Analysis of land use/land cover change and urbanization to achieving sustainable development in Lagos, Nigeria
Bibliographic record
Abstract
The perception of land use change dynamics is important for sustainable land resource management in developing countries such as Nigeria where most of the people depend on natural resources from the land for their livelihoods. Globally, land use and land cover (LULC) change is a major factor of environmental and climate change; rapid urban growth has caused some environmental problems such as loss of agricultural land, land degradation, water shortage, and pollution. This dissertation examines LULC change and urbanization, and the implications on sustainable development (SD) in Lagos Nigeria using spatial analysis. The dissertation contains three inter-related studies as follows: first research examines the urban sprawl and growth prediction for Lagos using GlobeLand30 product combined with GIS and Cellular Automata model. The next study ascertains land use change and impacts on urban and rural areas utilizing landscape metrics (LMs) and FRAGSTATS algorithm for statistical analysis. The final study reviews the agricultural land use and urban sustainability. This study demonstrated the efficiency of GlobeLand30 data and GIS integration with CA_Markov for LULC change analysis and prediction of future LULC scenarios. This research has shown that LMs are universal, potent, and useful to understand the spatial pattern of LULC change in urban-rural context. In conclusion, the outcomes of this study will help the urban planners, policymakers, and the governments in Nigeria to make crucial informed decisions about sustainable urban growth and improved land use management.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".