Classification of Daily Quantities of 110 Emergency Cases Segmented by Time Intervals Based on Bayesian Discriminant Analysis

The early literature on Naive Bayes classification focused on Bayes' theorem, gradually developing key theories such as the conditional independence assumption and probability smoothing techniques, whose applications expanded from text classification to fields like medical diagnosis and credit assessment. These theoretical foundations established the Naive Bayes classification method, demonstrating high efficiency and simplicity in handling classification problems. The conditional independence assumption significantly reduces computational complexity, enabling rapid classification decisions even in high-dimensional data spaces. Probability smoothing techniques effectively address data sparsity issues, enhancing the classifier's generalization capability under limited sample conditions. With deepening research, continuous optimizations of Naive Bayes classification have provided a solid theoretical foundation for subsequent empirical studies. This article divides a full day into 12 time periods. Based on daily quantities of 110 emergency cases across these periods, 6 distinct intervals with significantly different activity levels are selected to represent normal, peak, and off-peak periods for police incidents. Bayesian discriminant analysis is then employed to classify response volumes in other time periods. This methodology not only helps identify characteristics and patterns of police activities across different periods, but also enhances understanding of distribution of daily quantities of 110 emergency cases. It facilitates accurate expectation formation and provides both data support and theoretical justification for scientifically allocating police resources. The approach offers valuable insights in order to optimize police force deployment strategies and improve police response efficiency.