• Journal of Satellite, Information and Communications
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• v.9 no.4
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• pp.57-62
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• 2014

In this paper, we proposed and implemented a crime pattern analysis algorithm using big data. The proposed algorithm uses crime-related big data collected and published in the supreme prosecutors' office. The algorithm analyzed crime patterns in Seoul city from 2011 to 2013 using the spatial statistics analysis like the standard deviational ellipse and spatial density analysis. Using crime frequency, We calculated the crime probability and danger factors of crime areas, time, date, and places. Through a result we analyzed spatial statistics. As the result of the proposed algorithm, we could grasp differences in crime patterns of Seoul city, and we calculated degree of risk through analysis of crime pattern and danger factor.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2008.06a
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• pp.477-484
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• 2008

연쇄 살인과 같은 강력 범죄의 심각성이 사회적 이슈가 되면서 이에 대한 효과적인 과학 수사의 필요성이 증가되고 있다. 특히, 연쇄 범죄 데이타에 대한 공간 분석을 통해 범죄자의 거점 위치를 예측하는 지리적 프로파일링과 미래에 발생될 범행 장소의 위치, 즉 기존 범행에 이어 일어날 다음 범행 위치 예측에 관한 연구가 활발하다. 그러나, 이와 관련된 기존 연구는 물리적인 거리에 대한 통계적 기법을 적용하거나 단순한 공간적 분석만을 적용하므로 낮은 예측 정확도를 보이는 문제점이 있다. 본 논문에서는 이러한 문제를 해결하고 보다 효과적인 연쇄 범죄 수사를 지원하는 방법으로써 연쇄 범죄 발생에 대한 공간적 시간적 분포 특성에 따른 시공간 패턴을 기반으로 다양한 시공간 분석을 적용하는 거점 위치 예측 기법과 다음 범행 위치 예측 기법을 제안한다. 제안 기법은 중심축을 따라 나타나는 선형 분포의 연쇄 범죄에서도 정확도 높은 예측이 가능하고, 다수의 서로 다른 군집들에 대해 각 군집내 범행에 대한 지역적 예측과 대상 영역의 모든 범행에 대한 전역적 예측이 가능하다. 또한 방향 패턴을 활용하여 다음 범행 위치 예측 정확도도 개선하였다.

• Journal of Satellite, Information and Communications
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• v.10 no.2
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• pp.40-45
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• 2015

In this paper, we proposed and implemented a crime prediction algorithm based upon crime influential factors. To collect the crime-related big data, we used a data which had been collected and was published in the supreme prosecutors' office. The algorithm analyzed various crime patterns in Seoul from 2011 to 2013 using the spatial statistics analysis. Also, for the crime prediction algorithm, we adopted a Bayesian network. The Bayesian network consist of various spatial, populational and social characteristics. In addition, for the more precise prediction, we also considered date, time, and weather factors. As the result of the proposed algorithm, we could figure out the different crime patterns in Seoul, and confirmed the prediction accuracy of the proposed algorithm.

• Journal of the Korean Association of Geographic Information Studies
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• v.12 no.1
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• pp.12-25
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• 2009

The aim of this study is to analyze the periodical and spatial characteristics of urban crime and to find out the factors that affect the crime occurrence. For these, crime data of Masan City was examined and crime occurrence pattern is ploted on a map using crime density and criminal hotspot analysis. The spatial relationship of crime occurrence and factors affecting crime were also investigated using ESDA (Exploratory Spatial Data Analysis) and SAR (Spatial Auto-Regression) model. As a result, it was found that crimes had strong tendency of happening during a certain period of time and with spatial contiguity. Spatial contiguity of crimes was made clear through the spatial autocorrelation analysis on 5 major crimes. Especially, robbery revealed the highest spatial autocorrelation. However as a autocorrelation model, Spatial Error Model(SEM) had statistically the highest goodness of fit. Moreover, the model proved that old age population ratio, property tax, wholesale-retail shop number, and retail & wholesale number were statistically significant that affect crime occurrence of 5 most major crimes and theft crime. However population density affected negatively on assault crime. Lastly, the findings of this study are expected to provide meaningful ideas to make our cities safer with U-City strategies and services.

• Journal of Satellite, Information and Communications
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• v.11 no.1
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• pp.55-57
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• 2016

In this paper, we present an algorithm to determine the abnormal behavior through a CCTV-based behavioral recognition and a pattern of hand using ConvexHull. In the existing way that using CCTV for crime prevention, facial recognition is mainly used. Facial recognition is the way that compares the faces that are seen on the screen and faces of criminals for determining how dangerous targets are, however, this way is hard to predict future criminal behavior. Therefore, to predict more various situations, abnormal behaviours are determined with targets' incline of arms, legs and bodys and patterns of hand movements. it can forecast crimes when an acting has been getting within common normality out, comparing whose acting patterns with the crime patterns.

• Journal of Korea Spatial Information System Society
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• v.10 no.2
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• pp.63-79
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• 2008

With the recent development of advanced GIS and complex spatial analysis technologies, the more sophisticated technologies are being required to support the advanced knowledge for solving geographical or spatial problems in various decision support systems. In addition, necessity for research on scientific crime investigation and forensic science is increasing particularly at law enforcement agencies and investigation institutions for efficient investigation and the prevention of crimes. There are active researches on geographic profiling to predict the base location such as criminals' residence by analyzing the spatial patterns of serial crimes. However, as previous researches on geographic profiling use simply statistical methods for spatial pattern analysis and do not apply a variety of spatial and temporal analysis technologies on serial crimes, they have the low prediction accuracy. Therefore, this paper identifies the typology the spatio-temporal patterns of serial crimes according to spatial distribution of crime sites and temporal distribution on occurrence of crimes and proposes STA-BLP(Spatio-Temporal Analysis based Base Location Prediction) algorithm which predicts the base location of serial crimes more accurately based on the patterns. STA-BLP improves the prediction accuracy by considering of the anisotropic pattern of serial crimes committed by criminals who prefer specific directions on a crime trip and the learning effect of criminals through repeated movement along the same route. In addition, it can predict base location more accurately in the serial crimes from multiple bases with the local prediction for some crime sites included in a cluster and the global prediction for all crime sites. Through a variety of experiments, we proved the superiority of the STA-BLP by comparing it with previous algorithms in terms of prediction accuracy.

• Journal of the Economic Geographical Society of Korea
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• v.12 no.2
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• pp.179-194
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• 2009

The spatial distribution of crime incidences in urban neighborhoods is a reflection of their socio-economic environment and spatial inter-relations. Spatial interactions between offenders and victims lead to spatial autocorrelation of the crime incidences. The spatial autocorrelation among the incidences biases the interpretation of the ecological model in OLS framework. This research investigates residential crimes using residential burglaries and robberies occurred in the city of Columbus, Ohio, for 2000. In particular, the spatial distribution of incidence rates of residential crimes are accounted in OLS framework using eigenvectors, which reflect spatial dependence in crime patterns. Result presents that handling spatial autocorrelation enhanced model estimation, and both economic deprivation and crime opportunity are turned out significant in estimating residential crime rates.

• 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.

• Convergence Security Journal
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• v.16 no.3_2
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• pp.3-12
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• 2016

The purpose of this study is to analyse Modus Operandi of smishing. For the study, 87 cases of smishing crime reports and smishing experiences of victims were analysed and 10 police officers who investigates smishing crime were interviewed. The results indicated that smishing crime can be divided into the preparation stage and the implementation stage. In the preparation stage, two modus operandi patterns, collection of personal information and text message script composition, were identified. In the implementation stage, seven modus operandi patterns were identified: sending smishing text messages and installation of malicious mobile applications, leak personal information, sending personal information to smishing crime organization through online server, payment attempt using collected personal information, intercept authorization code, completion of payment using intercepted authorization code, and payment amount was delivered to victims. Further implications were discussed.

• Proceedings of the KAIS Fall Conference
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• 2011.05a
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• pp.400-403
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• 2011

다양한 특성의 도시공간의 확장은 경제 활동의 활성화는 물론 도시공간에서의 범죄발생 확대로 이어지고 있다. 이에 따라 국민의 생명과 재산보호, 사회질서유지 등을 위한 각종 대책과 경찰의 역할을 확대되고 있으나 효율적인 범죄예방 활동이 부족한 상태이다. 따라서 이 연구에서는 체계적인 범죄 모니터링을 위한 기초연구의 일환으로 도시공간에서의 범죄가 발생하는 공간에 대한 패턴을 이해하고 분석하여 도시 내 안전구역을 분석하여 GIS 공간분석기법의 효용성을 나타내었다.