Application of Geographic Information Systems (GIS) for Measuring the Impact Income Vulnerability on Rural Households: A Case Study of the 2010 Floods in Pakistan
Bibliographic record
Abstract
While the economic impact of natural disasters has been studied extensively, there are rather few studies that have addressed their impact on household income. This research tries to fill this gap by analyzing their actual effect on household income caused by the 2010 floods. We look at the impact of natural disasters on rural households in Pakistan after a massive flooding event in 2010. We used the difference-in-difference (DID) approach, which showed statistical significance at 1 percent. We also calculated the household distance from the rivers to see how vulnerable household income is to such kinds of shock-like floods. The results showed that the sample households living near had more impact as compared to the ones living far. Stata and Geographical Information System (GIS) software packages have been used for regression analysis and calculation of distance, respectively. This research will not only give insight in order to understand household income losses but will allow government, policymakers, and International Aid agencies to plan, make countermeasure strategies before designing post-disaster projects. After taking into account the effect of floods, which tend to have far more impact on the households, which are located near the source of the flooding. In this case, they need to focus more in terms of reconstruction of infrastructure, particularly for the households which are near these flooded areas. Firstly, this finding can give policymakers insight in terms of strategies to develop agriculture and non-agriculture employment opportunities. Secondly, it is essential to reduce income vulnerability and improve rural household finance economic conditions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".