What causes the spatiotemporal disparities in greenway use intensity? evidence from the central urban area of Beijing, China

Abstract

The reasonable layout of green sports space has positive significance for promoting residents’ outdoor sports. Existing studies rarely use large-scale trajectory data to analyze urban greenway physical activity; they mostly use on-site research and questionnaires to conduct research. This study, using central Beijing as a case study, identified the spatiotemporal relationships of greenway use intensity with the social, economic, and built environment. A multiscale geographically weighted regression (MGWR) model was used to explore the temporal heterogeneity of the factors, compensating for the lack of cross-sectional data in existing studies. The results demonstrated significant differences in the spatiotemporal patterns among the core, central and inner suburban areas, and they revealed residents’ preferences in different periods. The MGWR model indicated that there were obvious scale differences and spatial heterogeneity in the greenway use intensity among social, economic, and built environment factors. Housing prices, residential density, historical resource density, and greening quality of greenways contributed to greenway use intensity, which was weak in the core area and strong in the inner suburban area. The reason for the difference in the temporal dimension was related to commuting and the separation of workplaces and residences. The layout of the greenway should be combined with rivers, historical resources, and green space to improve its attractiveness. This paper improved the spatial awareness of greenway use and facilitated the ability of urban planners to generate appropriate strategies to improve land use efficiency in different regions and promote green spatial justice to meet the needs of green, healthy cities.

Meta-analysis on Associations Between the Built Environment and Mobile Physical Activity Using Volunteered Geographic Information

“…力活动的积极性和环境吸引力。土地利用混 合程度、建筑密度等土地利用与建筑环境因 素也对体力活动有显著影响 [9] 。2)社会环境 方面，人口密集的街区环境为社交活动提供 更多机会，同时更有利于促进移动型体力活 动 [7,10] ；社会凝聚力和社区治安也会显著影响 移动型体力活动行为的执行 [11] 。3)社会经济 属性对街区环境与体力活动的关系具有调节 作用，即不同性别、年龄、教育水平、收入 水平的群体对街区环境的使用需求和偏好不 同 [6] ，导致街区环境对不同人群的主动健康干 预方式存在一定差异。 1.2 自发地理信息为建成环境体力活动使 用效能评价提供新的可能 自 发 地 理 信 息( volunteered geographic information, VGI)是指个体通过移动互联设备 自发贡献的地理空间信息，具有数据来源广、 获取方式便捷、时效性高、空间定位准确等 特点。VGI被广泛应用于运动科学 [12] 、自然灾 害管理 [13] 、景观偏好 [14] 、交通运输 [15] 以及健康 环境 [16] 等领域。近年来，VGI也开始被应用 于城市建成环境和体力活动的关联研究。刘 堃等利用VGI获取体力活动数据，分析其与 城市绿道网络特征的关联，发现绿道网络越 发达，体力活动的多样性及使用效能越高 [7] 。 VGI也被应用于捕捉体力活动的时空分异特 征，进而测度不同街区环境的使用效能 [17][18] 。 [22] 。本研究的荟萃 分析方法严格遵循Saelens和Handy的荟萃分 析方法 [23] ，已有研究证明了该方法的科学性与 可靠性 [24] 。 McArthur等 [46] 城市 苏格兰格拉斯哥 [54] 。此外，相比于非自然 环境，在亲水空间和公园绿地等亲自然环境 中开展体力活动的疗愈效果更明显 [55] ；城市绿 地可通过减轻环境污染、提升环境质量等中 介作用来促进体力活动积极性 [56] 。因此，后续 研究可进一步对"自然环境-环境污染-体 力活动"的影响机制进行探讨，明确自然环 境影响体力活动的中介效应。 交通环境中，公交站点密度、道路密度、 人行道密度等影响可达性的交通环境指标均 与各类移动型体力活动存在一致的显著正相 关关系。移动型体力活动的空间范围与城市 道路网络高度复合，因此完善的交通基础设 施能显著激发体力活动意愿 [53] ，例如高品质的 步行空间是促进步行、减少机动车依赖、鼓 励健康出行的必要条件 [57] ，便捷的公共交通有 [58] ，但数据来源及样本代表性仍 受到质疑。例如，并非所有公园或绿道访客 都使用智能手机或体育应用程序 [17] ；与同区域 其他使用者相比，在健身平台上自愿发布地 理信息的人可能更年轻并且更喜欢体育活动 [59] ， 因此容易造成数据样本偏差的问题。此外， 不同体育应用程序(如悦跑圈、Keep、Strava、…”

Exploring the spatial-temporal distribution and push-pull factors of green exercise flow

“…The most important source of big data is mobile exercise application, which obtains a large amount of spatial behavior data of users by recording precise details such as location, time, speed and distance (28, 29). Green exercise studies using exercise application data focus on physical activity on street green paths (30,31) or a particular exercise activity (running and hiking) (29, 32). So far, few people have used large sample data and studied green exercise to explore when and where it ows.…”