Using Building Permits to Monitor Disaster Recovery: A Spatio-Temporal Case Study of Coastal Mississippi Following Hurricane Katrina

Article excerpt


Recovery from a natural disaster is understood as a dynamic and multifaceted process, yet we know little about its spatial and temporal variability. The inability of most methods to provide information about the pace and progression of disaster recovery leads to the problematic conclusion that recovery is spatially uniform and consistent from one time period to another (Cutter et al. 2006; Zottarelli 2008).

Hurricane Katrina stands as the most damaging disaster in U.S. history. One model suggests that long-term recovery of the Gulf Coast could take 11 years (Kates et al. 2006). Within that time, several billion dollars of aid and countless hours will be spent rebuilding the damaged structures and impacted institutions. While many studies following Hurricane Katrina have revealed initial recovery disparities driven by class and gender (Cutter et al. 2006; Elliott and Pais 2006), few methods or metrics are capable of capturing trends of recovery throughout the entire impacted area and over longer time frames (Pais and Elliott 2008; Zottarelli 2008). A disaster of this magnitude affords an opportunity to study how long-term recovery is manifested within an affected landscape and to uncover the drivers of recovery as they shift through space and time.

In this paper we use a spatial scan statistic, SaTScan, to examine the space-time trends of built environment recovery following Hurricane Katrina. Scan statistics are a common tool used to determine if points (in this case, rebuilding activities) are randomly distributed in space and time or if they are clustered (Kulldorff 1997; Kulldorff 2005). This research specifically investigates the spatial and temporal patterns of recovery using building permits issued in three municipalities on Mississippi's Gulf Coast. The spatio-temporal relationships between permits issued, damage level, and the pre-event number of housing units in the affected area form the basis for this inquiry. We question whether spatial and temporal clusters of building permits, if they exist, are related to the level of damage caused by the storm or the density of pre-event housing. The techniques employed here improve our understanding of recovery with data and methods which highlight recovery as a process rather than as an outcome.

What is Recovery?

Defining what recovery is and what it means for affected communities is fundamental to finding appropriate ways to measure it. Recovery varies depending on the context of the disaster, the level of impact and the extent of the damage, and the pre-event conditions (Bates and Peacock 1989; Quarantelli 1999). In addition to physical destruction and disruption, disasters interrupt the highly connected social fabric of communities (Bolin 1976). While some of the literature addresses recovery as a multi-dimensional concept from a theoretical perspective, most case studies of recovery distill a single aspect of the recovery process for in-depth analysis, focusing on specific recovery topics such psycho-social (Gault et al. 2005), institutional (Rubin and Barbee 1985), economic and business (Chang 2000; Webb et al. 2002), built environment (Liu and Plyer 2009; McCarthy and Hanson 2008), or the natural environment (Orr and Ogden 1992).

Oftentimes, the term recovery has been used interchangeably with rebuilding, restoration, and redevelopment (Mileti 1999). These phases are instrumental to recovery. For example, rebuilding of residential, commercial, and public structures not only requires the largest amount of resources, but is also an important precursor to repopulation, and the reestablishment of commerce and social networks (Rubin et al 1985; Kamel and LoukaitouSideris 2004). It must be acknowledged, however, that these other indicators are inadequate when trying to generalize about community recovery as a process, one that strives to restore, rebuild, and reshape the physical infrastructure, natural environment, and socio-economic systems through pre-event planning and post-event actions (Smith and Wenger 2006). …