Identification of Areas for Sustainable Settlements in Highly Conflicted Protected Areas Using ArcGIS Spatial Analyst: A Case of Chobe District, Botswana
Bibliographic record
Abstract
The increasing human settlements around protected areas is a significant cause of the shrinking size of conservation areas globally. This is an alarming situation, especially in countries where protected areas significantly contribute to the National economy. The use of new tools to solve Land use conflicts that exist around protected areas is needed. This study employed the weighted overlay analysis using ecological, social and economic factors to model suitability for developing settlements in Chobe District, an international tourist destination in Northern Botswana, using ArcGIS spatial analyst. The majority of the land (83.4%) in Chobe is not suitable for settlement, while suitable areas that could for settlement remain relatively small (0.3% of the land). This study suggests that more land for settling the increasing population be created out of Chobe District to avoid encroachment on the conservation areas. The study demonstrated that ArcGIS spatial analyst model builder could be integrated into the land resource planning process of protected areas, making it a handy tool for the analysis and the identification of ecological, economic and social factors, thus establishing an excellent area for further research, especially for sustainable management of protected areas.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".