• Journal of the Korea Society of Computer and Information
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• v.21 no.5
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• pp.91-98
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• 2016

We propose a method to train a model that can predict the probability of a crime being committed. CCTV data by matching criminal events are required to train the crime prediction model. However, collecting CCTV data appropriate for training is difficult. Thus, we collected actual criminal records and converted them to an appropriate format using variables by considering a crime prediction environment and the availability of real-time data collection from CCTV. In addition, we identified new specific crime types according to the characteristics of criminal events and trained and tested the prediction model by applying neural network partial least squares for each crime type. Results show a level of predictive accuracy sufficiently significant to demonstrate the applicability of CCTV to real-time crime prediction.

• ETRI Journal
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• v.44 no.2
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• pp.208-219
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• 2022

Crime amount prediction is crucial for optimizing the police patrols' arrangement in each region of a city. First, we analyzed spatiotemporal correlations of the crime data and the relationships between crime and related auxiliary data, including points-of-interest (POI), public service complaints, and demographics. Then, we proposed a crime amount prediction model based on 2D convolution and long short-term memory neural network (2DCONV-LSTM). The proposed model captures the spatiotemporal correlations in the crime data, and the crime-related auxiliary data are used to enhance the regional spatial features. Extensive experiments on real-world datasets are conducted. Results demonstrated that capturing both temporal and spatial correlations in crime data and using auxiliary data to extract regional spatial features improve the prediction performance. In the best case scenario, the proposed model reduces the prediction error by at least 17.8% and 8.2% compared with support vector regression (SVR) and LSTM, respectively. Moreover, excessive auxiliary data reduce model performance because of the presence of redundant information.

• Architectural research
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• v.24 no.1
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• pp.1-11
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• 2022

As the occurrence of a crime is dependent on different factors, their correlations are beyond the ordinary cognitive range. Owing to this limitation, systems face difficulty in correlating various factors, thereby requiring the assistance of artificial intelligence (AI) to overcome such limitations. Therefore, AI has become indispensable for crime prediction. Crimes can cause severe and irrevocable damage to a society. Recently, big data has been introduced for developing highly accurate models for crime prediction. Prediction of night crimes should be given significant consideration, because crimes primarily occur during nights, when the spatiotemporal characteristics become vulnerable to crimes. Many environmental factors that influence crime rate are applied for crime prediction, and their influence on crime rate may differ based on temporal characteristics and the nature of crime. This study aims to identify the environmental factors that influence sex and theft crimes occurring at night and proposes an artificial neural network (ANN) model to predict sex and theft crimes at night in random areas. The crime data of A district in Seoul for 12 years (2004-2015) was used, and environmental factors that influence sex and theft crimes were derived through multiple regression analysis. Two types of crime prediction models were developed: Type A using all environmental factors as input data; Type B with only the significant factors (obtained from regression analysis) as input data. The Type B model exhibited a greater accuracy than Type A, by 3.26 and 9.47 % higher for theft and sex crimes, respectively.

• Journal of Satellite, Information and Communications
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• v.10 no.2
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• pp.40-45
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• 2015

In this paper, we proposed and implemented a crime prediction algorithm based upon crime influential factors. To collect the crime-related big data, we used a data which had been collected and was published in the supreme prosecutors' office. The algorithm analyzed various crime patterns in Seoul from 2011 to 2013 using the spatial statistics analysis. Also, for the crime prediction algorithm, we adopted a Bayesian network. The Bayesian network consist of various spatial, populational and social characteristics. In addition, for the more precise prediction, we also considered date, time, and weather factors. As the result of the proposed algorithm, we could figure out the different crime patterns in Seoul, and confirmed the prediction accuracy of the proposed algorithm.

• ETRI Journal
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• v.43 no.6
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• pp.1058-1080
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• 2021

Crime is not a completely random event but rather shows a pattern in space and time. Capturing the dynamic nature of crime patterns is a challenging task. Crime prediction models that rely only on neighborhood influence and demographic features might not be able to capture the dynamics of crime patterns, as demographic data collection does not occur frequently and is static. This work proposes a novel approach for crime count and hotspot prediction to capture the dynamic nature of crime patterns using taxi data along with historical crime and demographic data. The proposed approach predicts crime events in spatial units and classifies each of them into a hotspot category based on the number of crime events. Four models are proposed, which consider different covariates to select a set of independent variables. The experimental results show that the proposed combined subset model (CSM), in which static and dynamic aspects of crime are combined by employing the taxi dataset, is more accurate than the other models presented in this study.

• KIPS Transactions on Computer and Communication Systems
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• v.5 no.9
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• pp.229-236
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• 2016

In the past, crime prediction methods utilized previous records to accurately predict crime occurrences. Yet these crime prediction models had difficulty in updating immense data. To enhance the crime prediction methods, some approaches used social network service (SNS) data in crime prediction studies, but the relationship between SNS data and crime records has not been studied thoroughly. Hence, in this paper, we analyze the relationship between SNS data and criminal occurrences in the perspective of crime prediction. Using Latent Dirichlet Allocation (LDA), we extract tweets that included any words regarding criminal occurrences and analyze the changes in tweet frequency according to the crime records. We then calculate the number of tweets including crime related words and investigate accordingly depending on crime occurrences. Our experimental results demonstrate that there is a difference in crime related tweet occurrences when criminal activity occurs. Moreover, our results show that SNS data analysis will be helpful in crime prediction model as there are certain patterns in tweet occurrences before and after the crime.

• 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.

• 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.

• ETRI Journal
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• v.43 no.2
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• pp.272-287
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• 2021

This article presents a multi-dimensional spatial pattern analysis of crime events in San Francisco. Our analysis includes the impact of spatial resolution on hotspot identification, temporal effects in crime spatial patterns, and relationships between various crime categories. In this work, crime prediction is viewed as a classification problem. When predictions for a particular category are made, a binary classification-based model is framed, and when all categories are considered for analysis, a multiclass model is formulated. The proposed crime-prediction model (HotBlock) utilizes spatiotemporal analysis for predicting crime in a fixed spatial region over a period of time. It is robust under variation of model parameters. HotBlock's results are compared with baseline real-world crime datasets. It is found that the proposed model outperforms the standard DeepCrime model in most cases.

• Journal of the Korea Society of Computer and Information
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• v.19 no.12
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• pp.309-317
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• 2014

Recently, crime prediction and prevention are the most important social issues, and global and local governments have tried to prevent crime using various methodologies. One of the methodologies, data mining can be applied at various crime fields such as crime pattern analysis, crime prediction, etc. However, there is few researches to find the relationships between the results of data mining and crime components in terms of criminology. In this study, we introduced environmental criminology, and identified relationships between environment factors related with crime and variables using at data mining. Then, using real burglary data occurred in South Korea, we applied clustering to show relations of results of data mining and crime environment factors. As a result, there were differences in the crime environment caused by each cluster. Finally, we showed the meaning of data mining use at crime prediction and prevention area in terms of criminology.