Graduation Year


Document Type




Degree Name

Doctor of Philosophy (Ph.D.)

Degree Granting Department

Geography, Environment and Planning

Major Professor

Graham Tobin, Ph.D.

Co-Major Professor

Ambe Njoh, Ph.D.

Committee Member

Fenda Akiwumi, Ph.D.

Committee Member

Robin Ersing, Ph.D.

Committee Member

Russell Ivy, Ph.D.


Disaster, emergency management, human geography


Significant damage and loss is experienced every year due to natural hazards such as hurricanes, tornadoes, droughts, floods, wildfires, volcanoes, and earthquakes. NOAA’s National Center for Environmental Information (NCEI) reports that in 2016 the United States experienced more than a dozen climate disaster events with damages and loss in excess of a billion dollars (NOAA National Centers for Environmental Information, 2017). Identifying vulnerabilities and risk associated with disaster threats is now a major focus of natural hazards research. Natural hazards research has yielded numerous theoretical frameworks over the last 25 years that have explained important elements of risk and vulnerability in disasters (Birkmann, 2016b). However, there has been much less progress made in operationalizing these frameworks. While the theory is well established, one of the more pressing challenges before us is the lack of development of user-friendly and flexible risk assessment techniques for emergency managers (Mustafa et al., 2011).

The trend in operationalizing natural hazards, theoretical frameworks has been the development of general, all-purpose, static models to measure vulnerability. However, important missing elements in the current hazards literature is the need for an operationalized risk model that is (1) simple, quick and easy to use, (2) flexible for changing conditions, and (3) site-specific for various geographic locations. Many of the current models for determining risk and vulnerability are very complex and time consuming to calculate and thus make them of little use for emergency and risk managers. In addition, little analysis has been conducted to see if a flexible risk identification measurement system could be developed. As vulnerability and risk become fluid due to changing conditions (environmental—hazard and location) and circumstances (social, economic, and political), our measurement tools need to be able to capture these differences in order to be effective.

This dissertation examines whether the Pressure and Release (PAR) natural hazards, theoretical framework can be operationalized using financial risk ratio methods. Specifically, it analyzes risk ratios using key vulnerability indicators to identify escalating vulnerability and ultimately predict risk. A structured modeling approach was used to identify key vulnerability indicators and develop risk ratios. These are applied to a case study to demonstrate whether this new approach can identify emerging risk trends. My research suggests that instead of operationalizing natural hazards theoretical frameworks using the current static, aggregate index method, a flexible risk ratio method could provide a new, viable option.

Included in

Geography Commons