• Title/Summary/Keyword: 범죄예측

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Extraction of Crime Vulnerable Areas Using Crime Statistics and Spatial Big Data (공간 빅데이터와 범죄통계자료를 이용한 범죄취약지 추출)

  • Park, So-Rang;Park, Jae-Kook
    • 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).

Prediction of the Number of Crimes according to Urban Environmental Factors in the Metropolitan Area (수도권 도시 환경 요인에 따른 범죄 발생 건수 예측)

  • Ye-Won Jang;Ye-Lim Kim;Si-Hyeon Park;Jae-Young Lee;Yoo-Jin Moon
    • Proceedings of the Korean Society of Computer Information Conference
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    • 2023.01a
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    • pp.321-322
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    • 2023
  • 본 논문에서는 Scikit-learn 패키지의 LinearRegression 모델과 Keras 딥러닝 모델을 활용하여 수도권 도시 환경 요인에 따른 범죄 발생 건수를 예측 모델을 제안한다. 연구 방법으로 범죄 발생과 유의미한 관계가 있다고 파악되는 수도권의 각 자치구 별 데이터셋을 분석하여, CCTV, 파출소, 가로등의 수가 범죄 발생에 유의미한 영향을 끼치는 것을 확인하였다. 독립 변수들 간에 Scale을 줄이고자 정규화를 진행했고, 종속변수의 정규성 확보를 위해 로그변환을 취했다. 손실 함수는 회귀문제에서 사용되는 'relu'함수를 사용했고 모델의 성능을 확인할 수 있는 지표로 MSE(Mean Squared Error)를 사용해 모델을 구성하였다. 본 논문에서 설계한 이 프로그램은 범죄 발생율이 높은 지역구에 경찰 인력의 추가적 배치, 안전 시설 확충 등 실무적 조치를 취함에 있어 근거를 제공할 수 있을 것으로 사료된다.

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A Study on Fuzzy Searching Algorithm and Conditional-GAN for Crime Prediction System (범죄예측시스템에 대한 퍼지 탐색 알고리즘과 GAN 상태에 관한 연구)

  • Afonso, Carmelita;Yun, Han-Kyung
    • 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.

A Study on Generation Methodology of Crime Prediction Probability Map by using the Markov Chains and Object Interpretation Keys (마코프 체인과 객체 판독키를 적용한 범죄 예측 확률지도 생성 기법 연구)

  • Noe, Chan-Sook;Kim, Dong-Hyun
    • Journal of the Korea Society of Computer and Information
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    • v.17 no.11
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    • pp.107-116
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    • 2012
  • In this paper we propose a method that can generate the risk probability map in the form of raster shape by using Markov Chain methodology applied to the object interpretation keys and quantified risk indexes. These object interpretation keys, which are primarily characteristics that can be identified by the naked eye, are set based on the objects that comprise the spatial information of a certain urban area. Each key is divided into a cell, and then is weighted by its own risk index. These keys in turn are used to generate the unified risk probability map using various levels of crime prediction probability maps. The risk probability map may vary over time and means of applying different sets of object interpretation keys. Therefore, this method can be used to prevent crimes by providing the ways of setting up the best possible police patrol beat as well as the optimal arrangement of surveillance equipments.

A Study on the Crime Prediction System using Big Data (빅데이터를 이용한 범죄 예측 시스템에 관한 연구)

  • Han, Sang-Jin
    • 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.

AI Crime Prediction Modeling Based on Judgment and the 8 Principles (판결문과 8하원칙에 기반한 인공지능 범죄 예측 모델링)

  • Hye-sung Jung;Eun-bi Cho;Jeong-hyeon Chang
    • Journal of Internet Computing and Services
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    • v.24 no.6
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    • pp.99-105
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    • 2023
  • In the 4th industrial revolution, the field of criminal justice is paying attention to Legaltech using artificial intelligence to provide efficient legal services. This paper attempted to create a crime prediction model that can apply Recurrent Neural Network(RNN) to increase the potential for using legal technology in the domestic criminal justice field. To this end, the crime process was divided into pre, during, and post stages based on the criminal facts described in the judgment, utilizing crime script analysis techniques. In addition, at each time point, the method and evidence of crime were classified into objects, actions, and environments based on the sentence composition elements and the 8 principles of investigation. The case summary analysis framework derived from this study can contribute to establishing situational crime prevention strategies because it is easy to identify typical patterns of specific crime methods. Furthermore, the results of this study can be used as a useful reference for research on generating crime situation prediction data based on RNN models in future follow-up studies.

Study on abnormal behavior prediction models using flexible multi-level regression (유연성 다중 회귀 모델을 활용한 보행자 이상 행동 예측 모델 연구)

  • Jung, Yu Jin;Yoon, Yong Ik
    • Journal of the Korean Data and Information Science Society
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    • v.27 no.1
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    • pp.1-8
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    • 2016
  • In the recently, violent crime and accidental crime has been generated continuously. Consequently, people anxiety has been heightened. The Closed Circuit Television (CCTV) has been used to ensure the security and evidence for the crimes. However, the video captured from CCTV has being used in the post-processing to apply to the evidence. In this paper, we propose a flexible multi-level models for estimating whether dangerous behavior and the environment and context for pedestrians. The situation analysis builds the knowledge for the pedestrians tracking. Finally, the decision step decides and notifies the threat situation when the behavior observed object is determined to abnormal behavior. Thereby, tracking the behavior of objects in a multi-region, it can be seen that the risk of the object behavior. It can be predicted by the behavior prediction of crime.

Crime Incident Prediction Model based on Bayesian Probability (베이지안 확률 기반 범죄위험지역 예측 모델 개발)

  • HEO, Sun-Young;KIM, Ju-Young;MOON, Tae-Heon
    • 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.

Prediction Model for Abnormal Behavior based on Multiple CCTV (다중 CCTV 연동 기반 비정상 행동 예측모델)

  • Jung, Yu-Jin;Yoon, Yong-Ik
    • Proceedings of the Korea Information Processing Society Conference
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    • 2014.11a
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    • pp.1023-1026
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    • 2014
  • CCTV 는 범죄상황 발생시 보안과 증거확보를 위해 사용되어 왔다. 실제 상황에서 범죄가 발생하기 전 예방을 하는 것 보다 사후 처리에 용도를 두고 있으며, 범죄 상황에서의 보행자에 대한 행동을 미리 예측하기 어렵다. 본 논문에서는 노상에서 CCTV 로 수집된 데이터를 통해 객체 인식 및 객체간의 관계를 파악한다. 파악된 객체를 다중의 CCTV 연동 카메라가 추적하고 객체의 행동을 분석한다. 객체가 이상행동이라고 판단될 시 위협을 받는 객체 및 가까운 기관에 알림을 줄 수 있는 모델을 제안한다. 이를 통해 범죄 발생 전 즉각적인 대응이 가능하며 빠른 상황판단이 가능하다.

A study on Introducing Intelligent Electronic Monitoring System through the Analysis of the Electronic Supervision (전자감독제도의 실태분석을 통한 지능형 전자발찌 도입 방안)

  • Cha, Minkyu;Kim, Donghee;Kim, Taehwan;Kwak, Daekyung
    • 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.