本文選題：數(shù)據(jù)挖掘 + 社區(qū)犯罪率��； 參考：《北京交通大學(xué)》2017年碩士論文

【摘要】：社區(qū)是人們生活的基本單元,與每個(gè)人的生活息息相關(guān),在犯罪手段和方式不斷翻新的今天,怎樣對社區(qū)犯罪進(jìn)行預(yù)警分析具有重大的意義。目前公安系統(tǒng)及相關(guān)行業(yè)都積累了海量數(shù)據(jù),這些都為數(shù)據(jù)挖掘創(chuàng)造了前提條件。本文利用數(shù)據(jù)挖掘的方法,以社區(qū)數(shù)據(jù)及相關(guān)犯罪數(shù)據(jù)為研究對象,針對社區(qū)犯罪率分析及預(yù)測這一目的,建立了社區(qū)犯罪率分析及預(yù)測體系,揭示出社區(qū)犯罪率與其影響因素之間的內(nèi)在規(guī)律性,得出影響社區(qū)犯罪率的關(guān)鍵因素,并對社區(qū)潛在犯罪率進(jìn)行預(yù)測,為警方合理部署有限資源與重點(diǎn)排查提供科學(xué)依據(jù)。本文的主要研究內(nèi)容如下:1、建立基于數(shù)據(jù)挖掘的社區(qū)犯罪率分析與預(yù)測體系。選用k-means聚類的方法將社區(qū)分成了犯罪率高、中、低三類,橫向?qū)Ρ染哂胁煌缸锫实纳鐓^(qū)之間的屬性差異,并通過嶺回歸與lasso回歸得出了影響社區(qū)犯罪率的關(guān)鍵因素,最后使用支持向量機(jī)對社區(qū)潛在犯罪率進(jìn)行預(yù)測,為合理評估社區(qū)的安全性提供技術(shù)及理論支持。2、多元社區(qū)數(shù)據(jù)與犯罪數(shù)據(jù)的處理。針對社區(qū)數(shù)據(jù)與相關(guān)犯罪數(shù)據(jù)具有數(shù)量較大且多元化的特點(diǎn),采用了拉格朗日插值法、z-score數(shù)據(jù)標(biāo)準(zhǔn)化方法以及主成分分析法(PCA)對社區(qū)及犯罪數(shù)據(jù)進(jìn)行了預(yù)處理,為挖掘工作的有序進(jìn)行創(chuàng)造了前提條件。3、社區(qū)犯罪率分析與預(yù)測體系的應(yīng)用。為了驗(yàn)證本文提出的社區(qū)犯罪率分析與預(yù)測體系的可行性,結(jié)合一組真實(shí)的社區(qū)數(shù)據(jù)與相關(guān)的犯罪數(shù)據(jù)實(shí)現(xiàn)犯罪率分析與預(yù)測體系的實(shí)例分析,取得了較好的分類及預(yù)測效果,篩選出了影響社區(qū)謀殺案件犯罪率的權(quán)重最大的幾個(gè)因素,并進(jìn)行社區(qū)潛在犯罪率的預(yù)測,因此可以使用本文提出的評價(jià)體系來評估社區(qū)的安全性指數(shù)。4、根據(jù)社區(qū)犯罪數(shù)量與社區(qū)人均收入、警察數(shù)量的數(shù)據(jù)圖,建立了三者之間的關(guān)系式,并通過實(shí)驗(yàn)擬合得到均衡解,從而量化社區(qū)警力資源分布,為犯罪預(yù)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(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP311.13;D917

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 湯毅平;;基于Apriori算法的重新犯罪關(guān)聯(lián)規(guī)則挖掘[J];指揮信息系統(tǒng)與技術(shù);2016年03期

2 鄭廷;張?jiān)茲?;基于領(lǐng)域本體的數(shù)據(jù)挖掘技術(shù)在賄賂犯罪偵查中的應(yīng)用[J];中國檢察官;2016年03期

3 王海燕;陳紅偉;;關(guān)聯(lián)規(guī)則在青少年違法犯罪預(yù)防中的應(yīng)用研究[J];電腦知識與技術(shù);2015年23期

4 劉冰;;DNA數(shù)據(jù)庫數(shù)據(jù)挖掘應(yīng)用研究[J];刑事技術(shù);2015年05期

5 齊立云;楊曉偉;;我國宏觀經(jīng)濟(jì)因素與犯罪率的關(guān)系研究——基于逐步回歸法的實(shí)證分析[J];中國物價(jià);2015年04期

6 孫菲菲;曹卓;肖曉雷;;基于隨機(jī)森林的分類器在犯罪預(yù)測中的應(yīng)用研究[J];情報(bào)雜志;2014年10期

7 張真理;許傳璽;;社會(huì)治安評價(jià)指標(biāo)體系的兩個(gè)基本問題[J];中國人民公安大學(xué)學(xué)報(bào)(社會(huì)科學(xué)版);2014年01期

8 葉明;葉猛;;數(shù)據(jù)挖掘在防范和打擊計(jì)算機(jī)犯罪中的應(yīng)用研究[J];信息網(wǎng)絡(luò)安全;2013年11期

9 胡思文;劉婷;白寶鋼;趙婷;周夢迪;林君;丁新新;;基于數(shù)據(jù)挖掘技術(shù)的犯罪規(guī)律分析研究——以溫州市近20年犯罪數(shù)據(jù)為例[J];科技視界;2013年08期

10 朱明;;犯罪數(shù)據(jù)的性質(zhì)與整理[J];湖北警官學(xué)院學(xué)報(bào);2012年12期

，

本文編號：2056557