准实验设计，以推断出行干预政策对深圳公园访问的因果影响，包括第一级反应（FLR）和返回上班 (RTW) 订单。结果显示，FLR 导致每个公园的每日访问量突然下降 2.21 次，并以每天 0.54 次的速度逐渐减少，而 RTW 订单帮助恢复了公园访问量，立即增加了 2.20 次每日访问量，并逐渐以 0 的速度增长. 每天 94 次访问。结果还表明，COVID-19 对公园参观的影响表现出社会和空间异质性：在规模小但没有运动设施和水景的低级别公园（例如，社区级公园）中，流动性降低的影响较小，而被紧凑型社区和土地使用包围的公园受大流行病的影响更大。这些发现为规划者提供了有关后 COVID 时代弹性绿地和可持续社区规划的重要见解。而被紧凑型社区和土地使用包围的公园受大流行的影响更大。这些发现为规划者提供了有关后 COVID 时代弹性绿地和可持续社区规划的重要见解。而被紧凑型社区和土地使用包围的公园受大流行的影响更大。这些发现为规划者提供了有关后 COVID 时代弹性绿地和可持续社区规划的重要见解。