• Journal of the Korean Geographical Society
• /
• v.36 no.3
• /
• pp.329-341
• /
• 2001

The purpose of this study focused on the distribution of crimes in Cheongju City. This study emphasized the characteristics of place and spatial pattern of crime in Central Business District(CBD). The crime core areas were delineated and explained through land-use based on fieldwork and GIS analysis For this aim. the police crime data of Cheongju Dongbu(east). Seobu(west) for 1998 were collected In which 3.909 indictable or similar offenses were reported. In this study, Included climes are murder. rape, robbery. arson, theft, burglary, assault and vandalism. Because theme crimes are related with site-specific crime. As a result. land-use patterns are often related to specific type of offenses. The climes in Cheongju City were concentrated in the CBD Most crimes were assaults and thefts Crime areas can be classified by the age of the offender Around Chungang and Pungmul Market in the CBD. the offender＇s ages were 30-50 dominantly Assaults and thefts were concentrated in Songan-gil(street). which is a place teen-ages and youngsters meet frequently The result of the buffering analysis with roads, explained 40% of crime within a 30m buffer area( including both sides) of a principal road The rest of the climes mainly occurred in the vicinity of narrow streets and alleys.

• Journal of Korea Spatial Information System Society
• /
• v.10 no.2
• /
• pp.63-79
• /
• 2008

With the recent development of advanced GIS and complex spatial analysis technologies, the more sophisticated technologies are being required to support the advanced knowledge for solving geographical or spatial problems in various decision support systems. In addition, necessity for research on scientific crime investigation and forensic science is increasing particularly at law enforcement agencies and investigation institutions for efficient investigation and the prevention of crimes. There are active researches on geographic profiling to predict the base location such as criminals' residence by analyzing the spatial patterns of serial crimes. However, as previous researches on geographic profiling use simply statistical methods for spatial pattern analysis and do not apply a variety of spatial and temporal analysis technologies on serial crimes, they have the low prediction accuracy. Therefore, this paper identifies the typology the spatio-temporal patterns of serial crimes according to spatial distribution of crime sites and temporal distribution on occurrence of crimes and proposes STA-BLP(Spatio-Temporal Analysis based Base Location Prediction) algorithm which predicts the base location of serial crimes more accurately based on the patterns. STA-BLP improves the prediction accuracy by considering of the anisotropic pattern of serial crimes committed by criminals who prefer specific directions on a crime trip and the learning effect of criminals through repeated movement along the same route. In addition, it can predict base location more accurately in the serial crimes from multiple bases with the local prediction for some crime sites included in a cluster and the global prediction for all crime sites. Through a variety of experiments, we proved the superiority of the STA-BLP by comparing it with previous algorithms in terms of prediction accuracy.

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

• The Journal of the Korea institute of electronic communication sciences
• /
• v.16 no.5
• /
• pp.917-930
• /
• 2021

Recently, as part of the crime prevention measures that focus on spatial characteristics on the determinants of crime-causing, interest in crime prevention (CPTED) through environmental design has been increasing. This study analyzed crime vulnerabilities in Ulju-gun for the purpose of establishing a master plan for crime prevention urban design (CPTED). The 12 indicators, including demographic, socioeconomic, and physical factors, were sampled from prior studies. As a next step, statistical analysis resulted in criminal vulnerability indexes. According to the analysis, districts with a high demographic crime vulnerability indexes were concentrated on apartment complexes, while districts with high socio-economic crime vulnerability indexes have low land prices and high proportion of female population. Also, the districts with high physical crime vulnerability indexes were found to be heavily distributed commercial ones with a large number of entertainment places. However, there was a limit to generalizing the indicators of previous studies to local governments with different regional characteristics. Therefore, further studies should be carried out by establishing additional indicators considering regional characteristics in the future.

• Korea journal of population studies
• /
• v.24 no.2
• /
• pp.149-177
• /
• 2001

This is a study on age-crime curve in Korea. Three data was used in this study as following: First is the crime statistics as aggregated data. Second is the police record(N=3.541 offences) of the male ex-offenders(N=988) who have been released in eleven prisons in 1987 as individual data. Third is the self-reported group-interview data(N=10.198 offences) administered to the male prisoners(N=979) in ten correctional facilities including eight adult prisons, one juvenile prison and one juvenile training center as another individual data. Generally, the right-skewness of age-crime curve has been explained through the difference of crime rate between early starters and late starters. Moffitt explains that this is because of the higher participation rate of the juvenile period of adolescence-limited offenders, but Godttfredson and Hirschi explain that this is because of a similar distribution in the crime rate of both early starters and late starters. the analysis of this study shows that Godttfredson and Hirschi’s explanation on the generality of age-crime-curve distribution is correct, but this can be modified by various factors like a economic crisis. And the peak age of juvenile period is consistent with the Moffitt’s hypothesis that the peak age is contributed to the increase of crime rate of late starters, not with Godttfredson and Hirschi’s one.

• Journal of Korean Society for Geospatial Information Science
• /
• v.18 no.3
• /
• pp.83-90
• /
• 2010

In modern society, there has been an increase in needs to protect the life and property of the people, because the number of various crimes is on the increase due to the sudden and complicated changes of the urban environment. For the needs, security persons in the urban area are expanding the role and skill of police for more effective crime prevention and surveillance, although the number of policeman/woman is insufficient and their tasks are hard. Recently, a system to observe and prevent crime in effective has been introduced by using such an artificial surveillance device as CCTV to monitor focusing on one area for 24 hours. However, the system brings such problems as the insufficiency of systematic criteria to install surveillance device and the invasion of privacy. Therefore, in this study, artificial surveillance zones to monitor crimes are selected by applying spatial features between artificial surveillance devices including CCTV and crime occurrence place, and using GIS spatial analysis techniques. As a result of selecting, it's found that the number of CCTV is absolutely insufficient and spatial distribution is not fully considered in the existing location of installed CCTV.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.20 no.4
• /
• pp.65-76
• /
• 2017

The purpose of this study was to present a prediction model that reflects crime risk area analysis, including factors and spatial characteristics, as a precursor to preparing an alternative plan for crime prevention and design. This analysis of criminal cases in high-risk areas revealed clusters in which approximately 25% of the cases within the study area occurred, distributed evenly throughout the region. This means that using a multiple linear regression model might overestimate the crime rate in some regions and underestimate in others. It also suggests that the number of deserted houses in an analyzed region has a negative relationship with the dependent variable, based on the multiple linear regression model results, and can also have different influences depending on the region. These results reveal that closure signs in a study area affect the dependent variable differently, depending on the region, rather than a simple or direct relationship with the dependent variable, as indicated by the results of the multiple linear regression model.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.20 no.4
• /
• pp.89-101
• /
• 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
• /
• v.39 no.1
• /
• pp.29-40
• /
• 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.

• The Korean Journal of Applied Statistics
• /
• v.23 no.2
• /
• pp.335-344
• /
• 2010

Reliability of the estimation is usually damaged in the situation where a linear regression model without spatial dependencies is employed to the spatial data analysis. In this study, we considered the conditional autoregressive model in order to construct spatial association structures and estimate the parameters via the Bayesian approaches. Finally, we compared the performances of the models with spatial effects and the ones without spatial effects. We analyzed the yearly total crime count data measured from each of 25 districts in Seoul, South Korea in 2007.