广州市社会治安状况预测研究

发布时间：2018-06-18 21:00

本文选题：社会治安状况 + 预测　；参考：《国防科学技术大学》2007年硕士论文

【摘要】： 中国人民解放军和武装警察部队担负有抵御外敌入侵、维护国家稳定以及保卫广大人民群众生命财产安全的神圣职责,因此维护社会治安秩序也是人民解放军和武装警察部队所应承担的责任之一。为有效打击违法犯罪活动,营造良好的社会治安环境以保障社会的正常秩序和人民群众的生命财产安全,广州市各级政府和政法部门投入了大量的人力、财力和物力以应对逐渐严峻的社会治安形势。为防患于未然,有效指导支持警务决策工作和打击违法犯罪活动,需要对未来时期广州市社会治安状况作出定量预测。本文围绕广州市社会治安状况预测和社会治安状况的相关性分析展开研究,主要研究内容如下: 首先阐述了开展广州市社会治安状况预测和分析社会治安状况影响机理的背景,对社会治安状况的主要内容和含义进行了介绍,最后分析了当前预测技术领域常用预测方法的国内外研究现状。第二,论文对广州市社会治安的总体状况以及广州市的发案量数据进行了深入分析,指出了常用预测方法用于广州市社会治安状况预测的不足,提出了确定广州市社会治安状况预测指标的基本原则,以及进行广州市社会治安状况预测的基本方法。第三,采用一个基本数学模型描述了广州市社会治安状况,奠定了定量预测广州市社会治安状况的基础。在深入分析广州市社会治安状况的基础上提出采用基于分段三次曲线拟合的预测方法对广州市社会治安状况进行预测。考虑到基于分段三次曲线拟合预测方法对离当前时间较远的数据信息注重的不多,为加大对离当前时间较远的数据信息的利用,改进了基于分段三次曲线拟合的预测方法。第四,由于基于分段三次曲线拟合预测方法假设了发案量变化过程的平稳,因此在有不确定事件出现时对发案量的平稳变化会造成较大影响。考虑到不确定事件对发案量造成的影响,提出利用线性回归分析模型修正不确定事件对基于分段三次曲线拟合预测模型预测结果。最后,以广州市周发案总量的预测为背景,利用本文所提出的基于分段三次曲线拟合预测模型对广州市周总发案量进行预测,并与另两种常用的预测模型——GM(1,1)模型、ARMA模型从预测效果上进行了对比,对本文所提出的基于分段三次曲线拟合预测模型的预测效果进行了验证。
[Abstract]:The Chinese people's Liberation Army and the armed police force have the sacred responsibility of resisting foreign enemies' invasion, maintaining national stability and safeguarding the lives and property of the broad masses of the people. Therefore, maintaining public order is also one of the responsibilities of the PLA and the armed police force. In order to effectively crack down on illegal and criminal activities and create a good social security environment to ensure the normal order of the society and the safety of the lives and property of the people, governments at all levels and political and legal departments in Guangzhou have invested a great deal of manpower. Financial and material resources to cope with the increasingly serious social security situation. In order to prevent trouble and effectively guide and support police decision-making and crack down on illegal and criminal activities, it is necessary to make a quantitative prediction of the social security situation in Guangzhou in the future. This paper focuses on the prediction of social security situation and the correlation analysis of social security situation in Guangzhou. The main research contents are as follows: firstly, the background of forecasting and analyzing the influence mechanism of social security in Guangzhou is expounded, and the main content and meaning of social security are introduced. Finally, the current research status of common prediction methods in the field of prediction technology is analyzed. Secondly, the paper analyzes the general situation of social security in Guangzhou and the data of the number of cases in Guangzhou, and points out the shortcomings of the common forecasting methods used in the prediction of social security in Guangzhou. This paper puts forward the basic principles for determining the prediction index of social security in Guangzhou and the basic method for forecasting the social security in Guangzhou. Thirdly, a basic mathematical model is used to describe the social security situation in Guangzhou, which lays a foundation for quantitative prediction of the social security situation in Guangzhou. Based on the in-depth analysis of the social security situation in Guangzhou, a prediction method based on piecewise cubic curve fitting is proposed to predict the social security situation in Guangzhou. Considering that the prediction method based on piecewise cubic curve fitting does not pay much attention to the data information far away from the current time, in order to increase the utilization of the data information far away from the current time, the prediction method based on piecewise cubic curve fitting is improved. Fourthly, because the prediction method based on piecewise cubic curve fitting assumes the stability of the process of the change of the quantity of the case, it will have a great influence on the steady change of the quantity of the case when there are uncertain events. Considering the influence of uncertain events on the number of cases, a linear regression model is proposed to modify the prediction results of uncertain events based on piecewise cubic curve fitting. Finally, based on the forecast of the total number of Zhou cases in Guangzhou, the paper uses the forecasting model based on piecewise cubic curve fitting to predict the total number of cases in Guangzhou. The prediction results are compared with those of the other two commonly used prediction models, the GM1 / 1) model and the ARMA model. The prediction results based on the piecewise cubic curve fitting model proposed in this paper are verified.
【学位授予单位】：国防科学技术大学
【学位级别】：硕士
【学位授予年份】：2007
【分类号】：D631.4

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