Fu Chun Chong, Fleta-Asín Jorge, Muñoz Fernando, Sáenz-Royo Carlos, Wei Loo Keat
Department of Biological Science, Faculty of Science, Universiti Tunku Abdul Rahman, Bandar Barat, 31900 Kampar, Perak, Malaysia.
IEDIS. Departamento de Dirección y Organización de Empresas, Facultad de Economía y Empresa, Universidad de Zaragoza, Gran Vía, 2, 50005 Zaragoza, Spain.
MethodsX. 2025 Jul 11;15:103497. doi: 10.1016/j.mex.2025.103497. eCollection 2025 Dec.
The rapid proliferation of scientometric and bibliometric analyses has emphasized the need for robust, scalable methods to visualize complex, large-scale research data. Conventional geospatial visualization techniques-most notably choropleth maps-often introduce significant distortions due to their inability to adequately account for spatial heterogeneity and overdispersion in bibliometric distributions. To address these methodological shortcomings, we propose GeoBM (Geographic Bibliometric Mapping), a computational framework that enables enhanced geovisualization of global scientific output and collaboration patterns. GeoBM integrates normalized country-level publication volumes with bilateral collaboration frequencies to produce high-resolution, interpretable geographic maps that reflect both research intensity and international connectivity. Implemented in Python, the framework leverages modular, algorithmically optimized routines for real-time data processing and visualization, incorporating statistical controls to mitigate overdispersion and enhance visual fidelity. The system supports extensive customization and is deployed via open-source platforms such as Google Colab and GitHub, facilitating broad accessibility and reproducibility. By providing a dual-focus representation of publication density and collaborative strength, GeoBM offers a powerful tool for the spatial analysis of global research networks, contributing to more nuanced evaluations in science policy, research management, and innovation studies.
科学计量学和文献计量学分析的迅速扩散凸显了对强大、可扩展方法的需求，以可视化复杂的大规模研究数据。传统的地理空间可视化技术——最显著的是分级统计图——由于无法充分考虑文献计量分布中的空间异质性和过度分散，常常会引入显著的失真。为了解决这些方法上的缺点，我们提出了GeoBM（地理文献计量映射），这是一个计算框架，能够增强对全球科学产出和合作模式的地理可视化。GeoBM将标准化的国家层面出版物数量与双边合作频率相结合，以生成反映研究强度和国际连通性的高分辨率、可解释的地理地图。该框架用Python实现，利用模块化、算法优化的例程进行实时数据处理和可视化，并纳入统计控制以减轻过度分散并提高视觉保真度。该系统支持广泛的定制，并通过谷歌Colab和GitHub等开源平台部署，便于广泛访问和重现。通过提供出版物密度和合作强度的双重聚焦表示，GeoBM为全球研究网络的空间分析提供了一个强大的工具，有助于在科学政策、研究管理和创新研究中进行更细致入微的评估。
