• International Journal of Advanced Culture Technology
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• v.9 no.1
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• pp.1-6
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• 2021

This study aims to test the relationship between the change in population size and population composition and crime changes. The analytical model includes variables representing changes in population size and population composition, three dependent variables for crime changes, and three control variables. Changes in popuvpdllation size and population composition are measured by indicators such as population size, gender, age groups, and immigrants and foreign workers, and crime changes by felonious, burglary, and violent crime volumes. The sample includes 154 cities and counties in South Korea, and the sample period is from 2010 to 2017. As a result of the analysis, I examine that the decrease in the number of young and men are likely to lead to a decline in felonious and violent crimes, but a high rate of the elderly and the increasing racial heterogeneity are likely to accelerate the fear and worries about crimes.

• Convergence Security Journal
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• v.14 no.6_2
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• pp.89-95
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• 2014

This study analyzed a correlation between crime and meteorological changes and discomfort index of Seoul and p resented a prediction expression through the regression analysis. For data used in this study, crime data from Januar y 2008 to December 2012 of Seoul Metropolitan Police Agency and meteorological records and discomfort index recor ded in the Meteorological Agency through the portal sites were used. Based on this data, SPSS 18.0 was used for the regression analysis and the analysis of correlation between crime and meteorological changes and discomfort index and a prediction expression was derived through the analysis and the risk index was shown in 5 steps depending on predicted values obtained through the prediction expression derived. The risk index of 5 steps classified like this is considered to be used as important data for crime prevention activities.

• International Journal of Advanced Culture Technology
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• v.10 no.3
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• pp.63-70
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• 2022

This study is to investigate that population change as a result of the decline in population has a correlation with a decrease in crime, with the change in the demographic composition by comparing with two models: model with growth in population and one with the decline in population. We collected demographic data for all cities in Korea from the 2010 Census to 2020 offered by the Korean Statistical Information Service, with crime data comprising serious reported crime events from the Korean Nation Police Agency through requesting data related to the total number of crimes at the same as the period of demographic data. This study can identify the impacts of demographic changes as a result of population change on crime change through a comparative analysis between areas with population growth and ones with population decline. We can confirm that there are differences in determinants of crime between areas with population increase and one with population decrease from the analysis of the impact of demographic change as a result of population change on crime change.

• Journal of Korea Multimedia Society
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• v.13 no.7
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• pp.983-990
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• 2010

The image processing technique is very sensitive to the variation of external environment, so it tends to lose a lot of accuracy when the external environment changes rapidly. In this paper, we propose a vehicle detecting and tracking system for crime prevention suitable for an external environments with various changes using the image processing technique. Because the vehicle camera detector for crime prevention extracts and tracks the vehicle within one lane, it is important to classify a characteristic region rather than the contour of a vehicle. The proposed system detects the entrance of the vehicle using optical flow and tracks the vehicle by classifying the headlights, the bonnet, the front-window and the roof area of the vehicle. Experimental results show that the proposed method is robust to the environmental changes such as type, speed and time of a vehicle.

• 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.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.95-102
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• 2023

This study aims to empirically verify the moderating effect of population drop on the relationship between demographic changes and crime decline in local cities facing population decline in Korea. The study employs changes in the population of young adults, men, elderly, foreign residents, and population movement as factors of demographic changes, including types of the city as a control variable in the research model. The rate of change in population drop is used as a moderator variable and the rate of change in crime decline as a dependent variable. Data are collected from 155 cities with declining populations from 2010 to 2022 through KOSIS, the National Statistics Portal, and information disclosure requests. Data collected are analyzed using moderated regression analysis. In the first and second stages of the analysis, they show that changes in the population of young adults, population movement, foreign people, population drop, and the type of city affect the change in crime. Moderated regression analysis shows that only the interaction terms among changes in the population of young adults, changes in population movement, and changes in the population of foreigners and the population drop affects change in crime significantly.

• KIPS Transactions on Computer and Communication Systems
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• v.5 no.9
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• pp.229-236
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• 2016

In the past, crime prediction methods utilized previous records to accurately predict crime occurrences. Yet these crime prediction models had difficulty in updating immense data. To enhance the crime prediction methods, some approaches used social network service (SNS) data in crime prediction studies, but the relationship between SNS data and crime records has not been studied thoroughly. Hence, in this paper, we analyze the relationship between SNS data and criminal occurrences in the perspective of crime prediction. Using Latent Dirichlet Allocation (LDA), we extract tweets that included any words regarding criminal occurrences and analyze the changes in tweet frequency according to the crime records. We then calculate the number of tweets including crime related words and investigate accordingly depending on crime occurrences. Our experimental results demonstrate that there is a difference in crime related tweet occurrences when criminal activity occurs. Moreover, our results show that SNS data analysis will be helpful in crime prediction model as there are certain patterns in tweet occurrences before and after the crime.

• Journal of Korea Multimedia Society
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• v.20 no.1
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• pp.66-73
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• 2017

Due to the recent rapid changes in society and wide spread of information devices, diverse digital information is utilized in a variety of economic and social analysis. Information related to the crime statistics by type of crime has been used as a major factor in crime. However, statistical analysis using only the structured data has the difficulty in the investigation by providing limited information to investigators and users. In this paper, structured data and unstructured data are analyzed by applying Korean Natural Language Processing (Ko-NLP) and the Latent Semantic Analysis (LSA) technique. It will provide a crime profile optimum system that can be applied to the crime profiling system or statistical analysis.

• Journal of Korea Multimedia Society
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• v.20 no.12
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• pp.1856-1864
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• 2017

The ultimate goal of multimedia in bio-signal research is to approach multimedia contents through bio-signal. Hence it is important to interpret user's emotions by analyzing his or her bio-signals. In this paper, we construct ERP task of oddball component to analyze EEG signal between normal stimuli and fear stimuli and measure EEG during ERP task. The results from extracted ERP component show that there is a difference in N200 in visual stimuli, P300 in auditory stimuli, and N100 and P300. Moreover, there are larger changes in audiovisual stimuli, indicating that users recognize greater fear of crime when visual and auditory stimuli are simultaneously presented.

• Journal of Environmental Science International
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• v.26 no.1
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• pp.97-107
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• 2017

In the recent trend where crime types are being diversified along with urban expansion, we are facing the reality of a 'taking-it-out-on-somebody' crime, which targets the public rather than a specific person with criminal intent, like a 'random target crime.' A park, which is a public space for urban dwellers' and residents' leisure and rest in the midst of social changes, can be called the spot where various crimes occur. This is because it is a space without boundaries, available for the influx of many and unspecified persons, thus making it difficult to specify users. For the purpose of keeping a park safe from crime and providing users with psychological stability, this study carried out its research to get basic knowledge of the introduction of CPTED (Crime Prevention Through Environmental Design) concept in park development. The park development guideline contents are analyzed based on the six principles of CPTED theory as per the space composition element of a park. As a result, this study found out a problem was that most guidelines applied to the space composition element of a park were fragmentary in applying the six principles of CPTED theory, and were focused on arrangement and installation of facilities.