The Roots of Community Resilience: A Comparative Analysis of Structural Change in Four Gulf Coast Hurricane Response Networks

The Roots of Community Resilience: A Comparative Analysis of Structural Change in Four Gulf Coast Hurricane Response Networks

by Thomas W. Haase, Gunes Ertan, and Louise K. Comfort

Abstract

Despite the emphasis on resilience, disasters continue to challenge the response capacities of communities around the United States. These challenges are generated by the complexities and uncertainties present in the post-disaster environment. This article presents the findings of an exploratory investigation into the development and evolution of four disaster response networks that formed along the Gulf Coast, Hurricane Katrina and Hurricane Rita in 2005, and Hurricane Gustav and Hurricane Ike in 2008. Using data collected from newspaper articles that referenced each hurricane during a period that spanned six days prior to landfall to twenty-two days after landfall, we identified the organizations that participated in each response network. We then used UCINET 6 to calculate network density and degree centralization, plotted longitudinally by date, and evaluated whether each network underwent structural change. The findings demonstrate that all four response networks underwent structural change, as a large heterogeneous collection of response organizations came together, collected and disseminated information, and sought to identify and implement solutions that would address the needs of those affected by the disaster event. While additional research is necessary to reveal the causal factors behind these structural changes, the findings presented in this article suggest that investments in information communication technologies, such as those made by the state of Louisiana after Hurricane Katrina, can help to facilitate the resilience of disaster response networks.

Suggested Citation

Haase, Thomas W., Gunes Ertan, and Louise K. Comfort. “The Roots of Community Resilience: A Comparative Analysis of Structural Change in Four Gulf Coast Hurricane Response Networks.” Homeland Security Affairs 13, Article 9 (October 2017). https://www.hsaj.org/articles/14095

 


 

Introduction

The concept of resilience has become a central focus of emphasis for disaster and emergency management researchers and policy-makers. The United States Department of State officially recognized resilience, defined as “the ability to adapt to changing conditions and prepare for, withstand, and rapidly recover from disruption.”1 The U.S. National Academy of Sciences (2012) further refined this definition in its report, Disaster Resilience: A National Imperative, which serves as a working guide to resilience studies in both research and practice. Likewise, the Department of Homeland Security indicated that strengthening resilience was one of its five critical missions in its 2014 Quadrennial Homeland Security Review.2 The Federal Emergency Management Agency (FEMA) also considers resilience to be a component of its National Preparedness Goal, which it identifies as “[a] secure and resilient nation with the capabilities required across the whole community to prevent, protect against, mitigate, respond to, and recover from the threats and hazards that pose the greatest risk.”3

Despite the emphasis on resilience as a public policy goal, disasters continue to challenge the response capacities of communities around the United States. These challenges are generated by the complexities and uncertainties present in the post-disaster environment. For public managers, complexity refers to the characteristics of a system, which means that complexity can refer to ill-structured administrative problems,4 mismatches between organizational structures and operational conditions,5 and the inability to identify and understand the linkages that exist within a system.6 Uncertainty, in contrast, refers to the sense of doubt that blocks or delays a decision maker’s actions.7 According to Elinor Ostrom, policy institutions often provide policy actors with the opportunity to pursue multiple policy choices.8 Since the choices taken by policy actors are often interdependent, this variety of choices can create uncertainty in the policy environment. Thus, a decision maker may know the type of action that she should take to obtain a certain outcome, but in an uncertain environment, she is unable to predict with any degree of confidence which of the possible actions will enable her to obtain the desired outcome.

In a disaster management context, complexities and uncertainties can undermine administrative effectiveness9 and generate cascades of failures.10 When such failures occur, the activities undertaken by disaster response organizations can become delayed, sporadic and ineffective, thereby leaving vulnerable populations subject to further risk. Recognizing this constraint, some governments have sought to manage uncertainty and complexity by using information technologies to facilitate information exchange and improve decision making.11 Thus, an important question is whether, and to what extent, access to information shapes the capacity of an organizational network to mobilize and structure disaster response operations?

This article presents the findings of an exploratory investigation into whether investments in information technology can affect the structural development and evolution of four disaster operations networks that formed in response to hurricanes along the Gulf Coast.12 Two of these networks formed in 2005 after Hurricane Katrina in Louisiana and Hurricane Rita in Texas. The other two networks formed in 2008 after Hurricane Gustav in Louisiana and after Hurricane Ike in Texas. After a brief introduction to resilience, this article explores three streams of literature relevant to this inquiry: inter-organizational network theory, complex adaptive systems theory, and social-technical systems theory. The second section reviews the four cases investigated by this study, focusing on the consequences of the events and the operational conditions under which the disaster response networks emerged. The third section presents the study’s research questions and methods of analysis. After the presentation of the findings, the article concludes by identifying policy implications for improving resilience of disaster response networks.

Resilience as an Evolving Concept

Aaron Wildavsky defined resilience as “the capacity to cope with unanticipated dangers after they have become manifest, learning to bounce back.”13 Alternative definitions construe resilience as the adaptability of systems to new environments through rapid transformation of existing resources to new demands. These approaches underline the role of information and information exchange in the facilitation of resilience.14 Although the focus of significant discussion, disaster management scholars and practitioners have yet to formulate a consensus as to what resilience means and how resilience should be evaluated. Recent research into the components and indicators of community resilience, however, has begun to advance the study of resilience.15 Ashley Ross, for example, conceptualizes resilience as a dynamic phenomenon that is driven by a set of adaptive capacities and processes.16 In this article, we applied this definition of resilience to the study of disaster response networks. Three streams of literature are relevant to this investigation of resilience in disaster response networks.

Inter-Organizational Networks

According to Michael McGuire, networks are “multiorganizational arrangements for solving problems that cannot be achieved, or achieved easily, by a single organization.”17 These networked arrangements are considered superior to traditional administrative structures. Networks provide an alternative to hierarchy and specialization, meaning they can accommodate a diversity of organizations – public, private, and nonprofit – which can work together to achieve collective goals.18 Networks are also highly flexible, enabling their constituent organizations to adapt their interactions in response to changes in the operational environment. Further, networks are scalable to the extent that their participants have the capacity to seek assistance from other organizations, whether vertically by level of jurisdiction or horizontally by source of funding. Finally, networks enable participants to identify and acquire the information and resources they need to complete their activities.19

Provan and Kenis note that networks provide a community of organizations with the structure they need to interact with one another and engage in learning activities.20 As such, a network of organizations designs a structure that enables its members to learn how to modify their activities within, and in response to, the complexities and uncertainties present in the operational environment.21 Similarly, a network’s interaction structure can facilitate the efficient distribution of resources, which is important for disaster response networks.22 As they work to structure and re-structure their relationships, organizations in a disaster response network can quickly locate resources such as information, money, personnel and equipment, and move these resources to where they are needed. Although the inter-organizational network literature suggests that networked governance structures are better positioned than traditional governmental structures to address the dynamic and ill-structured policy problems, the literature does not specify the processes that organizations would use to overcome the uncertainties and complexities present in the operational environment.

Proposition 1: A resilient disaster response network will be comprised of a heterogeneous collection of organizations that interact with one another to pursue and obtain collective goals.

Complex Adaptive Systems

A second stream of literature suggests that a disaster response network comprised of a heterogeneous collection of organizations may have the capacity to adapt in response to the uncertainties and complexities of changing environments.23 This adaptive capacity emerges when a system of organizations operates as a complex adaptive system, that is, a non-linear system of interdependent agents that collectively learn how to adjust their activities in reaction to environmental changes.24 The agents present in such systems receive information about the external environment. When a decision is needed, agents use internal models of rules to analyze the information they receive, which gives them insight into the actions that they should take. These rules draw upon internal cognitive building blocks, which agents employ to simplify their environment.25 In the public administration context, these building blocks take the form of signals that may be communicated through memoranda and directives, and boundaries that may be established by legislation and agency mission statements.26

Complex adaptive systems theory can be used to investigate the resilience of disaster response networks. In the words of Robert Axelrod and Michael Cohen, a system of agents can harness complexity by acknowledging the interdependent relationships that exist in a system and taking deliberate action to restructure the system to align with a desired measure of performance.27 For example, a policy-maker might encourage the organizations in a disaster response network to modify their actions by permitting them to exploit emergent opportunities and rewarding them when they identify novel solutions.28 Policy-makers may encourage organizations to modify their interaction patterns by enabling them to make internal procedural adjustments or by adjusting their operational environment, perhaps by exempting them from regulatory requirements during a crisis. Finally, policy-makers may encourage organizations to identify and select successful strategies by providing them with a clear understanding of what constitutes success, and rewarding them when they cast aside ineffective strategies.29 In a disaster context, inter-organizational relationships are a central aspect of this adaptive process because they lead to the development of “networks of reciprocal interaction that foster trust and cooperation.”30

Proposition 2: Learning, adaptation, and structural adjustment to environmental uncertainties and complexities are indicators of resilience in a disaster response network.

Sociotechnical Systems and Information Technology

A third stream of literature suggests that information technology can support a disaster response network’s capacity to adapt to uncertainty and complexity. That is, information technology represents a tool that can bring together a disconnected and spatially separated community of organizations.31 Information technology provides officials with the ability to scan the operational environment, detect and verify potential risks, and transmit risk and response information across an expansive network of organizations charged with disaster management and operational responsibilities.32

Albert Charns argued that the integration of technology within a social structure leads to the development of a sociotechnical system.33 Herbert Simon considered design as a means of structuring relationships among human beings, organizations, and technology.34 A sociotechnical system drives the processes of adaptation within a networked system, thereby enabling it to adjust and reorganize as required by changing conditions in the environment.35 There are several ways that technology can strengthen the capacities for performance and processes of adaptation in disaster response networks. The National Academies of Sciences identified three disaster management functions that were enabled through information technology: 1) robust, interoperable and priority-sensitive communications; 2) development of situational awareness and common operating picture; and 3) improved decision support, resource tracking, and resources allocation.36 All three functions are supported by information technology that, properly designed and implemented, can facilitate learning and adaption in a disaster response network.

Proposition 3: Information Communication Technologies (ICT), properly designed and implemented, can facilitate resilience (learning, adaptation, and structural adjustment) within disaster response networks.

An extensive body of literature focuses on networks and their roles in disaster management contexts. Empirical investigations of disaster response networks, for example, often focus on networks at a specific point in time (e.g., a single day) or as an aggregation of several points in time (e.g., a collective set of days or weeks). Investigations such as these have demonstrated the importance of disaster response networks and the existence of problematic resource and information gaps between organizations.37 In the context of resilience, however, these studies provide little insight into how disaster response networks emerge or evolve over time. Further, the literature on disaster response networks says little about network effectiveness, suggesting that the factors or conditions that promote or inhibit the resilience of disaster response networks are not yet fully identified. Relatedly, the extent to which policy changes might influence the emergence, evolution, and performance of disaster response networks is not yet known.

Case Study Selection

To evaluate the three propositions identified above, we conducted a small-n case study of the interaction structures of disaster response networks that formed after four Gulf Coast hurricanes. Specifically, we identify and compare the structural features of the organizational response networks that formed in 2005 following Hurricane Katrina in Louisiana and Hurricane Rita in Texas, with those that formed in 2008 following Hurricane Gustav in Louisiana and Hurricane Ike in Texas.

Louisiana: Hurricane Katrina, 2005 and Hurricane Gustav, 2008

Classified as one of the deadliest disaster events in the history of the United States, Hurricane Katrina struck the coast of Louisiana east of New Orleans the morning of August 29, 2005. Although New Orleans managed to withstand Hurricane Katrina’s impact, the storm surge and rainfall-induced flooding caused the subsequent failure of the levee systems, which inundated large portions of the city. In the days that followed, disaster management officials worked to avert an even larger humanitarian catastrophe. The federal government reported that Hurricane Katrina affected 41 of the state’s 64 parishes, caused approximately 1,100 deaths, and generated US$ 100 billion of damage.38 Approximately three years later, on September 1, 2008, Hurricane Gustav came ashore in Terrebonne Parish, Louisiana as a Category 2 storm. The National Weather Service reported that Hurricane Gustav weakened to a tropical depression, but continued to produce severe winds, tornados, and substantial rainfall, as much as twenty-one inches in some areas, as the storm slowly moved north beyond Baton Rouge.39 In Louisiana, Hurricane Gustav was responsible for seven deaths and an estimated US$ 4.618 billion of damages.

Texas: Hurricane Rita, 2005 and Hurricane Ike, 2008

Less than three weeks after Hurricane Katrina, a Category 5 Hurricane called Rita was moving towards the western coast of the Gulf of Mexico.40 Given the devastation wrought by Hurricane Katrina, government officials were concerned about the threats that Hurricane Rita posed to the oil and gas industry. Equally important, Hurricane Rita threatened the city of Houston, so officials ordered wide scale evacuations. According to the National Weather Service, Hurricane Rita came ashore between Sabine Pass, Texas and Johnson’s Bayou the morning of September 24, 2005 as a Category 2 Hurricane. Fortunately, the region avoided a catastrophe, with only two reported fatalities. Nevertheless, Hurricane Rita caused more than US$ 12 billion of damages. In after action reports, discussions about the governmental response to Hurricane Rita focused on the massive traffic jams caused by the evacuation orders. On September 13, 2008, almost three years after Hurricane Rita, a Category 2 storm named Hurricane Ike made landfall near Galveston, Texas.41 Along Galveston Bay, the storm surge increased to between ten and fifteen feet. Hurricane Ike’s sustained winds generated several tornados and severely damaged Houston’s downtown area. The storm took the lives of 21 Texans, and at least 16 people remained missing as of August 2011. Hurricane Ike became the third most expensive hurricane in the history of the United States, with damages estimated to be more than US$ 29.5 billion.42

Separated by a period of three years, these two sets of hurricane events make it possible to compare the structures of the disaster response networks that emerged to operate in the same general region of the United States. Many of the organizations, especially the public emergency management agencies, were present in both Louisiana and Texas for all four hurricanes, which increases the comparability of these disaster response networks. However, while activities of Texas and Louisiana were guided by federal laws and policies, each state had developed different perceptions of risk, and made different policy choices regarding the management of information in their respective communities in the months and years that followed the first hurricane event. Given that these four cases represent a valid small-n field study for the examination of the resilience of disaster response networks, we investigate the theoretical propositions stated above through an exploration of four comparative research questions:

  1. To what extent were the four disaster response networks characterized by heterogeneity in contrast to homogeneity in the respective sets of participating organizations?
  2. At what rate did response organizations interact with other organizations in these four disaster response networks?
  3. To what extent did the interactions exchanged among response organizations drive the structural evolution of these four disaster response networks?
  4. To what extent did investments in information technology and training between hurricane events facilitate structural changes in the disaster response networks?

Methods

This article investigates the resilience of the disaster response networks that emerged after hurricanes that occurred in 2005 and 2008: Hurricanes Katrina and Gustav in Louisiana and Hurricanes Rita and Ike in Texas. To answer the research questions stated above, we collected, coded, and analyzed data obtained from newspaper articles and government reports that covered the response activities that occurred before and after the hurricanes made landfall. The processes that we used to collect and analyze our data are discussed in the following subsections.

Data Collection and Coding

The data came from newspaper articles from the Times Picayune and the Houston Chronicle, which are respectively published in New Orleans, Louisiana and Houston, Texas. These articles covered the activities undertaken by the response networks that formed after each hurricane event, and constitute a day-to-day record of the activities undertaken by the organizations participating in each network. To focus our data collection activities, we used time and shared behavior to set the boundaries of the disaster response networks.43 Then, we conducted keyword searches in the LexisNexis Academic Database to identify articles that referenced each hurricane by name and were published between six days prior to landfall and twenty-two days after the storm made landfall. We classified articles as relevant if they referenced activities that fell within the fifteen Emergency Support Functions (ESFs) covered by the National Response Plan44 and the National Response Framework.45

We then coded the content of the newspaper articles and created Excel databases for each hurricane response network. To create these databases, we reviewed each article and identified the organizations reported to be involved in the response network. We assigned each organization a numerical identifier and an acronym, and classified the organizations by the date they became active in the response network, their source of funding (public, private, or nonprofit) and their level of jurisdiction (national, regional, state, county, or city). We also identified the interactions exchanged between organizations and coded each interaction as a separate transaction. All interactions were coded as non-directional and unweighted, since the news articles did not always indicate which organization initiated the transaction or the number of interactions that occurred.

We removed duplicate and irrelevant entries from the Excel databases and cleaned the data to ensure the consistency in organizational names, acronyms, source of funding, and level of jurisdiction. To ensure reliability, all co-authors participated in the coding processes and we conducted weekly comparisons to corroborate coding results. We also cross-referenced results from the content analysis with activities reported in government situation reports and found them to be consistent. After the databases were finalized, they were converted into four sets of relational matrices. We generated one set for each hurricane event, with each set comprised of twenty-eight separate relational matrices. Each relational matrix represented one day included in the analysis. We then refined each matrix by excluding isolated organizations, meaning we removed organizations that were not engaged in interactions with other organizations.

Data Analysis

We used multiple methods to analyze the data for each of the disaster response networks. We began by generating descriptive statistics to reveal the organizational composition of each network. We used Excel to generate tables that reported the numbers, jurisdictional levels, sources of funding and frequency distributions of the organizations detected in each disaster response network. We also plotted longitudinally, by date, the rate that the organizations became active in each disaster response system, as well as the number and type of interactions undertaken by each organization. We used these data to address our first and second research questions.

We used the network analyses software UCINET 6 to evaluate the data contained in our four sets of relational matrices.46 We used two common network level network measures to reveal the structure of the networks: density and degree centralization. We calculated these statistics for each of the twenty-four relational matrices included the period under analysis, and plotted the results longitudinally, by date, to evaluate whether each network underwent structural change. We then used these data to address our third research question. To address our final question, we reviewed governmental reports to determine whether Texas and Louisiana underwent policy changes or made investments in information technology between 2005 and 2008.

Research Assumptions

The application of the methods described above were subject to four assumptions, which enabled us to isolate the changes in interaction patterns within response networks that were stable in size across equivalent time slices. First, we assumed that the organizations did not enter a response network, but rather, they were always present in the network and became active when they started to interact with other organizations. Second, we assumed that organizations did not leave the response networks, but rather, they maintained a presence throughout the duration of the period under analysis. In line with these two assumptions, for all of the response networks, we used the total number of organizations detected in a network to normalize the number of nodes contained in each network’s daily matrices. Third, we assumed that the appropriate window of analysis for the investigation of structural change was twenty-four hours. This decision was driven by the nature of newspaper reporting, but also because larger time slices would undermine our ability to determine if, and when, structural changes might have occurred. Finally, we assumed that the detection of an interaction between two or more organizations represented the establishment of a permanent relationship that lasted throughout the duration of the disaster response. To capture this representation, we created our daily meta-matrices on a cumulative basis. As such, matrix one represented the interactions detected in day one, matrix two represented the interaction detected in day one and day two, and matrix three represented the interactions detected in day one, day two, and day three. This process continued until the creation of the final matrix, which represented the disaster response network in its entirety.

Findings

The findings indicate that the disaster response networks that operated after four hurricane events, Hurricane Katrina in Louisiana and Hurricane Rita in Texas in 2005 and Hurricane Gustav in Louisiana and Hurricane Ike in Texas in 2008, were comprised of a heterogeneous collection of response organizations. Additionally, these organizations modified their behaviors, at least in terms of their inter-organizational interaction patterns, which may suggest that these networks underwent the adaptive processes needed to overcome the uncertainties and complexities present in the post-disaster environment. However, as the findings presented below indicate, the characteristics of these four response networks were not identical.

System Composition

We began our analysis by generating frequency statistics that revealed the number and nature of the organizations that participated in the four response networks. In terms of numbers of organizational participants in the Louisiana networks, the data indicate that the Katrina response network was larger than the Gustav response network, at 372 and 222 organizations respectively. In Texas, the situation was reversed, with more organizations participating in the Ike response network than in the Rita response network, at 372 and 214 organizations respectively. This result is likely because Hurricane Katrina and Hurricane Ike were the biggest storm events under analysis. Further exploration of the data revealed that each response network depended on contributions of organizations from multiple levels of jurisdiction. Reflecting the idea that all disasters are local, the organizations from jurisdictions classified as county/parish level or lower were the most represented in all four response networks: Katrina (172 or 46.24%); Gustav (117 or 52.70%); Rita (113 or 53.24%); and Ike (235 or 63.17%). The organizations from jurisdictions classified as federal and national, which included both government agencies, nonprofit organizations, and private businesses were the second most represented in the response networks: Katrina (94 or 27.27%); Gustav (52 or 23.42%); Rita (44 or 20.37%); and Ike (84 or 22.58%). This was followed by the organizations from jurisdictions classified as state and regional: Katrina (79 or 21.24%); Gustav (48 or 21.62%); Rita (42 or 19.44%); and Ike (42 or 11.29%).

Table 1. Organizational Composition of Hurricane Response Networks by Source of Funding

LOUISIANA TEXAS
Katrina Gustav Rita Ike
N % N % N % N %
Nonprofit 61 16.40 42 18.92 36 16.82 98 26.34
Private 77 20.70 41 18.47 29 13.55 55 14.78
Public 234 62.90 139 62.61 149 69.63 219 58.87
Totals 372 100 222 100 214 100 372 100

Analysis of the organizational data by source of funding revealed similar findings. As Table 1 indicates, public organizations played a substantial role in response activities following each disaster event. More specifically, these data indicate that approximately 60% of the organizations detected interacting within all four response networks were public organizations. The other sectors also made important contributions to the response networks, but depending on the state, their participation reported slightly different numbers. Although there were fewer total organizations detected in the Gustav network than in the Katrina network, the Gustav network contained a higher percentage of nonprofit organizations than did the Katrina network, at 18.92% and 16.40% respectively. The opposite occurred in Texas, where both the number and percentage of nonprofit organizations increased from Hurricane Rita to Hurricane Ike. Further, in comparison to the other three hurricanes, more nonprofit organizations reported interacting in the response network that formed after Ike, at 26.34%, than in any other network. These data indicate that all four networks were comprised of a heterogeneous collection of organizations, necessary to promote adaptation in response to complexity and uncertainty.

System Growth and Development

We continued analysis of the response networks by plotting the date that each organization became active, meaning that an organization began to interact with one or more organizations in the network. Figure 1 presents the comparative results for Hurricane Katrina and Hurricane Gustav. These data indicate that both networks experienced steady growth over time.

Figure 1. Cumulative Percentage of Newly Active Organizations Detected in Response Network by Day: Hurricane Katrina and Hurricane Gustav

For both the Katrina and Gustav response networks, one quarter of the identified organizations were active by landfall. After landfall, the organizations in the Gustav network, as a percentage of all identified organizations, became active more quickly than the organizations in the Katrina network. By means of comparison, in the Gustav network, 75.7% of organizations were active six days after landfall. At that same time, only 60.5% of the organizations were active in the Katrina network. Subsequently, the organizations in the Gustav network continued to become active at a faster rate, allowing the network to reach 100% capacity sixteen days after landfall. In contrast, the Katrina response network did not reach 100% capacity until twenty days after landfall.

Figure 2. Cumulative Percentage of Newly Active Organizations Identified in Response Network by Day: Hurricane Rita and Hurricane Ike

Figure 2 presents the comparative results for Hurricane Rita and Hurricane Ike. These data indicate that both response networks also experienced steady growth over time. Unlike the Louisiana response networks, however, there were marked differences in the activation rates in the Texas response networks. For example, when Hurricane Rita made landfall, approximately three weeks after Hurricane Katrina, 39.9% of all organizations identified in the Rita network were active. Three years later, when Hurricane Ike made landfall, only 12.6% of the organizations in the response network were active, a substantial drop from the findings for the Rita response network. The expansion of the Ike response network also proceeded at a slower rate, with 61.3% of the identified organizations active thirteen days after landfall, and all identified organizations active in the network nine days later. In contrast, 57.5% of the organizations identified in the Rita response network were active four days after the hurricane came ashore.

System Structural Evolution Over Time

We generated social network measures for the four response networks for each date included in this study. For this article, we investigated two common network measures: density and degree centralization. Wasserman and Faust define network density as the “proportion of the possible [links] that are actually present in a [network].”47 In contrast, degree centralization evaluated the extent to which actors have links to each of the other actors in the network. When applied to the network, the degree centrality measure is a quantification of the “range or variability of the individual actor’s indices.”48

Network Density

The network density scores for Hurricane Katrina and Hurricane Gustav plotted over time are presented below in Figure 3. These results indicate that the overall density scores for both response networks were low, which is a common feature of large networks. Over time, however, the organizations in both networks became increasingly active. A closer look at the data reveals that the densities of the response networks began to diverge after the hurricanes came ashore. For the Katrina network, the density increased from 0.000884 at landfall to 0.007695 twenty-two days later. In contrast, for the Gustav network, density increased to 0.005585 five days after landfall. Then, on the sixth day, the network’s density increased substantially to 0.011088, after which density gradually increased to 0.014879. On the day that this substantial jump in density occurred, FEMA was working with several state and parish organizations to open a major aid center, which began to distribute assistance to communities affected by Hurricane Gustav.

Figure 3: Comparison of Response Network Density by Day: Hurricane Katrina and Hurricane Gustav

The network density scores for Hurricane Rita and Hurricane Ike plotted over time are presented in Figure 4. Like the response networks in Louisiana, the overall density scores for the response networks in Texas were also low. For the Rita network, response organizations began to establish linkages with one another at least four days before landfall. By September 24, 2005, the density of the Rita network had reached 0.002633, which was the highest landfall density of all response networks. In contrast, on the day of landfall, the density of the Ike network was 0.000551, which was the lowest density of all response networks. The Rita data also indicate that six days after landfall, the density of the network increased from 0.004475 to 0.006055. On this date, Texas counties were operating supply stations, crews were working to remove debris from the streets and to restore electrical services, and FEMA opened a disaster recovery center. From a comparative basis, however, these data suggest that the capacity of response organizations to become active decreased in the three years that followed Hurricane Rita.

Figure 4: Comparison of Response Network Density by Day: Hurricane Rita and Hurricane Ike

Network Degree Centralization

For the next step in our structural analysis, we calculated network degree centralization statistics for each of the hurricane response networks. Figure 5 reports the network degree centralization scores for Hurricane Katrina and Hurricane Gustav plotted over time. These data indicate that the organizations in both response networks gradually became increasingly connected to one another. At the time of landfall, both the Katrina network and the Gustav network were similar in structure. The Katrina network, however, was slightly more centralized than the Gustav network, at 0.039761 and 0.030728 respectively. The next day, the Gustav network became more centralized than the Katrina network, a finding that would remain constant over the next twenty-one days.

Figure 5: Comparison of Response Network Degree Centralization by Day: Hurricane Katrina and Hurricane Gustav

These data also reveal two points of structural change within these response networks. For the Rita network, the point of structural change occurred twelve days after landfall, when the network’s degree centralization score increased from 0.052306 to 0.108327. This finding parallels the density finding discussed in the previous subsection of this article. A point of structural change occurred much earlier in the Gustav network, on day six, when the degree centralization score increased from 0.067421 to 0.207980. On this day, there were multiple interactions exchanged between response organizations. These interactions reflected the collective response of city, county and state firefighters to fight fires in Terrebonne Parish, the Louisiana Department of Homeland Security and Emergency Preparedness working with state and local officials to establish and manage aid centers, and agencies such as FEMA and the Louisiana Department of Social Services working to provide food and social services to aid centers and citizens.

Figure 6: Comparison of Response Network Degree Centralization by Day: Hurricane Rita and Hurricane Ike

Finally, the network degree centralization scores for the Rita and Ike response networks, plotted longitudinally, are presented in Figure 6. Like the Katrina and Gustav networks, these data indicate that the organizations in both Texas response networks became increasingly connected. When Hurricane Rita came ashore, the storm’s response network centralization score was 0.018416. Twelve days later, the network’s centralization score reached 0.093897, its maximum level. In contrast, the Ike response network, which had a centralization score of 0.018416 when the storm came ashore, rapidly increased to 0.095680 three days later. Perhaps due to the size of the storm, the Ike network’s centralization score continued to increase over the next few days, reaching 0.167801 nineteen days after landfall. From a comparative basis, the data presented in Figure 5 and Figure 6 suggest that the organizations in all four response networks structured their interactions in a way that generated increasing levels of centralization, but once a certain centralization threshold level was reached, the centralization processes began to stabilize.

Investments in Training and Technology Post-2005

For the final stage of our analysis, we reviewed disaster policy changes that occurred after 2005.49 At the federal level, Congress strengthened the capacity of the federal government’s disaster management system. In reaction to problems encountered after Hurricane Katrina, legislation such as the Post-Katrina Emergency Management Reform Act of 2006 [Reform Act] reorganized the country’s disaster management institutions, strengthened and expanded the collection and dissemination of information, and reinforced communication and coordination capacities.50 The Reform Act also created the National Integration Center, which was charged to strengthen disaster management training and to promote collaboration among public, private, and non-profit organizations. Finally, the Reform Act required the Department of Homeland Security to modify its National Emergency Communications Plan so that officials and disaster responders had the ability to communicate with one another after a disaster event.51

In Louisiana, the legislature amended the Louisiana Homeland Security and Emergency Assistance and Disaster Act (Disaster Act) in 2006.52 In doing so, the legislature modified the state’s disaster management institutions, thereby improving their capacity to manage disaster events. The Disaster Act directed the Governor’s Office of Homeland Security and Emergency Preparedness (GOHSEP) to provide disaster management training and support throughout the state. The Disaster Act also established the state’s Emergency Operations Center, which coordinates the state’s emergency management operations. The Emergency Operation Center also assists local jurisdictions to coordinate response activities with their public, private and non-profit partners. Furthermore, the Louisiana legislature required the state’s parishes to develop emergency response plans and directed GOHSEP to provide technical assistance to parish authorities to help them to develop these plans.53 Finally, Louisiana spent more than US$180 million to strengthen the Louisiana Wireless Information Network (LWIN).54 Managed by GOHSEP and used by approximately 80,000 public and nonprofit personnel, the LWIN communication system can integrate with the communication networks used by neighboring states and maintain continuous communications in areas affected by disaster.

Finally, in Texas, the legislature also took steps to strengthen the state’s disaster management capacities. These changes, which were not adopted until 2007 because the Texas legislature convenes on a bi-annual basis, were made to the Texas Disaster Act of 1975. These amendments required that all public officials receive at least three hours of disaster management training before they assume their duties. The amendments also mandated that the Emergency Management Director be the presiding officer of the governing body of a city or country. Finally, the amendments established the Texas Statewide Mutual Aid system, which sets the conditions under which local governments may assist each other without a written agreement. Like Louisiana, Texas communities sought to improve their disaster management capacities. Communities like Houston upgraded their communications systems, conducted training, and disseminated information to the public. Despite such investments, the state of Texas reported that, three years after Hurricane Ike, its public safety communications shortcomings had yet to be addressed.55

Discussion

The findings generated by this study support the theoretical propositions that framed the analysis presented in this article. Our first proposition stated that a resilient disaster response network is comprised of a heterogeneous collection of organizations that interact with one another to pursue and obtain collective goals. This proposition is supported by the data presented in Tables 1 and 2. These tables indicate that public sector organizations from jurisdictions classified as county/parish or lower were the most prevalent in all four response networks. This finding not only reflects the idea that local agencies and officials are best positioned to respond to a disaster event, it also reflects the idea that communities in the United States expect their governments to deliver response assistance after a disaster. Moreover, these data indicate that federal and state organizations contributed to the response networks, often as coordinators or as the distributors of resources. More broadly, these data also indicate that private and nonprofit organizations participated in the response networks, bringing with them their resources and experience. Although their participation was documented in different numbers, organizations from these sectors represented between 13% and 24% of all organizations identified in each response network. In the Rita network, for example, nonprofit organizations represented 16.65% of all identified organizations. In the Ike network, however, they represented 26.34% of the detected organizations. Despite these differences, which appear to be influenced by the scale of the disasters, the organizations identified in all four response networks had the potential to use each other to locate information, money, personnel and equipment, and if a relationship was established, to move these resources to where they were needed.

Proposition 2 asserted that a resilient disaster response network can adapt its structure in response to the uncertainties and complexities present in the changing operational environment. In terms of adaptation as measured by network growth, the data presented in Figure 1 and Figure 2 indicate that all four response networks experienced steady growth over time. For example, in Louisiana, approximately 25% of all organizations that were active by the date of landfall for both Hurricane Katrina and Hurricane Gustav. After landfall, the organizations in the Gustav network became active more quickly than those in the Katrina network. In Texas, the data suggest that the opposite occurred, as the 2008 response network, which formed after Hurricane Ike, became active more slowly than the 2005 response network, which formed after Hurricane Rita. In terms of network structure, the findings presented in Figures 3 through 6 also indicate that all four response networks underwent change. Perhaps the best example of structural change occurred in the Gustav response network, when six days after landfall, the degree centralization score increased from 0.067421 to 0.207980, a result of an increase in reported organizational interactions related to firefighting, the management of aid centers, and the distribution of relief resources. An additional finding, reported in Figure 4, is the identification of points of structural change in the later stages of the Rita and Ike response networks, which may represent the system shifting from the response phase to the recovery phase. If so, this finding supports the transitions documented in the response and recover processes that occur after disaster events in large urban areas.56

The final proposition stated that information communication technologies (ICT), properly designed and implemented, facilitate the resilience of disaster response networks. The review of policy changes and investments in information technology revealed that steps taken at the federal level, and in the state of Louisiana, likely strengthened disaster resilience. At the federal level, Congress adopted the Post-Katrina Emergency Management Reform Act of 2006, which reorganized the country’s disaster management institutions, strengthened disaster management training, promoted organizational collaboration, and required the Department of Homeland Security to modify its National Emergency Communications Plan. In Louisiana, the legislature amended the state’s Louisiana Homeland Security and Emergency Assistance and Disaster Act in 2006. In doing so, the state directed GOHSEP to expand access to disaster management training, established the state’s Emergency Operations Center, and required the state’s parishes to develop emergency response plans. Central to these efforts was Louisiana’s decision to strengthen the Louisiana Wireless Information Network, the system used to maintain communications in disaster areas. In contrast, although the Texas legislature did adopt disaster management legislation, the changes were minor, and many did not come into effect until the later part of 2007, leaving little time for officials to implement these changes before the arrival of Hurricane Ike. Equally important, despite recognizing that it needed to strengthen its communication infrastructure, Texas as a state did not appear to take sufficient action prior to the arrival of Hurricane Ike.

Viewed collectively, all four disaster response networks demonstrated structural change. These structural changes, however, did not occur at the same rate nor did they evolve in the same manner, which suggests that each response network sought to find the appropriate “fit” for the context in which it operated. While all four networks were activated in response to different events, these findings suggest that Louisiana managed to strengthen the capacities and processes that generate resilience. Although these findings are subject to further inquiry, they indicate why the organizational response to Hurricane Gustav was more robust than the organizational response to Hurricane Katrina. In contrast, in Texas, the results generated for the Rita response network suggest that the network was likely influenced by the observed consequences from Hurricane Katrina three weeks earlier, an event that reinforced the need for preparedness and response throughout Texas. As the memory of Hurricane Katrina began to fade, and in line with the consensus that the response to Rita was constrained by shortcomings in evacuation processes, Texas did not take substantial steps to improve its disaster response capacities after 2005. Consequently, the level of resilience dropped over the course of three years, which may explain the slower response of the Ike network in 2008, in comparison to the Rita response network.

Conclusions

In the context of disaster response networks, resilience represents a set of adaptive capacities and a set of adaptive processes. Tied together in a series of feedback loops that facilitate learning, these capacities and processes provide the organizations in a disaster response network the ability to overcome the uncertainties and complexities present in the post-disaster environment through adaptation and change. The findings from this analysis demonstrate that disaster response networks undergo structural change, as a large heterogeneous collection of response organizations come together, collect and disseminate information, and seek to identify and implement solutions to address rapidly the needs of those affected by the disaster event. Although each of the response networks analyzed–Hurricanes Katrina and Gustav in Louisiana and Hurricanes Rita and Ike in Texas – experienced structural change, the rate at which these changes occurred differed in each network. A review of the policy changes and investments in information technology made in Louisiana following Hurricane Katrina, in contrast to those undertaken in Texas, suggests why the Hurricane Gustav response network was more robust than the Hurricane Ike response network. The findings support the well-established proposition that sustained investments in information technology infrastructure support the development of resilience in disaster response networks.

For the organizations and government officials responsible for protecting the United States from the consequences of terrorist attacks, technological disasters, and catastrophic natural events, the challenge is to determine how to reduce risk in an environment that is becoming increasingly interdependent and risk prone. As we advance further into the twenty-first century, risk reduction efforts will become more difficult, as policy-makers seek the means to manage the effects of urbanization, population growth, environmental change, and technological advancement. The promotion of resilience in disaster response networks may provide communities with a cost-effective tool that could be used to manage the consequences of a variety of risks. This means that, from a public policy perspective, federal, state and local governments should continue to update their institutional arrangements to facilitate administrative flexibility, organizational collaboration and cooperation, and the use of technology to share information across a heterogeneous community of organizations, decision-makers, and individual citizens.57

In addition to strengthening administrative capacities within the United States, it is essential to develop a conceptual framework that outlines the parameters of network resilience for disaster response organizations. This framework needs to identify factors that promote adaptive capacity in disaster response networks, as well as indicators that facilitate the measurement and assessment of network resilience. As the findings from this study suggest, the components and indicators of network resilience likely relate to the design of institutional arrangements, use of information technology, development of coordination plans and mutual aid agreements, and systematic use of disaster management training. To increase network performance, it is essential to evaluate whether public investments for disaster preparedness and response operations are producing the expected results. Although the concept of resilience does not provide a set of actionable solutions for communities exposed to recurring risk, such investigations contribute to more informed administrative adaptation.58

About the Authors

Thomas W. Haase received his Ph.D. from the Graduate School of Public and International Affairs at University of Pittsburgh. He is currently an Assistant Professor of Public Administration at the Department of Political Science at Sam Houston State University, where his teaching portfolio includes courses on international disaster management, community and social resilience, program evaluation, and Texas government. His research has focused on issues of disaster management, community resilience, and public administration education. He may be reached at twhaase@gmail.com

Güneş Ertan received her Ph.D. from the Graduate School of Public and International Affairs at University of Pittsburgh. She is currently an Assistant Professor of International Affairs at Koç University in Istanbul, Turkey where she teaches courses such as social networks and policy analysis. Her research focuses on the relationship between social networks and collective action. More specifically she studies the role of social networks in shaping collective action outcomes within the context of policy processes and social movements. She may be reached at gunesertan@ku.edu.tr.

Louise K. Comfort is Professor of Public and International Affairs and former director, Center for Disaster Management, University of Pittsburgh. She is a Fellow, National Academy of Public Administration, and author or coauthor of seven books, including Designing Resilience: Preparing for Extreme Events (University of Pittsburgh Press, 2010), Mega-Crises (Charles C. Thomas, 2012), and The Dynamics of Risk (Princeton University Press, forthcoming, 2018). Her primary research interests are in decision making under conditions of uncertainty and rapid change, and the uses of information technology to develop decision support systems for managers operating under urgent conditions. She has published articles on information policy, organizational learning, and sociotechnical systems, and serves as the Social Science Editor for Natural Hazards Review. She may be reached at comfort@gspia.pitt.edu.

Acknowledgements

The authors would like to acknowledge that support for this research was received from the National Science Foundation, Grant #0729456: DRU: Designing Resilience for Communities at Risk: Improving Decision Making to Support Collective Action under Stress, 9/1/2007–8/31/2012, the Graduate School of Public and International Affairs, University of Pittsburgh, and the College of Humanities and Social Sciences, Sam Houston State University. We are also grateful for the constructive comments received during the 10th Annual Homeland Defense/Security Education Summit, held at George Mason University on March 23rd and 24th, 2017.

Notes


1 United States and Department of State, National Security Strategy: 2010, (Washington, DC: The White House, U.S. Department of State, 2010), http://nssarchive.us/national-security-strategy-2010/. The full report, Disaster Resilience: A National Imperative, is published by the National Academies Press, Washington, DC, 2012.

2 Department of Homeland Security, “Quadrennial Homeland Security Review: 2014” (Washington DC: Department of Homeland Security, 2014), https://www.dhs.gov/sites/default/files/publications/2014-qhsr-final-508.pdf.

3 Federal Emergency Management Agency, 2015 National Preparedness Goal (Washington DC: Federal Emergency Management Agency, 2015), 1, https://www.fema.gov/media-library/assets/documents/25959.

4 William N. Dunn, Public Policy Analysis: An Introduction, 5th ed. (New York, N.Y.: Routledge, 2016).

5 Donald F. Kettl, The Transformation of Governance: Public Administration for the Twenty-First Century America (London, England: John Hopkins University Press, 2002).

6 Charles Perrow, Normal Accidents: Living with High-Risk Technologies (New York, N.Y.: Basic Books, Inc., 1984).

7 Raanan Lipshitz and Orna Strauss, “Coping with Uncertainty: A Naturalistic Decision-Making Analysis,” Organizational Behavior and Human Decision Processes 69, no. 2 (February 1, 1997): 149–63.

8 Elinor. Ostrom, Governing the Commons: The Evolution of Institutions for Collective Action (Cambridge, Massachusetts: Cambridge Univ. Press, 2005), 48–49.

9 Naim Kapucu et al., “Interorganizational Network Coordination under Stress Caused by Repeated Threats of Disasters,” Journal of Homeland Security and Emergency Management 7, no. 1 (January 30, 2010).

10 Louise K. Comfort, “Crisis Management in Hindsight: Cognition, Communication, Coordination, and Control,” Public Administration Review 67, no. 1 (2007): 189–197.

11 Louise K. Comfort, “Designing Policy for Action: The Emergency Management System,” in Managing Disaster: Strategies and Policy Perspectives, ed. Louise K. Comfort (Durham: Duke University Press Books, 1988), 3–21; Louise K. Comfort, Shared Risk: Complex Systems in Seismic Response (New York, N.Y.: Pergamon, 1999); Comfort, “Crisis Management in Hindsight”; Qian Hu and Naim Kapucu, “Information Communication Technology Utilization for Effective Emergency Management Networks,” Public Management Review 18, no. 3 (March 15, 2016): 323–48; Bruce Cutting and Alexander Kouzmin, “From Chaos to Patterns of Understanding: Reflections on the Dynamics of Effective Government Decision Making,” Public Administration 77, no. 3 (1999): 475–508; Louise K. Comfort, Arjen Boin, and Chris C. Demchak, eds., Designing Resilience: Preparing for Extreme Events, vol. 90, 2 vols. (Pittsburgh, Pa: University of Pittsburgh Press, 2010).

12 These four hurricanes are explored in greater detail in a separate working paper that investigates the nature of the cross-jurisdictional linkages that formed between disaster response organizations after each hurricane event. Comfort, Louise, Thomas W. Haase and Gunes Ertan (2017) The Dynamics of Change Following Extreme Events: Shattered Communities, Emergent Networks, and Sustainable Resilience (unpublished).

13 Aaron Wildavsky, Searching for Safety, Social Theory and Social Policy (New Brunswick: Transaction Press, 1988), 77, http://www.transactionpub.com/title/978-0-912051-18-5.html.

14 Comfort, Shared Risk: Complex Systems in Seismic Response, 21.

15 Susan L. Cutter, “The Landscape of Disaster Resilience Indicators in the USA,” Natural Hazards 80, no. 2 (January 2016): 741–58; Susan L. Cutter et al., “A Place-Based Model for Understanding Community Resilience to Natural Disasters,” Global Environmental Change-Human and Policy Dimensions 18, no. 4 (October 2008): 598–606; Patricia H Longstaff et al., “Building Resilient Communities: A Preliminary Framework for Assessment,” Homeland Security Affairs 6, no. 3 (September 2010).\\uc0\\u8221{} {\\i{}Global Environmental Change-Human and Policy Dimensions} 18, no. 4 (October 2008).

16 Ashley D. Ross, Local Disaster Resilience: Administrative and Political Perspectives, Routledge Research in Public Administration and Public Policy (New York: Routledge, 2013).

17 Michael McGuire, “Collaborative Policy Making and Administration: The Operational Demands of Local Economic Development,” Economic Development Quarterly 14, no. 3 (2000): 278.

18 Jan Kooiman, Modern Governance: New Government-Society Interactions (London: Sage, 1994); Donald F. Kettl, System under Stress: Homeland Security and American Politics (Washington, D.C.: CQ Press, 2004).

19 Comfort, Shared Risk: Complex Systems in Seismic Response; Louise K. Comfort and Thomas Haase, “Communication, Coherence, and Collective Action: The Impact of Hurricane Katrina on Communications Infrastructure,” Public Works Management & Policy 10, no. 4 (April 1, 2006): 328–43.

20 Keith G. Provan and Patrick Kenis, “Modes of Network Governance: Structure, Management, and Effectiveness,” Journal of Public Administration Research and Theory 18, no. 2 (April 1, 2008): 229–52, doi:10.1093/jopart/mum015.

21 Ibid., 229.

22 William L. Waugh and Gregory Streib, “Collaboration and Leadership for Effective Emergency Management,” Public Administration Review 66, s1 (2006): 131–140.

23 Naim Kapucu, “Interorganizational Coordination in Dynamic Context: Networks in Emergency Response Management,” Connections 26,2 (2005): 33–48.

24 Robert Axelrod and Michael D. Cohen, Harnessing Complexity: Organizational Implication of a Scientific Frontier (New York, N.Y.: Basic Book, Inc., 2000); Murray Gell-Mann, The Quark and the Jaguar: Adventures in the Simple and the Complex (New York, N.Y.: W. H. Freeman and Company, n.d.); John H. Holland, Hidden Order: How Adaptation Builds Complexity (Reading, Mass.: Addison-Wesley Pub. Co., 1995).

25 John H. Holland, Hidden Order: How Adaptation Builds Complexity (Reading, Mass.: Addison-Wesley Pub. Co., 1995)

26 John H. Holland, Signals and Boundaries: Building Blocks for Complex Adaptive Systems (Cambridge, Massachusetts: MIT Press, 2014).

27 Axelrod and Cohen, Harnessing Complexity: Organizational Implication of a Scientific Frontier, 9.

28 Axelrod and Cohen, Harnessing Complexity: Organizational Implication of a Scientific Frontier.

29 Ibid.

30 Ibid., 156.

31 Hu and Kapucu, “Information Communication Technology Utilization for Effective Emergency Management Networks.”; Naim Kapucu, “Interorganizational Coordination in Dynamic Context: Networks in Emergency Response Management,” Connections 26, no. 2 (2005): 33–48; Comfort, “Crisis Management in Hindsight.”; Comfort and Haase, “Communication, Coherence, and Collective Action.”

32 Louise K. Comfort et al., “Designing Adaptive Systems for Disaster Mitigation and Response: The Role of Structure,” in Designing Resilience: Preparing for Extreme Events, ed. Louise K. Comfort, Arjen Boin, and Chris C. Demchak (Pittsburgh, Pa.: University of Pittsburgh Press, 2010), 349.

33 Albert Charns, “The Principles of Sociotechnical Design,” Human Relations 29, no. 8 (1976): 783–89.

34 Herbert A. Simon, The Sciences of the Artificial, 3. ed., [Nachdr.] (Cambridge, Mass.: MIT Press, 2008); Herbert A. Simon, “The Architecture of Complexity,” General Systems 10, no. 1965 (1965): 63–76.

35 Elayne Coakes, Knowledge Management in the Sociotechnical World: The Graffiti Continues (London: Springer, 2002).

36 Ramesh R. Rao, Jon Eisenberg, and Ted Schmitt, eds., Improving Disaster Management: The Role of IT in Mitigation, Preparedness, Response, and Recovery (Washington D.C.: National Research Council of the National Academies, 2007), http://nap.edu/11842.

37 William L. Waugh and Gregory Streib, “Collaboration and Leadership for Effective Emergency Management,” Public Administration Review 66, no. s1 (2006): 131–140; Comfort, “Crisis Management in Hindsight.”; Donald P. Moynihan, “Learning under Uncertainty: Networks in Crisis Management,” Public Administration Review 68, no. 2 (2008): 350–365; D. P. Moynihan, “The Network Governance of Crisis Response: Case Studies of Incident Command Systems,” Journal of Public Administration Research and Theory 19, no. 4 (October 1, 2009): 895–915; Carter T. Butts, Ryan M. Acton, and Christopher Marcum, “Interorganizational Collaboration in the Hurricane Katrina Response,” Journal of Social Structure 13, no. 1 (2012); Naim Kapucu, Multi-Agency and Cross-Sector Coordination in Response to Disasters: The World Trade Center Attack in New York City, September 11, 2001 (LAP Lambert Academic Publishing, 2009); Kapucu, “Interorganizational Coordination in Dynamic Context.”; Comfort and Haase, “Communication, Coherence, and Collective Action.”September 11, 2001 (LAP Lambert Academic Publishing, 2009).

38 U.S. House of Representatives, “A Failure of Initiative: Final Report of the Select Bipartisan Committee to Investigate the Preparation for and Response to Hurricane Katrina” (Washington D.C.: U.S. Government Printing Office, 2006), http://katrina.house.gov/full_katrina_report.htm.

39 National Hurricane Center, “Tropical Cyclone Report: Hurricane Gustav,” (National Hurricane Center, September 9, 2014), www.nhc.noaa.gov/data/tcr/AL072008_Gustav.pdf.

40 National Weather Service, “Post Storm Data Acquisition: Hurricane Rita,” November 14, 2005, www.nws.noaa.gov/om/data/pdfs/Rita.pdf.

41 National Hurricane Center, “Tropical Hurricane Report: Hurricane Ike” (National Hurricane Center, February 4, 2009), www.nhc.noaa.gov/data/tcr/AL092008_Ike.pdf.

42 Ibid.

43 Edward O. Laumann, Peter V. Marsden, and David Prensky, “The Boundary Specification Problem in Network Analysis,” in Research Methods in Social Network Analysis (Transaction Publishers, 1983), 22–32.

44 Federal Emergency Management Agency, “National Response Plan” (Washington D.C.: Department of Homeland Security, 2004), http://www.au.af.mil/au/awc/awcgate/nrp/plan.pdf.

45 Federal Emergency Management Agency, “National Response Framework,” (Washington, D.C: Department of Homeland Security, 2008), https://www.fema.gov/pdf/emergency/nrf/nrf-core.pdf.

46 Steven P. Borgatti, Martin G. Everett, and Linton C. Freeman, Ucinet for Windows: Software for Social Network Analysis (Harvard, MA: Analytic Technologies, 2002), https://sites.google.com/site/ucinetsoftware/home.

47 Stanley Wasserman and Katherine Faust, Social Network Analysis: Methods and Applications, Structural Analysis in the Social Sciences 8 (Cambridge, Massachusetts: Cambridge University Press, 1994), 101.

48 Ibid., 180.

49 The policy changes and their potential impact on the disaster response activities that occurred in Louisiana and Texas after Hurricane Gustav and Hurricane Ike in 2008 are explored in greater detail in a separate working paper. This working paper investigates the nature of the cross-jurisdictional linkages that formed between disaster response organizations after each hurricane event. Comfort, Louise, Thomas W. Haase and Gunes Ertan (2017) The Dynamics of Change Following Extreme Events: Shattered Communities, Emergent Networks, and Sustainable Resilience (unpublished).

50 Post-Katrina Emergency Management Reform Act, Pub. L. No. 109-295, 120 Stat. 1355, 2006.

51 Ibid.

52 Governor’s Office of Homeland Security and Emergency Preparedness, “A Decade After Hurricanes Katrina and Rita – 10 Initiatives That Make Louisiana Smarter + Safer + Stronger + More Resilient.” (Baton Rouge, LA: Governor’s Office of Homeland Security and Emergency Preparedness, 2015), http://gohsep.la.gov/recover/katrina-rita-10-years-later.

53 Ibid., 12.

54 Ibid., 14–15.

55 Texas Department of Public Safety, “Texas Department of Public Safety Report on Interoperable Communications to the Texas Legislature,” (Austin, Texas: Texas Department of Public Safety, August 31), www.dps.texas.gov/LawEnforcementSupport/communications /interop/ documents/interopRpt.pdf.

56 Vale J. Lawrence and Thomas J. Campanella, “Resilience Axioms,” in In the Resilient City: How Modern Cities Recover from Disasters, ed. Vale J. Lawrence and Thomas J. Campanella (Oxford, United Kingdom: Oxford University Press, 2005), 335–55.

57 Joseph W Pfeifer, “Network Fusion: Information and Intelligence Sharing for a Networked World,” Homeland Security Affairs 8, no. 1 (2012).

58 Ann Marie Thomson and James L. Perry, “Collaboration Processes: Inside the Black Box,” Public Administration Review 66, no. s1 (2006): 20–32.


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