Examining urban expansion in Abeokuta through the lens of its economic development cluster: A geospatial approach utilising Random Forest and Batty’s entropy
Bibliographic record
Abstract
This study analysed urban expansion of Abeokuta within the framework of an economic development cluster initiative pivoted on the city. It addressed the trend, distance, direction and pattern of urban expansion to provide baseline information on targeted supports and interventions for the viability of the initiative. Previous studies have analysed urban expansion using concentric zone theory which typically considers a single city centre. This is contrary to what is obtainable in contemporary cities like Abeokuta as critiqued by the multiple nuclei theory (MNT). We therefore considered the effects of three major centres in Abeokuta based on the MNT. Google Earth Engine was used for data collection and analysis. Data were acquired and preprocessed from Landsat 7 ETM+, Landsat 8 OLI/TIRS and Landsat 9 OLI-2/TIRS-2 sensors for 2003, 2013 and 2023, respectively. Supervised image classification through a Random Forest algorithm was used to analyse land use and land cover change. Batty’s entropy was used to estimate urban expansion in concentric ring zones around three major city centres. Findings revealed a decline of 43.3% in dense vegetation and 29.3% increase in both built-up areas and bare land within two decades (2003-2023). Urban expansion is higher around Old Governor's Office ( ∆ H B norm = 0.13) than the King's Palace ( ∆ H B norm = 0.11) which is higher than the New Governor's Office ( ∆ H B norm = 0.07). This study established that observing multiple growth points using concentric ring zoning other than the city centre provides a better theoretical understanding of urban expansion of contemporary polycentric cities. • Decline of 43.3% in dense vegetation and 29.3% increase in both built-up and bare land within two decades • High Batty’s entropies indicate increasing urban expansion from three different centres • Urban expansion is higher around the Old Governor's Office than the King's Palace which is higher than the New Governor's Office • Multiple growth points using concentric ring zoning provide a better theoretical understanding of urban expansion of contemporary polycentric cities
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".