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基于轨迹大数据的城市公园绿地使用分析:来自中国一个中等城市的经验

Urban park green space use analysis based on trajectory big data: Experience from a medium-sized city in China.

作者信息

Xu Shuna, Yuan Shengyuan, Li Jingzhong, Gao Xin, Hu Jinhua

机构信息

College of Urban and Environmental Sciences, Xuchang University, 88 Bayi Road, Xuchang, Henan Province, China.

出版信息

Heliyon. 2024 Feb 19;10(4):e26445. doi: 10.1016/j.heliyon.2024.e26445. eCollection 2024 Feb 29.

DOI:10.1016/j.heliyon.2024.e26445
PMID:38420409
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10900791/
Abstract

Regular visits to park green space offer remarkable benefits for the physical and mental health of urban residents. Achieving a comprehensive understanding of the utilization across the entire city is a prerequisite for improving the overall utilization rate of park green spaces. Traditional social survey methods are limited by their sample size and time-consuming nature, while methods based on geographic location big data are gaining momentum. This study focuses on Xuchang, a medium-sized city in China, and systematically analyzes the current state and influencing factors of park green space utilization by mining GPS trajectory big data from April 3 to 12, 2022. Results indicate that residents' choices of park green spaces are highly diverse. Approximately 20% of visitors on holidays and weekends, and about 25% of visitors on weekdays, prefer the park green space closest to their homes. Notably, the distance threshold for park green space visits on weekdays, weekends, and holidays is 3633, 3824, and 4127 m, respectively. These distances are significantly higher than the several hundred meters specified in planning documents or commonly used in accessibility analyses. For individuals who frequently visit park green spaces, distance is the most critical influencing factor. Conversely, for those who occasionally visit, distance is not the primary consideration. For individuals who rarely or never visit park green spaces, personal attitudes play an essential role. In comparison to weekdays, the number of visitors on holidays and weekends is larger, the travel distance is longer, and they are more inclined to choose larger parks. Visits are concentrated in the afternoon and evening, and weather changes remarkably affect park green space utilization. Importantly, no compensatory effect is observed between the frequency and duration of park green space visits. These findings hold important implications for urban planning, management, and the promotion of park green space utilization.

摘要

定期前往城市公园绿地对城市居民的身心健康具有显著益处。全面了解整个城市的公园绿地利用情况是提高公园绿地整体利用率的前提条件。传统的社会调查方法受样本量和耗时的限制,而基于地理位置大数据的方法正逐渐兴起。本研究聚焦于中国的中型城市许昌,通过挖掘2022年4月3日至12日的GPS轨迹大数据,系统分析了公园绿地利用的现状及影响因素。结果表明,居民对公园绿地的选择具有高度多样性。节假日和周末约20%的游客,工作日约25%的游客更喜欢选择离家最近的公园绿地。值得注意的是,工作日、周末和节假日公园绿地的访问距离阈值分别为3633米、3824米和4127米。这些距离明显高于规划文件中规定的几百米或可达性分析中常用的距离。对于经常访问公园绿地的个人来说,距离是最关键的影响因素。相反,对于偶尔访问的人来说,距离不是主要考虑因素。对于很少或从不访问公园绿地的个人来说,个人态度起着至关重要的作用。与工作日相比,节假日和周末的游客数量更多,出行距离更长,他们更倾向于选择较大的公园。访问集中在下午和晚上,天气变化对公园绿地的利用有显著影响。重要的是,未观察到公园绿地访问频率和时长之间的补偿效应。这些发现对城市规划、管理以及促进公园绿地利用具有重要意义。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9840/10900791/4138d968bc39/gr7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9840/10900791/d5414dca52e3/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9840/10900791/7387ab8a51c2/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9840/10900791/0d93e13dbec1/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9840/10900791/1992c611d6c3/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9840/10900791/a3faaae743fd/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9840/10900791/05d2c4084f73/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9840/10900791/4138d968bc39/gr7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9840/10900791/d5414dca52e3/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9840/10900791/7387ab8a51c2/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9840/10900791/0d93e13dbec1/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9840/10900791/1992c611d6c3/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9840/10900791/a3faaae743fd/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9840/10900791/05d2c4084f73/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9840/10900791/4138d968bc39/gr7.jpg

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