• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.561-564
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• 2016

If using crime map which represents criminal section that violent crimes targeting women frequently happened, the police could prevent additional crimes by positioning themselves intensively in expected crime zones and each individual could avoid being damaged by referring information of criminal zones. In this paper, by analyzing crimes targeting women and offender information which is provided in public-opened datum portal, we suppose a system which prevents crimes that calculates locational danger and, by considering location and age group of users, provides user-customized information of danger. By crawling the criminals datum which is provided in public-opened datum portal, It collects them. About the areas which happened sexual crimes, calculating danger of crime based on statistical crime information including criminal information, residence of offenders, areas which happened sexual crimes, sentences and the number of crime, this system is able to visualize the areas which sexual crimes happened based on information of danger grade representing on user's location. The score of danger calculated in location unit can provide criminal information according to location and ages of users by interacting GIS.

• Journal of Korean Society for Geospatial Information Science
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• v.22 no.4
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• pp.135-142
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• 2014

As interest in social safety has recently increased at the national level, the various activities which can effectively prevent crimes are being carried out. Because the existing maps related to crimes provide the information about the present condition of crimes by administrative district for users, women and pedestrians who go by night could not actually grasp safe roads in advance. Therefore, this study developed the methodology that can easily extract dangerous areas due to crimes by the digital map 2.0. In the digital map 2.0, location and attribute information of center-lines of roads and building layers were used to find dangerous areas of crimes in these layers. Pavement materials and road width which are already built by the attribute information were used in the center-lines of roads. Crossing angles that roads and roads cross each other were additionally extracted and utilized. The attribute information about building types were input in the building layers of the digital map 2.0. The areas that are more the threshold values set by totaling up all the risk scores when considering pavement materials, road width, crossing angles of road, and building types in the center-lines of roads and road crossings were extracted as the dangerous areas that crimes can occur. Verification of the developed methodology was done by experiment. In the spatial apsect, the dangerous areas of crimes could be found by using the digital 2.0, roads, and building layers only through the experiment. In the administrative aspect to prevent crimes, additional installation of safety facilities such as street lights and security lights in the identified areas which are vulnerable for crimes is thought to be increasing safety of dangerous areas.

• 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.

• 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 Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.4
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• pp.221-229
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• 2015

The urban crimes that threat individual's safety are parts of the serious social problems. However. the information of crime in Korea has only been provided by forms of hot spots around place of crime, or forms of crime statistics without positional information. Those could not provide enough information to users in identifying the vulnerable areas for substantive crimes. Therefore, this study suggested a methodology of extraction in criminal vulnerable areas by using the spatial information, the statistical information and the public sector information. The crime vulnerable areas were extracted through the grid-based spatial analysis and the overlapping analysis from each of the information. In fact, the extracted areas were able to provide detailed vulnerability information than the traditional hot spot-based crime information. Following the study, the extracted results in crime vulnerable areas have displayed highly coincide with Korea safety map, provided by national disaster management institute, which regards to be able to provide crime risk rating in terms of administrative business in future.

• Proceedings of the Korean Society of Disaster Information Conference
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• 2023.11a
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• pp.222-223
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• 2023

본 연구는 여성 안전에 대한 다양한 정책이 시행되었지만, 공공장소에서의 여성의 안전을 위협하는 사건과 사고는 지속해서 발생하고 있다. 최근 여성 대상 범죄, 특히 성폭력 범죄가 매우 높아지면서 공공장소에서의 여성 안전에 대한 문제가 대두되고 있다. 이러한 문제를 해결하기 위해서는 여성 안전사고 발생 원인을 분석하여 그 원인을 해결할 수 있는 전략과 방안들을 개발하는 연구가 필요하며, 이를 통해 여성들이 공공장소에서 안전하게 생활할 수 있는 환경을 조성할 수 있다. 따라서 공공장소에서의 여성 안전 개선방안 연구는 매우 중요하다. 그러기 위해서는 첫째 범죄율을 분석, 공공장소에서 여성 범죄의 유형 및 발생 경로에 대해 분석하여 언제, 어디서, 어떤 유형의 범죄가 발생하는지 파악한다. 둘째 교육 및 정보 제공, 여성들에게 주변 위험 요소와 대처 방법, 보호시설 위치 등을 교육하고 정보를 제공하여 위험 상황으로부터 탈출하거나 대처할 수 있도록 한다. 셋째 시설 개선, 공공장소의 설계나 조명, 치안 등을 개선하여 여성들이 안전하게 사용할 수 있는 환경을 조성한다. 넷째 적극적인 관리, 공공장소에서 범죄가 발생하거나 이에 대한 우려가 있을 때, 적극적으로 주변 상황을 관찰하고 대처한다. 다섯 번째 커뮤니티 참여, 지역사회와 함께 여성 안전에 대한 캠페인을 전개하고, 지역주민들의 적극적인 참여와 지원을 유도하여 여성 안전의식을 확산시킨다. 이러한 방법들을 적극적으로 활용하여 여성들이 더욱 안전하고 편안한 환경에서 일상생활을 영위할 수 있는 사회적으로 안전한 여성 안전 문제에 대한 개선방안을 제시한다.

• Journal of the Korea Society of Computer and Information
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• v.14 no.10
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• pp.207-215
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• 2009

Recently, the research of the analysis of the crime spatial is increased by using the computer information technology and GIS (Geometric Information System) in order to prevent the urban crime so as to increase the urbanization rate. In this paper, a probability map formed by the raster is organized by the quantification of crime risk per the cell using the region property of the urban spatial information in the static environment. Also, a map of the risk probability is constructed based on the relative risk by the region property, the relative risk by the facility, the relative risk by the woody plant and the river, and so on. And, this integrated risk probability map is calculated by averaging the individual cell risk applied to the climatic influence and the seasonal factor. And, a probability map of the overall risk is generated by the interpretation key of the crime occurrence relative risk index, and so, this information is applied to the probability map quantifying the occurrence crime pattern. And so, in this paper, a methodology of the modeling and the simulation that this crime risk probability map is modified according to the passage of time are proposed.

• 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.

• 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.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.4
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• pp.72-85
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• 2019

The goal of this study is to predict sexual violence crimes, which is an routine risk. The study used to the Weight of Evidence on sexual violence crimes that occurred in partly Cheongju-si for five years from 2011 to 2015. The results are as follows. First, application and analysis of the Weight of Evidence that considers the weight of evidence characteristics showed 8 out of total 26 evidences that are used for a sexual violence crimes risk prediction. The evidences were residential area, date of use permission for building, individual housing price, floor area ratio, number of basement floor, lot area, security light and recreational facility; which satisfied credibility in the process of calculating weight. Second, The weight calculated 8 evidences were combined to create the prediction map in the end. The map showed that 16.5% of sexual violence crimes probability occurs in 0.3㎢, which is 3.3% of the map. The area of probability of 34.5% is 1.8㎢, which is 19.0% of the map and the area of probability of 75.5% is 2.0㎢, which is 20.7% of the map. This study derived the probability of occurrence of sexual violence crime risk and environmental factors or conditions that could reduce it. Such results could be used as basic data for devising preemptive measures to minimize sexual violence, such as police activities to prevent crimes.