Spatial Mismatch Between Street Space Quality and Public Sentiment: A Multilevel Spatial Diagnostic Approach

Against the background of urban renewal, urban governance has increasingly pursued the coordinated development of spatial provision and public needs. Taking the central urban area of Shanghai as a case study, this study integrates street-view imagery and geotagged Weibo data to examine the spatial matching relationship between Street Space Quality (SSQ) and public sentiment. The results show that although SSQ and public sentiment both exhibit significant spatial clustering, no stable synchronization is observed between their spatial patterns. Most mismatches remain localized, whereas some display clear neighborhood continuity; moreover, the stable matching types exhibit distinct SSQ profiles. Overall, spatial mismatch in central Shanghai is characterized by the coexistence of locality and neighborhood continuity and by pronounced differences in quality profiles across types. These findings provide a spatial analytical basis for coordinated diagnosis and fine-grained governance in urban renewal.