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

Chun Chong Fu1, Jorge Fleta-Asín2,3, Fernando Muñoz3,4

  • 1Department of Biological Science, Faculty of Science, Universiti Tunku Abdul Rahman, Bandar Barat, 31900 Kampar, Perak, Malaysia.

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|July 30, 2025
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Summary
This summary is machine-generated.

GeoBM (Geographic Bibliometric Mapping) offers a new way to visualize global research, overcoming limitations of traditional maps. This framework enhances understanding of scientific output and international collaboration for better science policy.

Keywords:
Bibliometric analysisBibliometrixCitespaceCountry collaboration mapGithubGoogle colabNetworkProductionPython algorithmVOSviewer

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Area of Science:

  • Bibliometrics and Scientometrics
  • Geographic Information Systems (GIS)
  • Science of Science Policy

Background:

  • Traditional geospatial visualizations like choropleth maps struggle with the heterogeneity and overdispersion common in bibliometric data.
  • Existing methods often distort the representation of global scientific output and collaboration patterns.
  • There is a need for scalable and robust methods to visualize complex, large-scale research data.

Purpose of the Study:

  • To introduce GeoBM (Geographic Bibliometric Mapping), a computational framework for enhanced geovisualization of global scientific output and collaboration.
  • To address the methodological shortcomings of conventional geospatial techniques in bibliometric analysis.
  • To provide a tool for more nuanced spatial analysis of global research networks.

Main Methods:

  • GeoBM integrates normalized country-level publication volumes with bilateral collaboration frequencies.
  • The framework uses Python with modular, algorithmically optimized routines for real-time data processing.
  • Statistical controls are incorporated to mitigate overdispersion and enhance visual fidelity.

Main Results:

  • GeoBM produces high-resolution, interpretable geographic maps reflecting research intensity and international connectivity.
  • The framework enables a dual-focus representation of publication density and collaborative strength.
  • Open-source deployment on platforms like Google Colab and GitHub ensures accessibility and reproducibility.

Conclusions:

  • GeoBM offers a powerful tool for the spatial analysis of global research networks.
  • The framework contributes to more nuanced evaluations in science policy, research management, and innovation studies.
  • Enhanced geovisualization of bibliometric data is crucial for understanding contemporary research landscapes.