Journal of Resources and Ecology >
The Associations of Green Spaces with Older Adults’ Mental Health in Perspective of Spatiality, Sociality and Historicality
YUE Yafei, E-mail: yfyue@mail.dlut.edu.cn |
Received date: 2022-03-14
Accepted date: 2022-06-25
Online published: 2023-02-21
Supported by
The National Natural Science Foundation of China(52078095)
The National Natural Science Foundation of China(51638003)
The China Scholarship Council(202006060186)
The theory of health fairness requires that improving the benefits of green spaces on health should take into account the characteristics of vulnerable groups such as older adults. Until now, the comprehensive associations of green spaces metrics with older adults’ mental health are unclear in China. This study aimed to systematically assess their associations from the perspective of spatiality, sociality and historicality. Data of 879 respondents aged 60 or older in Dalian, China were used, and multilevel linear models were conducted in HLM6.08. Results indicated that in the spatial dimension, green spaces metrics derived from overhead view had a stronger association with older adults’ mental health than from street view. The park with gentle slopes and irregular boundaries was more positively related to older adults’ mental health. In the social dimension, green spaces could adjust and buffer the negative effects of socio-demographic characteristics such as having no homeownership. In the historical dimension, green spaces had a lasting effect on older adults’ mental health, especially for the group of middle income. Our findings confirm the necessity of multidimensional assessments for green spaces to examine associations with older adults’ mental health in Chinese settings. The results can provide empirical evidence for realizing fairer allocation of urban green spaces resources taking into account individual differences.
Key words: green spaces; street view; older adults; mental health; multilevel linear model
YUE Yafei , YANG Dongfeng , XU Dan . The Associations of Green Spaces with Older Adults’ Mental Health in Perspective of Spatiality, Sociality and Historicality[J]. Journal of Resources and Ecology, 2023 , 14(2) : 299 -308 . DOI: 10.5814/j.issn.1674-764x.2023.02.008
Fig. 1 Mental health benefits of green spaces metrics on older adults in perspective of spatiality, sociality, historicality |
Fig. 2 Location of the study area and interviewed residential areas |
Table 1 Design and calculation methods of green spaces metrics |
Characteristics | Calculation method | Data name | |
---|---|---|---|
Availability | NDVI | Extracted from remote sensing images by software ENVI | Landsat-8 images |
Vegetation cover rate | Extracted from globe land cover data by software ArcGIS | GLC_FCS30-2020 | |
Visuality | Streetscape green spaces rate | Extracted from Tencent Street View data by a fully convolutional neural network | Tencent Street View data |
Accessibility | Distance to park | Calculating the spatial networks distance to the latest park by software ArcGIS | Park distribution data in Dalian |
Park attraction | Total number | Calculating the total number of parks in the residential buffer zone by software ArcGIS | Park distribution data in Dalian |
Number of types | Calculating the type number of parks in the residential buffer zone by software ArcGIS | Park distribution data in Dalian | |
Area | Calculating the area of parks in the residential buffer zone by software ArcGIS | Park distribution data in Dalian | |
Slope | Calculating the average slope of parks in the residential buffer zone by software ArcGIS | Digital Elevation Model data | |
Plant diversity | Calculating the average number of plant species in the park in the residential buffer zone by software ArcGIS | GLC_FCS30-2020 and Park distribution data in Dalian | |
Special distribution of parks | Landscape Pattern Indexes | Calculating 10 typical LPI in area, density, shape, diversity, aggregation, proximity, etc. dimensions by software Fragstats | Park distribution data in Dalian |
Fig. 3 Architecture of the fully convolutional network (adopted from Long et al., 2015)Note: The attached numbers refer to a layer’s convolution kernel size. |
Table 2 Description of socio-demographic characteristics |
Variable | Levels | N | Percentage (%) | Variable | Levels | N | Percentage (%) |
---|---|---|---|---|---|---|---|
Age | 60-70 | 394 | 44.8 | Number of co-occupants | 1 | 116 | 13.2 |
71-80 | 293 | 33.3 | 2 | 388 | 44.1 | ||
> 81 | 192 | 21.8 | 3 and more | 375 | 42.7 | ||
Gender | Male | 406 | 46.2 | Monthly income level (yuan) | 0-1000 | 116 | 13.2 |
Female | 473 | 53.8 | 1001-2000 | 63 | 7.2 | ||
Pre-retirement occupation | Intellectual work | 314 | 35.7 | 2001-3000 | 242 | 27.5 | |
Manual work | 505 | 57.5 | 3001-4000 | 242 | 27.5 | ||
Other | 60 | 6.8 | >4000 | 216 | 24.6 | ||
Education level | Homeownership | Own | 571 | 65.0 | |||
Primary school or lower | 300 | 34.1 | Kinsfolks | 261 | 29.7 | ||
Middle school | 274 | 31.2 | Others | 47 | 5.3 | ||
High school | 192 | 21.8 | Number of years living at the current address | 0-4 | 169 | 19.2 | |
College/university | 113 | 12.9 | 5-10 | 167 | 19.0 | ||
> 10 | 543 | 61.8 |
Table 3 Description of green spaces metrics |
Characteristics | Min | Max | Mean | Standard deviation | |
---|---|---|---|---|---|
Availability | NDVI | 0.043 | 0.110 | 0.073 | 0.015 |
Vegetation cover rate | 0.002 | 0.614 | 0.116 | 0.119 | |
Visuality | Streetscape green spaces rate | 0.059 | 0.457 | 0.183 | 0.073 |
Accessibility | Distance to park (m) | 0.000 | 1189.782 | 295.049 | 244.775 |
Park attraction | Total number (number) | 0.000 | 8.000 | 2.020 | 2.061 |
Number of types (number) | 0.000 | 3.000 | 1.130 | 0.763 | |
Area (m2) | 0.000 | 98633.000 | 14039.260 | 17834.518 | |
Slope (degree) | 0.000 | 20.000 | 6.550 | 4.927 | |
Plant diversity (number) | 0.000 | 5.000 | 1.030 | 1.268 | |
Special distribution of parks | Average patch size (ha) | 0.000 | 11.340 | 0.816 | 2.289 |
Average perimeter-area ratio | 0.000 | 1333.333 | 226.188 | 403.214 | |
Average proximity index | 0.000 | 23.200 | 0.533 | 2.988 | |
Degree of aggregation (%) | 0.000 | 100.000 | 19.980 | 35.222 |
Fig. 4 Green spaces metrics in Dalian: (A) GLC_FCS30-2020 (B) Parks distribution in Dalian (C) NDVI (D) Streetscape green spaces rate |
Table 4 The results of multilevel linear models |
Variable | Null model | Random-coefficient models | Intercept Model | Full Model |
---|---|---|---|---|
Coef. (S.E.) | Coef. (S.E.) | Coef. (S.E.) | Coef. (S.E.) | |
Fixed effects | ||||
Socio-demographic characteristics (individual level) | ||||
Age | 0.126***(0.041) | 0.117***(0.039) | ||
Number of years living at the current address | -0.096*(0.043) | -0.106**(0.043) | ||
Homeownership (ref: not own) | 0.135**(0.074) | 0.161**(0.075) | ||
Number of co-occupants (ref: single) | ||||
Living with another person | 0.091(0.032) | 0.087(0.032) | ||
Living with two or more other people | 0.293***(0.095) | 0.266***(0.093) | ||
Green spaces metrics (neighborhood level) | ||||
NDVI | 0.304***(0.093) | 0.307***(0.094) | ||
Vegetation cover rate | 0.181**(0.074) | 0.173**(0.073) | ||
Streetscape green spaces rate | 0.095*(0.060) | 0.094*(0.060) | ||
Distance to park | 0.057(0.060) | 0.052(0.060) | ||
Total number | 0.089(0.074) | 0.078(0.072) | ||
Slope | -0.205***(0.066) | -0.128**(0.054) | ||
Plant diversity | 0.089(0.070) | 0.073(0.070) | ||
Perimeter to area ratio | 0.192***(0.051) | 0.195***(0.048) | ||
Degree of aggregation | -0.111**(0.053) | -0.112**(0.053) | ||
Interaction term | ||||
Age×NDVI | -0.131**(0.053) | |||
Homeownership×NDVI | -0.090*(0.059) | |||
Constant | -0.139***(0.065) | -0.303***(0.101) | -0.154***(0.054) | -0.323***(0.100) |
Random effects | ||||
ICC | 16.87% | 16.78% | 10.41% | 9.98% |
Var (Neighborhood-level constant) | 0.172 | 0.168 | 0.099 | 0.092 |
Var (Individual level) | 0.848 | 0.833 | 0.852 | 0.829 |
AIC | 2146.033 | 2137.124 | 2145.544 | 2132.525 |
Note: *, ** and *** indicate the significance at 0.1, 0.05 and 0.01 levels respectively. |
Table 5 Long-term associations of green spaces metrics with mental health in different elderly groups |
Year | Sequential Model 1 (Whole respondents) | Sequential Model 2 (Low-income groups) | Sequential Model 3 (Groups with middle-low income) | Sequential Model 4 (Groups with middle-high income) | Sequential Model 5 (High-income groups) |
---|---|---|---|---|---|
Coef. (S.E.) | Coef. (S.E.) | Coef. (S.E.) | Coef. (S.E.) | Coef. (S.E.) | |
2019 | 0.389***(0.117) | 0.029(0.127) | 0.212***(0.074) | 0.209***(0.061) | 0.131***(0.080) |
2018 | 0.350***(0.082) | 0.083(0.109) | 0.226***(0.053) | 0.251***(0.076) | 0.142***(0.091) |
2017 | 0.341***(0.107) | 0.022(0.133) | 0.218***(0.059) | 0.219***(0.065) | 0.104**(0.084) |
2016 | 0.228***(0.069) | 0.044(0.133) | 0.273***(0.073) | 0.294***(0.081) | 0.095(0.096) |
2015 | 0.224***(0.070) | 0.019(0.134) | 0.273***(0.071) | 0.267***(0.081) | 0.087(0.098) |
2014 | 0.217***(0.068) | 0.016(0.143) | 0.248***(0.063) | 0.240***(0.082) | 0.081(0.094) |
2013 | 0.168**(0.075) | 0.041(0.102) | 0.245***(0.082) | 0.229***(0.075) | 0.085(0.101) |
2012 | 0.098(0.075) | 0.045(0.101) | 0.209**(0.084) | 0.159**(0.086) | 0.062(0.104) |
2011 | 0.089(0.078) | -0.047(0.137) | 0.157**(0.075) | 0.094(0.104) | 0.029(0.081) |
2010 | 0.109(0.081) | -0.070(0.155) | 0.178**(0.069) | 0.092(0.095) | 0.093(0.109) |
Note: The bold numbers indicate at significant level; *, ** and *** indicate the significance at 0.1, 0.05 and 0.01 levels respectively. |
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