建成环境是否影响居民健康生活方式?——基于北京22个社区的实证分析

doi:10.18306/dlkxjz.2021.04.010

Abstract

Abstract:

The construction of healthy communities is an important trend in the post-pandemic era. While many studies have investigated the effects of single lifestyle behaviors on health, the minimization of the individual risk of disease might only be achieved by a combination of these behaviors.This paper uses the 2019 Beijing City Physical Examination questionnaire survey data to construct an evaluation index system for the healthy lifestyle behaviors of community residents. Based on this, we use the hierarchical linear model (HLM) to investigate the impact of community built environment on residents' lifestyle. Results from the HLM show that the differences between neighborhoods can explain 6.93% of the differences in residents' healthy lifestyle. And within the 500 m buffer zone of the community boundary, the built environment with a low degree of urban function mix, a well-developed public transportation system, and a high proximity to parks is more conducive for residents to form a healthy lifestyle. And from the perspective of community type, the healthy degree of lifestyle of residents in policyhousing communities is significantly lower than that of other communities, which is manifested in the low level of social communication and mental health. This study provides new ideas for the construction of healthy communities from the perspective of promoting a healthy lifestyle for community residents.

Key words: healthy community, lifestyle, built environment, Beijing

XU Jingxue, CHEN Li, ZHANG Wenzhong. Does the built environment impact residents’ healthy lifestyle? Empirical analysis based on 22 communities in Beijing[J].PROGRESS IN GEOGRAPHY, 2021, 40(4): 660-670.

Figures/Tables 6

Tab.1

Tab.2

Tab.3

Fig.1

Tab.4

Description of spatial data and statistics of research samples"

分类	变量名	测量方式及数据来源	均值/标准差
社区建成环境特征
密度	人口密度 (万人/km²)	以LandScan 2018全球人口栅格数据为基础,采用GIS空间分析与数理统计方法对不同搜索口径范围内的人口密度进行估算	500 m搜索口径：0.96/0.64 1000 m搜索口径：0.91/0.62
混合度	POI类型的熵指数	$EI = ∑ S i × ln (1 / S i)$ ,其中S_i为搜索口径范围内i类POI数量^＃占总POI数量的比重,POI数据来源于2017年8月百度地图网站抓取数据	500 m搜索口径：2.32/0.19 1000 m搜索口径：2.33/0.20
设计	道路交叉口密度(个/km²)	次干道及支路道口交叉口密度,路网数据来源于2018年北京市百度交通路网数据	500 m搜索口径：70.13/62.58 1000 m搜索口径：66.41/55.75
目的地可达性	最邻近公园距离 (km)	社区矢量中心到最邻近面状公园距离,2017年北京市公园面状矢量数据来源于北京城市实验室^[39]( https://www.beijingcitylab.com/)	0.58/0.57
公交邻近度	公共交通线路密度(条/km²)	不同搜索口径范围内公交和地铁线路密度,公共交通站点及其线路数据来源于网络	500 m搜索口径：23.03/11.44 1000 m搜索口径：22.49/11.57
区位	距离天安门距离	社区矢量中心到天安门城楼的欧式距离	13.15 km
个体属性统计
年龄	30岁以下(24.4%);30~39岁(21.5%);40~49岁(17.6%);50~59岁(15.9%);60岁以上(20.6%)
性别	男性(55.1%);女性(44.9%)
学历	初中及以下(22.31%);高中(30.38%);大专(19.36%);大学及以上(27.95%)
职业	雇员(49.2%);个体经营者(3.4%);自由职业者(9.7%);待业(4.9%);退休(29.4%);学生(3.4%)
家庭年收入	10万元以下(37.9%);10万~19.9万元(38.1%);20万~29.9万元(12.7%);30万~49.9万元(8.1%);50万元以上(3.2%)
户籍	本地户籍(64.0%);外地户籍(36.0%)

Tab.4

Tab.5

Regression results of hierarchical linear modeling (HLM)"

变量	模型1 (500 m)	模型2 (1000 m)	模型3 (500 m)	模型4
变量	截距	0.899^***(0.135)	0.901^***(0.114)	模型4	0.877^***(0.102)	0.779^***(0.022)
社区层级变量
人口密度	-0.037(0.031)	-0.013(0.037)
POI类型的熵指数	-0.018^***(0.051)	-0.029^***(0.031)	-0.010^***(0.041)
道路交叉口密度	0.045(0.023)	0.023(0.035)
最邻近公园距离	-0.032^**(0.015)	-0.024^**(0.014)	-0.019^**(0.012)
公共交通线路密度	0.033^*(0.022)	0.009(0.024)	0.033^*(0.022)
区位(参照组：五环内社区)
距离天安门距离	-0.084^**(0.043)	-0.052^*(0.029)	-0.054^**(0.025)
社区类型变量(参照组：普通商品房社区)
中高档商品房社区				-0.026(0.022)
胡同社区				0.002(0.028)
老旧社区				0.010(0.023)
政策性住房社区				-0.082^**(0.031)
城中村				-0.018(0.039)
个体层级变量
年龄(参照组：30岁及以下)
30~39岁	-0.011(0.014)	-0.012(0.014)	-0.011(0.014)	-0.012(0.014)
40~49岁	-0.041^**(0.015)	-0.042^**(0.015)	-0.041^**(0.015)	-0.042^**(0.015)
50~59岁	-0.041^**(0.019)	-0.042^**(0.019)	-0.040^**(0.019)	-0.041^**(0.019)
60岁及以上	0.032(0.023)	0.033(0.023)	0.031(0.023)	0.032(0.023)
性别(参照组：女性)
男性	0.039^***(0.009)	0.039^***(0.009)	0.039^***(0.009)	0.038^***(0.009)
学历(参照组：本科以下学历)
本科及以上学历	-0.002(0.011)	-0.002(0.011)	-0.001(0.011)	-0.002(0.011)
职业(参照组：雇员)
个体经营者	0.023(0.022)	0.024(0.023)	0.023(0.022)	0.022(0.023)
自由职业者	-0.006(0.015)	-0.005(0.015)	-0.006(0.015)	-0.005(0.015)
待业	-0.023(0.019)	-0.023(0.019)	-0.024(0.019)	-0.024(0.019)
退休	-0.018(0.019)	-0.018(0.019)	-0.018(0.019)	-0.019(0.019)
学生	0.052^**(0.024)	0.051^**(0.024)	0.051^**(0.024)	0.051^**(0.024)
家庭年收入(参照组：10万元以下)
10万~19.9万元	0.034^**(0.009)	0.034^**(0.009)	0.034^**(0.009)	0.033^**(0.009)
20万~29.9万元	0.013^**(0.014)	0.013^**(0.014)	0.013^**(0.014)	0.012^**(0.014)
30万元以上	0.045^**(0.015)	0.045^**(0.015)	0.044^**(0.015)	0.043^**(0.015)
户籍(参照组：外地户籍)
本地户口	0.007(0.009)	0.007(0.009)	0.007(0.009)	0.007(0.009)
ICC	5.56%	5.83%	5.67%	5.01%
Deviance	1209.17	1213.82	1224.44	1230.71

Tab.5

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维度	指标层	计算/量化方法
营养行为	BMI	肥胖(BMI≥28)赋值为1;偏瘦或超重(BMI<18.5或24≤BMI<28)赋值为2;正常(18.5≤BMI<24)赋值为3
体力活动行为	日常出行	询问居民最主要的通勤方式,主动式出行(步行或骑行)赋值为3;小汽车出行赋值为1;其他出行方式赋值为2
体力活动行为	体育锻炼	经常参加体育锻炼(每周参加体育锻炼3次及以上,每次持续时间30 min及以上,运动强度达到中等及以上)赋值为3;几乎不锻炼赋值为1;其他赋值为2
社会适应行为	社会交往	社区人际关系满意度使用5点李克特量表衡量,范围从1(非常不满意)到5(非常满意)
心理健康状况	心理健康	居民自评心理健康数据使用《中国城市居民亚健康症状自评量表》中衡量“心理状况”的13道问题进行测量,每个问题的答案以5点李克特量表(最不健康为1,最健康为5)进行评分。该指标得分范围为13~65分,得分越低表示心理健康状况越差。该量表的信度(Cronbach'sα)为0.914,效度(KMO)为0.940,表明使用的心理健康量表是可靠的,并能够准确测量出居民真实的心理健康状况

熵值法计算权重结果汇总
指标	信息熵值e	信息效用值d	权重系数w/%
BMI	0.9959	0.0041	21.93
日常出行	0.9976	0.0024	12.77
体育锻炼	0.9925	0.0075	39.64
社会交往	0.9970	0.0030	16.00
心理健康	0.9982	0.0018	9.66
居民生活方式健康度得分统计性描述
指标	均值/标准差	偏度系数(Skewness)	峰值系数(Kurtosis)
居民生活方式健康度综合得分	0.80/0.14	-1.69	3.22

Does the built environment impact residents’ healthy lifestyle? Empirical analysis based on 22 communities in Beijing

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社区类型	社区名称(编号)
普通商品房	南湖中园社区(8)、中信新城西区(22)、安宁里社区(1)、天桥湾社区(11)
中高档商品房	新龙城社区(19)、万科星园社区(12)、金蝉南里社区(7)、长阳半岛社区(21)、万泉新新家园社区(13)、武夷花园牡丹园社区(14)
老旧社区	呼家楼北社区(6)、西里第二社区(17)、合成公社区(4)、三街坊西社区(10)、西井社区(16)、铁道部住宅区第四社区(15)
胡同社区	国子监社区(3)、大栅栏西街社区(2)、延寿街社区(20)
政策性住房	弘善家园第二社区(5)、三嘉信苑社区(9)
城中村	西苑挂甲屯社区(18)

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