• 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 Korea Institute of Information and Communication Engineering
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• v.13 no.8
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• pp.1687-1692
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• 2009

Cyber forensics is used the process and technology of digital forensics as a criminal investigation in cyber space. Cyber crime is classified into cyber terror and general cyber crime, and those two classes are connected with each other. The investigation of cyber terror requires high technology, system environment and experts, and general cyber crime is connected with general crime by evidence from digital data in cyber space. Accordingly, it is difficult to determine relational crime types, collect evidence and the legal admissibility of evidence. Therefore, we considered the classifications of cyber crime, the collection of evidence in cyber space and the application of laws to cyber crime. In order to efficiently investigate cyber crime, it is necessary to integrate those concepts for each cyber crime-case. In this paper, we constructed a cyber forensics domain ontology for cyber criminal investigation using the concepts, relations and properties, according to categories of cyber crime, laws, evidence, and information of criminals and crime-cases. This ontology can be used in the process of investigating of cyber crime-cases, and for data mining of cyber crime; classification, clustering, association and detection of crime types, crime cases, evidences and criminals.

• 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.

• International Journal of Contents
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• v.8 no.2
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• pp.89-96
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• 2012

Scientific analysis of crime hot spots is essential in preventing and/or suppressing crime. However, results could be different depending on the analytic methods, which highlights the importance of choosing adequate tools. The purpose of this study was to introduce two advanced techniques for detecting crime hot spots, GAM and Local Moran's I, hoping for more police agencies to adopt better techniques.GAM controls for the number of population in study regions, but local Moran's I does not. That is, GAM detects high crime rate areas, whereas local Moran's I identifies high crime volume areas. For GAM, physical disorder was used as a proxy measure for population at risk based on the logic of the broken windows theory. Different regions were identified as hot spots. Although GAM is generally regarded as a more advanced method in that it controls for population, it's usage is limited to only point data. Local Moran's I is adequate for zonal data, but suffers from the unavoidable MAUP(Modifiable Areal Unit Problem).

• 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.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.6
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• pp.1439-1449
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• 2015

This study, based on the sociological crime theory, is to examine the relation between urban characteristics and the incidence of crime, helping establish effective crime prevention measures. For doing so, the study employs crime data from the Supreme Prosecutors' Office and socio-demographic data including the regional Statistical Yearbooks -both from 2005 to 2012- to build the study's panel data, and analyzes the panel model on the 16 subordinate districts in the city of Busan. To reduce the incidence of crime and prevent crimes from occurring based on the analysis results, first, prevention measures specific to each region by its attributes are needed rather than general ones; second, new institutional frameworks or policies are required for utilizing accurate crime data; third, interdisciplinary research in which various fields including urban engineering are associated to that of social science is necessary to further the study.

• 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.

• Journal of the Korea Society of Computer and Information
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• v.20 no.1
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• pp.93-101
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• 2015

In modern society, various crimes is occurred. It is necessary to predict the criminal in order to prevent crimes, various studies on the prediction of crime is in progress. Crime-related data, is announced to the statistical processing of once a year from the Public Prosecutor's Office. However, relative to the current point in time, data that has been statistical processing is a data of about two years ago. It does not fit to the data of the crime currently being generated. In This paper, crime prediction data was apply with Naver trend data. By using the Web traffic Naver trend, it is possible to obtain the data of interest level for crime currently being generated. It was constructed a modeling that can predict the crime by using traffic data of the Naver web search. There have been applied to Markov chains prediction theory. Among various crimes, murder, arson, rape, predictive modeling was applied to target. And the result of predictive modeling value was analyzed. As a result, it got the same results within 20%, based on the value of crime that actually occurred. In the future, it plan to advance research for the predictive modeling of crime that takes into the characteristics of the season.

• Convergence Security Journal
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• v.14 no.6_2
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• pp.89-95
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• 2014

This study analyzed a correlation between crime and meteorological changes and discomfort index of Seoul and p resented a prediction expression through the regression analysis. For data used in this study, crime data from Januar y 2008 to December 2012 of Seoul Metropolitan Police Agency and meteorological records and discomfort index recor ded in the Meteorological Agency through the portal sites were used. Based on this data, SPSS 18.0 was used for the regression analysis and the analysis of correlation between crime and meteorological changes and discomfort index and a prediction expression was derived through the analysis and the risk index was shown in 5 steps depending on predicted values obtained through the prediction expression derived. The risk index of 5 steps classified like this is considered to be used as important data for crime prevention activities.