A Review of Geospatial Information Technology for Natural Disaster Management in Developing Countries
Bibliographic record
Abstract
Disasters are deadly and destructive events, particularly in developing countries where economic, social, political and cultural factors increase natural hazard vulnerability. The recent devastation of the Haiti earthquake on January 12th, 2010 was a prime example of the human toll a natural disaster can take in developing regions of the world. There is an imminent need to improve natural disaster management capacity in developing countries to reduce disaster impacts. Given that disasters are spatial phenomenon, the application of geospatial information technology (GIT) is essential to the natural disaster management process. However, in developing countries there are numerous barriers to the effective use of GIT, especially at the local level, including limited financial and human resources and a lack of critical spatial data required to support GIT use to improve disaster management related decision making processes. The results of a thorough literature review suggests that currently available free and open source GIT (FOS GIT) offers great potential to overcome some of these barriers. Thus, disaster management practitioners in developing countries could harness this potential in an attempt to reduce hazard vulnerability and improve disaster management capacity. The use of FOS GIT significantly reduces software costs and can help build local level GIT knowledge/technical skills that are required for successful GIT implementation.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".