Spatiotemporal Distribution Pattern and Spatial Clustering of Landslide-and Flood-Prone Areas in Metropolitan Palapa, Indonesia
Bibliographic record
Abstract
The rapid development of urban areas is one of the driving factors for hazard exposure which causes vulnerability and environmental degradation.Environmental degradation causes vulnerability to disasters which will have implications for a decrease in economic benefits and an increase in community poverty.Landslides and floods are frequent natural disasters that cause environmental degradation.To reduce the risk of landslides and floods and formulate development policies and strategies in disaster-prone areas is necessary to identify landslides and flood areas.This study aims to identify and detect areas prone to landslides and floods in Metropolitan Palapa using GIS analysis and Moran's Local Index based on LISA statistics.The analysis results show that the northern to northeastern areas of Metropolitan Palapa (Pariaman regency and Pariaman municipal) are areas with high vulnerability to landslides, with high positive spatial associations of landslide occurrence and intensity.The area has an altitude between 350 to >1100 above sea level and a slope between 15 to >45%.Meanwhile, Padang and Pariaman municipal are areas that are vulnerable to flood hazards, and this is because these areas are lowlands/coastal areas.We can implement strategies and policies for the Palapa Metropolitan area, including a) limiting development in the northern region, b) applying engineering to areas prone to landslides by applying cut and fill techniques, c) improvement of drainage systems in areas prone to flooding; d) increase public awareness to care and provide incentives to people who care about the environment.Appropriate strategies and policies will reduce environmental degradation.We required disaster-based development planning to reduce environmental degradation, but the data is often an obstacle.
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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 | 0.001 |
| 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) | 0.001 | 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".