In Environmental research ; h5-index 67.0
BACKGROUND : Numerous studies in developed countries have demonstrated that greenspace(GS) exposure is associated with improvements in the health of individuals with hypertension and diabetes. Currently, limited research examined associations between multiple GS exposures and chronic health conditions in developing countries.
METHODS : Geospatial data and spatial analysis were employed to objectively measure the total vegetative cover of the community (mean value of normalised difference vegetation index [NDVI] within specific buffer zone) and proximity to park-based GS (network distance from home to the entrance of park-based GS). Street view imagery and machine learning techniques were used to measure the subjective perceptions of the street GS quality. A multiple linear regression model was then applied to examine the associations between multiple GS exposures and the prevalence of hypertension and diabetes in the neighbourhoods located in Qingdao, China.
RESULTS : The model explains 29.8% and 28.2% of the prevalence of hypertension and diabetes, respectively. The results suggested that: 1) the total vegetative cover of the neighbourhood was inversely correlated with the prevalence of hypertension (β = -0.272, p = 0.013, 95% confidence interval (CI): [-1.332, -0.162]) and diabetes (β = -0.230, p = 0.037, 95% CI: [-0.720, -0.008]). 2) The street GS quality was negatively correlated with the prevalence of hypertension (β = -0.303, p = 0.007, 95% CI: [-2.981, -0.491]) and diabetes (β = -0.309, p = 0.006, 95% CI: [-1.839, -0.314]). 3) Proximity to park-based GS and the prevalence of hypertension and diabetes mellitus were not significantly correlated.
CONCLUSIONS : This study used subjective and objective methods to comprehensively assess the greenspace exposure from overhead to eye level, from quantity, proximity to quality. The results demonstrated the beneficial relationships between street GS quality, total vegetative cover, and chronic health in a rapidly urbanising Chinese city. It further addressed that the street GS quality was more pronounced in potentially mitigating chronic health problems, and improving the quality of street GS might be an efficient and effective intervention pathway in chronic health issues in cities with high population densities.
Liu Yawen, Zhao Bing, Cheng Yingyi, Zhao Tianyi, Zhang Ao, Cheng Siqi, Zhang Jinguang
2023-Jan-21
Diabetes, Greenspace exposure, Hypertension, Machine learning, Streetscape
