Global open data in local urban development: an actionable framework for adopting Global Human Settlement Layer (GHSL) in the Global South
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.038 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.026 | 0.035 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".