LASSO Model of Postdisaster Housing Recovery: Case Study of Hurricane Sandy

AbstractUnderstanding the recovery phase of the disaster cycle is still in its infancy. A thorough grasp of recovery can lead to effective postdisaster planning, which is essential for enabling communities to recover quickly from natural and human-made catastrophes. Recent major disasters such as Hurricanes Sandy and Katrina have revealed the inability of existing policies and planning to promptly restore infrastructure, residential properties, and commercial activities in affected communities. While many studies examined the macroeconomic effects of certain policies on overall recovery, very few, if any, have focused on modeling the housing recovery decisions of households and their contributions to the community’s recovery. The reestablishment of housing is a crucial parameter in understanding recovery processes because it has a ripple effect on the overall timing of recovery. The objective of this study is to create a model capable of predicting postdisaster housing recovery decisions founded on the da...

[1]  A. Fothergill,et al.  Race, ethnicity and disasters in the United States: a review of the literature. , 1999, Disasters.

[2]  Andrew Curtis,et al.  Spatial video data collection in a post-disaster landscape: The Tuscaloosa Tornado of April 27th 2011 , 2012 .

[3]  Jacob Cohen,et al.  A power primer. , 1992, Psychological bulletin.

[4]  Jorge Cadima Departamento de Matematica.  Loading and correlations in the interpretation of principle compenents , 1995 .

[5]  Susan L. Cutter,et al.  Using Building Permits to Monitor Disaster Recovery: A Spatio-Temporal Case Study of Coastal Mississippi Following Hurricane Katrina , 2010 .

[6]  V. Keith,et al.  Church-based social capital, networks and geographical scale: Katrina evacuation, relocation, and recovery in a New Orleans Vietnamese American community , 2008 .

[7]  K. Roeder,et al.  Screen and clean: a tool for identifying interactions in genome‐wide association studies , 2010, Genetic epidemiology.

[8]  V. Storr,et al.  “There’s No Place like New Orleans”: Sense of Place and Community Recovery in the Ninth Ward after Hurricane Katrina , 2009 .

[9]  James M. Dahlhamer,et al.  Business Disruption, Preparedness And Recovery: Lessons From The Northridge Earthquake , 1997 .

[10]  Enrico L Quarantelli,et al.  General and Particular Observations on Sheltering and Housing in American Disasters , 2010 .

[11]  Peter Bühlmann,et al.  p-Values for High-Dimensional Regression , 2008, 0811.2177.

[12]  A. Curtis,et al.  Identifying Spatial Patterns of Recovery and Abandonment in the Post-Katrina Holy Cross Neighborhood of New Orleans , 2010 .

[13]  Ivan Damnjanovic,et al.  Agent‐Based Modeling of Behavioral Housing Recovery Following Disasters , 2012, Comput. Aided Civ. Infrastructure Eng..

[14]  Edward L Kick,et al.  Repetitive flood victims and acceptance of FEMA mitigation offers: an analysis with community-system policy implications. , 2011, Disasters.

[15]  Stephanie E. Chang,et al.  ResilUS: A Community Based Disaster Resilience Model , 2011 .

[16]  Stephanie E. Chang,et al.  Modeling Community Recovery from Earthquakes , 2006 .

[17]  Scott B. Miles,et al.  Foundations of community disaster resilience: well-being, identity, services, and capitals , 2015, Environmental Hazards and Resilience.

[18]  Geoffrey J. D. Hewings,et al.  Retrofit Priority of Transport Network Links under an Earthquake , 2003 .

[19]  Adam Rose,et al.  Business Interruption Impacts of a Terrorist Attack on the Electric Power System of Los Angeles: Customer Resilience to a Total Blackout , 2007, Risk analysis : an official publication of the Society for Risk Analysis.

[20]  I. Jolliffe.  Rotation of principal components: choice of normalization constraints , 1995 .

[21]  Walter Gillis Peacock,et al.  Inequities in Long-Term Housing Recovery After Disasters , 2014 .

[22]  L. Wasserman,et al.  HIGH DIMENSIONAL VARIABLE SELECTION. , 2007, Annals of statistics.

[23]  Thomas E. Shriver,et al.  Contested Environmental Hazards and Community Conflict over Relocation. , 2005 .

[24]  Robert I. Kutak.  The Sociology of Crises: The Louisville Flood of 1937 , 1938 .

[25]  M. Lindell,et al.  Housing reconstruction after two major earthquakes: the 1994 Northridge earthquake in the United States and the 1999 Chi-Chi earthquake in Taiwan. , 2004, Disasters.

[26]  Bernard Amadei,et al.  A qualitative comparative analysis of neighborhood recovery following Hurricane Katrina , 2014 .

[27]  S. Sanders,et al.  Chapter 2 Lessons Learned on Forced Relocation of Older Adults , 2004 .

[28]  M. Lindell,et al.  Household Adjustment to Earthquake Hazard , 2000 .

[29]  Peng Zhao,et al.  On Model Selection Consistency of Lasso , 2006, J. Mach. Learn. Res..

[30]  Yang Zhang.  Will Natural Disasters Accelerate Neighborhood Decline? A Discrete-Time Hazard Analysis of Residential Property Vacancy and Abandonment before and after Hurricane Andrew in Miami-Dade County (1991–2000) , 2012 .

[31]  Jacques Henry,et al.  Return or relocate? An inductive analysis of decision-making in a disaster. , 2013, Disasters.

[32]  Masanobu Shinozuka,et al.  Integrating Transportation Network and Regional Economic Models to Estimate the Costs of a Large Urban Earthquake , 2001 .

[33]  Walter Gillis Peacock,et al.  Planning for Housing Recovery? Lessons Learned From Hurricane Andrew , 2009 .

[34]  W. Peacock,et al.  Sheltering and Housing Recovery Following Disaster , 2007 .

[35]  Charlene K. Baker,et al.  Rebuild or Relocate? Resilience and Postdisaster Decision-Making After Hurricane Sandy , 2015, American journal of community psychology.

[36]  Sukumar Ganapati,et al.  Enabling Participatory Planning After Disasters: A Case Study of the World Bank's Housing Reconstruction in Turkey , 2008 .

[37]  R. Tibshirani,et al.  A SIGNIFICANCE TEST FOR THE LASSO. , 2013, Annals of statistics.