Crime Prediction Methods Based on Machine Learning:A Survey

The objective of crime prediction,one of the most important technologies in social computing,is to extract useful information from many existing criminal records to predict the next process-related crime.It can aid the police in obtaining criminal information and warn the public to be vigilant in certain areas.With the rapid growth of big data,the Internet of Things,and other technologies,as well as the increasing use of artificial intelligence in forecasting models,crime prediction models based on deep learning techniques are accelerating.Therefore,it is necessary to classify the existing crime prediction algorithms and compare in depth the attributes and conditions that play an essential role in the analysis of crime prediction algorithms.Existing crime prediction methods can be roughly divided into two categories:those based on conventional machine learning and those based on contemporary deep learning.This survey analyses the fundamental theories and procedures.The most frequently used data sets are then enumerated,and the fundamental procedures of various algorithms are also analyzed in this paper.In light of the insufficient scale of existing data in this field,the ambiguity of data types used to predict crimes,and the absence of public data sets that have a significant impact on the research of algorithm models,this survey proposes the construction of a machine learning-based big data research model to address these issues.Future researchers who will enter this field are provided with a guide to the direction of future research development.

Comparison of Model Performance for Basic and Advanced Modeling Approaches to Crime Prediction

A good machine learning model would greatly contribute to an accurate crime prediction. Thus, researchers select advanced models more frequently than basic models. To find out whether advanced models have a prominent advantage, this study focuses shift from obtaining crime prediction to on comparing model performance between these two types of models on crime prediction. In this study, we aimed to predict burglary occurrence in Los Angeles City, and compared a basic model just using prior year burglary occurrence with advanced models including linear regressor and random forest regressor. In addition, American Community Survey data was used to provide neighborhood level socio-economic features. After finishing data preprocessing steps that regularize the dataset, recursive feature elimination was utilized to determine the final features and the parameters of the two advanced models. Finally, to find out the best fit model, three metrics were used to evaluate model performance: R squared, adjusted R squared and mean squared error. The results indicate that linear regressor is the most suitable model among three models applied in the study with a slightly smaller mean squared error than that of basic model, whereas random forest model performed worse than the basic model. With a much more complex learning steps, advanced models did not show prominent advantages, and further research to extend the current study were discussed.

Predicting the Type of Crime: Intelligence Gathering and Crime Analysis

Crimes are expected to rise with an increase in population and the rising gap between society’s income levels.Crimes contribute to a significant portion of the socioeconomic loss to any society,not only through its indirect damage to the social fabric and peace but also the more direct negative impacts on the economy,social parameters,and reputation of a nation.Policing and other preventive resources are limited and have to be utilized.The conventional methods are being superseded by more modern approaches of machine learning algorithms capable of making predictions where the relationships between the features and the outcomes are complex.Making it possible for such algorithms to provide indicators of specific areas that may become criminal hot-spots.These predictions can be used by policymakers and police personals alike to make effective and informed strategies that can curtail criminal activities and contribute to the nation’s development.This paper aims to predict factors that most affected crimes in Saudi Arabia by developing a machine learning model to predict an acceptable output value.Our results show that FAMD as features selection methods showed more accuracy on machine learning classifiers than the PCA method.The naïve Bayes classifier performs better than other classifiers on both features selections methods with an accuracy of 97.53%for FAMD,and PCA equals to 97.10%.

Analysis of the Resolution of Crime Using Predictive Modeling

There has been evidence of crime in the US since colonization. In this article, we analyze the crime statistics of San Francisco and its resolution of crime recorded from January to September of the year 2018. We define resolution of crime as a target variable and study its relationship with other variables. We make several classification models to predict resolution of crime using several data mining techniques and suggest the best model for predicting resolution.

Prediction of Extremist Behaviour and Suicide Bombing from Terrorism Contents Using Supervised Learning

This study proposes an architecture for the prediction of extremist human behaviour from projected suicide bombings.By linking‘dots’of police data comprising scattered information of people,groups,logistics,locations,communication,and spatiotemporal characters on different social media groups,the proposed architecture will spawn beneficial information.This useful information will,in turn,help the police both in predicting potential terrorist events and in investigating previous events.Furthermore,this architecture will aid in the identification of criminals and their associates and handlers.Terrorism is psychological warfare,which,in the broadest sense,can be defined as the utilisation of deliberate violence for economic,political or religious purposes.In this study,a supervised learning-based approach was adopted to develop the proposed architecture.The dataset was prepared from the suicide bomb blast data of Pakistan obtained from the South Asia Terrorism Portal(SATP).As the proposed architecture was simulated,the supervised learning-based classifiers na飗e Bayes and Hoeffding Tree reached 72.17%accuracy.One of the additional benefits this study offers is the ability to predict the target audience of potential suicide bomb blasts,which may be used to eliminate future threats or,at least,minimise the number of casualties and other property losses.

基于GWR模型的高铁站域犯罪风险预测及影响因素空间分异研究

为掌握城市建成环境对高铁站域犯罪的影响规律,确保高铁站域公共安全,通过两个典型新老高铁站域的对比,研究不同发展阶段的高铁站域犯罪关联性因素及其空间异质性。结合CPTED理论与圈层理论,运用地理加权回归与GIS空间分析等方法,构建了犯罪风险预测模型,测度新老高铁站域犯罪风险程度,探究了不同圈层犯罪分布特征,分析了不同建成环境对高铁站域犯罪的影响机制。

基于大数据的危害国家安全犯罪预测侦查研究

由算法所主导的预测性侦查正成为大数据侦查的新分支。危害国家安全犯罪的预测侦查更加强调干预节点的前移和涉危群体或人员的预判,而这一节点已前移至涉危线索发现之前。以大数据为基础的预测性侦查模式可以塑造国家安全维护新思维、实现涉危情报信息的整合共享、有效判别罪与非罪、此罪与彼罪及确保涉危证据留痕与追溯。涉危领域的预测侦查有着一套固有的流程模型,其有效运行依赖涉危情报数据可视化开发和利用、涉危数据融合共享的双向推进、涉危重点群体或人员的分级确定和侦查等予以保障。在大数据预测的运行过程中,需要秉承尊重隐私原则、坚守算法透明原则、坚持“人机共治”,以应对大数据使用所带来的风险和挑战。

基于随机森林的分类器在犯罪预测中的应用研究

犯罪预测一直是公安部门亟待解决的突出问题。基于随机森林这种模型组合分类器,结合机器学习技术在犯罪预测中的应用现状,提出了一种用于预测犯罪的新的分类方法,并通过模拟实验来展示这种分类方法比一般的随机森林分类会有更高的可信度。创新之处在于提出的这种随机森林分类器的每一棵树都是退化的决策树,并且根据在线学习的结果在下一轮的分类中选择区分度更高的决策树。最终给出一个应用于犯罪预测的较为成功的分类器的思路和模式,得出准确有效的预测结论。

基于多模态信息特征融合的犯罪预测算法研究

近年来,对犯罪预测方法进行了很多研究,这些方法在犯罪预测中存在高度非线性关系、冗余和多个数据集之间的依赖关系等问题。为了提高犯罪预测模型正确率,设计一种多模态信息特征融合的犯罪预测算法。将空间、时间、环境和上下文信息特征融合提高群集聚类正确率,充分利用特征目标函数计算逻辑损失,从而提高全局特征的正确率和局部特征的精确率。实验结果表明,在不同比例数据训练集条件下,该算法比现有方法的正确率和精确率分别提高了约12%和4%。

基于GIS的犯罪分析系统研究与设计

GIS在我国公安领域中的应用大多局限于数据显示和查询等较浅层次。针对该问题,作者设计出一套犯罪分析系统。旨在利用空间分析和空间数据挖掘等技术对犯罪数据进行分析、并提炼成情报,使GIS在情报导向警务的工作模式下得到更好应用。本文结合相关犯罪理论,引入热点分析、犯罪预测等一些经典或新的犯罪分析技术和方法,并提出六个业务模块及系统框架,具有理论性与实用性。

基于随机森林的犯罪风险预测模型研究

犯罪预测是犯罪预防的前提,也是公安部门亟待解决的问题.随机森林作为一种组合分类方法,具有准确率高、速度快、性能稳定的特性,且能够给出指标重要性评价,本文将其应用于犯罪风险预测中.实验证明,随机森林方法选出的指标集可以显著地提高预测准确率,基于该方法构建的预测模型相较于神经网络与支持向量机具有更高的准确性和稳定性,能够满足犯罪风险预测的需求.

我国犯罪预测及其研究的现状、问题与发展趋势——对“中国知网”的内容分析

对"中国知网"犯罪预测论文的内容分析显示,我国犯罪预测及其研究的滞后主要体现在三方面:一是技术准备不足,预测方法落后;二是预测目的错位,脱离实务需求;三是缺乏时代视野,与犯罪预测的主流趋势脱节。相应地,改进预测方法和技术,强化犯罪预测与犯罪控制实务的联系,紧跟犯罪预测的国际主流,是我国犯罪预测发展的应然趋势。

大数据背景下的犯罪预测侦查

将大数据深度融合到犯罪侦查活动中,构建大数据时代的新型犯罪预测侦查模式,将有效地降低发案率,节约警务资源。数据引导的侦查机制、数据信息的深度合成、数据分析的先进技术,是大数据背景下犯罪预测侦查的三大实现要素。大数据背景下的我国犯罪预测侦查存在思维观念滞后、资源整合不够等问题。应进一步加强预测和防范机制的建设、整合资源、培养人才,推进犯罪预测侦查的标准化、规范化,以适应大数据背景下犯罪预测防范警务模式的要求。

多尺度视角特征动态融合的盗窃犯罪预测模型

针对盗窃犯罪时空预测特征融合不精、时序动态适应性不足问题,提出自注意力和多尺度多视角特征动态融合的预测模型。首先,以盗窃发案的位置信息为基础,将数据投射到地图栅格内,通过构建一种可将不同时序长度案件数据匹配为自适应长度数据的方法,并组合向量映射后的天气、作案时间、地理位置等属性,构造多维度特征融合的输入向量;其次,采用自注意力机制生成多视角特征动态融合的向量;最后,通过采用多尺度窗口CNN对多视角特征动态融合向量进行编码后送入分类器,预测出每个地图栅格内的发案态势。在某市盗窃数据集上验证,本文方法在3种地理栅格尺度下,预测准确率最高可达到0.899,显著优于其他对比模型。

基于Logistic回归的犯罪概率预测研究

国内能够指导基层公安机关工作的犯罪微观预测研究甚少.基于Logistic回归模型构建了一个犯罪概率预测模型,对模型指标体系选取、数据预处理、模型训练等关键问题进行了重点研究,并进行了理论检验和实证分析证明.

犯罪活动计算机预测预警体系研究

在分析犯罪预测的现状和具体方式及适用对象后,重点阐述了尚不为人熟知的犯罪中观预测预警体系(简称犯罪预测预警系统)。该系统是各级公安指挥机关和政府对社会治安形势分析和犯罪趋势预测相结合的一体化预测预警体系。给出了基于统计概率的具体预测预警算法的实现方法。

人文奥运与北京流动人口犯罪的刑事政策

人文奥运的北京 ,在流动人口犯罪的预防和控制方面 ,应体现以人为本的精神 ,给予流动人口应有的尊重 ,将“严打”与社会治安综合治理紧密结合 ,开展犯罪预测 ,建立犯罪预警机制。

灰色系统理论在犯罪动态预测中的应用

犯罪动态预测,是指采用定量分析方法,依据时间数列资料的自身规律对某些现象未来发展的趋向、水平和程度进行的一种计量和推测。影响犯罪的变量错综复杂,因此反映治安状况的发案数经常上下波动。运用灰色系统理论分析、预测犯罪动态发展变化规律,对科学决策、优化警力部署,实现对犯罪的精准打击都有着极其重大的意义。

弱势群体的犯罪趋势预测

弱势群体的形成原因较为复杂,具有相对性、变动性、交叉性等特点。弱势群体与犯罪的关联性相对较强,选择恰当的分析视角并运用科学的分析方法,科学、理性地预测其犯罪趋势,对社会政策和刑事政策的制定具有积极意义。从整体情况来看,近些年来弱势群体的发案率有上升趋势,团伙式犯罪和自救式犯罪比例上升;从个体情况来看,农民工、未成年人、残疾人和刑释解教人员等弱势群体的犯罪都呈现出不同的发展趋势。

基于马尔科夫链的民族地区毒品犯罪预测研究

毒品的非法生产、需求以及贩运呈现出一种巨大规模的上升趋势,特别是边疆少数民族地区的毒品犯罪问题更加引人关注。针对此问题,大多是从社会学的角度进行研究,提出了采用马尔科夫链的方法,对边疆少数民族地区收集的犯罪数据进行统计分析、建模。实验结果表明,预测毒品犯罪案件数与实际发生的毒品犯罪案件数相比,达到了96.52%的预测准确率。结论是,通过马尔科夫链来进行民族地区的毒品犯罪预测研究,可获得更加合理、准确的预测结果,具有更高的有效性和实用性。