Observational Scale and Modeled Potential Residential Loss from a Storm Surge

Two geographically related questions with regard to hurricane-induced storm-surge impacts were investigated: (1) What observational scale of analysis is appropriate? (2) Is the effect of observational scale on model results predictable? These two research questions were investigated in the context of storm surge-induced impacts to single-family residential structures in Florida. The study was conducted for 21 coastal counties in Florida at five spatial scales of analysis: parcel, block, block group, tract, and county. The research findings reveal a monotonically decreasing relationship between predicted standardized residential loss (the ratio of predicted loss at scale X and the predicted loss at parcel scale) and the observational scale of analysis. This monotonic relationship was consistent for most Florida counties, primarily due to the notable spatial distribution of housing units and proximity to the coastline.

[1]  William A. V. Clark,et al.  The Effects of Data Aggregation in Statistical Analysis , 2010 .

[2]  Michael E. Hodgson,et al.  Effects of lidar post‐spacing and DEM resolution to mean slope estimation , 2009, Int. J. Geogr. Inf. Sci..

[3]  A modelling approach for estimating the frequency of sea level extremes and the impact of climate change in southeast Australia , 2009 .

[4]  W. S. Robinson,et al.  Ecological correlations and the behavior of individuals. , 1950, International journal of epidemiology.

[5]  E. Baker,et al.  The Costs of Hurricane Emergency Management Services: A Risk-Based Method for Calculating Property Owners' Fair Share , 2009 .

[6]  Stephen J. Walsh,et al.  Population and Environment Interactions: Spatial Considerations in Landscape Characterization and Modeling , 2008 .

[7]  J. A. Tullis,et al.  Rapid Assessment of Storm-Surge Inundation after Hurricane Katrina Utilizing a Modified Distance Interpolation Approach , 2007 .

[8]  J. Woodruff,et al.  Intense hurricane activity over the past 5,000 years controlled by El Niño and the West African monsoon , 2007, Nature.

[9]  Brent Yarnal,et al.  Vulnerability of Hampton Roads, Virginia to Storm-Surge Flooding and Sea-Level Rise , 2007 .

[10]  T. Hastie,et al.  Principal Curves , 2007 .

[11]  Tevfik Kosar,et al.  Towards a faster and improved ADCIRC (Advanced Multi-Dimensional CIRCulation) model , 2007 .

[12]  Gary Higgs,et al.  Measuring Potential Access to Primary Healthcare Services: The Influence of Alternative Spatial Representations of Population , 2006 .

[13]  Philip J. Schneider,et al.  HAZUS — Its Development and Its Future , 2006 .

[14]  Jianguo Wu,et al.  The modifiable areal unit problem and implications for landscape ecology , 1996, Landscape Ecology.

[15]  P. Webster,et al.  Changes in Tropical Cyclone Number, Duration, and Intensity in a Warming Environment , 2005, Science.

[16]  K. Emanuel.  Increasing destructiveness of tropical cyclones over the past 30 years , 2005, Nature.

[17]  M. E. Johnson,et al.  Hurricane Loss Estimation Models: Opportunities for Improving the State of the Art , 2004 .

[18]  V. Meentemeyer.  Geographical perspectives of space, time, and scale , 1989, Landscape Ecology.

[19]  T. Beer,et al.  Impact of Sea-level Rise and Storm Surges on a Coastal Community , 2003 .

[20]  K. Cheung,et al.  Modeling of storm-induced coastal flooding for emergency management , 2003 .

[21]  C. V. Anderson,et al.  The Federal Emergency Management Agency (FEMA) , 2002 .

[22]  V. Gornitz,et al.  Impacts of sea level rise in the New York City metropolitan area , 2001 .

[23]  Cynthia A. Brewer,et al.  Dasymetric Mapping and Areal Interpolation: Implementation and Evaluation , 2001 .

[24]  Mick Green,et al.  Behaviour of regression models under random aggregation , 2001 .

[25]  P. Atkinson,et al.  Spatial Scale Problems and Geostatistical Solutions: A Review , 2000 .

[26]  William H. Hooke,et al.  U.S. PARTICIPATION IN INTERNATIONAL DECADE FOR NATURAL DISASTER REDUCTION , 2000 .

[27]  D. Marceau.  The Scale Issue in the Social and Natural Sciences , 1999 .

[28]  D. Steel.  Using Local Census Data to Investigate Scale Eeects , 1997 .

[29]  Susan L. Cutter,et al.  The Role of Geographic Scale in Monitoring Environmental Justice , 1996 .

[30]  Peter F. Fisher,et al.  Modeling Sensitivity to Accuracy in Classified Imagery: A Study of Areal Interpolation by Dasymetric Mapping* , 1996 .

[31]  Kam‐biu Liu,et al.  Lake-sediment record of late Holocene hurricane activities from coastal Alabama , 1993 .

[32]  S. Levin.  The problem of pattern and scale in ecology , 1992 .

[33]  A. Raftery,et al.  Ice Floe Identification in Satellite Images Using Mathematical Morphology and Clustering about Principal Curves , 1992 .

[34]  G. Holland.  An Analytic Model of the Wind and Pressure Profiles in Hurricanes , 1980 .

[35]  S. Openshaw.  A million or so correlation coefficients : three experiments on the modifiable areal unit problem , 1979 .

[36]  D. Commerce.  Statistical abstract of the United States , 1978 .

[37]  B. Mandelbrot.  How Long Is the Coast of Britain? Statistical Self-Similarity and Fractional Dimension , 1967, Science.

[38]  C. E. Gehlke,et al.  Certain Effects of Grouping upon the Size of the Correlation Coefficient in Census Tract Material , 1934 .