本文選題：數據挖掘 + 社區犯罪率　； 參考：《北京交通大學》2017年碩士論文

【摘要】：社區是人們生活的基本單元,與每個人的生活息息相關,在犯罪手段和方式不斷翻新的今天,怎樣對社區犯罪進行預警分析具有重大的意義。目前公安系統及相關行業都積累了海量數據,這些都為數據挖掘創造了前提條件。本文利用數據挖掘的方法,以社區數據及相關犯罪數據為研究對象,針對社區犯罪率分析及預測這一目的,建立了社區犯罪率分析及預測體系,揭示出社區犯罪率與其影響因素之間的內在規律性,得出影響社區犯罪率的關鍵因素,并對社區潛在犯罪率進行預測,為警方合理部署有限資源與重點排查提供科學依據。本文的主要研究內容如下:1、建立基于數據挖掘的社區犯罪率分析與預測體系。選用k-means聚類的方法將社區分成了犯罪率高、中、低三類,橫向對比具有不同犯罪率的社區之間的屬性差異,并通過嶺回歸與lasso回歸得出了影響社區犯罪率的關鍵因素,最后使用支持向量機對社區潛在犯罪率進行預測,為合理評估社區的安全性提供技術及理論支持。2、多元社區數據與犯罪數據的處理。針對社區數據與相關犯罪數據具有數量較大且多元化的特點,采用了拉格朗日插值法、z-score數據標準化方法以及主成分分析法(PCA)對社區及犯罪數據進行了預處理,為挖掘工作的有序進行創造了前提條件。3、社區犯罪率分析與預測體系的應用。為了驗證本文提出的社區犯罪率分析與預測體系的可行性,結合一組真實的社區數據與相關的犯罪數據實現犯罪率分析與預測體系的實例分析,取得了較好的分類及預測效果,篩選出了影響社區謀殺案件犯罪率的權重最大的幾個因素,并進行社區潛在犯罪率的預測,因此可以使用本文提出的評價體系來評估社區的安全性指數。4、根據社區犯罪數量與社區人均收入、警察數量的數據圖,建立了三者之間的關系式,并通過實驗擬合得到均衡解,從而量化社區警力資源分布,為犯罪預防工作提供決策支持。
[Abstract]:Community is the basic unit of people's life, which is closely related to everyone's life. At present, the public security system and related industries have accumulated massive data, which have created a prerequisite for data mining. Based on the method of data mining and taking community data and related crime data as the research object, this paper establishes a community crime rate analysis and prediction system for the purpose of community crime rate analysis and prediction. This paper reveals the inherent regularity between the crime rate in the community and its influencing factors, obtains the key factors affecting the crime rate in the community, and forecasts the potential crime rate in the community, which provides a scientific basis for the police to reasonably deploy the limited resources and focus on the investigation. The main contents of this paper are as follows: 1. A community crime rate analysis and prediction system based on data mining is established. The k-means clustering method is used to divide the community into three groups: high, middle and low crime rates. The attribute differences among communities with different crime rates are compared horizontally, and the key factors influencing the crime rate in communities are obtained by ridge regression and lasso regression. Finally, support vector machine is used to predict the potential crime rate in the community, which provides technical and theoretical support for the reasonable evaluation of community security. In view of the large quantity and diversity of community data and related crime data, the Lagrange interpolation method and principal component analysis (PCA) are used to preprocess community and crime data. It creates the precondition. 3. The application of community crime rate analysis and prediction system. In order to verify the feasibility of the community crime rate analysis and prediction system proposed in this paper, combining a group of real community data and related crime data to realize the crime rate analysis and prediction system, a case study has been carried out, and good classification and prediction results have been obtained. Selected the most important factors that influence the crime rate of murder cases in the community, and predicted the potential crime rate in the community. Therefore, the evaluation system proposed in this paper can be used to evaluate the community security index .4.According to the data graph of community crime and community per capita income, police number, the relationship between them is established, and the equilibrium solution is obtained by experiment fitting. So as to quantify the distribution of community police resources and provide decision support for crime prevention.
【學位授予單位】：北京交通大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP311.13;D917

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