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In the era of data-intensive science, the complexity and volume of geospatial data have grown exponentially. Compared to traditional data sources, non-traditional sources are more complex and structured, necessitating sophisticated methods and a series of decisions to transform raw data inputs into usable and actionable data products. This Special Issue, “Sustainable geospatial analytics and geoinformatics with repeatable, reproducible, and expandable (RRE) framework and design,” brings together a collection of seven pioneering papers that address the critical need for consistency and transparency in geospatial research. These studies explore diverse domains, including explainable machine learning, disaster risk assessment, urban ecological health, infectious disease control and scientific workflow management. Collectively, they advocate for the adoption of an RRE framework to ensure that results can be verified and reproducible across different environments and expanded with new data or methodologies. By integrating visual programming, service-oriented strategies, as well as Findable, Accessible, Interoperable, and Reusable (FAIR) principles, the featured research lowers technical barriers for non-experts while enhancing the robustness of complex models. This editorial synthesizes the contributions of these papers, highlighting how they foster a sustainable and collaborative geospatial knowledge ecosystem. This collection serves as a roadmap for the next generation of geoinformatics, where transparency and flexibility are foundational to addressing global environmental and social challenges.
Wang et al. (Fri,) studied this question.