K-means聚类分析公安视频监控点位风险评估与治理的Python实现——以C市J区为例

Python Implementation of K-Means Clustering Analysis for Risk Assessment and Governance of Public Security Video Surveillance Points—Taking J District of C City as an Example

我国“十四五”规划明确提出将“一网统管”理念融入城市建设与管理,推动传统网格化治理模式向跨部门、多维度综合治理转型。公安机关作为城市管理的重要力量,在视频监控系统的建设与应用中面临建设进度不一、运营效率待提升及资源分配不均等问题。基于C市J区公安视频监控点位现状,运用K-means算法对监控点位进行的聚类分析,基于K-means算法的公安视频监控布局分类思想,将网格化治理模式融入视频监控点位布局,提出了C市J区的公安视频监控风险评估与点位治理研究方案。通过精准的点位布局,可显著提高城市视频监控防控体系的科学性和有效性。为公安部门提供了视频监控风险评估及点位治理的科学指导,实现网格区域内城市地区分类,提高了城市视频监控防控体系的智慧性。China’s 14th Five-Year Plan clearly proposes to integrate the concept of “one network unified management” into urban construction and management, and promote the transformation of traditional grid governance mode to cross departmental and multi-dimensional comprehensive governance. As an important force in urban management, public security organizations face problems such as inconsistent construction progress, the need to improve operational efficiency, and uneven resource allocation in the construction and application of video surveillance systems. Based on the current situation of public security video surveillance points in J district of C city, a clustering analysis of the surveillance points was conducted using the K-means algorithm. This analysis, grounded in the classification principles of K-means, integrates a gridding-based governance model into the layout of the surveillance points. The study proposes a risk assessment and point management research scheme for public security video surveillance in District J of City C. Through precise point layout, the scientificity and effectiveness of the urban video surveillance and prevention system can be significantly improved, which provides scientific guidance for video surveillance risk assessment and point management to the public security department, achieves urban area classification within the grid area, and improves the intelligence of the urban video surveillance prevention and control system.