Drivers and Spatial extent of Urban Development in Flood-prone Areas in Metropolitan Lagos
Bibliographic record
Abstract
Urban development in flood-prone areas has created many environmental challenges in many cities in Nigeria. This survey-based study examined the drivers and spatial extent of development in floodplains in metropolitan Lagos. A total of 1,031 (7.2%) buildings out of 14,273 were systematically selected along 211 streets out of 1,403 prone to flood, and a structured questionnaire was administered to heads of households to determine the factors influencing development in floodplains. A Thematic Mapper of 1990, Enhanced Thematic Mapper of 2000 and the Google Earth Landsat of 2014 were also analysed in ILWIS 3.3 Academic and Arc-GIs 10.2 to determine the extent of development in floodplains. The study revealed, in order of significance, closeness to place of work, nearness to market, closeness to children’s schools, low rent, low income, and family affinity as factors that influenced the development in floodplains in the Lagos metropolis. Ineffective control of development and inadequate compliance with planning and building regulations were additional factors. Urban development in flood-prone areas in Lagos increased from 9.3 km2 in 1990 to 10.50 km2 in 2000 and 17.80 km2 in 2014. The study recommends that floodplains should be acquired and effectively protected to prevent any form of physical development; they should serve as natural sink for storm water and urban green for passive recreation. Urban development regulations should be strictly enforced by the relevant government agencies, such as the Lagos State Building Control Agency in the Ministry of Physical Planning and Urban Development.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".