Crime Analysis Research Articles

A survey on crime analysis and prediction

A lot of police forces around the world have adopted mechanisms that use statistical data to guide their decision-making which is coined as predictive policing. As urbanization is increasing day by day, it is highly demanding to keep an eye on the criminal activities of each region so as to reduce the occurrences of unwanted behaviours. Prediction of crimes can be done only using the analysis of the patterns of criminal activities using the past data available with the concerned personals. This mainly makes use of the historic data and analyze them using Deep learning, Statistical Models and Algorithms. This paper makes a study on different approaches used worldwide for the prediction and forecast of crime occurrences. The methods are categorized and their effectiveness in various areas based on the precision and accuracy in their prediction is studied so as to show a light to the existing methodologies and to the need for future developments.

Contemporary Crimes Prevalent within Africa’s Banking Industry and a Threat Analysis of Such Crimes on Africa’s Development

Contemporary Crimes Prevalent within Africa’s Banking Industry and a Threat Analysis of Such Crimes on Africa’s Development

Ekološka bezbednost i ekološki kriminalitet

Consideration of the complex security issue in modern conditions requires the inclusion of various aspects, among which a very current environmental aspect is unavoidable. The security consideration based on it, results in a special form of environmental security, as an unavoidable part of the overall security. Thereby two analytical security levels stand out: national and international. The analysis of environmental security includes a very important issue of tort in the domain of environment and appropriate forms of crime, which require special attention. Environmental crime begins from the environmental law, and is related to the normative approach in defining, monitoring and opposing various forms and degrees of commiting a tort. A complex analysis of environmental security and environmental crime indicates a causal relationship and the necessary activity to reduce environmental crime in order to increase environmental security and overall security. The considered connection indicates the need to increase the analytical attention to the reduction of environmental crime through the increase of human and organizational capacities, in the domain of general, and especially environmental security.

我国刑法中的暴力犯罪与相关概念之辨析

我国刑法中的暴力犯罪与相关概念之辨析

A Welfare Analysis of Medicaid and Crime

A Welfare Analysis of Medicaid and Crime

Analysis of crimes related to the violation of the inviolability of the home

В статье говорится об анализе преступлений, которые связаны с нарушением неприкосновенности жилища. А неприкосновенность жилища является одним из основных прав человека и гражданина. Нарушение неприкосновенности жилища считается преступлением, совершенным действием.

Fairness in Algorithmic Policing

AbstractPredictive policing, the practice of using of algorithmic systems to forecast crime, is heralded by police departments as the new frontier of crime analysis. At the same time, it is opposed by civil rights groups, academics, and media outlets for being ‘biased’ and therefore discriminatory against communities of color. This paper argues that the prevailing focus on racial bias has overshadowed two normative factors that are essential to a full assessment of the moral permissibility of predictive policing: fairness in the social distribution of the benefits and burdens of policing as well as the distinctive role of consent in determining fair distribution. When these normative factors are given their due attention, several requirements emerge for the fair implementation of predictive policing. Among these requirements are that police departments inform and solicit buy-in from affected communities about strategic decision-making and that departments favor non-enforcement-oriented interventions.

Analysis of Hate Speech and Hate Crimes From A Social Psychological Perspective: A Review

Hate crime and hate speech are extreme examples of negative intergroup relations. It is thought that it would be very useful to analyze the variables that lead up to for dealing with hate speech and crimes that have many physical and psychological destructive consequences for the exposed group members. Therefore, the aim of the present study is to address some of the social psychological variables associated with hate speech and hate crimes and to suggest solutions to reduce hate speech and hate crimes in this context. For this purpose, first of all, hate speech and hate crimes were defined and various examples were presented in this direction. Later, hate crimes and hate speech were examined in terms of social identity identification, social dominance orientation, system justification, realistic and symbolic threat perception, frustration and scapegoat concepts. The relationship between hate speech and crimes of this concept has been embodied with research findings and examples from various regions in Turkey and the world. Finally, some solution suggestions have been presented by making use of this theoretical knowledge in terms of combating hate crimes and hate speeches. Keywords: Hate crime, hate speech, intergroup relations, social psychology

Evaluation of crime pattern in Khanna local government using geographic information system

Sustainable development can only take place in an atmosphere of peace and tranquility. The state of insecurity in Rivers State and Nigeria at large has been on the rise over the past decade. The Nigerian security agencies have not been able to effectively tackle and nib crime activities in the bud. This has led to the loss of lives, businesses, and other attendant socio-economic effects. The distribution of crime across the landscape is geographically random since crime is a human phenomenon, hence geography plays an important role in law enforcement and criminal justice. Geographic information system (GIS) can help the security agency in tactical planning and response; allocating resources quickly and more effectively, determine mitigation priorities, analyze historical events, and predict future trends and patterns. The main aim of this paper is crime modeling and analysis using the Geo-information system. This paper uses GIS to visualize, analyze, and explain criminal activities within Khanna local government in a spatial context. To achieve this, crime data and records covering over seven years were obtained from the Nigerian Police Force in Bori. Supplementary and attribute data were collected through fieldwork using the Global Positioning System (GPS) to provide the coordinates of crime points and scenes in the local government and other associated data. A spatial database was created using ArcGIS software. The crime hotspot map and crime density map of the study area was produced using the ArcGIS pro tool- Gi-Bin fixed 234-FDR at 99%,95%, and 90% confidence level respectively for the hot spots and cold spots respectively. The maps and results from the spatial analysis performed can help the police to carry out patterns and trends analysis across multiple jurisdictions; enlist community action, enhancing implementation of various policies to reduce crime in the study area. It can also provide tools and techniques to capture, forecast future crime occurrence in the area and reduce crime incidences.

Comparative and legal analysis of crimes against national security in the criminal codes of Ukraine and Poland

The article presents a comprehensive comparative legal analysis of crimes against national security according to the criminal codes of Ukraine and Poland. The authors analyze the common approaches of the legislator of countries to the criminalization of socially dangerous acts in the relevant field, the differences in these approaches. Based on the analysis of the current

Multivariate analysis and characterization of low impact crime in Mexico City

Background: Multivariate analysis is fast becoming a key instrument that can be used to address crimes or incidents. It may be helpful to assess government policies on crime prevention. Methods: To distinguish between the 25 official crime categories reported in Mexico City (Jan 2019 - Jun 2019), principal coordinates analysis was used to determine the quality of a characteristic in this context. This study used cluster analysis via K-means and Biplot based on time and location in terms of crime occurrence. Results: The results obtained from this preliminary analysis indicates that around 70% of crime occurrence is shown in the following boroughs: Cuauhtémoc, Iztapalapa, Gustavo A. Madero, Benito Juárez, Álvaro Obregón, Coyoacán, and Miguel Hidalgo. Conclusion: There are two factors that contribute to the difficulties in crime analysis in Mexico City, namely, the lack of people’s trust in authorities and the insufficiency of tools for data analysis. The latter is an integral part in achieving justice for the victims of crimes because it impedes the process of observing patterns and predicting the perpetrators’ next actions, which may help in solving a number of types of crimes. It is then imperative for law enforcement to utilize data analysis tools that aid in identifying crime patterns and trends, such that the occurrences of crime show a downward trend and consequentially increase the people’s trust in law enforcement agencies.

A Study on Characteristics Analysis and Countermeasures of Digital Sex Crimes in Korea

In the modern knowledge–information age, various information and communication technologies provide us with many benefits and at the same time, bring various side effects such as cybercrime. The number of cybercrimes is increasing gradually, and in particular, the number of digital sex crimes has been increasing recently. In addition, digital sex crimes are becoming increasingly violent, so national measures are needed. In this study, statistical data at the national level were used to investigate the overall characteristics of digital sex crimes in Korea. First, statistical analysis shows that the victims are mainly women in their teens and 20s. Typical types of digital sex crimes are distribution of illegal contents and illegal filming, the perpetrators are mainly unknown, and digital sex crimes were less often recognized by others and more often by the victims themselves. Based on these results, countermeasures against various digital sex crimes are suggested.

Forensic Analysis of Crimes in the Sphere of Information Technologies

The article deals with the issues of foren-sic support for preventing commission of crimes in the field of information tech-nology. It is called to attention that alt-hough introduction of modern infor-mation processes in various spheres of government activities, production and public relations has brought positive re-sults, it has also led to an increase in the cases of committing cyber crimes. Cyber crimes are a new phenomenon in general crime, therefore, the development of ad-vanced techniques to combat them is re-quired. In this process, the forensic sci-ence plays an important role, first of all, for the preparation of recommendations on methods for investigating cyber crimes. Forensic analysis of information technology crimes which are called cyber crimes indicates that their investigation is different from the investigation of other crimes. This is largely due to the fact that computer objects can be both means of committing such crimes and objects of criminal encroachment. Information stored on computer media can be per-ceived and evaluated solely by using computer equipment and digital technol-ogy. Without this, it is impossible to iden-tify persons involved in committing a crime and obtain the evidence necessary to solve the crime. To study and evaluate them as sources of evidence, one needs to resort to the help of specialists, com-puter equipment and sophisticated soft-ware. It distinguishes computer objects from other material objects that is why it is necessary to put computer objects into a separate category of sources of evi-dence in forensic science and criminal procedure.

A robbery is a robbery is a robbery? Exploring crime specificity in official police incident data

ABSTRACT Police crime data guide police operations, direct funding, underpin theory testing and policy evaluation, aide private sector decisions, and inform the public. Those applications would benefit from greater crime specificity. The National Incident-Based Reporting System (NIBRS) should improve police crime data specificity. Using robbery incident data (n = 1,341) with NIBRS variables and narrative reports from the Cincinnati Police Department and qualitative analysis, this study identified thirteen robbery subtypes in the data. Tables and data visualizations demonstrate the same thirteen robbery subtypes cannot be identified using just NIBRS variables. Rather the qualitative narrative reports are most informative. While an improvement, NIBRS data are unlikely to increase the crime specificity police crime data users need. Police crime data users are urged to think carefully about what official crime data measures are used when testing theory, conducting crime analysis, evaluating policy, and so on. Recommendations for improving police data recording practices are also offered.

Crime and punishment in times of pandemics

How should we think about crime deterrence in times of pandemics? The economic analysis of crime tells us that potential offenders will compare the costs and the benefits from crime and from innocence and then choose whichever option is more profitable. We must therefore ask ourselves how this comparison is affected by the outbreak of a pandemic and the policy changes which may accompany it, such as governmental restrictions, social distancing, and responses to economic crises. Using insights from law and economics, this paper investigates how the various components in the cost-benefit analysis of crime might change during a pandemic, focusing on Covid-19 as a test case. Building on classical theoretical models, existing empirical evidence, and behavioral aspects, the analysis reveals that there are many potentially countervailing effects on crime deterrence. The paper thus highlights the need to carefully consider which aspects are applicable given the circumstances of the pandemic, as whether crime deterrence will increase or decrease should depend on the strength of the effects at play.

Analysis of Hate Crimes in Transgender Communities

Research shows that the LGBTQ population is disproportionately affected by hate crimes and those against transgender individuals are especially violent. Given the considerable underreporting of these crimes, better insight into the victimization experiences and reasons for underreporting is necessary to improve the safety of the transgender community and secure necessary services for these victims. The current study takes a community-based participatory research (CBPR) approach by creating and utilizing an Advisory Board made up of service providers in the transgender community in Los Angeles, to assist in the development of a survey instrument and focus group questions and provide venues for the focus groups. The study examines results from a short survey and five focus groups with transgender individuals on their experiences of hate crimes and reporting activities. Results indicate nearly all participants experienced some type of hate incident or crime based on their gender identity and/or expression. Almost half of the respondents did not report these crimes to the police because they did not think police would do anything, were afraid of being arrested, or were afraid of being victimized by the police. Narrative accounts describe mostly, though not entirely, negative encounters with the police and how participants take preventative measures to reduce their potential for hate-based victimization. Based on our findings several recommendations have been made to help improve relations between transgender communities and law enforcement with the goal of creating a safer environment for transgender individuals and increasing the reporting of hate crimes.

A spatial Analysis of Crime and Neighborhood Characteristics in Detroit Census Block Groups

Abstract. Crime has an inherent geographical quality and when a crime occurs, it happens within a particular space making spatiality essential component in crime studies. To prevent and respond to crimes, it is first essential to identify the factors that trigger crimes and then design policy and strategy based on each factor. This project investigates the spatial dimension of violent crime rates in the city of Detroit for 2019. Crime data were obtained from the City of Detroit Data Portal and demographic data relating to social disorganization theory were obtained from the Census Bureau. In the presence of spatial spill over and spatial dependence, the assumptions of classical statistics are violated, and Ordinary Least Squares estimations are inefficient in explaining spatial dimensions of crime. This paper uses explanatory variables relating to the social disorganization theory of crime and spatial autoregressive models to determine the predictors of violent crime in the City for the period. Using GeoDa 1.18 and ArcGIS Desktop 10.7.1 software package, Spatial Lag Models (SLM) and Spatial Error Models were carried out to determine which model has high performance in identifying predictors of violent crime. SLM outperformed SEM in terms of efficiency with (AIC:5268.52; Breusch-Pagan test: 9.8402; R2: 16% & Log Likelihood: −2627.26) > SEM (AIC: 5275.24; Breusch-Pagan test: 9.7601; R2: 15% & Log Likelihood: −2630.6194). Strong support is found for the spatial disorganization theory of crime. High percent ethnic heterogeneity (% black) and high college graduates are the strongest predictors of violent crime in the study area.

More crime in cities? On the scaling laws of crime and the inadequacy of per capita rankings\u2014a cross-country study

Crime rates per capita are used virtually everywhere to rank and compare cities. However, their usage relies on a strong linear assumption that crime increases at the same pace as the number of people in a region. In this paper, we demonstrate that using per capita rates to rank cities can produce substantially different rankings from rankings adjusted for population size. We analyze the population–crime relationship in cities across 12 countries and assess the impact of per capita measurements on crime analyses, depending on the type of offense. In most countries, we find that theft increases superlinearly with population size, whereas burglary increases linearly. Our results reveal that per capita rankings can differ from population-adjusted rankings such that they disagree in approximately half of the top 10 most dangerous cities in the data analyzed here. Hence, we advise caution when using crime rates per capita to rank cities and recommend evaluating the linear plausibility before doing so.

Use of selected types of crime analysis in modern criminological science

The article considers methods of analysis of criminological significant information, which are used in modern criminological science. Based on the analytical study, it was concluded that at the current stage of the development of criminological science, regression (as a type of mathematical method), factor (causal), and monitoring criminological analysis are most often used. These methods of criminological analysis were analyzed, their advantages and disadvantages were determined, their recommendations for their application in criminological science were given. It was established that the use of regression analysis is not found in many criminological studies, but today this direction has been updated due to the increase in crime rates both in Ukraine and in the world. However, in criminology, this direction is now one of the promising in the formation of crime prevention measures. It has been proved that most often this type of criminological analysis is used as monitoring. It is quite often used in criminological research by analyzing statistical reporting, which is publicly available to law enforcement agencies (in particular, on the official website of the Office of the Prosecutor General). In this regard, science identifies types of monitoring analysis, which is most often used in our country.

Applying Machine Learning Models to Classify Xenophobic Tweets Against Asians, With Data Analysis of Hate Crimes

This paper offers insight to the COVID-19 pandemic and its effect on people's attitudes towards certain minority groups, particularly Asians, Asian-Americans, and Pacific Islanders. With the Coronavirus first being identified in Wuhan, China, xenophobia, and racism towards groups pertaining to the supposed origins of the COVID-19 pandemic have been on the rise. Along with the violent physical attacks on these groups, this paper will focus on the online hate and xenophobia that Asians face due to their race, ethnicity, country of origin, and/or others. In this paper, Python is employed as the primary programming language; external libraries such as pandas, NumPy, sklearn, WordCloud, and matplotlib are imported for handling data. In analyzing the racism against Asians, keywords such as “Asian Hate,” “Hate Crime” and “anti-Asian” are utilized, and the Python programming language is employed to sift through Google News articles with these keywords and identify patterns in the words’ usages. Furthermore, the frequencies of the keywords’ usages on online platforms such as Twitter are also analyzed in the form of comma-separated files, with patterns of usage over time before and after the COVID-19 pandemic began being identified. Randomly selected tweets are classified into five categories: anti-Asian, not anti-Asian, not English, hate against others racial groups, and support towards Asians. These tweets are classified by artificial intelligence using machine learning methods of logistic regression, support vector machine, and Naive Bayes; the artificial intelligence was taught using pre-classified data sets. Classified tweets represent the implication and relevance between the tweets and xenophobia. This classification model of xenophobia is expected to be used in social media content censoring and enhance the internet chatting etiquette. The goal of this classification model is to terminate anti-Asian hatred and lower the overall level of societal racism.