Research on the Spatial Distribution Characteristics of Global Cybercrime and Prevention Strategies Based on Geographically Weighted Regression
DOI:
https://doi.org/10.62051/tr5f2089Keywords:
Cybercrime; Geographically Weighted Regression (GWR); Spatial Heterogeneity; Transnational Prevention; Policy Efficacy.Abstract
Against the backdrop of deep global digital integration, the transnational nature and spatial heterogeneity of cybercrime have intensified governance challenges. This study employs a Geographically Weighted Regression (GWR) model to integrate cybercrime data from 129 countries (including 12 indicators such as crime incidents, success rate, and prevention rate) from the International Telecommunication Union (ITU) 2023 Global Cybersecurity Index (GCI) and the VERIS Community Database (VCDB), constructing a three-dimensional analytical framework of "Spatial Distribution - Influencing Factors - Policy Efficacy". The results show that four countries including the United States and the United Kingdom are high-incidence target countries for cybercrime (crime incidents ≥ 138 times), while five countries such as Singapore and South Korea achieve a crime prevention rate of over 89%. Economic level (GDP), internet penetration, and cybersecurity policy efficacy (GCI index) exhibit significant spatially heterogeneous impacts on crime distribution, with well-developed and strictly enforced legal policies accounting for 80% of the crime inhibition effect. Geospatial proximity and technical investment intensity show positive inhibitory effects, and regional collaboration mechanisms can enhance prevention efficiency by over 30%. The study reveals the dual laws of cybercrime—"economic centers attracting crime" and "spatial differentiation of policy efficacy"—providing data support for optimizing global cybersecurity policies.
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