Title page for ETD etd-11092012-092032


Type of Document Dissertation
Author Ward, Steven Matthew
Author's Email Address steven.matthew.ward@gmail.com
URN etd-11092012-092032
Title A Discourse on Geospatial Technology Applications in Predictive Analytics and Evidence-Based Decision Support for Disaster Research and Management
Degree Doctor of Philosophy (Ph.D.)
Department Geography & Anthropology
Advisory Committee
Advisor Name Title
Leitner, John Committee Chair
Gall, Melanie Committee Member
Rohlie, Robert Committee Member
Wang, Fahui Committee Member
Chang, Sun Joseph Dean's Representative
Keywords
  • Hurricane Katrina
  • Disaster Science
  • Geospatial Technology
  • Resilience
  • Spatial Recovery
  • Recovery
  • Geography
  • Hazards
  • GIS
Date of Defense 2012-11-01
Availability unrestricted
Abstract
Continued population growth and development in vulnerable locations across the world are creating a new geography of hazards and disasters. Increasing storm frequencies coupled with unrelenting efforts to control flooding through structural means will undoubtedly intensify the intersection between flood hazards and humans. Accordingly, the baseline capacity of places to prepare for and rebound from disaster events adequately is negatively impacted. Hurricane Katrina brought this reality to the forefront of disaster science and management in 2005. Concurrent with the increased awareness of evolving hazardscapes has been the identification of deficiencies in how components of disasters are studied and managed. The topic of recovery represents one of the least understood elements in hazards geography, owing most of its existing catalogue of knowledge to social sciences and public administration. This dissertation summarizes an effort to develop a spatial metric which quantifies recovery from flood events as well as the evaluation of applying these research based methods in practical environments. The study theorizes that recovery can be measured by assessing the proximity of critical elements within the built environment. These elements (buildings) represent hubs of social activity necessary for social networks to flourish in post disaster settings. It goes on to evaluate and apply this metric in both New Orleans, LA and Carinthia, Austria, in order to identify cultural bias in model design prior to conducting a case study where research based predictive analytics are used in a real world mitigation plan. The outcome of the study suggests that recovery is indeed measurable spatially and is heavily influenced by culture and scale. By integrating this new understanding of recovery into potential mitigation strategies, planning for risk reduction expenditures can more appropriately consider the drivers of place-specific vulnerability.
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