基于SNA网络匹配的老旧社区公共空间优化研究OACHSSCD

The COVID-19 pandemic upended neighborhood community interactions,leading to a demand for their rapid return after the pandemic restrictions eased.This shift further accentuated the structural mismatch in aging communities between the uneven distribution and monofunctional nature of public spaces and residents'real needs.Existing research mostly analyzes physical space or activity from a single dimension,failing to conduct systematic matching.This study,conducted using social network analysis(SNA)and the Simian Community in Xi'an Textile City as the case study,reveals the structural mismatch between public spaces and resident activity.Some targeted hierarchical optimization strategies are proposed to solve the mismatch between public space supply and demand in aging communities.Social relationship characteristics of the permanent population,behavioral patterns,and basic information of 41 public spaces in communities were collected through questionnaire surveys,field observations,path tracking,and in-depth interviews.A dual-network model of public spaces and residents'behaviors was then constructed.UCINET software at the global,local,and nodal levels quantitatively analyzed core indicators,such as density,clustering coefficient,and degree centrality,thus revealing the model's matching analysis.The study found that a rapidly aging population,whose social relationships have shifted from primarily"work-based"to"interests-based",is characteristic of the Simian Community.Its public spaces show a significant structural mismatch with the behavioral network of residents.At the global level,the public space network is homogeneously dispersed and lacks a clear hierarchy.The activity network shows a"multi-core parallel"structure,revealing low overall cohesion.Existing spaces are inadequate to support the collective interaction needs of residents.At the local level,spatial clusters are unevenly distributed,accompanied by weak connections between the northern and southern districts,and a lack of gathering spaces for social interactions.Street spaces are inadequate to support the actual needs of residents.At the nodal level,more than half of the public space nodes have inadequate vitality and single functions,which can be recognized as key nodes,potential nodes,and weak nodes.On this basis,an empirical pathway of"network matching analysis-structural mismatching identification-hierarchical optimization of strategies"was built in this study.It proposed a three-layer spatial optimization strategy,which integrates the network system,strengthens cluster connections,and activates the vitality of the nodes,according to the analysis framework of"integration-region-node".First,it revitalizes underutilized land by adding spatial nodes,solving traffic jams by constructing"connected pathways",and reconstructing the spatial network skeleton of communities.Second,it compensates for missing clusters by introducing small-scale gathering spaces to strengthen linkages between regions and clusters.Third,it realizes function diversification and facility humanization at nodes of different levels,thus precisely stimulating nodal vitality.This study verifies the effectiveness of SNA in matching the analysis between the public space network and the residents'behavioral network,and it establishes a spatial optimization strategy from precise diagnosis to targeted renewal.It also provides feasible methods and practical references for solving the structural mismatch of public spaces in old communities.Moreover,it determines that the optimization of public spaces in old communities shall shift from simple physical renewal to developing residents'social relationships.However,this study has a single sample and lacks dynamic tracking of residents'long-term needs.Future studies can further explore collaborative evolutionary mechanisms of spatial,behavioral,and social relationships through comparative analysis of multiple types of communities,aiming to improve theories and methods related to optimizing public spaces in older communities.