Effects of Land Use Data Spatial Resolution on SDG Indicator 11.3.1 (Urban Expansion) Assessments: A Case Study Across Ethiopia
Bibliographic record
Abstract
Geospatial data play a significant role in the United Nation’s Sustainable Development Goals, particularly through assessments of monitoring indicators. Sustainable Development Goal (SDG) Indicator 11.3.1 assessments utilize land and population geospatial data to monitor urban expansion, and were implemented to enhance inclusive and sustainable urbanization, and capacity for participatory, integrated and sustainable human settlement planning and management in all countries by 2030. Data-limited countries often rely on accessible, wide-coverage geospatial datasets for SDG Indicator 11.3.1 assessments which may have characteristics (e.g., coarse spatial resolution) influential to assessment outcomes. The presented work examines the effect of land use data spatial resolution on SDG Indicator 11.3.1 assessment components including urban area delineation, SDG Indicator 11.3.1 and supporting spatial metrics, spatial patterns of urban land development, and land use change patterns for urbanizing areas in Ethiopia from 2016 to 2020. A comparison was made between a single land use map at the spatial resolution in which it was originally produced, 30 m, and at a majority-resampled spatial resolution comparable to many global coverage datasets, 90 m. Analyses revealed changes in the urban areas identified, observed boundaries of urban areas, and all quantified metrics from 30 m resolution to 90 m resolution, with the decrease in resolution resulting in smaller urban areas being missed and differences in the delineated hinterland areas connected to an urban core. Statistical testing indicated significant differences in SDG Indicator 11.3.1 values, developed land use area per capita, and spatial patterns of urban development between the two spatial resolutions. The relative order of land use conversion types remained similar across both resolutions, with agricultural land experiencing the greatest conversion to developed land, followed by rangeland and forest, although the total area of each conversion type differed. This study illustrates the possible discrepancies in SDG Indicator 11.3.1 and related outputs when utilizing differing resolution datasets and the importance of data characteristic consideration when conducting SDG Indicator 11.3.1 assessments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".