Analyzing the Spatiotemporal Dynamics and Social Influences on the Transportation Cybersecurity Industry Through a Business Visitor Flow Perspective

The study aims to explore the spatiotemporal dynamics of the U.S. Transportation Cybersecurity Industry (TCI) from the perspective of Business Visitor Flow (BVF) and analyze the impact of economic and social factors. We innovatively constructed the BiTransGCN model by integrating an attention-based Transformer with a GCN backbone to predict the spatial distribution changes of BVF over the next cycle. Additionally, we applied the game theory-based Geoshapley method to analyze the contribution of spatial clustering and features, explaining the impact and potential associations of sensitive factors on industry visit volume. The study found that the three different industries within TCI exhibit distinct spatial distributions and cluster levels, with varying degrees of influence from socioeconomic factors. The analysis also highlights that geographic location and education level are the most significant social variables affecting industry clustering. Notably, the predictive model indicates that TCI visit volume is expected to increase across all 51 U.S. states in the next cycle, with an average growth rate of approximately 14.16%. The findings provide valuable insights for policymakers, business decision-makers, and industry professionals.