Quantifying spatial associations between effective green spaces and cardiovascular and cerebrovascular diseases by applying volunteered geo-referenced data

Abstract

Among the top public health risks, cardiovascular and cerebrovascular diseases cause more than 1 million deaths annually globally. Due to the calming effect of green spaces and their ability to trap air pollutants, urban green spaces are considered have close associations with cardiovascular and cerebrovascular diseases. However, ignoring the spatial heterogeneity of different urban green space types and considering only the configuration or compositions of urban green spaces has resulted in inconsistent and contradictory conclusions. Therefore, by introducing Tencent urban density data, four effective green spaces (EGSs) were categorized. Category 1 EGSs, which exhibit a high increasing of visitors and areas, accounted for the smallest areal percentage (0.81%). Category 2 EGSs, which exhibit a low increasing of visiting and high increasing of areas, accounted for the highest areal percentage (42.51%). Category 3 EGSs, which exhibit a high increasing of visiting and low increasing of areas, accounted for 13.70% of the total EGS areas. Category 4 EGSs, which exhibit a low increasing of visiting and areas, accounted for 3.75% of the total EGS areas. Using a geographically weighted regression model, spatial associations between EGS and cardiovascular and cerebrovascular diseases were quantified. Consequently, these spatial associations varied among EGS types and seasons. EGS configurations (perimeters of vegetation and areas of vegetation) have a more significant association with cardiovascular and cerebrovascular diseases than the composition (normalized difference vegetation index) of EGS. Spatial associations implying stronger relationships were observed in EGS1. The strongest association was found in summer. Enlarge the coverage of evergreen vegetation in all EGS is first considered to enhance the negative association between EGS and chronic diseases. A methodology framework was provided to classify urban green space types using multi-source data. Suggestions for how to plan different urban green spaces for developing sustainable cities have been provided in this study, which offer scientific support to urban managers and planners for effective decision making.