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

• Journal of Satellite, Information and Communications
• /
• v.10 no.1
• /
• pp.6-11
• /
• 2015

In this paper, we analyzed current states and spatial characteristics of crime occurring in A city of Colombia using big data of crime. The analysis draws on the crime statistics of Colombia National Police Agency from 2013 January to September. We also investigated spatial autocorrelation of crime using global and local Moran's Index. Spatial autocorrelation analysis shows significant spatial autocorrelation in the high frequency of crime. Global Moran's I analysis indicates that there are statistically significant value of crime area. Using local Moran's Index analysis, we also implement Local Indicators of Spatial Association(LISA) map and hot spot analysis helps us identify crime distribution.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.21 no.4
• /
• pp.64-80
• /
• 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.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.13 no.1
• /
• pp.169-174
• /
• 2018

There are many CCTV cameras connected to the video surveillance and monitoring center for the safety of citizens, and it is difficult for a few monitoring agents to monitor many channels of videos. In this paper, we propose an intelligent video surveillance system utilizing a safety map to efficiently monitor many channels of CCTV camera videos. The safety map establishes the frequency of crime occurrence as a database, expresses the degree of crime risk and makes it possible for agents of the video surveillance center to pay attention when a woman enters the crime risk area. The proposed gender classification method is processed in the order of pedestrian detection, tracking and classification with deep training. The pedestrian detection and tracking uses Adaboost algorithm and probabilistic data association filter, respectively. In order to classify the gender of the pedestrian, relatively simple AlexNet is applied to determine gender. Experimental results show that the proposed gender classification method is more effective than the conventional algorithm. In addition, the results of implementation of intelligent video security system combined with safety map are introduced.

• Journal of Digital Convergence
• /
• v.14 no.11
• /
• pp.125-133
• /
• 2016

In recent years, various crimes such as "random killing' crime continue to rise. Despite the government's crime prevention efforts and crime related researches, crime increases and a different approach is needed. Therefore, this study proposes the alternative for crime prevention by analyzing big data. To achieve this objective, this study was to perform visualization utilizing the histogram, the bubble chart and the hit map and association analysis. To analyze the relationship between crime and some variables, this study analyzed data of Seongnam city, Korea National Police Agency and etc. The results of analysis showed that CCTV will be to reduce the crime rate and security light is not significantly relevant. And the result showed that other types of crime focused by time of the day and day of the week and showed that an increase of the foreigners and crime increase are associated. This study presents a scheme for reducing the crime rate on the basis of this analysis result.

• Journal of the Korea Convergence Society
• /
• v.11 no.11
• /
• pp.75-80
• /
• 2020

Since the Fourth Industrial Revolution, important technologies such as big data analysis, robotics, Internet of Things, and the artificial intelligence have been used in various fields. Generally speaking it is understood that the big-data technology consists of gathering stage for enormous data, analyzing and processing stage and distributing stage. Until now crime records which is one of useful big-sized data are utilized to obtain investigation information after occurring crimes. If crime records are utilized to predict crimes it is believed that crime occurring frequency can be lowered by processing big-sized crime records in big-data framework. In this research the design is proposed that the smart system can provide the users of smart devices crime occurrence probability by processing crime records in big-data analysis. Specifically it is meant that the proposed system will guide safer routes by displaying crime occurrence probabilities on the digital map in a smart device. In the experiment result for a smart application dealing with small local area it is showed that its usefulness is quite good in crime prevention.

• Journal of Korean Society for Geospatial Information Science
• /
• v.15 no.1 s.39
• /
• pp.3-7
• /
• 2007

To deal with the modern intellectual criminal acts, various efforts have been tried. Especially, it is not difficult to watch the recent activities to analyze the criminal characteristics spatially using computing and GIS technology. In this study, the spatial features and patterns of crime are investigated. Based on the real criminal record in Seoul Korea, the crime is reconstituted with four major categories such as assault, larceny, robbery, and rape. Then the variables are derived based on the theory of criminology. The kernal density analysis is performed to investigate the criminal distribution, and the correlation between the main criminal causes and the criminal outbreak is examined by buffering analysis. In addition, the land price and land usages are correlated with social-economic factors of criminal patterns to produce the final crime map.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.12 no.1
• /
• pp.12-25
• /
• 2009

The aim of this study is to analyze the periodical and spatial characteristics of urban crime and to find out the factors that affect the crime occurrence. For these, crime data of Masan City was examined and crime occurrence pattern is ploted on a map using crime density and criminal hotspot analysis. The spatial relationship of crime occurrence and factors affecting crime were also investigated using ESDA (Exploratory Spatial Data Analysis) and SAR (Spatial Auto-Regression) model. As a result, it was found that crimes had strong tendency of happening during a certain period of time and with spatial contiguity. Spatial contiguity of crimes was made clear through the spatial autocorrelation analysis on 5 major crimes. Especially, robbery revealed the highest spatial autocorrelation. However as a autocorrelation model, Spatial Error Model(SEM) had statistically the highest goodness of fit. Moreover, the model proved that old age population ratio, property tax, wholesale-retail shop number, and retail & wholesale number were statistically significant that affect crime occurrence of 5 most major crimes and theft crime. However population density affected negatively on assault crime. Lastly, the findings of this study are expected to provide meaningful ideas to make our cities safer with U-City strategies and services.

• Journal of Convergence for Information Technology
• /
• v.8 no.1
• /
• pp.161-171
• /
• 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 Korean Association of Geographic Information Studies
• /
• v.13 no.3
• /
• pp.14-28
• /
• 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.