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Mapping Tornado Hotspots in the U.S.: Spatial and Temporal Analysis Across the U.S.

Authors: Seong Nam Hwang;

Mapping Tornado Hotspots in the U.S.: Spatial and Temporal Analysis Across the U.S.

Abstract

Tornadoes rank among the most damaging weather events in the US. They put lives at risk, wreck infrastructure, and upset economic systems. This study examines data from 1950 to 2022 using Geographic Information Systems (GIS) to uncover tornado hotspots, pinpoint high-risk areas, and grasp why some regions face bigger threats. States such as Texas, Alabama, and Oklahoma proved to be risk zones due to high tornado activity, which leads to major damage, deaths, and money losses. Texas tops the list as the state tornadoes hit hardest, with the most recorded events and severe economic blows. Season patterns show tornadoes strike most in late spring and early summer creating a pressing need to get ready during these months. The research also sheds light on the toll on key infrastructure, such as U.S. interstate highways. These results offer key insights to steer disaster management and mitigate risks for the most exposed areas.

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Powered by OpenAIRE graph
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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