Analysis of Grassroots Grid-based Governance in the Context of Big Data
Abstract
This study evaluates the feasibility and optimization mechanisms of grid-based grassroots governance in China under the influence of big data technologies. By integrating a four-layer governance simulation model—comprising infrastructure, data support, application support, and service layers—with stratified community sampling in Hangzhou, the research adopts a mixed-methods approach combining SPSS, ArcGIS, AnyLogic, and NVivo analyses. Findings reveal that grid-based governance significantly improves infrastructure provision, data accuracy, service responsiveness, and community satisfaction. The model facilitates more efficient policy implementation, enhances party-building integration, and strengthens localized service delivery. This study concludes that grid-based governance, supported by institutional alignment and big data, offers a scalable and human-centered solution for modernizing grassroots administration in China.
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