Increasing internal state security requires an understanding of the factors that influence the commission of repetitive crimes (recidivism) since the crime is not caused by public danger but by the criminal person. Against the background of informatization of the information activities of law enforcement agencies, there is no doubt about the expediency of using artificial intelligence algorithms and blockchain technology to predict and prevent crimes. The prediction machine-learning models for identifying significant factors (individual characteristics of convicts), which affect the propensity to commit criminal recidivism, were applied in this article. For predicting the probability of propensity for criminal recidivism of customers of Ukrainian penitentiary institutions, a Decision Tree model was built to suggest the probability of repeated criminal offenses by convicts. It was established that the number of convictions to the actual punishment and suspended convictions is the main factors that determine the propensity of customers of penitentiary institutions to commit criminal recidivism in the future. Decision Tree models for the classification of convicts prone or not prone to recidivism were built. They can be used to predict new cases for decision-making support in criminal justice. In our further research, the possibility of using the technology of distributed registers/blockchain in predictive criminology will be analyzed.

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Prediction Machine Learning Models on Propensity Convicts to Criminal Recidivism

Information technologies, such as big data analytics (data mining, and predictive analytics) are rapidly developing and penetrating into critically important areas of modern technocratic society, including law enforcement and security sectors. Successful applications of artificial intelligence (AI) are able not only to provide informational support for decision-making, but also to replace the person himself in this process. Global digitization has also affected criminal justice in recent years. The justice system adapts to modern challenges and megatrends using digital platforms and data science. Solving crimes is a complex task and requires significant resources and expertise. Data mining models can provide effective solutions for solving complex non-standard problems of crime detection. AI algorithms are increasingly used in predictive justice to support judicial decision-making, in particular the qualification of crimes and their relevance to the criminal process. Text Mining algorithms provide reliable support in the formation of the evidence base, judicial analytics, and reasonable decision-making in criminal proceedings. Predictive machine learning algorithms are not always 100% accurate, but the criminal justice system is becoming more and more technologically complex. Its effective functioning includes the processing of large volumes of unordered and unstructured data. Nowadays, association rules, i. e. one of the important machine learning models, are widely used to identify patterns and discover new knowledge in such masses of information. The work uses associative rule mining for the extract correlations and co-occurrences between the historical crime information of convicted people. An associative rule mining model was built to search for non-obvious interesting connections between historical crime information of convicted and repeated offenses. The frequent item sets, which are a combination of individual characteristics of convicts who commit repeated criminal offenses, and the strong association rules, have been revealed. The obtained results give grounds for asserting that early dismissals and suspended convictions are the main factors (antecedents), that cause the risk of recidivism (consequent).

Associative Rule Mining for the Assessment of the Risk of Recidivism

Abstract Crime is a threat to any nation’s security administration and jurisdiction. Therefore, crime analysis becomes increasingly important because it assigns the time and place based on the collected spatial and temporal data. However, old techniques, such as paperwork, investigative judges, and statistical analysis, are not efficient enough to predict the accurate time and location where the crime had taken place. But when machine learning and data mining methods were deployed in crime analysis, crime analysis and predication accuracy increased dramatically. In this study, various types of criminal analysis and prediction using several machine learning and data mining techniques, based on the percentage of an accuracy measure of the previous work, are surveyed and introduced, with the aim of producing a concise review of using these algorithms in crime prediction. It is expected that this review study will be helpful for presenting such techniques to crime researchers in addition to supporting future research to develop these techniques for crime analysis by presenting some crime definition, prediction systems challenges and classifications with a comparative study. It was proved though literature, that supervised learning approaches were used in more studies for crime prediction than other approaches, and Logistic Regression is the most powerful method in predicting crime.

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A study on predicting crime rates through machine learning and data mining using text

Crime analysis and prevention is a systematic approach for identifying and analyzing patterns and trends in crime. The system can predict regions which have high probability for crime occurrence and can visualize crime prone areas. With the increasing advent of computerized systems, crime data analysts can help the Law enforcement officers to speed up the process of solving crimes. Using the concept of data mining, real time and location data, the system can extract unknown, useful information from an unstructured data. Here we have an approach between computer science and criminal justice to develop a data mining, real time and location data procedure that can help solve crimes faster. Instead of focusing on causes of crime occurrence like criminal background of offender, political enmity etc. we are focusing mainly on crime factors of each day. To have a better response towards criminal activity, it is very important that one should understand the patterns in crime. I analyses these patterns by taking crime datasets from the Nigeria Police zone six (6) Calabar, Cross River State and town planning bodies. This dataset includes different streets of the city of Calabar. The major aim of this mission is to expect which category of crime is most probably to take place at a detailed time and places in

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Okeke Ogochukwu C

Papers

An overview of crime analysis, prevention and prediction using data mining based on real time and location data