• Korea Information Processing Society Review
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• v.2 no.4
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• pp.40-43
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• 1995

• Journal of Digital Convergence
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• v.15 no.10
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• pp.513-524
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• 2017

This study proposed an 'Crime Victim Care Specialist(CVCS)' convergence curriculum for psychology and police students in college. First, the courses and the curriculums of counseling, psychology, psychotherapy, police, and police administration departments in nationwide were listed. After consulting with professors from 2 majors, 3-levels curriculum and courses were drafted. Second, for the inputs from the field, a panel of 51 crime victim care/counseling/psychotherapy experts were Delphi surveyed about goodness of fit and importance. The result were following. First, the curriculum were consisted of required, basics, advanced courses. The required course were 5 courses for each department. The basics were for the minors and 7 courses for each. The advanced were for the double majors and 4 courses for each. Finally, the implication and further studies were suggested.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.4
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• pp.89-101
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• 2017

Crime occurs differently based on not only place locations and building uses but also the characteristics of the people who use the place and the spatial structures of the buildings and locations. Therefore, if spatial big data, which contain spatial and regional properties, can be utilized, proper crime prevention measures can be enacted. Recently, with the advent of big data and the revolutionary intelligent information era, predictive policing has emerged as a new paradigm for police activities. Based on 7420 actual crime incidents occurring over three years in a typical provincial city, "J city," this study identified the areas in which crimes occurred and predicted risky areas. Spatial regression analysis was performed using spatial big data about only physical and environmental variables. Based on the results, using the street width, average number of building floors, building coverage ratio, the type of use of the first floor (Type II neighborhood living facility, commercial facility, pleasure use, or residential use), this study established a Crime Incident Prediction Model (CIPM) based on Bayesian probability theory. As a result, it was found that the model was suitable for crime prediction because the overlap analysis with the actual crime areas and the receiver operating characteristic curve (Roc curve), which evaluated the accuracy of the model, showed an area under the curve (AUC) value of 0.8. It was also found that a block where the commercial and entertainment facilities were concentrated, a block where the number of building floors is high, and a block where the commercial, entertainment, residential facilities are mixed are high-risk areas. This study provides a meaningful step forward to the development of a crime prediction model, unlike previous studies that explored the spatial distribution of crime and the factors influencing crime occurrence.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.4
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• pp.64-80
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• 2018

In Korea, citizens can only know general information about crime. Thus it is difficult to know how much they are exposed to crime. If the police can predict the crime risky area, it will be possible to cope with the crime efficiently even though insufficient police and enforcement resources. However, there is no prediction system in Korea and the related researches are very much poor. From these backgrounds, the final goal of this study is to develop an automated crime prediction system. However, for the first step, we build a big data set which consists of local real crime information and urban physical or non-physical data. Then, we developed a crime prediction model through machine learning method. Finally, we assumed several possible scenarios and calculated the probability of crime and visualized the results in a map so as to increase the people's understanding. Among the factors affecting the crime occurrence revealed in previous and case studies, data was processed in the form of a big data for machine learning: real crime information, weather information (temperature, rainfall, wind speed, humidity, sunshine, insolation, snowfall, cloud cover) and local information (average building coverage, average floor area ratio, average building height, number of buildings, average appraised land value, average area of residential building, average number of ground floor). Among the supervised machine learning algorithms, the decision tree model, the random forest model, and the SVM model, which are known to be powerful and accurate in various fields were utilized to construct crime prevention model. As a result, decision tree model with the lowest RMSE was selected as an optimal prediction model. Based on this model, several scenarios were set for theft and violence cases which are the most frequent in the case city J, and the probability of crime was estimated by $250{\times}250m$ grid. As a result, we could find that the high crime risky area is occurring in three patterns in case city J. The probability of crime was divided into three classes and visualized in map by $250{\times}250m$ grid. Finally, we could develop a crime prediction model using machine learning algorithm and visualized the crime risky areas in a map which can recalculate the model and visualize the result simultaneously as time and urban conditions change.

• The Journal of Bigdata
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• v.4 no.2
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• pp.23-34
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• 2019

Recently, the crime that utilizes the digital platform is continuously increasing. About 140,000 cases occurred in 2015 and about 150,000 cases occurred in 2016. Therefore, it is considered that there is a limit handling those online crimes by old-fashioned investigation techniques. Investigators' manual online search and cognitive investigation methods those are broadly used today are not enough to proactively cope with rapid changing civil crimes. In addition, the characteristics of the content that is posted to unspecified users of social media makes investigations more difficult. This study suggests the site-based collection and the Open API among the content web collection methods considering the characteristics of the online media where the infringement crimes occur. Since illegal content is published and deleted quickly, and new words and alterations are generated quickly and variously, it is difficult to recognize them quickly by dictionary-based morphological analysis registered manually. In order to solve this problem, we propose a tokenizing method in the existing dictionary-based morphological analysis through WPM (Word Piece Model), which is a data preprocessing method for quick recognizing and responding to illegal contents posting online infringement crimes. In the analysis of data, the optimal precision is verified through the Vote-based ensemble method by utilizing a classification learning model based on supervised learning for the investigation of illegal contents. This study utilizes a sorting algorithm model centering on illegal multilevel business cases to proactively recognize crimes invading the public economy, and presents an empirical study to effectively deal with social data collection and content investigation.

• Journal of the Korean Institute of Landscape Architecture
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• v.47 no.1
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• pp.49-56
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• 2019

Urban green spaces have been contributing to the improvement of environmental, mental, and physical health for humans. In addition, recent studies showed the potential role of vegetation in reducing the amount of crime in inner-city neighborhoods at the micro-scale level. However, little is known about the positive role of urban green areas in improving urban safety at the regional level. The purpose of this study is to examine the relationship between urban green areas and actual outdoor crime rates, while also considering socio-demographic factors. The study area is the city of Austin, Texas, USA, which consists of 506 block groups. This study utilized socio-demographic factors based on U.S. Census data and vegetation-related factors utilizing GIS and ENVI software. For analyses, the analysis of variance (ANOVA) and an ordinary least square (OLS) regression were utilized. The results from ANOVA showed that yearly crime rates per acre for areas having 0%~25% trees in their neighborhoods were 0.46% and 1.05% higher than those of having 25%~50% and >50% trees in the neighborhoods, respectively. The results from the OLS regression represented that income, NDVI and park rates in neighborhoods were negatively associated with the crime rate per acre, whereas the percentage of minorities and the percentage of teenage school dropouts were positively associated with the crime rate per acre. This study implies that urban green areas may help to improve the safety of urban areas.

• Journal of the Korean Home Economics Association
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• v.50 no.1
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• pp.41-50
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• 2012

The purpose of this study was to examine the response pattern of children of early childhood (ages 4 to 5) to strangers' luring behaviors that suggest imminent crime. Data were collected from registrants offered by four kindergartens and daycare centers. Individual interviews were performed (N = 100) by using a questionnaire. Results were as follows: First, children of early childhood responded unfavorably to strange adults' kindness and request for aids. However, it was found that they did not make the right decision in relation to strange adults' luring behaviors that lead to crime when family related clues were manipulated. Second, children of early childhood responded favorably to strange adults' luring behaviors that are suggestive of criminal intent when candy, ice cream, and toys were offered. Especially, youngsters were more prone to be deceived by these indices than the older children. Third, older children responded unfavorably to strange adults' luring behaviors that suggest a criminal intent In addition, youngsters did not respond cleverly to strange adults' luring behaviors that suggest a criminal intent while showing a reluctant response.

• Review of KIISC
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• v.31 no.5
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• pp.39-45
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• 2021

인터넷이 발달되고 소셜미디어의 사용이 증가함에 따라 공개정보와 소셜네트워크를 통해 국제 범죄조직, 테러리스트 그룹, 주변 국제 안보환경, 사이버 범죄에 대한 정보 분석의 요구가 늘어나고 있다. 하지만 아직 국내에서 OSINT와 SOCMINT 활동에 대한 공개된 정보가 많지 않아 이에 대한 연구가 많지 않다. 저자는 OSINT와 SOCMINT 조사 분석을 실제 수행하면서 알게 된 문제점과 이를 극복하는 방안을 제시하고자 한다. 다만 Intelligence 업무의 특성상 정보 보안이 매우 중요하여 구체적인 내용에 대해서 제시하기 보다는 업무에서 발생되는 문제를 보편화하여 작성하였다.

• Review of KIISC
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• v.13 no.6
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• pp.67-78
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• 2003

최근 컴퓨터 및 정보통신기술 등의 급속한 발전과 국가 정책에 따른 세계 최고수준의 If 인프라 구축에 따라 인터넷 이용률 및 인터넷에 의한 범죄 등이 급증하고 있다. 특히, 가장 많은 인터넷 사용 연령층이 초등학교 고학년에서 대학생들로 그들의 인터넷 이용률은 90％이상을 차지하고 있으며 사이버 범죄의 절반정도가 이들에 의하여 이루어지고 있다. 그러나 이들 학생을 위한 정보보호관련 교육내용과 교육환경의 열악함은 학생들이 죄의식 없이 범죄를 저지르거나 그들 자신들이 피해자가 되는 사회로 방치되고 있다. 본 연구에서는 제8회 정보보호심포지움에서 발표하였던 '초중고 정보보호교육 강화'의 내용을 보완하여 대학교를 포함한 정규학교에서의 정보보호교육내용을 분석하고 정보보호교육환경의 현황을 파악하였다. 또한, 이에 관한 결론으로 정보보호교육 강화 방안을 제시하였다.

• Proceedings of the Korea Multimedia Society Conference
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• 2012.05a
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• pp.231-233
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• 2012

컴퓨터 및 프린터 기술의 발전으로 디지털 문서 활용 사례가 전 분야에 확산되면서 디지털 문서의 위 변조 범죄가 증가하고 많은 사회적인 문제를 야기하고 있다. 이러한 컴퓨터를 이용한 범죄의 증거를 수집하고 분석하기 위해 디지털 포렌식 기술의 발전이 더욱 중요해지고 있다. 디지털 포렌식은 PC나 휴대폰 등 각종 디지털 매체 등에 남아 있는 디지털 정보들을 수집 분석해 범죄 단서를 찾는 컴퓨터 법의학이다. 본 논문에서는 프린터기로 출력된 문서의 고해상도 영상현미경 이미지를 사용하여 원본 여부를 판별 할 수 있는 프린터기 동일 여부 판별 기술을 제안한다.