A dasymetric method to spatially apportion tornado casualty counts

This paper describes a dasymetric technique to spatially apportion casualty counts from tornado events in the US Storm Prediction Center's database. Apportionment is a calculation of the number of casualties within the area of the tornado damage path and with respect to the underlying populatio...

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Bibliographic Details
Published in:Geomatics, natural hazards and risk natural hazards and risk, 2017-12, Vol.8 (2), p.1768-1782
Main Authors: Fricker, Tyler, Elsner, James B., Mesev, Victor, Jagger, Thomas H.
Format: Article
Language:English
Subjects:
Casualties
Dasymetric mapping
Demographic variables
Demographics
Demography
fatalities
Housing
injuries
Mathematical analysis
Methods
Metropolitan areas
Minority & ethnic groups
Population density
Poverty
Social factors
Spatial distribution
Spatial variations
Storm damage
Storm forecasting
Storms
tornado
Tornado damage
Tornadoes
Vulnerability
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Summary:This paper describes a dasymetric technique to spatially apportion casualty counts from tornado events in the US Storm Prediction Center's database. Apportionment is a calculation of the number of casualties within the area of the tornado damage path and with respect to the underlying population density. The method is illustrated with raster grids on tornadoes occurring between 1955 and 2016 within the most tornado-prone region of the United States. Results suggest a relatively uniform spatial distribution of tornado-induced casualties with slightly higher rates in the mid-south, particularly in northern Mississippi and Alabama, and also in many metropolitan areas. In addition, there is some degree of spatial variation over time, particularly clusters of high injury rates across the northern half of Alabama. Validation of the results at the county- and grid-level indicate that casualty numbers correlate strongly with the dasymetric estimates. Future work that includes socioeconomic variables (demographics, ethnicity, poverty and housing stock/value) might allow populations to be profiled with regards to vulnerability.
ISSN:1947-5705
1947-5713
DOI:10.1080/19475705.2017.1386724