Crime Analysis Research Articles

NIRBHAY: Analysis and Prediction of Crime

NIRBHAY: Analysis and Prediction of Crime

An Inchoate Crime in Criminal Law: Prospects of a Constructionist Approach

The paper considers the possibilities of solving some problems of crime qualification using the juridicalconstructionist approach. The focus of the legal analysis is on the structural features of a specific normative prescription of the criminal law with the peculiarities of the connections between its elements. First, the author shows how the application of different legal structures to similar facts of the case changes the legal assessment of what has been done from a completed crime through an attempt to commit it and to conclusions about the absence of a criminally punishable act. Second, the author discusses the issue of determining not only the moment of the legal completion of the crime, but also the moment of the legal beginning of the crime to find the boundaries between the preparation for the commission of a crime and the attempt to commit it. The author provides his understanding of the legal structure in criminal law, as well as its content. Using the example of judicial normative interpretation and law enforcement practice, the author proves that legal structures can change, despite the fact that the disposition of an article of a Special part of the Criminal Law may remain unchanged. According to the author, the main work on the description and modification of legal structures is carried out by the Supreme Court of the Russian Federation, including its Plenum. The paper demonstrates positive aspects of the constructionist approach in the legal analysis of an unfinished crime, as well as the difficulties that may be caused by it. For clarity, using the analysis of legal structures, the author elucidates approaches to the qualification of attacks on the life of two or more persons, when one victim was killed, and one or more other victims were not killed due to circumstances independent of the will of the perpetrator. In the process of this analysis, the author demonstrates the distinction between attempted murder of two or more persons and preparations for such a crime, and provides his considerations concerning decisions of the courts of appeal of general jurisdiction.

Using remote sensing data to derive built-form indexes to analyze the geography of residential burglary and street thefts

ABSTRACT By deploying remotely sensed data together with spatial statistical modeling, we use regression modeling to investigate the relationship between the density of the built environment and two types of crime. We show how the Global Human Settlement Layer (GHSL) data set, which is a measure of building density generated from Sentinel 2A satellite imagery, can be used to create different indexes to describe the built environment for the purpose of analyzing crime patterns for indoor crimes (residential burglary) and open space crimes (street theft). Analysis is at neighborhood level for Stockholm, Sweden. Modeling is then extended to incorporate six planning areas which represent different neighborhood types within the city. Modeling is further extended by adding selected social, economic, demographic and land use variables that have been found to be significant in explaining spatial variation in the two crime categories in Stockholm. Significant associations between the GHSL-based indexes and the two crime rates are observed but results indicate that allowance for differences in neighborhood type should be recognized. Average income and transport hubs were also significant variables in the investigated crime categories. The article provides a practical demonstration and assessment of the use of high-resolution satellite data to examine the association between urban density and two common types of crime and offers reflections about the use of satellite image data in crime analysis.

Analysis of Crimes of Violence against Children in Gorontalo Province

Currently, many criminal acts are often committed, such as violence. Violence is an arbitrary act committed by someone with the aim of causing physical or psychological harm. Parents have an important role to protect and protect, but instead acts of violence against children are carried out by those closest to them, such as parents. Child Victims of violence who are part of a society whose development is immature both physically and psychologically require special assistance in handling it. This study aims to determine the emergence of factors that cause victims of physical violence against children in the Gorontalo region to reduce the number of victims of acts of violence against children. Which examines the existence of violence perpetrated against children. Data collection was done by interview technique. So a way is needed so that acts of violence against children are not continuously carried out and strict action must be given by the government to the perpetrators who commit violence. So it is deemed necessary to emphasize the regulations that have been implemented in the government, especially Law Number 23 of 2002 concerning child protection

Research on the Application of Social Network Data Mining Technology in Crime Analysis and Prevention

Abstract Social network data mining (SNDM) technology shows great application potential in crime analysis and prevention. This study focuses on revealing the characteristics, laws, and trends of criminal behavior through an in-depth analysis of criminal information in social networks. Using data mining techniques such as association rule mining, cluster analysis, and community discovery, the key information and organizational structure of criminal networks are successfully mined, which provides a powerful means of investigation and prevention for public security departments. It is found that important criminal clues are hidden in the user communication data in social networks, and the communication mode and hidden information between criminals can be revealed through association rule mining technology. Cluster analysis helps to identify gangs and hot spots with similar criminal behaviors, which provides important clues for further investigation. In addition, community discovery technology further reveals the internal structure and membership relationship of criminal gangs, which is helpful in deeply understanding the operation mode of criminal organizations and the spread path of criminal acts. Based on historical data and mining results, this study also constructs a crime trend prediction model, which provides timely early warning information for public security departments and helps to take measures to prevent and crack down on criminal acts in advance. On the whole, this study not only enriches the application theory of SNDM technology in the field of crime analysis but also provides new ideas and tools for actual crime investigation and prevention.

Potentiel argumentatif du vocabulaire juridique et orientation axiologique du discours judiciaire

This article proposes a research programme at the intersection of linguistic semantics, law, and judicial practice. Its aim is to explain and argue for a judicial and meta-judicial semantics, complementary to legal linguistics, by giving it theoretical and methodological foundations in the perspective of the semantics of argumentative possibilities. The hypothesis is that legal terms with a dual affiliation (legal and general lexicons), deploy in the contradictory arguments of judicial practice, their discursive potential (specified by conventional definitions), but also the argumentative potential of the meaning that these lexical entities have in the general lexicon. The illustration that supports this hypothesis is based on the analysis of crime, in the contradictory pleadings of judicial and post-judicial practices in French judicial system.

Psychological Analysis of Individual Extreme Violent Crimes from the Perspective of Mental Disorders and Countermeasures

Psychological Analysis of Individual Extreme Violent Crimes from the Perspective of Mental Disorders and Countermeasures

Leveraging Machine Learning Methods for Crime Analysis in Textual Data

Leveraging Machine Learning Methods for Crime Analysis in Textual Data

A Detailed Analysis of Cyber Crimes Against Women

Today’s world is a technologically advanced world. In this era, gadgets have become a very essential parts of our lives. Every coin has two sides and so is the case with the technological advancement. On one hand, technology has made the lives of the women very easy as they are able to communicate, study online, etc. and at the same time, there is a rapid increase in the number of cyber crimes against them. In this paper, the researchers shall analyse as to what kind of cyber crimes are happening against women, what are the laws to protect them against these crimes, what are the loopholes and solutions to the same.

Wykorzystanie analizy kryminalnej w działalności organów ścigania

Crime analysis is a very useful tool supporting law enforcement authorities in the process of a establishing an objective truth. This article provides an insight into scientific views on its definition, kinds, methods, forms, analytical techniques, and the legal grounds for its application by law enforcement agencies who are the guardians of public order. It also refers to the use of data derived from open sources in the process of a crime analysis. It also presents the practical aspects of the Prosecutors’ Support System – Analysis & Investigative Module (System Wsparcia Prokuratora – Moduł Analityczno-Śledczy) and provides positive predictions for its integration with the PROK-SYS system. This article also mentions a controversial issue such as the evidentiary value of this subsidiary tool which allows to review multi – source evidence (gathered on various planes of research) as well as the role played by a crime analyst in the criminal procedure. Based on those considerations, the author makes de lege ferenda conclusions on the equivalence of the position of a crime analyst and an expert witness so that crime analysis can become a fully valid evidence as compared with crime expert’s opinion.

Enforcement of Law Against Corporations That Commit Environmental Crime and Transnational Crime

Objective: This research wants to explore the following. Analysis of Environmental Crime as a Transnational Crime Involving Corporations and Analysis of Law Enforcement Problems Regarding Company Responsibility as Perpetrators of Environmental Crime. Methodology: This research is a normative legal research based on empirical cases using a statutory approach. Techniques for obtaining through Normative law by researching legal material, primary, secondary legal material, and also tertiary legal material. In empirical legal research, data collection techniques exist 2 (two) techniques that can be used, either individually or together. An interview is a direct question-and-answer session between researchers and respondents or sources for getting information. Results: The results of this research show that the problems in law enforcement against corporations in environmental crimes can be grouped as follows: There is no legal provision that regulates the definition of corporations, or sanctions for corporations that commit environmental crimes; Law enforcement is still offender oriented without any legal protection for victims; Weak evidence and lack of expert witnesses. Document study is a method of collecting data use and study documentation or documents in the form of archives, notes, and tables, places used as research. Conclusion: Environmental crime is a transnational crime because most environmental crimes often involve corporations. Legal regulations for environmental crimes against corporations are still very difficult to implement and encounter various problems. The problem of law enforcement against corporations can be grouped with legal provisions governing corporations, for corporations that commit environmental crimes, due to weak evidence and a lack of expert witnesses.

Study and analysis of Hate Crimes in Spain (2013-2020)

Introduction: In this article, we analyze the evolution of hate crimes in Spain from 2013 to 2020. Hate crimes are based on the relationship between racism and fear, shaped by a social and economic rejection based on race-ethnic identity, religion, sexual orientation-identity, sex/gender, and social class. Method: These crimes are examined by type and population affected with the figures obtained from the reports of the Ministry of the Interior of the Report on the Evolution of hate crimes. The methodology used has been a critical documental analysis of sources and data to reveal an X-ray of hate crimes within Spanish society and in comparison, to the European Union. The typology of the crimes is identified and analyzed. Results: The results indicate an overall increase in such crimes, particularly in five autonomous communities, with a significant increase in crimes motivated by sexual orientation and identity. Moreover, the data shows a significant increase in certain crimes related to the COVID-19 pandemic. Conclusions: Further training for law and health officials is required to improve the precision of the registry, cataloguing and data generation in order to identify trends. In addition, pedagogy for the general population is necessary to create awareness of these crimes to the public.

Explaining Theft Using Offenders’ Activity Space Inferred from Residents’ Mobile Phone Data

Both an offender’s home area and their daily activity area can impact the spatial distribution of crime. However, existing studies are generally limited to the influence of the offender’s home area and its immediate surrounding areas, while ignoring other activity spaces. Recent studies have reported that the routine activities of an offender are similar to those of the residents living in the same vicinity. Based on this finding, our study proposed a flow-based method to measure how offenders are distributed in space according to the spatial mobility of the residents. The study area consists of 2643 communities in ZG City in southeast China; resident flows between every two communities were calculated based on mobile phone data. Offenders’ activity locations were inferred from the mobility flows of residents living in the same community. The estimated count of offenders in each community included both the offenders living there and offenders visiting there. Negative binomial regression models were constructed to test the explanatory power of this estimated offender count. Results showed that the flow-based offender count outperformed the home-based offender count. It also outperformed a spatial-lagged count that considers offenders from the immediate neighboring communities. This approach improved the estimation of the spatial distribution of offenders, which is helpful for crime analysis and police practice.

Multiplicative Version of First Zagreb Index in Fuzzy Graph and its Application in Crime Analysis

Multiplicative Version of First Zagreb Index in Fuzzy Graph and its Application in Crime Analysis

An Empirical Analysis of Violent Crimes Committed by Foreign Nationals in South Korea

As the number of foreigners residing in South Korea has increased, the overall scale of crimes committed by non-Korean nationals and the associated public anxiety have both risen. This paper seeks to explore the trends in violent crimes committed by foreigners in Korea by examining crime statistics from the Korean National Police Agency and immigration data from the Korean Ministry of Justice. Additionally, this paper empirically examines the attributes of the home countries of foreign criminals who are arrested in Korea. The results of our analysis, based on the violent crime statistics for foreign nationals of twelve types from 2003 to 2019, reveal that foreigners from higher-income countries with better educational environments are more likely to be involved in violent criminal activities. Individuals from countries with relatively strong law enforcement are less likely to engage in violent crime while residing in South Korea.

London street crime analysis and prediction using crowdsourced dataset

To effectively prevent crimes, it is vital to anticipate their patterns and likely occurrences. Our efforts focused on analyzing diverse open-source datasets related to London, such as the Met police records, public social media posts, data from transportation hubs like bus and rail stations etc. These datasets provided rich insights into human behaviors, activities, and demographics across different parts of London, paving the way for a machine learning-driven prediction system. We developed this system using unique crime-related features extracted from these datasets. Furthermore, our study outlined methods to gather detailed street-level information from local communities using various applications. This innovative approach significantly enhances our ability to deeply understand and predict crime patterns. The proposed predictive system has the potential to forecast potential crimes in advance, enabling government bodies to proactively deploy targeted interventions, ultimately aiming to prevent and address criminal incidents more effectively.

Legal Analysis of Transnational Crimes in the Scope of Tax Crimes in View of International Law Words

In order to achieve the ideals of the Unitary State of the Republic of Indonesia, which will bring prosperity to its people, resilience in the economic sector plays a significant role. However, the circulation of black money still haunts Indonesia in realizing its goals. One practice that is detrimental to the State of Indonesia is tax evasion. Although Indonesia already has several regulations regarding preventing and eradicating money laundering crimes. However, it is still difficult to prosecute the perpetrators of these crimes. Therefore, this research focuses on the regulation of Indonesian law regarding transnational crimes within the scope of financial transactions resulting from criminal acts in the field of taxation. Second, Indonesian law enforcement acts against transnational crimes within the scope of financial transactions resulting from criminal acts in the field of taxation. The focus of the study uses normative research methods with a juridical-qualitative analysis method. The results of this study conclude that even though Indonesia already has legal instruments related to the prevention and eradication of money laundering, especially the proceeds of crime in the field of taxation, there is a legal loophole in the regulation of Special Purpose Vehicles (SPV) so that it is necessary to reorganize SPV arrangements so that they no longer become loopholes for criminals and strengthen the FIU in terms of law enforcement.

Prediction of Criminal Suspect Characteristics with Application of Wavelet Neural Networks

Abstract Crime incidents grow at a certain rate every year and become more complicated day by day, but at this stage, the development of the field of crime analysis and prediction is still lagging, so this paper applies a wavelet neural network to predict the characteristics of criminal suspects. Analyze the applicability of the WNN model, explore the principle of criminal suspect feature prediction, and study the basis of wavelet neural network analysis. Establish the face feature recognition framework, use the Morlet function and Mexican Hat function pair to optimize and improve the wavelet neural network, and construct the model evaluation index for evaluating the model prediction effect. Preprocessing the suspect feature point data by using the grayscale reflection in Haar features. Simulation experiments are used to analyze the performance of different algorithms and the prediction effect of criminal suspect features on wavelet neural networks. The proposed algorithm in this study is superior and has a lesser number of parameters than ResNet. From the loss curves of different algorithms, the algorithm proposed in this study has a faster-descending loss curve and a smaller loss rate in the epoch value of [100, 200] interval. The overall range of the evaluation indexes of the algorithms proposed in this study is above 88%, and the highest accuracy rate can reach 94.234%, which is a good performance of the algorithms and accurate prediction.

Study of crimes in the city of Kokand using GIS technologies and sociological questionnaires

Formulation of the problem. It is known that the influence of the demographic, social and economic conditions of the region on criminal activity is significantly high. In this place, researching the types of crime characteristic of cities with different functions, and identifying hotspots where criminal activity has intensified within the city will further enrich the scientific and practical aspects of the geography of crime. The use of geographic information systems (hereinafter GIS) in the implementation of these tasks is an important issue on the agenda of the field today. Analysis of recent research and publications. Today, in most developed countries, GIS is widely used to identify common crime areas and organize preventive measures for crime prevention. Research in this regard is almost undeveloped in the Republic of Uzbekistan. Formulation of the purpose of the article. In this study, theft and drug-related crimes committed in the city of Kokand, Fergana region, were investigated using the Hot Spot analysis method of the Arc GIS (version 10.8) program. The problems of combating crime in identified theft and drug-related crime centres were studied with the help of sociological surveys (conducted with prevention inspectors) and an action plan for crime prevention was developed. Presentation of the main research material. The indicators of crime levels in the small administrative areas of Kokand, in the Fergana region, have been examined in this study. In addition to the types of crimes that are most commonly committed in large social infrastructures (for example, markets, parks, public places) and their specific characteristics, we also discussed the levels of these types of crimes according to age, gender and population size. Problems and prospects of the development of crime geography in Kokand city. Hotspot analysis consists of identifying hotbeds of crime in the area and developing proposals and recommendations based on this. Proposals and recommendations for reducing crime in the city of Kokand. First, this technology is effective in identifying crime trends, organizing preventive measures, planning crime prevention strategies, and improving public safety. Second, GIS is adapted for data processing, mapping, and spatial analysis, thereby increasing the effectiveness of crime analysis and quickly creating a quality criminal analysis map. Thirdly, according to the results of this technology, optimization of the distribution of law enforcement officers, proper and planned deployment, and development of more effective crime reduction measures will help.

A novel cross-domain adaptation framework for unsupervised criminal jargon detection via pre-trained contextual embedding of darknet corpus

As the regulation on the surface web becomes more stringent, criminals are gradually turning to the darknet markets for illicit operations. Moderating and studying the content on the marketplaces contribute to the combat of criminal forces in the darknet. Nevertheless, to evade the surveillance of law enforcement, jargons are widely used in criminal conversations as a disguise. These jargons misinterpret the meaning of seemingly innocuous words in cryptic ways, creating a huge challenge for criminal investigation. Current research on Chinese jargon detection focuses on keyword matching. However, this approach cannot keep up with the rapid update of new jargons from various domains. To the best of our knowledge, we are the first to conduct Chinese jargons detection research in the darknet markets. Specifically, we design an unsupervised cross-domain adaptation Chinese jargon detection framework (CJD-Framework) integrated with the pre-trained language model. Firstly, six underground markets in Chinese are crawled to build the first dataset of darknet corpus (DC-dataset). Next, a pre-training model based on Chinese word is proposed to extract contextual embeddings for darknet words. Finally, relying on semantic similarity analysis, a cross-corpus framework is constructed to effectively identify Chinese jargons in the darknet. Comprehensive experiments demonstrate the effectiveness and generalizability of the CJD-framework over the state-of-the-art models, with a detection accuracy of 91.5%. The darknet corpus dataset and innovative framework proposed in this research can provide sources and ideas for future analysis of underground crimes in the darknet markets.