Occurrence Of Crime Research Articles

Multiscale analysis of the influence of street built environment on crime occurrence using street-view images

Assessing the effect of street built environment on crime occurrence is a hot research subject in environmental criminology, which also plays an important role in crime prevention or even urban planning. Recent development in emerging geotagged big data (e.g., the street-view images) makes it possible to quantify the influence of street built environment on crime. However, previous studies have often neglected the multiscale problem in exploring the association between environmental features and crime occurrence. Therefore, in this study, a multiscale analysis method was proposed to quantitatively study the influence of street built environment on crime occurrence using street-view images. Firstly, inspired by the theory of crime prevention through environmental design, we established a multiscale descriptive framework for environmental features with simultaneous consideration of the physical features and scene perception of street built environment. Then, a multiscale geographically weighted regression model was used to explore the spatial scale of influence for different streetscape features on crime occurrence. Experimental results indicated that the proposed method could reflect the difference of the spatial scale of various environmental features on crime, thereby uncovering the association between environmental features and crime occurrence with better accuracy. This study may enrich the theory in environmental criminology, and it provides useful insights for crime prevention through urban streetscape design.

Principles of Selective Immigration Policy in Relation to Visa Free Entry for Tourism Purposes

Tourism has an important role in enhancing the economic growth of a country. As a country with growing tourism industry, Indonesia is trying to increase foreign tourist visits by rolling out a visa-free policy. However, the visa-free policy has not yet fully provided benefits. In practice, this policy triggers the occurrence of transnational and international crimes, the rise of illegal foreign workers and the large number of foreigners who abuse their residence permits. The purpose of this research is to analyze the visa-free policy from the perspective of selective policy theory. The research method is normative legal research with a statutory approach and a conceptual approach. The results of the study show that the visa-free policy as contained in Presidential Regulation Number 21 of 2016 is not fully based on the theory of selective policy. In conclusion, the visa-free policy cannot be implemented optimally because the formulation of this policy does not represent the theory of selective policy. Consequently, the implementation of visa-free policy generates various matters that have an adverse impact on Indonesia. In order to overcome this problem, it is urgent to evaluate and improve the visa-free policy and to make this policy truly based on the theory of selective policy. Thus, the visa-free policy will be very selective in choosing and determining countries that can receive a visa-free policy.

Trafficking crime policy in a philosophical, sociological and juridical perspective

This study aims to discuss government policies in tackling the crime of trafficking as a criminal act that violates human rights. Trafficking is a transnational crime and Indonesia as a country with a large number of trafficking victims. Trafficking victims are women/children, various endemics are the reasons for the occurrence of Trafficking crimes against vulnerable groups based on social, economic, and cultural conditions. The method used in this research is a descriptive method with a qualitative approach. In dealing with the crime of trafficking, it is necessary to do it bilaterally/multilaterally. Various prevention efforts have been carried out by the government through law enforcement (penal) against perpetrators as well as a form of protection for victims such as providing compensation to both victims and their families. However, the lack of understanding and professionalism of law enforcement officers in dealing with the crime of trafficking has resulted in this crime not being able to be resolved optimally, so there needs to be an integrated handling of cases by prioritizing non-penal efforts and involving the community in crime prevention and control, namely through preventive and repressive efforts.

Relationship between the coronavirus pandemic and criminal activities: Emerging evidence from Fiji Islands

Introduction: The spread of the coronavirus has led to strict containment measures around the world, simply to restrict the gathering of a large number of people. In Fiji, the COVID-19 lockdown measures are affecting different social aspects, including crime rates and criminal activities.
 Methods: This study was based on a secondary analysis of aggregated crime data, presenting preliminary analyses on crime trends across the Fiji Islands, during the first six months of the curfew period. It considers how the crime patterns shifted due to the numerous containment restrictions. We studied the crime data during the first six months of the curfew period, starting from March 31 to September 30, 2020, against the average of crime occurrences for the same period over the last four years (2016-2019).
 Results and Discussion: The study shows an overall increase of 18.8% in crime occurrences, during the curfew period. The disobedience against lawful orders were largely made up of curfew breaches (with an astounding increase of 2602 cases), with the southern and western divisions registering the majority of offences. The study also provides evidence of a decrease in offences against public morality (-41.7%) and property (-26.8%) during the curfew period, which could most likely be linked to strict stay-home restrictions and limited mobility. Criminal offences such as burglary (-24.6%), theft (-22%) and aggravated robbery (-23.3%) show a decreasing trend in the curfew period. However, a worrying increase in offences against the drugs ordinance act (104.4%), common assault (28.6%), serious assault (97.4%) and criminal intimidation (36.8%) is noted in this study. It could be interpreted that the central division (-46.4%) was possibly the safest area in Fiji during the first few months of the curfew, while the southern (17.9%) and western (29.2%) continue to show an increasing crime trend.
 Conclusion: The findings of this research are consistent with the predictions of the routine activity theory, which estimates crime rates to fluctuate during an exceptional event. Regardless of some limitations and directions for future research, the current study contributes to the literature on exceptional events and crime through an ongoing pandemic in the South Pacific.
 Keywords: Coronavirus, Fiji, lockdown, crime rates

Efforts of Bhabinkamtibmas in Preventing the Criminal Act of Togel Gaming in the Jurisdiction of the Kebumen Police

The law must be enforced to achieve the goals and ideals of the Indonesian nation as stated in the Preamble to the 1945 Constitution of the Republic of Indonesia. This study aims to find out what efforts have been made by Bhabinkamtibmas in preventing the crime of lottery gambling in Kebumen . And what is the general situation of the Kebumen Regency and know what factors support and hinder Bhabinkamtibmas in preventing the occurrence of lottery gambling crimes in the jurisdiction of the Kebumen Police. The method used in method used is through interviews, observation, and library research. Data analysis using qualitative data analysis. The results obtained can be said to be not optimal because of the factors that might hinder the performance of Bhabinkamtibmas. Inhibiting factors include the lack of public awareness of the law, so many are still afraid of the police, then the lack of personnel in the field which makes Bhabinkamtibmas members have to hold concurrent positions. In addition to inhibiting factors, there are also supporting factors that help Bhabinkamtibmas in carrying out their duties, there is budget support for Bhabinkamtibmas, making additions to optimize Bhabinkamtibmas activities in the field, maximizing breakthroughs from the Kebumen Police Chief which makes Awards & Punishments for Bhabinkamtibmas members to encourage them to carry out their duties in the field so that its performance can be optimal.

Textile damage science—Is it a reliable science?

AbstractForensic science has long been used in criminal investigations and the justice system. The standards applied to contemporary forensic practice have changed dramatically in the past decade. Presented here is a modern‐day review of textile damage science in the context of these changing standards, and how they have challenged the presentation of scientific evidence. The prevalence of textile damage examinations is fluctuating in the current forensic environment. Some laboratories are conducting more examinations than ever, and other laboratories are no longer able to conduct examinations due to limitations in resourcing or case numbers. This dynamic landscape will no doubt continue as the occurrence of crime fluctuates throughout and beyond the pandemic affecting the world.This article is categorized under: Crime Scene Investigation > Crime Scene Reconstruction Forensic Chemistry and Trace Evidence > Trace Evidence Jurisprudence and Regulatory Oversight > Expert Evidence and Narrative

More sports, more crimes? An analysis for the Metropolitan Region of Porto Alegre

Does the occurrence of crimes increase or decrease on sporting event days? There is no conclusive answer in the literature. This article uses a database that combines crime occurrences in the Metropolitan Region of Porto Alegre and the performance of soccer matches in a panel of daily data for the period 2014-2018. Considering matches as an exogenous variable, we estimated an autoregressive distributed lag model (ARDL). We found that the hypothesis of reduction of crime due to the greater security adopted in 2014 - the year in which Brazil hosted the World Cup - is supported. Also, we found a 4-day delayed small, but positive effect of matches on crime.

The Motivation of Criminality During the Covid-19 Pandemic in Central Sulawesi

With the news of the crime were often pushed in a variety of digital platforms and non- digital and criminal acts that are still happening in the community, make this topic endlessly to be discussed. Moreover, during the current pandemic, so many demands for life are not in line with the situation as a result of the implementation of the lockdown policy and the implementation of restrictions on community activities (IRCA) from the government which requires some people to be willing to lose their livelihood. Meanwhile, out of 100,000 people in Indonesia, 140 of them are at risk of being exposed to crime. The high crime rate is influenced by several factors such as education, less strict laws, high unemployment and inadequate wages. The purpose of this study was to determine the characteristics of crime and determine the factors that influence the occurrence of criminal acts in Central Sulawesi during the Covid-19 pandemic. This type of research is a type of descriptive qualitative research and descriptive quantitative. The data used in this study is secondary data from the Central Statistics Agency and the Central Sulawesi Regional Police. The research method used is multiple linear regression. The results of this study show that the characteristics of crime in Central Sulawesi during the pandemic, namely ordinary theft cases became the highest indicator in criminal cases, while theft in the family became the lowest indicator in criminal cases. In addition, it is known that the dominant criminal acts are carried out by men with self-employed and unemployed jobs, with the last education being high school or equivalent. Partially, the variable Number of Poor People has a significant effect on crime that occurs in Central Sulawesi and simultaneously or together the four variables, namely education, unemployment, Gross Regional Domestic Product (GRDP) and Number of Poor Population have an effect on the occurrence of crime in Central Sulawesi. The result of the coefficient of determination in this study was 79.99% it means that the four independent variables are able to explain the dependent variable of 79.99% and the remaining 20.01% are other variables that have not been used as variables in this study.

범죄분석을 통한 안전도시 구축의 효율화 방안

This study is a study on the construction of a crime-safe city as a countermeasure by analyzing the occurrence of crimes due to the new urban concentration in the outskirts of the city in order to escape the urban concentration phenomenon. The subject of this study is Pyeongtaek, south Gyeonggi-do. In 2020, the number of crimes nationwide was 1,611,906, 3,108.7 per 100,000 people, and the Gyeonggi Nambu Provincial Police Agency had 379,794 crimes, which was 3,877.5 per 100,000, while Pyeongtaek City had 28,270, which was 5,510.4 per 100,000. This is more than 2,000 cases higher than the national crime rate per 100,000 people, and 1,500 cases higher than the crime rate in Gyeonggi-do. In other words, the study began with the need for an alternative for the safety and order maintenance and risk prevention of Pyeongtaek citizens due to crime. As a result, the problem with the occurrence of crimes in Pyeongtaek City is First, the occurrence of crimes due to insufficient security conditions. Second, there is a lack of CCTV, an infrastructure for crime prevention. Third, it is the deterioration of crime prevention activities through people-to-people cooperation, which is one of the police activities of the local community, which is outside the traditional police activities. Based on these problems, suggestions were made on how to increase the efficiency of the construction of a crime-safe city. First, the factors vulnerable to crime and improvement of the environment. Second, the strengthening of police patrol power in response to crime. Third, crime prevention is the duty of the police.

HAGEN: Homophily-Aware Graph Convolutional Recurrent Network for Crime Forecasting

The goal of the crime forecasting problem is to predict different types of crimes for each geographical region (like a neighborhood or censor tract) in the near future. Since nearby regions usually have similar socioeconomic characteristics which indicate similar crime patterns, recent state-of-the-art solutions constructed a distance-based region graph and utilized Graph Neural Network (GNN) techniques for crime forecasting, because the GNN techniques could effectively exploit the latent relationships between neighboring region nodes in the graph if the edges reveal high dependency or correlation. However, this distance-based pre-defined graph can not fully capture crime correlation between regions that are far from each other but share similar crime patterns. Hence, to make a more accurate crime prediction, the main challenge is to learn a better graph that reveals the dependencies between regions in crime occurrences and meanwhile captures the temporal patterns from historical crime records. To address these challenges, we propose an end-to-end graph convolutional recurrent network called HAGEN with several novel designs for crime prediction. Specifically, our framework could jointly capture the crime correlation between regions and the temporal crime dynamics by combining an adaptive region graph learning module with the Diffusion Convolution Gated Recurrent Unit (DCGRU). Based on the homophily assumption of GNN (i.e., graph convolution works better where neighboring nodes share the same label), we propose a homophily-aware constraint to regularize the optimization of the region graph so that neighboring region nodes on the learned graph share similar crime patterns, thus fitting the mechanism of diffusion convolution. Empirical experiments and comprehensive analysis on two real-world datasets showcase the effectiveness of HAGEN.

Dimension of justice of restoration justice paradigm in the criminal system for sexual violence in Indonesia

The act of sexual violence is a crime that is classified as a violation of human rights (HAM). The increase in cases of sexual violence in the world, including in Indonesia, shows that this shows that the existing justice system is unable to guarantee justice for victims, and most importantly recovery for victims. For this reason, a justice restoration approach is needed as an alternative in law enforcement against sexual crimes. In practice, marriage is used as a way to approach justice restoration. This article uses a normative and juridical approach to discuss law enforcement on sexual crimes through a restoration justice approach. It can be concluded that law enforcement on sexual crimes must look at the criminological, victimological and ontological aspects, in order to be able to place the problem objectively. As a complaint offense, sexual crimes may not be passed on to the criminal process, if there is peace between the victim and the perpetrator, provided that there is an agreement between the victim, the perpetrator, the family and the community without coercion from various parties. The main thing in the justice restoration approach for sexual crimes is to provide protection and assistance to victims from various parties, so that victims can be separated from the trauma or psychological impact caused by sexual violence experienced by the victim or the impact received after the occurrence of the sexual crime.

Weather Conditions in Relation to Crimes Committed in Pagadian City

This study aimed to determine the relationship between weather conditions as to wet and dry as recorded in the Department of Science and Technology (DOST) – Philippine Atmospheric, Geophysical and Astronomical Services Administration (PAGASA) Weather Forecast Station and the crimes committed in Pagadian City in calendar years 2014-2018. Weather conditions as to wet and dry were correlated to crimes against person and property that was committed in Pagadain City. It employed a quantitative descriptive correlational method of research using the data of crime occurrences recorded in the Philippine National Police Pagadian City Station and the rainfall data from Department of Science and Technology (DOST) – Philippine Atmospheric, Geophysical and Astronomical Services Administration (PAGASA) Weather Forecast Station in the city statistically treated using frequency count, mean, paired sample t-test, and chi-square test. Findings of the study revealed that physical injuries were profiled the highest in crimes against persons, followed by murder, homicide, and rape. At the same time, theft cases were dominantly registered for crimes against properties, followed by robbery, carnapping, and cattle rustling. The average distribution of murder, homicide, and physical injuries during the wet season outnumbered the cases during the dry season, except for rape cases that were higher during the dry season. Likewise, robbery, theft, carnapping, and cattle rustling were higher during the wet season than the dry season. The average distribution differences of crimes committed between the wet and the dry seasons tested at a 0.05 level of significance were not significant. The relationship between crimes against persons and the weather conditions was not significant. The relationship between crimes against properties and the weather conditions was also not significant. Moreover, crimes against persons and properties in the city do not depend on weather conditions. Murder, homicide, physical injuries, rape, robbery, theft, carnapping, cattle rustling, and the like could happen in the city regardless of whether it is wet or dry weather conditions, and there is a need to implement the crime prevention programs designed by the researcher.

Exploring multi-scale variability in hotspot mapping: A case study on housing prices and crime occurrences in Heerlen

This paper examines the use of varying scaling in hotspot mapping as an exploratory spatial data analysis technique. The ambiguous geographic relationships between different crime types and housing prices are used to highlight the necessity of the multi-scale approach in effective pattern recognition. Hotspot maps were created using crime and housing price data from the city of Heerlen, The Netherlands. First, insights generated from observing the hotspot maps suggest a consistent spatial pattern of all crime types except home burglaries. By analyzing these relationships further with correlation analysis, it was found that all crime types except home burglaries have a persistent negative relationship with housing prices. The relationship between home burglaries and housing prices was found to differ in direction and significance depending on location and scale of the hotspot maps. A method for fine-tuning the scale is developed and presented. The fine-tuned results enrich hotspot mapping and highlight the need to consider scale variability properties in spatial data analysis.

A Design to Predict and Analyze Crime

Abstract: Crime is one of the dominant and alarming aspect of our society. Over the past few years, the crime rate across globe has increased exponentially. So, preventing the crime from occurring is a vital task. In the recent time, it is seen that artificial intelligence has shown its importance in almost all the field and crime prediction is one of them. However, it is necessary to maintain a proper database of the crime that has occurred. The ability to predict the crime on the basis of time, location and so on which can occur in future can help the law enforcement agencies in preventing the crime before it occurs from a strategical perspective. However, predicting the crime accurately is a challenging task because crimes are increasing at an alarming rate. Thus, the crime prediction and analysis methods are very important to detect the future crimes and reduce them. In Recent time, many researchers have conducted experiments to predict the crimes using various machine learning methods and particular inputs. For crime prediction, KNN, K-means and Random Forest and some other algorithms are used. Our system can predict regions which have high probability for crime occurrence and can visualize crime prone areas. The main purpose is to highlight the worth and effectiveness of machine learning in predicting violent crimes occurring in a particular region in such a way that it can be used by police to reduce crime rates in the society.

Perlindungan Hukum Konsumen Pengguna e-Banking: Ditinjau dari Undang-Undang Perlindungan Konsumen dan Undang-Undang Perbankan

The purpose of the research is to analyze the legal protection of e-banking users, and the rules needed to perfect the legal protection of e-banking users. This research is a normative legal research that focuses on literature research. The results show that in Indonesia, e-banking transactions are currently not fully protected. Forms of legal protection for customers/consumers from abuse of circumstances in standard agreements according to Law Number 8 Year 1999. Implementation of consumer protection for e-banking service users is carried out by emphasizing the aspects of e-banking technology security, customer complaints, and consumer education. In terms of technological security, 3 principles are applied, namely confidentiality, integrity and availability. Improvement of consumer protection is done by cooperation, increased coordination, online information exchange and designated contact person by involving various parties to reduce the occurrence of crime against the e-banking system. DOI: https://doi.org/10.26905/mlj.v3i1.9176

CRIME ANALYSIS USING MACHINE LEARNING

Crimes are the significant threat to the humankind. as crimes are increasing at a rapid rate approach for identifying trends in crime. Our project can predict regions which have high probability for crime occurrence and can visualize crime prone areas by visual representation of data by bar graphs, pie charts etc. It speed up the classification of criminal activities by calculating the average accuracy rate .It uses crime data set and predicts the types of crimes in a particular area which help in Accurate results based on the predictive analysis of logistic regression. The objective would be train a model for prediction using logistic regression classification algorithm. Logistic regression used for classification problems and it is a predictive analysis algorithm based on concept of probability. Crime analysis project is a systematic approach for identifying trends in crime. .It uses crime data set and predicts the types of crimes in a particular area which help in Accurate results based on the predictive analysis of logistic regression.

Crime Type Prediction in Saudi Arabia Based on Intelligence Gathering

Abstract One of the biggest social problems currently facing major cities around the globe is the high rate of crime. The largest part of the social-economic loss globally is ascribed to criminal activities. Crime also has direct impacts on the nation’s economy, social constructs and country’s global repute. Inadequate policing capital is one of the biggest challenges facing many global economies. As a result, these resources have to be rationed. This implies that some areas will not be covered extensively thus providing favorable environs for perpetrators. To combat crime, more innovative security measures are needed. In this sense, traditional methods are being replaced with modern approaches of machine learning systems that can predict the occurrence of crime. These crime forecasts can be used by legislatures and law enforcers to make effective and informed approaches that can efficiently eradicate criminals and facilitate nation building. This paper seeks to review the literature on the application of machine learning models in crime prediction and to find the influences that have an impact on crimes in Saudi Arabia. The results show that after the four models were trained and tested, the random forest classifier had the highest accuracy of 97.84%.

Mplementation of Community Policing in Efforts to Prevent Crime in the City of Banjarmasin

This study discusses the implementation of Polmas (community policing) which is done by members of Bhabinkamtibmas (People’s security and order supervising officer). The Banjarmasin police are collaborating with FKPM through a problem-solving programme. The goal is to find supporting and inhibiting factors, and identify a Polmas implementation model in an effort to prevent the occurrence of crime in Banjarmasin. This study uses a qualitative descriptive approach in accordance with the main objectives of the research, namely to study, describe and analyse data and information according to needs. The techniques used were through observation, interviews and documentation.This study focuses on several topics related to a problem which lifted that is to know and analyse the factors that support and hinder the implementation of community policing. This is an effort to prevent crime in Banjarmasin by using information based on Theory George C. Edward III, who held the view that policy implementation is influenced by four factors, namely: 1) communication; 2) resources; 3) disposition; and, 4) bureaucracy structure. To obtain an effective and efficient model for implementing Polmas programme policies in Banjarmasin using a SWOT (strengths, weaknesses, opportunities and threats) approach by considering internal factors consisting of strength and weakness components and external factors consisting of opportunity and threat components.In this study it was found that the implementation of the Polmas / Community policing policy in the city of Banjarmasin had been carried out well due to the accuracy of the appointment of the right program implementers, the acceptance of the program by the targeted community members and the active role of the potential community involved in implementing the policy, but this was not the case. Without any obstacles that must be overcome, for example budget constraints in implementation, program implementers / Bhabinkamtibmas who have other duties, there is a public perception that the responsibility of Kamtibmas is only on the security forces and the legal awareness of citizens is low. This research is expected to have significant practical benefits for readers and related institutions - especially police institutions - in implementing policies. It can provide positive information for police officers, especially those carrying out Polmas duties, while tasked with maintaining security and public order through preventing the occurrence of crime in their respective areas of duty. Keywords: Implementation, Community Policing, Bhabinkamtibmas and problem-solving DOI: 10.7176/RHSS/12-8-04 Publication date: April 30 th 2022

Documentary and oral Scientific expert Evidence and Modes of Scientific Examination under Pakistani Laws

The crime scene operation and the investigative process is important stage of pretrial construction of the events for assessment of occurrence of crime which helps to collect the material or physical evidence to be presented before the court during a trial. This process hinges upon intelligent and expert handling of the crime scene so that the prosecution may build the case beyond reasonable doubt and to link the evidence with real criminal instead of conviction of innocent. This paper presents an overview of the kind of evidence with relation to the crime scene which is important for the assessment that what expertise and scientific examinations might be needed for a potential crime scene investigation. In doing so, this paper underlines the kinds of expert evidence with reference to law of evidence in Pakistan and emphasizes legal perspective to be kept in mind while investigative operation is conducted.

A model for predicting crimes using big data and neural‐fuzzy networks

AbstractBig data analytics is important for identifying and analyzing different patterns, relations, and trends within a large volume of data such as crime prediction. In this paper, we propose a new model for predicting crimes using neural‐fuzzy model. For this purpose, time of occurrence and longitude and latitude of the crime scene are used. We use k‐means algorithms to cluster crime records based on their locations. The crime pattern of each cluster was learnt by its own neural‐fuzzy network, in addition using shuffled frog leaping algorithm to determine the optimal cluster radius. The accuracy and Mean Absolute Error and Root Mean Squared Error of this method were compared with: Gaussian Process Regression, Classification and Regression Tree, and Artificial Neural Network. The experiment results showed that the proposed model was the most accurate model and had the least error to predict the occurrence of crimes so can be used as an effective tool in real‐world applications.