袁媛^,1,2, 陈玉洁¹, 刘晔^,1,2, 丁凯丽³1.中山大学地理科学与规划学院,广州 510275
2.广东省城市化与地理环境空间模拟重点实验室,广州 510275
3.浙江万维空间信息技术有限公司,杭州 310051

The neighborhood effect of residential greenery on residents' self-rated health:A case study of Guangzhou, China

YUAN Yuan^,1,2, CHEN Yujie¹, LIU Ye^,1,2, DING Kaili³1. School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
2. Guangdong Key Laboratory for Urbanization and Geo-simulation, Guangzhou 510275, China
3. Zhejiang ZJUGIS Space Information Technology Co., Ltd., Hangzhou 310051, China

通讯作者: 刘晔(1986-), 男, 广东广州人, 教授, 博士生导师, 研究方向为人口地理、健康地理和城市地理。E-mail: liuye25@mail.sysu.edu.cn

收稿日期:2020-06-12修回日期:2021-07-8

基金资助:

国家自然科学基金项目(51678577) 国家自然科学基金项目(41871161) 国家自然科学基金项目(41871140) 国家自然科学基金项目(41971194) 广东省科技创新青年拔尖人才项目 广州市科技项目(201804010241)

Received:2020-06-12Revised:2021-07-8

Fund supported:

National Natural Science Foundation of China(51678577) National Natural Science Foundation of China(41871161) National Natural Science Foundation of China(41871140) National Natural Science Foundation of China(41971194) Guangdong Provincial Technical Innovation Program for the Top Young Talents Guangzhou Science and Technology Project(201804010241)

作者简介 About authors
袁媛(1976-), 女, 浙江绍兴人, 教授, 博士生导师, 研究方向为健康社区与规划、城市贫困等。E-mail: yyuanah@163.com






摘要
良好的城市生态环境有益于居民的健康福祉。至今鲜有研究阐明绿化环境对中国城市居民健康的心理—社会—行为机制。本文运用广州调查问卷数据、遥感影像数据和百度街景数据,提取多种社区绿化指标,并运用多层线性回归模型和中介效应分析技术,阐明社区绿化环境影响居民自评健康的路径和机制,定量测度社区绿化环境的健康效应在不同社会群体间的差异。研究发现：① 社区绿化水平与居民的自评健康水平存在显著关联;② 社区绿化通过缓解心理压力提升居民的自评健康水平;③ 社区绿化的健康效应在不同收入群体和不同性别群体之间存在明显差异,表现为社区绿化水平与健身时长和心理压力的关系,中低收入群体强于高收入群体,女性群体强于男性群体。本文以期丰富健康地理学视角下的绿化环境与公共健康的实证研究,并为健康社区建设和人居环境提升提供科学依据。
关键词： 社区绿化环境;自评健康;邻里影响;弱势群体;广州

Abstract
Urban natural environment especially greenery environment has been consistently linked to better health outcome. Previous studies have shown that the greenery environment of neighborhood is related to residents' health, but scant attention has been paid to biopsychosocial pathways through which neighborhood greenery affects resident' wellbeing. In addition, a limited number of studies have evaluated the healthy pathways between neighborhood greenery and residents' wellbeing among Chinese vulnerable groups in urban setting. Based on neighborhood effect, three potential mediating pathways (namely, increasing physical activity duration, reducing psychological stress and facilitating social interaction) linking neighborhood greenery to residents' self-rated health were explored from 757 participants of Guangzhou, China. In view of questionnaire data involving 30 communities and greenery data obtained from remote sensing image and streetscape data and etc., this research employed multilevel linear regression model and mediating effect model and propensity score matching method to examine biopsychosocial pathways and mechanism linking neighborhood greenery to residents' self-rated health, as well as the difference among different social strata (e.g. different income and gender groups). According to this empirical analysis, the following conclusions can be achieved. Firstly, residential neighborhood greenery is significantly associated with residents' self-rated health outcome. Secondly, reduction in psychological stress is a kind of pathway linking residential neighborhood greenery to residents' self-rated health. Thirdly, the impact of neighborhood greenery on residents' self-rated health differs significantly among different social strata, and neighborhood greenery has greater impact on the self-rated health of low-middle income residents and female residents group, which means the beneficial effect of reduction in psychological stress on self-rated health is strengthened by neighborhood greenery. This research findings enrich the empirical research on greenery environment and public health from the perspective of health geography, and suggest the necessity of improving active spatial intervention and greenspace construction of the "healthy community" at the neighborhood scale in promoting residents' wellbeing in Chinese urban settings.
Keywords：residential greenery;self-rated health;neighborhood effect;vulnerable groups;Guangzhou


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本文引用格式
袁媛, 陈玉洁, 刘晔, 丁凯丽. 广州社区绿化环境对居民自评健康的邻里影响. 地理学报, 2021, 76(8): 1965-1975 doi:10.11821/dlxb202108011
YUAN Yuan, CHEN Yujie, LIU Ye, DING Kaili. The neighborhood effect of residential greenery on residents' self-rated health:A case study of Guangzhou, China. Acta Geographica Sinice, 2021, 76(8): 1965-1975 doi:10.11821/dlxb202108011

1 引言

中国共产党“十九大”将“健康中国”上升到国家战略高度,如何通过提升人居环境增进人民健康福祉成为学术界关注的热点。近年来,地理****开始关注城市社区环境对居民健康的邻里影响^[1]。邻里影响是指邻里环境直接或间接地影响居民的行为方式与个人境遇,从而对他们的就业机会、子女教育、犯罪倾向和健康状况带来一定的影响^[2,3]。作为邻里环境的重要组成部分,社区绿化环境的社会生态服务功能能够增进居民健康水平^[4,5,6]。因此,从地理学视角阐明社区绿化环境对居民健康的影响路径和机制,探究提升居民健康的主动式空间干预手段,可为健康社区建设和人居环境提升提供科学依据和理论支持。

基于城市生态系统理论,****们开始关注绿色空间在健康社区建设中的核心地位,以及绿化环境与居民健康之间的关系^{[5, 7]}。社区绿化水平的测度指标包括绿量指数（如归一化植被指数NDVI）、绿化覆盖率、绿地率、街道绿视率和绿地可达性等^[8]。植物种类、规模、质量和可达性等绿色空间的属性不仅影响其生态和社会效益,也影响人们使用绿色空间的动机和机会,而单一绿化指标无法表征绿色空间的不同属性。因此,综合运用多种指标测度社区的绿化水平,能够更全面地揭示社区绿化环境影响居民健康的复杂机制。社区绿化环境对居民健康的影响可归结为3方面^[5]：① 缓解环境压力路径（Reducing Harm）：社区绿化通过改善空气质量、消除噪音和调节气候增进居民健康^{[6, 9-11]};② 恢复力路径（Restoring Capacities）：观赏绿植有助于降低精神压力并修复涣散的注意力,从而降低高血压和心理疾病等慢病风险^{[10, 12-17]};③ 建设力路径（Building Capacities）：绿色空间为周边居民开展体育锻炼提供舒适宜人的场所,从而增强居民的身体素质并缓解他们的精神压力;绿色空间也为周边居民开展邻里互动和集体活动提供了便利的场所,提升社区凝聚力,约束不健康行为并防范健康风险^[18,19,20]。

由于社区绿化环境的健康效应具有复杂性,测度社区绿化水平的各项指标与居民健康结局之间的关联强度并不一致。例如,相较于植被覆盖度而言,街道绿化（绿视率）在防尘降噪和缓解心理压力所起到的作用更强^[15]。相对而言,植被覆盖度、街道绿视率和公园绿地可达性均能发挥鼓励体力活动和促进邻里交往的作用,从而促进居民健康水平^{[11, 16-20]}。由于不同社会群体使用绿地的机会和动机不同,且弱势群体（如老幼群体、低收入群体、女性等）获取资源能力有限^[21],社区绿化的健康效应存在差异^[4]。既有研究表明老年人更加依赖社区绿地资源^[22],老年人受社区绿化的健康效应更显著^[23],社区绿化水平高能降低儿童身体质量指数并提高儿童心理健康水平^[24,25],低收入社区的绿地可达性较差^[26,27],社区绿化环境好能降低收入造成的健康不平等^[28]。社区绿化的健康效应存在性别差异,例如绿化环境可达性好能够增强女性户外体力活动的意愿^[29];社区绿化能够降低女性的出行危险感知,对女性的健康影响更显著^[30],因此提高邻里绿化的可步行性能够提升女性运动水平^[21]。

国内健康地理研究方兴未艾。早期研究大多在中宏观尺度上开展,研究主题包括居民健康水平的区域差异^[31]、城镇化进程与健康的关系^[32]和环境污染物与健康的关系^[33]等。近年来开始转向邻里尺度,评估各种城市建成环境因素（如土地利用、交通网络、景观设计和公共服务设施）对各种健康结局（如生理健康、心理健康、肥胖与超重、慢病发生率和主观幸福感）^{[34,35,36,37,38]}的影响强度,以及个人家庭因素（如年龄、性别、社会经济地位、行为习惯和家庭构成）对各种建成环境健康效应的调节作用^[34,35,36]。然而既有研究存在以下局限：首先,尽管有****估计城市社区绿化环境与居民整体健康水平的相关关系,但至今鲜有研究阐明不同特征的城市社区绿化环境对居民整体健康水平的心理—社会—行为机制。其次,****们往往假设受访对象随机地被分配到调查社区中,从而忽视了居住自选择机制,可能导致邻里环境健康效应结果的偏误^[35]。此外,既有研究缺乏对城市弱势群体关注,尤其缺乏绿化环境健康效应的中间机制在不同社会群体间的比较。

本文运用广州调查问卷数据、遥感影像数据和百度街景数据,提取多种社区绿化指标,运用多层线性回归模型和中介效应分析技术,阐明社区绿化环境影响居民自评健康的路径和机制,定量测度社区绿化环境的健康效应在不同社会群体间的差异,以期丰富健康地理学视角下绿化环境与公共健康的实证研究,并为健康社区建设和人居环境提升提供科学依据。

2 研究设计

2.1 研究区域与数据来源

研究区域为广州内城6区（越秀、荔湾、海珠、天河、白云、番禺）。主要数据来源于2016年1月开展的社区问卷调查。调查抽样方法如下：① 采用多阶段分层PPS抽样调查法选取30个案例社区,涵盖商品房、历史街区、单位社区、保障房及城中村5种社区类型（图1）;② 基于六普常住人口比例,采用分层比例抽样确定各社区问卷数量;③ 采用Kish法在受访家庭内抽取1名成年家庭成员。调查共收集了1029份有效问卷,剔除缺失值后,最终样本量为757份。因采用严谨的抽样方法,样本代表性较高。

图1

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图1案例社区分布(数字代表30个社区的编号)

Fig. 1Location of sample neighborhoods in Guangzhou, China



街景数据来源于百度地图街景时光机的全景模式下爬取的2016年街景照片。案例社区街景采样点选取原则为：在各社区出入口、外围街道按50m间隔进行采样点布置,以平视视角（人眼真实视角）在东南西北（对应0°、90°、180°、270°）4个方位爬取街景图片。其他数据源为2016年12月7日的Landsat-8精度为30 m×30 m卫星遥感影像。

2.2 研究框架与变量选取

2.2.1 研究框架 鉴于本文可获取的数据类型,参考已有文献,社区绿化环境指标从周围绿化程度和可达性两方面选取^[8],周围绿化程度包括NDVI、公园绿地占比和绿视率3个指标,分别表示社区周边绿色植物量（水平视角）、水平绿化情况和街道两旁的垂直绿化情况（垂直视角,即人眼视角）;可达性水平由离最近公园的距离所测度,该指标反映了社区居民使用周边大型公共绿地的便利程度。影响路径则重点关注恢复力和建设力两方面来建构框架,即促进健身时长、缓解心理压力和促进社会交往（图2）。不同年龄、性别、社会经济地位的人对绿色空间的使用偏好和使用机会有所不同,导致绿色空间对不同人群的健康效应存在差别^[4]。因此,本文不仅探究绿化环境影响居民自评健康的3组中介机制（健身时长、心理压力、社会交往）,还探究受访者的性别和收入所起到的调节作用。

图2

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图2研究框架

Fig. 2Research framework



2.2.2 变量选取 被解释变量选取自评健康水平,该变量为连续变量,取值范围是5~15。该变量基于SF-36量表（健康调查量表）中的一般健康状况（General Health）和躯体疼痛（Bodily Qain）模块共3道问题,采用Likert-5量表测度。其中,一般健康状况的问题为“您对自己身体健康状况的评价”,答案为“非常好”“良好”“一般”“较差”和“非常差”,分别赋值5~1。躯体疼痛的问题为“过去一个月内身体疼痛吗?”和“过去一个月内身体疼痛是否影响您的工作或其他日常活动”,答案为“没有”“偶尔”“有时”“经常”和“总是”,分别赋值5~1。该量表信度系数α为0.80,表明可信度较高。

解释变量采用NDVI、公园绿地占比、最近公园的距离和绿视率（Green View Index）,分别表征社区周边的绿色植物量、水平绿化情况、大型公共绿地可达性和街道两旁的垂直绿化情况。NDVI为社区边界周边的1km缓冲区内所有像元NDVI的平均值;公园绿地占比基于百度地图所绘制公园绿地的边界,为社区边界周边1 km缓冲区内公园绿地（G1）用地的占比;最近公园的距离为社区重心到最近公园绿地边界的路网距离;绿视率水平为该社区所有已爬取的街景图片绿色植物像元占比的平均值^[39],由北京城市象限科技有限公司（ http://www.urbanxyz.com/#projects）开发的“猫眼象限”程序计算而得。多重共线性检验表明,所有社区绿化指标的VIF均小于3,不存在严重的多重共线性。

中介变量选取健身时长、心理压力、和社会交往,均为连续变量^①(①中介变量选取问题为：健身时长为“最近一周户外健身时间是__小时”;心理压力为“在过去一个月内是否由于情绪问题（如沮丧焦虑）影响到工作或其他日常活动?”（总是、经常、有时、很少、没有分别赋值为5~1）;社会交往包括邻里交流、信任、社区参与等,包括“人们愿意互相帮助”“邻居们经常相互串门”“小区的人值得信任”“小区居民见面能打招呼”“小区居民能共同处理问题”“小区居民经常交流健康相关信息”（非常同意、同意、一般、不同意、非常不同意分别赋值5~1,该指标取值范围为30~6）。);控制变量选取年龄、性别、学历、个人月收入、户口的居民社会经济地位和人口学属性变量。所有变量的描述性统计如表1所示。

Tab. 1
表1
表1变量的描述性统计
Tab. 1Descriptive statistics of variables in sampled neighborhoods

变量	均值/比重 (标准差)	变量	均值/比重 (标准差)	变量	均值/比重 (标准差)
年龄	42.81(13.753)	性别		自评健康水平	12.624(1.864)
户口		男性占比(%)	49.10	NDVI_1000 m	0.117(0.093)
本市(%)	77.30	女性占比(%)	50.90	公园绿地占比	0.033(0.023)
外地(%)	22.70	个人月收入(元/月)		绿视率	0.215(0.074)
学历		0~1499(%)	2.40	最近公园的距离	0.843(0.328)
初中及以下占比(%)	16.90	1500~4999(%)	44.30	健身时长	4.485(3.395)
高中/中专/大专占比(%)	65.40	5000~8999(%)	46.20	心理压力	1.794(0.790)
本科及以上占比(%)	17.70	> 9000(%)	7.10	社会交往	17.913(3.480)

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2.3 研究方法

2.3.1 多层模型 本文运用STATA 14.0软件开展多层线性回归模型分析。多层回归模型充分考虑数据嵌套性,能精确计算不同地理层级要素的贡献程度。在本文中,第一层为757名受访者个体,第二层为30个调查社区^②(②多层模型公式为：Y_ij = α+βX_ij+γZ_j+μ_j+ε_ij。式中：个体i（1~757）嵌套于社区单元j（1~30）中;Y_ij为社区j中i的自评健康得分;X_ij为个体层级变量;Z_j为社区层级变量;α为截距;μ_j为社区层级残差;ε_ij为个体层级残差。)。

2.3.2 中介效应分析 中介效应（Mediation Effect）是指自变量Z（社区绿化环境）通过中介变量M对因变量Y（自评健康）产生影响。多层级中介效应模型是多层线性模型和中介效应模型的结合,可探讨多层数据结构下不同层级变量间的影响,尤其是高层级自变量对个体层面因变量的影响,强化了中介效应的解释力度^③(③中介效应分析的公式为：（1）Y_ij=α+βX_ij+γZ_j+μ_j+ε_ij;（2）M_ij=α+βX_ij+γZ_j+μ_j+ε_ij;（3）Y_ij=α+βX_ij+γZ_j+M_ij+μ_j+ε_ij。式中：M_ij代表中介变量。)。本文采用此模型探究各影响路径,用逐步回归检验系数法检验其中介效应。

2.3.3 倾向值匹配法 倾向值匹配（Propensity Score Matching, PSM）通过对处理组与控制组样本的逐一匹配,达到随机分配效果,以控制可观察变量导致的自选择机制干扰。PSM基本过程为：根据可观测的混淆变量^④(④基于城市生态系统理论,引入性别、月收入、年龄、受教育年限、户口等个体社会经济属性作为混淆变量加以控制。),通过Logit模型估计每个样本归入处理组的概率,得到其倾向得分,然后将倾向值最为接近、但分属两组的样本进行逐一匹配。为稳健起见,本文采用邻近匹配、半径匹配^⑤(⑤参考以往文献,本文半径采用样本估计倾向值标准差的1/4。)和核匹配3种方法,以获得类似于随机试验的设计效果。最后,得出各绿化指标对健康的平均影响效应（Average Treatment Effect on Treated, ATT）及显著性。

3 结果分析

3.1 绿化环境对居民自评健康的影响和路径

构建空模型（Null Model）估计社区层和个体层因素对受访者自评健康水平总体差异的贡献。自评健康空模型组内相关系数（ICC）为0.2513,即社区层因素解释居民自评健康差异的25.13%。当纳入个体层和社区层变量,似然比从空模型的2920.599下降至2632.262,表明绿化环境能有效解释居民健康在社区层面的异质性,适合建立多层模型。表2中模型1为基准模型,仅纳入核心解释变量与控制变量,性别（β = -0.374,P < 0.05）、年龄（β = -0.770,P < 0.01）和本科及以上教育水平（β = 0.579,P < 0.05）与自评健康呈显著相关;NDVI（β = 1.584,P < 0.01）和公园绿地占比（β = 1.489,P < 0.1）与自评健康呈显著正相关;绿视率（β = -1.418,P < 0.01）和最近公园的距离（β = -1.970,P < 0.01）与自评健康呈显著负相关。将健身时长、心理压力、社会交往作为因变量开展多层级线性回归分析（模型1a~1c）,发现NDVI和绿视率与居民健身时长呈显著正相关,最近公园的距离与健身时长呈显著负相关;公园绿地占比、绿视率和最近公园的距离与心理压力呈显著正相关;最近公园的距离与社会交往呈显著负相关。当采用不同指标测度社区绿化时,社区绿化环境与居民自评健康的关联不同。

Tab. 2
表2
表2社区绿化环境与自评健康、健身时长、心理压力和社会交往的关联及中介效应
Tab. 2The association and mediation effect between neighborhood greenery, self-rated health, physical activity duration, psychological stress and social interaction

指标	模型1 因变量：自评健康(路径A)			模型1a 因变量：健身时长(路径B)			模型1b 因变量：心理压力(路径C)			模型1c 因变量：社会交往(路径D)			模型2 因变量：自评健康(路径A、B'、C'、D')
	系数估计值	标准误		系数估计值	标准误		系数估计值	标准误		系数估计值	标准误		系数估计值	标准误
绿化环境
NDVI_1000m	1.584***	0.589		3.353**	1.318		0.202	0.338		1.091	1.266		1.757***	0.571
公园绿地占比	1.489*	0.692		2.364	1.545		0.296*	0.304		2.101	1.157		1.719**	0.670
绿视率	-1.418***	0.537		2.890**	1.190		0.838***	0.303		0.287	1.157		-1.428**	0.521
最近公园的距离	-1.970***	0.533		-2.868**	1.189		0.591*	0.304		-2.036*	1.147		-1.786***	0.519
中介变量
健身时长													0.022	0.017
心理压力													-0.431***	0.075
社会交往													0.003	0.016
个体属性
性别(参照组：男)	-0.374**	0.101		-0.389*	0.208		-0.055	0.048		-0.565*	0.235		-0.397***	0.010
年龄	-0.770***	0.062		0.958***	0.131		0.086***	0.030		0.134	0.148		-0.699***	0.065
户口(参照组：本市)	-0.021	0.054		0.157	0.309		0.147	0.096		-0.376	0.679		-0.003**	0.147
教育(参照组：初中及以下)
高中中专大专	0.394	0.172		0.095	0.352		-0.052	0.081		0.526	0.397		0.385	0.168
本科及以上	0.579**	0.227		0.531	0.466		-0.124	0.108		0.395	0.524		0.555	0.221
收入	0.152	0.119		-0.429*	0.174		-0.524***	0.162		-0.237	0.198		0.135	0.084
组间方差	0.442			2.251			0.151			1.985			0.407
组内方差	1.752			7.351			0.391			9.467			1.679
对数似然值	-1313.042			-1858.136			-750.564			-1949.368			-1296.430
AIC	2655.731			3750.272			1535.564			3928.736			2628.861

注：^*P < 0.10;^**P < 0.05;^***P < 0.01。

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表2中模型2在基准模型上同时加入了健身时长、心理压力和社会交往3个中介变量,发现NDVI（β = 1.757,P < 0.01）及公园绿地占比（系数β = 1.719,P < 0.05）与自评健康呈显著正相关,绿视率（β = -1.428,P < 0.05）和最近公园的距离（β = -1.786,P < 0.01）与自评健康呈显著负相关;心理压力（β = -0.431,P < 0.01）与自评健康呈显著负相关。采用Sobel检验判断以上变量是否起中介作用,仅心理压力通过检验（Z = 5.136,P < 0.05）。由于案例社区多数位于人口密度较大的老城区,社区周边大中型公园数量与外围地区仍存在一定差距,公园距离越近,越能增强居民去公园的意愿,且眺望绿色植物能在一定程度上减少肾上腺素分泌和交感神经的兴奋,从而缓解心理压力,进而影响居民自评健康水平。

对4种绿化指标采用PSM进行稳健性分析（Sensitivity Analyses）,将处于各绿化指标排名前50%的社区居民作为处理组,其余居民作为控制组,再将表1中控制变量加以控制。在部分控制了自选择机制干扰后,社区绿化和居民自评健康仍存在显著相关（表3）。

Tab. 3
表3
表3社区绿化与自评健康的关联：基于PSM的分析
Tab. 3PSM analysis of neighborhood greenery on self-rated health

匹配方法	NDVI_1000 mATT值	公园绿地占比ATT值	绿视率ATT	最近公园的距离ATT值
邻近	0.792*	0.670*	-0.560**	0.590*
半径	0.184*	0.398***	-0.378**	1.075***
核心	0.201*	0.392***	-0.392**	1.076***

注：^*P < 0.10;^**P < 0.05;^***P < 0.01。

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3.2 绿化环境对居民自评健康影响的群体差异

3.2.1 绿化环境对不同收入群体自评健康的影响差异 由于低收入群体（<1499元）样本量仅占2.4%,考虑样本数量和广州人均月收入情况,选取个人月收入低于5000元的群体作为中低收入（353份）进行分层分析,探讨绿化环境对不同收入群体健康的差异影响。中低收入群体和高收入群体的空模型ICC分别为0.1559和0.4359,表明两类群体健康水平的整体差异在一定程度上均源自于社区间的差异。

模型3为基准模型,探求绿化环境对不同群体自评健康总效应,结果显示所有绿化指标均呈显著相关。模型3a′是将健身时长、心理压力、社会交往引入,在中低收入和高收入群体中,绿视率、最近公园的距离的系数均小于模型3系数,中低收入群体中健身时长（β = -0.049,P < 0.1）、心理压力（β = -0.538,P < 0.01）与自评健康呈显著负相关,高收入群体中心理压力（β = -0.354,P < 0.01）对自评健康呈显著负相关,表明对中低收入和高收入两类群体而言,促进体力活动和缓解压力是社区绿化影响居民健康的中间机制（表4）。

Tab. 4
表4
表4社区绿化对不同群体健康影响的分层分析(收入与性别)
Tab. 4Stratified analysis of neighborhood greenery and self-rated health by income and gender

指标	模型3 因变量：自评健康 (路径A)			模型3a' 因变量：自评健康(路径A、B'、C'、D')			模型4 因变量：自评健康 (路径A)			模型4a' 因变量：自评健康(路径A、B'、C'、D')
	系数 (中低收入)	系数 (高收入)		系数 (中低收入)	系数 (高收入)		系数 (女性)	系数 (男性)		系数 (女性)	系数 (男性)
绿化环境
NDVI_1000 m	1.163*	1.856***		1.448**	1.857***		1.366**	1.647**		1.629***	1.532**
公园绿地占比	1.630**	1.483**		1.941**	1.498**		0.998**	2.059***		1.439**	1.974***
绿视率	1.273*	1.527***		0.710**	1.299**		-0.802	-1.887***		-0.251	-1.894***
最近公园的距离	-1.273*	-2.431***		-1.112***	-2.142***		-2.319***	-1.549***		-2.034***	-1.359**
中介变量
健身时长				-0.049*	0.034					-0.063**	0.033
心理压力				-0.538***	-0.354***					-0.612***	-0.173*
社会交往				0.001	-0.005					0.010	-0.003
组间方差	0.526	0.428		0.481	0.338		0.462	0.489		0.403	0.430
组内方差	2.302	1.245		2.137	1.215		1.787	1.513		1.616	1.506
对数似然值	-664.696	-640.479		-651.443	-633.171		-677.395	-626.098		-657.723	-623.857
AIC	1357.393	1308.957		1336.886	1300.455		1386.790	1284.195		1353.446	1285.715

注：^*P < 0.10;^**P < 0.05;^***P < 0.01。模型已控制了表1所列的所有控制变量。

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进一步采用Sobel Test检验中介效应。低收入群体中,压力缓解在最近公园的距离（Z = 3.015,P = 0.003）、绿视率（Z = -3.227,P = 0.001）对自评健康的影响中发挥部分中介效应;在高收入群体中,压力缓解在最近公园的距离（Z = -3.951,P = 0.00007）发挥部分中介作用。即压力缓解在最近公园距离和绿视率对低收入群体自评健康的影响中发挥中介效应,压力缓解仅在最近公园的距离对高收入群体自评健康的影响中发挥中介效应,说明社区绿化对中低收入群体健康效应的影响程度大于高收入群体。邻里绿化环境好、绿地可达性高促进中低收入群体到户外减压,而高收入群体仅受可达性影响,原因为高收入家庭机动车拥有率较高,日常生活方式和活动范围与中低收入群体有所差异。调研过程中高收入受访者表示平时休憩娱乐时间较少,会选择健身等其他方式减压,因此对社区绿化依赖程度低。

3.2.2 绿化环境对不同性别群体自评健康的影响差异 进一步测度社区绿化环境的健康效应在不同性别群体间的差异。女性群体空模型和男性群体空模型的ICC分别为0.2640和0.3125,表明女性和男性群体健康水平的整体差异在一定程度上来自于社区间的差异。

模型4为基准模型,探求绿化环境的自评健康总效应。NDVI、公园绿地占比、最近公园的距离对女性群体自评健康呈显著相关,4种绿化指标对于男性群体自评健康呈显著相关。模型4a′将健身时长、压力、社会交往引入,结果显示,女性群体中最近公园的距离系数小于模型4系数,男性群体中NDVI、公园绿地占比、最近公园的距离均小于模型4的系数;女性群体中,健身时长（β = -0.063,P < 0.05）对女性自评健康呈显著负相关,但并未发挥部分中介效应;女性和男性群体心理压力对自评健康的影响均呈显著负相关,但女性（β = -0.612,P < 0.01）受心理压力的中介影响明显高于男性（β = -0.173,P < 0.1）（表4）。

Sobel Test检验结果表明,女性群体中,压力在最近公园的距离（Z = -2.035,P < 0.05）对自评健康的影响中发挥部分中介效应;男性群体中,压力在最近公园的距离（Z = -3.001,P < 0.05）发挥部分中介作用。总体上女性群体的压力中介效应程度更大。由于性别和体质差异,女性用于日常家务和照顾老幼的时间多于男性,因此活动范围较小,相较男性而言更偏向于在周边的公园绿地进行减压;而男性群体不排斥去更远的公园进行减压。另外,调研过程男性受访者表示,男性减压更多源自其他途径,相较女性更少通过绿地进行减压。

4 结论与讨论

本文采用广州问卷、遥感影像和城市街景,提取多种绿化指标,并使用多层模型和中介效应技术,探讨了社区绿化环境影响居民自评健康的“心理—社会—行为”机制,以及对不同社会群体间的影响差异。结果表明：社区绿化环境与居民自评健康存在显著关联;社区绿化通过减缓心理压力提升居民自评健康;社区绿化环境的健康效应在不同社会阶层和性别的群体间存在差异,对中低收入群体和女性群体的健康生活方式及健康水平的影响更大。

对比以往研究,本文证实了中国城市背景下同样存在健康的群体差异,即心理压力对中低收入和女性群体的中介效应更加显著。原因为中低收入群体对社区绿化依赖程度更高,高收入群体机动车拥有率较高,可以到远距离绿地休憩;女性相较于男性更倾向于在家周边开展运动进行减压。然而本研究中促进健身时长和社会交往的路径并不显著,可能是本研究区绿化环境对健身时长具有遮掩效应;中心城区居民社会交往差异较小,社区绿化促进社交的作用有限。本文从地理学视角阐明中国高密度城市中不同特征的绿化环境对居民健康的复杂影响路径和机制,补充了西方发达国家低密度城市所开展的研究结论。随着人文主义思潮兴起,健康地理更加关注微观个体与地方的关系,基于“心理—社会—行为”的分析框架,解释环境影响健康的社会行为机制,凸显地方（社区）所承载社会文化意义。

本文具有以下几点政策启示：在旧城改造过程中,建议在高密度的老城区通过见缝插针方式,增加和设置可达性高的绿地和开放空间以提高绿地的服务水平,强化居民使用绿地的意愿;加强社区城市绿色空间的连续性,规划建设覆盖面广和15 min内步行可达的绿地公园;突出公平性、以人为本的价值导向,根据不同群体社会经济特征,不同社区绿化空间的规划布局应有所差异,重点改善和提升弱势群体社区的绿化环境。

本文存在以下几点不足：① 本文使用截面数据揭示了绿化环境与健康结局之间的关联,未能识别两者间的因果关系,未来研究可结合追踪调查数据;② 本文所使用的中介变量（健身时长、心理压力水平和社会交往水平）均为自评指标,未来的研究可使用计步器、手持GPS和心理学仪器等仪器,收集这些中介变量的客观数据;③ 未考虑个人饮食习惯、基因体质和行为偏好等因素的影响,未来研究可以把相关因素纳入到问卷调查中。

参考文献 View Option 原文顺序
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被引期刊影响因子

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[5] Markevych I, Schoierer J, Hartig T, et al. Exploring pathways linking greenspace to health: Theoretical and methodological guidance
Environmental Research, 2017, 158:301-317.
DOI:S0013-9351(17)30306-7PMID:28672128 [本文引用: 3]
In a rapidly urbanizing world, many people have little contact with natural environments, which may affect health and well-being. Existing reviews generally conclude that residential greenspace is beneficial to health. However, the processes generating these benefits and how they can be best promoted remain unclear.During an Expert Workshop held in September 2016, the evidence linking greenspace and health was reviewed from a transdisciplinary standpoint, with a particular focus on potential underlying biopsychosocial pathways and how these can be explored and organized to support policy-relevant population health research.Potential pathways linking greenspace to health are here presented in three domains, which emphasize three general functions of greenspace: reducing harm (e.g. reducing exposure to air pollution, noise and heat), restoring capacities (e.g. attention restoration and physiological stress recovery) and building capacities (e.g. encouraging physical activity and facilitating social cohesion). Interrelations between among the three domains are also noted. Among several recommendations, future studies should: use greenspace and behavioural measures that are relevant to hypothesized pathways; include assessment of presence, access and use of greenspace; use longitudinal, interventional and (quasi)experimental study designs to assess causation; and include low and middle income countries given their absence in the existing literature. Cultural, climatic, geographic and other contextual factors also need further consideration.While the existing evidence affirms beneficial impacts of greenspace on health, much remains to be learned about the specific pathways and functional form of such relationships, and how these may vary by context, population groups and health outcomes. This Report provides guidance for further epidemiological research with the goal of creating new evidence upon which to develop policy recommendations.Copyright © 2017 Elsevier Inc. All rights reserved.

[6] Nieuwenhuijsen M J, Khreis H, Triguero-Mas M, et al. Fifty shades of green: Pathway to healthy urban living
Epidemiology, 2017, 28(1):63-71.
PMID:27525811 [本文引用: 2]
Currently half the world population lives in cities, and this proportion is expected to increase rapidly to 70% over the next years. Over the years, we have created large, mostly grey cities with many high-rise buildings and little green space. Disease rates tend to be higher in urban areas than in rural areas. More green space in cities could reduce these rates. Here, we describe the importance of green space for health, and make recommendations for further research. Green space has been associated with many beneficial health effects, including reduced all-cause and cardiovascular mortality and improved mental health, possibly through mediators, such as reduced air pollution, temperature and stress, and increased physical activity, social contacts, and restoration. Additional studies are needed to strengthen the evidence base and provide further guidelines to transport planners, urban planners, and landscape architects. We need more longitudinal studies and intervention studies, further understanding of the contribution of various mechanisms toward health, and more information on susceptible populations and on where, when, how much, and what type of green space is needed. Also needed are standardized methods for green space quality assessments and evaluations of effectiveness of green prescriptions in clinical practice. Many questions are ideally suited for environmental epidemiologists, who should work with other stakeholders to address the right questions and translate knowledge into action. In conclusion, a growing evidence base supports the hypothesis that greener cities are healthier cities.

[7] Cohen-Cline H, Turkheimer E, Duncan G E. Access to green space, physical activity and mental health: A twin study
Journal of Epidemiology and Community Health, 2015, 69(6):523-529.
DOI:10.1136/jech-2014-204667PMID:25631858 [本文引用: 1]
Increasing global urbanisation has resulted in a greater proportion of the world's population becoming exposed to risk factors unique to urban areas, and understanding these effects on public health is essential. The aim of this study was to examine the association between access to green space and mental health among adult twin pairs.We used a multilevel random intercept model of same-sex twin pairs (4338 individuals) from the community-based University of Washington Twin Registry to analyse the association between access to green space, as measured by the Normalised Difference Vegetation Index and self-reported depression, stress, and anxiety. The main parameter of interest was the within-pair effect for identical (monozygotic, MZ) twins because it was not subject to confounding by genetic or shared childhood environment factors. Models were adjusted for income, physical activity, neighbourhood deprivation and population density.When treating twins as individuals and not as members of a twin pair, green space was significantly inversely associated with each mental health outcome. The association with depression remained significant in the within-pair MZ univariate and adjusted models; however, there was no within-pair MZ effect for stress or anxiety among the models adjusted for income and physical activity.These results suggest that greater access to green space is associated with less depression, but provide less evidence for effects on stress or anxiety. Understanding the mechanisms linking neighbourhood characteristics to mental health has important public health implications. Future studies should combine twin designs and longitudinal data to strengthen causal inference.Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

[8] Dadvand P, Bartoll X, Basagaña X, et al. Green spaces and general health: Roles of mental health status, social support, and physical activity
Environment International, 2016, 91:161-167.
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[9] Hartig T, Mitchell R, de Vries S, et al. Nature and health
Annual Review of Public Health, 2014, 35:207-228
DOI:10.1146/annurev-publhealth-032013-182443PMID:24387090 [本文引用: 1]
Urbanization, resource exploitation, and lifestyle changes have diminished possibilities for human contact with nature in urbanized societies. Concern about the loss has helped motivate research on the health benefits of contact with nature. Reviewing that research here, we focus on nature as represented by aspects of the physical environment relevant to planning, design, and policy measures that serve broad segments of urbanized societies. We discuss difficulties in defining "nature" and reasons for the current expansion of the research field, and we assess available reviews. We then consider research on pathways between nature and health involving air quality, physical activity, social cohesion, and stress reduction. Finally, we discuss methodological issues and priorities for future research. The extant research does describe an array of benefits of contact with nature, and evidence regarding some benefits is strong; however, some findings indicate caution is needed in applying beliefs about those benefits, and substantial gaps in knowledge remain.

[10] Jiang B, Chang C Y, Sullivan W C. A dose of nature: Tree cover, stress reduction, and gender differences
Landscape and Urban Planning, 2014, 132:26-36.
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[11] Liu Y, Wang R Y, Grekousis G, et al. Neighbourhood greenness and mental wellbeing in Guangzhou, China: What are the pathways?
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[12] van den Berg A E, Maas J, Verheij R A, et al. Green space as a buffer between stressful life events and health
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[13] de Vries S, Van Dillen S, Groenewegen P, et al. Streetscape greenery and health: Stress, social cohesion and physical activity as mediators
Social Science & Medicine, 2013, 94:26-33.
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[14] Grazuleviciene R, Dedele A, Danileviciute A, et al. The influence of proximity to city parks on blood pressure in early pregnancy
International Journal of Environmental Research and Public Health, 2014, 11(3):2958-2972.
DOI:10.3390/ijerph110302958PMID:24619158
This study investigated the effect of proximity to city parks on blood pressure categories during the first trimester of pregnancy. This cross-sectional study included 3,416 female residents of the city of Kaunas, Lithuania, who were enrolled in the FP7 PHENOTYPE project study. The women were classified into four blood pressure categories: optimal, normal, high-normal blood pressure, and hypertension. Multinomial regression models were used to investigate the association between three women's groups with respect to the residence distances from city parks (300, >300-1,000, and >1,000 m) and four blood pressure categories. When using the optimal blood pressure as the reference group, the crude and adjusted odds ratios (OR) for normal blood pressure and for high-normal blood pressure proved to be statistically significantly higher after the inclusion of the selected covariates into the regression analysis. The probability of normal blood pressure increased by 9%, and that of high-normal blood pressure-by 14% for every 300 m increase in the distance to green spaces. The findings of this study suggest a beneficial impact of nearby city parks on blood pressure amongst 20- to 45-year-old women. This relationship has important implications for the prevention of hypertension and the reduction of hypertension-related morbidity.

[15] Wang R Y, Helbich M, Yao Y, et al. Urban greenery and mental wellbeing in adults: Cross-sectional mediation analyses on multiple pathways across different greenery measures
Environmental Research, 2019, 176:108535. DOI: 10.1016/j.envers.2019.108535.
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[16] Chiang Y C, Li D Y. Metric or topological proximity? The associations among proximity to parks, the frequency of residents' visits to parks, and perceived stress
Urban Forestry & Urban Greening, 2019, 38:205-214.
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[17] Kaplan S. The restorative benefits of nature: Toward an integrative framework
Journal of Environmental Psychology, 1995, 15(3):169-182.
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[18] Lu Y. Using Google Street View to investigate the association between street greenery and physical activity
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[19] Pietilä M, Neuvonen M, Borodulin K, et al. Relationships between exposure to urban green spaces, physical activity and self-rated health
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[20] Maas J, van Dillen S M E, Verheij R A, et al. Social contacts as a possible mechanism behind the relation between green space and health
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[21] Althoff T, Sosič R, Hicks J L, et al. Large-scale physical activity data reveal worldwide activity inequality
Nature, 2017, 547(7663):336-339.
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[22] de Vries S, Verheij R A, Groenewegen P P, et al. Natural environments-healthy environments? An exploratory analysis of the relationship between greenspace and health
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[23] Cummings J L, Braboy Jackson P. Race, gender, and SES disparities in self-assessed health, 1974-2004
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[24] Bell J F, Wilson J S, Liu G C. Neighborhood greenness and 2-year changes in body mass index of children and youth
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[25] McCormick R. Does access to green space impact the mental well-being of children: A systematic review
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[26] Estabrooks P A, Lee R E, Gyurcsik N C. Resources for physical activity participation: Does availability and accessibility differ by neighborhood socioeconomic status?
Annals of Behavioral Medicine, 2003, 25(2):100-104.
PMID:12704011 [本文引用: 1]
The purpose of this study was to determine whether the availability and accessibility of physical activity resources differed by neighborhood socioeconomic status (SES) in a small U.S. city (population = 133,046). U.S. census tracts (N =32) were used to represent neighborhoods and categorized into high, medium, or low SES on the basis of the percentage of unemployed individuals, per capita income, and percentage of the population below the poverty threshold. We developed a geographic information system to generate a comprehensive list of physical activity resources available within each census tract in the city. We identified 112 parks, 33 sport facilities, 15 fitness clubs, 11 community centers, and 5 walking/biking trails. The total number of physical activity resources varied by neighborhood SES (p <.05); low-SES (M =4.5-/+2.3) and medium-SES (M =4.9-/+2.6) neighborhoods had significantly fewer resources than high-SES (M =8.4-/+3.5) neighborhoods. Low-, medium-, and high-SES neighborhoods did not differ on the number of pay-for-use facilities; however, low-SES (M =3.1-/+1.5) and medium-SES (M =3.8-/+1.6) neighborhoods had significantly fewer free-for-use resources than high- (M =6.1-/+2.4) SES neighborhoods (p <.01). Data suggest that individuals from lower SES neighborhoods may have limited ability to control their physical activity in the face of inaccessible environments. Community research and promotion efforts should include assessment and targeting of available and accessible physical activity resources.

[27] Harris C D, Paul P, Zhang X Y, et al. Park access among school-age youth in the United States
Journal of Physical Activity and Health, 2015, 12(S1):S94-S101.
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[28] Mitchell R, Popham F. Effect of exposure to natural environment on health inequalities: An observational population study
The Lancet, 2008, 372(9650):1655-1660.
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[29] Cerin E, Leslie E, Toit L D, et al. Destinations that matter: Associations with walking for transport
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[30] Richardson E A, Mitchell R. Gender differences in relationships between urban green space and health in the United Kingdom
Social Science and Medicine, 2010, 71(3):568-575.
DOI:S0277-9536(10)00345-XPMID:20621750 [本文引用: 1]
Natural environments, or 'green spaces', have been associated with a wide range of health benefits. Gender differences in neighbourhood effects on health have been found in a number of studies, although these have not been explored in relation to green space. We conducted the first UK-wide study of the relationship between urban green space and health, and the first such study to investigate gender differences in this relationship. An ecological approach was used. Two land use datasets were used to create a proportional green space measure (% by area) at the UK Census Area Statistic ward scale. Our sample consisted of 6432 urban wards, with a total population of 28.6 million adults aged 16-64 years in 2001. We selected health outcomes that were plausibly related to green space (cardiovascular disease mortality, respiratory disease mortality and self-reported limiting long-term illness) and another that was expected to be unrelated (lung cancer mortality). Negative binomial regression models examined associations between urban green space and these health outcomes, after controlling for relevant confounders. Gender differences in these associations were observed and tested. Male cardiovascular disease and respiratory disease mortality rates decreased with increasing green space, but no significant associations were found for women. No protective associations were observed between green space and lung cancer mortality or self-reported limiting long-term illness for either men or women. Possible explanations for the observed gender differences in the green space and health relationship are gender differences in perceptions and usage of urban green spaces. We conclude that it is important not to assume uniform health benefits of urban green space for all population subgroups. Additionally, urban green space measures that capture quality as well as quantity could be more suited to studying green space and health relationships for women.Copyright 2010 Elsevier Ltd. All rights reserved.

[31] Zhao Xueyan, Wang Weijun, Wan Wenyu. Regional inequalities of residents' health level in China: 2003-2013
Acta Geographica Sinica, 2017, 72(4):685-698.
DOI:10.11821/dlxb201704010 [本文引用: 1]
As one of the core problems clsoely related with human development, health inequality has aroused an increasing concern in the world. Through an integration of the coefficient of variation, Theil index, exploratory spatial data analysis (ESDA) and spatial panel econometric model, we examine the regional inequality, spatial-temporal dynamic patterns and the key factors of the residents' health level (RHL) in China from 2003 to 2013. The aim is to provide scientific basis for policy making on regional health inequality reduction in China. The results are shown as follows: (1) China's RHL index decreased from 0.404 to 0.295 in 2003-2013, with an annual rate of 2.698%. Specifically speaking, the growing rate in the western region was higher than that in the eastern and central regions, but the there is no change in RHL in terms of the basic pattern which decreases from the east to central part then to the west. (2) The regional inequality of RHL presented an extending trend in 2003-2013. Among this, the RHL inequality between regions presented a reducing trend, but that within a region presented an expanding trend. And the growing rate of inequalities of RHL in the western region was higher than that of the eastern and central regions. (3) The spatial distribution of RHL has tended into the letter "T" shape, and the RHL presented a stepped decrease from the east to the central part then to the west and a symmetric decrease from the north to the central part then to the south. (4) By observing the change of Moran's I in 2003, 2008 and 2013, we found that the spatial agglomeration range of RHL presented a narrowing trend. All the hot spots and cold spots presented a shrinking tendency, the RHL in the west formed a stable cold spot, including Xinjiang, Qinghai and Xizang, but that in the east coastal area formed a stable hot spot, including Shandong, Henan, Qinghai, Hubei, Anhui, Jiangsu and Shanghai. (5) The selected explanatory variables, such as per capita GDP, per capita spending on health, urbanization level and environment quality, have significant direct impacts on the RHL in China. With the increase of per capita GDP, per capita spending on health and urbanization level and the improvement of environment quality, the RHL will be raised. Finally, this paper points out the attention should be focused on the research of the regional inequality of RHL, such as the problems of the residents' multi-time-domain, multi-scale and multi-influencing mechanism.
[ 赵雪雁, 王伟军, 万文玉. 中国居民健康水平的区域差异: 2003—2013
地理学报, 2017, 72(4):685-698.]
[本文引用: 1]

[32] Gu Lijuan, Mark Rosenberg, Zeng Juxin. The impacts of socioeconomic and environmental factors on self-rated health status among different income groups in China
Geographical Research, 2017, 36(7):1257-1270.
DOI:10.11821/dlyj201707006 [本文引用: 1]
China's rapid socioeconomic growth in recent years and the simultaneous increase in many forms of pollution are generating contradictory pictures of residents' health status. To understand this twofold phenomenon and explore the influencing factors of health is the key point to realize the "healthy China" strategy and the prerequisite to take any effective action. Given the rapid economic development, the severe environmental degradation, and the rising health inequalities among different income groups in modern China, this paper applies multilevel analysis to the 2013 China General Social Survey data on social development and health and the China Statistical Yearbook data. Three-level logistic models from the individual, community and provincial levels are developed to investigate the impact of socioeconomic development and environmental degradation on self-reported health, differentiating among lower, middle and higher income groups. The results of the multi-level logistic analysis demonstrate that for all the three income groups, individual-level factors contribute more to the explanation of health than community-level or provincial-level factors. Income, job and education increase the likelihood of rating health positively for the lower and middle groups but have little or no effects on the higher-income group. Compared to the lower and middle groups, residents from the high-income group can buffer themselves from the adverse effect of the environmental degradation. Environmental risks have a mediating effect on the relationship between socioeconomic development and health. These outcomes indicate that the complex interconnections among socioeconomic development and environmental degradation have differential effects on health status among different income groups. Besides social inequality, environmental injustice is another cause of health inequities among different income groups in modern China. This study is the first empirical research exploring the interplay of socioeconomic development and environmental degradation on health by conducting analysis on lower, middle and higher income groups respectively. The results provide an in-depth understanding of health and its key impacting factors and offer some concise policy implications to improve the health status of general populations in a more efficient way.
[ 顾丽娟, Mark Rosenberg, 曾菊新. 社会经济及环境因子对不同收入群体自评健康的影响
地理研究, 2017, 36(7):1257-1270.]
[本文引用: 1]

[33] Ma Jing, Zhou Chuangwen, Pryce G. Spatial analysis and modelling of air pollution and death rates in Hebei province, China
Human Geography, 2019, 34(6):45-52.
[本文引用: 1]

[ 马静, 周创文, Gwilym Pryce. 环境公正视角下空气污染和死亡人数的空间分析及关系研究: 以河北省为例
人文地理, 2019, 34(6):45-52.]
[本文引用: 1]

[34] Sun Bindong, Yan Hong, Zhang Tinglin. Impact of community built environment on residents' health: A case study on individual overweight
Acta Geographica Sinica, 2016, 71(10):1721-1730.
DOI:10.11821/dlxb201610005 [本文引用: 2]
With the rapid economic development and living condition improvement in China, the problem of obesity becomes increasingly serious and threats to the residents' health. Based on the data of China family panel studies, this study examined the association among community built environment, individual commuting mode choice and overweight of adults with the structural equation model. It is concluded that community built environment has a significant effect on individual commuting mode choice and overweight. After social economic factors are controlled, improving population density and degree of accessibility of facilities, shortening the distance to the nearest bus station will lower the likelihood of overweight by the intermediary role of reducing private motorized commuting; however, the direct and total effects are positive. The latter conclusion contrasts with findings from western developed countries. In order to alleviate the obesity problem of China through geographical perspective, we should make urban planning policy according to China's own conditions and rules.
[ 孙斌栋, 阎宏, 张婷麟. 社区建成环境对健康的影响: 基于居民个体超重的实证研究
地理学报, 2016, 71(10):1721-1730.]
[本文引用: 2]

[35] Zhang Yanji, Qin Bo, Tang Jie. The impact of urban built environment on residential physical health: Based on propensity score matching
Acta Geographica Sinica, 2018, 73(2):333-345.
[本文引用: 3]

[ 张延吉, 秦波, 唐杰. 基于倾向值匹配法的城市建成环境对居民生理健康的影响
地理学报, 2018, 73(2):333-345.]
[本文引用: 3]

[36] Liu Yi, Liu Yuqi, Liu Ye, et al. Impacts of neighborhood environments on migrants' subjective wellbeing: A case study of Guangzhou, China
Progress in Geography, 2018, 37(7):986-998.
[本文引用: 2]

[ 刘义, 刘于琪, 刘晔, 等. 邻里环境对流动人口主观幸福感的影响: 基于广州的实证
地理科学进展, 2018, 37(7):986-998.]
[本文引用: 2]

[37] Zhang L, Zhou S H, Kwan M P, et al. Impacts of individual daily greenspace exposure on health based on individual activity space and structural equation modeling
International Journal of Environmental Research and Public Health, 2018, 15(10):E2323. DOI: 10.3390/ijerph15102323.
[本文引用: 1]

[38] Qiu Yingzhi, Chen Hongsheng, Li Zhigang, et al. Exploring neighborhood environmental effects on mental health: A case study in Guangzhou, China
Progress in Geography, 2019, 38(2):283-295.
DOI:10.18306/dlkxjz.2019.02.011 [本文引用: 1]
When China enters the "urban era," the environmental and social problems brought about by rapid urbanization have posed a serious threat to urban residents' physical and mental health outcomes. Urban health problems have become an important concern both in academic circles and in the public sphere. As an indispensable dimension of health, mental health is an important prerequisite for achieving "good life." In Western urban studies, the last decade has seen an acceleration in research examining the close relationship between urban environment and mental health outcomes. A growing body of literature has reported significant impact of neighborhood characteristics on residents' mental health, such as housing condition, green space, and neighborhood social support, among others. In China, however, mechanism of mental health associated with neighborhood environment remains poorly understood. This article systematically explores the direct impact of neighborhood environment on mental health, which helps to promote the "Healthy City" and new urbanization strategies. Based on the data collected from a questionnaire survey involving 23 neighborhoods and 1,150 respondents in Guangzhou City, this research employed the General Health Questionnaire (GHQ-12) and multilevel linear regression models to examine the surveyed residents' mental health and its determinants. It particularly focused on the extent to which and the ways in which neighborhood built and social environments influence their residents’ mental health status. The results indicate that mental health status of residents living in different neighborhoods was significantly different. Multilevel modeling shows that residents' mental health is influenced by both individual and neighborhood factors. Regarding individual variables, low educational level is negatively related to mental health. Residents with housing property rights have better mental health outcomes than those without housing property rights. In addition, good physical health condition contributes to better mental health status. After controlling for individual variables, the impact of neighborhood environment is still significant. For variables related to the built environment, residents living in neighborhoods with abundant facilities and parks around have better mental health outcomes. With regard to neighborhood social environment, high neighborhood interactions and adequate community groups lead to better mental health status, but community disputes can negatively affect mental health of residents. This study confirms that neighborhood characteristics have significant influence on mental health in Guangzhou City, China, which provides valuable implications for policies and practices seeking to promote residents’ mental health. First, facilities and parks should be taken into consideration in neighborhood design and city planning. Another useful approach to improving mental health is for community committees and residents to make joint efforts to build a cohesive and supportive neighborhood, including enriching community groups and strengthening neighborhood interactions.
[ 邱婴芝, 陈宏胜, 李志刚, 等. 基于邻里效应视角的城市居民心理健康影响因素研究: 以广州市为例
地理科学进展, 2019, 38(2):283-295.]
[本文引用: 1]

[39] Helbich M, Yao Y, Liu Y, et al. Using deep learning to examine street view green and blue spaces and their associations with geriatric depression in Beijing, China
Environment International, 2019, 126:107-117.
DOI:10.1016/j.envint.2019.02.013URL [本文引用: 1]