• Korean Security Journal
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• no.3
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• pp.175-204
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• 2000

In modern society, a spontaneous phenomenon that community residents' participation in crime prevention sphere is increasing now due to the recognition that it is extremely difficult to control crime only by the limited official power(police) is spreading in the society. In the consideration of crime occurrence, the fear of crime does not belong to a certain person any more, but it turns to a matter of common interest for all people. If the recognition of community problems is well understood and analysed, the effect of community residents' participation in crime prevention will appear more eminent and definite. For a long period of time, thanks to the community residents' participation in crime prevention, it has been possible to sustain safety and stability of local community. Ultimately not only the police could reduce the crime problems but local community could decrease a fear of crime. In this study, for maximized effect of crime prevention, it is focused on the role of the community residents' participation in crime prevention. For desirable interrelations among service group and residents, police, first the review of situational crime prevention and community crime prevention theory was studied. And the community residents participation to crime prevention activity in U.S.A, Japan etc was reviewed. By interviewing actual participants in community crime prevention activity, the problems and countermeasures of the community residents' participation in crime prevention activity and desirable direction was suggested

• 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.

• Journal of the Korean housing association
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• v.25 no.3
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• pp.85-92
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• 2014

The purpose of this study is to explore the relationship between housing types and crime and to suggest the appropriate strategies and interventions of housing policies for crime prevention. For spatial analysis of crime data, spatial autocorrelation is tested by Moran's I Test. A Ordinary Least Squares-based regression model is employed because crime data used in this study fails to show spatial autocorrelation. Results show that housing type variables except non-residential housing type are not associated with crime. Among land-use characteristics, the percentage of commercial areas is likely to better explain crime occurrence rather than housing types. It is surprising that residents' satisfaction to housing environment has a positive direction in its relationship with crime even though it cannot have a statistical significance. However, fear of crime shows a negative direction with crime although it fails to have a statistical significance. The findings of this study can contribute to understand the association between housing types and crime when setting housing policies for crime prevention.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.36 no.5
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• pp.23-32
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• 2020

Recently, women's fear of crime continues to increase in space of everyday. By the way, the fear of crime has the spatial properties as crime. Therefore, The purpose of this study is to evaluate the spatial dependence of fear of crime and to suggest the physical environmental factors influencing fear of crime. For this, a spatial regression analysis using spatial weights was conducted based on the location data of the fear of crime measured through a survey. The results of this study are as follows; First, the fear of crime felt by women in their twenties who are vulnerable to crime has spatial dependence. Therefore, it is necessary to consider the spatial characteristics in analyzing the environmental factors affecting this. Second, in order to reduce the fear of crime, it is necessary to improve the environments of old housing and entertainment facilities. There is also a need for ongoing management. Third, careful consideration is needed in the installation of CCTV and street lights, which are factors influencing the fear of crime. It is necessary to establish a reasonable arrangement standard for CCTV and to analyze the street lighting in detail.

• Korean Journal of Artificial Intelligence
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• v.10 no.1
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• pp.21-25
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• 2022

Korea has relatively less crime than other countries. However, the crime rate is steadily increasing. Many people think the crime rate is decreasing, but the crime arrest rate has increased. The goal is to check the relationship between CCTV and the crime rate as a way to lower the crime rate, and to identify the correlation between areas without CCTV and areas without CCTV. If you see a crime that can happen at any time, I think you should use a random forest algorithm. We also plan to use machine learning random forest algorithms to reduce the risk of overfitting, reduce the required training time, and verify high-level accuracy. The goal is to identify the relationship between CCTV and crime occurrence by creating a crime prevention algorithm using machine learning random forest techniques. Assuming that no crime occurs without CCTV, it compares the crime rate between the areas where the most crimes occur and the areas where there are no crimes, and predicts areas where there are many crimes. The impact of CCTV on crime prevention and arrest can be interpreted as a comprehensive effect in part, and the purpose isto identify areas and frequency of frequent crimes by comparing the time and time without CCTV.

• Korean Journal of Artificial Intelligence
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• v.9 no.1
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• pp.15-20
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• 2021

This paper was conducted to prevent and respond to crimes by predicting crimes based on artificial intelligence. While the quality of life is improving with the recent development of science and technology, various problems such as poverty, unemployment, and crime occur. Among them, in the case of crime problems, the importance of crime prediction increases as they become more intelligent, advanced, and diversified. For all crimes, it is more critical to predict and prevent crimes in advance than to deal with them well after they occur. Therefore, in this paper, we predicted crime types and crime tools using the Multiclass Logistic Regression algorithm and Multiclass Neural Network algorithm of machine learning. Multiclass Logistic Regression algorithm showed higher accuracy, precision, and recall for analysis and prediction than Multiclass Neural Network algorithm. Through these analysis results, it is expected to contribute to a more pleasant and safe life by implementing a crime prediction system that predicts and prevents various crimes. Through further research, this researcher plans to create a model that predicts the probability of a criminal committing a crime again according to the type of offense and deploy it to a web service.

• Journal of the Korea Society of Computer and Information
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• v.22 no.1
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• pp.41-48
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• 2017

In this study, we proposed GIS-based Regional Crime Prevention Index (RCPI) development method designed to support local governments with systematic crime prevention activities. The public interest in safe urban environment is increasing rapidly. The government is putting efforts into crime prevention activities to eliminate the criminal opportunities in advance. CPTED is method to prevent crimes in the city by improving environmental factors that cause crime. It is used by local governments to promote the crime prevention activities centering on the expansion of CCTVs and street lamps and the improvement of street environment. However, most policies were terminated as one-off programs and it is necessary to monitor the effect of such policies on a continuous basis. In order to alleviate issues, this study proposed RCPI as part of crime safety assessment in urban environments. The estimation of RCPI in City A of Gyeonggi-do showed relative differences in 31 districts (dong), indicating that it is also possible to evaluate the crime safety in the local community on the level of the administrative dong, the smallest administrative district in the urban environments. As a crime map, the RCPI will be used effectively as he reference to support the decision making process for local government in the future.

• ETRI Journal
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• v.43 no.2
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• pp.272-287
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• 2021

This article presents a multi-dimensional spatial pattern analysis of crime events in San Francisco. Our analysis includes the impact of spatial resolution on hotspot identification, temporal effects in crime spatial patterns, and relationships between various crime categories. In this work, crime prediction is viewed as a classification problem. When predictions for a particular category are made, a binary classification-based model is framed, and when all categories are considered for analysis, a multiclass model is formulated. The proposed crime-prediction model (HotBlock) utilizes spatiotemporal analysis for predicting crime in a fixed spatial region over a period of time. It is robust under variation of model parameters. HotBlock's results are compared with baseline real-world crime datasets. It is found that the proposed model outperforms the standard DeepCrime model in most cases.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.5
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• pp.217-221
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• 2017

Proactive prevention is important for crime. Past crimes have focused on coping after death and punishing them. But with Big Data technology, crime can be prevented spontaneously. Big data can predict the behavior of criminals or potential criminals. This article discusses how to build a big data system for crime prevention. Specifically, it deals with the way to combine unstructured data of big data with basic form data, and as a result, designs crime prevention system. Through this study, it is expected that the possibility of using big data for crime prevention is described through fingerprints, and it is expected to help crime prevention program and research in future.

• Proceedings of the Korea Contents Association Conference
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• 2015.05a
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• pp.17-18
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• 2015

To improve urban safety there is an increasing social need for environmental design against crime, which is defined as the creation of inconvenient environments or situations for criminal offenders. By using a cluster analysis, we aimed to clarify crime occurrence patterns from the perspective of land-use. Osaka Prefecture was chosen as the study area because it has the highest crime rate in Japan. The results revealed that there are six patterns of crime occurrence, and that cities of medium-level of mixed land-use have the lowest crime rates.