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

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.