A methodological approach for enhancing visualization of country data representation in the presence of significant spatial disparity.

In this article, we present a methodological approach to address spatial disparity in global data representation, introducing an algorithm called Flexible Mapping to Understand Spatial Analysis (FLEMUSA). We utilize world maps to depict various data points across countries, revealing substantial variation among them. However, conventional choropleth maps often fail to effectively represent regions with sparse data, obscuring valuable insights. To mitigate this issue, we propose interactive graphical methods in both two and three dimensions, implemented through open-source Python code accessible via Google Colab. Our approach includes several contributions such as excluding countries without data from the representation, scaling magnitudes within country borders, focusing on regional analysis, and using logarithmic scales for bubble maps proportional to country sizes. Additionally, we offer interactive 2D and 3D representations, rotatable 3D representations, and zoomable options, facilitating enhanced visualization of regional similarities amidst data heterogeneity. Through this algorithm, we aim to improve the clarity and interpretability of spatial data analysis, integrating solutions for extreme data overdispersion, all programmed with open-source code.-Utilization of world maps for visual representation of data across countries mitigating the overdispersion step by step.-Implementation of graphical methods, including interactive 2D and 3D maps, to address spatial disparity.-Provision of open-source code for customizable graphical representations, facilitating implementation in online journals as interactive code snippets.