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Construction of Data-Driven Urban Conflict Prevention and Governance Model
Abstract
The surge of urban operation data provides a new opportunity for prior identification and accurate intervention of contradictions and disputes. Based on the data of 12,345 work orders, police receiving, and judicial mediation in a sub-provincial city in recent three years, this paper constructs a closed-loop model of “perception-prediction- intervention-feedback”: it opens up semantic mapping and synchronization of cross-departmental heterogeneous data, integrates multi-scale spatio-temporal characteristics, and embeds LightGBM-Text cellular neural network (CNN) dual-channel model to realize minute-level prediction, differentiate intervention according to risk level, and optimize the closed-loop through visual dashboard. The six-month A/B test shows that the dispute response time is shortened by 36.9%, the incident resolve rate is increased by 22.6%, and the satisfaction of the masses is increased by 18.1%. Under the premise of clear responsibilities, the model realizes efficient multi-sectoral linkage and adaptive governance and provides a replicable paradigm for social governance in megacities.
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