Data Analysis of Land Use Change and Urban and Rural Impacts in Lagos State, Nigeria
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study examines land use change and impacts on urban and rural activity in Lagos State, Nigeria. To achieve this, multi-temporal land use and land cover (LULC) datasets derived from the GlobeLand30 product of years 2000 and 2010 for urban and rural areas of Lagos State were imported into ArcMap 10.6 and converted to raster files (raster thematic maps) for spatial analysis in the FRAGSTATS situated in the Patch Analyst. Thus, different landscape metrics were computed to generate statistical results. The results have shown that fragmentation of cultivated lands increased in the rural areas but decreased in the urban areas. Also, the findings display that land-use change resulted in incremental fragmentation of forest in the urban areas, and reduction in the rural areas. The fragmentation measure of diversity increased in the urban areas, while it decreased in the rural areas during the period of study. These results suggest that cultivated land fragmentation is a complex process connected with socio-economic trends at regional and local levels. In addition, this study has shown that landscape metrics can be used to understand the spatial pattern of LULC change in an urban-rural context. Finally, the outcomes of this study will help the policymakers at the three levels of governments in Nigeria to make crucial informed decisions about sustainable land use.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it