Bayesian Network for Modeling Determinant Factors Influencing Offenders to Commit Crime (The Case of Addis Ababa Police Commission)

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Addis Ababa University


The identification of causes and phenomena associated with crime is one of the most popular goals in criminology, especially in view of its practical value and the belief that such identifications are useful when seeking to correct or control criminal behavior. The utility of discovering causes must, however, be qualified. Understanding and processing of offenders’ records is one method to learn about both crime and the individuals who involve in misdeeds so that police can take crime prevention measures accordingly. Though data on criminals are continuously being gathered, they are not effectively being utilized for extracting patterns that can be used for effective management of crimes. This is mainly due to the inadequacy of the human brain to search for complex and multifactor dependencies in data and the lack of objectiveness in such analysis demanded a computerized approach. Developments in the information and communication technologies have made it possible for organizations to collect, store and manipulate massive amount of data. One such development is Bayesian Network. In this study, the main objective of the research is to develop a predictive model for factors that constitute higher crime trends in Addis Ababa which makes use of Bayesian Network modeling techniques. For this purpose, published literatures in related areas have been studied together with the review of different Bayesian Network modeling approaches. Different tools and techniques supporting such task were examined by taking into consideration their application to the problem domain. In addition, an experiment is conducted to explore the potential of Bayesian IV network in modeling factors that constitute higher crime trend using personal identification record of criminals. For the purpose of the experimentation 1572 criminal records were collected from the Addis Ababa Police Commission. The records were manually and automatically further preprocessed to make them compatible with software used. Important attributes that are considered relevant for the constructing predictive model for higher crime trends were selected. After preprocessing the data, a learning classifier is used to learn from the training data and use this classifier to classify new data. A model is constructed for the best learned model from data. Based on the experimental data, a Bayesian performance prediction model was developed where 73.25 % prediction accuracy was first observed. Further experiments and modification of the prediction model increased the level of prediction accuracy to 75.78 %. Finally, Three Phase Dependency Analysis in particular and Bayesian network in general is found applicable for modeling determinant factors for higher crime trends.



Bayesian Network for Modeling Determinant Factors