GeoBM: A Python-based tool for integrated visualization of global bibliometric data

•GeoBM enhances global research mapping beyond traditional choropleth limits.•Combines publication volume and collaboration for richer geovisualization.•Open-source, Python-based tool with real-time, customizable visual outputs. The rapid proliferation of scientometric and bibliometric analyses has...

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Bibliographic Details
Published in:MethodsX Vol. 15; p. 103497
Main Authors: Fu, Chun Chong, Fleta-Asín, Jorge, Muñoz, Fernando, Sáenz-Royo, Carlos, Wei, Loo Keat
Format: Journal Article
Language:English
Published: Netherlands Elsevier B.V 01.12.2025
Elsevier
Subjects:
Bibliometric analysis
Bibliometrix
Citespace
Country collaboration map
Economics/Business
Github
Google colab
Network
Production
Python algorithm
VOSviewer
Python algorithm
Github
Citespace
Bibliometrix
Production
Network
Bibliometric analysis
Country collaboration map
VOSviewer
Google colab
ISSN:2215-0161, 2215-0161
Online Access:Get full text
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Summary:•GeoBM enhances global research mapping beyond traditional choropleth limits.•Combines publication volume and collaboration for richer geovisualization.•Open-source, Python-based tool with real-time, customizable visual outputs. 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. [Display omitted]
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ISSN:2215-0161
2215-0161
DOI:10.1016/j.mex.2025.103497