Global open data in local urban development: an actionable framework for adopting Global Human Settlement Layer (GHSL) in the Global South - Sujit Kumar Sikder; Mohit Kapoor; Prajwal Parmar; Md. Tariqul Islam; Kh Md Nahiduzzaman (Sustainable Communities)
Introduction This study presents a comprehensive analysis of the applicability and usage trends of Global Human Settlement Layer (GHSL) in local urban development . The persistent scarcity of high-quality local data for urban areas in these regions remains a critical concern. This data deficiency underscores the potential value of adopting global open data products such as GHSL to bridge information gaps.Material and method This research adopts a bibliometric analysis and a systematic literature review to critically explore the existing state-of-arts on GHSL products and their utilization patterns within the context of the Global South. A total of 830 journal articles were extracted from Scopus (376) and Web of Science (453), 57 articles considered for further review and analysis.Results The findings reveal significant limitations of widely used population grids, e.g. GHS-Pop, particularly for their coarse spatial resolution. Recent studies have reported their poor performance, particularly when applied at local scales. Consequently, this study emphasizes the necessity of incorporating additional field-level data collection efforts and engaging stakeholders for validations when utilizing GHSL products for local policy-making. An actionable conceptual framework introduced based on the Social–Ecological–Technological–System (SETS), which can enhance the adoption of GHSL to facilitate local urban development strategies in the Global South.Conclusion The proposed conceptual framework offers a holistic methodology for integrating GHSL into local urban policy frameworks, addressing socio-ecological vulnerabilities, technological limitations, and policy implementation challenges. Future research should focus on integrating GHSL with national and local datasets to enhance spatial resolution and contextual relevance.