Mapping spatial and temporal dynamics in urban growth: The case of secondary cities in northern Ghana
Bibliographic record
Abstract
Urbanization induced growth of secondary cities presents several issues and challenges for sustainable development. Yet, secondary cities continue to receive less attention from scholars, city planners and policymakers in Africa. Understanding the spatial and temporal dynamics of secondary cities is critical for achieving Sustainable Development Goal 11. This paper examines the emerging spatial and temporal evolution of two secondary cities in Northern Ghana. The paper utilizes raster data (1990–2019) and applied landscape metrics to analyze spatial development in Wa and Bolgatanga municipalities along three concentric rings. The results show significant increase in built areas over the study period. Urban development in the two cities is becoming more or less fragmented, dispersed and contiguous. Inadequate spatial planning, weakly regulated development and uncoordinated land markets account for the fragmentated spatial forms. The two cities exhibit a monocentric form that fluctuates, is dynamic, and discontinuous. The paper reflects on the implications of the findings and suggests the need for a planned extension of secondary cities in Africa to generate efficient urban forms, curtail sprawl and protect the natural environment.
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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 | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".