Green and Blue Space Availability and Self-Rated Health among Seniors in China: Evidence from a National Survey
Abstract
:1. Introduction
2. Materials and Methods
2.1. Outcome Variable: Self-Rated Health
2.2. Predictor Variables: Availability of Green and Blue Space
2.3. Covariates: Individual and Neighborhood-Level Attributes
2.4. Statistical Analysis
3. Results
3.1. Descriptive Statistics
3.2. Multivariate Regressions
4. Discussion
4.1. Associations between Green Space and Seniors’ SRH
4.2. Associations between Blue Space and Seniors’ SRH
4.3. Limitations and Future Studies
5. Conclusions
Author Contributions
Funding
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
Urban (n = 1061) | Rural (n = 712) | |||||
---|---|---|---|---|---|---|
Variables | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 |
Coverage of waterbody in 1 km | −0.498 | 0.212 | ||||
(0.474) | (0.716) | |||||
Coverage of vegetation in 1 km | −0.200 | −0.048 | ||||
(0.176) | (0.316) | |||||
The nearest distance to park (log) | −0.031 | |||||
(0.027) | ||||||
The nearest distance to waterbody (log) | −0.029 | 0.028 | ||||
(0.028) | (0.045) | |||||
The nearest distance to river (log) | 0.038 | −0.008 | ||||
(0.036) | (0.035) | |||||
Within 0–0.3 km of park | 0.033 | |||||
(0.091) | ||||||
Within 0.3–0.5 km of park | −0.056 | |||||
(0.097) | ||||||
Within 0.5–1 km of park | 0.033 | |||||
(0.088) | ||||||
Within 0–0.3 km of waterbody | 0.210 * | −0.013 | ||||
(0.100) | (0.319) | |||||
Within 0.3–0.5 km of waterbody | 0.124 | −0.437 | ||||
(0.115) | (0.290) | |||||
Within 0.5–1 km of waterbody | 0.041 | 0.194 | ||||
(0.070) | (0.195) | |||||
Within 0–0.3 km of river | −0.099 | 0.195 | ||||
(0.144) | (0.217) | |||||
Within 0.3–0.5 km of river | −0.237 + | 0.388 | ||||
(0.140) | (0.430) | |||||
Within 0.5–1 km of river | −0.093 | 0.202 | ||||
(0.085) | (0.252) | |||||
Within 1 km of coastline | −1.196 + | |||||
(0.686) | ||||||
Within 1–5 km of coastline | −0.379 + | −0.969 | ||||
(0.200) | (0.629) | |||||
Within 5 km of lake | −0.156 | 0.091 | ||||
(0.515) | (0.235) | |||||
Age | −0.014 *** | −0.014 *** | −0.013 *** | 0.003 | 0.004 | 0.003 |
(0.003) | (0.003) | (0.003) | (0.005) | (0.005) | (0.005) | |
Gender | 0.145 ** | 0.139 ** | 0.157 *** | 0.003 | −0.005 | 0.004 |
(0.046) | (0.047) | (0.046) | (0.065) | (0.065) | (0.065) | |
Ethnic group | 0.099 | 0.096 | 0.093 | 0.059 | 0.072 | 0.058 |
(0.112) | (0.113) | (0.113) | (0.141) | (0.144) | (0.141) | |
Marriage | 0.515 + | 0.509 + | 0.533 * | −0.301 | −0.314 | −0.311 |
(0.262) | (0.266) | (0.264) | (0.232) | (0.233) | (0.232) | |
Local Hukou | −0.061 | −0.057 | −0.063 | −0.231 | −0.247 | −0.259 |
(0.056) | (0.057) | (0.056) | (0.167) | (0.170) | (0.168) | |
No. of person(s) in household | 0.003 | 0.001 | 0.003 | 0.009 | 0.010 | 0.012 |
(0.012) | (0.012) | (0.012) | (0.016) | (0.016) | (0.016) | |
Living alone | −0.018 | −0.034 | −0.022 | −0.061 | −0.061 | −0.057 |
(0.048) | (0.049) | (0.048) | (0.068) | (0.068) | (0.068) | |
Employed | 0.373 *** | 0.350 *** | 0.371 *** | 0.055 | 0.066 | 0.035 |
(0.100) | (0.102) | (0.101) | (0.184) | (0.185) | (0.185) | |
Not able to work | 0.080 | 0.062 | 0.074 | −0.293 | −0.286 | −0.310 |
(0.104) | (0.106) | (0.104) | (0.189) | (0.190) | (0.191) | |
Retired | 0.209 * | 0.201 * | 0.196 * | −0.113 | −0.099 | −0.176 |
(0.094) | (0.095) | (0.094) | (0.236) | (0.237) | (0.239) | |
Homemaker | 0.430 *** | 0.415 ** | 0.411 ** | 0.243 | 0.254 | 0.234 |
(0.125) | (0.126) | (0.125) | (0.272) | (0.273) | (0.274) | |
<5000 CNY as reference | ||||||
5000–15,000 CNY | 0.049 | 0.047 | 0.054 | 0.087 | 0.088 | 0.083 |
(0.081) | (0.082) | (0.082) | (0.074) | (0.074) | (0.074) | |
15,000–30,000 CNY | −0.027 | −0.037 | −0.034 | 0.109 | 0.110 | 0.123 |
(0.088) | (0.089) | (0.089) | (0.120) | (0.120) | (0.120) | |
>30,000 CNY | 0.074 | 0.070 | 0.067 | −0.367 | −0.376 | −0.372 |
(0.102) | (0.104) | (0.103) | (0.249) | (0.251) | (0.251) | |
No answer | 0.113 | 0.113 | 0.121 | −0.155 | −0.156 | −0.172 |
(0.089) | (0.090) | (0.090) | (0.105) | (0.106) | (0.106) | |
Below elementary school as reference | ||||||
Elementary school | −0.114 + | −0.101 | −0.108 | 0.040 | 0.038 | 0.032 |
(0.066) | (0.068) | (0.066) | (0.071) | (0.072) | (0.071) | |
Middle school | −0.011 | −0.003 | −0.001 | 0.076 | 0.082 | 0.080 |
(0.077) | (0.078) | (0.077) | (0.101) | (0.102) | (0.101) | |
High school | 0.075 | 0.100 | 0.092 | 0.307 | 0.318 | 0.318 |
(0.100) | (0.101) | (0.100) | (0.364) | (0.364) | (0.362) | |
Technical secondary school | −0.068 | −0.050 | −0.058 | 0.602 * | 0.669 * | 0.630 * |
(0.098) | (0.100) | (0.099) | (0.275) | (0.289) | (0.277) | |
Technical Junior college | −0.314 | −0.298 | −0.327 | −0.833 | −0.862 | −0.883 |
(0.305) | (0.307) | (0.305) | (0.548) | (0.546) | (0.545) | |
Junior college | −0.041 | −0.034 | −0.032 | 0.490 | 0.475 | 0.538 |
(0.112) | (0.115) | (0.113) | (0.463) | (0.465) | (0.463) | |
College | −0.076 | −0.066 | −0.084 | 0.798 | 0.829 | 0.924 |
(0.121) | (0.122) | (0.121) | (0.743) | (0.751) | (0.756) | |
Graduate | 0.098 | 0.063 | 0.046 | |||
(0.662) | (0.667) | (0.670) | ||||
Pension benefit | 0.071 | 0.076 | 0.083 | −0.075 | −0.071 | −0.073 |
(0.060) | (0.061) | (0.061) | (0.083) | (0.084) | (0.085) | |
Medical insurance | 0.065 | 0.069 | 0.062 | 0.126 | 0.121 | 0.118 |
(0.071) | (0.072) | (0.071) | (0.106) | (0.108) | (0.107) | |
Owns car | 0.074 | 0.073 | 0.063 | 0.175 | 0.189 | 0.128 |
(0.086) | (0.087) | (0.086) | (0.160) | (0.164) | (0.162) | |
Owns housing property | 0.037 | 0.037 | 0.034 | 0.071 | 0.069 | 0.070 |
(0.041) | (0.042) | (0.041) | (0.067) | (0.068) | (0.068) | |
The nearest distance to road (log) | 0.030 | 0.033 | 0.019 | −0.016 | −0.017 | −0.015 |
(0.023) | (0.025) | (0.024) | (0.032) | (0.032) | (0.032) | |
GDP per km2 | 0.001 * | 0.001 + | 0.001 * | 0.001 | 0.001 | −0.001 |
(0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | |
Number of person(s) per km2 | 0.000 | 0.000 | 0.000 | −0.000 | 0.000 | 0.000 |
(0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | |
Constant | 1.810 *** | 1.807 *** | 1.758 *** | 2.278 ** | 2.065 * | 2.308 ** |
(0.464) | (0.544) | (0.473) | (0.763) | (0.846) | (0.723) | |
Observations | 1061 | 1061 | 1061 | 712 | 712 | 712 |
R-squared | 0.215 | 0.214 | 0.225 | 0.230 | 0.232 | 0.241 |
Adjusted R-squared | 0.109 | 0.104 | 0.110 | 0.095 | 0.097 | 0.097 |
F test model | 2.03 | 1.95 | 1.96 | 1.71 | 1.70 | 1.68 |
p-value of F model | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
Community type dummy | Entered | Entered | Entered | Entered | Entered | Entered |
Housing type dummy | Entered | Entered | Entered | Entered | Entered | Entered |
City dummy | Entered | Entered | Entered | Entered | Entered | Entered |
Variables | Urban Buffer 0.5 km | Urban Buffer 2 km | Urban Buffer 3 km | Rural Buffer 0.5 km | Rural Buffer 2 km | Rural Buffer 3 km |
---|---|---|---|---|---|---|
Coverage of waterbody in 0.5 km | −0.274 | 0.298 | ||||
(0.453) | (0.489) | |||||
Coverage of vegetation in 0.5 km | −0.172 | 0.050 | ||||
(0.159) | (0.292) | |||||
Coverage of waterbody in 2 km | 0.103 | 0.176 | ||||
(0.516) | (1.280) | |||||
Coverage of vegetation in 2 km | −0.087 | 0.060 | ||||
(0.207) | (0.384) | |||||
Coverage of waterbody in 3 km | 0.346 | 0.090 | ||||
(0.558) | (1.580) | |||||
Coverage of vegetation in 3 km | 0.119 | 0.053 | ||||
(0.239) | (0.424) | |||||
Age | −0.014 *** | −0.014 *** | −0.014 *** | 0.003 | 0.003 | 0.003 |
(0.003) | (0.003) | (0.003) | (0.005) | (0.005) | (0.005) | |
Gender | 0.145 ** | 0.144 ** | 0.143 ** | 0.001 | 0.003 | 0.003 |
(0.046) | (0.046) | (0.046) | (0.065) | (0.065) | (0.065) | |
Ethnic group | 0.097 | 0.096 | 0.100 | 0.060 | 0.060 | 0.060 |
(0.112) | (0.113) | (0.113) | (0.141) | (0.141) | (0.142) | |
Marriage | 0.515 + | 0.515 + | 0.508 + | −0.303 | −0.296 | −0.296 |
(0.263) | (0.263) | (0.263) | (0.232) | (0.232) | (0.232) | |
Local Hukou | −0.063 | −0.066 | −0.066 | −0.230 | −0.235 | −0.234 |
(0.055) | (0.056) | (0.055) | (0.167) | (0.167) | (0.167) | |
No. of person(s) in household | 0.003 | 0.002 | 0.002 | 0.009 | 0.009 | 0.009 |
(0.012) | (0.012) | (0.012) | (0.016) | (0.016) | (0.016) | |
Living alone | −0.021 | −0.023 | −0.025 | −0.059 | −0.062 | −0.063 |
(0.048) | (0.048) | (0.048) | (0.068) | (0.068) | (0.068) | |
Employed | 0.370 *** | 0.367 *** | 0.362 *** | 0.052 | 0.053 | 0.052 |
(0.100) | (0.100) | (0.101) | (0.184) | (0.184) | (0.184) | |
Not able to work | 0.075 | 0.075 | 0.067 | −0.295 | −0.292 | −0.293 |
(0.104) | (0.104) | (0.105) | (0.189) | (0.189) | (0.189) | |
Retired | 0.209 * | 0.207 * | 0.203 * | −0.119 | −0.116 | −0.116 |
(0.094) | (0.094) | (0.094) | (0.236) | (0.236) | (0.236) | |
Homemaker | 0.423 *** | 0.425 *** | 0.419 *** | 0.234 | 0.241 | 0.241 |
(0.125) | (0.125) | (0.125) | (0.272) | (0.272) | (0.272) | |
<5000 CNY as reference | ||||||
5000–15,000 CNY | 0.049 | 0.047 | 0.050 | 0.086 | 0.086 | 0.086 |
(0.081) | (0.081) | (0.081) | (0.074) | (0.074) | (0.074) | |
15,000–30,000 CNY | −0.028 | −0.031 | −0.027 | 0.111 | 0.109 | 0.109 |
(0.088) | (0.088) | (0.088) | (0.120) | (0.120) | (0.120) | |
>30,000 CNY | 0.074 | 0.071 | 0.077 | −0.366 | −0.369 | −0.367 |
(0.102) | (0.102) | (0.103) | (0.249) | (0.249) | (0.250) | |
No answer | 0.115 | 0.116 | 0.122 | −0.155 | −0.158 | −0.158 |
(0.089) | (0.089) | (0.089) | (0.105) | (0.105) | (0.105) | |
Below elementary school as reference | ||||||
Elementary school | −0.114 + | −0.110 + | −0.110 + | 0.042 | 0.040 | 0.040 |
(0.066) | (0.066) | (0.066) | (0.071) | (0.071) | (0.071) | |
Middle school | −0.010 | −0.005 | −0.004 | 0.079 | 0.078 | 0.077 |
(0.077) | (0.077) | (0.077) | (0.101) | (0.101) | (0.101) | |
High school | 0.076 | 0.080 | 0.079 | 0.317 | 0.318 | 0.317 |
(0.100) | (0.099) | (0.100) | (0.362) | (0.364) | (0.363) | |
Technical secondary school | −0.068 | −0.062 | −0.061 | 0.608 * | 0.606 * | 0.604 * |
(0.099) | (0.098) | (0.098) | (0.276) | (0.275) | (0.275) | |
Technical Junior college | −0.307 | −0.304 | −0.298 | −0.849 | −0.848 | −0.847 |
(0.305) | (0.305) | (0.305) | (0.547) | (0.546) | (0.546) | |
Junior college | −0.044 | −0.040 | −0.042 | 0.495 | 0.492 | 0.490 |
(0.112) | (0.112) | (0.112) | (0.463) | (0.463) | (0.463) | |
College | −0.079 | −0.077 | −0.079 | 0.800 | 0.796 | 0.796 |
(0.121) | (0.121) | (0.121) | (0.743) | (0.743) | (0.743) | |
Graduate | 0.071 | 0.071 | 0.074 | |||
(0.662) | (0.663) | (0.663) | ||||
Pension benefit | 0.070 | 0.071 | 0.071 | −0.074 | −0.076 | −0.075 |
(0.060) | (0.060) | (0.060) | (0.083) | (0.084) | (0.084) | |
Medical insurance | 0.065 | 0.072 | 0.071 | 0.127 | 0.125 | 0.125 |
(0.071) | (0.071) | (0.071) | (0.106) | (0.106) | (0.106) | |
Owns car | 0.075 | 0.076 | 0.077 | 0.172 | 0.175 | 0.174 |
(0.086) | (0.086) | (0.086) | (0.160) | (0.160) | (0.160) | |
Owns housing property | 0.036 | 0.038 | 0.039 | 0.070 | 0.072 | 0.072 |
(0.041) | (0.041) | (0.041) | (0.067) | (0.068) | (0.068) | |
The nearest distance to road (log) | 0.031 | 0.030 | 0.029 | −0.016 | −0.015 | −0.015 |
(0.024) | (0.023) | (0.023) | (0.032) | (0.032) | (0.032) | |
GDP per km2 | 0.001 * | 0.001 * | 0.001 * | 0.001 | 0.001 | 0.001 |
(0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | |
Number of person(s) per km2 | 0.000 | 0.000 | 0.000 | −0.000 | 0.000 | 0.000 |
(0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | |
Constant | 1.787 *** | 1.781 *** | 1.747 *** | 2.207 ** | 2.195 ** | 2.200 ** |
(0.464) | (0.465) | (0.466) | (0.758) | (0.765) | (0.776) | |
Observations | 1061 | 1061 | 1061 | 712 | 712 | 712 |
Adjusted R2 | 0.108 | 0.107 | 0.107 | 0.096 | 0.095 | 0.095 |
F test model | 2.02 | 2.01 | 2.01 | 1.71 | 1.71 | 1.71 |
p-value of F model | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
Community type dummy | Entered | Entered | Entered | Entered | Entered | Entered |
Housing type dummy | Entered | Entered | Entered | Entered | Entered | Entered |
City dummy | Entered | Entered | Entered | Entered | Entered | Entered |
References
- Markevych, I.; Schoierer, J.; Hartig, T.; Chudnovsky, A.; Hystad, P.; Dzhambov, A.M.; de Vries, S.; Triguero-Mas, M.; Brauer, M.; Nieuwenhuijsen, M.J.; et al. Exploring pathways linking greenspace to health: Theoretical and methodological guidance. Environ. Res. 2017, 158, 301–317. [Google Scholar] [CrossRef] [PubMed]
- White, M.P.; Elliott, L.R.; Gascon, M.; Roberts, B.; Fleming, L.E. Blue space, health and well-being: A narrative overview and synthesis of potential benefits. Environ. Res. 2020, 191, 110169. [Google Scholar] [CrossRef] [PubMed]
- Lachowycz, K.; Jones, A.P. Towards a better understanding of the relationship between greenspace and health: Development of a theoretical framework. Landsc. Urban Plan. 2013, 118, 62–69. [Google Scholar] [CrossRef]
- Akpinar, A. How is quality of urban green spaces associated with physical activity and health? Urban. For. Urban Green. 2016, 16, 76–83. [Google Scholar] [CrossRef]
- Gunawardena, K.R.; Wells, M.J.; Kershaw, T. Utilising green and bluespace to mitigate urban heat island intensity. Sci Total Environ. 2017, 584–585, 1040–1055. [Google Scholar] [CrossRef] [PubMed]
- Yin, S.; Shen, Z.; Zhou, P.; Zou, X.; Che, S.; Wang, W. Quantifying air pollution attenuation within urban parks: An experimental approach in Shanghai, China. Environ. Pollut. 2011, 159, 2155–2163. [Google Scholar] [CrossRef]
- Nowak, D.J.; Hirabayashi, S.; Doyle, M.; McGovern, M.; Pasher, J. Air pollution removal by urban forests in Canada and its effect on air quality and human health. Urban For. Urban Green. 2018, 29, 40–48. [Google Scholar] [CrossRef]
- Jeanjean, A.P.R.; Buccolieri, R.; Eddy, J.; Monks, P.S.; Leigh, R.J. Air quality affected by trees in real street canyons: The case of Marylebone neighbourhood in central London. Urban For. Urban Green. 2017, 22, 41–53. [Google Scholar] [CrossRef]
- Irga, P.J.; Burchett, M.D.; Torpy, F.R. Does urban forestry have a quantitative effect on ambient air quality in an urban environment? Atmos. Environ. 2015, 120, 173–181. [Google Scholar] [CrossRef] [Green Version]
- Rey Gozalo, G.; Barrigon Morillas, J.M.; Montes Gonzalez, D.; Atanasio Moraga, P. Relationships among satisfaction, noise perception, and use of urban green spaces. Sci. Total Environ. 2018, 624, 438–450. [Google Scholar] [CrossRef]
- Shanahan, D.F.; Bush, R.; Gaston, K.J.; Lin, B.B.; Dean, J.; Barber, E.; Fuller, R.A. Health Benefits from Nature Experiences Depend on Dose. Sci. Rep. 2016, 6, 28551. [Google Scholar] [CrossRef] [Green Version]
- Duan, Y.; Wagner, P.; Zhang, R.; Wulff, H.; Brehm, W. Physical activity areas in urban parks and their use by the elderly from two cities in China and Germany. Lands. Urban Plan. 2018, 178, 261–269. [Google Scholar] [CrossRef]
- Wood, L.; Hooper, P.; Foster, S.; Bull, F. Public green spaces and positive mental health—Investigating the relationship between access, quantity and types of parks and mental wellbeing. Health Place 2017, 48, 63–71. [Google Scholar] [CrossRef]
- Liu, H.; Li, F.; Li, J.; Zhang, Y. The relationships between urban parks, residents’ physical activity, and mental health benefits: A case study from Beijing, China. J. Environ. Manag. 2017, 190, 223–230. [Google Scholar] [CrossRef]
- Wang, H.; Dai, X.; Wu, J.; Wu, X.; Nie, X. Influence of urban green open space on residents’ physical activity in China. BMC Public Health 2019, 19, 1093. [Google Scholar] [CrossRef] [Green Version]
- Mytton, O.T.; Townsend, N.; Rutter, H.; Foster, C. Green space and physical activity: An observational study using Health Survey for England data. Health Place 2012, 18, 1034–1041. [Google Scholar] [CrossRef] [Green Version]
- McMorris, O.; Villeneuve, P.J.; Su, J.; Jerrett, M. Urban greenness and physical activity in a national survey of Canadians. Environ. Res. 2015, 137, 94–100. [Google Scholar] [CrossRef]
- Huang, B.; Liu, Y.; Feng, Z.; Pearce, J.R.; Wang, R.; Zhang, Y.; Chen, J. Residential exposure to natural outdoor environments and general health among older adults in Shanghai, China. Int. J. Equity Health 2019, 18, 178. [Google Scholar] [CrossRef]
- Chen, Y.; Yuan, Y. The neighborhood effect of exposure to blue space on elderly individuals’ mental health: A case study in Guangzhou, China. Health Place 2020, 63, 102348. [Google Scholar] [CrossRef]
- Helbich, M.; Yao, Y.; Liu, Y.; Zhang, J.; Liu, P.; Wang, R. Using deep learning to examine street view green and blue spaces and their associations with geriatric depression in Beijing, China. Environ. Int. 2019, 126, 107–117. [Google Scholar] [CrossRef] [PubMed]
- Clappier, A.; Martilli, A.; Grossi, P.; Thunis, P.; Pasi, F.; Krueger, B.C.; Calpini, B.; Graziani, G.; van den Bergh, H. Effect of Sea Breeze on Air Pollution in the Greater Athens Area. Part I: Numerical Simulations and Field Observations. J. Appl. Meteorol. 2000, 39, 546–562. [Google Scholar] [CrossRef]
- Fleming, L.E.; Kirkpatrick, B.; Backer, L.C.; Walsh, C.J.; Nierenberg, K.; Clark, J.; Reich, A.; Hollenbeck, J.; Benson, J.; Cheng, Y.S.; et al. Review of Florida Red Tide and Human Health Effects. Harmful Algae 2011, 10, 224–233. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Volker, S.; Kistemann, T. The impact of blue space on human health and well-being—Salutogenetic health effects of inland surface waters: A review. Int. J. Hyg. Environ. Health 2011, 214, 449–460. [Google Scholar] [CrossRef] [PubMed]
- Pasanen, T.P.; White, M.P.; Wheeler, B.W.; Garrett, J.K.; Elliott, L.R. Neighbourhood blue space, health and wellbeing: The mediating role of different types of physical activity. Environ. Int. 2019, 131, 105016. [Google Scholar] [CrossRef]
- Vert, C.; Gascon, M.; Ranzani, O.; Marquez, S.; Triguero-Mas, M.; Carrasco-Turigas, G.; Arjona, L.; Koch, S.; Llopis, M.; Donaire-Gonzalez, D.; et al. Physical and mental health effects of repeated short walks in a blue space environment: A randomised crossover study. Environ. Res. 2020, 188, 109812. [Google Scholar] [CrossRef]
- Perchoux, C.; Kestens, Y.; Brondeel, R.; Chaix, B. Accounting for the daily locations visited in the study of the built environment correlates of recreational walking (the RECORD Cohort Study). Prev. Med. 2015, 81, 142–149. [Google Scholar] [CrossRef] [Green Version]
- White, M.P.; Wheeler, B.W.; Herbert, S.; Alcock, I.; Depledge, M.H. Coastal proximity and physical activity: Is the coast an under-appreciated public health resource? Prev. Med. 2014, 69, 135–140. [Google Scholar] [CrossRef] [Green Version]
- Wilson, L.-A.M.; Giles-Corti, B.; Burton, N.W.; Giskes, K.; Haynes, M.; Turrell, G. The Association between Objectively Measured Neighborhood Features and Walking in Middle-Aged Adults. Am. J. Health Promot. 2011, 25, e12–e21. [Google Scholar] [CrossRef]
- Ying, Z.; Ning, L.D.; Xin, L. Relationship between Built Environment, Physical Activity, Adiposity, and Health in Adults Aged 46-80 in Shanghai, China. J. Phys. Act. Health 2015, 12, 569–578. [Google Scholar] [CrossRef]
- Gilmer, M.J.; Harrell, J.S.; Miles, M.S.; Hepworth, J.T. Youth characteristics and contextual variables influencing physical activity in young adolescents of parents with premature coronary heart disease. J. Pediatr. Nurs. 2003, 18, 159–168. [Google Scholar] [CrossRef]
- Nutsford, D.; Pearson, A.L.; Kingham, S.; Reitsma, F. Residential exposure to visible blue space (but not green space) associated with lower psychological distress in a capital city. Health Place 2016, 39, 70–78. [Google Scholar] [CrossRef] [PubMed]
- White, M.; Smith, A.; Humphryes, K.; Pahl, S.; Snelling, D.; Depledge, M. Blue space: The importance of water for preference, affect, and restorativeness ratings of natural and built scenes. J. Environ. Psychol. 2010, 30, 482–493. [Google Scholar] [CrossRef]
- Thoma, M.V.; Mewes, R.; Nater, U.M. Preliminary evidence: The stress-reducing effect of listening to water sounds depends on somatic complaints: A randomized trial. Medicine (Baltimore) 2018, 97, e9851. [Google Scholar] [CrossRef] [PubMed]
- Neutens, T.; Schwanen, T.; Witlox, F.; De Maeyer, P. Equity of Urban Service Delivery: A Comparison of Different Accessibility Measures. Environ. Plan. A 2010, 42, 1613–1635. [Google Scholar] [CrossRef]
- Ekkel, E.D.; de Vries, S. Nearby green space and human health: Evaluating accessibility metrics. Landsc. Urban Plan. 2017, 157, 214–220. [Google Scholar] [CrossRef]
- Labib, S.M.; Lindley, S.; Huck, J.J. Spatial dimensions of the influence of urban green-blue spaces on human health: A systematic review. Environ. Res. 2020, 180, 108869. [Google Scholar] [CrossRef]
- McDougall, C.W.; Quilliam, R.S.; Hanley, N.; Oliver, D.M. Freshwater blue space and population health: An emerging research agenda. Sci Total Environ. 2020, 737, 140196. [Google Scholar] [CrossRef] [PubMed]
- Garrett, J.K.; White, M.P.; Huang, J.; Ng, S.; Hui, Z.; Leung, C.; Tse, L.A.; Fung, F.; Elliott, L.R.; Depledge, M.H.; et al. Urban blue space and health and wellbeing in Hong Kong: Results from a survey of older adults. Health Place 2019, 55, 100–110. [Google Scholar] [CrossRef]
- Finlay, J.; Franke, T.; McKay, H.; Sims-Gould, J. Therapeutic landscapes and wellbeing in later life: Impacts of blue and green spaces for older adults. Health Place 2015, 34, 97–106. [Google Scholar] [CrossRef]
- Wheeler, B.W.; Cooper, A.R.; Page, A.S.; Jago, R. Greenspace and children’s physical activity: A GPS/GIS analysis of the PEACH project. Prev. Med. 2010, 51, 148–152. [Google Scholar] [CrossRef]
- Song, Y.; Huang, B.; Cai, J.; Chen, B. Dynamic assessments of population exposure to urban greenspace using multi-source big data. Sci. Total Environ. 2018, 634, 1315–1325. [Google Scholar] [CrossRef] [PubMed]
- Xiao, Y.; Wang, D.; Fang, J. Exploring the disparities in park access through mobile phone data: Evidence from Shanghai, China. Landsc. Urban Plan. 2019, 181, 80–91. [Google Scholar] [CrossRef]
- United Nations. World Urbanization Prospects: The 2018 Revision; United Nations: New York, NY, USA, 2018; p. 30. [Google Scholar]
- Jim, C.Y. Sustainable urban greening strategies for compact cities in developing and developed economies. Urban Ecosyst. 2012, 16, 741–761. [Google Scholar] [CrossRef] [Green Version]
- National Bureau of Statistics of China. Tabulation on the Population Census of the People’s Republic of China by County; China Statistics Press: Beijing, China, 2010.
- The State Council of The People’s Republic of China. National Population Development Plan (2016–2030); The State Council of The People’s Republic of China: Beijing, China, 2017.
- National Statistical Bureau. China Statistic Yearbook; National Statistical Bureau: Beijing, China, 2010.
- Li, P.; Li, W.; Chen, G.; Zou, Y.; Cui, Y.; Ren, L.; Tian, Z.; Zhang, L.; Fan, L.; Wang, K.; et al. 2011 Chinese Social Survey, V1 ed.; Chinese Academy of Social Sciences: Beijing, China, 2018. [Google Scholar] [CrossRef]
- Chen, B.; Nie, Z.; Chen, Z.; Xu, B. Quantitative estimation of 21st-century urban greenspace changes in Chinese populous cities. Sci. Total Environ. 2017, 609, 956–965. [Google Scholar] [CrossRef] [PubMed]
- Hansen, M.C.; Potapov, P.; Moore, R.; Hancher, M.; Turubanova, S.A.; Tyukavina, A.; Thau, D.; Stehman, S.V.; Goetz, S.J.; Loveland, T.; et al. High-Resolution Global Maps of 21st-Century Forest Cover Change. Science 2013, 342, 850–853. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Reid, C.E.; Kubzansky, L.D.; Li, J.; Shmool, J.L.; Clougherty, J.E. It’s not easy assessing greenness: A comparison of NDVI datasets and neighborhood types and their associations with self-rated health in New York City. Health Place 2018, 54, 92–101. [Google Scholar] [CrossRef]
- Higgs, G.; Fry, R.; Langford, M. Investigating the Implications of Using Alternative GIS-Based Techniques to Measure Accessibility to Green Space. Environ. Plan. B Plan. Des. 2012, 39, 326–343. [Google Scholar] [CrossRef]
- Wei, F. Greener urbanization? Changing accessibility to parks in China. Landsc. Urban Plan. 2017, 157, 542–552. [Google Scholar] [CrossRef]
- Reyes, M.; Páez, A.; Morency, C. Walking accessibility to urban parks by children: A case study of Montreal. Landsc. Urban Plan. 2014, 125, 38–47. [Google Scholar] [CrossRef]
- Qiu, Y.; Liu, Y.; Liu, Y.; Li, Z. Exploring the Linkage between the Neighborhood Environment and Mental Health in Guangzhou, China. Int. J. Environ. Res. Public Health 2019, 16, 3206. [Google Scholar] [CrossRef] [Green Version]
- Lu, S.; Ma, J.; Ma, X.; Tang, H.; Zhao, H.; Hasan Ali Baig, M. Time series of the Inland Surface Water Dataset in China (ISWDC) for 2000–2016 derived from MODIS archives. Earth Syst. Sci. Data 2019, 11, 1099–1108. [Google Scholar] [CrossRef] [Green Version]
- Dempsey, S.; Devine, M.T.; Gillespie, T.; Lyons, S.; Nolan, A. Coastal blue space and depression in older adults. Health Place 2018, 54, 110–117. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- National Bureau of Statistics of China. Compilation Regulation of Zoning Code and Urban-Rural Code for National Census. Available online: http://www.stats.gov.cn/tjsj/tjbz/200911/t20091125_8667.html (accessed on 19 March 2020).
- State Bureau of Surveying and Mapping of China. National Roads and Highways of China; China Communications Press: Beijing, China, 2009. [Google Scholar]
- Gaode Map. Gaode Map. Available online: https://www.amap.com/ (accessed on 19 March 2020).
- Google. Google Map. Available online: https://www.google.com/maps (accessed on 2 August 2020).
- National Geomatics Center of China. Lakes and Waterbodies of China; National Geomatics Center of China: Beijing, China, 2014.
- National Geomatics Center of China. China River Basins; National Geomatics Center of China: Beijing, China, 2011.
- Wessel, P.; Smith, W.H.F. A global, self-consistent, hierarchical, high-resolution shoreline database. J. Geophys. Res. Solid Earth 1996, 101, 8741–8743. [Google Scholar] [CrossRef] [Green Version]
- Xu, X. China 1 km Grid Population Data for Year 2010; Chinese Academy of Sciences, Data Center of Resources and Environmental Sciences: Beijing, China, 2017. [Google Scholar] [CrossRef]
- Xu, X. China 1 km Grid GDP Spatial Distribution Data for Year 2010; Chinese Academy of Sciences, Data Center of Resources and Environmental Sciences: Beijing, China, 2017. [Google Scholar]
- Yuan, L.; Shin, K.; Managi, S. Subjective Well-being and Environmental Quality: The Impact of Air Pollution and Green Coverage in China. Ecol. Econ. 2018, 153, 124–138. [Google Scholar] [CrossRef]
- MacKerron, G.; Mourato, S. Life satisfaction and air quality in London. Ecol. Econ. 2009, 68, 1441–1453. [Google Scholar] [CrossRef]
- Bertram, C.; Rehdanz, K. The role of urban green space for human well-being. Ecol. Econ. 2015, 120, 139–152. [Google Scholar] [CrossRef] [Green Version]
- Shumway-Cook, A.; Patla, A.E.; Stewart, A.; Ferrucci, L.; Ciol, M.A.; Guralnik, J.M. Environmental Demands Associated with Community Mobility in Older Adults with and without Mobility Disabilities. Phys. Ther. 2002, 82, 670–681. [Google Scholar] [CrossRef] [Green Version]
- Ministry of Housing and Urban-Rural Development. Evaluation Standard for Urban Landscaping and Greening; Ministry of Housing and Urban-Rural Development: Beijing, China, 2010; p. 98. [Google Scholar]
- Van den Berg, M.; Wendel-Vos, W.; van Poppel, M.; Kemper, H.; van Mechelen, W.; Maas, J. Health benefits of green spaces in the living environment: A systematic review of epidemiological studies. Urban For. Urban Green. 2015, 14, 806–816. [Google Scholar] [CrossRef]
- Astell-Burt, T.; Feng, X.; Kolt, G.S. Mental health benefits of neighbourhood green space are stronger among physically active adults in middle-to-older age: Evidence from 260,061 Australians. Prev. Med. 2013, 57, 601–606. [Google Scholar] [CrossRef]
- Maas, J.; Verheij, R.A.; Groenewegen, P.P.; de Vries, S.; Spreeuwenberg, P. Green space, urbanity, and health: How strong is the relation? J. Epidemiol. Community Health 2006, 60, 587–592. [Google Scholar] [CrossRef] [Green Version]
- Xie, B.; An, Z.; Zheng, Y.; Li, Z. Healthy aging with parks: Association between park accessibility and the health status of older adults in urban China. Sustain. Cities Soc. 2018, 43, 476–486. [Google Scholar] [CrossRef]
- Coppel, G.; Wüstemann, H. The impact of urban green space on health in Berlin, Germany: Empirical findings and implications for urban planning. Landsc. Urban Plan. 2017, 167, 410–418. [Google Scholar] [CrossRef]
- Liu, Y.; Wang, R.; Grekousis, G.; Liu, Y.; Yuan, Y.; Li, Z. Neighbourhood greenness and mental wellbeing in Guangzhou, China: What are the pathways? Landsc. Urban Plan. 2019, 190. [Google Scholar] [CrossRef]
- Kaczynski, A.T.; Besenyi, G.M.; Stanis, S.A.; Koohsari, M.J.; Oestman, K.B.; Bergstrom, R.; Potwarka, L.R.; Reis, R.S. Are park proximity and park features related to park use and park-based physical activity among adults? Variations by multiple socio-demographic characteristics. Int. J. Behav. Nutr. Phys. Act. 2014, 11, 146. [Google Scholar] [CrossRef] [Green Version]
- Van den Berg, A.E.; Maas, J.; Verheij, R.A.; Groenewegen, P.P. Green space as a buffer between stressful life events and health. Soc. Sci. Med. 2010, 70, 1203–1210. [Google Scholar] [CrossRef] [Green Version]
- Akpinar, A.; Barbosa-Leiker, C.; Brooks, K.R. Does green space matter? Exploring relationships between green space type and health indicators. Urban For. Urban Green. 2016, 20, 407–418. [Google Scholar] [CrossRef]
- Richardson, E.A.; Mitchell, R.; Hartig, T.; de Vries, S.; Astell-Burt, T.; Frumkin, H. Green cities and health: A question of scale? J. Epidemiol. Community Health 2012, 66, 160–165. [Google Scholar] [CrossRef] [Green Version]
- Völker, S.; Heiler, A.; Pollmann, T.; Claßen, T.; Hornberg, C.; Kistemann, T. Do perceived walking distance to and use of urban blue spaces affect self-reported physical and mental health? Urban For. Urban Green. 2018, 29, 1–9. [Google Scholar] [CrossRef]
- Gascon, M.; Zijlema, W.; Vert, C.; White, M.P.; Nieuwenhuijsen, M.J. Outdoor blue spaces, human health and well-being: A systematic review of quantitative studies. Int. J. Hyg. Environ. Health 2017, 220, 1207–1221. [Google Scholar] [CrossRef]
- Aspinall, P.A.; Ward Thompson, C.; Alves, S.; Sugiyama, T.; Brice, R.; Vickers, A. Preference and relative importance for environmental attributes of neighbourhood open space in older people. Environ. Plan. B Plan. Des. 2010, 37, 1022–1039. [Google Scholar] [CrossRef]
- Coleman, T.; Kearns, R. The role of bluespaces in experiencing place, aging and wellbeing: Insights from Waiheke Island, New Zealand. Health Place 2015, 35, 206–217. [Google Scholar] [CrossRef] [PubMed]
- Sikorska, D.; Łaszkiewicz, E.; Krauze, K.; Sikorski, P. The role of informal green spaces in reducing inequalities in urban green space availability to children and seniors. Environ. Sci. Policy 2020, 108, 144–154. [Google Scholar] [CrossRef]
- Bell, S.L.; Phoenix, C.; Lovell, R.; Wheeler, B.W. Green space, health and wellbeing: Making space for individual agency. Health Place 2014, 30, 287–292. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Name of Data | Sources | Year of Data Collected |
---|---|---|
Chinese Social Survey (CSS) | Li, Li, Chen, Zou, Cui, Ren, Tian, Zhang, Fan, Wang and Hu [48] | 2011 |
Neighborhood location (point) | Gaode Map [60] | 2010 |
Landsat images (30 m) | Hansen, Potapov, Moore, Hancher, Turubanova, Tyukavina, Thau, Stehman, Goetz, Loveland, Kommareddy, Egorov, Chini, Justice and Townshend [50] | 2012 |
Park entrance (point) | Gaode Map [60], Google [61] | 2010 |
Water surface area (250 m) | Lu, Ma, Ma, Tang, Zhao and Hasan Ali Baig [56] | 2011 |
China major lake (polygon) | National Geomatics Center of China [62] | 2014 |
China river (polyline) | National Geomatics Center of China [63] | 2011 |
Coastal line (polyline) | Wessel and Smith [64] | 2010 |
China population density (1 km) | Xu [65] | 2010 |
China GDP per area (1 km) | Xu [66] | 2010 |
China major road | State Bureau of Surveying and Mapping of China [59] | 2009 |
Urban (n = 1061) | Rural (n = 712) | |||||||
---|---|---|---|---|---|---|---|---|
Variables | Mean | S.D. | Min. | Max. | Mean | S.D. | Min. | Max. |
Outcome | ||||||||
Self-rate health | 2.011 | 0.685 | 1.000 | 3.000 | 1.901 | 0.733 | 1.000 | 3.000 |
Predictors | ||||||||
Coverage of waterbody in 1 km | 0.035 | 0.072 | 0.000 | 0.380 | 0.010 | 0.048 | 0.000 | 0.460 |
Coverage of vegetation in 1 km | 0.339 | 0.273 | 0.000 | 1.000 | 0.830 | 0.245 | 0.064 | 0.999 |
The nearest distance to park (m) | 3167 | 7144 | 19 | 45,649 | ||||
The nearest distance to waterbody (m) | 2944 | 4595 | 1 | 48,240 | 8135 | 8652 | 1 | 44,417 |
Nearest distance to river (m) | 4497 | 6126 | 28 | 29,731 | 9138 | 7548 | 25 | 38,546 |
Within 0–0.3 km of park | 0.237 | 0.425 | 0.000 | 1.000 | ||||
Within 0.3–0.5 km of park | 0.171 | 0.377 | 0.000 | 1.000 | ||||
Within 0.5–1 km of park | 0.259 | 0.438 | 0.000 | 1.000 | ||||
Within 0–0.3 km of waterbody | 0.134 | 0.341 | 0.000 | 1.000 | 0.018 | 0.134 | 0.000 | 1.000 |
Within 0.3–0.5 km of waterbody | 0.069 | 0.254 | 0.000 | 1.000 | 0.041 | 0.197 | 0.000 | 1.000 |
Within 0.5–1 km of waterbody | 0.194 | 0.395 | 0.000 | 1.000 | 0.025 | 0.157 | 0.000 | 1.000 |
Within 0–0.3 km of river | 0.065 | 0.246 | 0.000 | 1.000 | 0.040 | 0.197 | 0.000 | 1.000 |
Within 0.3–0.5 km of river | 0.033 | 0.180 | 0.000 | 1.000 | 0.014 | 0.118 | 0.000 | 1.000 |
Within 0.5–1 km of river | 0.135 | 0.342 | 0.000 | 1.000 | 0.032 | 0.177 | 0.000 | 1.000 |
Within 1 km of coastline | 0.001 | 0.030 | 0.000 | 1.000 | 0.000 | 0.000 | 0.000 | 0.000 |
Within 1–5 km of coastline | 0.071 | 0.257 | 0.000 | 1.000 | 0.007 | 0.083 | 0.000 | 1.000 |
Within 5 km of lake | 0.003 | 0.053 | 0.000 | 1.000 | 0.027 | 0.161 | 0.000 | 1.000 |
Covariates | ||||||||
Demographical information | ||||||||
Age | 69.5 | 7.3 | 60 | 92 | 68.5 | 7.0 | 60 | 101 |
Gender (1 = male, 0 = female) | 0.469 | 0.499 | 0.000 | 0.000 | 0.513 | 0.500 | 0.000 | 1.000 |
Ethnic group (1 = Han, 0 = other) | 0.948 | 0.222 | 0.000 | 1.000 | 0.885 | 0.319 | 0.000 | 1.000 |
Marriage (1 = married, 0 = single) | 0.994 | 0.080 | 0.000 | 1.000 | 0.985 | 0.123 | 0.000 | 1.000 |
Local Hukou (1 = local, 0 = migrant) | 0.783 | 0.412 | 0.000 | 1.000 | 0.966 | 0.180 | 0.000 | 1.000 |
Number of person(s) in household | 4 | 2 | 1 | 21 | 4 | 2 | 1 | 21 |
Living alone (1 = yes, 0 = no) | 0.631 | 0.483 | 0.000 | 1.000 | 0.624 | 0.485 | 0.000 | 1.000 |
Occupation (1 = yes, 0 = no) | ||||||||
Employed | 0.149 | 0.356 | 0.000 | 1.000 | 0.628 | 0.484 | 0.000 | 1.000 |
Not able to work | 0.122 | 0.328 | 0.000 | 1.000 | 0.273 | 0.446 | 0.000 | 1.000 |
Retired | 0.604 | 0.489 | 0.000 | 1.000 | 0.052 | 0.222 | 0.000 | 1.000 |
Homemaker | 0.056 | 0.229 | 0.000 | 1.000 | 0.020 | 0.139 | 0.000 | 1.000 |
Income level (1 = yes, 0 = no) | ||||||||
<5000 CNY | 0.156 | 0.363 | 0.000 | 1.000 | 0.552 | 0.498 | 0.000 | 1.000 |
5000–15,000 CNY | 0.181 | 0.385 | 0.000 | 1.000 | 0.232 | 0.423 | 0.000 | 1.000 |
15,000–30,000 CNY | 0.407 | 0.492 | 0.000 | 1.000 | 0.091 | 0.288 | 0.000 | 1.000 |
>30,000 CNY | 0.156 | 0.363 | 0.000 | 1.000 | 0.017 | 0.129 | 0.000 | 1.000 |
No answer | 0.100 | 0.300 | 0.000 | 1.000 | 0.108 | 0.310 | 0.000 | 1.000 |
Education level (1 = yes, 0 = no) | ||||||||
Below elementary school (lowest) | 0.189 | 0.392 | 0.000 | 1.000 | 0.407 | 0.492 | 0.000 | 1.000 |
Elementary school | 0.287 | 0.453 | 0.000 | 1.000 | 0.434 | 0.496 | 0.000 | 1.000 |
Middle school | 0.240 | 0.427 | 0.000 | 1.000 | 0.130 | 0.337 | 0.000 | 1.000 |
High school | 0.083 | 0.277 | 0.000 | 1.000 | 0.007 | 0.083 | 0.000 | 1.000 |
Technical secondary school | 0.083 | 0.277 | 0.000 | 1.000 | 0.014 | 0.118 | 0.000 | 1.000 |
Technical Junior college | 0.005 | 0.068 | 0.000 | 1.000 | 0.003 | 0.053 | 0.000 | 1.000 |
Junior college | 0.061 | 0.240 | 0.000 | 1.000 | 0.004 | 0.065 | 0.000 | 1.000 |
College | 0.051 | 0.220 | 0.000 | 1.000 | 0.001 | 0.037 | 0.000 | 1.000 |
Graduate (highest) | 0.001 | 0.030 | 0.000 | 1.000 | 0.000 | 0.000 | 0.000 | 0.000 |
Assets | ||||||||
Pension benefit (1 = yes, 0 = no) | 0.681 | 0.466 | 0.000 | 1.000 | 0.376 | 0.485 | 0.000 | 1.000 |
Medical insurance (1 = yes, 0 = no) | 0.881 | 0.324 | 0.000 | 1.000 | 0.910 | 0.286 | 0.000 | 1.000 |
Owns car (1 = yes, 0 = no) | 0.074 | 0.262 | 0.000 | 1.000 | 0.035 | 0.184 | 0.000 | 1.000 |
Owns housing property | 1.062 | 0.549 | 0.000 | 5.000 | 1.136 | 0.467 | 0.000 | 6.000 |
Environmental features | ||||||||
The nearest distance to road (m) | 1843.3 | 2237.9 | 7.0 | 22,037.6 | 5365.6 | 4831.9 | 7.7 | 23,496.0 |
GDP per km2 (10,000 CNY) | 11,660.4 | 10,720.8 | 0.0 | 50,051.8 | 1844.5 | 4081.1 | 0.0 | 26,769.4 |
Number of person(s) per km2 | 10,707.6 | 10,187.3 | 0.0 | 41,878.0 | 638.2 | 1164.4 | 0.0 | 14,839.0 |
Urban (n = 1061) | Rural (n = 712) | |||||
---|---|---|---|---|---|---|
Variables | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 |
Coverage of waterbody in 1 km | −0.498 | 0.212 | ||||
(0.474) | (0.716) | |||||
Coverage of vegetation in 1 km | −0.200 | −0.048 | ||||
(0.176) | (0.316) | |||||
The nearest distance to park (log) | −0.031 | |||||
(0.027) | ||||||
The nearest distance to waterbody (log) | −0.029 | 0.028 | ||||
(0.028) | (0.045) | |||||
The nearest distance to river (log) | 0.038 | −0.008 | ||||
(0.036) | (0.035) | |||||
Within 0–0.3 km of park | 0.033 | |||||
(0.091) | ||||||
Within 0.3–0.5 km of park | −0.056 | |||||
(0.097) | ||||||
Within 0.5–1 km of park | 0.033 | |||||
(0.088) | ||||||
Within 0–0.3 km of waterbody | 0.210 * | −0.013 | ||||
(0.100) | (0.319) | |||||
Within 0.3–0.5 km of waterbody | 0.124 | −0.437 | ||||
(0.115) | (0.290) | |||||
Within 0.5–1 km of waterbody | 0.041 | 0.194 | ||||
(0.070) | (0.195) | |||||
Within 0–0.3 km of river | −0.099 | 0.195 | ||||
(0.144) | (0.217) | |||||
Within 0.3–0.5 km of river | −0.237 + | 0.388 | ||||
(0.140) | (0.430) | |||||
Within 0.5–1 km of river | −0.093 | 0.202 | ||||
(0.085) | (0.252) | |||||
Within 1 km of coastline | −1.196 + | |||||
(0.686) | ||||||
Within 1–5 km of coastline | −0.379 + | −0.969 | ||||
(0.200) | (0.629) | |||||
Within 5 km of lake | −0.156 | 0.091 | ||||
(0.515) | (0.235) | |||||
Constant | 1.810 *** | 1.807 *** | 1.758 *** | 2.278 ** | 2.065 * | 2.308 ** |
(0.464) | (0.544) | (0.473) | (0.763) | (0.846) | (0.723) | |
Observations | 1061 | 1061 | 1061 | 712 | 712 | 712 |
R-squared | 0.215 | 0.214 | 0.225 | 0.230 | 0.232 | 0.241 |
Adjusted R-squared | 0.109 | 0.104 | 0.110 | 0.095 | 0.097 | 0.097 |
F test model | 2.03 | 1.95 | 1.96 | 1.71 | 1.70 | 1.68 |
p-value of F model | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
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Lin, C.; Wu, L. Green and Blue Space Availability and Self-Rated Health among Seniors in China: Evidence from a National Survey. Int. J. Environ. Res. Public Health 2021, 18, 545. https://doi.org/10.3390/ijerph18020545
Lin C, Wu L. Green and Blue Space Availability and Self-Rated Health among Seniors in China: Evidence from a National Survey. International Journal of Environmental Research and Public Health. 2021; 18(2):545. https://doi.org/10.3390/ijerph18020545
Chicago/Turabian StyleLin, Chensong, and Longfeng Wu. 2021. "Green and Blue Space Availability and Self-Rated Health among Seniors in China: Evidence from a National Survey" International Journal of Environmental Research and Public Health 18, no. 2: 545. https://doi.org/10.3390/ijerph18020545