A Survey of Crime Scene Investigation Image Retrieval Using Deep Learning

Crime scene investigation(CSI)image is key evidence carrier during criminal investiga-tion,in which CSI image retrieval can assist the public police to obtain criminal clues.Moreover,with the rapid development of deep learning,data-driven paradigm has become the mainstreammethod of CSI image feature extraction and representation,and in this process,datasets provideeffective support for CSI retrieval performance.However,there is a lack of systematic research onCSI image retrieval methods and datasets.Therefore,we present an overview of the existing worksabout one-class and multi-class CSI image retrieval based on deep learning.According to theresearch,based on their technical functionalities and implementation methods,CSI image retrievalis roughly classified into five categories:feature representation,metric learning,generative adversar-ial networks,autoencoder networks and attention networks.Furthermore,We analyzed the remain-ing challenges and discussed future work directions in this field.

The Windy City’s Dark Side: A Statistical Exploration of Crime in the City of Chicago

This paper presents a detailed statistical exploration of crime trends in Chicago from 2001 to 2023, employing data from the Chicago Police Department’s publicly available crime database. The study aims to elucidate the patterns, distribution, and variations in crime across different types and locations, providing a comprehensive picture of the city’s crime landscape through advanced data analytics and visualization techniques. Using exploratory data analysis (EDA), we identified significant insights into crime trends, including the prevalence of theft and battery, the impact of seasonal changes on crime rates, and spatial concentrations of criminal activities. The research leveraged a Power BI dashboard to visually represent crime data, facilitating an intuitive understanding of complex patterns and enabling dynamic interaction with the dataset. Key findings highlight notable disparities in crime occurrences by type, location, and time, offering a granular view of crime hotspots and temporal trends. Additionally, the study examines clearance rates, revealing variations in the resolution of cases across different crime categories. This analysis not only sheds light on the current state of urban safety but also serves as a critical tool for policymakers and law enforcement agencies to develop targeted interventions. The paper concludes with recommendations for enhancing public safety strategies and suggests directions for future research, emphasizing the need for continuous data-driven approaches to effectively address and mitigate urban crime. This study contributes to the broader discourse on urban safety, crime prevention, and the role of data analytics in public policy and community well-being.

Monitoring and Early Warning of New Cyber-Telecom Crime Platform Based on BERT Migration Learning

The network is a major platform for implementing new cyber-telecom crimes.Therefore,it is important to carry out monitoring and early warning research on new cyber-telecom crime platforms,which will lay the foundation for the establishment of prevention and control systems to protect citizens’property.However,the deep-learning methods applied in the monitoring and early warning of new cyber-telecom crime platforms have some apparent drawbacks.For instance,the methods suffer from data-distribution differences and tremendous manual efforts spent on data labeling.Therefore,a monitoring and early warning method for new cyber-telecom crime platforms based on the BERT migration learning model is proposed.This method first identifies the text data and their tags,and then performs migration training based on a pre-training model.Finally,the method uses the fine-tuned model to predict and classify new cyber-telecom crimes.Experimental analysis on the crime data collected by public security organizations shows that higher classification accuracy can be achieved using the proposed method,compared with the deep-learning method.

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%.

Quantitative Analysis of Crime Incidents in Chicago Using Data Analytics Techniques

In this paper we aim to identify certain social factors that influence,and thus can be used to predict,the occurrence of crimes.The factors under consideration for this analytic are social demographics such as age,sex,poverty,etc.,train ridership,traffic density and the number of business licenses per community area in Chicago,IL.A factor will be considered pertinent if there is high correlation between it and the number of crimes of a particular type in that community area.

Temporal-spatial Distribution of Various Types of Crime in the Special Wards of Tokyo

Based on the official criminal data released by the Tokyo Metropolitan Police Department in 2019,this paper discusses the temporal-spatial distribution of various types of crimes in the special wards of Tokyo.The results show that:(1)The times of high and low incidence of different types of crime differ significantly.Although vicious crime and violent crime present no obvious monthly distribution,property crime clearly differs between the first and second half of a calendar year.(2)The month before the new year sees a surge in most types of crime.(3)Vicious crime peaks in the hours between night and early morning.Violent crime and property crime correlate positively with the frequency of human interaction and peak in the morning and evening commuting hours.(4)The spatial distribution of crime resembles the concentric circles of the three rings of the special wards of Tokyo,with a central high-incidence area,a center-peripheral low-incidence area,and a marginal high-incidence area.In addition,the center sees more personal crime than the periphery,whereas property crimes show the opposite trend.(5)A spatial autocorrelation analysis shows that the special wards of Tokyo may be grouped into the“high-high”and“low-low”agglomeration modes of different types of crime,with marked differences between the various types of crime.The crime can be divided into three types:central agglomeration,double central agglomeration,and decentralized agglomeration.

The dynamics of poverty and crime

Poverty and crime are two maladies that plague metropolitan areas. The economic theory of crime[1]demonstrates a direct correlation between poverty and crime. The model considered in this study seeks to examine the dynamics of the poverty-crime system through stability analysis of a system of ordinary differential equations in order to identify cost-effective strategies to combat crime in metropolises.

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.

GIS Based Spatial and Temporal Analysis of Crimes, a Case Study of Mardan City, Pakistan

This paper is aimed to study the crime with special reference to its spatial and temporal distribution. It also tries to analyze and estimates the impact of weather on the prevalence of criminal activity in Mardan City. Studying the crimes with reference to its spatial and temporal distribution is important because information about the occurrence of crime is one of the most crucial tools in combating crime. It allows understanding how crimes are distributed through space, and hence can help in combating them. The Geographical Information System is an important tool for dealing with spatial and temporal analysis and thus fighting crime. The advent of desktop computers has significantly increased the role of computer mapping. The availability of user-friendly GIS applications has further increased the use of GIS in crime mapping. For the present study the data was collected about homicide and aggressive assaults from all the three police stations covering Mardan City. The collected data was analyzed under different scenarios. The temporal based analysis of data reveals that almost 50% crimes reported in 2009, took place from May-September (summer) compared to 32% in winter (October to February). The spatial analysis of data reveals 05 hotspots. Sikindary area was identified as the most crucial hotspot where within a 05 km radius, 11 crimes were committed in 2009 followed by Kalpani Bridge area from where a total of 10 crimes of aggressive nature were reported. Similarly police station wise the City Police Station accounts for 47% of the aggressive crimes committed in 2009.

Practice and Thinking of Grid Management to Promote Crime Prevention and Control in Urban Community

Practice and Thinking of Grid Management to Promote Crime Prevention and Control in Urban

Perceptions of People on Police Efficiency and Crime Prevention in Urban Areas in Malaysia

Crime cases from snatch thefts to murders, are becoming increasingly common in Malaysia according to the database of the Royal Malaysian Police （RMP）, the current overall Crime Index in Malaysia was 147,062 for 2013 compared with 65,237 in 1977. The database also revealed that the number of property crimes reported in this country has always exceeded the number of violent crimes. Although in 2013, crimes related to property are higher （117,687） than violent crime （29,375）, the gradual increase in the latter does worry Malaysians. Likewise, Malaysian Government had implemented a ＂safe community＂ concept, inspired by the Malaysian Crime Prevention Foundation （MCPF）, a non-government organization, as they were concerned that the threat of violent crime and non-violent crime would eventually reduce the quality of life for many individuals. The concept is very important to the public as it is perceived as improved safety for the public with less crime. This study analyzes the public safety through their perceptions on the effectiveness of the policeman and suggesting few prevention actions against crime. The data were collected using a self-administered survey questionnaire. Specifically, this study focuses on the more developed states in this country. From the cross tabulation analysis, the majority of respondents agreed that the police are effective in controlling crime. Approximately, 72.1% of respondents felt that the police are efficient in controlling crime.

Preliminary Study on Crime Prevention of Left-behind Children in Rural China

In the process of social and economic development in China,there appears the inevitable problem of crime of left-behind children.Extensive researches and disputes prove that it is,to a greater extent,a social phenomenon resulted from social factors. It is recommended to improve the treatment of migrant workers and solve the problem of school admission for their children,provide effective legal education for those left-behind children whose problem can not be solved in a short term,and supplement the guardianship role to control the crime rate of leftbehind children. Another more effective method is to resume effective control of people's social life through the rural cultural tradition,to fundamentally prevent crime of left-behind children.

基于CRIME理论的^(*)ST宜生财务舞弊案例研究

资本市场不断完善对我国经济发展起到重要的推动作用,但同时财务舞弊事件也层出不穷。上市公司财务舞弊行为严重侵害了投资者权益,扰乱了我国资本市场的正常运行,不利于社会经济健康发展。运用CRIME理论,分别从造假行为人、造假手段、造假动因、监督机制、造假结果5个方面对^(*)ST宜生财务舞弊事件进行分析,厘清了财务舞弊的来龙去脉,并针对性地从公司、会计师事务所和监管机制3个角度提供启示,希望对相关主体具有借鉴意义,减少财务舞弊行为的发生,肃清资本市场不良风气。

The Influences of CPTED on Fear of Crime and Sense of Community

Fear of crime varies considerably depending on its attitude towards environmental conditions. Sense of community and CPTED （crime prevention through environmental design） are believed to have an influence on fear of crime. This paper investigates the hypotheses of influences of CPTED on FOC （fear of crime） and SOC （sense of community）. A total of 171 respondents from the designated residential areas were involved in this study. The structural equation modeling technique was employed and the results indicate that CPTED has a direct influence on fear of crime and sense of community. However, no significant relationship was found between SOC and FOC.

Committing a Crime with the Employment of a Toxic Substance

Poisons are harmful chemicals of all sorts. They are detrimental to health and can have deadly effects on the organism. Many poisonous substances have been used for poisoning others, deliberately. This had been done throughout the ages. In comparison to visibly violent and traumatic crimes involving bloodshed, acts involving poisons have occurred in more subtle methods and accordingly been preferred by actors somehow suitable for indirect and concealed modus operandi. （Doping in sports or horse racing being a lesser form of offense, it is beyond the scope of this text）.

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.

Assessing the Threat of Nexus Between Organized Crime and Democratic Politics： Mapping the Factors

In recent years, increased attention from the media, the international community and policy makers has highlighted the destabilizing effects criminal networks have on the legitimacy of democratic politics, as well as the capacity of democratic systems to deliver basic services. Indeed, the Organization for Economic Co-operation and Development highlighted in 2014 how illicit financial flows drain the state from resources needed to provide basic services （OECD, 2014）. While this problem affects not only developing and fragile states, these countries are particularly affected as this phenomenon tends to exacerbate inequality （Briscoe, Perdomo ＆ Uribe Burcher, 2014）. Mapping the factors that make politics vulnerable to the influence of organized crime is a key element in the effort to implement adequate strategies to prevent and mitigate this phenomenon. This paper explores 21 threat factors identified, understood as some of the conditions that may contribute to the likelihood that political corruption linked to organized crime takes place. These threat factors underline institutional weaknesses--including those related to illicit political fmance---and organized crime activities, which create opportunities for illicit networks to penetrate democratic political systems. The paper also discusses how these institutional weaknesses interrelate to specific criminal markets and networks. The paper draws from extensive desk research in 2015, which complements previous desk and field research on the same topic carded out in 2011-2014 in the Baltic States, Latin America and West Africa.

Crimes of Violence in Our Arab Society

Violence is an inherited phenomenon, a social phenomenon of concern, and violence is a complex social phenomenon that is not based on one factor and is not the result of one. It is the result of a combination of factors and causes because it is an individual and social phenomenon. In the context of the humanitarian nature that makes this behavior fake that will satisfy the requirements and needs or achieve the goals but we find that the reality is the opposite when we use force, methods, and violence in social relations under any justification, it is customary and violation of social norms, defined violence as ＂any behavior expressed Any reaction intended to inflict harm, self or otherwise, self-destruction or other property of pain. It is important to note that the phenomenon of violence is a global social phenomenon that is not limited to Arab society. Violence is also prevalent in other societies of the world. Violence causes a rupture in our Arab society, and therefore we are called upon to fight it. In recent years, violence has increased among young people in Arab society in particular.＂

Revisiting History Through Crime Fiction： Shiono Nanami＇s Scarlet Venice in The Renaissance Trilogy of Murder

The paper explores an ambiguous intersection between history and fiction in Scarlet Venice （Hi no Venezia, in original） （1988） by Japanese novelist and historiographer, Shiono Nanami （1937-）. As a prominent transnational writer who has worked on the Italian Renaissance and the Roman antiquity for about 40 years, Shiono fictionalizes the political culture of 16th century Venice in the context of the Eastern Mediterranean history. Based on her The Tales of the Sea Capital （1981）, historiography par excellence, the novel portrays the political maneuver of the Republic of Venice from viewpoints of diplomacy. With this thematic approach to history, the novel centers on the life and death of Alvise Gritti （1480-1534）, an illegitimate son of famed Venetian Doge, Andrea Gritti. In the metaphorical/metafictional structure of historical crime fiction, the novel unveils the austerity of Venetian polity, while contrasting it with the liberalism fostered in Sultan Suleiman＇s Ottoman court. In conclusion, the author interprets that Alvise Gritti is a victim of the political intricacy with which Venice was wrestling, implying that the republic is the metaphorical murderer. Whereas Alvise＇s death commemorates a drastic step taken by Venice for political negotiation, Shiono＇s recent writings suggest that Japan should model the rigid pragmatism in Venetian politics, along with its effective use of intelligence in diplomacy

Alteration of Crime Risk Levels and Formal Aspect of Typical LowCost Housing Design Due to Site Conditions and Building Codes

As a component of the process of rationalization, lowcost housing Institution, as well as major investiment companies, commonly reuse a typical design as a standard. The goal of this procedure is to obtain cheaper costs both in the construction and in the maintenance of an homogeneous lot of buildings. The paper shows that, nevertheless an identical design being proposed in different towns, the final results are buildings with a different aspect. This is mainly due to the influence of building codes of the urban plan of the specific town, and to the site conditions, such as street orientation, ground inclination and so on. As a result the paper offers the ability to evaluate the role of the external factors on a standard design （with the afore said scale vantages） under the criminological point of view as well. This may be considered a further vantage that aims to obtain a lower crime risk level in town design.