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This paper proposes a data-driven framework for quantifying disaster vulnerability using social media analytics, repurposing a previously collected Twitter dataset originally intended for evacuation behavior analysis. After refining the dataset to isolate signals of distress and need, a category based classification strategy is introduced in which thematic dictionaries guide the grouping of Tweets based on the semantic similarity of their embeddings. Focusing on Hurricane Dorian, a compound disaster during the COVID-19 pandemic characterized by high distress and negative sentiment, a weighted amplification factor is incorporated that prioritizes Tweet categories based on the immediacy of impact on human life, while normalizing by Tweet volume and population density. The resulting Media Impact Index (MII) is calculated at the Census Block Group (CBG) level for the United States. To demonstrate the cross-cultural flexibility of the pipeline, the same methodology is applied to Typhoon Hagibis in Japan, with a comparable vulnerability index generated at the district level. The findings suggest that the proposed framework can provide emergency management agencies with a scalable and adaptable tool for identifying and prioritizing vulnerable regions in diverse types of disasters and sociocultural contexts.
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Dasymetric population mapping based on US census data and 30-m gridded estimates of impervious surface
Abstract Assessment of socio-environmental problems and the search for solutions often require intersecting geospatial data on environmental factors and human population densities. In the United States, Census data is the most common source for information on population. However, timely acquisition of such data at sufficient spatial resolution can be problematic, especially in cases where the analysis area spans urban-rural gradients. With this data release, we provide a 30-m resolution population estimate for the contiguous United States. The workflow dasymetrically distributes Census block level population estimates across all non-transportation impervious surfaces within each Census block. The methodology is updatable using the most recent Census data and remote sensing-based observations of impervious surface area. The dataset, known as the U.G.L.I (updatable gridded lightweight impervious) population dataset, compares favorably against other population data sources, and provides a useful balance between resolution and complexity.
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- PAR ID:
- 10378047
- Publisher / Repository:
- Nature
- Date Published:
- Journal Name:
- Scientific Data
- Volume:
- 9
- Issue:
- 1
- ISSN:
- 2052-4463
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1038/s41597-022-01603-z
