城市体征工程中的统计模型构建

Construction of statistical models in urban sign engineering

城市体征工作旨在为城市建设自动化的感知能力,以支撑城市科学规划、社会精准治理及高效公共服务。相较于全量感知、逐一分类、精准李生等基础手段,基于统计模型的感知能力建设具有成本低、问题导向特征强等特点。文章对统计建模难点进行了识别,并在梳理一般统计方法的基础上,重点关注了阈值评估方法和复杂体征统计量构建方法,旨在推进智慧城市建设中的城市运行体征相关统计理论的创新和建模水平的提升,从而辅助更多城市低成本地用好城市大数据,进而助力城市管理服务水平提升。

The purpose of urban sign work is to enhance the perception ability of urban construction automation,in order to support scientific urban planning,precise social governance,and efficient public services.Compared to basic methods such as full perception,individual classification,and precise twinning,the construction of perception capabilities based on statistical models has the characteristics of low cost and strong problem oriented characteristics.The article identifies the difficulties in statistical modeling and,based on a review of general statistical methods,focuses on threshold evaluation methods and complex sign statistics construction methods.The aim is to promote innovation in statistical theories related to urban operational signs and improve modeling levels in smart city construction,thereby assisting more cities to make good use of urban big data at low cost and improving the level of urban management services.