长株潭城市群居民步行锻炼去哪儿？
注：本文为删减版，不可直接引用。原中英文全文刊发于《景观设计学》（Landscape Architecture Frontiers）2023年第1期“城市森林与全球气候变暖”。获取全文免费下载链接请点击https://journal.hep.com.cn/laf/EN/10.15302/J-LAF-1-020075；参考引用格式见文末。
导 读
本文以中国长株潭城市群绿色基础设施与居民步行体力活动的频率和强度为研究对象，分析步行体力活动的空间分布特征，并运用多元线性回归模型分析城市群绿色基础设施对居民步行体力活动频率和强度的影响机制。研究发现，长株潭城市群居民的步行体力活动轨迹主要分布在绿色基础设施的连接廊道和小型场地中。绿色基础设施的内部环境、外部环境与空间格局均对居民步行体力活动存在不同程度的影响。最后，本文提出城市群绿色基础设施的建设及更新策略，以期改善居民步行体力活动环境，充分发挥城市群绿色基础设施的生态和社会价值。
关键词
绿色基础设施；步行体力活动；影响机制；长株潭城市群；空间格局
中国长株潭城市群绿色基础设施对步行体力活动的影响机制研究
Research of the Influence Mechanisms of Green Infrastructure on Walking Physical Activities in Changsha–Zhuzhou–Xiangtan Urban Agglomeration, China
作 者
李博，欧阳浩，刘秋宏
中南大学建筑与艺术学院
01 引言
步行体力活动是降低慢性疾病发生率、改善居民健康和提高生活质量的重要途经之一。绿色基础设施（green infrastructure，以下简称GI）是一种由自然区域和其他开放空间组成的相互连接的网络及其附带的工程设施。GI的构成要素为网络中心、连接廊道和小型场地，是可供步行体力活动的主要场所。现有对GI体系的系统性研究较少探讨其对步行体力活动的影响。相关研究还需探索GI整体格局与步行体力活动的相关性，以及进一步综合分析GI对运动频率和强度的影响差异和影响机制。
城市群GI将是保障大部分城乡居民进行步行体力活动的基本设施。为了进一步理解城市群GI和步行体力活动之间的关系，本文对中国长株潭城市群GI和居民步行体力活动展开研究。通过构建多元线性回归模型进行差异分析，探索城市群GI对步行体力活动频率和强度的影响机制，并制定有利于促进居民步行运动和健康城市环境建设的策略，以期提升城市居民福祉。
在城市公园中进行步行体力活动的人们 © 欧阳浩
02 研究方法
研究方法
本文研究区域为湖南省长株潭城市群（长沙市、株洲市、湘潭市）的中心城区，包括长沙市的天心区、芙蓉区、开福区、雨花区和岳麓区；株洲市的天元区、芦淞区、石峰区和荷塘区；以及湘潭市的岳塘区和雨湖区。
数据来源
研究所用的步行体力活动数据来源于“多锐运动”APP上记录的2016~2019年的运动数据在数据采集期间，研究区域的用户量为3785人。采集到的步行轨迹数据的内容包含空间位置、运动类型、运动时长、运动距离、运动日期和运动频率等信息。
本研究中的城市用地分类数据采用长株潭相关城市总体规划数据。路网数据基于由“开放街道地图”获取到的数据进行分类处理。2016~2020年日平均降水和日平均气温数据来源于国家气象信息中心。归一化植被指数（NDVI）数据来源于USGS网站2019年12月Landsat 8数据。房价数据通过获取安居客、链家和房天下三个房地产平台2016~2019年的月度数据，并进行去重处理后计算平均值得出。人口数据来源于第七次全国人口普查统计数据。最终，通过高德地图爬取2019年12月的各类POI数据，并进行纠偏与清洗。
分析方法
城市群GI的划定
已被广泛认可和应用的GI范围的划定方法一般包括确定GI的目标和定位，确定其构成要素（即网络中心、连接廊道和小型场地），以及识别网络格局三个步骤。
网络中心是指较少受到外界干扰、面积较大的自然栖息地斑块，包括处于原生状态的土地、生态保护区、郊野公园、森林、湖泊、湿地、农田、牧场和林地等。连接廊道是指线性的、连接网络中心和小型场地的生态廊道，主要包括河流和城市道路周边，以及防护绿带等带状绿地。小型场地是对网络中心和连接廊道的补充，为人们提供兼具生态和社会价值的休闲场地，主要包括小型城市公园、广场、街旁绿地、社区公园等。
网格单元划分
本文中格网尺度的选择主要依据人均步行10分钟的运动距离（800m）来判定。通过ArcGIS 10.6对研究区构建800m×800m的格网体系，并筛选出1436个包含城市群GI的网格单元作为研究样本区域。
研究区居民步行体力活动轨迹与网格单元分布图 © 李博，欧阳浩，刘秋宏
指标体系构建
本文参考相关研究成果结合研究区现状，从GI的外部环境、内部环境和景观格局三个层面构建GI指标体系，得到分析模型的自变量。
外部环境指标包括人口指标，即人口密度、居住密度；经济指标，即房价水平、土地利用混合度；环境指标，即日平均气温、日平均降水。内部环境指标指GI内部的基础服务设施，即公共厕所、停车场地、城市广场和公交站点密度；以及景观要素，包括运动路径（步道交叉口数量、步道密度），水体（水体面积占比、距水体距离）和绿地（NDVI、绿地面积占比）指标。空间格局指标包括景观数量，即斑块密度（PD）、最大斑块面积占比（LPI）；景观形状，即景观形状指数（LSI）、斑块边缘密度（ED）；景观斑块间关系，即斑块聚合度指数（AI）、斑块分离指数（DIVISION）、斑块蔓延度指数（CONTAG）。步行体力活动指标体系主要包括步行体力活动的频率和强度，是分析模型的因变量。
GI指数计算与数据预处理
将各项指标数据转换为栅格数据（像素精度为30m），通过Fragstats4.0移动窗口命令进行计算，并在ArcGIS软件中，分别计算每个网格单元内所有像素各个指标的平均值。本研究采用Z-Scores法对计算所得网格单元中的平均值进行标准化处理。
研究运用方差膨胀因子对23项指标进行多重共线性检验，剔除具有显著共线性的4个自变量（斑块密度、斑块边缘密度、斑块分离度指数和斑块蔓延度指数），最终得到19项自变量指标进行后续分析。
多元线性回归模型
本研究利用多元线性回归模型对19项城市群GI指标进行差异分析，讨论GI外部环境、内部环境和空间格局指标与步行体力活动频率和强度之间的关系。
03 研究结果
步行体力活动空间分析
通过对长株潭城市群GI的功能分析和空间识别，划定9类GI空间，占城市群总面积的44%。网络中心主要包括长株潭城市群绿心和大型城市公园。连接廊道的识别主要包括湘江景观带、芙蓉大道绿带，以及城市主、次、支三级道路沿线的其他城市道路绿带。小型场地主要包括小型城市绿地、城市广场、社区公园和小型城市公园。
城市群GI网络中心及连接廊道分布图 © 李博，欧阳浩，刘秋宏
经计算GI内部的轨迹数量与步行体力活动轨迹总数的百分比，结果显示88.3%的步行活动发生在GI内部，且集中分布在三个城市中心城区的城市绿道、绿地和公园等区域。在空间结构上，连接廊道中有最多的步行轨迹数量和最大的轨迹总长，而网络中心拥有最少的步行轨迹数量和最短的长度。从具体类型来看，其他城市道路绿带和其他城市绿地这两类中的轨迹数量和长度均较大，城市群绿心、大型城市公园的轨迹数量和长度均较小。
城市群GI小型场地分布图 © 李博，欧阳浩，刘秋宏
步行体力活动频率和强度
通过对1436个网格单元内的指标进行计算（最小值、最大值和平均值），并在SPSS软件中对标准化后的数据进行回归分析。多元回归分析结果显示多元线性回归模型拟合效果较好，且自变量之间不存在多重共线性，回归模型显著性检验成立。
部分城市群GI指标对步行体力活动的频率或强度均表现出显著影响（P≤0.05），包括居住密度、房价水平、公共厕所密度、城市广场密度、公交站点密度、绿地公园占比、最大斑块面积占比和斑块聚合度指数，其中公共厕所密度、LPI和步行体力活动的频率与强度呈负相关关系。而部分城市群GI指标则对步行体力活动的频率或强度影响均不显著（P＞0.05），包括人口密度、日平均降水、距水体距离、NDVI和LSI。
部分城市群GI指标对步行体力活动的频率或强度的影响存在差异。在外部环境指标中，LM和日平均气温和步行体力活动强度具有显著正相关关系，而对步行体力活动频率影响不显著；在内部环境指标中，步道密度和步行体力活动频率有显著正相关关系，而水体面积占比和步行体力活动强度显著负相关。
整体而言，居住密度、房价水平、步道密度对步行体力活动频率的影响最显著（P≤0.01），居住密度、房价水平、LM、日平均气温、公共厕所密度、城市广场密度、公交站点密度、步道交叉口数量、绿地面积占比和AI对步行体力活动强度的影响最显著（P≤0.01）。
04 讨论
步行体力活动主要集中在连接廊道和小型场地中，说明居民更倾向于沿绿道等线性空间或者是在小型城市开放空间中开展步行体力活动。连接廊道可以有效连接各类城市开放空间，适宜的步行尺度使其可以满足健身锻炼和休闲游憩的多种需求。小型场地常见于居住地周边，距离居民通勤路线较近，具备相对完善的基础设施和趣味灵活的游线，从而易于提高居民的步行活动体验。
城市群GI可有效连接各类运动设施 © 欧阳浩
此外，仅少部分步行体力活动轨迹分布在城市群绿心和大型城市公园中（如岳麓山、石燕湖等）。因自然保护管控政策要求，城市群绿心和大型城市公园较少分布在土地开发强度较大的高密度城市中心区域，而更多分布在市郊、乡村等区域，对于居住在城市中的居民来说，路程中花费的时间较长且游憩设施较少，因而出现在网络中心的步行体力活动较少。
从外部环境指标来看，回归分析结果显示城市群GI中居住密度和房价水平与步行体力活动的频率和强度呈正相关关系。居住密度较高的区域可能人口数量更多，总体出行需求更高，同时，这类区域具有更高的街道连通性、可达性，更易于为居民带来良好的步行运动体验。其次，LM与步行体力活动的强度呈现正相关关系，LM越高的区域往往在步行范围内分布有多种设施，以便于居民在一次步行活动过程中完成多项任务；日平均气温对居民步行体力活动的强度具有显著正向影响，本研究中步行轨迹数据主要集中在春季和秋季，较高的温度可以提高人体感知舒适度，减少疲惫感。
城市群GI可以为人们提供多样的活动场所 © 欧阳浩
从内部环境指标中来看，长株潭城市群GI中城市广场和公交站点密度与步行体力活动的频率和强度均呈正相关关系，这表明可以通过适当增加城市广场和公交站点来提高基础设施的可达性。而公共厕所密度和步行体力活动的频率和强度均呈负相关关系。这与以往相关研究存在一定的差异，后续需要进一步细分研究继续探讨公共厕所空间配置与步行体力活动之间的关系。运动路径方面，步道密度和步行活动频率呈正相关关系；步道交叉口数量和步行活动强度呈正相关关系，步道交叉口数量和步道密度往往代表了一个地区连通性的强弱，更好的连通性不仅易于行走，还能有效疏解聚集性人群；同时步道交叉口能有效降低道路交通中的机动车车速，提高步行环境的安全性。水体面积占比和步行体力活动的强度呈负相关关系，长株潭城市群GI中的水体主要为面积较大的江河湖泊或水源涵养区，生态保护和水源保护的要求限制了公众的亲水活动，从而在一定程度上限制了步行体力活动。
从景观格局指标来看，LPI与步行体力活动的频率和强度呈负相关关系，而AI与步行体力活动的频率和强度呈正相关关系，说明城市群核心GI斑块面积越大，相应的居民步行活动的频率和强度越低；而斑块间聚合关系越好，越能促进居民步行活动的频率和强度。长株潭GI大型景观斑块主要位于城郊，远离城市核心区，可达性相对较差，并且因生态保护要求在一定程度上限制步行等人为活动。城市群GI中良好的景观聚合关系则可以在加强各景观类型之间的连通性、保障步行运动通畅和步行运动中的可选择性，从而提高居民进行步行体力活动的意愿。
从城市群GI对频率和强度的影响差异来看，景观格局指标对频率的影响更显著，而外部环境指标和内部环境指标对强度的影响更显著。这表明城市群GI的景观空间结构和内部景观连接关系会直接影响居民步行体力活动的频率，原因可能在于频率代表了居民进行周期性步行体力活动的意向，而连接性更强、聚合度更高的GI斑块通常具有更有序、更稳定的景观系统为步行提供有利的城市群环境。强度表示居民进行步行体力活动持续的时间和运动状态，丰富的土地利用和基础设施类型，以及适宜的环境温度和降水能有效满足居民在长时间、长距离运动中对景观多样化的需求，降低高负荷运动的疲惫感，提升高强度活动的体验感。
05 结论
基于GI相关理论，本研究识别并构建了长株潭城市群GI体系，探讨了在长株潭城市群GI中步行体力活动的空间分布和各项GI指标对步行体力活动频率和强度的影响差异。
依据本文研究结果，研究团队提出三项城市群GI建设策略。在外部环境方面，打造与公园、步行绿道和其他绿地临近的多业态、功能型住区环境，可以整体增强居民步行体力活动强度。在内部环境方面，增加GI中城市广场、公交站点等设施的数量，构建相对集中的人行步道网络，并适当增加步道交叉口数量，可有效提升居民进行步行体力活动的频率。在空间格局方面，在保障生态功能的前提下适当控制大型绿地面积，增加可步行的中小型绿地面积和数量，合理规划连接各类GI的步行绿道系统，可以保证步行运动过程的连续性，进而全面提升步行体力活动的频率和强度。
受采集到的数据所限，本研究还存在一些不足。首先，多锐APP的主要用户是青年和中年人，因此本文未能体现城市群GI对未成年人和老年人步行体力活动的影响，未来将进一步采集这两类人群的步行体力活动数据开展专项研究。其次，本研究仅使用客观环境要素作为自变量来研究GI与步行体力活动的关系，缺少对居民环境感知、心理感受等主观因素的探讨，后续将进一步拓展这方面的研究。
参考文献
[1] Liu, J., Chen, Z., Yang, F., He, X., Chen, W., & Deng, S. (2014). Current situation and strategies to chronic diseases of the elderly in China. China & Foreign Medical Treatment, 33(23), 194-195, 198.
[2] World Health Organization. (2014). Global status report on noncommunicable diseases 2014.
[3] Caspersen, C. J., Powell, K. E., & Christenson, G. M. (1985). Physical activity, exercise, and physical fitness: Definitions and distinctions for health-related research. Public Health Reports, 100(2), 126-131.
[4] Wu, Z., Wang, Z., & Song, Y. (2018). The meta analysis on the built environment’s influence on the physical activity of the elderly. Journal of Shanghai University of Sport, 42(1), 64-71, 78.
[5] Chen, C., Hailili, T., & Chen, Y. (2018). Built environment's influence on obesity of the older women and its planning responses. Human Geography, 33(4), 76-81.
[6] Beaulieu, K., Hopkins, M., Blundell, J., & Finlayson, G. (2018). Homeostatic and non-homeostatic appetite control along the spectrum of physical activity levels: An updated perspective. Physiology & Behavior, (192), 23-29.
[7] Edholm, O. G., Adam, J. M., Healy, M. J., Wolff, H.S., Goldsmith, R., & Best, T. W. (1970). Food intake and energy expenditure of army recruits. British Journal of Nutrition, (24), 1091-1107.
[8] Mayer, J., Roy, P., & Mitra, K. P. (1956). Relation between caloric intake, body weight, and physical work: Studies in an industrial male population in West Bengal. The American Journal of Clinical Nutrition, 4(2), 169-175.
[9] Frank, L. D., Sallis, J. F., Conway, T. L., Chapman, J. E., Saelens, B. E., & Bachman, W. (2006). Many pathways from land use to health: Associations between neighborhood walkability and active transportation, body mass index, and air quality. Journal of the American Planning Association, 72(1), 75-87.
[10] Giles-Corti, B., Vernez-Moudon, A., Reis, R., Turrell, G., Dannenberg, A. L., Badland, H., Foster, S., Lowe, M., Sallis, J. F., Stevenson, M., & Owen, N. (2016). City planning and population health: A global challenge. Lancet, (388), 2912-2924.
[11] Tainio, M., Andersen, Z. J., Nieuwenhuijsen, M. J., Hu, L., de Nazelle, A., An, R., Garcia, L. M. T., Goenka, S., Zapata-Diomedi, B., Bull, F., & de Sá, T. H. (2021). Air pollution, physical activity and health: A mapping review of the evidence. Environment International, (147), 105954.
[12] Tyrväinen, L., Ojala, A., Korpela, K., Lanki, T., Tsunetsugu, Y., & Kagawa, T. (2014). The influence of urban green environments on stress relief measures: A field experiment. Journal of Environmental Psychology, (38), 1-9.
[13] Li, K. (2009). Green infrastructure: Concept, theory and practice. Chinese Landscape Architecture, 25(10), 88-90.
[14] Benedict, M. A., & McMahon, E. (2006). Green Infrastructure: Linking Landscapes and Communities. Island Press.
[15] Zhang, Y., Yu, B., & Che, S. (2014). The comparison and blending of green infrastructure and low impact development. Chinese Landscape Architecture, 30(3), 49-53.
[16] Ding, J., & Wang, M. (2016). The planning of the green infrastructure in the country with water network—A case study of the west area of Lili Town. Chinese Landscape Architecture, 32(1), 98-102.
[17] Zhang, Q. (2009). Exploration on the protection and utilization mode of urban green heart area—Case study of the green heart planning of Changsha–Zhuzhou–Xiangtan urban agglomeration, China. Paper Collection for 2009 International Forum on Urban Development and Planning, 16-18, 45.
[18] Ferreira, J. C., Monteiro, R., & Silva, V. R. (2021). Planning a green infrastructure network from theory to practice: The case study of Setúbal, Portugal. Sustainability, 13(15), 8432.
[19] National Recreation and Park Association. (2016). NRPA Americans’ Engagement with Parks Survey.
[20] Coombes, E,. Jones, A. P., Hillsdon, M. (2010). The relationship of physical activity and overweight to objectively measured green space accessibility and use. Social Science & Medicine, (70), 816-822.
[21] Sallis, J., Bauman, A., & Pratt, M. (1998). Environmental and policy interventions to promote physical activity. American Journal of Preventive Medicine, 15(4), 379-397.
[22] Astell-Burt, T., Feng, X., & Kolt, G. S. (2014). Green space is associated with walking and moderate-to-vigorous physical activity (MVPA) in middle-to-older-aged adults: Findings from 203883 Australians in the 45 and up study. British Journal of Sports Medicine, (48), 404-406.
[23] Owen, N., Humpel, N., Leslie, E., Bauman, A. & Sallis, J. F. (2004). Understanding environmental influences on walking: Review and research agenda. American Journal of Preventive Medicine, 27(1), 1-76.
[24] Coutts, C., Chapin, T., Horner, M., & Taylor, C. (2013). County-level effects of green space access on physical activity. Journal of Physical Activity and Health, (10), 232-240.
[25] Li, F., Fisher, K., Brownson, R., & Bosworth, M. (2005). Multilevel modelling of built environment characteristics related to neighborhood walking activity in older adults. Journal of Epidemiology and Community Health, (59), 558-564.
[26] Akpinar, A., & Cankurt, M. (2017). How are characteristics of urban green space related to levels of physical activity: Examining the links. Indoor and Built Environment, 26(8), 1091-1101.
[27] Schipperijn, J., Cerin, E., Adams, M. A., Reis, R., Smith, G., Cain, K., Christiansen, L. B., van Dyck, D., Gidlow, C., Frank, L. D., Mitáš, J., Pratt, M., Salvo, D., Schofield, G., & Sallis, J. F. (2017). Access to parks and physical activity: An eight country comparison. Urban Forestry & Urban Greening, (27), 253-263.
[28] Fu, X. (2015). Green infrastructure planning and the enlightenment to our country. Urban Development Studies, 22(4), 52-58.
[29] Kim, G., & Miller, P. A. (2019). The impact of green infrastructure on human health and well-being: The example of the Huckleberry Trail and the Heritage Community Park and Natural Area in Blacksburg, Virginia. Sustainable Cities and Society, (48), 101562.
[30] Lu, D. (2015). Function orientation and coordinating development of subregions within the Jing-Jin-Ji urban agglomeration. Progress in Geography, 34(3), 265-270.
[31] Research group of China Development Research Foundation. (2022). Report on the integration of urban agglomerations in China. China Economic Report, (5), 109-119.
[32] Peng, Z., Wu, Y., Wu, T., & Xiao, Y. (2008). Conception and suggestion about frame of green space system in Chang-Zhu-Tan urban cluster region. Journal of Anhui Agricultural Sciences, 36(3), 1043-1045.
[33] Dong, X., Liang, Y., Hou, B., & Chen, L. (2021). Research on greenway route selection method of urban agglomeration based on resource and environment assessment system: Taking Beijing-Tianjin-Hebei urban agglomeration as an example. Urban Development Studies, 28(12), 118-127.
[34] Wang, F., & Wang, K. (2019). Connectivity and distribution pattern of regional greenways and ecological recreation spaces in the Pearl River Delta urban agglomeration. Progress in Geography, 38(3), 428-440.
[35] Jiao, S., Wei, C., Liu, S., He, S., & Guan, C. (2009). The protective development strategies of green heart in the urban agglomeration in Chang-Zhu-Tan urban agglomeration. Economic Geography, 29(10), 1716-1719, 1742.
[36] Li, B., & Gan, T. (2019). Construction of water security pattern of Changsha-Zhuzhou-Xiangtan urban agglomeration based on ArcGIS and GAP analysis. Water Resources Protection, 35(4), 80-88.
[37] Wang, J., Yuan, Y., Zhang, X., & Wang, Z. (2018). Comprehensive evaluation of the urban green space in the Beijing-Tianjin-Hebei urban agglomeration. Urban and Environmental Studies, (1), 55-65.
[38] Ao, Y., Wang, Z., Zhao, Y., Jiang, L., & Zhang, M. (2022). Effect of green space landscape elements on temperature under different urbanization levels: A case study of Guanzhong Plain urban agglomeration. Chinese Journal of Ecology, 1-15.
[39] Yang, F., Duan, N., Xu, Y., & Tao, Y. (2019). Smart planning and cross regional interaction: Dilemma and planning response of regional green space protection. Planners, 35(21), 52-58.
[40] Zhang, Y., Liu, Z., Wang, J., & Shao, D. (2020). Analysis of regional differences in urban park green space per capita of urban agglomerations in China from 1996 to 2016. Huazhong Architecture, 38(5), 65-70.
[41] Andersson, E., Barthel, S., Borgström, S., Colding, J., Elmqvist, T., Folke, C., & Gren, Å. (2014). Reconnecting cities to the biosphere: Stewardship of green infrastructure and urban ecosystem services. AMBIO, 43(4), 445-453.
[42] Chen, Y., Zhou, G., Wang, H., Cui, S., Bi, F., & Yu, X. (2023). The evolution characteristics and enhancement paths of the economy-society-ecology coordinated development level of Changsha-Zhuzhou-Xiangtan urban agglomeration. Tropical Geography, 43(3), 519-531.
[43] Changsha Municipal Bureau of Natural Resources and Planning. (2014). Urban Comprehensive Planning of Changsha (2003–2020) (Revised in 2014).
[44] Xiangtan Municipal Bureau of Natural Resources and Planning. (2017). Urban Comprehensive Planning of Xiangtan (2010–2020) (Revised in 2017).
[45] Zhuzhou Municipal Bureau of Natural Resources and Planning. (2017). Urban Comprehensive Planning of Zhuzhou (2006–2020) (Revised in 2017).
[46] Li, W., & Li, J. (2017). Identification and optimization of small and medium-sized green infrastructure based on GIS: A case study of Nanxi Wetland Ecological Tourist Area in Xuancheng, Anhui Province. Journal of Hefei University of Technology (Social Sciences), 31(3), 128-134.
[47] Cai, Y., Wen, Z., & Lei, M. (2016). Green infrastructure system planning strategy for “sponge city” development. Planners, 32(12), 12-18.
[48] Fu, X., & Wu, R. (2009). Introduction to green infrastructure assessment (GIA)—A case study of Maryland, USA. Chinese Landscape Architecture, 25(9), 41-45.
[49] Yang, J., Qi, Z., & Peng, S. (2022). Construction of multi-level green infrastructure network in Changsha City. Journal of Chinese Urban Forestry, 20(2), 36-42.
[50] Wu, W., & Fu, X. (2009). The concept of green infrastructure and review of its research development. Urban Planning International, 24(5), 67-71.
[51] Gong, C., Wu, H., & Hu, C. (2022). Ecological function optimization strategy of urban public open space based on green infrastructure network: A case study of central Nanjing. New Architecture, (1), 49-54.
[52] Wickham, J. D., Riitters, K. H., Wade, T. G., & Vogt, P. (2010). A national assessment of green infrastructure and change for the conterminous United States using morphological image processing. Landscape and Urban Planning, 94(3-4), 186-195.
[53] Ai, J., Yu, K., Zeng, Z., Yang, L., Liu, Y., & Liu, J. (2022). Assessing the dynamic landscape ecological risk and its driving forces in an island city based on optimal spatial scales: Haitan Island, China. Ecological Indicators, (137), 108771.
[54] Jin, Y., Li, A., Bian, J., Nan, X., Lei, G., & Muhammad, K. (2021). Spatiotemporal analysis of ecological vulnerability along Bangladesh-China-India-Myanmar economic corridor through a grid level prototype model. Ecological Indicators, (120), 106933.
[55] García-Llamas, P., Calvo, L., De la Cruz, M., & Suárez-Seoane, S. (2018). Landscape heterogeneity as a surrogate of biodiversity in mountain systems: What is the most appropriate spatial analytical unit? Ecological Indicators, (85), 285-294.
[56] Yang, L., Yu, B., Liang, P., Tang, X., & Li, J. (2022). Crowdsourced data for physical activity-built environment research: Applying Strava data in Chengdu, China. Frontiers in Public Health, (10), 883177.
[57] Liu, Y., Hu, J., Wang, W., & Luo, C. (2022). Effects of urban park environment on recreational jogging activity based on trajectory data: A case of Chongqing, China. Urban Forestry & Urban Greening, (67), 127443.
[58] Zhong, Q., Li, B., & Chen, Y. (2022). How do different urban footpath environments affect the jogging preferences of residents of different genders? Empirical research based on trajectory data. International Journal of Environmental Research and Public Health, (19), 14372.
[59] Chen, W., Xiao, D., & Li, X. (2002). Classification, application, and creation of landscape indices. Chinese Journal of Applied Ecology, 13(1), 121-125.
[60] Moore, S., & Kestens, Y. (2011). Neighbourhood environmental correlates of perceived park proximity in Montreal. Canadian Journal of Public Health, 102(3), 176-179.
[61] Arifwidodo, S. D., Chandrasiri, O., Rasri, N., Sirawarong, W., Rattanawichit, P., & Sangyuan, N. (2022). Association between park visitation and physical activity among adults in Bangkok, Thailand. Sustainability, 14(19), 12938.
[62] Wang, Z., Ettema, D., & Helbich, M. (2021). Objective environmental exposures correlate differently with recreational and transportation walking: A cross-sectional national study in the Netherlands. Environmental Research, (194), 110591.
[63] Wang, H., Dai, X., Wu, J., Wu, X., & Nie, X. (2019). Influence of urban green open space on residents’ physical activity in China. BMC Public Health, (19), 1093.
[64] Li, X., Hong, Z., Yuan, Y., Zhao, L., & Xu, M. (2015). Research on residence outdoor space suitable for elders and children’s activities. Urban Development Studies, 22(5), 104-111.
[65] Acar, H., Yavuz, A., Eroğlu, E., Acar, C., Sancar, C., & Değermenci, A. S. (2021). Analysis of activity, space and user relations in urban squares. Indoor and Built Environment, 30(9), 1466-1485.
[66] Qin, B., & Zhang, Y. (2019). The effects of urban built environment on residents’ physical activity: A study on neighborhood survey in Beijing. Urban Development Studies, 26(3), 65-71.
[67] Wang, H., & Yang, Y. (2019). Neighbourhood walkability: A review and bibliometric analysis. Cities, (93). 43-61.
[68] Xue, H., Cheng, X., Jia, P., & Wang, Y. (2020). Road network intersection density and childhood obesity risk in the US: A national longitudinal study. Public Health, (178), 31-37.
[69] Cai, Z., Fang, C., Zhao, H., Zhang, X., & Zhang, Q. (2022). A review and future research framework on association between blue space and physical activity. Journal of Environmental and Occupational Medicine, 39(10), 1165-1171.
[70] Aliyas, Z. (2020). A qualitative study of park-based physical activity among adults. Journal of Public Health, (28), 623-632.
[71] Poppe, L., Deforche, B., Van Cauwenberg, J., Brondeel, R., Mertens, L., Van de Weghe, N., Benoit, S., Veitch, J., & Van Dyck, D. (2022). The association between the number of parks near home and levels of physical activity among community-dwelling older adults: A longitudinal study. Cities, (130), 103931.
[72] Ma, L., Bo, J., Li, X., Fang, F., & Cheng, W. (2019). Identifying key landscape pattern indices influencing the ecological security of inland river basin: The middle and lower reaches of Shule River Basin as an example. Science of the Total Environment, (674), 424-438.
[73] Zhao, F., Li, H., Li, C., Cai, Y., Wang, X., & Liu, Q. (2019). Analyzing the influence of landscape pattern change on ecological water requirements in an arid/semiarid region of China. Journal of Hydrology, (578), 124098.
[74] Kim, M., Song, K., & Chon, J. (2021). Key coastal landscape patterns for reducing flood vulnerability. Science of the Total Environment, (759), 143454.
[75] Li, B., Liu, Q., Wang, T., He, H., Peng, Y., & Feng, T. (2022). Analysis of urban built environment impacts on outdoor physical activities—A case study in China. Frontiers in Public Health, (10), 861456.
[76] Xu, Z., Kong, X., & Wei, J. (2014). Clustering analysis on disciplines innovation capabilities of research-oriented universities in China—An example of 36 “Project 985” universities. Science and Technology Progress and Policy, 31(23), 165-168.
[77] Qian, Y., & Fang, X. (2022). Multiple linear regression model and application. China Science and Technology Information, (4), 73-74.
[78] Liu, K., Siu, K. W. M., Gong, X., Gao, Y., & Lu, D. (2016). Where do networks really work? The effects of the Shenzhen greenway network on supporting physical activities. Landscape and Urban Planning, (152), 49-58.
[79] He, D., Lu, Y., Xie, B., & Helbich, M. (2021). Large-scale greenway intervention promotes walking behaviors: A natural experiment in China. Transportation Research Part D: Transport and Environment, (101), 103095.
[80] Li, Y., Wang, J., & Wang, Z. (2011). Discussions on North American experiences about linear open space planning and management. Urban Planning International, 26(4), 85-90.
[81] Seong, E. Y., Lee, N. H., & Choi, C. G. (2021). Relationship between land use mix and walking choice in high-density cities: A review of walking in Seoul, South Korea. Sustainability, (13), 810.
[82] Huang, R., Moudon, A. V., Zhou, C., & Saelens, B. E. (2019). Higher residential and employment densities are associated with more objectively measured walking in the home neighborhood. Journal of Transport & Health, (12), 142-151.
[83] Sung, H., Lee, S., & Cheon, S. Operationalizing Jane Jacobs’s urban design theory: Empirical verification from the Great City of Seoul, Korea. Journal of Planning Education and Research, 35(2), 117-130.
[84] Obuchi, S. P., Kawai, H., Garbalosa, J. C., Nishida, K., & Murakawa, K. (2021). Walking is regulated by environmental temperature. Scientific Reports, 11(1), 12136.
[85] Ma, Q., Wang, L., Gong, X., & Li, K. (2022). Research on the rationality of public toilets spatial layout based on the POI data from the perspective of urban functional area. Journal of Geo-information Science, 24(1), 50-62.
[86] Zang, P., Qiu, H., Xian, F., Zhou, X., Ma, S., & Zhao, Y. (2021). Research on the difference between recreational walking and transport walking among the elderly in mega cities with different density zones: The case of Guangzhou City. Frontiers in Public Health, (9), 775103.
[87] Smith, K. R., Brown, B., Yamada, I., Kowaleski-Jones, L., Zick, C. D., & Fan, J. X. (2008). Walkability and body mass index density, design, and new diversity measures. American Journal of Preventive Medicine, 35(3), 237-244.
[88] Wang, F., Wen, M., & Xu, Y. (2013). Population-adjusted street connectivity, urbanicity and risk of obesity in the U.S. Applied Geography, (41), 1-14.
[89] Jansen, M., Kamphuis, C. B., Pierik, F. H., Ettema, D. F., & Dijst, M. J. (2018). Neighborhood-based PA and its environmental correlates: A GIS- and GPS based cross-sectional study in the Netherlands. BMC Public Health, (18), 233.
[90] Perchoux, C., Kestens, Y., Brondeel, R., & Chaix, B. (2015). Accounting for the daily locations visited in the study of the built environment correlates of recreational walking (the record cohort study). Preventive Medicine, (81), 142-149.
[91] Van Cauwenberg, J., Nathan, A., Barnett, A., Barnett, D. W., & Cerin, E. (2018). Relationships between neighbourhood physical environmental attributes and older adults’ leisure-time physical jogging: A systematic review and meta-analysis. Sports Medicine, (48), 1635-1660.
[92] Spring, A. (2018). Short- and long-term impacts of neighborhood built environment on self-rated health of older adults. Gerontologist, 58(1), 36-46.
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Li, B., Ouyang, H., & Liu, Q. (2023). Research of the influence mechanisms of green infrastructure on walking physical activities in Changsha–Zhuzhou–Xiangtan Urban Agglomeration, China. Landscape Architecture Frontiers, 11(1), 30–57. https://doi.org/10.15302/J-LAF-1-020075
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