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- W3202048830 abstract "Urban green spaces (UGSs) provide numerous irreplaceable environmental and social benefits to humankind, but the lack of baseline information makes it difficult to propose a reasonable greening strategy so as to achieve an equitable allocation of community green spaces. This paper divides UGSs into three classes using the spatial design network analysis (sDNA) and quantifies the UGS accessibility of communities in central Wuhan. Based on these results and the Gini coefficient, we analyze the UGS equity of the spatial distribution at the community level, then propose future greening strategies both at the city and community levels. The results show that the railway station and old Wuhan city are the core areas of traffic network strength (TNS). UGSs are evenly distributed in the core areas of TNS, but the number of UGSs in non-core areas is small, and their distribution is relatively uneven, and the number of communities with medium UGS accessibility is the largest, carrying the densest residential population. Most communities perform well in terms of UGS equity, but the UGS equity of 163 communities, covering a population of more than one million, remains to be improved. The method and conclusions of this study will contribute to the future greening policy making of 965 communities in central Wuhan, thus promoting the orderly planning and high-quality construction of community living circles." @default.
- W3202048830 created "2021-10-11" @default.
- W3202048830 creator A5011282132 @default.
- W3202048830 creator A5013979754 @default.
- W3202048830 creator A5061433881 @default.
- W3202048830 date "2021-09-28" @default.
- W3202048830 modified "2023-10-09" @default.
- W3202048830 title "Community-Level Urban Green Space Equity Evaluation Based on Spatial Design Network Analysis (sDNA): A Case Study of Central Wuhan, China" @default.
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- W3202048830 doi "https://doi.org/10.3390/ijerph181910174" @default.
- W3202048830 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8507989" @default.
- W3202048830 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/34639480" @default.
- W3202048830 hasPublicationYear "2021" @default.
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