Traditional methods of crime analysis, modeling, and prediction encounter several obstacles that hinder their efficacy in practical scenarios. These challenges encompass:
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Data Sparsity: The scarcity of data poses a significant hurdle in the effective examination and forecasting of criminal activities.
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Interpretability and Transparency: Predictive models often suffer from a lack of clarity and openness, which obstructs their usefulness in real-life situations.
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Evaluation Systems: Conventional techniques typically employ relatively basic evaluation systems devoid of standardization and comprehensive metrics.
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Decision-Making Applications: There is an insufficient amount of research concerning decision-making applications, limiting the practical deployment of crime prediction models.
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Manual Analysis: The manual scrutiny of crime data is susceptible to misinterpretation and forecasting errors, diminishing the precision of traditional security systems.
In contrast, machine learning methodologies have demonstrated potential in predicting crimes. For example, an experiment utilizing machine learning algorithms attained a 69% accuracy rate in forecasting if a crime will transpire and a 47% accuracy rate in predicting the count of crimes. Nonetheless, issues such as real-time forecasts of future criminal incidents continue to persist due to data constraints.
