Beyond the Bloodstains: Behavioral Clusters and Over-Kill Dynamics in Chinese Homicides

This study investigates behavioral patterns and predictive limitations in Chinese homicides through an integrative analysis of crime scene dynamics, offender characteristics, and cultural context. Leveraging a dataset of 254 adjudicated single-victim cases (2013–2022), we employed partitioning-around-medoids (PAM) clustering, similarity network analysis, and machine learning to address two objectives: (1) identifying crime-scene predictors of prior criminal records and (2) uncovering latent homicide typologies. Results revealed three distinct clusters: Sharp-Weapon/Over-kill (knife-dominated, 79% over-kill), Strangulation/Control (high coercive control, low over-kill), and Mixed Method (heterogeneous tactics). These clusters deviate from Western instrumental-expressive dichotomies, reflecting China’s unique weapon accessibility (61% bladed tools vs. <3% firearms) and rural-urban divides. Logistic regression demonstrated that female victims were six times more likely to experience over-kill (OR = 6.05), while sharp-weapon use reduced its odds (OR = 0.31). However, machine learning models failed to predict prior criminal records (AUC ≈ 0.50–0.54), challenging assumptions of behavioral consistency. Findings underscore the interplay of method rationality (e.g., weapon efficiency) and emotional context (e.g., gendered violence), while highlighting structural barriers such as rural socioeconomic precarity and data fragmentation. This study advocates for culturally grounded criminological frameworks that integrate crime scene analysis with offender biographies, offering policy implications for violence prevention and investigative prioritization in non-Western contexts.