• KIISE Transactions on Computing Practices
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• v.22 no.9
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• pp.455-460
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• 2016

Recently, with more severe types felonies such as robbery and sexual violence, the importance of crime prediction and prevention is emphasized. For accurate and prompt crime prediction and prevention, both a classification model of crime with high accuracy based on past criminal records and well-designed system interface are required. However previous studies on the analysis of crime factors have limitations in terms of accuracy due to the difficulty of data preprocessing. In addition, existing crime monitoring systems merely offer a vast amount of crime analysis results, thereby they fail to provide users with functions for more effective monitoring. In this paper, we propose a classification model for types of crime based on random-forest algorithms and system design factors for real-time crime prediction. From our experiments, we proved that our proposed classification model is superior to others that only use criminal records in terms of accuracy. Through the analysis of existing crime monitoring systems, we also designed and developed a system for real-time crime monitoring.

• 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 the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.4
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• pp.213-220
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• 2017

This study examines the limitations of existing crime mapping that relies mainly on reported crime data, suggests a crime mapping method based on experts' and users' assessments of a neighborhood environment as an alternative approach, and conducts a case study on a real-world site by applying the suggested approach. According to the results of the case analysis, while the areas adjoining arterial roads with heavy pedestrian traffic were shown as high crime risk areas in the crime map based on actual reported crime data, the areas adjoining local roads with low pedestrian traffic were high-risk areas in the crime risk area map based on experts' and residents' evaluations. This study makes a contribution to the field in that it demonstrates the detailed application process of crime risk area mapping according experts' and residents' evaluations, compares the results with those of an existing crime map, and finally shows that the former can function as a complement to the latter.

• 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.

• Journal of The Korean Digital Architecture Interior Association
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• v.10 no.1
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• pp.57-63
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• 2010

This study examines the correlations of crime-prone locations with the urban space configuration in residential district. CPTED(Crime Prevention Through Environmental Design) is applied to this study and it is intended to control the architectural environment in order to restrain criminal activities. For this reason, an objective analysis for explaining the spatial characteristics of the places where the crimes have occurred is more important than statistical and descriptive approach for analyzing the criminal data. Visibility graph analysis (VGA) supports the CPTED theory in this study for objective interpretation of crime-prone locations and quantitative analysis for built environment. The comparative analysis on object streets and areas are used and the results are followings. The analysis by streets showed that street crimes are correlated with connectivity, control, integration, and integration(r=3) and burglary cases are correlated with control. The analysis by areas showed that street crimes are correlated with connectivity and integration. The T-tests results of crime area and whole area showed that street crimes are correlated with integration and burglary cases have negative correlation with connectivity. Several localized environmental design for crime prevention are also proposed on the basis of this study.

• Architectural research
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• v.24 no.1
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• pp.1-11
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• 2022

As the occurrence of a crime is dependent on different factors, their correlations are beyond the ordinary cognitive range. Owing to this limitation, systems face difficulty in correlating various factors, thereby requiring the assistance of artificial intelligence (AI) to overcome such limitations. Therefore, AI has become indispensable for crime prediction. Crimes can cause severe and irrevocable damage to a society. Recently, big data has been introduced for developing highly accurate models for crime prediction. Prediction of night crimes should be given significant consideration, because crimes primarily occur during nights, when the spatiotemporal characteristics become vulnerable to crimes. Many environmental factors that influence crime rate are applied for crime prediction, and their influence on crime rate may differ based on temporal characteristics and the nature of crime. This study aims to identify the environmental factors that influence sex and theft crimes occurring at night and proposes an artificial neural network (ANN) model to predict sex and theft crimes at night in random areas. The crime data of A district in Seoul for 12 years (2004-2015) was used, and environmental factors that influence sex and theft crimes were derived through multiple regression analysis. Two types of crime prediction models were developed: Type A using all environmental factors as input data; Type B with only the significant factors (obtained from regression analysis) as input data. The Type B model exhibited a greater accuracy than Type A, by 3.26 and 9.47 % higher for theft and sex crimes, respectively.

• 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 Digital Convergence
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• v.14 no.11
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• pp.125-133
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• 2016

In recent years, various crimes such as "random killing' crime continue to rise. Despite the government's crime prevention efforts and crime related researches, crime increases and a different approach is needed. Therefore, this study proposes the alternative for crime prevention by analyzing big data. To achieve this objective, this study was to perform visualization utilizing the histogram, the bubble chart and the hit map and association analysis. To analyze the relationship between crime and some variables, this study analyzed data of Seongnam city, Korea National Police Agency and etc. The results of analysis showed that CCTV will be to reduce the crime rate and security light is not significantly relevant. And the result showed that other types of crime focused by time of the day and day of the week and showed that an increase of the foreigners and crime increase are associated. This study presents a scheme for reducing the crime rate on the basis of this analysis result.

• Journal of the Korea Convergence Society
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• v.9 no.5
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• pp.15-20
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• 2018

In modern society, crime is one of the major social problems. Crime has a great impact not only on victims but also on those around them. It is important to predict crimes before they occur and to prevent crime. Various studies have been conducted to predict crime. One of the most important factors in predicting crime is frequency of crime occurrence. The frequency of crime is widely used as basic data for predicting crime. However, the frequency of crime occurrence is announced about 2 years after the statistical processing period. In this paper, we propose a frequency analysis of crime - related key words retrieved from the web as a way to indirectly grasp the frequency of crime occurrence. The relationship between the number of frequency of crime occurrence and frequency of actual crime occurrence was analyzed by correlation coefficient.

• 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.