Understanding inequalities in geographic accessibility to emergency cyclone shelters in Bangladesh under climate change
Bibliographic record
Abstract
This research aims to explore inequalities in geographic accessibility to emergency cyclone shelters in Bangladesh, a country in the Global South that is prone to natural disasters. We begin by quantifying the walking time to the nearest cyclone shelters as a basic measure of accessibility. Additionally, we compute a more practical measure of accessibility by considering crowding effects in shelters due to the interactions between supply (e.g., shelter capacity) and demand (e.g., population levels) using the two-step floating catchment area (2SFCA) method. Using these accessibility measures as a basis, we examine accessibility inequalities through the Gini index. Furthermore, we perform a statistical analysis with an equity lens to examine whether marginalized populations are disproportionately located in underserved areas with limited accessibility. The results reveal substantial inequalities in 2SFCA-based cyclone shelter accessibility across different regions. The statistical analysis results show that seniors, females, individuals with physical disabilities, and religious minorities are disproportionately located in areas where walking time to the nearest shelters exceeds the government guideline of 20 min. This study is one of the first attempts to understand the inequalities in geographic accessibility to emergency cyclone shelters in an under-examined low- and middle-income country (LMIC) in the Global South such as Bangladesh. By shedding light on the inequalities faced in accessing these critical facilities, our research contributes to the broader understanding of human mobility and accessibility in response to the increasing intensity and frequency of unexpected disruption events in the context of climate change.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".