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Title
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Measuring Community Resiliance to Natural Disasters: A Case Study of Thurston [County], Washington
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Date
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2014
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Creator
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Eng
Rhoads, Kyli Anne
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Subject
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Environmental Studies
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extracted text
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MEASURING COMMUNITY RESILIENCE
TO NATURAL DISASTERS
A CASE STUDY OF THURSTON COUNTY, WASHINGTON
by
Kyli Anne Rhoads
A Thesis
Submitted in partial fulfillment
of the requirements for the degree
Master of Environmental Studies
The Evergreen State College
June 2014
©2014 by Kyli Anne Rhoads. All rights reserved.
This Thesis for the Master of Environmental Studies Degree
by
Kyli Anne Rhoads
has been approved for
The Evergreen State College
by
________________________
Edward Whitesell, Ph. D.
Member of the Faculty
________________________
Date
ABSTRACT
Measuring Community Resilience to Natural Disasters: A Case Study of
Thurston County, Washington
Kyli Anne Rhoads
Strengthening our communities to improve resilience to natural disasters is a growing
focus, as many regions are already seeing an increase in frequency and intensity of
climate change impacts. Additionally, a strong push from the Federal Emergency
Management Agency (FEMA) has encouraged communities to focus on natural hazard
mitigation projects to avoid and reduce impacts from a variety of disasters before they
occur. Overall, this push has been greatly successful in strengthening community
recoveries from disaster events. However, new research in social resilience and
vulnerability science has found that most natural hazard mitigation plans lack an
understanding of and attention to populations vulnerable to natural hazards and how these
affect community resilience. In an effort to address this research gap, this study used GIS
to identify vulnerable populations and rank community resilience to flood hazards within
Thurston County, Washington by identifying social indicators and combining them to see
how they interact to affect overall community resilience. Social demographic data from
the 2000 census, in combination with 100-year floodplain data, were used to analyze the
levels of community resilience according to a 100-year flood event among county census
block groups. Levels of community resilience were calculated according to four social
characteristics: per capita income, populations living below 150% of the federal poverty
level, populations over the age of 65, and racial minority populations. Findings indicate
that, at the 2000 census block group level, low resilience areas are not disproportionately
exposed to the 100-year floodplain. With an understanding of the flood risks and the
ability of a community to rebound from a natural disaster, specific areas can be identified
where natural hazard mitigation projects should be focused. Additionally, community
development, emergency response, and climate adaptation plans can be improved to
specifically address low-resilient areas.
Table of Contents
List of Figures………………………………………………………………
v
List of Tables……………………………………………………………….
vii
Acknowledgements…………………………………………………………
viii
1. Introduction……………………………………………………………...
1
2. Literature Review……………………………………………………….
15
a. Natural Hazard Mitigation Plans…………………….……………..
15
b. Community Resilience…………………….………………………..
20
c. Social Vulnerability……….…………………………………..........
23
d. Social Construction and Interdisciplinarity of Disasters.……..........
26
e. Environmental Justice………………………………………………
28
f. Determinants of Community Resilience……………………………
31
g. Modeling Community Resilience: Advantages and Disadvantages..
35
h. The Next Step: Measuring Community Resilience…..……….........
38
i. Conclusion: Community Resilience in Thurston County…………..
39
3. Methods…………………………………………………………...……...
41
a. The 100-year floodplain……...…………………………...................
45
b. Social Indicators……………………………………………………..
47
c. Low Income Populations and Poverty Status………………………
47
d. Racial Minority Populations………………………………………..
50
e. Elderly Populations……...…………………………………………
51
f. Moran’s I……………………...……………………………………
52
g. Spatial Weighted Overlay………………...………………………..
54
4. Results………...…………………..……………………………………..
57
a. Low Income and Poverty Status…...………………………………..
57
b. Per Capita Income………...…………………………………………
58
c. Racial Minorities…………...………………………………………..
59
d. Elderly Populations………...………………………………………..
60
e. Spatial Weighted Overlay………………...…………..……………..
61
5. Discussion and Conclusion...……..……………………………………..
67
a. Income and Poverty as Social Indicators……………….…………...
67
b. Race as a Social Indicator………...……………………..…………..
70
c. Age as a Social Indicator……………………………….…………...
73
d. Block Groups and the 100-Year Floodplain…………..…………….
74
e. Implications……………………...……………………..……………
75
f. Considerations for Future Research………...…………..…………...
78
g. Conclusion……...……………………………………..…………….
81
List of References…………………………………………………………..
84
Appendix……………………………………………………...…………….
89
v
List of Figures
Figure
Page
1.
Map of Washington State….....…………………………………
6
2.
Community resilience model…………………………………..
32
3.
Stress resistance and resilience model…………………………..
33
4.
Bronfenbrenner’s systems and their interactions………………..
38
5.
Thurston County 100-Year Flood Plain………………………...
47
7.
Distribution of per capita income in Thurston County by block
group………………………………………………………..…..
49
8.
Percentage of people living below 150% of the poverty
threshold in 2000 by block group……………………………….
49
9.
Percentage of racial minorities per block
group…………………………………………………………….
51
10.
Percentage of elderly populations by block
group…...………………………………………………………..
52
11.
Anselin local Moran’s I for ratio of income to
poverty…………..........................................................................
58
12.
Anselin local Moran’s I for per capita
income……………………………..............................................
59
13.
Anselin local Moran’s I for racial
minorities…………………………..............................................
60
14.
Anselin local Moran’s I for over 65
population……………………………………………………….
61
15.
Spatial weighted overlay…………………………………..........
62
16.
Spatial weighted overlay results with 100-year
floodplain………………..............................................................
62
17.
Block groups with a community resilience ranking of 1……......
63
18.
Block groups with a community resilience ranking of 10………
65
vi
List of Tables
Table
1.
Thurston County adoption of NHMP by jurisdiction and
date…………………………………………………………..
11
2.
Indicators for the DROP model……………………………..
37
3.
Social demographics for the two block groups with a
ranking of 1………………………………………………….
64
Social demographics for the two block groups with a
ranking of 10………………………………………………...
66
Average exposure area to the 100-year floodplain (%) by
community resilience ranking………………………………
66
Complete list of ranked block groups (Appendix)………….
95
4.
5.
6.
Page
vii
Acknowledgements
The completion of this thesis would not have been possible without the
tireless help and guidance from my faculty and thesis reader, Dr. Edward
Whitesell. His persistent guidance throughout planning, writing, revising, and
editing processes continually challenged and encouraged me over the past year. I
am very thankful and grateful for the amount of time and thought he put into
helping me produce this body of research. I also would like to thank Dr. Martha
Henderson, who offered inspiration and continual support in my understanding
and passion toward my thesis research topic, as well as my other faculty of The
Evergreen State College: Dr. Kevin Francis, Dr. Carri LeRoy, Dr. Erin Martin,
and Dr. Gregory Steward. To the individuals and organizations that directly
contributed to my research, I would also like to express my thanks and
appreciation for making time to meet with me or provide feedback on my
research: the Thurston County Regional Planning Council, Thurston County
Housing Authority, Homes First! of Thurston County, and Tom Crawford of
Thurston Climate Action Team. Lastly, many thanks to my wonderful fiancé,
friends and family who constantly provide me with encouragement, motivation
and inspiration.
viii
Chapter 1: Introduction
There is a general consensus in the scientific community that global climate
change, in combination with rising populations of people exposed to natural hazards, the
frequency and severity of natural disasters are increasing globally (IPCC, 2013). Natural
hazard research and mitigation planning have greatly decreased structural and financial
exposure to a variety of natural disasters. It is estimated that for every one dollar spent on
mitigation, four dollars are saved in recovery efforts, greatly reducing the impacts felt by
a community in a disaster event (Multi-Hazard Mitigation Council, 2005). Over the last
two decades, a significant paradigm shift within the field of natural disasters has led to an
increase in more active planning and mitigation efforts before a disaster ensues, rather
than depending so heavily on a strong response effort after a disaster ensues.
This paradigm shift began with the Robert T. Stafford Disaster Relief and
Emergency Assistance Act of 1988 (2013). This act amended the Disaster Relief Act of
1974 (1988) which states that the Federal Emergency Management Agency (FEMA) is
responsible for government-wide assistance to victims of a presidentially declared
disaster. The Stafford Act amendment was passed following a series of devastating
disasters in the United States during the 1980’s and required FEMA to place natural
hazard mitigation as the highest priority prior to a disaster, forcing FEMA to begin
shifting from a strong response-driven effort to a more planning and mitigation mindset
on the federal level. It did not take long for communities to begin seeing the benefits and
importance of natural hazard mitigation and planning. In 2000, the Stafford Act was
amended with the Disaster Mitigation Act (DMA) of 2000 (2000) which replaced former
mitigation requirements with new requirements that each state must meet to qualify for
1
disaster assistance, creating strong incentives for states and local governments to
implement and coordinate their own natural hazard mitigation plans (NHMPs). Incentives
for local governments to create their own NHMPs included access to grants and funding
that would allow communities to strengthen their communities through mitigation
projects like structural reinforcements or raising buildings in a floodplain, as well give
communities who have enacted NHMPs access to more disaster funding in the case of a
natural disaster in their area (Lindell, Prater, & Perry, 2006).
While this shift has been a significant one, mitigation and disaster research
continue to work to enhance our understanding of what makes our communities
vulnerable and how to decrease those vulnerabilities before a natural disaster strikes.
Many communities are beginning to see the growing importance of climate adaptation
due to climate change as weather events become more extreme and frequent, and in many
ways, this requires us to step outside of our single-discipline mindset and incorporate
various fields of knowledge to understand how the different facets of natural disasters
influence each other. However, adopting the interdisciplinary mindset required to
accomplish this is no small feat. It involves incorporating disciplines that range from
emergency management, climatology, and community development planning to social
science, environmental justice, economics and education. A combination of some of these
disciplines has led to the creation of more specific, but interdisciplinary fields, such as
vulnerability science and community resilience science.
Research in these fields studies the relationships that a multitude of variables have
on a community, and how, in turn, these variables interact with each other to increase or
2
decrease the overall health of the community when stressed by a natural event. Although
community resilience and vulnerability science are relatively new fields, significant
progress has been made on what these terms mean, why they’re important, and even how
to measure or model them for specific communities. Research on these subjects has
repeatedly shown that we are more in control of how our communities are affected by
disaster than originally thought. Disasters are not entirely randomly distributed, or evenly
damaging across the affected populations (Godschalk, 1999). These fields of research
have led many communities all over the United States to develop and implement NHMPs
and climate adaptation plans.
Thurston County, Washington (Figure 1) has been one of the many places that
have worked hard to develop plans that reduce their vulnerability to natural disasters.
FEMA has declared a total of twenty-three natural disasters in Thurston County since
1962, six of which were between 2003 and 2009 (Thurston County NHMP, 2009). And
the overall average is one almost every two years. Besides the Thurston County NHMP,
which was first developed and implemented in 2003 by the Thurston County Regional
Planning Council, other examples of measure the community has taken to increase
awareness and better prepare for climate adaptation can be found within organizations
like Thurston Climate Action, and Transition Olympia. These examples encompass the
understanding that a more proactive stance is in order to make the places we live safer
and more resilient to future natural disasters through community education and reestablishing connections and cohesion within our communities. While this need is easier
to see now, approaching such a complex subject and changing the way we handle
emergencies is not such a simple task.
3
Already, developing an NHMP on a city, county or statewide scale involves
enormous collaboration with community and city planners, emergency management, fire
and school districts, city transit organizations, colleges and the public. Drafting and
implementing an NHMP combined with a budget that shifts from year-to-year can
seriously limit the resources available and ability of planners to incorporate every item
they might like to include within the county NHMP. Past research has addressed that
NHMPs tend to carry more weight in the ‘fact-based elements’ and ‘economic risk
assessment’ portions, and leave much to be desired as far as socioeconomic data and
vulnerable populations go (Frazier, Walker, Kumair, & Thompson, 2013b). Gathering
useful social information can be more difficult and time consuming than gathering
economic data or outlining facts about hazards that exist in a given area, and some
planning councils just do not have the time or resources to carry their plans much further
than is absolutely required. Not only can gathering social information be difficult, but
each community, city, or county will vary in which vulnerable populations they need to
work to include in NHMPs. Another difficult hurtle, is how to assemble all of this
information in a useful, meaningful way that will truly benefit planning efforts.
Geospatial Information Systems (GIS) have provided a solution to these problems
over the last few years. Combined with useful social census data like age, race, and
financial income, we now have a powerful tool to input all of these data into and see how
they interact with each other over a given region and gather an approximate idea of how
impacted specific areas would be impacted by a natural disaster event. Foundational
studies world-wide in this area of research have provided helpful methods that can be
implemented in most parts of the United States to study how our societal factors work in
4
combination to affect vulnerability to natural hazards and show community resilience to
natural disasters in an area (Cutter, Boruff, & Shirley, 2003; Cutter, Mitchell, & Scott,
2000; Frazier, et al., 2013b; Gunawardhana, Budge, & Abeyrathna, 2013). Some of these
studies helped serve as a valuable guide in developing the research design and methods
for this study to identify vulnerable populations and measure community resilience using
Thurston County as a case study. The vulnerable populations in Thurston County that are
focused on in this study have also been identified in previous research of this kind;
populations over the age of 65, racial minorities, persons living below 150% of the
federal poverty threshold, and per capita income are identified on the block-group level.
The purpose of this study was to identify vulnerable populations within Thurston
County using publicly available social demographic data and spatial technology to
determine a foundational “community resilience” ranking in relation to the 100-year
floodplain. More specifically, this study shows how certain factors can be usefully
incorporated to spatially analyze ways in which areas might be affected by natural
disaster and which areas might be at an increased risk to an event. In this study, Thurston
County is the study area and focus, but these methods are applicable to other counties,
cities, and communities as well and can serve as a basis for other regions to better
understand vulnerable populations and community resilience in their own areas.
Although the Thurston County NHMP includes a basic demographic profile for the
region, which includes many tables and graphs of statistics, this study takes some of this
information and combines them into a useful tool to create a stronger understanding of
how those variables interact to increase or decrease vulnerability, and ultimately impact
community resilience across the spatial plane.
5
Figure 1: Map of Washington State. Thurston County is identified in black.
Like much of the Pacific Northwest, Thurston County is exposed to a variety of
natural disasters, most notably earthquakes, volcanic activity, tsunamis, and extreme
flooding. As was stated before, Thurston County is not a stranger to federally declared
disasters. Of the various natural hazards that exist in this region, flooding poses the most
frequent and expensive problem to the county. Roughly 5.1% of the county exists in a
floodplain (Washington State NHMP, 2010), and since 1962, Thurston County has had
twenty-three federally declared disasters, seventeen of which were major flooding events
(Thurston County NHMP, 2009). This information has encouraged local planners to
heavily stress floodplain management techniques within the county plans, and this is the
reason this study has chosen to look at community resilience in relation to the 100-year
floodplain. While this natural hazard was chosen for this study to contain scope and keep
this thesis manageable, it should be noted that Thurston County’s community resilience is
6
also relevant and important to study in relation to other natural hazards in the area that
pose a disaster risk to local communities. Although there are differences in how a
community will be impacted depending on what type of disaster event happens, if a
community is socially vulnerable (i.e. high populations of low income families, elderly,
or high populations of diverse, racial minorities) they are likely to be more severely
impacted by any natural disaster than less vulnerable communities, whether it be a flood,
earthquake, or severe winter storm due to more limited resources, physical inabilities,
language barriers or cultural barriers; understanding this is why it’s important to view and
increase community resilience to a variety of hazards rather than just to one in particular.
Before delving into the past research on these subjects, it is important to define some
basic but important terms, and to discuss the current state of the Thurston County NHMP.
Important Terms and NHMP Basics
A hazard is defined as something that has the potential to cause harm, generally
used in the English language to describe something that can cause damage to life or
property (Merriam-Webster, 2013). Hazards can be human induced, like hazardous waste
sites, hazardous fumes, or hazardous commercial structures. A natural hazard is a
physical, biological, or ecological process that exists in nature as part of Earth’s
meteorological, ecological, and/or geological systems. Some of the most frequent natural
hazards in the Pacific Northwest, for instance, are earthquakes, landslides, severe storms,
volcano eruptions, flooding, and wildfires. A natural hazard becomes a disaster when the
hazard impacts human life or developments (Abbott, 2012; Tierny, 2007). In the absence
of humans, there are no natural disasters, only natural hazards. As Earth becomes more
7
densely populated, natural hazards have increasingly become natural disasters, causing
over 2 million deaths globally since 1970, and costing over $210 billion globally in 2011
alone (Abbott, 2012).
Emergency Management (EM) is “the management of risk so that societies can
live with environmental and technical hazards and deal with the disasters they cause,”
(Waugh, 2000, p. 3). Within EM, they identify four primary phases of the EM process—
natural hazard mitigation (NHM), preparedness, response, and recovery (FEMA, 1998).
Prior to the last two decades, EM strongly focused on responding to natural disasters as
they happened, but over the last twenty years, there has been a shift in this disasterresponse focus, to a stronger emphasis on mitigation, planning, and community resilience
to help avoid as much damage as possible prior to an disastrous event taking place (Berke
& Campanella, 2006; Board on Natural Disasters, 1999; Cutter, Barnes, Berry, Burton,
Evans, Tate, & Webb, 2008; Manyena, 2014; McEntire, Fuller, Johnson, & Weber, 2002;
Allen, 2006). This paradigm shift is the result of Earth’s growing populations, climate
change, and the realization of the limited capabilities of large response organizations.
Natural Hazard Mitigation (NHM) is defined as “any sustained action taken to
reduce or eliminate long-term risk to people and property from natural hazards and their
effects” (FEMA, 2013). The differences between mitigation and preparedness can be
difficult to separate because the two terms are often used interchangeably and overlap
each other in several aspects, however, the two do have differences. Mitigation can best
be understood as long-term, on-going actions that provide “passive protection” at the
time of an event (Godschalk, 2005; Lindell & Perry, 2007). These actions can be handled
8
by individuals, city, county, state and federal agencies, and can include, but are not
limited to the following: establishing flood-plains and appropriate land-use codes;
changing development zoning; strengthening building codes; and public education
campaigns about home, property, and business damage reduction. Preparedness, on the
other hand, can involve active planning on individual, community and government levels
and aims to enhance effective response once a disaster happens. Preparedness tasks can
include, but are not limited to, stock piling food and water; preparing emergency kits for
homes; collaborating with other community or state organizations; and becoming
educated about individual and local emergency management plans. Mitigation and
preparedness give local communities, and state and federal agencies the opportunity to
plan ahead of a disaster, reducing exposure and making our communities capable of a
quicker recovery after an event occurs. Primary agencies responsible for mitigation and
preparedness range from small local organizations (e.g., schools and churches) to city and
county jurisdictions or larger state and federal organizations (e.g., State Emergency
Management Divisions and FEMA).
Plans are comprised of structural and non-structural mitigation strategies (Lindell,
et al., 2006). Structural strategies consist of five primary areas of focus—hazard source
control, community protection works, land use practices, building construction practices,
and building contents protection. Non-structural mitigation strategies are more vague, but
various, including things such as “reducing chemical quantities stored at water treatment
plans,” and “purchasing undeveloped floodplains and dedicating them to open space,”
(Lindell, et al., 2006, p. 195).
9
As was stated earlier, Thurston County first introduced its NHMP in 2003. Since
then, significant progress has been made to make the area safer in the face of a variety of
natural hazards. The plan includes structural and non-structural mitigation strategies for
earthquakes, storms, floods, landslides, wildfires, and volcanic eruptions, as well as
potential climate change impacts in Thurston County’s future. Thurston’s NHMP is a
multi-jurisdictional plan, meaning the plan covers Thurston County as a whole, but
nineteen cities, tribes and local organizations have decided to adopt the plan as their own,
rather than writing a localized plan for their areas of jurisdiction. It is important to note
the differences in each community’s needs, strengths, and weaknesses when looking at a
multi-jurisdictional plan. Thurston’s NHMP includes urban areas such as Olympia and
Lacey, as well as smaller rural areas such as Bucoda, Yelm, and Rainier (see Table 1).
Thurston’s NHMP also includes social and economic data in the form of basic
profiles and tables for each area that has adopted the plan. While the profiles and tables
are informative, this section is not conducive to showing how these populations are
distributed throughout the county, understanding how these different characteristics
interact with one another, or what part they play in community resilience to the natural
disasters being mitigated. By using GIS, these data can be compiled into a more useful
tool to better understand what these characteristics mean for regions throughout the
county, and how these areas might be differently impacted by a natural disaster.
Moving forward, the literature review chapter of this study compiles the primary
research done in the fields of community resilience to natural disasters; these studies
cover a multitude of factors that influence how humans are affected by natural disasters,
10
including vulnerability science, vulnerable population identification, environmental
justice, community planning, and modeling and measuring community resilience to
natural disasters. This chapter also helps define many of the more complex terms like
community resilience and vulnerability and addresses why much work is still needed in
our planning and mitigation activities to incorporate a systems view to strengthen our
NHMPs.
Table 1: Jurisdiction Adoption and Approval Dates of the 2003 NHMP for the Thurston Region. Plan is
updated every five years; the latest version will be available by November, 2014.
Jurisdiction
Adoption
Approval
Thurston County
August 4, 2003
October 6, 2003
Town of Bucoda
May 24, 2005
August 17, 2005
City of Lacey
September 11, 2003
October 6, 2003
City of Olympia
December 9, 2003
October 6, 2003
City of Rainier
March 2, 2005
April 6, 2005
City of Tenino
July22, 2003
October 6, 2003
City of Tumwater
July15, 2003
October 6, 2003
City of Yelm
August 13, 2003
October 6, 2003
Confederated Tribes of the Chehalis Reservation
July 19, 2003
October 6, 2003
Fire District 4 – Rainier
August 12, 2003
October 6, 2003
Fire District 9 – McLane
August 14, 2003
October 6, 2003
Fire District 13 – Griffin
August 14, 2003
October 6, 2003
Intercity Transit
June 2, 2004
October 6, 2003
Providence St. Peter Hospital
May 6, 2004
August 25, 2004
School District, North Thurston Public Schools
January 18, 2005
February 28, 2005
School District, Olympia
August 9, 2004
October 6, 2003
School District, Rainier
---------------October 6, 2003
School District, Tumwater
June 12, 2003
October 6, 2003
School District, Yelm Community Schools
November 23, 2004 December 23, 2004
The Evergreen State College
July 9, 2003
October 6, 2003
In chapter three, the research methods involved in this study will be addressed and
discussed. Methods for this study were chosen based on previous, similar studies covered
in chapter two. Variables used were chosen based on availability of data (only publicly
available census data were used) and on whether these vulnerable populations had been
identified in past research as indicators of community resilience. Spatial analyses are
discussed and explained, as well as the final, spatial weighted overlay analysis, which
11
resulted in a ranked community resilience map of Thurston County. These methods were
chosen to produce foundational methods for measuring community resilience to natural
disasters in Thurston County, and to provide meaningful information about what
community resilience in Thurston County looks like in relation to the 100-year
floodplain.
The 100-year floodplain used in this study was created using HAZUS, rather than
depending on existing floodplain maps. This was done for two reasons. By using
HAZUS, a more detailed depth grid and floodplain can be developed using more current
elevation data provided by the USGS. Further, using HAZUS allows us to go beyond just
mapping a natural hazard area. HAZUS incorporated hundreds of useful tools and data
within its disaster models. Although no other HAZUS capabilities were included in this
particular study (e.g., damage estimations for the study region), it was used to provide
future research with a stronger format for measuring community resilience in Thurston
County. With the inclusion of HAZUS, the foundation is laid for similar studies to
incorporate flood and earthquake models for the region, and to see in more detail how
areas of low and high community resilience might be impacted by an event. This will be
further discussed in the section on considerations for future research, in chapter six.
Chapter four covers the results of all spatial analyses done on the four variables
and the spatial weighted overlay. Maps that display the spatial statistics conducted are
provided and explained. The spatial analyses provided information about the degree to
which a variable influences itself. If each of the individual social variables is correlated
with themselves, this is important to understand before combining the variables into the
12
spatial weighted overlay. These relationships also help show how community resilience
throughout the county could also be auto-correlated with itself—in other words, by
strengthening or weakening one jurisdictions community resilience to natural disasters,
the neighboring jurisdictions might also indirectly experience increased or decreased
community resilience. The analyses of social indicators in this study helped show the
complex and systemic nature of community resilience.
The spatial weighted overlay analysis and the ultimate findings of community
resilience in Thurston County are the result of the final analysis. The outcome of the
weighted analysis assigned a community resilience ranking to each individual area within
Thurston County. Identifying areas of highest and lowest resilience within the county
provided a very useful tool for things such as conducting hazard mitigation projects,
educational preparedness campaigns, or community planning and restructuring. It will
also allow planners and community members to identify where particularly low resilience
areas lie within proximity to specific hazards.
This study used the 100-year floodplain as a natural hazard for comparison, but
the resilience ranking could also be studied in relation to fault lines, landslide zones, or
areas prone to wildfires. Knowing the community resilience information of an area can
also allow emergency responders to better prepare. For example, knowledge about low
resilience due to lower-than-average financial income would allow groups like the
American Red Cross to be better prepared to dispatch aid to those specific areas, to
provide needed resources and locate alternative housing for impacted populations.
Included in this chapter is the final map of Thurston County with a choropleth map
13
indicating block groups with their corresponding community resilience ranking. The
highest and lowest groups are identified and shown up close in relation to the 100-year
floodplain.
Lastly, the discussion chapter goes further in depth on the areas of highest and
lowest resilience within Thurston County. Some of the social characteristics that strongly
influenced their ranking are looked at in detail for the block groups of highest and lowest
community resilience. Implications for this study and considerations for future research
are discussed, to identify ways this research is helpful and what is needed from further
research on this topic.
The transition of natural disaster management from a response mindset to a
stronger focus on mitigation and planning has greatly decreased many communities’
exposure to natural hazards. As is discussed in chapter two, most NHMPs tend to place a
higher priority on mitigating for structural and economic damage than on understanding
the underlying social structures that interact with each other to increase or decrease
community resilience to natural disasters. Recent research has focused on these
shortcomings, and many have interrogated the meanings of terms like “community
resilience” and “vulnerability.” These studies have greatly strengthened our
understanding of the importance of underlying social factors and the roles they play in
how a community is impacted by and recovers from an event. Other studies have taken
community resilience and vulnerability sciences further into modeling stages, and even
measuring stages. The following chapter addresses and reviews the primary existing
literature on these subjects, and helps clarify the methods chosen for this study.
14
Chapter 2: Literature Review
As was mentioned in the first chapter, research in the fields of natural hazard
mitigation and emergency management have come a long way in the past two or three
decades in revealing inter-linking and underlying factors that influence and affect the way
populations are impacted by disasters. These areas of study are constantly growing and
incorporating a more systemic perspective, which continues to influence and strengthen
overall community resilience and reduce vulnerability. In the first portion of this review,
research on the quality and effectiveness of NHMPs is discussed and two major
consistent themes are found throughout these studies. The first is that there is a general
lack of inclusion of many important social indicators that should be taken into
consideration when creating an effective NHMP that encourages community resilience to
natural disasters. Secondly, multiple researchers have emphasized the need to incorporate
community-specific and interdisciplinary aspects outside of the “cookie-cutter” format
that FEMA currently requires from all NHMPs to make our mitigation more effective and
localized to each community’s needs. A review of these studies leads us into the second
half of the review, into more complex questions. What does community resilience really
mean? What has vulnerability and who are vulnerable? How can we begin to measure our
own community resilience? Fortunately, research has made significant progress in these
areas as well.
Natural Hazard Mitigation Plans
Since the implementation of the Disaster Management Act of 2000, some work
has been done to try and measure the quality and effectiveness of the implemented
15
NHMPs. One of these studies focused on 139 cities across five states, including
Washington State. This study (Berke & Roenigk, 1996) sought to find out if statemandated NHMPs resulted in an increase in the development and effectiveness of local
community mitigation plans within the state. What they found was that not only were
local communities more likely to develop and implement their own NHMPs if plans were
mandated on the state level, but many of the plans developed were of higher quality than
communities who developed plans in a state without state-mandated NHMPs.
Washington State was unique in this study, because it has a state mitigation plan, but
does not mandate local communities to implement their own. Despite the lack of a state
mandate, 29 of the 30 local governments examined in this study have prepared and
adopted local NHMPs. The researchers attributed this anomaly to the fact that
Washington State may have a more “progressive and environmentally conscious culture
to take collective action through planning,” (Berke & Roenigk, 1996, p. 5). While this
very well might be the case, this study looked at plan effectiveness based on requirements
and recommendations set by FEMA. The effectiveness of these plan requirements within
Washington State have been seen by some as less effective than their potential, due to
deficiencies in several areas. It has been argued that this is due to the low, “cookie-cutter”
style standards that FEMA requires of them, and the lack of incentive for county planners
to go further than required to create NHMPs that are more specially designed for their
specific communities.
One such study has analyzed county plans within Washington State, to look at
their effectiveness on the county level based on FEMA requirements, and then to analyze
them a second time based on further needed additions recommended in NHMP research.
16
Keeping with the same methods and analysis style of previous studies, this study was
conducted by performing a comparative analysis of eight Washington State county
NHMPs—Skagit, Clallam, King, Kitsap, Pierce, Thurston, Pacific, and Lewis (Frazier, et
al., 2013b). The researchers found significant differences between rural and urban
counties, and they argued that the base-level criteria that FEMA requires of NHMPs is
broad and is not always sufficient for smaller, local community needs. Of all eight plans
included in the study, Thurston County was scored the highest (88%) out of the basic
FEMA mitigation plan requirements (a total of 57 requirements). This high score was
mostly attributed to the fact that Thurston County developed its plan internally (through
the Thurston County Regional Planning Council), rather than contracting it out, and is
better established, resourced, and sophisticated when compared to smaller or rural
jurisdictions. However, since FMEA produced the format recommendations for NHMPs,
other research has expanded on areas that should be included in a strong plan (Berke,
Smith, & Lyles, 2012; Godschalk et al., 1999; Hoch, 2002; Hopkins, 2001). These
expanded areas included topics such as “issue identification and visioning, internal
consistency, implementation, monitoring and evaluation, organization and presentation,
and integration and coordination with other plans and compliance with governmental
mandates” (Frazier, et al., 2013b, p. 55). Frazier, et al., then re-evaluated the eight
Washington counties a second time within the same study (2013b) to include these
expanded items, for a total possible score of 293. When factoring in these expanded
items, Thurston County again scored highest, but at a mere 54%, showing potential for
improvement in many of the expanded factors.
17
These authors determined that overall, most of the NHMPs were strongest in the
risk assessment and “fact-based elements,” while “sections requiring more analysis and
time-consuming detail and review, such as socioeconomic analysis and identification of
special needs populations” were less focused on, and therefore less effective, in all plans
studied (Frazier, et al., 2013b, p. 58). The researchers concluded that sub-par mitigation
plans are the result of jurisdictions following the “cookie-cutter” format of local
mitigation planning, based on the large, general NHMP format recommendations
provided by FEMA. Counties generally follow this format, performing close to the bare
minimum necessary to qualify for mitigation and post-disaster funding. Overall, this
research found that local hazard mitigation planning is in need of a “more place-based
approach,” which includes more specific mitigation based on local hazards and
community needs. Frazier et al. concluded with the recommendation that a,
“collaborative, interdisciplinary approach to hazard mitigation planning and NHMP
development has the potential of increasing overall plan quality. This in turn increases
community resilience and reduced vulnerability,” (Frazier, et al., 2013b, p. 59). While
including socioeconomic information and identifying vulnerable populations in an
NHMP might be more time-consuming, incorporating it would offer a useful tool for
understanding the relationships between hazards and particularly vulnerable populations,
and would increase over-all community resilience by providing information of where
resources would be most needed. It also would offer planners a better idea of weak areas
where mitigation projects should be prioritized, to increase over-all community
resilience.
18
Other groups of researchers have performed similar comparative analyses. One
study selected fifty-seven counties spread over three different states in the southeastern
United States, and found that significant differences existed among county NHMPs
within each state, separated by rural and urban areas (Horney, Naimi, Lyles, Simon,
Salvesen, & Berke, 2012). Counties that classified as “urban” had stronger direction
setting principals (goals, fact bases, and policies) than counties classified as “rural,”
while the “urban” county plans were inferior to “rural” plans in the action-oriented
principals (implementation and monitoring, inter-organizational coordination, and
participation). The significant differences in the urban NHMPs compared to the rural
NHMPs show that there is room for improvement at both scales. While the states focused
on in this study were Florida, Georgia, and North Carolina, the results suggest that
similar weaknesses may lie within other state and county NHMPs, including Washington
State. As was noted earlier, Thurston County’s NHMP is a multi-jurisdictional plan, and
has been adopted by large and small cities within the county, so for an NHMP to be most
effective for all communities included, it must take into account the differences in each
community’s needs, strengths, and weaknesses when mitigating natural hazards.
In sum, reviewing the literature on quality and effectiveness of NHMPs reveals
two strong themes: (1) there is a general lack of inclusion of many important social
indicators that should be taken into consideration when creating an effective NHMP; and
(2) in order to include factors outside of the “cookie-cutter” format currently used to
develop plans, an interdisciplinary approach must be taken. Incorporating data from
social disciplines provides a more rounded picture of where community vulnerability
exists, and ultimately, leads to a strengthened NHMP. These conclusions raise questions
19
about two particular terms and concepts that need to be addressed and better understood
before moving on, namely “community resilience” and “social vulnerability.”
Community Resilience
As discussed, past NHMPs have been strongest in providing natural hazard
factual information along with structural and economic risk assessments. This rings true
for Thurston County’s mitigation plan, as well. Although the plan does incorporate social
demographic profiles for each jurisdiction covered, these lists and tables are not useful
for understanding the relationship these characteristics have with each other (social
vulnerability), nor the impact they have on community resilience in the area. In
Thurston’s NHMP, as well as in most NHMPs in general, risk assessments only measure
potential economic loss in terms of workforce and infrastructure (Washington State
Enhanced Mitigation Plan, 2010). The main reason for a purely economic focus in
measuring vulnerability and risk is due to the difficulty in measuring social factors, such
as social capital, value of place and income disparity. However, by including other noneconomic factors into our assessment of what is at risk in times of natural disasters, and
including economic data as one piece of the puzzle (rather than the main piece), we can
begin to measure a community’s resilience to natural disasters.
Community resilience is a broadly used and still shifting term, without one single
definition. However, the Community And Regional Resilience Institute (CARRI) has
been helping track community resilience-related research since 2009, while compiling
and implementing the Community Resilience System (CRS), later adopted by FEMA in
2011. CARRI (2013) provides the various accepted definitions of resilience and shows
20
the evolution of the term from 1978 to present day. The most common definitions used
today in the natural hazard emergency management fields are the ecological system
versions (rather than their older, strictly physics and engineering-based definitions), and
CARRI lists forty-six separate definitions that have been cited throughout resiliencerelated literature. Although the early ecological versions of the definition were not
originally developed with human community resilience in mind, many disaster
researchers have adopted these versions because of their capacity to capture the complex
changes involved in community resilience with respect to a natural disaster event
(Gunderson, 2010).
Two commonly cited definitions listed by CARRI stem from Holling, who
defined resilience as, “the persistence of relationships within a system; a measure of the
ability of systems to absorb changes of state variables, driving variables, and parameters,
and still persist,” (Holling, 1973, p. 17) and as the “buffer capacity or the ability of a
system to absorb perturbation, or the magnitude of disturbance that can be absorbed
before a system changes its structure,” (Holling, 1996, p. 53). Both of these definitions of
ecological resilience include the importance of absorbance, but the second, more recent
definitions, incorporates the specific terms ‘perturbation,’ and ‘disturbance,’ stressing the
importance of stress or disruption along with system response. Klein, Nicholls, and
Thomalla, (2003) go further, to say that resilience is, “the ability of a system that has
undergone stress to recover and return to its original state; more precisely (i) the amount
of disturbance a system can absorb and still remain within the same state or domain of
attraction and (ii) the degree to which the system is capable of self-organization
[emphasis added],” (Klein, et al., 2003, p. 43). This definition has been adopted by many
21
researchers to help increase communication between natural hazard research
communities and climate change research communities. It incorporates important climate
change language so that natural hazard discussions can more easily incorporate aspects of
climate change into discussions about hazard prediction, disaster prevention and
mitigation (Klein, et al., 2003). Another benefit to this definition is its incorporation of
the term ‘self-organization,’ which is an important characteristic of any system and
necessary for resilience to take place at all. While the Klein, et al., (2003) version isn’t
the most frequently cited definition of resilience, it will be the definition used throughout
the rest of this paper when speaking of community resilience. This study will identify
characteristics that influence a community’s resilience to natural disasters by using a
holistic, systems perspective, thus it is important to keep in mind the importance of selforganization as a trait of a resilient community.
Resilience
“The
ability
of
a
system
that
has
undergone
stress
to
recover
and
return
to
its
original
state;
more
precisely
(i)
the
amount
of
disturbance
a
system
can
absorb
and
still
remain
within
the
same
state
or
domain
of
attraction
and
(ii)
the
degree
to
which
the
system
is
capable
of
self-‐organization”
(Klein,
et
al.,
2003,
p.
43).
Systems are characterized by several factors, including emergent properties, selforganization, non-linear change, and unpredictability (Gunderson, 2010; Meadows,
2008), so to view community resilience through a systems approach, it is important to
understand that resilience is a system process rather than an achieved state or stability
(Norris, Stevens, B. Pfefferbaum, Wyche, & R. Pfefferbaum, 2008). Because social,
physical and economic variables within a community are constantly changing, so is a
22
community’s resilience to natural disasters. It is helpful to understand that although
community resilience and social vulnerability are closely, negatively correlated, the
absence of vulnerability does not necessarily mean high community resilience.
Community resilience is complex and includes many, interlinking factors, including
physical and economic facets, all of which are constantly changing, and it would be
extremely difficult to incorporate them all. It is necessary to keep this in mind, and
understand that this study is only the beginning in understanding local community
resilience.
It has been discussed that the economic aspects of NHMPs tend to be given
priority in identifying and executing mitigation projects. The physical aspect plays a
somewhat less predictable role, but NHMPs also have been shown to be stronger in their
hazard fact-based elements, to provide as much information as possible about the
potential hazard at hand. Where does this leave the social variables? How do they interact
with each other and what can they tell us about a community’s over all resilience to a
natural disaster? These types of social vulnerability questions are the focus of
vulnerability science, and they have helped contribute to our understanding of community
resilience.
Social Vulnerability
Vulnerability science is another social science field that, like resilience science,
depends on an ever-shifting system as the population increases and decreases. Also, like
resilience science, it is an evolving field with large degrees of uncertainty due to the
difficult nature of measuring and quantifying vulnerability (Cutter, Mitchell, & Scott,
23
2000). Vulnerability is generally accepted in the natural hazard realm as a term referring
to the potential loss of life or property due to hazards (Cutter, 1996). Other researchers
stress the importance of identification and inclusion of vulnerable people into
preparedness and mitigation plans; such as the elderly, poor, children, disabled, and
minority communities (Berke & Campanella, 2006; Colten, 2008; Kiter-Edwards, 1998;
Tobin, 1999; Vink & Takeuchi, 2013). Cutter, et al. (2000) created a “place
vulnerability” map and scoring system that used GIS mapping and social data to visualize
biophysical and social vulnerability in Georgetown County, South Carolina. Their study
was able to delineate areas where vulnerable people (females, non-whites, <18 and >65
years of age, and low income neighborhoods) were spatially situated in relation to areas
with high risk of hazards such as chemical spills, earthquakes, floods, hurricanes,
windstorms, tornados and wildfires. Like previous studies that assessed the effectiveness
and quality of NHMPs, this study showed the need for the inclusion of social indicators
when assessing and mitigating a community’s vulnerability to natural hazards. Because
vulnerability and social factors are linked, effective mitigation strategies must usefully
incorporate these factors into the overall mitigation plan in order to increase community
resilience. With this understanding, other studies have worked to determine exactly
which social characteristics are most helpful and important to include when identifying
vulnerable populations and assessing a community’s resilience to natural disasters.
In a study of all 3,141 counties in the United States, researchers developed a
Social Vulnerability Index (SoVI) to quantitatively measure factors that most strongly
influenced a county’s vulnerability to natural disasters (Cutter, Boruff, & Shirley, 2003).
In their review of the literature, these authors identify seventeen factors that past research
24
has shown to influence vulnerability. Within these seventeen factors, these authors note
that four factors are most commonly cited in literature as influential on vulnerability—(1)
age, (2) gender, (3) race, and (4) socioeconomic status. These researchers then used 1990
U.S. census data to quantitatively assess each of the seventeen factors that past research
had attributed as an influence on vulnerability to identify the amount of variance each
trait individually contributed to overall vulnerability.
They found that of the seventeen factors identified in past research, eleven of
these showed a stronger influence on overall vulnerability. These eleven factors
explained the highest percentage of variance when assessing vulnerability, and when
combined, explained 76.3% of the variance in vulnerability from county to county—
personal wealth, age (children), age (elderly), density of the built environment, singlesector economic dependence, house stock and tenancy, race (African American and
Asian), ethnicity (Native American), ethnicity (Hispanic), Occupation, and infrastructure
dependence (Cutter et al., 2003). Density of the built environment describes how
developed an area is; explaining that areas that are more structurally dense contain higher
vulnerability than areas with less structural development because there are more
buildings and homes that could be damaged during a natural disaster. Single-sector
economic dependence represents areas that are heavily dependent on one or two major
industries for their economic stability, such as a town where a large portion of the
residents are employed in the logging industry, or oil refineries. Areas that are largely
dependent on these areas tend to see over-all economic growth in times of prosperity, but
the entire area suffers when these industries struggle or suffer. The characteristic of racial
inequality show areas where racial minorities have unequal access to resources or a
25
tendency to be marginalized due to social, cultural, or language differences. This study
identifies African Americans (particularly African American women-led households) and
Asian populations as the most vulnerable.
While this study further shows the importance of understanding social
vulnerability and helps show the complex relationship these factors have with each other
to ultimately increase or decrease vulnerability, these methods could be applied at a more
local scale to identify vulnerability in more detail. Incorporating social aspects into our
understanding of natural disaster impacts helps us understand that, ultimately, we are
more in control of how we are impacted by natural disasters. This relationship between
vulnerability and community resilience to natural disasters is being called by some in the
disaster research community the “social construction of disasters.”
The Social Construction and Interdisciplinarity of Disasters
The primary theme behind much of the research surrounding disaster resilient
communities lies in the foundational understanding of the social construction of disasters.
This concept addresses specific measures a community takes that can have the ability to
increase or decrease a community’s resilience as a whole. Literature on the social
construction of disasters (Clarke, 2006; Klinenberg, 2002; Peacock, Hearn, Morrow, &
Gladwin, 1997; Wisner & Walker, 2005) has stressed the need for understanding the
theory of how social factors—such as inequality, political structure, gender, racial and
ethnic relations, the environment, and culture—all tie together to either enhance or hinder
a community’s resilience to disasters. “Structured Destruction,” a term referring to
“patterns of suffering that follow divisions of race or class” (Clarke, 2006, p.134), is a
26
concept that calls attention to the relationship between devastation following a disaster
and the “ways humans organize their societies: along lines of wealth and poverty,
division of labor, access to healthcare, and membership in organizations,” (Clarke, 2006,
p.129). Sociologists have made some headway in showing that “disasters are not random
or equal probability events but are the result of existing social and economic conditions.
Therefore, it should come as no surprise that those disenfranchised from political and
economic power disproportionately suffer the consequences of these events and have the
greatest difficulties in recovering from them. In fact, disasters serve to bring to the
forefront the social inequalities that characterize contemporary societies,” (Rodriguez &
Barnshaw, 2005, p.222).
Joining social aspects with the physical and economic aspects of natural disaster
research requires tying together multiple disciplines of research, such as community
development and planning, local and federal policy, hazard mitigation and emergency
management, climate science, and local voices. This provides a systems view that allows
us to better plan for future events for everyone in the community. Viewing community
resilience through the social constructionist and interdisciplinary lens can help us identify
weak areas in our communities that can become starting points for building community
resilience. Understanding the social construction of disasters brings light to the
environmental justice portion of the literature on these subjects, which begin to address
the moral and ethical issues associated with how we socially construct natural disasters.
27
Environmental Justice
Environmental Justice (EJ) is an area of social science research that studies how
evenly environmental benefits and burdens are distributed across various populations
(Schlosberg, 2008). It posits that “environmental safety and health cannot be a luxury
reserved for the privileged classes or wealthy countries,” (Bolin & Stanford, 1998).
Environmental justice research varies widely, but includes research on minorities and
vulnerable populations’ exposure to hazardous pollution from activities such as nuclear
facilities and natural gas production sites, as well as studies of populations most affected
by climate change and natural disasters. Bolin and Stanford (1998) point out that
“disasters occur at the intersection of environmental hazards and vulnerable people, and
as such are social products,” … “their risks and effects are mediated by prevailing social
practices and their material forms in a given place,” (Bolin & Stanford, 1998, p.218).
Low-income populations and racial minorities have been identified as bearing a heavier
burden of environmental hazards such as pollution, contaminated drinking water, and
exposure to waste sites than their white and/or wealthier counterparts (Bullard, 1993);
this is just one example of the necessity of studying the relationships between community
social characteristics and hazards. Environmental justice is inherently interdisciplinary
and, when discussing community resilience and vulnerability to natural disasters,
environmental justice is an essential component.
Cohen and Bradley (2008) argue that the higher exposure to natural hazards by
vulnerable populations is not just a community planning failure, or a shortcoming in our
understanding, it is a human rights issue; many of our own vulnerabilities are within our
28
power to prevent, or at least reduce. These researchers agree with previous studies
discussed here in regards to the social construction theory of disasters and reiterate that
disasters expose serious social inequalities that already exist in a community or
population. They recommend that we begin increasing awareness through public policy,
and that local and national leaders need to be repeatedly reminded to keep vulnerable
populations and their rights at the top of their list of priorities. It is ultimately their job to
be a voice of protection for these underserved and vulnerable populations, and to ensure
they get the help they need before and after natural disaster events. This will only prove
more urgent as the frequency and severity of natural disasters due to climate change rise
and continue to affect our ever-increasing populations.
Not only is prioritizing in this way the right thing to do, but as we have seen with
natural hazard mitigation—although expensive at times—prevention and active planning
are more economically efficient as well, when compared to the damages incurred when
these vulnerabilities are neglected. Cohen and Bradley discuss examples of governments
that are learning that neglecting to put vulnerable populations at the forefront of hazard
planning and community development has proven costly. Examples they give are global,
but some reside here in the United States as well, such as the on-going lawsuits being
filed in the courts showing “monumental negligence” in failing to take sufficient
preventative measures in New Orleans,” for hurricane Katrina (Cohen and Bradley, 2010,
p.126). The onset of these types of lawsuits is reminding leaders of their responsibility to
protect the people they represent, and of the serious consequences when they fail to do
so.
29
In essence, vulnerability, community resilience, and human rights issues are
strongly intertwined, and many researchers have repeatedly encouraged solutions that
involve methods that address the social inequalities underlying community
vulnerabilities; these solutions would include political change, economic reform, and
focusing public policy to protect people and nature across all scales from risk (Wisner,
Blaikie, Canon, & Davis, 2003). Clarke (2006) suggests that we need to change what we
deem “critical infrastructure,” to include social network structures (the social fabric upon
which we all ultimately depend in times of crisis), and reduce our dependence on large
organizations for help, which are often strangled by red tape in moments when they need
to make quick, life-saving decisions. Clarke advocates preemptive resilience, allowing
every day citizens (who are often some of the most effective first responders in a disaster)
to play a larger role in policy and planning efforts.
This review has discussed many topics—natural hazard mitigation, emergency
management, social vulnerability, and environmental justice. The connections,
importance and shortfalls of these topics are well summarized by Bolin and Stanford
when they say:
“…people, particularly low income minorities, elders, and financially
stressed middle-class households, often have to balance limited resources
against the constant hazards of illness, unemployment, rising living costs,
and even homelessness. Given persistent general risks, it should not be
overly surprising that many appear willing to ‘take their chances’ against
the infrequent earthquake or occasional flood and do not purchase
expensive but minimal coverage insurance”…”If disasters are to be taken
as opportunities to make safer communities, developing an informed
understanding of the local realities of daily life surely must be as important
as imposing new building codes, zoning regulations, or insurance
requirements. Efforts to discipline disaster victims by denying them
assistance if they have not taken adequate self-protection measures may fit
30
neoliberal ideological agendas, but are likely to only increase the social
inequalities that are too often already amplified by disaster,” (Bolin &
Stanford, 1998, p.226).
Past research has significantly contributed to the importance of
interdisciplinarity and incorporating social factors when looking at how a
community is affected by, and recovers from, a natural disaster. So far, this
review has helped identify reoccurring factors that intermingle to affect a regions’
overall vulnerability. But what does this mean for community resilience? What
specific factors have been found useful in determining community resilience?
And how has knowing these factors helped in strengthening community
resilience? Specific characteristics of a resilient community can be even more
difficult to identify and measure than identifying the definition of community
resilience. There is, however, a substantial body of research working on
identifiable traits of a resilient community, how these traits connect to one
another, and how they influence community resilience to natural disasters.
Determinants of Community Resilience
One group of researchers (Norris, et al., 2008) takes a systems perspective,
and identifies four primary capacities of a resilient community—economic
development, social capital, information and communication, and community
competence—which are networked (interlinked) and adaptive (changing), as
shown in Figure 2. Factors within the four primary capacities include resource
equity and diversity, social network structure and support, effective systems for
informing and communicating with the public, and collective action and
31
empowerment. They also present a basic model of stress resistance and resilience
over time (Figure 3) that expresses the pre-event environment (social, economic
and physical structures) as vital to the system’s ability to either absorb stress with
existing resources (resistance) or adapt when resources are exhausted (resilience).
Failure to resist or adapt leads to persistent dysfunction within the community
and, ultimately, a longer recovery period.
Figure 2: Community resilience as a set of networked adaptive capacities (Norris, et al., 2008).
32
Figure 3: Model of stress resistance and resilience over time (Norris, et al., 2008). When a
community is struck by a crisis, it either resists and adapts, or resilience/vulnerability systems take
root. Resilient communities adapt and adopt a new environment, whereas communities
outweighed by vulnerabilities continue in a state of persistent dysfunction.
Godschalk (2003) lists the primary elements of a resilient community as
effective land-use and raw materials, physical capital, accessible housing, health
services schools, and employment opportunities. Godschalk stresses that natural
hazard mitigation needs to be incorporated into sustainable development plans to
increase resilience to future disturbance without debilitating damage to physical,
social or economic community systems. Another group of researchers (B.
Pfefferbaum, Reissman, R. Pfefferbaum, Klomp, & Gurwitch, 2005) identified
seven interrelated factors that contribute to over-all community resilience;
connectedness, commitment, and shared values; structure, roles and
responsibilities; resources; support and nurturance; critical reflection and skill
building; and communication. They placed part of the responsibility on the
leadership of public health officials to incorporate strategies that focus on
33
community risk as a whole (termed prevention paradox) and to work toward a
more community-based approach to disaster prevention. This technique stresses
the importance of addressing underlying issues causing low public health prior to
a disaster by linking public health with sectors of the community, such as
education, criminal justice, faith communities, and businesses.
Research in urban and sustainable development has also contributed to our
understanding of community resilience, by working to improve future
development plans and land-use codes as a part of enhanced new urbanist design.
This coincides with recommendations from Godschalk (2003) to merge natural
hazard planning and sustainable development. Recommendations show the
continued need for a paradigm shift about how humans interact with their
environment, enhanced preparedness and mitigation techniques such as larger
green spaces, wetland restoration, and reinforcing vertically built living structures,
thus improving overall community resilience (Berke & Campanella, 2006;
Stevens, Burke, & Song, 2010).
The existing research on these topics identify a variety of factors that may
be determinants of community resilience, and while they definitely all contribute
toward community resilience, some factors might play a larger role in overall
community resilience than others depending on the strengths and weaknesses of
the individual communities the researchers are assessing. Although these
researchers do not all commonly agree on the specific determinants of community
resilience, the overall theme is that a variety of social factors are underutilized in
34
planning and mitigation efforts, and communities need to find ways of
incorporating social components that are important in their regions of planning
and mitigation to increase their overall community resilience.
Modeling Community Resilience: Advantages and Disadvantages
Identifying key traits of a disaster-resilient community is the first step in
modeling or measuring a community’s resilience in the face of natural disasters.
Over the last fifteen years, significant research has emerged using a systems
approach to design a model that communities can use to visualize or measure the
networks involved in the natural disaster process. By doing so, communities are
better able to identify areas of strength and weakness, or continue the model over
time to show temporal increases or decreases in resilience.
Two examples from the research on modeling community resilience are
the Disaster Resilience of Place Model (DROP) and the Baseline Resilience
Indicators for Communities (BRIC) model (Cutter, et al., 2008; Cutter, et al.,
2010). In the DROP model, a working set of indicators is identified for measuring
community resilience, using factors of ecological, social, economic, institutional,
infrastructure, and community competence systems as a framework (Table 2). The
BRIC model, developed two years after the DROP, simplifies the model further
by listing just five major categories; societal, economical, institutional,
infrastructure, and community capital (Cutter, et al., 2010).
As was discussed earlier, ecological models have been used as of late to
incorporate a more interdisciplinary view of disasters. Kiter-Edwards (2008)
35
explains that using ecological models to view the multiple social, physical and
political systems involved in a natural disaster provides the advantage of
incorporating different levels of components. For example, including current
community and organization factors, as well as relationships involved in an
individual’s development ties, such as mental development gained in a persons’
lifetime from experiences through local family and neighborhood networks,
community networks, and ultimately state, national, or even global network
structures that help explain a persons’ current social, physical or political state.
Using an ecological approach also allows researchers to incorporate difficult-tomeasure factors like an individual’s mental health before and after a disaster.
Boon, Cottrell, King, Stevenson, & Miller (2012) take this same approach by
using Bronfenbrenner’s bioecological theory as a framework for modeling
community resilience. This theory was originally designed for modeling human
development by incorporating individual factors (microsystem) and working
outwards to social and environmental factors that influence human development
on the meso-system, exo-system, and macro-system scale (Bronfenbrenner,
2005). Using this as a framework for studying how different system levels tie
together to influence how a person or community is affected by and responds to a
natural disaster has several advantages. It allows researchers or a community to
benchmark social resilience, target priority interventions required and measure
progress over time (Boon, et al., 2012). This model, as illustrated in Figure 4, also
allows the incorporation of sociological and psychological perspectives, resulting
in a more holistic “snap-shot” of a community’s health and stability. Although
36
modeling has been foundational in our understanding of the complex systems
involved in community resilience, modeling is not well suited if the objective is to
quantitatively measure community resilience and provide specific spatial context
of these resilience indicators.
Table
2:
Community
Resilience
Indicators,
DROP
Model
(Cutter,
et
al.,
2008)
Dimension
Candidate
variables
Dimension
Candidate
variables
Ecological
Social
Economic
Wetlands
acreage
and
loss,
erosion
rates,
%
impervious
surface.
biodiversity
#
coastal
defense
structures
Institutional
Demographics
(age,
race,
class,
gender,
occupation),
social
networks
and
social
embeddedness,
community
values-‐
cohesion,
faith-‐based
organizations
Employment
Value
of
property
Wealth
generation
Municipal
finance/revenues
Community
Competence
Local
understanding
of
risk,
counseling
services,
absence
of
psychopathologies
(alcohol,
drug,
spousal
abuse),
health
and
wellness
(low
rates
mental
illness,
stress-‐
related
outcomes)
quality
of
life
(high
satisfaction)
Lifelines
and
critical
infrastructure,
transportation
Infrastructure
network,
residential
housing
stock
and
age
commercial
and
manufacturing
establishments
Participation
in
hazard
reduction
programs
(NFIP,
Storm
Ready),
hazard
mitigation
plan,
emergency
services,
zoning
and
building
standards,
emergency
response
plans,
interoperable
communications,
continuity
of
operations
plans
37
Figure 4: Conceptual scheme of Bronfenbrenner’s systems and their interactions. Diagram
constructed by Boon et al. (2012).
The Next Step: Measuring Community Resilience
While community resilience models have been a big step forward, new
research is now emerging that works to quantify the indicators of resilience.
These measurements have begun to provide a much more detailed and precise
idea of what community resilience looks like in a defined area, and they address
the spatial relationships between variables, making the results easier to understand
and incorporate into existing NHMPs. For example, Frazier et al. (2013a) use a
framework similar to the Place Vulnerability Model discussed earlier (Cutter, et
al., 2000), but take it several steps further by focusing on overall resilience, rather
than only vulnerability. In this research, they conducted a case study of Sarasota
38
County, Florida, which consisted of four phases—interviews with planning
officials, reviews of existing plans, a focus group, and a spatial analysis. A
univariate analysis was conducted on Sarasota County’s elevation, per capita
income, percent below poverty level, populations over 65, and the total
populations. The data were then combined with the other three phases of the study
to produce a countywide spatial indicator map. With this study, these researchers
effectively demonstrated that, “although national resilience quantification metrics
are useful, local scale resilience estimates appear more useful if community
hazard mitigation and climate change adaptation are the primary goal,” (Frazier,
et al., 2013a, p.1). Their findings and recommendations laid the groundwork for a
very helpful tool for quantitatively and spatially measuring community resilience
to natural disasters.
Conclusion: Community Resilience in Thurston County
As this review of the literature shows, past research has identified a
multitude of important, interrelated factors that contribute to community
resilience to natural disasters. The implementation and growth of local natural
hazard mitigation plans has helped sway the focus from response-style emergency
management to a more preventative approach. Many of the NHMPs that exist
today tend to carry a stronger focus on economic and structural damage mitigation
due to time and financial limitations. Despite this, disaster and community
resilience research continues to call for more emphasis on social factors and their
effects on overall community resilience. In fact, we are reaching a day and age
39
where not incorporating these social factors is growing more costly and unjust the
longer this is neglected.
Several studies discussed have identified a variety of vulnerable
populations that can help understand a communities overall resilience. Of the
vulnerable populations discussed, a few are reoccurring and/or overlapping, and
have frequently been cited as important factors when determining a community’s
resilience; children and elderly, females, racial minorities, and low income
populations (Bolin & Stanford, 1998; Buckle, Marsh, & Smale, 2001; Cutter, et
al., 2003; Cutter, et al., 2000; Frazier, et al., 2013a; Freudenburg, et al., 2008).
While modeling community resilience has advanced the conceptualization of
community resilience, it has not proven to be as useful for designing NHMPs or
for implementing disaster management and response measures. More recent
research is now emerging, using GIS tools and spatial statistics, that allow us to
quantitatively measure vulnerability and community resilience in our local
communities, providing a helpful new tool by which to view our own
communities (Cutter, et al., 2003; Cutter, et al., 2000; Frazier, et. al., 2013a). By
measuring community resilience with a holistic, systems approach including
social data, a baseline of strengths and weaknesses can be provided that can help
influence various aspects of our communities, including enhanced emergency
management plans and response, development regulations, environmental
conservation and restoration, local policies, climate change adaptation, and an
enhanced NHMP.
40
Chapter 3: Methods
The object of this study is to provide a foundational understanding of the
spatial distribution of community resilience in Thurston County by quantitatively
measuring specific vulnerable populations and comparing results to the 100-year
floodplain. It is important to note that although they are closely negatively
correlated, community resilience is not simply the absence of vulnerability, but
vulnerability is a very important component. Future studies should incorporate
other indicators of community resilience to provide a more accurate picture of
what community resilience looks like in Thurston County, as will be discussed in
the final chapter. This study combines HAZUS, ArcGIS, and publicly available
data to answer the question of where the highest and lowest areas of community
resilience lie in relation to the 100-year floodplain. Of the determinants of
community resilience usable in a quantitative measurement such as this, only a
few are appropriate for the scope of this paper. Thurston County has done an
excellent job and accomplished great strides in decreasing vulnerability to natural
hazards that are prevalent in the Pacific Northwest. As discussed earlier, primary
hazards in the county are earthquakes, severe storms, landslides, flooding, and
volcanic activity. Flooding is by far the most frequent event, with devastating
floods happening about every three years (Washington State NHMP, 2010). In
their 2012 Natural Hazard Mitigation report summary, members of the Thurston
County Regional Planning Council indicated that they have continued to update
and enhance flood risk maps and will be using recently available data from the
41
FEMA software modeling program, HAZUS-MH, to include potential impacts for
a variety of different flooding scenarios.
HAZUS is an extremely powerful natural disaster modeling software
created by FEMA and ESRI to give damage estimations for floods, earthquakes
and hurricanes. Because flooding is the most frequent hazard in the study area, it
was chosen as the natural hazard to view spatially in relation to community
resilience. However, the earthquake model could also be powerful to use in future
studies since many areas in the Pacific Northwest lie on or near fault lines that are
at risk of earthquakes. HAZUS is not merely a mapping software, but provides the
ability to know in a very short amount of time the extent of damage done to a
specific area in a disaster scenario. For instance, by using Thurston County as the
study area and running a 100-year flood model, HAZUS can provide the user with
information about the water depth levels all across the county based on elevation;
demographic data; hazardous wastes; critical structures at risk; the amount of
structural damage and type of building material that sustained the damage; the
amount of building contents that are damages; economic loss estimations;
business disruptions, human lives lost, and the amount of debris generated in the
area, as well as time and cost estimations for debris removal. This is not an
exhaustive list of all of HAZUS’s capabilities, but provides the reader with a brief
understanding of how monumentally helpful this type of information can be to
local emergency planners and responders in the stressful moments following a
disaster event. While these estimations are not error-proof, they are the most
accurate information available to planners and responders at this time.
42
To keep this paper manageable, these loss estimations available from
HAZUS were not included in this study. However, HAZUS was used to build the
100-year floodplain in the study area (Thurston County) to provide the most
accurate floodplain based on most recent elevation data, and to ‘set the stage’ for
future studies to incorporate this portion further if they choose. By creating the
100-year floodplain using HAZUS, rather than using federally provided
floodplain maps, a more detailed flood map was created based on current USGS
data elevation models. The floodplain was then compared with the community
resilience rankings. By combining the understanding that a community resilience
measurement offers with a powerful disaster modeling software like HAZUS
affords researchers, planners and responders access to significantly more
information, a much more realistic understanding of how their local areas would
be impacted by an event, and knowledge about which areas are going to have the
most difficult times recovering. This will be discussed further in the discussion
chapter.
Variables were chosen for this study based on identification of important
characteristics in the literature, specifically, Cutter, et al., (2003) and Frazier et al.
(2013b). Chose indicators for this study were per capita income, income to
poverty ratios, racial minority populations, and populations over the age of sixtyfive. All variable data were taken from the U.S. Census Bureau’s 2000 census.
One limitation to this study was the use of 2000 census data, rather than 2010
census data. All four variable data were available at the census tract level for
2010, but census tracts are significantly larger than block groups and would result
43
in a much less well-defined picture of community resilience in the study area. The
size and shapes of census tracts and block groups vary widely, and change from
census-to-census depending on a variety of factors. For instance, for the 2000
census, Thurston had 49 census tracts, some of which included multiple cities or
combinations of rural and urban areas. These tracts were further broken down into
block groups (132 total), and conducting the spatial analyses at the block group
level allowed for a much more defined look at community resilience in Thurston
County.
Age and racial demographics were available at the block group level for
2010, but financial data were not incorporated into the 2010 census. Due to lack
of responses and privacy issues with the financial information that used to be
included in the census, these data are no longer collected. Since 2005, the U.S.
Census Bureau began sampling about 250,000 individuals per month through the
American Community Survey, and this is now the primary means of obtaining
and tracking financial data on an annual basis. These data are only released in two
formats, either at the census tract level, or as a summary document at the block
group level. Because the block group location, shape, and area vary significantly
between the 2000 census and the 2010 census, data integrity was preserved by
using all social indicator data from the 2000 census only. This makes the data
used in this study fourteen years old at the time it was conducted. This issue will
be revisited in the discussion section. All data used were free and public, and were
taken from the 2000 census at the block group level. Social indicators (SIs) used
in this study were chosen to represent populations that are vulnerable to a
44
flooding event in Thurston County based on social demographics presented in the
literature that influence or help identify community resilience.
While these social indicators have been identified in the literature as
strong indicators of community resilience, another limitation to this study is its
small scope. To make this study manageable and meet time requirements and
deadlines, only four SIs were used for these analyses and the study was conducted
in relation to one natural hazard (i.e., floods). While these variables are sufficient
to express and display a foundation for measuring community resilience using
these methods, they represent only a small percentage of all the facets that
encompass true community resilience. By identifying and incorporating other
detailed social variables—homes with children, single parent homes, disabled
populations, housing vacancy rates, or areas with high homeless populations, for
example—the accuracy and helpfulness of a community resilience measurement
like this would be increased. This will be discussed further in the discussion
chapter; for now, a brief explanation of each SI will be presented and its purpose
within this study will be explained.
The 100-Year Floodplain
To achieve the greatest stream definition specific to the elevation and
hydrology network within Thurston County, a 100-year floodplain was mapped
for this study using HAZUS, and all of Thurston County was selected for the
study region. All shapefiles and data used were re-projected into the NAD83
UTM Zone 10 coordinate system to preserve shape, area and distance. A digital
45
elevation model for Thurston County was then imported from USGS using the
HAZUS Digital Elevation Model (DEM) extent tool. The purpose of the DEM
extent tool is to overlay the most recent elevation information from the National
Elevation Dataset onto the study region. A stream network was created with an
input of 1.0 square mile, to show areas in the region that accumulate water in a
rain or storm event. The output results show the highly defined stream network
system within all of Thurston County’s watersheds. Since some of these streams
begin or flow outside of the study area, all stream segments that fall within
Thurston County were selected for the spatial analysis of SIs. Hydrology analysis
was run, and a 100-year flood event was chosen for the delineation of analysis.
The result is a polygon representing the 100-year floodplain in Thurston County
(Figure 5) and this is the floodplain layer used in all analyses in this study.
HAZUS offers the opportunity for a user to model a 100-, 200-, or 500year flood. The term “100-year flood,” despite the misleading name, means that in
any given year, there is a one-percent chance that this level of a flooding event
would take place. For a “200-year flood,” there is a 0.2 percent chance of it
occurring in any given year, etc. Of these different flooding levels, a 100-year
flood is the most likely and probabilistic event in Thurston County (although
more extreme flood events are not impossible) and is the base-line boundary that
is used by many flood-related agencies when assessing flood-prone areas
(Thurston County Flood Plan, 2013). The Thurston County Flood Plan provides
information based on a 100- and 200-year flood. Floodplains created in HAZUS
and used by FEMA are measured,
46
…using a discharge probability, which is the probability that a
certain river discharge (flow) level will be equaled or exceeded in a
given year. Flood studies use historical records to determine the
probability of occurrence for the different discharge levels. The
flood frequency equals 100 divided by the discharge probability. For
example, the 100-year discharge has a 1-percent chance of being
equaled or exceeded in any given year. These measurements reflect
statistical averages only; it is possible for two or more floods with a
100-year or higher recurrence interval to occur in a short time
period. The same flood can have different recurrence intervals at
different points on a river (Thurston County Flood Plan, 2013, p. 61).
Figure 5: Hydrology and delineation results for water accumulation of 1mi2. Thurston County
100-Year Flood Plain overlaying the Digital Elevation Model (DEM).
Social Indicators
Low Income and Poverty Status
Individuals and families that are considered low-income or living below
the federal poverty threshold are at greater risk of a difficult recovery from a
natural disaster like a major flooding event due to financial and resource
47
limitations. This SI uses two resources to create the layers used in the final
analyses—ratio of income to poverty and per capita income data—which were
retrieved from the 2000 census (U.S. Census Bureau, 2000). Low income is
defined, per the U.S. Census Bureau American Community Survey and the U.S.
Department of Education, as taxable income not exceeding 150% of the federal
poverty threshold. In 2000, the federal poverty threshold for a singled individual
was $8,350 and a family of four qualified at $17,050. To incorporate persons
living below 150% of the poverty threshold, ratio data was used to show the
percent of individuals per block group living below $12,525 annually (Figure 6).
Per capita income is a mean figure for income in a given area, so it is influenced
by high and low figures. Per capita income figures include these outliers, which
can raise or lower the per capita income figures. This is why per capita income
ranges for 2000 are between $11,796 and $40,250, despite the fact that there are
probably many people living above and below those figures (Figure 7). Median
income is not affected by high and low outliers, but since past studies similar to
this study aim to identify areas where concentrations of outliers (specifically
low-income) lie, per capita has been chosen as a more representative means to
include low-income and poverty stricken individuals. A Global Moran’s I
analysis was applied to census data to test for autocorrelation, followed by a
Local Indicator of Spatial Analysis (LISA).
48
Figure 6: Percentage of people living below 150% of the poverty threshold in 2000 by block
group (U.S. Census Bureau, 2000). Flood Plain and map created using HAZUS and ArcMap.
Figure 7: Distribution of per capita income in Thurston County by block group (U.S. Census
Bureau, 2000) and the 100-year floodplain. Flood Plain and map created using HAZUS and
ArcMap.
49
Racial Minority Populations
Minority populations make up about 17% of Thurston County’s 2000
total population. A review of the literature shows that minority populations may
have a more difficult recovery from an event due to cultural, language, or
economic barriers. Census data (2000) defines minority populations as non-white
persons that are Hispanic/Latino, African American, Native American,
Hawaiian/Pacific Islander, Asian, Other, or two or more of these races combined.
By identifying where racial minorities are spatially distributed throughout the
county, natural hazard mitigation planners and emergency response groups can
strengthen their plans prior to an event, or strengthen their response after an
event occurs. Planning and response organizations can effectively plan for
response to certain areas that contain a higher racial minority population by
incorporating educational campaigns in predominant minority languages, provide
translators within planning or response teams to encourage these populations
input and feedback in planning and mitigation efforts, or connect with local
cultural groups prior to an event to help aid in response efforts in communities
that contain potentially marginalized groups during a disaster.
Persons of different races can be of Hispanic/Latino ethnicity, including
Caucasians. Because of this, all non-Hispanic racial minority groups and persons
who only identified as Hispanic or Latino were combined to avoid “doublecounting” individuals that identify as Hispanic/Latino and as a member of an
already included, non-white race. Groups were selected and totaled per census
50
block group, then normalized by total census block group population (Figure 8).
A Global Moran’s I analysis was applied to the census data to test for
autocorrelation, followed by a Local Indicator of Spatial Analysis (LISA).
Figure 8: Percentage of racial minorities per block group (U.S. Census Bureau, 2000). Flood
Plain and map created using HAZUS and ArcMap.
Elderly Populations
Populations over the age of 65 may be more vulnerable to effects of a
natural disaster due to various factors such as decreased mobility, financial
limitations, or lack of a support network. In the instance of a flood, mold and
water borne illnesses can also be a significant problem in affected areas. People
in this age range are more likely to be immunocompromised, making them more
susceptible to illness and disease. Locating areas with high concentrations of this
age group can help communities prepare to have necessary resources available to
51
help elderly populations in a disaster event. Census demographic data from 2000
were used to identify the number of person over the age of 65 at each block
group in Thurston County (Figure 9). A Global Moran’s I analysis was applied to
census data to test for autocorrelation, followed by a Local Indicator of Spatial
Analysis (LISA).
Figure 9: Percentage of elderly populations by block group (U.S. Census Bureau, 2000). Flood
Plain and map created using HAZUS and ArcMap.
Moran’s I Analysis
After individual maps were created for each SI, a Global Moran’s I analysis
was applied to the census data to test for autocorrelation, followed by a Local
Indicator of Spatial Analysis (LISA) called an Anselin Local Moran’s I. Due to the
complexity of community resilience, it is important to be aware of how much each
individual variable is correlated with itself. The Global Moran’s I statistic provides
52
three helpful values to test for autocorrelation—an I Index, a z-value, and a pvalue. This spatial analysis is used to determine the amount of “clustering” of a
single variable and explains how much the variable influences itself. It answers the
question, for instance, whether a block group that contains a high rate of lowincome individuals tends to be spatially organized next to other block groups that
also contain high rates of low-income individuals, and if so, how significant is this
clustering? A positive I value indicates a positive spatial autocorrelation, a
negative I value indicates a negative spatial autocorrelation, and zero shows that
the variable is perfectly randomly dispersed. The p-value shows at what
significance the variable is autocorrelated with itself, with significance being a pvalue<0.05.
The Local Indicator of Spatial Analysis (LISA) statistic used within
ArcGIS is a more precise cluster and outlier analysis called the Anselin Local
Moran’s I. This statistic identifies the same three values as the Global Moran’s I,
but rather than three single values for the entire county, the output is for each
individual block group. This test results in either a High-High (HH), High-Low
(HL), Low-High (LH), or Low-Low (LL) value association for each block group to
identify specific block groups that are surrounded by block groups that either share
similar values (HH or LL), or are surrounded by block groups with different values
(HL or LH). The analysis also provides a p, z, and I value for each individual block
group. Natural Jenks classifications were used in the spatial statistic mapping
process to preserve natural breaks in the data distributions.
53
Spatial Weighted Overlay
The last step in this study’s analyses was a weighted spatial overlay using
the weighted spatial weighted overlay tool in ArcGIS. This tool takes multiple data
layers (variables) into account using a common measurement scale, and weights
identified variables according to their importance. For the purposes of this study,
the output values identify community resilience in Thurston County for each block
group based on chosen SIs according to the evaluation scale defined in the
weighted overlay tool. To begin to identify and quantify community resilience in
this study, four vulnerability indicators were chosen—per capita income, poverty
status, elderly populations and minority populations. Values within each variable
for each block group were ranked along a scale from one to ten according to the
spread of the variable values across the county. This gave all block groups
containing SI values of the highest percentage of people living below 150% of the
federal poverty threshold, highest elderly populations, highest racial minority
populations, and lowest per capita income a value of one. On the other end of the
scale, block groups containing SI values with the highest per capita incomes, and
lowest percentages of poverty, elderly populations and racial minority populations
were given a value of ten. All four SIs were then evenly weighted (25%) and
assessed in each block group with equal importance to identify the community
resilience ranking per block group. In other words, only individual variable values
were weighted, while each of the variables themselves were deemed to be just as
important in determining community resilience as each of the other three variables.
54
The spatial overlay tool output resulted in a single ranking for each block group.
The resulting map was then overlaid with the 100-year floodplain.
As was discussed in the literature review chapter, the field of
environmental justice and research on vulnerable population exposure to natural
hazards shows that vulnerable populations are often marginalized to areas that are
at an increased risk to natural hazards (Cutter, et al., 2003; Rodriguez & Barnshaw,
2005; Clarke, 2006; Cohen & Bradley, 2008). To assess the relationship between
low community resilience and increased exposure to the 100-year floodplain, the
100-year floodplain was then intersected with the ranked block group layer. The
area of the 100-year floodplain was totaled for each block group, then all block
groups within each ranking were totaled and divided by the summed area of all
block groups within that rank to give an average percentage of the area per block
group that is exposed to the 100-year floodplain.
By conducting autocorrelation analyses on the SIs, the relationship a
variable has with itself over an area can be better understood. If a variable or
variables are strongly autocorrelated and then combined to measure community
resilience, it would indicate that community resilience is also autocorrelated. In
other words, areas of low community resilience would be spatially distributed
closer to other areas of low community resilience and the other way around. After
understanding where community resilience is low and high throughout the county,
natural hazard areas can be compared to identify if low resilience areas tend to
exist within or closer to natural hazards than areas of higher community resilience.
55
This study sought to compare ranked community resilience to the 100-year
floodplain to find out if, in fact, areas with concentrations of vulnerable
populations do tend to be exposed at a higher rate to this natural hazard in
Thurston County.
56
Chapter 4: Results
Social Indicators
Low Income and Poverty Status
The Global Moran’s I analysis indicated that block groups with populations
living below 150% of the 2000 federal poverty threshold were slightly
autocorrelated, but not significantly (I=0.87, p=0.068). The Anselin Local Moran’s
I identified eight individual block groups that were significantly similar to or
different from neighboring block groups. The low number of significant individual
block groups confirms the Global Moran’s I analysis’ findings of non-significance.
For the Local Moran’s I, I value ranged from -5.88 – 9.37, and p-values for
significant block groups ranged from p=0.000 – 0.032. Block groups identified as
HH and LL are of particular interest, since these areas show where clustering
occurs.
57
Figure 10: Anselin Local Moran’s I for Ratio of Income to Poverty (2000). I-value Range: -5.88
– 9.37. p-value range for significant block groups only: 0.000 – 0.032.
Per Capita Income
The Global Moran’s I analysis indicated that the per capita income of a
block group is significantly autocorrelated with the per capita income of a
neighboring block group (I=0.327, p<0.000). The Anselin Local Moran’s I
identified twenty-four block groups that were significantly similar to or different
from neighboring block groups. The high number of significant block groups
confirms the Global Moran’s I value of significance. The individual I-values for
statistically significant block groups ranged from 3.337 – 14.638, and the p-values
ranged from p=0.000 – 0.049. Block groups identified as HH and LL are of
particular interest, since these areas show where clustering (HH, LL) exists.
58
Figure 11: Anselin Local Moran’s I for Per Capita Income (2000). I-value range: 3.337 – 14.638.
p-value range for significant block groups only: 0.000 – 0.049.
Racial Minority Populations
The Global Moran’s I analysis indicates that the racial minority
populations of a block group are significantly autocorrelated with neighboring
block groups (I=0.327, p<0.000). The Anselin Local Moran’s I identified thirteen
block groups that were significantly similar to or different from neighboring block
groups. The high number of significant block groups identified confirm the
Global Moran’s I value of significance. I-value ranges for statistically significant
block groups ranged from I=4.205 – 24.516, and p-values ranged from p=0.000 –
0.048. Block groups identified as HH and LL are of particular interest, since these
areas show where clustering (HH, LL) exists.
59
Figure 12: Anselin Local Moran’s I for Racial Minorities (2000 Census Data). I-value range:
4.205 – 42.516. p-value range: 0.000 – 0.048.
Elderly Populations
Results from the Global Moran’s I analysis indicate that the elderly
populations (over 65) were positively correlated with neighboring block groups,
but not significantly (I=0.40, p=0.329). The Anselin Local Moran’s I identified
six block groups that were significantly similar to or different from neighboring
block groups. The low number of significant block groups confirm the Global
Moran’s I statistic of non-significance. Statistically significant Local Moran’s I
values ranged from -5.178 – 11.617, and p-values ranged from 0.000 – 0.033.
Block groups identified as HH and LL are of particular interest, since these areas
show where clustering (HH, LL) exists.
60
Figure 13: Anselin Local Moran’s I for Over 65 population (2000 Census Data). I-value range: 5.178 – 11.617. p-value range: 0.000 – 0.033.
Spatial Weighted Overlay
Results from using the spatial weighted overlay tool measure community
resilience based on the four chosen SIs and gave each block group a community
resilience ranking on a scale from one to ten. This analysis output maps the spatial
arrangement of community resilience across Thurston County in 2000. A full
output table, ranking each block group from least resilience to most resilient, as
well as showing original social indicator values, is appended to the end of this
study (Appendix A). A ranking of one indicates block groups with the lowest
community resilience based on the four chosen SIs, while a ten indicates block
groups with the highest community resilience based on the four chose SIs. The
spatial weighted overlay, along with community resilience with respect to the
100-year floodplain is shown in Figures 14 and 15.
61
Figure 14: Spatial weighted overlay results show community resilience ranking based on four
chosen social indicators. Block groups given a rank of ten indicate areas of high per capita
income, low rates of people living below 150% of the 2000 federal poverty threshold, and low
rates of elderly and racial minority populations. A rank of one indicates areas of low per capita
income, high rates of people living below 150% of the 2000 federal poverty threshold, and
high rates of elderly and racial minority populations (U.S. Census Bureau, 2000).
Figure 15: Spatial weighted overlay results with 100-year floodplain. Both of the block groups
identified as a rank one either partially or fully intersect with the 100-year floodplain. Map
created using HAZUS and ArcGIS.
62
Two block groups were identified that were identified that were given a
ranking of one, making them the least resilience block groups based on chosen
SIs. These block groups contain the highest combination of low-value SIs based
on 2000 census data; low per capita income, high populations living below 150%
of the federal poverty threshold, and high rates of elderly and racial minority
populations. One block group is located in Lacey while the other is the block
group incorporating the Nisqually Indian Reservation on the eastern side of the
county. The 100-year floodplain covers almost half of the block group containing
the Nisqually Indian Reservation, while the block group in Lacey only slightly
intersects with the floodplain in its northwestern corner. These block groups can
be seen in Figure 16, along with Table 3 showing the specific social demographic
data from the 2000 census that corresponds with each block group.
Figure 16: Block groups with a community resilience ranking of 1 with parcels and the 100-Year
floodplain. Maps created using ArcGIS and HAZUS.
63
Table 3: Social demographics for the two block groups with a ranking of 1 (U.S.
Census Bureau, 2000).
The spatial weighted overlay identified six block groups that fulfilled
criteria for a ranking of ten. These block groups represent areas of the highest
community resilience based on the chosen social indicators—high per capita
income, low percentage of people living below 150% of the federal poverty
threshold, and low percentages of elderly and racial minority populations. Two
are located in Olympia, two are located in Tumwater, and the last two fall in
unincorporated areas in the northern portion of Thurston County. They can be
seen in Figure 17, along with the specific social demographic data from the 2000
census that coincides with each block group in Table 4.
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Figure 17: Block groups with a community resilience ranking of 10 with parcels and the 100-Year
floodplain. Maps created using ArcGIS and HAZUS.
All other block groups fell within the two-to-nine range and will not all be
discussed here. A table has been included in the appendix at the end of this study,
showing each block group ranked from one to ten and their corresponding social
demographic data as well within the county.
The exposure of block groups to the 100-year floodplain may be shown as
the percentage of the area of each block group that contains the 100-year
floodplain. The average of such exposures is summarized in Table 5 per group
ranking.
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Table 4: Social demographics for the six block groups with a ranking of 10
(U.S. Census Bureau, 2000).
Table 5: Average exposure area to the 100-year floodplain (%) by community resilience ranking.
66
Chapter 5: Discussion and Conclusion
Thurston County planners work on mitigation projects to increase the
county’s community resilience to natural disasters, but they face increasing
challenges with populations growth, increasing urban density, an aging
population, climate change, and an economic recession that continues to impact
local planning budgets as well as the financial health of communities large and
small. Likewise, the emergency response community continues to work to
increase efficiency and response efforts in their communities, but suffer from
many of the same challenges. The research community that focuses on
community resilience continues to stress the importance of incorporating
meaningful social demographic data into mitigation planning efforts, shifting our
NHMPs from the current “cookie-cutter” plans heavily laden with natural disaster
facts and economic and structural risk assessments. There is not yet an official
protocol on how planners should go about incorporating social demographic data
more meaningfully. This study offers one example of how social demographic
within the local NHMPs can be more usefully implemented for education,
planning, preparedness programs, and motivation for other local officials to
reduce existing social inequality.
Income and Poverty as Social Indicators
Individual and family incomes are major factors to take into consideration
when looking at community resilience to natural disasters, and are repeatedly
stressed as strong indicators of community resilience in the literature. People and
67
families with a higher expendable income would have increased purchasing
power to replace lost resources or obtain alternative housing if needed, whereas
those with less expendable income would have significantly more difficulty. As
was mentioned in the methods section, availability of 2010 financial data
restricted the use of 2010 data for all four variables. There is a significant
difference in the individual and family income between 2000 and 2010, mostly
due to the effects of the national economic recession that began in 2007, which
are currently still being felt in 2014. A comparative analysis of the changes
between the 2000 and 2010 censuses indicate that most Americans have seen a
decrease in financial income while cost of living expenses continue to increase
(Bishaw, 2013). The same study found that the percentage of people living below
150% of the federal poverty threshold in Washington State increased from 11.6%
in 2000 to 13.5% in 2010, meaning that, if the study were conducted again using
all 2010 census block group data, income figures would probably be lower when
inflation adjustments are taken into consideration.
The clustering analyses were particularly interesting for the percentage of
people living below 150% of the federal poverty threshold, because their
insignificant p-value seems to contradict the significant p-value of the per capita
income analysis. It would make sense that either both financial variables would
show significant clustering or both would not. The ratio of income to poverty did
output a positive variable, showing correlation (p=0.0676), just not as
significantly as per capita income (p<0.000). The ratio of income-to-poverty used
for this study focused on individuals living at or below 150% of the federal
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poverty threshold, because that is the standard “low-income” line in the United
States. To put this into perspective for this discussion, 150% of the federal
poverty threshold in 2014 dollars equates to $17,505. But earning this figure or
just above this figure might not be enough to increase community resilience by
much, and therefore, 150% might be too low of a threshold when using this as an
indicator of community resilience.
The Massachusetts Institute of Technology has created the “Living Wage
Calculator,” which calculates the minimum required annual income for survival
and is dependent on the number of individuals in the household (Glasmeier,
2014). According to the MIT calculator, for a single individual living in Thurston
County, the minimum income required to cover very basic living expenses is
$17,697 before taxes are deducted, and an annual income of $15,768 after tax
deductions. The costs incorporated into this figure are extremely basic and not
very realistic for the average person, and greatly under-estimate the cost of living
expenses for individuals and families. For example, monthly expenses for housing
are estimated at $609 per month, according to the MIT calculator. This estimation
is intended to cover rent/mortgage and utilities. According to the Thurston County
2013 Profile, the average (2013) monthly mortgage for home buyers was $719 per
month, while the average (years 1995-2012) one-to-two bedroom home or duplex
rental cost was $745 per month in Lacey, $719 per month in Olympia, and $895
per month in Tumwater. These average rates for local rental and mortgage rates
do not include utility costs. An individual making 200% of the federal poverty
threshold would be earning an annual income of $23,340 and it could be argued
69
that even this person would be extremely financially strained if they were to be
seriously impacted by a flooding event or other natural disaster. If this person
were paying $745 per month on rent in Lacey, they would be spending 38% of
their monthly income on housing. According to the Housing Authority of
Thurston County (HATC), low-income housing is offered to people who are
spending more than 30% of their monthly income on housing, so this person
would qualify for low-income housing, despite not being defined as “lowincome” per the current definition of 150% of the federal poverty threshold
(HATC, 2013). For future studies, researchers should try to identify and
incorporate realistic income values that would enable a family to have the
purchasing power required to recover from an event, and then weight the income
social indicator accordingly. As has been explained here, 150% of the federal
poverty threshold is far too low for Thurston County, to realistically represent all
persons vulnerable to natural hazard events due to low-income.
Race as a Social Indicator
The Global and Local Moran’s I analyses indicated a significant positive
relationship between block groups with a high and low racial minority population.
Because income to poverty ratio data and age both showed a relatively low
influence in this analysis, it was surprising to see that race played a stronger role.
Areas with higher racial minority populations tend to be concentrated in the
northeastern portion of Thurston County.
70
One of the two least resilient block groups identified in this study was the
block group that includes a portion of the Nisqually Indian Reservation. In 2000,
this block group had a total population of 752, a per capita income of $15,284,
3.6% of the population was living below 150% of the federal poverty threshold,
and only 4.4% of its population was over the age of 65. When comparing only
these variable to other block groups, this area would have ranked higher on the
community resilience scale than it did when including racial minority populations,
but because the four variables were ranked at 25% importance, this block group
was identified lower on the resilience scale due to its high racial minority
population of 68.6%.
A high minority population does not necessarily decrease community
resilience. One could argue quite the opposite, in fact. A majority (60%) of the
non-white population in this block group is Native American. It could be argued
that this is an instance of a culture that can have a very strong social cohesion and
a tight community network—traits that increase community resilience. One of the
primary reasons that racial minorities are identified as vulnerable populations in
emergency situations is that language and/or cultural barriers may exist and be
exacerbated by natural disasters when emergency responders are of a different
culture and/or speak a different language. While language and cultural barriers
may still exist between the residents of the Nisqually Tribe and some emergency
responders in the event of a natural disaster, the fact that this racial minority
group exists so densely in this area could serve more strongly as a positive trait
than a negative one.
71
In contrast to the Nisqually block group, the second identified block group
that was given a ranking of one lies within Lacey. It has a population of 818, per
capita income in 2000 was $14,488, 24.2% of the block group was living below
150% of the federal poverty threshold, 3.8% were over the age of 65, and 50.9%
identify as a racial minority. Unlike the Nisqually block group, this block group is
inhabited by a more diverse array of different races; 49% white, 12.7% black,
2.4% Native American, 13.8% Asian, 0.01% Hawaiian or Pacific Islander, 12.5%
Hispanic or Latino, and 8.9% being of two or more of these races. While there are
many advantages to a culturally diverse community, in times of emergency and
high-stress situations, it can inhibit very important communication between
community members due to language and/or cultural differences. This does not,
however, need to be the case. Emergency responders and community members
can be trained to learn how to effectively communicate important information to
populations of another race or background. This can be done by forming
emergency response teams that speak the predominant minority population’s
language, or by training other emergency responders about ways to better address
certain issues to avoid cultural conflict or increased confusion. Planners could
also connect with local cultural organizations that may be willing to help in
response and/or translation with non-English speaking residents. Sometimes,
simple steps such as these can greatly increase the effectiveness of a community’s
recovery following a disaster.
72
Age as a Social Indicator
As was discussed in the literature review, a significant amount of research
has been done to study how natural disasters affect elderly populations, and there
is overwhelming evidence that seniors are disproportionately negatively impacted
by natural disasters when compared to other age groups. Several reasons exist for
this—decrease mobility, isolation, or compromised immune systems and preexisting conditions that are exacerbated by natural-disaster-induced stress. A
community resilience ranking can help local areas become aware of a high
percentage of elderly populations within their own community. In the event of a
natural disaster, the first people on site are generally the community members
impacted. If community members are aware of specific vulnerable populations
that exist in their neighborhood or block group, they can begin right away
searching for isolated elderly individuals still in their hopes, or be prepared for
earlier treatment of exacerbated health issues, such as heart attacks or other stressinduced problems.
Further, by being aware of where high densities of elderly populations
exist, community groups and members that work with elderly populations can
help educate them on the hazards in their area and how to prepare. From a
preparedness and mitigation perspective, areas of low community resilience due
to high elderly populations could be very helpful to know about when
implementing new mitigation projects, or attempting to target this specific
73
population for natural disaster awareness, as organizations like the American Red
Cross frequently do.
Block Groups and the 100-Year Floodplain
Both block groups with a community resilience ranking of one intersected
the 100-year floodplain. The Nisqually block group contains the Nisqually River
along its east side, with the 100-year floodplain extending substantially into this
block group. Of the highest ranked groups, the 100-year floodplain only
intersected three out of six identified block groups. Throughout all 132 block
groups, there was not a significant relationship between ranking score and
percentage of floodplain area. Each ranked group had at least 50% of the block
groups intersecting the floodplain, but to varying extents, as can be seen in Table
3. This was unexpected because the literature shows that vulnerable populations
tend to be marginalized toward more hazard-prone areas. This study found that,
with respect to the 100-year floodplain in Thurston County, this is not the case.
It is possible that exposure to the 100-year floodplain is equally dispersed
across the county. The stream network and floodplains in Thurston County are
extensive, and many areas are at high risk due to low elevation, geological
structures, and high annual precipitation. However, since community resilience is
fluid, future studies could incorporate more detailed variables, use a smaller, more
defined scale than block groups, or incorporate more recent data. By doing these
things, the ranking of community resilience would be different than is found here,
and patterns of vulnerable populations and exposure to flood hazards might come
74
into view. This will be further discussed in the section on considerations for future
research.
Implications
As with the definition of community resilience, the study of this concept
using GIS is still in the early stages of its development. It was noted in the
literature review that community resilience is not a state to be achieved, but rather
an ongoing process. By incorporating and attempting to measure community
resilience on a county, city, or neighborhood level, municipalities and local
governments can enhance their own natural hazard mitigation plans or
preparedness plans to address weak areas identified by their own community
resilience measurements, to strengthen their overall community resilience to
natural disasters. The social indicators chosen for such a study would be unique to
each individual area, and based upon prevalent vulnerable groups for that specific
region.
Awareness of community resilience at this scale could help local agencies
such as natural hazard mitigation planners, the Red Cross, emergency
management divisions, or local leaders to target especially vulnerable areas for
education campaigns, more detailed plans for specific resources needed from
vulnerable populations during an emergency, or policies that actively try to reduce
social vulnerability. Currently, Thurston County’s inclusion of social data into the
NHMP and flood management plans consists of several table profiles for each
incorporated area within the county. While this format offers a general
75
understanding of the social state of the area, the inclusion of a more detailed
spatial distribution of these social factors and how they interact with one another
in reaction to a natural disaster carries much more potential. A paradigm shift
happened when people became aware of the effectiveness of mitigation and
planning, and the same paradigm shift can happen when people become aware of
the social construction of disasters, environmental justice and importance of
community resilience. The Frazier, et al. (2013b) study showed that Thurston
County is already a leader in NHMPs across the state, and it should continue to be
so by embracing community resilience and striving to understand the underlying
factors of how our community is affected by natural events and why.
As Cohen and Bradley (2008) point out, the inclusion of social data into
our mitigation and disaster sectors is now a human rights issue, and vulnerable
populations need to have a stronger voice and presence in the way we plan for and
respond to natural disasters. Historically, disasters in the U.S. are increasingly
bringing social inequalities to the forefront, and many governments are learning
the hard way that, with the knowledge and research available on the importance of
these issues and their connectedness to environmental justice, ignoring them is
neglectful and irresponsible.
Following Tropical Storm Irene and Hurricane Sandy, Mayor Michael
Bloomberg and New York City were sued for neglect and discrimination with
respect to the city’s 900,000 disabled people. The court ruled that the city violated
the American’s with Disabilities Act, the Rehabilitation Act, and New York City
76
human rights laws. Victims spent several days trapped in high-rise housing due to
lack of preparedness and evacuation plans (Brooklyn Center for Independence of
the Disabled, Center for Independence of the Disabled, and Tania Morales v.
Michael Bloomberg and the City of New York, 2011). In 2005, Hurricane Katrina
posed a significant threat to elderly populations in the area. This storm system
killed 1,330 people, and 71% of these victims in Louisiana were over the age of
60 (Klein, 2009). Over 200 of these victims lived in nursing homes, and another
class action lawsuit was filed after the owners chose to “wait out the storm,”
resulting in the drowning and heat exhaustion in m any of their patients (Klein,
2009). These are just two examples, of many, where vulnerable populations had
not been included in emergency preparedness, mitigation and response plans and
local city governments were hit with expensive legal fees and penalties for it.
On March 22, 2014, during the writing of this thesis, the town of Oso,
Washington was struck by a massive, devastating landslide that wiped out 24
homes and resulted in the death of at least 41 people. This area has suffered
severe landslides of similar proportions, with the most recent event happening just
in 2006, but Snohomish County officials are now under investigation to learn why
these areas were redeveloped and new housing developments permitted, despite
the known hazard. As of April 29, 2014, $3.5 million dollars in claims have been
filed (Vaugn, 2014). The population of Oso in 2010 was 180 (U.S. Census
Bureau, 2010). The American Community Survey estimated that the per capita
income in 2012 was $15,801, and 42% of the population was living with some
form a disability (ACS, 2012), indicating that there were high percentages of
77
vulnerable populations living in this area. Families filing lawsuits against the city
officials and Snohomish County say they were not warned of the landslide hazard,
despite multiple studies finding the region to be extremely hazardous (Brunner,
Doughton, & Welch, 2014). It is not yet known whether the courts will find these
city and county official guilty of negligence, but a significant amount of damage
and devastation could have been avoided, had city and county officials taken
preemptive action through natural hazard mitigation and zoning to protect these
families from being sold homes they believed to be safe. If it is found that prior
knowledge of this dangerous hazard was ignored, and developers were allowed to
re-build homes in this area, knowingly putting dozens of vulnerable families in
harm’s way—this could stand as a prime example of structured destruction.
Understanding community resilience and ways in which we “structured
destruction” in our own communities can prove invaluable in times of a natural
disaster. By implementing an effective community resilience map into Thurston
County’s NHMP, areas of low community resilience and where they lie in
proximity to natural hazards can be better understood, and if needed, mitigated to
avoid a devastating event. Thurston County’s NHMP is strong, and has proven
effective in many aspects, but could be greatly strengthened by including the
inter-linked social factors that make up our community’s resilience.
Considerations for future research
The four indicators used here are not exhaustive of all the factors that
influence a community’s resilience to natural disasters. As was stated in the
78
methods chapter, inclusion of other variables, such as homeless populations or
housing vacancy rates, would help strengthen the accuracy of a community
resilience measurement and provide a more accurate picture of the health of the
community. A more defined measurement of community resilience in Thurston
County may provide a more accurate representation of vulnerable populations
exposed to natural hazards such as floods, earthquakes, or landslides.
Further, incorporating critical facilities or structures would allow
consideration of where these communities lie in relation to needed resources, or
more hazards, during a natural disaster. This element would further enhance
accuracy and provide a more realistic picture of community resilience in the study
area. For instance, while this study identified two block groups that were ranked
lower than all the other block groups in the county, these variables could
potentially be weighted differently if we knew that emergency facilities, such as
hospitals, emergency shelters, or fire stations also existed in close proximity to
affected populations. Communities could also be identified that might exist closer
to hazards that could be exacerbated by a natural disaster, such as a water
treatment facility or building that houses large amounts of toxic chemicals. By
identifying and incorporating resources and other hazards that are important or
valuable during an emergency, the community resilience ranking could be more
accurately detailed.
Lastly, the four chosen indicators were each weighted evenly at 25% to
not give preference to one indicator over another. Low per capita income, high
79
rates of elderly, minorities and persons living below the poverty threshold were
viewed as equally impactful on the community’s resilience. In reality, some
variables may play a stronger role in community resilience than others. If more
variables are included in future studies, the weighting of each variable should be
carefully selected to accurately represent how much influence each variable has
on an area’s resilience. In times of economic depression or recession, income
variables might be weighted slightly higher than other variables, since financial
resources will be more strictly limited. Another example of a variable that might
be considered a higher influence on community resilience would be disabled
populations. If an area contains a high percentage of physically or mentally
disabled individuals, that would indicate an area of increased need during an
emergency. Incorporating data that include locations of high populations of
disabled (assisted living centers, retirement communities, rehabilitation centers or
psychological treatment facilities) and then weighting this variable higher than
some others that may not be as influential on community resilience would present
a more accurate measurement of how that area might actually be affected by a
natural disaster.
HAZUS offers a multitude of opportunities in future community resilience
research. This software allows users the opportunity to run a simulation of a
natural disaster in any chosen study area. The output of these powerful simulation
models is the most accurate estimate currently available of the damage that would
ensue from a flood, earthquake, or hurricane. If cities and counties have available
community resilience data to identify areas of lower community resilience, a
80
simulation model could be run in that area to see specific types of damage that
would result from the assigned event. Output values include a variety of
information, such as casualties, impact of event (depth of water in any given area,
in a flood or hurricane scenario), number and types of residences and businesses
affected, which residences and business had insurance for such a disaster, amount
and type of debris generated, cost estimation and time estimates for cleanup and
recovery, and effects of employment and labor disruption. By incorporating these
simulation models in areas of low community resilience, these specific areas can
be targeted for enhanced mitigation projects to reduce possible damage in the
future or educational awareness campaigns to help people actively prepare.
Conclusion
This study examined the spatial relationships of community resilience in
Thurston County in relation to the 100-year floodplain. Although Thurston
County planners have made great strides in their natural hazard mitigation plans
and projects to reduced damage in the event of a natural disaster, social
demographic data could be more strongly incorporated. As of now, the Thurston
NHMP contains fifty-two pages of tables with social demographic data on each
incorporated area. While this information is helpful in providing a general
understanding of a city’s social demographic makeup, the current format does not
help planners or community members understand the interlinked complexity of
what these demographic characteristics reveal about our own social construction
of natural disasters. By including and strengthening the concept of community
81
resilience rankings, these complex relationships come to light and bring into focus
many areas of weakness that should be actively targeted in future mitigation
projects. If Thurston County wants to continue as a leader in Washington State
Natural Hazard Mitigation, and embrace true environmental justice, these social
inequality need to be included and addressed to be more effective in mitigating
disaster.
In the bigger picture, cities, counties and states which choose to embrace
the concept of community resilience and actively include the social construction
of disasters into their community development and mitigation plans, would be
setting the stage for the continuing paradigm shift in natural disaster preparedness,
planning and response. As climate change impacts continue to increase in
frequency and intensity, communities who most strongly embrace this concept of
community resilience and begin working toward strengthening it now will be
communities who buffer disaster impacts better than communities who continue
to stick to the basic FEMA, “cookie-cutter” format. Research in community
resilience and social vulnerability have significant strides to make before they will
be incorporated and implemented at the federal level, so a localized, ground-up
approach would be more effective at this time. By understanding how community
resilience is spatially situated in our communities, we can identify areas of
weakness to enhance mitigation, preparedness, education, and response efforts
nation-wide. This would result in an overall increase in local community
resilience, but also community resilience as a nation.
82
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89
Appendix
2
2
2
106
106
115
1
3
4
2
123.1
1
2
123.1
2
2
2
3
3
3
3
3
3
3
3
3
3
123.3
123.3
105
105
112
112
113
113
114.1
114.1
114.2
114.2
1
2
3
4
2
3
1
4
1
2
1
2
3
115
3
3
116.1
1
3
3
3
116.1
116.2
116.2
3
1
2
% of area that is 100year floodplain
2
% of pop. over 65 per
block group
123
% of pop. racial minority
per block group
1
Under 150% of federal
pov. threshold per block
group
1
Per cap. income
Block group
115
Population
Census tract
1
City/area
Rank
List of block groups from lowest to highest resilience rank.
Lacey
Nisqually
Reservati
on
Olympia
Olympia
Lacey
Unincorp
.
Unincorp
.
Lacey
Lacey
Olympia
Olympia
Lacey
Lacey
Lacey
Lacey
Lacey
Lacey
Lacey
Lacey
Unincorp
.
Lacey/U
nincorp.
818
14,448
24.2%
50.9
3.8
2.14
752
15,248
3.6%
68.6
4.4
33.59
616
22,017
30.8%
36.7
12.5
0.00
1512
14,930
46.5%
38.3
5.4
1628
15,632
32.4%
41.5
7.7
0.00
0.00
1,658
18,404
11.6%
43.3
3.5
2.21
2629
13,233
14.2%
31.3
12.7
0.72
1,741
19,368
13.7%
35.3
5.1
206
18,342
21.0%
37.6
4.2
2,469
22,255
23.3%
26.6
5.5
1,399
15,097
39.7%
22.8
15.6
2304
15,203
38.2%
24.7
18.8
1289
21,454
20.6%
25.4
11.2
1440
19,865
32.1%
25.0
19.5
1,072
18,214
16.6%
23.6
9.6
961
18,926
6.1%
29.8
9.5
879
19,216
32.7%
25.4
7.5
2,635
23,371
10.4%
25.2
12.1
2,123
19,149
29.0%
26.1
5.4
0.00
0.00
11.03
30.04
7.11
11.31
5.21
0.00
0.00
0.00
0.00
12.38
1144
26,966
14.4%
31.0
13.9
0.00
1991
18,983
10.1%
26.0
6.6
9.12
1,472
21,977
3.1%
26.4
20.2
5,057
18,872
14.6%
25.1
9.7
4,724
21,398
7.8%
26.4
6.7
20.39
2.63
8.54
Lacey
Lacey
Lacey
90
123.1
3
3
127
8
4
4
4
4
103
103
105
107
1
4
1
1
4
108
4
4
109
3
4
112
1
4
4
4
4
114.1
114.2
115
115
4
3
2
5
4
116.1
4
4
116.2
4
4
117
4
4
4
4
4
122.1
122.2
123.2
124.1
3
4
1
2
4
127
3
5
5
5
101
101
102
2
4
1
% of area that is 100year floodplain
3
% of pop. over 65 per
block group
3
% of pop. racial minority
per block group
122.2
Under 150% of federal
pov. threshold per block
group
3
Per cap. income
Block group
4
Population
Census tract
122.1
City/area
Rank
3
Olympia/
Lacey
1,710
15,434
27.8%
26.3
15.6
0.38
785
29,162
11.6%
22.5
14.8
10.73
2,105
25,506
9.6%
22.7
6.3
1.34
1,332
11,796
55.1%
24.8
2.5
24.20
2030
15,677
40.8%
19.8
6.0
686
18,091
38.0%
21.7
19.0
1,470
16,646
20.5%
21.5
35.0
2,417
24,840
4.0%
19.1
5.9
11.66
0.00
12.50
0.00
937
16,053
38.3%
22.3
13.8
0.00
1,826
20,216
16.5%
22.5
7.6
6.36
715
12,989
31.5%
22.2
11.9
6.38
2,446
19,898
14.0%
20.9
13.1
1,665
19,098
2.9%
18.5
6.3
1,041
17,642
15.9%
20.4
7.0
1,024
29,482
9.4%
17.4
7.6
0.00
9.93
10.42
0.00
2170
21,701
10.6%
19.0
8.1
12.04
2699
23,371
8.2%
20.8
5.7
6.02
1,423
22,160
5.5%
17.2
1.1
23.90
3,823
19,566
14.9%
18.9
23.9
3,298
28,160
4.2%
20.3
6.9
1,946
20,330
17.5%
20.2
10.4
1,555
16,365
19.9%
20.2
7.7
9.51
2.02
9.25
6.83
863
14,827
18.3%
20.7
11.6
5.68
588
18,252
41.6%
16.4
5.8
832
23,740
18.9%
16.6
9.4
1239
29,828
3.2%
15.9
17.8
14.94
0.00
24.03
Lacey
Unincorp
.
Unincorp
.
Olympia
Olympia
Olympia
Olympia
Tumwate
r
Tumwate
r
Olympia/
Lacey
Lacey
Lacey
Lacey
Lacey
Unincorp
.
Unincorp
.
Unincorp
.
Olympia
Lacey
Lacey
Yelm
Unincorp
.
Olympia
Olympia
Olympia
91
117
1
5
122.1
5
5
122.2
1
5
124.2
3
5
125
5
5
127
1
5
127
4
6
102
3
6
104
2
6
108
5
6
109
4
6
6
111
114.1
2
3
6
118.1
1
6
119
1
6
120
2
6
124.1
3
% of area that is 100year floodplain
5
% of pop. over 65 per
block group
5
% of pop. racial minority
per block group
109
Under 150% of federal
pov. threshold per block
group
5
Per cap. income
Block group
4
3
Population
Census tract
106
107
City/area
Rank
5
5
Olympia
Olympia
Tumwate
r
Olympia/
Unincorp
Unincorp
.
Unincorp
.
Unincorp
.
Unincorp
.
Unincorp
.
Unincorp
.
Olympia/
Unincorp
.
Olympia/
Tumwate
r
Tumwate
r
Tumwate
r
Olympia
Lacey
Unincorp
.
Unincorp
.
Unincorp
.
Yelm/Un
incorp.
2,509
26,989
22.1%
15.7
8.6
15,555
27,250
5.7%
15.5
12.5
14.52
0.00
3,237
25,485
8.4%
15.6
5.8
5.38
2,291
24,405
6.3%
15.4
9.1
2.50
1,197
33,579
6.5%
17.1
15.8
2.02
1,234
31,840
8.1%
15.0
10.7
4.10
1,355
20,693
7.2%
14.8
3.8
7.49
1,567
16,597
13.0%
16.1
7.0
8.27
1,697
20,014
18.2%
15.3
13.7
15.33
1,944
17,466
9.5%
14.8
8.7
2.96
2,101
19,823
15.1%
13.6
9.4
0.00
1,004
28,517
9.9%
13.3
7.8
12.25
2,307
24,906
23.9%
13.3
13.7
1.15
756
23,230
9.2%
13.0
14.3
0.00
1143
27,049
10.1%
13.1
9.0
820
20,329
10.1%
13.8
18.8
9.21
0.00
2,022
26,198
7.1%
14.3
6.5
1.96
2,022
24,829
10.3%
13.6
6.8
0.89
2598
20,700
13.0%
13.1
4.3
13.27
4,132
17,285
21.0%
13.5
12.2
13.58
92
101
101
102
104
105
106
107
1
3
2
1
2
2
2
7
108
1
7
109
2
7
110
1
7
111
1
7
116.1
2
7
116.2
3
7
118.2
3
7
122.1
1
7
124.1
1
7
126
4
7
127
6
8
8
103
117
2
3
% of area that is 100year floodplain
7
7
7
7
7
7
7
% of pop. over 65 per
block group
3
% of pop. racial minority
per block group
126
Under 150% of federal
pov. threshold per block
group
6
Per cap. income
Block group
6
Population
Census tract
125
1357
17,052
18.8%
12.5
10.4
4.95
1,659
17,123
26.3%
13.7
12.4
6.71
696
16,504
58.2%
11.1
24.7
610
31,564
11.5%
10.5
17.0
1,462
21,764
13,2%
9.7
7.2
1,678
26,877
5.3%
9.8
16.2
1,690
22,780
22.6%
10.7
12.1
1,153
28,099
7.2%
11.1
13.4
793
18,864
23.6%
12.0
13.7
51.42
33.69
0.00
7.81
0.00
0.00
17.07
926
31,068
6.5%
10.5
14.4
0.02
1778
26,667
15.0%
12.3
14.4
5.97
1482
27,124
5.6%
9.9
15.7
6.07
1309
29,669
16.9%
10.8
10.9
18.99
1,064
31,655
4.6%
11.4
19.5
5.74
Lacey
Tumwate
r/Unincor
p.
Unincorp
orated
2,061
29,157
12.3%
12.0
19.1
14.58
1,890
20,742
8.4%
9.8
7.0
5.30
1,078
30,659
5.9%
11.7
16.5
5.50
Yelm
2,382
15,962
26.3%
11.4
10.0
6.48
1,585
22,709
8.5%
10.5
12.4
5.77
1,867
20,523
13.0%
11.5
6.8
8.00
1577
18,864
23.1%
8.9
8.2
1899
33,807
2.3%
9.6
17.6
0.01
16.02
City/area
Rank
6
Rainier/
Unincorp
.
Tenino/U
nincorp.
Olympia
Olympia
Olympia
Olympia
Olympia
Olympia
Olympia
Tumwate
r
Tumwate
r
Tumwate
r/Unincor
p.
Unincorp
.
Lacey/U
nincorp.
Tenino/U
nincorp.
Unincorp
.
Olympia
Unincorp
93
119
2
8
120
1
8
121
1
8
121
2
8
122.2
2
8
124.2
2
8
125
2
8
125
4
8
126
1
8
127
2
8
127
7
9
104
3
9
108
3
9
109
6
9
110
2
9
9
113
113
2
3
% of area that is 100year floodplain
8
% of pop. over 65 per
block group
5
% of pop. racial minority
per block group
118.2
Under 150% of federal
pov. threshold per block
group
8
Per cap. income
4
Population
118.2
City/area
Block group
Census tract
Rank
8
.
Unincorp
.
Unincorp
.
Unincorp
.
Unincorp
.
Unincorp
.
Unincorp
.
Unincorp
.
Unincorp
.
Unincorp
.
Unincorp
.
Unincorp
.
Unincorp
.
Unincorp
.
Olympia/
Tumwate
r
Tumwate
r
Tumwate
r
Tumwate
r/Unincor
p.
Lacey
Lacey
2,035
28,991
11.2%
9.0
14.2
14.33
1378
20,899
20.9%
8.3
13.3
12.32
1,297
29,692
5.6%
8.0
7.6
18.71
2,585
40,250
8.4%
9.2
15.2
32.79
902
32,279
7.0%
8.4
9.5
6.54
709
28,435
11.8%
9.2
15.8
40.72
1787
27,886
7.7%
7.8
8.5
5.50
716
23,706
16.8%
9.1
9.8
3.70
2083
17180
14.0%
8.5
6.0
5.13
1,244
16,467
20.0%
9.5
13.7
4.83
1,785
23,205
14.1%
9.3
8.0
13.38
736
23,341
19.8%
8.0
10.6
11.73
1499
17,778
24.1%
9.1
11.6
4.99
813
27,613
13.7%
6.6
17.1
0.00
1,122
20,799
7.8%
7.0
8.4
33.24
931
21,405
19.2%
7.4
22.3
5.90
3,498
23,853
10.2%
7.7
8.0
24.85
1277
26,232
19.5%
4.4
76.8
772
18,063
9.7%
6.7
15.4
0.00
0.00
94
118.2
1
9
118.2
2
9
119
5
9
121
3
9
121
4
9
122.1
2
9
124.2
1
9
125
1
9
125
3
9
126
2
9
126
5
9
126
6
9
127
5
10
103
3
10
108
2
10
109
1
10
117
2
% of area that is 100year floodplain
9
% of pop. over 65 per
block group
2
% of pop. racial minority
per block group
118.1
Under 150% of federal
pov. threshold per block
group
9
Unincorp
.
Unincorp
.
Unincorp
.
Unincorp
.
Unincorp
.
Unincorp
.
Unincorp
.
Unincorp
.
Unincorp
.
Rainier/
Unincorp
.
Unincorp
.
Unincorp
.
Tenino/U
nincorp.
Bucoda/
Unincorp
.
Unincorp
.
Olympia
Tumwate
r
Tumwate
r
Unincorp
.
Per cap. income
Block group
5
Population
Census tract
117
City/area
Rank
9
1,565
20,067
6.1%
7.3
9.0
9.30
1,450
21,552
13.7%
6.9
9.8
14.09
843
18,682
8.0%
6.2
8.8
6.17
587
27343
12.8%
5.6
7.5
13.58
765
27,585
11.9%
7.1
13.3
4.96
776
25,654
9.9%
6.3
25.5
18.83
1,525
26,102
9.2%
7.5
10.6
32.89
1,016
31,367
15.7%
7.3
13.4
8.01
1,368
31,557
6.1%
7.0
9.3
9.88
1464
21695
13.1%
7.2
8.1
7.81
2135
20052
16.1%
6.8
11.3
12.82
1069
20031
2.7%
6.7
7.1
6.56
1511
19243
16.8%
6.3
11.9
4.08
817
15637
32.6%
7.0
11.1
10.12
1162
17281
16.5%
5.7
9.9
17.88
855
18191
21.8%
3.0
9.9
0.00
949
34201
5.5%
0.6
16.2
0.00
498
21185
34.5%
0.0
56.8
10.56
968
30942
9.5%
3.2
8.1
0.06
95
Census tract
Block group
119
3
10
119
4
Per cap. income
Under 150% of federal
pov. threshold per block
group
% of pop. racial minority
per block group
% of pop. over 65 per
block group
% of area that is 100year floodplain
Unincorp
.
Unincorp
.
Population
City/area
Rank
10
1083
39798
2.6%
4.2
13.7
18.34
1082
29674
5.1%
2.8
19.5
4.87
96
97
