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邻里环境感知与个体健康对通勤模式选择的影响研究——以南京市为例

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曹晨,1, 甄峰,2,3, 姜玉培2,31.南京大学地理与海洋科学学院,南京 210023
2.南京大学建筑与城市规划学院,南京 210093
3.江苏省智慧城市设计仿真与可视化工程实验室,南京 210093

The influence of neighborhood environmental perception and individual health on commuting mode choice: A case study of Nanjing city, China

CAO Chen,1, ZHEN Feng,2,3, JIANG Yupei2,31. School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China
2. School of Architecture and Urban Planning, Nanjing University, Nanjing 210093, China
3. Provincial Engineering Laboratory of Smart City Design Simulation & Visualization, Jiangsu, Nanjing 210093, China

通讯作者: 甄峰（1973-）,男,陕西汉中人,博士,教授,博士生导师,主要研究方向为智慧城市、大数据与城市规划。E-mail: zhenfeng@nju.edu.cn

收稿日期:2020-11-4修回日期:2021-04-27
基金资助:国家社会科学基金重点项目(20AZD040)


Received:2020-11-4Revised:2021-04-27
作者简介 About authors
曹晨（1993-）,女,甘肃平凉人,博士研究生,主要研究方向为健康地理、土地利用与区域发展。E-mail: caochen_nju@163.com





摘要
通勤是大部分城市就业者日常工作所必须面对的问题。相比宏观层面的城市建成环境,微观层面的邻里环境感知及个体健康与通勤模式选择的联系更为密切。以南京市主城区8个社区为案例地,利用结构方程模型探究邻里环境感知与个体健康对城市就业者通勤模式选择的影响。首先借助问卷调查数据,将通勤模式分为积极通勤、公共交通通勤、电动车/摩托车通勤以及小汽车通勤四类;其次使用因子分析法确定邻里环境感知的潜变量,并将个体健康分为身体健康、心理健康与健康行为3个潜变量;最后构建结构方程模型的理论框架并进行实证分析。研究发现：① 邻里环境感知变量中服务设施感知、环境品质感知对通勤模式选择存在显著的直接效应,交通安全感知对通勤模式选择存在显著的间接效应。② 个体健康变量中,心理健康与健康行为对通勤模式选择存在显著的直接效应。③ 通勤距离与社会经济属性对通勤模式选择存在显著的直接效应,同时社会经济属性对通勤模式选择亦存在显著的间接效应。基于研究结果,提出有效提升就业者对于社区环境的主观感受,进而提高就业者参与健康行为的意愿与就业者的健康水平,从而引导其选择更加健康和绿色的通勤模式。
关键词： 通勤模式选择;邻里环境感知;个体健康;结构方程模型;南京

Abstract
Commuting is an issue facing urban employees in their daily work. Compared to the macro-level urban built environment, the macro-level neighborhood environmental perception and individual health are more closely related to commuting mode choice. This paper takes eight typical communities with different traffic environments, leisure environments and socioeconomic statuses in the main urban area of Nanjing city as the research area, and uses the structural equation model to investigate the influence of neighborhood environmental perception and individual health on urban employees' commuting mode choice. Firstly, based on questionnaire survey data, according to the amount of physical activity consumed by the employees when they choose commuting modes during the commuting process, the commuting modes are divided into four categories: active commuting, public transportation commuting, electric vehicle/motorcycle commuting and car commuting. Secondly, the latent variables of neighborhood environmental perception are identified using factor analysis, and individual health is divided into three latent variables: physical health, mental health and health behavior. Finally, the theoretical framework of the structural equation model is constructed and an empirical analysis is conducted using the structural equation model. The results show that: (1) The perception of service facilities and environmental quality have significant direct effects on commuting mode choice, while traffic safety perception has significant indirect effect. (2) Mental health and health behavior in the individual health variables have significant direct effects on commuting mode choice. (3) Commuting distance and socioeconomic attributes have significant direct effects on commuting mode choice, while socioeconomic attributes have significant indirect effects, which come from the impact of socioeconomic attributes on commuting distance, neighborhood environmental perception and individual health. Based on the results of the study, it is proposed to effectively enhance the subjective perception of the neighborhood environment, and increase the willingness to participate in healthy behaviors and health awareness of the urban employees so as to choose a more healthy and environmentally friendly commuting mode.
Keywords：commuting mode choice;neighborhood environmental perception;individual health;structural equation model (SEM);Nanjing


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本文引用格式
曹晨, 甄峰, 姜玉培. 邻里环境感知与个体健康对通勤模式选择的影响研究——以南京市为例[J]. 地理研究, 2021, 40(10): 2823-2837 doi:10.11821/dlyj020201072
CAO Chen, ZHEN Feng, JIANG Yupei. The influence of neighborhood environmental perception and individual health on commuting mode choice: A case study of Nanjing city, China[J]. Geographical Research, 2021, 40(10): 2823-2837 doi:10.11821/dlyj020201072


1 引言

通勤是城市就业者日常交通出行的重要组成部分,在中国,居民通勤出行量占日常总出行量的比例约为40%[1]。在当前经济发展迅速、城市空间扩张、小汽车保有量剧增的背景下,选择小汽车通勤的就业者比例不断上升。大量的小汽车通勤出行给城市交通带来了巨大压力,造成交通拥堵与环境污染等问题,同时对小汽车通勤的过度依赖还增加了就业者的久坐时间,使就业者的身体活动不足并承受着更大的精神压力和消极情绪,进而引起一系列健康问题[2,3,4]。而当前,中国政府与居民个人越来越重视健康问题。2016年国务院印发并实施了全国首个健康领域的中长期战略规划——《“健康中国2030”规划纲要》,体现了中国政府对于国民健康问题与健康环境的重视。随着“健康中国”上升为一项国家战略,同时城市就业者对通勤出行中的环境与健康问题也日益关注,势必对就业者通勤模式选择产生新的影响。

目前已有大量文献探究建成环境与出行行为之间的关系,但这些研究多关注宏观层面的建成环境,如密度（Density）[5,6,7,8]、多样性（Diversity）[9,10,11,12]、设计（Design）[13,14,15,16]、目的地可达性（Destination accessibility）[17,18]等的影响。环境感知是人们对周围环境及其变化的主观感觉和心理判断,是人们环境行为的心理基础[19]。即使居住在建成环境相同的区域,也可能会因个人经历、态度与偏好、社会经济属性等不同而对环境的感知度不同,并影响其自身行为。当评价建成环境是否成功时,不能只关注客观建成环境[20]8,建成环境的主观感知与人的联系更为密切,因此也应该将其考虑进去。但目前较少有研究探究建成环境感知是否与通勤行为的变化有关。在少数研究中,Humpel等认为居民感知的路线便利性改善可以促进步行行为[21];Panter等发现骑自行车或过马路时危险感的增加与汽车出行正相关,骑自行车时安全性增加与减少汽车使用有关[22]。现有相关研究对于建成环境感知的度量指标暂无统一的认识,如Booth等使用可用的自行车道、体育锻炼设施、休闲娱乐设施、商店可达性、步行的安全性、步行道的安全性等指标分析环境感知与步行运动、休闲活动的关系[23];张舒怡等从物质环境感知（包括环境品质感知、服务设施感知及体育设施感知）与社会环境感知（包括社区组织感知、社区归属感及社区安全感）两方面对社区建成环境感知进行评价[19];冯建喜等提出可达性、安全性和吸引力三方面的的建成环境感知评价要素[24]。

此外,在通勤与健康的相关研究中,主要以两种独立的关系作为研究对象,即通勤对健康的影响与健康对通勤的影响。大多数****认为就业者选择不同通勤方式时身体活动量不同,因此会产生不同的健康效应[3,4,25-27]。但相反,就业者自身的健康状况、健康行为也可能会影响其通勤模式的选择[28,29,30,31,32]。目前,通勤对健康的影响研究更为普遍,而健康对通勤的影响研究相对较少。在少数研究中,Carse等发现,通勤模式选择与心理健康、身体健康、BMI有关[28]。Hansson等认为身体不健康的人可能不会开始或继续选择步行、骑自行车通勤[29]。Mattisson等和Kroesen等发现,肥胖与积极通勤呈负相关[30,31],同时Kroesen等还发现心理健康对积极通勤有正向影响[31]。现有的研究对于健康影响通勤的机制解释较弱,有****认为身体健康使积极通勤更容易或更可行[33]。随着研究的深入,逐渐有****认识到通勤与健康二者之间的潜在联系与作用机制,如有****认为由于积极通勤而增加的良好的心理感受对通勤者来说是一种“奖励”,反之又形成积极通勤的内在动机[31]。但由于个体纵向数据集获取的限制,目前关于通勤与健康相互作用的研究较少。

综上,国内外关于建成环境感知对通勤模式选择的影响尚未得出一致的研究结论,并且个体健康对通勤模式选择的影响需进行进一步的验证。因此,本文以南京市为案例地,选择微观层面的邻里环境感知与个体健康相关因素,探究其对就业者通勤模式选择的影响。理论方面,本文的研究有助于拓展中国城市通勤问题的研究视角,明确邻里环境感知度量的指标,深化个体健康对通勤模式选择的影响研究。实践方面,有助于相关城市管理部门制定完善城市建成环境、提升就业者对建成环境的主观感受及提升个体健康水平的策略,以期引导就业者选择更加健康和绿色的通勤模式,从而减少对小汽车通勤的依赖,缓解城市交通拥堵、环境污染等问题。

2 数据来源与研究方法

2.1 数据来源

本文研究所用的数据来源于2017年12月至2018年1月开展的“南京市居民身体活动及健康状况”问卷调查。选取南京市主城区交通性环境、休闲性环境与社会经济地位不同的8个典型社区作为调研区域（图1）。其中,云南路社区和俞家巷社区位于老城区,交通性环境较好而休闲性环境较差,属于Ⅰ类社区;中奥社区和凤栖苑社区位于河西新城,交通性环境和休闲性环境均较好,属于Ⅱ类社区;汇林绿洲社区和锁金村社区临近玄武湖景区,交通性环境较差而休闲性环境较好,属于Ⅲ类社区;景明佳园社区和兴卫村社区位于主城边缘,交通性环境和休闲性环境均较差,属于Ⅳ类社区,不同类型社区的建成环境特征与样本特征见表1。采用偶遇抽样方法进行纸质问卷填写,对18岁及以上全职就业者的通勤行为、邻里环境感知、个体健康状况以及社会经济属性等信息进行记录,共获取有效样本622个,样本的社会经济属性见表2所示。

图1

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图1调研社区的分布图

Fig. 1Distribution of the case study communities



Tab. 1
表1
表1调研社区类型及特征
Tab. 1Types and characteristics of the case study communities
社区类型平均通勤
距离(km)		建成环境特征样本特征
交通性环境休闲性环境拥有小汽车占比(%)个人月收入大于10000元占比(%)本科/大专及以上学历占比(%)本地户籍
占比(%)
Ⅰ类社区4.80较好较差46.6721.2170.3064.24
Ⅱ类社区6.72较好较好69.6627.5977.9370.34
Ⅲ类社区9.24较差较好80.5227.2788.3185.71
Ⅳ类社区6.86较差较差62.6622.7875.3265.19

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Tab. 2
表2
表2样本社会经济属性
Tab. 2Socioeconomic attributes of the survey samples
变量变量符号样本量变量描述变量变量符号样本量变量描述
性别a1622（100.00%）虚拟家庭人口结构a5622（100.00%）等级
男331（53.22%）0单身68（10.93%）1
女291（46.78%）1两口之家123（19.77%）2
年龄a2622（100.00%）连续三口之家265（42.60%）3
个人月收入(元)a3622（100.00%）等级四口之家85（13.67%）4
≤200030（4.82%）1五口之家67（10.77%）5
2001~4000115（18.49%）2六口之家14（2.25%）6
4001~6000134（21.54%）3最高学历a6622（100.00%）虚拟
6001~800096（15.43%）4高中/中专及以下138（22.19%）0
8001~1000094（15.11%）5本科/大专及以上484（77.81%）1
10001~1500077（12.38%）6户籍a7622（100.00%）虚拟
>1500076（12.22%）7外地179（28.78%）0
是否有小汽车a4622（100.00%）虚拟本地443（71.22%）1
否221（35.53%）0
是401（64.47%）1

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2.2 研究方法

结构方程模型（structural equation model,SEM）是基于变量的协方差矩阵分析变量之间关系的多元数据分析工具,整合了因子分析（factor analysis）与路径分析（path analysis）两种统计方法,可以检验模型中潜变量、观测变量与误差变量之间的关系,并且获得自变量对因变量影响的直接效应、间接效应和总体效应[34,35]。由于本文探究邻里环境感知与个体健康对通勤模式选择的影响,因变量通勤模式为四分类变量,且存在通勤距离等中介变量,传统的多元回归方法难以支撑分析,因此采用结构方程模型,通过效应值来分析邻里环境感知与个体健康对通勤模式选择的影响。

3 变量选取与理论模型构建

3.1 变量选取

3.1.1 通勤模式分类 本文根据就业者通勤过程中选择不同通勤方式时所消耗身体活动量的大小,将通勤模式划分为四类（表3）。其中,步行和骑自行车身体活动量最大,将其作为积极通勤模式;乘坐公交和地铁的过程中可能会采取不同通勤方式的组合,如步行、骑自行车转换乘公共交通等,包含一定量的身体活动,作为公共交通通勤模式;电动车和摩托车作为在中国较常见的短距离出行的机动化交通方式,为电动车/摩托车通勤模式;小汽车通勤模式包括私家车、出租车以及单位班车,就业者基本以静坐为主,身体活动量最小[26,36-39]。

Tab. 3
表3
表3通勤模式变量描述性统计
Tab. 3Descriptive statistics of commuting mode variable
变量变量符号样本量变量描述
通勤模式t622（100.00%）类别
积极通勤192（30.87%）1
公共交通通勤195（31.35%）2
电动车/摩托车通勤66（10.61%）3
小汽车通勤169（27.17%）4

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3.1.2 邻里环境感知变量的探索性分析 邻里环境感知数据来源于问卷调查,针对就业者对于邻里环境的感受设置若干问题,选项设置选用5点李克特量表,1代表“非常不同意”,5代表“非常同意”。

利用SPSS 20.0对南京市就业者邻里环境感知问卷调查数据进行信度和效度检验,数据的总体Cronbach's Alpha值为0.676,基于标准化项的Cronbach's Alpha值为0.695,显示问卷中邻里环境感知各题项之间的内部一致达到最小可接受值。同时,问卷中邻里环境感知各题项的Cronbach's Alpha if Item Deleted值绝大多数未达到0.695（表4）,表明问卷中数据效度较好。

Tab. 4
表4
表4南京市就业者邻里环境感知调查数据的效度检验
Tab. 4Validity test of the survey data on the neighborhood environmental perception of the employees in Nanjing city
邻里环境感知观测变量变量符号Cronbach's Alpha if Item Deleted
步行到最近的大型超市或商场轻松方便D10.644
步行到最近的公交车站轻松方便D20.667
步行到最近的地铁站轻松方便D30.652
步行到最近的公园或街头绿地轻松方便D40.635
小区周边交叉路口多D50.671
小区周边有很多不同的道路可供选择D60.656
小区周边道路卫生情况很好D70.660
小区周边道路夜晚照明情况很好D80.656
小区周边街道路面平坦D90.653
小区周边大部分道路设有步行道D100.665
小区周边设有行人过街设施D110.656
小区周边有吸引人的自然景观D120.643
小区周边有吸引人的人文景观D130.658
小区周边没有很多快速行驶的机动车辆D140.690
小区周边不经常发生交通事故D150.703
小区周边没有很多障碍（如占道车辆等）D160.718
小区周边治安很好D170.645
小区周边夜晚治安很好D180.646

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为了确定邻里环境感知各个观测变量的可行性,对问卷中关于邻里环境感知的18个变量进行探索性因子分析。为了分析邻里环境感知变量是否满足因子分析的前提条件,即题项之间是否具有较强的相关性,对邻里环境感知的18个变量进行KMO和Bartlett检验。检验出KMO值为0.777,Bartlett球形度检验值的显著性为0.000,表明各题项间相关系数显著,因此适合做因子分析。

进一步采用最大方差法将因子进行正交旋转,使得因子更具有说服性和解释性,5个公因子的累计方差贡献率为58.22%。由旋转成份矩阵表（表5）,结合各题项所表达的含义,将5个公因子分别定义为服务设施感知、环境品质感知、道路情况感知、交通安全感知和社区安全感知,形成了结构方程模型中邻里环境感知的5个潜变量。邻里环境感知潜变量的均值、标准差与标准误见表6。

Tab. 5
表5
表5旋转成份矩阵
Tab. 5Rotation component matrix
成份
12345
D10.579
D20.650
D30.668
D50.563
D60.680
D40.714
D120.724
D130.779
D70.550
D80.599
D90.641
D100.773
D110.732
D140.772
D150.654
D160.688
D170.870
D180.879
注：提取方法为主成份分析法;旋转法为具有Kaiser标准化的正交旋转法。

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Tab. 6
表6
表6邻里环境感知潜变量的均值、标准差及标准误
Tab. 6Mean, standard deviation and standard error of latent variables of neighborhood environmental perception
邻里环境感知潜变量变量符号样本量均值标准差均值的标准误
服务设施感知service6223.7850.6240.025
环境品质感知environment6222.6830.9820.039
道路情况感知road6223.6230.6300.025
交通安全感知traffic6222.9270.7780.031
社区安全感知community6223.8350.7560.030
3.1.3 个体健康变量的选取 在通勤与健康关系的研究中,大多数研究将个体健康分为身体健康与心理健康[27,31,40],也有研究将个体健康分为主观健康与客观健康[41],只有少数研究将健康行为作为个体健康的一部分[42,43]。综上,本文将个体健康分为心理健康、身体健康以及健康行为三类,形成个体健康的3个潜变量。

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心理健康指人的精神、情绪和意识方面的良好状态,包括精力充沛、注意力集中、无经常性的失眠现象、与他人关系良好等方面[29,44,45]。因此选择浑身乏力、易疲倦,注意力不集中、反应慢,失眠、多梦以及邻里关系4个观测变量来表示。

身体健康包括身体质量指数（BMI）、是否患慢性疾病以及自评总体健康3个观测变量。BMI用来表征就业者是否肥胖或超重;慢性疾病包括糖尿病、高脂血症、高血压、脑卒中以及冠心病等典型慢性疾病[40];自评总体健康是居民对自身健康状况的总体评价,被证明是评价身体健康的良好指标[46,47,48]。

健康行为包括睡眠时长、运动健身、做家务3个观测变量,反映居民日常行为的健康程度[42,43]。睡眠时长影响就业者身心健康,如睡眠时间短与BMI升高有关[49],对通勤方式选择存在间接影响;是否运动健身、做家务可以反映就业者日常身体活动状况以及对身体健康的重视程度,可能影响其日常通勤模式的选择。南京市就业者个体健康变量描述性统计见表7。

Tab. 7
表7
表7个体健康变量描述性统计
Tab. 7Descriptive statistics of individual health variables
个体健康潜变量变量符号个体健康观测变量变量符号样本量变量描述
心理健康mental浑身乏力、易疲倦b1622（100.00%）虚拟
否532（85.53%）0
是90（14.47%）1
注意力不集中、反应慢b2622（100.00%）虚拟
否555（89.23%）0
是67（10.77%）1
失眠、多梦b3622（100.00%）虚拟
否546（87.78%）0
是76（12.22%）1
邻里关系b4622（100.00%）等级
较差11（1.77%）1
一般389（62.54%）2
较好196（31.51%）3
亲近26（4.18%）4
身体健康physicalBMIb5622（100.00%）等级
体重偏瘦或正常393（63.18%）1
超重185（29.74%）2
肥胖44（7.07%）3
是否患慢性疾病b6622（100.00%）虚拟
否546（87.78%）0
是76（12.22%）1
自评总体健康b7622（100%）等级
不好23（3.70%）1
一般220（35.37%）2
好296（47.59%）3
非常好83（13.34%）4
健康行为behavior睡眠时长b8622（100.00%）连续
运动健身b9622（100.00%）虚拟
否465（74.76%）0
是157（25.24%）1
做家务b10622（100.00%）虚拟
否206（33.12%）0
是416（66.88%）1
注：根据2010年中国卫生部发布的《营养改善工作管理办法（卫疾控发[2010]73号）》,其中规定BMI<18.5为偏瘦,18.5≤BMI<24为正常,24≤BMI<28为超重,BMI≥28为肥胖。

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3.1.4 控制变量的选取 控制变量包括通勤距离与社会经济属性。通勤距离是通勤模式选择的重要影响因素[50],因此将通勤距离（变量符号：d）纳入模型。同时,性别、年龄、个人月收入、最高学历、是否有小汽车等社会经济属性对通勤模式选择均存在显著影响[5,51,52],因此也将其纳入模型。

3.2 理论模型构建

由于人的行为具有非理性的一面,因此即使面对相同的城市建成环境,人因其生活经历、态度偏好及社会经济属性不同,对建成环境的主观感受与通勤模式选择也会存在差异。因此,仅从宏观层面城市建成环境的角度不能全面阐释通勤模式选择背后的机理,还需考虑微观层面邻里环境感知的影响。同时,微观层面的个体健康相关因素对通勤模式选择的影响亦长期存在。此外,通勤距离与性别、年龄、个人月收入等社会经济属性对通勤模式选择存在显著影响,在基于微观层面的研究中,如果忽视这些潜在影响因素,可能会放大某些环境感知变量或个体健康变量对通勤模式选择的影响,得出错误的结论,因此需控制这些变量的影响。

综上,得出本文的理论模型框架如图2所示。假设邻里环境感知、个体健康、通勤距离及社会经济属性对通勤模式选择存在直接影响,同时社会经济属性对邻里环境感知与通勤距离存在直接影响,对个体健康与通勤模式选择存在间接影响。此外,邻里环境感知通过对个体健康产生影响,进而对通勤模式产生间接影响。

图2

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图2邻里环境感知、个体健康及控制变量对通勤模式选择的影响模型框架

Fig. 2A conceptual model of the influence of neighborhood environmental perception, individual health, and control variables on commuting mode choice



4 结构方程模型结果分析

4.1 模型适配度指数

使用AMOS 22软件建立初始模型,采用极大似然法（Maximum Likelihood Method）对数据进行估算,经过模型评价与模型修正,得到最终模型。将适配度指数分别与参考值进行比较[34]52-53（表8）,发现模型拟合效果较好。

Tab. 8
表8
表8模型适配度指标
Tab. 8Model fitness indexes
适配度指数参考值模型结果
绝对适配度指数
χ2显著性概率值P<0.050.006
GFI>0.900.953
AGFI>0.900.932
RMR<0.050.064
RMSEA<0.050.017
增值适配度指数
NFI>0.900.888
RFI>0.900.847
IFI>0.900.982
TLI（NNFI）>0.900.974
CFI>0.900.981
简约适配度指数
PGFI>0.50.658
PNFI>0.50.647
NC（χ2自由度比值）,CMIN/DF1~31.170

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4.2 邻里环境感知对通勤模式选择的影响

邻里环境感知一方面直接影响就业者通勤模式选择,同时通过影响个体健康,进而间接影响就业者通勤模式选择（表9）。

Tab. 9
表9
表9邻里环境感知对通勤模式选择的影响
Tab. 9The effects of neighborhood environmental perception on commuting mode choice
变量符号效应serviceenvironmentroadtrafficcommunity
t总体效应-0.118**-0.049-0.038-0.0220.047
直接效应-0.196**-0.543*-0.5170.758*0.339
间接效应0.0780.4940.479-0.780**-0.292
mental总体效应-0.0670.0320.358***0.0900.200*
直接效应-0.0670.0320.358***0.0900.200*
间接效应...............
physical总体效应-0.150**0.0510.267***0.154**0.104
直接效应-0.150**0.0510.267***0.154**0.104
间接效应...............
behavior总体效应0.0760.456*0.430-0.721**-0.260
直接效应0.0760.456*0.430-0.721**-0.260
间接效应...............
注：数值均为标准化之后的值;*、**、***分别代表在10%、5%、1%的置信水平上显著;...表示模型中无此关系。

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邻里环境感知变量中服务设施感知、环境品质感知与交通安全感知对通勤模式选择存在显著的直接效应。服务设施感知对通勤模式选择存在显著的负向直接效应,表明就业者感知的服务设施越好,选择积极通勤模式的可能性越大。良好的服务设施包括商业设施、公共交通设施等,在一定程度上可以缩小就业者日常活动的空间范围,进而促使其选择积极通勤模式[10,53]。环境品质感知对就业者通勤模式选择也存在显著的负向直接效应,表明就业者感知的环境品质越好,选择积极通勤模式的可能性越大。这与Van Dyck等的研究结论较为一致,即感知的环境美学与积极通勤显著正相关[54,55]。交通安全感知对通勤模式选择存在显著的正向直接效应,表明就业者感知的交通安全越好,越倾向于选择小汽车通勤。此外,道路情况感知与社区安全感知对通勤模式选择的直接影响路径不显著。

邻里环境感知变量中交通安全感知对通勤模式选择存在显著的间接效应,该效应来自于邻里环境感知对就业者个体健康的影响。交通安全感知对就业者身体健康存在显著的正向直接效应,对健康行为存在显著的负向直接效应,同时对通勤模式选择存在显著的负向间接效应,表明就业者感知的交通安全越好,其身体健康状况越好,而进行健康行为的可能性越小,同时更倾向于选择积极通勤模式。由此可见,个体健康变量在邻里环境感知对通勤模式选择的影响中存在中介作用,尤其针对交通安全感知变量,交通安全感知对通勤模式选择的间接影响比直接影响更显著。此外,服务设施感知、环境品质感知、道路情况感知及社区安全感知对个体健康均存在一定的直接影响,进而对通勤模式选择产生间接影响,但间接影响的路径并不显著。

4.3 个体健康对通勤模式选择的影响

个体健康对通勤模式选择影响的效应值如表10所示。就业者的心理健康对通勤模式选择存在显著的负向直接效应,表明就业者心理健康水平越好,越倾向于选择积极通勤模式。这与Kroesen等与Palomino等的研究结论较为一致,即心理健康对积极通勤存在正向影响[31,46]。就业者的健康行为对通勤模式选择存在显著的正向直接效应,说明日常更注重健康行为的就业者选择小汽车通勤模式的可能性更大,而日常无运动健身、做家务等健康行为的就业者选择小汽车通勤模式的可能性更小。可能的原因是步行、骑自行车上下班可以更好地将身体活动融入日常生活,而不需要固定的运动时间和场所,因此没有运动健身、做家务等健康行为的就业者将通勤过程中的步行、骑自行车等身体活动视为日常生活中的锻炼方式。就业者的身体健康对通勤模式选择的影响路径不显著。

Tab. 10
表10
表10个体健康对通勤模式选择的影响
Tab. 10The effects of individual health on commuting mode choice
变量符号效应mentalphysicalbehavior
t总体效应-0.080*0.0580.084***
直接效应-0.080*0.0580.084***
间接效应.........
注：数值均为标准化之后的值;*和***分别代表在10%和1%的置信水平上显著;...表示模型中无此关系。

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4.4 控制变量对通勤模式选择的影响

通勤距离与社会经济属性对通勤模式选择存在显著的直接效应（表11、表12）。通勤距离对通勤模式选择存在显著的正向直接效应,表明就业者的通勤距离越长,越倾向于选择小汽车通勤。这与张雪等的研究结论较为一致[50]。社会经济属性中,性别、个人月收入、是否有小汽车以及家庭人口结构对通勤模式选择均存在显著的直接效应。其中,女性就业者选择积极通勤模式的可能性大于男性就业者,而男性就业者更倾向于选择小汽车通勤模式,可能的原因是男性更喜欢开车,并且在家庭中拥有小汽车使用的优先权[5,20]。个人月收入对通勤模式选择存在显著的正向直接效应,表明个人月收入高的就业者选择小汽车通勤模式的可能性更大,可能的原因是个人月收入高的就业者能更好地承担小汽车通勤的成本[5,8]。是否有小汽车对通勤模式选择存在显著的正向直接效应,即有小汽车的就业者更倾向于选择小汽车通勤模式。家庭人口结构对通勤模式选择存在显著的正向直接效应,表明家庭人数越多的就业者选择小汽车通勤模式的可能性越大,可能是由于人数较多的家庭有老人与儿童,对小汽车使用的需求更大,因此就业者选择小汽车通勤模式的可能性更大[8,33]。

Tab. 11
表11
表11通勤距离对通勤模式选择的影响
Tab. 11The effects of commuting distance on commuting mode choice
变量符号效应d
t总体效应0.246***
直接效应0.246***
间接效应...
注：数值均为标准化之后的值;***代表在1%的置信水平上显著;...表示模型中无此关系。

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Tab. 12
表12
表12社会经济属性对通勤模式选择的影响
Tab. 12The effects of socioeconomic attributes on commuting mode choice
变量符号效应a1a2a3a4a5a6a7
t总体效应-0.147**-0.0060.075*0.204***0.084**0.0770.099
直接效应-0.128**-0.0180.091*0.172***0.091**0.0430.065
间接效应-0.0190.012-0.0160.032*-0.0070.034*0.034*
d总体效应-0.097**0.0440.071*0.070-0.0070.114***0.096*
直接效应-0.097**0.0440.071*0.070-0.0070.114***0.096*
间接效应.....................
service总体效应0.047-0.002-0.010-0.078*0.005-0.030-0.033
直接效应0.047-0.002-0.010-0.078*0.005-0.030-0.033
间接效应.....................
environment总体效应0.140**0.102**-0.0300.014-0.0610.0780.121**
直接效应0.140**0.102**-0.0300.014-0.0610.0780.121**
间接效应.....................
road总体效应-0.141***-0.078-0.129**0.102**-0.014-0.0260.014
直接效应-0.141***-0.078-0.129**0.102**-0.014-0.0260.014
间接效应.....................
traffic总体效应-0.0070.474***0.031-0.0770.120**0.204***-0.103
直接效应-0.0070.474***0.031-0.0770.120**0.204***-0.103
间接效应.....................
community总体效应-0.0400.077*-0.0490.145***0.0080.042-0.029
直接效应-0.0400.077*-0.0490.145***0.0080.042-0.029
间接效应.....................
mental总体效应0.043**0.089**0.039**-0.0080.0150.041-0.014
直接效应.....................
间接效应0.043**0.089**0.039**-0.0080.0150.041-0.014
physical总体效应0.0330.108***0.034-0.0110.0190.051*-0.012
直接效应.....................
间接效应0.0330.108***0.034-0.0110.0190.051*-0.012
behavior总体效应-0.106-0.442**-0.0520.049-0.067-0.207**0.030
直接效应.....................
间接效应-0.106-0.442**-0.0520.049-0.067-0.207**0.030
注：数值均为标准化之后的值;*、**、***分别代表在10%、5%、1%的置信水平上显著;...表示模型中无此关系。

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社会经济属性中是否有小汽车、最高学历及户籍对通勤模式选择还存在显著的间接效应,该效应来自于社会经济属性对通勤距离、邻里环境感知与个体健康的影响。有小汽车的就业者感知的道路情况与社区安全更好,可能是由于此类就业者个人月收入较高、居住的社区品质更好,因此更倾向于选择小汽车通勤模式。学历较高的就业者通勤距离更长,可能的原因是高学历的就业者会在更大的空间范围内求职,或增加通勤距离以获得具有更高收入的职业[56,57];同时高学历的就业者对交通安全的感知与身体健康状况更好,因此选择小汽车通勤模式的可能性更大。本地户口的就业者通勤距离更长,对环境品质的感知好于外地户口的就业者,也更倾向于选择小汽车通勤模式。可能的原因是多数本地户口的就业者已购买房屋而无需租房,因此通勤距离比通常租住在工作地附近的外地户口的就业者更长,因此选择小汽车通勤模式的可能性更大。由此可见,邻里环境感知与个体健康对通勤模式选择的影响因就业者的社会经济属性不同而存在差异。此外,性别、年龄、个人月收入及家庭人口结构对通勤距离与邻里环境感知存在不同的直接影响,进而对通勤模式选择产生间接影响,但间接影响的路径并不显著。

5 结论与讨论

5.1 结论

本文利用结构方程模型分析微观层面的邻里环境感知、个体健康以及通勤距离、社会经济属性等控制变量对南京市就业者通勤模式选择的影响,主要得到以下结论：① 邻里环境感知对就业者通勤模式选择存在显著影响。具体来说,邻里环境感知变量中服务设施感知、环境品质感知对通勤模式选择存在显著的负向直接效应,交通安全感知对通勤模式选择存在显著的负向间接效应,该效应来自于邻里环境感知对个体健康的影响。② 个体健康变量中,心理健康对就业者通勤模式选择存在显著的负向直接效应,而健康行为对就业者通勤模式选择存在显著的正向直接效应。③ 通勤距离与性别、个人月收入、是否有小汽车、家庭人口结构等社会经济属性对通勤模式选择存在显著的直接效应,是否有小汽车、最高学历及户籍对通勤模式选择存在显著的间接效应,该效应来自于社会经济属性对通勤距离、邻里环境感知与个体健康的影响。

5.2 讨论

鉴于邻里环境感知与个体健康对通勤模式选择存在显著影响,本文认为,城市建成环境规划与建设中应体现“以人为本”的理念,注重居民对于建成环境的主观感受。城市建成环境中服务设施、环境品质尤其要受到重视,可通过完善社区服务设施,提升社区环境品质,有效提升居民对于社区环境的主观感受,进而吸引居民采用更加积极和绿色的通勤模式。同时,个体健康对通勤模式选择不仅存在显著的直接影响,并且在邻里环境对通勤模式选择的影响中存在中介作用,相关城市管理部门在宣传引导上,可以通过组织开展健康宣传活动与全民健身活动,提高就业者健康行为的参与意愿和健康水平,从而引导更多的就业者选择积极通勤模式。

尽管本文的研究得到了一些有意义的发现,但本文仍然存在一些不足之处。关于个体健康的测度,本文分为心理健康、身体健康与健康行为,由于个体健康的衡量标准十分复杂,本文所考虑的健康指标不够全面。此外,由于本文使用的截面数据的限制,仅分析了个体健康对于通勤模式选择的影响,未能讨论通勤与健康的双向因果关系。未来可尝试建立更加系统和全面的健康指标体系,并通过收集个体纵向数据集,探究个体健康与通勤模式选择的双向因果关系。

致谢：

真诚感谢匿名评审专家在论文评审中所付出的时间和精力,评审专家对本文研究综述、理论模型、指标选取、结果分析、参考文献引用等方面详实的修改意见,使本文受益匪浅,深表谢意。


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中国大城市的郊区化加剧了居民的职住分离与长距离通勤,进而影响其健康状况。本文以北京典型近郊巨型居住区天通苑为案例,研究城市郊区居民通勤模式对健康的影响。天通苑全职就业者通勤空间总体上呈现以天通苑为中心不均匀的放射状格局;根据通勤距离、时间与方式,将天通苑全职就业者的通勤模式划分为短距离-积极-公交通勤、中长距离-公交-小汽车通勤、超长距离-公交-小汽车通勤3种模式。本文借助二项Logistic回归模型,在控制其他社会经济属性的前提下验证不同通勤模式对生理健康和心理健康2个维度6个指标的影响。研究发现,整体上通勤模式对睡眠质量差、经常请病假、疲惫不堪、压力大等健康风险的影响均呈现出倒“U”形的趋势,表明适度通勤可能有利于健康,而过长通勤却不利于健康,尤其是超长距离-公交-小汽车通勤模式显著地增加了睡眠质量差、经常请病假、压力大等的健康风险。最后,本文指出改善大城市郊区职住关系不仅意味着城市运行效率的提升,更意味着居民健康状况及生活质量的提升。
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Preventive Medicine, 2012, 55(5):453-457. DOI: 10.1016/j.ypmed.2012.08.019.
PMID:22964003
To quantify the association between time spent in active commuting and in moderate to vigorous physical activity (MVPA) in a sample of working adults living in both urban and rural locations.In 2009, participants in the Commuting and Health in Cambridge study were sent questionnaires enquiring about sociodemographic characteristics and weekly time spent in active commuting. They were also invited to wear an accelerometer for seven days. Accelerometer data were used to compute the time spent in MVPA. Multiple regression models were used to examine the association between time spent in active commuting and MVPA.475 participants (70% female) provided valid data. On average, participants recorded 55 (SD: 23.02) minutes of MVPA per day. For women, reporting 150 or more minutes of active commuting per week was associated with an estimated 8.50 (95% CI: 1.75 to 51.26, p=0.01) additional minutes of daily MVPA compared to those who reported no time in active commuting. No overall associations were found in men.Promoting active commuting might be an important way of increasing levels of physical activity, particularly in women. Further research should assess whether increases in time spent in active commuting are associated with increases in physical activity.Copyright © 2012 Elsevier Inc. All rights reserved.

何晓龙, 翁锡全, 林文弢. 适宜白领规律性步行/自行车通勤健康效应的评价指标分析
中国体育科技, 2017, 53(5):125-132.
[本文引用: 1]

[ He Xiaolong, Weng Xiquan, Lin Wentao. Analysis on appropriate indicators for evaluating Chinese white-collar workers' health benefits of regular walking/bicycling commuting
China Sport Science and Technology, 2017, 53(5):125-132.] DOI: 10.16470/j.csst.201705015.
[本文引用: 1]

Jacob N, Munford L, Rice N, et al. Does commuting mode choice impact health?
Health Economics, 2021, 30(2):207-230. DOI: 10.1002/hec.4184.
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Künn-Nelen A. Does commuting affect health?
Health Economics, 2016, 25(8):984-1004. DOI: 10.1002/hec.3199.
PMID:26010157 [本文引用: 1]
This paper analyzes the relation between commuting time and health in the UK. I focus on four different types of health outcomes: subjective health measures, objective health measures, health behavior, and healthcare utilization. Fixed effect models are estimated with British Household Panel Survey data. I find that whereas objective health and health behavior are barely affected by commuting time, subjective health measures are clearly lower for people who commute longer. A longer commuting time is, moreover, related to more visits to the general practitioner. Effects turn out to be more pronounced for women and for commuters driving a car. For women, commuting time is also negatively related to regular exercise and positively to calling in sick. Copyright © 2015 John Wiley & Sons, Ltd.Copyright © 2015 John Wiley & Sons, Ltd.

Christian T J. Trade-offs between commuting time and health-related activities
Journal of Urban Health, 2012, 89(5):746-757. DOI: 10.1007/s11524-012-9678-6.
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孙斌栋, 吴江洁, 尹春, 等. 通勤时耗对居民健康的影响: 来自中国家庭追踪调查的证据
城市发展研究, 2019, 26(3):59-64.
[本文引用: 2]

[ Sun Bindong, Wu Jiangjie, Yin Chun. The impact of commuting duration on residents' health: Evidence from China family panel studies
Urban Development Studies, 2019, 26(3):59-64.] DOI: 10.3969/j.issn.1006-3862.2019.03.008.
[本文引用: 2]

Bond L, Kearns A, Mason P, et al. Exploring the relationships between housing, neighbourhoods and mental wellbeing for residents of deprived areas
BMC Public Health, 2012, 12:48. DOI: 10.1186/1471-2458-12-48.
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周素红, 何嘉明. 郊区化背景下居民健身活动时空约束对心理健康影响: 以广州为例
地理科学进展, 2017, 36(10):1229-1238.
DOI:10.18306/dlkxjz.2017.10.005 [本文引用: 1]
本文从健康地理跨学科的视角入手,以广州市典型郊区的102个样本为研究对象,并选择10个城区街道的927个样本作为参照组进行对比研究,重点探讨中国式快速郊区化背景下,郊区居民健身活动时空约束对心理健康状况的影响。通过构建多元线性回归模型,从微观层面探讨居民城市建成环境、健身行为和心理健康之间的相互关系。结果表明：基于WHO-5反映心理健康量表的评分,郊区样本心理健康状况平均分值只有8.411分,远低于城区样本的平均12.788分,郊区居民的心理健康问题需要引起重视。相对于城区居民,郊区居民健身活动受长距离的通勤及不完善的公共交通系统的时空约束更为明显,健身活动频率更低、时间更短、空间上主要集中在住宅附近。这种差异除了受个人经济社会属性、邻里社区融入等因素影响外,还明显受到建成环境因素的影响。研究结论对弥补过度市场化逐利下造成的城市公共性缺失,维护社会空间公平,完善中国式郊区化下的公共服务设施配套体系,改善郊区居民心理健康状况有重要意义,同时也可为郊区规划及公共政策制定提供参考。
[ Zhou Suhong, He Jiaming. Effects of spatial-temporal constraints of suburban residents on fitness activities to mental health in the context of rapid suburbanization: A case study in Guangzhou, China
Progress in Geography, 2017, 36(10):1229-1238.] DOI: 10.18306/dlkxjz.2017.10.005.
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Palomino J, Sarrias M. The monetary subjective health evaluation for commuting long distances in Chile: A latent class analysis
Papers in Regional Science, 2019, 98(3):1397-1417. DOI: 10.1111/pirs.12416.
[本文引用: 2]
Long-distance commuting (LDC) is a growing phenomenon in specialized countries in extractive industries such as Chile. There has also been a growing concern about the potential impacts on the health of long-distance commuters. This paper formalizes the relationship between commuting distance and self-assessed health status and shows the monetary valuation of health costs for commuting long distances using a latent class approach. This econometric approach allows us to capture both preference and threshold heterogeneity. The results show that there are two classes of workers: the first group is not sensitive to commuting distance, whereas the monetary valuation of workers in the second group is equivalent to CLP $431 (US$0.68).

Wu S Q, Wang R, Zhao Y F, et al. The relationship between self-rated health and objective health status: A population-based study
BMC Public Health, 2013, 13:320. DOI: 10.1186/1471-2458-13-320.
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Mu R. Regional disparities in self-reported health: Evidence from Chinese older adults
Health Economics, 2014, 23(5):529-549. DOI: 10.1002/hec.2929.
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Bayon V, Leger D, Gomez-Merino D, et al. Sleep debt and obesity
Annals of Medicine, 2014, 46(5):264-272. DOI: 10.3109/07853890.2014.931103.
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张雪, 柴彦威. 基于结构方程模型的西宁城市居民通勤行为及其影响因素
地理研究, 2018, 37(11):2331-2343.
DOI:10.11821/dlyj201811016 [本文引用: 2]
近年来,中国城市转型中居民职住关系的变化对通勤交通方式、通勤时间的影响,以及不同居住区居民通勤行为的差异性,引起了****们的广泛关注。基于2013年西宁市居民活动日志调查数据,分析西宁居民通勤行为的居住区差异,利用结构方程模型分析通勤距离、通勤交通方式、通勤时间三者之间的关系,探讨居住区类型及个人社会经济属性因素对于通勤行为的影响。研究发现,通勤距离对通勤交通方式、通勤时间有显著的正向影响;男性、自有房者、高收入者、兼职就业者、高学历者采用机动化通勤方式的比例较高;在控制个人社会经济属性之后,居住区类型属性仍然对居民通勤行为产生显著影响。基于以上发现,对西宁城市交通发展和空间布局提出政策建议。
[ Zhang Xue, Chai Yanwei. Influencing factors of residents' commuting behavior in Xining: A SEM analysis using activity-travel diary survey data
Geographical Research, 2018, 37(11):2331-2343.] DOI: 10.11821/dlyj201811016.
[本文引用: 2]

Santos G, Maoh H, Potoglou D, et al. Factors influencing modal split of commuting journeys in medium-size European cities
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Ko J, Lee S, Byun M. Exploring factors associated with commute mode choice: An application of city-level general social survey data
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Kitamura R, Mokhtarian P L, Laidet L. A micro-analysis of land use and travel in five neighborhoods in the San Francisco Bay Area
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Van Dyck D, Cerin E, Conway T L, et al. Perceived neighborhood environmental attributes associated with adults' transport-related walking and cycling: Findings from the USA, Australia and Belgium
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