• Journal of the Korea Society of Computer and Information
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• v.19 no.12
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• pp.309-317
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• 2014

Recently, crime prediction and prevention are the most important social issues, and global and local governments have tried to prevent crime using various methodologies. One of the methodologies, data mining can be applied at various crime fields such as crime pattern analysis, crime prediction, etc. However, there is few researches to find the relationships between the results of data mining and crime components in terms of criminology. In this study, we introduced environmental criminology, and identified relationships between environment factors related with crime and variables using at data mining. Then, using real burglary data occurred in South Korea, we applied clustering to show relations of results of data mining and crime environment factors. As a result, there were differences in the crime environment caused by each cluster. Finally, we showed the meaning of data mining use at crime prediction and prevention area in terms of criminology.

• 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.

• Spatial Information Research
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• v.8 no.2
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• pp.213-232
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• 2000

It is important to scrutinize spatial patterns in crime analysis since crime data has geographical attribute in itself. We focus on the development of ¨Crime Analysis GIS¨ prototype which can discover spatial patterns in crime data by integrating mapping functions of GIS and spatial analysis techniques. The structure of this system involves integration of DBMS and GIS, and the major functions of the system include (i) exploring spatial distribution of point data, (ii) mapping hot-spot, (iii) clustering analysis of crime occurrence, and (iv) analyzing aggregated areal data. The process of design and implementation of this system is based on object-oriented methodologies. A web-based extension of the prototype using 3-tier architecture is currently under development.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.11a
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• pp.1143-1145
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• 2012

본 연구에서는 인천 지역의 범죄의 특성 지역을 분석하기 위하여 기존의 이론적인 논의에 기초하며 AHP와 GIS와 연계를 통해 분석하고자 하였다. 이를 위해 인천의 범죄 우발지역을 볼 수 있는 공간적 패턴과 경찰서 내지 파출소의 적지를 선정하고 자료 분석 및 데이터 수집과 통틀어 공간 분석을 수행하며 이러한 분석을 통해 얻어진 여러 가지 요인들과 범죄 율을 나타내어 최종 자료를 구현하기 위한 연구를 한다. 범죄 율의 공간적 표현을 위해서 수식을 이용하여 범죄 율을 구했으며 인구 및 정확한 데이터들을 수집하여 분석하고 그대로 반영한다. AHP를 이용하여 설문조사 및 객관적인 판단과 가중치를 판단할 수 있는 중요한 척도가 되었으며, 이것들을 반영하여 최종 결과물에 현실성을 더하여, 실제로 예방조치를 취할 수 있도록 할 수 있을 것이다. 또한 GIS 소프트웨어에서 제공되는 좌표체계와 데이터베이스 및 SQL 등을 이용하여 현실성과 정밀성을 더하여 활용될 수 있음을 확인하였다.

• Journal of the Korean Association of Geographic Information Studies
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• v.13 no.3
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• pp.14-28
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• 2010

The aim of this study is to investigate crime hotspot areas using the spatio-temporal cluster analysis which is possible to search simultaneously time range as well as space range as an alternative method of existing hotspot analysis only identifying crime occurrence distribution patterns in urban area. As for research method, first, crime data were collected from criminal registers provided by official police authority in M city, Gyeongnam and crime occurrence patterns were drafted on a map by using Geographic Information Systems(GIS). Second, by utilizing Ripley K-function and Space-Time Scan Statistics analysis, the spatio-temporal distribution of crime was examined. The results showed that the risk of crime was significantly clustered at relatively few places and the spatio-temporal clustered areas of crime were different from those predicted by existing spatial hotspot analysis such as kernel density analysis and k-means clustering analysis. Finally, it is expected that the results of this study can be not only utilized as a valuable reference data for establishing urban planning and crime prevention through environmental design(CPTED), but also made available for the allocation of police resources and the improvement of public security services.

• Journal of the Korea Society of Computer and Information
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• v.11 no.4 s.42
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• pp.221-230
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• 2006

Changing drastically, the life in a modern city has forced citizens to gradually shorten their average period of settlement, which has weakened the identity of city habitation, thus causing serious crimes and damaging the security of city greatly. Haying a highly composite structure with not only macro, but micro characteristics, city is grasped as a very composite phenomenon shown in the social, economic and spatial constitution relationships, including the personal motives of criminals. Accordingly, this study puts stress on the necessity of any crime prediction program to predict the occurrence of crimes by analyzing the occurrence patterns of sharply increasing intra-city crimes of violence on a typical, time and spatial basis and clarifying their structural dynamic relationships in a both macro and micro manner. Moreover, the deduction of various factors closely related to crime occurrence will contribute to elucidating the occurrence structure of city crimes.

• Proceedings of the KGS Conference
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• 2001.05a
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• pp.229-232
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• 2001

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.5
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• pp.791-805
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• 2022

In recent years, the number of scams related to voice phishing has been on the decline, but the number of messenger phishing attacks, a new type of crime, is increasing. In this study, by analyzing SNS posts containing messenger phishing cases, criminal trends of the main methods, imposture of trusted relative and fake payment were identified. Through the analysis, main words and patterns composing the message and the similarity and continuity of the phone numbers used were derived as criminal attributes, and criminal organizations were grouped. As the results of the analysis, we propose a cooperative system to prevent damage from messenger phishing by disseminating the criminal information collected by investigative agencies to private operators, and a plan to respond to messenger phishing predicted through grouping of criminal organizations.

• Korean Journal of Culture and Social Issue
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• v.13 no.3
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• pp.127-153
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• 2007

The study aims to examine the effects of crime on societal anxiety. For this purpose, two studies were conducted. In study 1, the data were collected from 286 students(male 160, female 126) regarding perceived seriousness of crime, estimation of crime occurrence, societal anxiety, and estimation of change in these 3 variables every 3 years from 1993 to 2011. The means of the 3 variables were higher than the midpoint of the scale. And the means of all the 3 variables gradually increased from 1993 to 2011 and the change patterns were very similar. Furthermore, the estimation of crime occurrence and perceived seriousness of crime were significant predictors of societal anxiety. Study 2 was conducted to explore the effect of frequency estimation of the criminal acts on the societal anxiety. The data from 259 subjects(male 141, female 118) were analysed. The occurrence of frequency of 7 types of crimes including 25 criminal acts were estimated and societal anxiety was measured. The results of study 2 showed that only the major crime was significant a predictor of societal anxiety, and among the major crimes, arson was a significant predictor of the dependent variable. Implications and limits of the studies are discussed.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.1
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• pp.29-40
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• 2021

Risk factors that threaten public safety such as crime, fire, and traffic accidents have spatial characteristics. Since each region has different dangerous environments, it is necessary to analyze the spatial pattern of risk factors for each sector such as traffic accident, fire, crime, and living safety. The purpose of this study is to analyze the spatial distribution pattern of local safety level index, which act as an index that rates the safety level of each sector (traffic accident, fire, crime, living safety, suicide, and infectious disease) for basic local governments across the nation. The following analysis tools were used to analyze the spatial autocorrelation of local safety level index : Global Moran's I, Local Moran's I, and Getis-Ord's G⁎i. The result of the analysis shows that the distribution of safety level on traffic accidents, fire, and suicide tends to be more clustered spatially compared to the safety level on crime, living safety, and infectious disease. As a result of analyzing significant spatial correlations between different regions, it was found that the Seoul metropolitan areas are relatively safe compared to other cities based on the integrated index of local safety. In addition, hot spot analysis using statistical values from Getis-Ord's G⁎i derived three hot spots(Samchuck, Cheongsong-gun, and Gimje) in which safety-vulnerable areas are clustered and 15 cold spots which are clusters of areas with high safety levels. These research findings can be used as basic data when the government is making policies to improve the safety level by identifying the spatial distribution and the spatial pattern in areas with vulnerable safety levels.