基于空间大数据的城市派出所选址优化研究

Research on Optimization of Urban Police Station Site Selection Based on Spatial Big Data

人口密度的提升及城市功能的升级,导致城市公共安全的管理越发困难。为提升派出所对城市公共安全的管理能力,提出了一种基于空间大数据的城市派出所位置选址模型。该模型利用城市兴趣点数据,根据不同的功能特征对城市空间进行区域划分,再根据不同区域的城市管理权重,优化派出所的选址。结果表明,经优化后,城市派出所数量可降至3个,同时派出所的出警时间仍可保持在50 min以内,最高出警时间为48 min。研究构建的模型可根据城市功能区域的划分,减少城市派出所的数量,同时保证派出所对城市公共安全的管理能力,有效提升了城市空间的利用能力,强化了派出所的城市公共安全管理职责。

The increase in population density and the upgrading of urban functions have made it increasingly difficult to manage urban public safety.A spatial big data based model for the location selection of urban police stations is proposed to improve their management capabilities in urban public safety.This model utilizes urban interest point data to divide urban space into regions based on different functional features,and then optimizes the location of police stations according to the urban management weights of different regions.The results show that after optimization,the number of urban police stations can be reduced to 3,while the response time of police stations can still be maintained within 50 min,with a maximum response time of 48 min.The model constructed by the research can reduce the number of urban police stations based on the division of urban functional areas,while ensuring the management ability of police stations for urban public safety,effectively improving the utilization ability of urban space,and strengthening the urban public safety management responsibilities of police stations.