Deriving the impact mechanism of urban green space environment on community social capital based on multi-source data integration: a case study of nanjing city, China - Feng Zhen; Mohan Liu; Runlin Yang (Computational Urban Science)
Abstract With the orderly progression of urban renewal in China, social capital, as an important factor in analyzing the relationship between stakeholders, has once again become a key topic in urban planning research. Green spaces have proved to have a more notable impact on social capital than other built environment factors. In light of this, the present study is based on the data of 1,282 residents’ questionnaire surveys conducted in Nanjing in 2022 and extracts spatial characteristic information of green space through multisource big data such as remote sensing data, street-view data, and points of interest, to investigate the influence mechanism of physical and perceived attributes of urban green space on community social capital. The study found that there are differences in the influence mechanisms and dimensions of these dual attributes of green space on social capital. The direct effect of perceptual attributes is more substantial, while physical attributes almost exclusively affect social capital indirectly through the perception of green space. Notably, among the physical attributes, only the total coverage of neighborhood vegetation has a considerable direct effect on neighborhood relations, whereas community sentiment, a willingness to participate, and larger and more aggregated green spaces do not enhance community social capital effectively. Lastly, community social capital is affected substantially by exogenous variables of socioeconomic attributes, and there is group differentiation. Results reveal the direction of renewal and optimization of urban green spaces from the perspective of promoting social capital, which provides a reference for the synergistic and high-quality development of the community’s physical and nonphysical environments.