Doctor of Philosophy / Department of Geography / Bimal K. Paul / Given the recent recognition that not only physical processes, but social, political and economic aspects of hazards determine vulnerability and impact of an event, the next logical step would seem to be the development of classification systems that address those factors. Classifications for natural disasters, such as the Fujita Scale for tornadoes and the Saffir-Simpson hurricane scale, focus on the physical properties of the event, not the impact on a community. Pre-event vulnerability to a natural hazard is determined by many factors, such as age, race, income and gender, as well as infrastructure such as density of the built environment and health of the industrial base. The behavior of residents in the community, construction quality of shelters and warning system effectiveness also affect vulnerability. If pre-event vulnerability is to be determined by such factors, post-event impact should, at least in part, be as well. The goal of this research was to develop the Tornado Impact-Community Vulnerability Index (TICV) that utilizes variables such as the number of persons killed, economic impacts and social vulnerability to describe to the level of impact a tornado event has on community. As tornadoes that strike unpopulated areas are often difficult to classify, even in the traditional sense, the TICV will take into consideration only events that strike communities with defined political boundaries, or “places” according to the U.S. Census Bureau. By assigning a rating to the impact, this index will allow the severity of the storm to be understood in terms of its effect on a specific community and hence its impact, rather than an physically-based rating that gives only a broad, general indication of its physical strength.
| Identifer | oai:union.ndltd.org:KSU/oai:krex.k-state.edu:2097/8531 |
| Date | January 1900 |
| Creators | Stimers, Mitchel James |
| Publisher | Kansas State University |
| Source Sets | K-State Research Exchange |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
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