Faculty of Accounting and Informatics

This dissertation aims to investigate the application of data analytics in forecasting serious crime trends in South Africa. The escalating rates of serious crimes, including homicide, robbery, and sexual assault, present significant challenges to the country's economic growth and the safety of its citizens. Recent South African crime statistics indicate a notable increase of over 9.6% in serious crimes, rising from 444,452 incidents in December 2021 to 486,960 in December 2022. This upward trajectory underscores the urgency to predict future serious crimes preemptively, facilitating the development of proactive strategies by law enforcement agencies, policymakers, and community organizations to prevent and mitigate criminal activities. To achieve this objective, this study employs a comprehensive dataset comprising historical crime records and spatial data to analyse serious crime trends across South Africa's nine provinces from 2005 to 2020. Data pre-processing techniques are applied to clean and normalize the data, ensuring its suitability for subsequent analysis. Exploratory data analysis is conducted using Python (Anaconda) and the Flourish studio environment to identify patterns, relationships, and potentially influential factors associated with serious crimes in South Africa. Various data analytics techniques, including machine learning algorithms, time series analysis, and spatial analysis, are utilized to construct models for predicting serious crime trends. These predictive models are trained using historical crime data and relevant contextual features, facilitating the identification of patterns and correlations that could inform future crime trends. The evaluation of these predictive models involves rigorous performance metrics and validation techniques to assess their predictive power, stability, and generalizability. The results reveal an increase in serious crime across South Africa, with certain provinces emerging as hotspots for specific serious crimes, such as Gauteng with a 21% increase in sexual crimes, KwaZulu-Natal with a 23.1% increase in murders, and the Western Cape with a 38% increase in drug-related crimes. This dissertation contributes to the field of crime analysis by presenting a comprehensive approach to predicting serious crime trends in South Africa. The insights gained from this research can inform the development of proactive strategies and resource allocation by law enforcement agencies, policymakers, and community organizations to address serious crimes effectively. Furthermore, this study lays the groundwork for future research in crime prediction and prevention, highlighting the potential of data analytics techniques in tackling complex societal issues. Future research may explore advanced techniques such as ensemble learning and deep learning to enhance the accuracy and robustness of predictive models.

Cities across the world suffer significantly from traffic congestion. Governments are trying to harness the power of today's computing, networking, and communication technologies to build system that can improve the efficiency of current road traffic and conditions. The study investigated the purpose efficiencies of intelligent system to assess their performance. Considering the findings, it can be said that traffic flow forecasting (TFF) possibilities are numerous, involve a variety of technologies, and can significantly reduce most traffic issues in smart cities. The studies were later evaluated to find similarities, content, benefits, and disadvantages of traffic congestion. By applying the project management tools such as the performance metrics and SQERT model were used to evaluate and prioritize the state-of-the-art methods. A classical model was proposed to improve upon and determine the traffic dangers that affect road users and aggregate the information about traffic from vehicles, traffic lights, and roadside sensors. These on-road sensors (ORS) performance are used for analyses such are vehicle classification, speed calculations, and vehicle counts.

Faculty of Accounting and Informatics

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