• 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.

• 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 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.

• 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).

• Journal of Convergence for Information Technology
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• v.8 no.1
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• pp.161-171
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• 2018

This study set out to identify crime vulnerable areas with the GIS spatial analysis technique for the prediction of crimes. Crime vulnerable areas were extracted from the statistics of crimes with the GIS hotspot analysis technique and the inverse distance weighted(IDW) method applied to different crimes according to places and use districts. The scope of surveillance and weight were calculated for each of CPTED surveillance elements including CCTV, streetlamp, patrol division, and police substation. Maps of crime vulnerable areas were overlapped one after another to make a CPTED-based one expressed in four grades(safety, attention, warning, and risk).

• Journal of The Korea Institute of Healthcare Architecture
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• v.28 no.4
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• pp.31-39
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• 2022

The purpose of this study is to analyse the hazard risk by examining the magnitude and severity of each type of hazard in order to mitigate and prepare for disasters in medical facilities. Methods: The hazard risk analysis for hazard types was surveyed for team leaders of medical facilities. The questionnaire analyzed data from 27 facilities, which were returned from 41 Local Medical Centers. Results: When looking at the 'Risk' by category type of hazard, the influence of health safety and fire/energy safety comes first, followed by natural disaster, facility safety, and crime safety. On the other hand, as for 'Magnitude', facility safety and crime safety come first, followed by health safety, fire/energy safety, and natural disasters. Most of the top types of disaster judged to have high hazard in medical facilities are health types. The top five priorities of hazard in medical facilities, they are affected by the geographical and industrial conditions of the treatment area. In the case of cities, the hazard was found to be high in the order of infectious disease, patient surge, and wind and flood damage. On the other hand, in rural areas, livestock diseases and infectious diseases showed the highest hazard. In the case of forest areas, the hazard was high in the order of wildfire, fire accident, lightning, tide, earthquake, and landslide, whereas in coastal areas of industrial complexes, the hazard was high due to fire, landslide, water pollution, marine pollution, and chemical spill accident. Implications: Through the research, standards will be established for the design of hospitals with disaster preparedness, and will contribute to the preparation of preemptive measures in terms of maintenance.

• 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 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 Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.1
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• pp.29-40
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• 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.

• Journal of the Korean Association of Geographic Information Studies
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• v.13 no.3
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• pp.14-28
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• 2010

The aim of this study is to investigate crime hotspot areas using the spatio-temporal cluster analysis which is possible to search simultaneously time range as well as space range as an alternative method of existing hotspot analysis only identifying crime occurrence distribution patterns in urban area. As for research method, first, crime data were collected from criminal registers provided by official police authority in M city, Gyeongnam and crime occurrence patterns were drafted on a map by using Geographic Information Systems(GIS). Second, by utilizing Ripley K-function and Space-Time Scan Statistics analysis, the spatio-temporal distribution of crime was examined. The results showed that the risk of crime was significantly clustered at relatively few places and the spatio-temporal clustered areas of crime were different from those predicted by existing spatial hotspot analysis such as kernel density analysis and k-means clustering analysis. Finally, it is expected that the results of this study can be not only utilized as a valuable reference data for establishing urban planning and crime prevention through environmental design(CPTED), but also made available for the allocation of police resources and the improvement of public security services.