Where renewal works best? Unrevealing spatiotemporal effects of community renewal via DID and RF analysis

Datos Bibliográficos

ID22236403
AutoresHong Ni (0000-0002-2711-1793, Suzhou University of Science and Technology), Jinliu Chen (0000-0002-5343-0851, Soochow University, autor de correspondencia), Pengcheng Li (0009-0007-5302-9105, Suzhou University of Science and Technology)
Año2025
Volumen13
Número1
Fecha de publicación2025-12-10
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaHumanities and Social Sciences Communications (JOURNAL)
Identificadores de la revistaISSN: 2662-9992 • E-ISSN: 2662-9992
EditorialSpringer Science and Business Media LLC (PUBLISHER)
DOI10.1057/s41599-025-06378-3
OpenAlexW7113898612
IdiomaEN
Citas recibidas2
Referencias citadas122

Community renewal has emerged as a pivotal strategy to mitigate spatial obsolescence and address the multifaceted challenges of rapid urbanization. However, its effects on urban spatial vitality remain spatially heterogeneous across different development stages, and the social factors shaping residential life satisfaction during renewal are still in dispute. To address these dual challenges, this study employs a socio-spatial theoretical framework and develops a two-stage analytical model. First, utilizing multi-source big and small data (e.g., geotagged social media data, geospatial POIs, questionnaire survey data), we implement a Difference-in-Differences (DID) analysis to quantify the causal effects of community renewal on urban vitality across spatiotemporal dimensions. Specifically, central districts exhibit statistically significant vitality enhancements: Gusu (DID: 0.063, p < 0.001), SIP (DID: 0.054, p < 0.01), and SND (DID: 0.052, p < 0.01), along with Suzhou overall (DID: 0.054, p < 0.001). Conversely, Wuzhong and Xiangcheng, as peripheral regions, demonstrate negative effects. Second, combining Ordinary Least Squares (OLS) and Random Forest (RF) models to identify key determinants of resident satisfaction. Green space coverage (β = 0.123, RF = 30) and educational facility density (β = 0.315, RF = 41.5) exhibit the strongest positive correlation with satisfaction. These findings highlight the necessity of fostering community cohesion through localized socio-spatial integration by bridging ex-post impact evaluation with actionable socio-spatial strategies. The study advances urban renewal theory by demonstrating how spatial interventions can catalyze social integration, while providing policymakers with evidence-based guidelines for optimizing renewal efficiency in heterogeneous urban contexts

Big data · Geographic information system · Geospatial analysis · Obsolescence · Subdivision · Urban planning · Vitality · Place Attachment and Urban Studies · Urban Green Space and Health · Urban Planning and Governance