• KIISE Transactions on Computing Practices
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• v.22 no.9
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• pp.455-460
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• 2016

Recently, with more severe types felonies such as robbery and sexual violence, the importance of crime prediction and prevention is emphasized. For accurate and prompt crime prediction and prevention, both a classification model of crime with high accuracy based on past criminal records and well-designed system interface are required. However previous studies on the analysis of crime factors have limitations in terms of accuracy due to the difficulty of data preprocessing. In addition, existing crime monitoring systems merely offer a vast amount of crime analysis results, thereby they fail to provide users with functions for more effective monitoring. In this paper, we propose a classification model for types of crime based on random-forest algorithms and system design factors for real-time crime prediction. From our experiments, we proved that our proposed classification model is superior to others that only use criminal records in terms of accuracy. Through the analysis of existing crime monitoring systems, we also designed and developed a system for real-time crime monitoring.

• Korean Journal of Artificial Intelligence
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• v.9 no.1
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• pp.15-20
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• 2021

This paper was conducted to prevent and respond to crimes by predicting crimes based on artificial intelligence. While the quality of life is improving with the recent development of science and technology, various problems such as poverty, unemployment, and crime occur. Among them, in the case of crime problems, the importance of crime prediction increases as they become more intelligent, advanced, and diversified. For all crimes, it is more critical to predict and prevent crimes in advance than to deal with them well after they occur. Therefore, in this paper, we predicted crime types and crime tools using the Multiclass Logistic Regression algorithm and Multiclass Neural Network algorithm of machine learning. Multiclass Logistic Regression algorithm showed higher accuracy, precision, and recall for analysis and prediction than Multiclass Neural Network algorithm. Through these analysis results, it is expected to contribute to a more pleasant and safe life by implementing a crime prediction system that predicts and prevents various crimes. Through further research, this researcher plans to create a model that predicts the probability of a criminal committing a crime again according to the type of offense and deploy it to a web service.

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

• 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.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.6
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• pp.1113-1122
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• 2020

Recently, as violent crimes of crime without reason (Korea : Do not ask), women and the elderly are getting serious. In the existing system, many CCTVs are installed, but it is difficult to prevent crime due to only follow-up measures after a crime occurs. This device prevents crime through this device for incidents in shaded areas and closed spaces such as apartments and buildings. To do this, we research this technology to develop products and software. It sends an alarm signal using communication technology to a specific place where you want to receive an event of an alarm or a CCTV device operated using image analysis big data technology and convergence sensor technology for a specific target of the behavior expected to be a crime or movement. Develop the device. This development device researches and develops this device and supplies low-cost devices to consumers, which is used as a device that predicts the occurrence of crime in advance, processes it as an alarm signal in real time, and transmits it, and constitutes a standalone device and a server. Will provide the device to be connected.

• Journal of the Society of Disaster Information
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• v.10 no.3
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• pp.374-387
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• 2014

Since the sexual violence crime has a high probability of repeated crime, the electronic monitoring system has been introduced as a measure to it. And this system allows the police to know the location of former criminal around the clock through the electronic device, the former criminal has the psychological/mental oppression which can restrain the intention of crime to a degree. However, there is a limit in blocking criminals with strong will from repeated crime. The next-generation intelligent electronic anklet currently under study collects and analyzes the change bio-data in real time through the location information of electronic monitoring target and attached sensor. This study is aimed to predict the symptom of crime occurrence in advance based on this and block the crime intention in advance or stop the ongoing crime before it is expanded.

• Electronics and Telecommunications Trends
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• v.34 no.1
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• pp.111-122
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• 2019

As society develops, the demand for safety and security services increases. Developed nations such as the United States use advanced technology to lower crime rate and promote intelligent security services. First, this article examines intelligent systems that are used for monitoring and detecting crimes and dangerous situations. Recently, we have been studying technologies that enable preemptive responses through prediction of crime and hazardous situations. In this paper, we examine the cases of security services based on a crime/risk prediction model and explain the structure and major technologies of an intelligent security system. In addition, we propose a direction for technological development for achieving future security services.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.2
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• pp.149-160
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• 2021

In this study, artificial intelligence-based algorithms were proposed, which included a fuzzy search for matching suspects between current and historical crimes in order to obtain related cases in criminal history, as well as conditional generative adversarial networks for crime prediction system (CPS) using Timor-Leste as a case study. By comparing the data from the criminal records, the built algorithms transform witness descriptions in the form of sketches into realistic face images. The proposed algorithms and CPS's findings confirmed that they are useful for rapidly reducing both the time and successful duties of police officers in dealing with crimes. Since it is difficult to maintain social safety nets with inadequate human resources and budgets, the proposed implemented system would significantly assist in improving the criminal investigation process in Timor-Leste.

• Convergence Security Journal
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• v.16 no.6_2
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• pp.23-32
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• 2016

Rapid advances in information and communications technology are new challenges and also opportunities for the police. For the purpose of identifying its implications, this study reviews utilization cases of information and communications technology in the field of public security in South Korea and other countries. As theoretical basis for utilization of information and communications technology, this study introduces intelligence-led policing, predictive policing and evidence-based policing. Also, utilization of big-data based crime analysis and crime prediction technology, as well as advancement of information and communications system and command and control technology of the police, are discussed. Based on the identified implications in this study, the following proposals are made. They are (1) procuring basic data, (2) creating an integrated database, (3) increasing utilization of policy decision-makers, (4) exchange and cooperation between related institutions, (5) training professional analyzers, (6) establishing legal basis and practical guidelines for an integrated database.

• Electronics and Telecommunications Trends
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• v.36 no.5
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• pp.74-81
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• 2021

Based on data provided by the science and technology knowledge infrastructure (ScienceON, 2017-2021), this paper reviews the research trends of domestic police services and related technologies, and describes the research and development direction of policing technology. For this purpose, the research was searched using the keywords science policing, smart policing, predictive policing, and policing. Policing technology is used for crime investigation (prevention), such as crime analysis and crime prediction. The collection of related data use urban infrastructure, the processing of data collected using technologies, such as artificial intelligence, and the utilization of data in police services (system) were summarized. In future, on-site support technology and crime investigation (prevention) technology for a preemptive correspondence to social threats and effective police activities must be developed. In addition, the quality of police services should be improved, a system to use police-related data should be developed, and the capabilities of police experts need to be strengthened.