Explore human-factor impacts on the occurrence of general aviation accidents

General aviation accidents often result in severe consequences, including casualties and property losses. Identifying and analyzing risk factors from past accidents is of great importance for improving aviation safety. This study proposes a hybrid approach that integrates an improved HFACS framework, incorporating the 24 Model, with complex network theory to systematically examine human and organizational factors in general aviation accidents. First, a risk factor dataset was established based on accident reports, and chi-square tests were applied to identify hierarchical factors with significant relationships, thereby constructing causal accident chains. Second, ArcGIS was employed to analyze the spatiotemporal distribution of accidents, revealing high-risk regions, seasonal patterns, as well as regional variations and activity density effects on accident frequency. Third, a complex network model was constructed from the causal chains, consisting of 34 nodes and 54 edges, and its topological characteristics—including node degree, node strength, average path length, and network diameter—were analyzed using the open-source visualization software Gephi. In addition, vulnerability analysis was performed to evaluate the robustness of the network and to identify critical nodes. The results provide both theoretical support and practical implications for safety management in general aviation and offer a novel perspective for accident analysis in other high-risk domains.