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Tornado Risk and Impact Analysis Using Dasymetric Modeling

Explore the impact of accurate population data in disaster planning and recovery using Dasymetric Modeling for tornado risk analysis. This presentation demonstrates a technique to improve data quality and create precise population distributions for better risk assessment. Learn how to disaggregate data sources and analyze tornado data effectively with dasymetric mapping.

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Tornado Risk and Impact Analysis Using Dasymetric Modeling

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  1. Tornado Risk and Impact Analysis Using Dasymetric Modeling John D. Crabtree, Ph.D. Department of Computer Science and Information Systems University of North Alabama

  2. Abstract Accurate information is critical to planners and first responders. Inaccuracies and inconsistencies can have a deleterious impact on disaster planning, preparedness, emergency response, disaster recovery and risk mitigation. This presentation demonstrates a technique that can be used to improve the quality of any population data set and its application to the analysis of eight years of tornado data from the National Weather Service. Population maps created from any data source (e.g., census tract, census block, tax assessment, etc.) can be intelligently disaggregated to create more accurate population distributions that can then be analyzed through the intersection of actual or projected risk polygons.

  3. Problem Statement • Accurate disaster maps are important • Facilitate emergency response efforts • Aid in disaster recovery • Improve risk mitigation and management • Population data are critical in map creation • Common data analysis methods are poor • FEMA HAZUS-MH algorithm excludes the entire census unit if the centroid of the unit is not within the risk area (e.g., tornado warning polygon)

  4. Dasymetric Mapping • Dasymetric: Russian transliteration of the Greek terms for density and measuring (dazimetricheskiy) translated into English • Displays surface data by partitioning space into zones that represent the underlying surface variation • Often used to disaggregate population data • Uses ancillary data set

  5. 41,635 94,298 62,138

  6. Hot Spot Analysis

  7. Current and Future Research • Statistical comparison of accuracy of density distribution • HAZUS-HM • Census Tract • Dasymetric • Census Block • Compare dasymetric census block to property tax assessment data • Compare various dasymetric algorithms

  8. Acknowledgements • Cameron Martin, Geography Student, University of North Alabama • Conference Poster Display • Jeremy Mennis, Associate Professor, Temple University • Torrin Hultgren, Graduate Student, University of Colorado

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