• International Journal of Advanced Culture Technology
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• v.8 no.3
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• pp.61-72
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• 2020

The fear of crime, discussed in the early 1960s in the United States, is a psychological response, such as anxiety or concern about crime, the potential victim of a crime. These anxiety factors lead to the burden of the individual in securing the psychological stability and indirect costs of the crime against the society. Fear of crime is not a good thing, and it is a part that needs to be adjusted so that it cannot be exaggerated and distorted by the policy together with the crime coping and resolution. This is because fear of crime has as much harm as damage caused by criminal act. Eric Pawson has argued that the popular impression of violent crime is not formed because of media reports, but by official statistics. Therefore, the police should watch and analyze news related to fear of crime to reduce the social cost of fear of crime and prepare a preemptive response policy before the people have 'fear of crime'. In this paper, we propose a deep - based news classification system that helps police cope with crimes related to crimes reported in the media efficiently and quickly and precisely. The goal is to establish a system that can quickly identify changes in security issues that are rapidly increasing by categorizing news related to crime among news articles. To construct the system, crime data was learned so that news could be classified according to the type of crime. Deep learning was applied by using Google tensor flow. In the future, it is necessary to continue research on the importance of keyword according to early detection of issues that are rapidly increasing by crime type and the power of the press, and it is also necessary to constantly supplement crime related corpus.

• Honam Mathematical Journal
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• v.44 no.4
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• pp.572-593
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• 2022

Crime committed by civilians and criminals using legal and illegal firearms and conversion of legal firearms into illegal ones has become a common practice around the world. As a result, policies to control civilian gun ownership have been debated in several countries. The issue arose because the linkages between firearm-related mortality, weapon accessibility, and violent crime data can imply diverse options for addressing criminality. In this paper, we have projected a mathematical model in terms of the Caputo fractional derivative to address the issues viz. input of legal guns, crime committed by legal and illegal guns, and strict government policies to monitor the license of legal guns, strict action against violent crime. The boundedness, existence and uniqueness of solutions and the stability of points of equilibrium are examined. It is observed that violent crime increases with the increase of crime committed by illegal guns, crime committed by legal guns and, decreases with the increase of legal guns, the deterrent effect of civilian gun ownership, and action of law against crime. Further, legal guns increase with the increase of the limitation of trade of illegal guns and decrease with the increase of conversion of legal guns into illegal guns and increase of the growth rate of illegal guns. Again, as crime is committed by legal guns also, the policy of illegal gun control does not assure a crime-free society. Weak gun control can lead to a society with less crime. Theoretical aspects are numerically verified in the present work.

• Korea journal of population studies
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• v.24 no.2
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• pp.149-177
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• 2001

This is a study on age-crime curve in Korea. Three data was used in this study as following: First is the crime statistics as aggregated data. Second is the police record(N=3.541 offences) of the male ex-offenders(N=988) who have been released in eleven prisons in 1987 as individual data. Third is the self-reported group-interview data(N=10.198 offences) administered to the male prisoners(N=979) in ten correctional facilities including eight adult prisons, one juvenile prison and one juvenile training center as another individual data. Generally, the right-skewness of age-crime curve has been explained through the difference of crime rate between early starters and late starters. Moffitt explains that this is because of the higher participation rate of the juvenile period of adolescence-limited offenders, but Godttfredson and Hirschi explain that this is because of a similar distribution in the crime rate of both early starters and late starters. the analysis of this study shows that Godttfredson and Hirschi’s explanation on the generality of age-crime-curve distribution is correct, but this can be modified by various factors like a economic crisis. And the peak age of juvenile period is consistent with the Moffitt’s hypothesis that the peak age is contributed to the increase of crime rate of late starters, not with Godttfredson and Hirschi’s one.

• Journal of the Korean Association of Geographic Information Studies
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• v.14 no.4
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• pp.194-207
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• 2011

This study tries to find the possibility to prevent crime by improving urban spatial environment through the analysis of spatial environment property that mutually coincides or differs by comparing the place where crime actually occurs and the place where citizen is afraid of crime. The method of study is as follows. First, the ontents scope and method of study was established by theoretic investigation of case study related to crime. Second, as crime cannot be prevented by police power only, CPSCP(Citizen Participation System for Crime Prevention) was developed so that all citizen can cooperatively participate in the crime prevention anytime and anywhere. Third, the data on the place where people feel fear in the region was collected by directly indicating the place where citizen is afraid of crime in the space by utilizing CPSCP. Fourth, the place where crime actually occurs and the place where citizen is afraid of crime are redundantly analyzed for comparative analysis of 2 places. The result shows that environmental design improving physical environment of urban space is necessary to prevent crime and to eliminate the fear of crime. The CPSCP developed by this study which will be advanced to U-crime prevention system will contribute to making citizen's own neighborhood a smart safety city autonomously.

• The Journal of Industrial Distribution & Business
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• v.12 no.6
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• pp.47-55
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• 2021

Purpose: Organizational crime is an offense committed by an individual or an official in a corporate entity for organizational gain. This study aims to explore the literature on challenges facing digital forensics and further discuss possible solutions to such challenges as far as the protection of corporate crime is concerned. Research design, data and methodology: Qualitative textual methodology matches the interpretative approach since it is a quality method meant to consider the inductivity of strategies. Also, a qualitative approach is vital because it is distinct from the techniques used in optimistic paradigms linked to science laws. Results: For achieving justice through the investigation of digital forensic, there is a need to eradicate corporate crimes. This study suggests several solutions to reduce corporate crime such as 'Solving a problem to Anti-forensic Techniques', 'Cloud computing technique', and 'Legal Framework' etc. Conclusion: As corporate crime increases in rate, the data collected by digital forensics increases. The challenge of analyzing chunks of data requires digital forensic experts, who need tools to analyze them. Research findings shows that a change of the operating system and digital evidence interpretation is becoming a challenge as the new computer application software is not compatible with older software's structure.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.119-130
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• 2022

Crime is a common social problem that affects the quality of life. As the number of crimes increases, it is necessary to build a model to predict the number of crimes that may occur in a given period, identify the characteristics of a person who may commit a particular crime, and identify places where a particular crime may occur. Data privacy is the main challenge that organizations face when building this type of predictive models. Federated learning (FL) is a promising approach that overcomes data security and privacy challenges, as it enables organizations to build a machine learning model based on distributed datasets without sharing raw data or violating data privacy. In this paper, a federated long short- term memory (LSTM) model is proposed and compared with a traditional LSTM model. Proposed model is developed using TensorFlow Federated (TFF) and the Keras API to predict the number of crimes. The proposed model is applied on the Boston crime dataset. The proposed model's parameters are fine tuned to obtain minimum loss and maximum accuracy. The proposed federated LSTM model is compared with the traditional LSTM model and found that the federated LSTM model achieved lower loss, better accuracy, and higher training time than the traditional LSTM model.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.4
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• pp.78-89
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• 2012

The purpose of this study is to examine the possibility of crime prevention in consideration of urban physical environment by analyzing the spatial distribution characteristics and pattern using actual crime occurrence data of the case city. The crime data was rebuilt by transforming them into geographic information system to analyze the spatial aspect of crime occurrence. The findings are as follows: a change from 2008 to 2011 is indicated with similar trend. But the local movements of crime hot spots are found. Moreover crimes were happening along the roads in linear pattern rather than inside of blocks in commercial area. This indicates the importance of environmental improvement of roads and open spaces. In addition it was found that the crime occurrence in a dangerous district can be reduced and prevented through the physical environment design and urban planning. The findings will contribute to promoting fundamental crime prevention as the physical environmental improvement in a city and to building a safe community as its result.

• The Journal of the Korea Contents Association
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• v.15 no.10
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• pp.150-159
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• 2015

This study attempted to test the scale effect of MAUP, particularly focusing on the spatial autocorrelation of sex crime, correlations among neighborhood structural variables, and causal mechanism leading to sex crime. Analysis results of nation-wide Eup-Myon-Dong and Si-Gun-Gu data discovered that the spatial autocorrelation, correlations among independent variables, and determinant coefficient of multiple regression of Si-Gun-Gu level were generally bigger and stronger than those of Eup-Myon-Dong, which appeared to be due to the averaging effect. Regarding the causal effect to sex crime, two interesting results were found: First, the ratio of non-apartment residency lowered sex crime at both levels contrary to the hypothesis. Second, the ratio of food and lodging increased sex crime only at Eup-Myon-Dong level. These suggested that future research need to perform more detailed analyses dividing data into subsets such as urban vs. rural and/or economically advantaged vs. disadvantaged areas.

• International Journal of Advanced Culture Technology
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• v.10 no.4
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• pp.499-510
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• 2022

This study examines the reporting factors of crime against business in Korea and proposes a corresponding predictive model using machine learning. While many previous studies focused on the individual factors of theft victims, there is a lack of evidence on the reporting factors of crime against a business that serves the public good as opposed to those that protect private property. Therefore, we proposed a crime prevention model for the willingness factor of theft reporting in businesses. This study used data collected through the 2015 Commercial Crime Damage Survey conducted by the Korea Institute for Criminal Policy. It analyzed data from 834 businesses that had experienced theft during a 2016 crime investigation. The data showed a problem with unbalanced classes. To solve this problem, we jointly applied the Synthetic Minority Over Sampling Technique and the Tomek link techniques to the training data. Two prediction models were implemented. One was a statistical model using logistic regression and elastic net. The other involved a support vector machine model, tree-based machine learning models (e.g., random forest, extreme gradient boosting), and a stacking model. As a result, the features of theft price, invasion, and remedy, which are known to have significant effects on reporting theft offences, can be predicted as determinants of such offences in companies. Finally, we verified and compared the proposed predictive models using several popular metrics. Based on our evaluation of the importance of the features used in each model, we suggest a more accurate criterion for predicting var.

• Journal of the Korean Geographical Society
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• v.42 no.2 s.119
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• pp.243-257
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• 2007

This paper aims to analyze fear of crime which is considered socio-psychologically important in our daily lives from a geographical point of view. Especially, the spatial patterns of the fear of crime which were analyzed in the area of Youngtong in Suwon city. First, this paper takes a look at the correlation between the incidence of crime and the fear of crime. Most people feel fear in actual crime scenes, but they do not always coincide with place where people feel the high level of fear. Fear of crime is closely connected with physical environments as well as the incidence of crime. The level of fear is high in places where the light is dark, unfrequented paths, especially in parks and around mountains. Several factors which have effects on fear of crime operate differently upon place. Second, a survey which measures the fear of crime was quantitatively analyzed. Factor analysis was employed to find out whether questions are appropriate for measuring the fear of crime as well as to reduce the amount of data so that more exact result can be derived from the data. Through the factor analysis, seven factors were extracted and it is found that a factor of incivility accounts for 24.032% of variance. Other factors which affect fear of crime are community cohesion, warning, incidence of crime, victimization, morality and authority.