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Adoptability of the Federal Emergency Management Agency National Risk Index's interactive online map viewer

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Title:
Adoptability of the Federal Emergency Management Agency National Risk Index's interactive online map viewer
Creator:
DiBetitto, Stephanie
Place of Publication:
Denver, CO
Publisher:
University of Colorado Denver
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Language:
English

Thesis/Dissertation Information

Degree:
Master's ( Master of public administration)
Degree Grantor:
University of Colorado Denver
Degree Divisions:
School of Public Affairs, CU Denver
Degree Disciplines:
Public administration
Committee Chair:
Boylard, Wendy
Committee Members:
Thomas, Deborah

Notes

General Note:
Fall 2017

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University of Colorado Denver
Holding Location:
Auraria Library
Rights Management:
Copyright Stephanie DiBetitto. Permission granted to University of Colorado Denver to digitize and display this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.

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Running head: ADOPTABILITY OF NRFS MAP VIEWER
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Adoptability of the Federal Emergency Management Agency National Risk Index’s Interactive
Online Map Viewer Stephanie DiBetitto
University of Colorado Denver School of Public Affairs
This client-based project is submitted in partial fulfillment of the requirements for the degree of Master of Public Administration in the School of Public Affairs at the University of Colorado Denver Denver, Colorado
Fall
2017


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Capstone Project Disclosures
This client-based project was completed on behalf of the Federal Emergency Management Agency and supervised by PUAD 5361 Capstone course instructor Wendy L. Bolyard, PhD and second faculty reader Deborah Thomas, PhD. This project does not necessarily reflect the views of the School of Public Affairs or the faculty readers. Raw data were not included in this document, rather relevant materials were provided directly to the client. Permissions to include this project in the Auraria Library Digital Repository are found in the final appendix. Questions about this capstone project should be directed to the student author.


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Table of Contents
Executive Summary.....................................................................4
Literature Review and Statement of Purpose............................................7
Measuring Risk......................................................................7
Social Vulnerability................................................................8
Resiliency within the Context of Vulnerability......................................9
Resiliency within the Context of Risk Reduction and Hazard Mitigation..............12
Decision-Support Tools.............................................................12
Federal Emergency Management Agency (FEMA) and the National Risk Index.............13
The National Risk Index............................................................14
Literature Review Findings.........................................................14
Methodology..........................................................................15
Research Questions and Hypotheses..................................................15
Measurement and Data Collection....................................................17
Sampling Plan......................................................................17
Validity and Reliability...........................................................19
Data Analysis......................................................................20
Results..............................................................................20
Survey Findings....................................................................21
Demographic information..........................................................21
Testing hypotheses 1-1, 1-2, and 1-3.............................................21
Testing hypotheses 2-1 and 3-1...................................................22
Interview Findings.................................................................22
Community information and emergency management...................................22
Map viewer findings..............................................................23
Discussion...........................................................................24
Limitations......................................................................25
Recomm endati ons................................................................25
Research Next Steps..............................................................27
Conclusion...........................................................................28
References...........................................................................29


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Appendix A: Models Used to Measure Social Vulnerability.............................33
Pressure and Release Model.........................................................33
Social Vulnerability Index.........................................................34
The Hazards-of-Place Model of Vulnerability........................................35
The Disaster Resilience of Place Model............................................36
Appendix B: Measurement Table........................................................38
Appendix C: Survey Questions.........................................................39
Appendix D: Interview Protocol.......................................................43
Appendix E: Postcard Notification....................................................45
Appendix F: Statistical Outputs......................................................46
Crosstab Outputs and Chi-Square Values............................................46
Spearman’s rho Generated by SPSS...................................................46
Descriptive Statistics Generated by SPSS...........................................47
Measures of Central Tendency Generated by Qualtries...............................48
Appendix G: Research Questions, Hypotheses, Results and Recommendations.............59
Appendix H: High-Priority Recommendations Infographics...............................62


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Executive Summary
The Federal Emergency Management Agency has created a National Risk Index (NRI). The NRI is unique, as it incorporates resilience, which is often excluded from historic hazard risk frameworks. One component of the NRI is a public facing interactive online map viewer. The map viewer is an example of a decision-support tool; decision-support tools include map viewers, software and online platforms. These tools help to inform stakeholders on hazard probability and associated impacts. The intended use of the interface is to inform emergency managers, local officials and community planners on their natural hazard risk. Though the interactive online viewer is primarily for use by public entities, it has not been tested at the community level. To understand the role the interactive online viewer can play in local community decision-making, this study performs a mixed methods analysis on the tool’s adoptability by emergency and floodplain managers throughout Colorado.
Surveys and interviews were used to collect data. Responses reveal that there is a statistically significant relationship between map viewer adoption potential and simplicity, as well as between map viewer adoption potential and usefulness. Therefore, emergency and floodplain managers that find the interactive map viewer simple to use and the information provided useful will be more likely to indicate interest in adopting it to aid future decisions (i.e., mitigation activities). This research provides insight into the ways the tool can be modified to better serve the needs of practitioners. This is important information to gather because the intent is to offer users a tool that will enhance resilience in decision-support.


Running head: ADOPTABILITY OF NRFS MAP VIEWER
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Adoptability of the Federal Emergency Management Agency National Risk Index’s Interactive
Online Map Viewer
There are many ways to measure risks associated with natural hazards. Existing models
and frameworks associated with natural hazards vary in methodologies and theoretical
frameworks. Traditional risk models focus on physical rather than social vulnerabilities
associated with natural risks. However, as models and research evolve, researchers shift their
focus towards measuring social vulnerability to understand the root causes of post-disaster
casualties and pre-disaster risk reduction (Klein, Nicholls, & Thomalla, 2003; Strategy, 1994;
Thomas, Phillips, Lovekamp, & Fothergill, 2013). Recent research suggests that studying
physical hazards without incorporating social vulnerability does not provide a full understanding
of risk. Thomas, Phillips, Lovekamp, and Fothergill, (2013) find that
Researchers have studied and written about human vulnerability to disasters for decades. Yet, far too frequently, efforts to reduce vulnerability occur only after a major event has claimed lives and destroyed individual and community assets, including homes, businesses, and savings. Measures to reduce vulnerability tend to rely on established practices, analyzing current policies and revising already-existing plans, but recent research on vulnerability has much to offer managers and practitioners in disaster risk reduction, (p. 2)
Measuring and understanding social vulnerability is an important component because it provides insight into the ways risks can be reduced and life and property at the individual and community level can be protected (Thomas, Phillips, Lovekamp, & Fothergill, 2013). This is because natural hazards are unavoidable, though disasters and damages can be prevented. As the body of knowledge surrounding social vulnerability grows, it becomes clear that human actions influence a community’s susceptibility and by default vulnerability to risk; therefore, models continue to develop and incorporate more progressive metrics. As a result, models are now


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starting to include preparedness and mitigation metrics to operationalize a system’s resilience, providing a more informed understanding of risk.
The Federal Emergency Management Agency’s (FEMA) Natural Hazards Risk Assessment Program with regional office support has created a National Risk Index (NRI), beta published October 2017 - http://tiny.cc/NRI_beta. The NRI is unique, as it incorporates resilience, which is often excluded from historic hazard risk frameworks and is an emerging concept within social vulnerability research. One component of the NRI is a public facing interactive online viewer. The intended use of the interface is to inform emergency managers, local officials, and community planners on their natural hazard risk, generate baseline and custom risk reports, and display visual products to inform hazard mitigation plans (state and local), promote risk awareness, engage community members, provide a baseline risk assessment, guide hazard mitigation grant funding prioritization, and reduce future risk, among other uses. The NRI and its outputs work to strengthen FEMA initiatives (FEMA, PowerPoint slides, December 16, 2016).
Though the interactive online viewer is primarily for use by local governments, it has not been tested at the community practitioner level. To understand the role the interactive online viewer can play in local community decision-making, this study performs a mixed methods analysis on the tool’s adoptability by emergency and floodplain managers throughout Colorado. The research question asks: What is the likelihood of emergency and floodplain managers in Colorado will adopt the National Risk Index’s interactive online viewer? Understanding how the tool can help emergency and floodplain managers inform risk related decisions will allow FEMA to determine if the online viewer is successfully designed and includes relevant and understandable information. This research provides insight into the ways the tool can be


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modified to better serve the needs of practitioners. This is important information to gather for FEMA because their intent is to give users a tool that can inform decision-making and risk reducing action, which can further enhance resilience by decreasing vulnerability. Users include practitioners making on the ground emergency management decisions (i.e., emergency managers, mitigation and operational planners, floodplain managers). This report includes the following sections: literature review and statement of purpose; methodology; results; discussion and recommendations; conclusion; references; and appendices.
Literature Review and Statement of Purpose
This section outlines the major themes present in the literature and provides readers with a more in-depth understanding of the importance of measuring social vulnerability and resilience within the context of risks caused by natural hazards. Furthermore, it explains the use of decision-support tools to measure risks, which can help to increase mitigation and resiliency. Measuring Risk
Measuring risk in the context of natural hazards varies by frameworks used and how researchers define terms. To understand the fundamentals of how risks are measured, it is important to understand the two paradigms of disaster. Thomas, Phillips, Lovekamp, and Fothergill (2013) explain the two paradigms in contrast to one another, as the first focuses on physical vulnerabilities and the second focuses on social vulnerabilities. The measure of physical risks is the dominant paradigm, which portrays nature as the primary cause of disasters.
Solutions within the dominant paradigm avoid disasters through hierarchies that apply technology, science, and engineering. This approach promotes conquering rather than co-existing with natural systems (Hewitt 1983; Strategy, 1994; Thomas, Phillips, Lovekamp, & Fothergill, 2013). Measuring risk through the lens of the dominant paradigm may inform the existent


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literature as to why risks occur but does not explain why disasters result. Recent research finds that risks are natural, though disasters are a result of human action and exposure to risk (Blankoff, 2006; Mileti, 1999). For these reasons, the dominant paradigm has many shortcomings, including its lack of a full consideration of all forms of disaster, its command and control approach to management, and its inability to acknowledge social systems (Thomas, Phillips, Lovekamp, & Fothergill, 2013). “Overall, the dominant paradigm provides a limited understanding of the causes of, and solutions to, disasters and fails, in particular, to recognize the true nature of vulnerability and the capacity that related populations bring to bear on their own risk as well as that of the large society” (Thomas, Phillips, Lovekamp, & Fothergill, 2013, p.10). A better understanding of social vulnerability within the context of risk provides greater insight into the causes of disasters (Blaikie, Cannon, Davis, & Wisner, 1994).
Social Vulnerability
The vulnerability paradigm incorporates social characteristics, which take into consideration why certain groups of people are more susceptible to risk. This paradigm incorporates political and socioeconomic influences into measurements. Metrics used to measure social vulnerability include class, gender, age, ethnicity, and disabilities, to name a few.
Solutions to reduce social vulnerability include grassroots initiatives, prioritized by the community, that work with nature (Blankoff, 2006; Thomas, Phillips, Lovekamp, & Fothergill, 2013). It is critical that social vulnerabilities are incorporated into risk management and reduction, as structural fixes may only stand until the next event and continue to keep those most susceptible to risk vulnerable. Furthermore, rebuilding in ways that reduce social vulnerability and mitigate future disasters promotes community resilience (Mileti, 1999).


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Social vulnerability is measured to gain insight into a community's potential for loss postdisaster and as a way to guide mitigation strategies (Cutter, 1996). Adger (2006) finds studying vulnerability to be “a powerful analytical tool for describing states of susceptibility to hard, powerlessness, and marginality of both physical and social systems, and for guiding normative analysis of actions to enhance well-being through reduction of risk” (p. 268). For example, when reflecting on Hurricane Katrina in New Orleans, Louisiana in 2005, 70% of those who perished were elderly (65 years of age or older) and African American (Thomas, Phillips, Lovekamp, & Fothergill, 2013). Measuring vulnerability allows researchers and practitioners to analyze and predict what causes loss and makes individuals and communities susceptible to risk. Though there is no standard set of metrics used to measure vulnerability, Cutter, Boruff, and Shirley (2003) explain:
There is a general consensus within the social science community about some of the major factors that influence social vulnerability. These include: lack of access to resources (including information, knowledge, and technology); limited access to political power and representation; social capital, including social networks and connections; beliefs and customs; building stock and age; frail and physically limited individuals; and type and density of infrastructure and lifelines, (p. 245)
Vulnerability theory focuses on three themes, which include the measure of vulnerability from
exposure, societal resilience or resistance, or place (Burton, Kates, & White, 1993; Blaikie,
Cannon, Davis, & Wisner, 1994; Cutter, 1996; Cutter, Boruff, & Shirley, 2003; Hewitt 1997;
Kasperson, Kasperson, & Turner, 1995). Within the existent literature, there are varying
definitions of the term vulnerability, which complicates how it is operationalized. However,
several models are commonly used to measure social vulnerability (See Appendix A).
Resiliency within the Context of Vulnerability
As research in the social vulnerability field progresses, there is greater attention on the
multiple stressors and multiple pathways contributing to vulnerability (Adger, 2006). New


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research focuses on the integration of social-ecological systems and resiliency. “The concept of a social-ecological system reflects the idea that human action and social structures are integral to nature and hence any distinction between social and natural systems is arbitrary” (Adger, 2006, p. 268). Resiliency fits into this body of knowledge, as it helps to explain the extent of disturbance a system can absorb before the state of that system is significantly altered. It also explains the capacity a social-ecological system has to self-organize or adapt to an altered condition (Adger, 2006). Additionally, it can be posited from Klein, Nicholls, and Thomalla’s (2003) research that there is an inverse relationship between resilience and vulnerability: as resilience increases, vulnerability decreases.
Resilience came to the forefront as a way to reduce risk as unsustainable technological solutions were not reducing dollar losses resulting from natural hazards (Mileti, 1999). Moving away from structural or technological solutions towards resiliency was spurred by an assessment completed by Mileti (1999). This assessment was the second of two studies completed 25 years apart and commissioned by the US government as a way to determine why losses from natural disasters continued to increase. Research finds that disaster-resistant communities avoid greater damages and loss. “This shift emphasized the interactive nature of natural and human systems, the built environment, and the role of human agency in producing hazards and disasters (acts of people, not acts of God)” (Cutter et al., 2008). The US government through FEMA was in support of developing disaster-resilient communities. In 1994, the National Mitigation Strategy was created as a way to decrease losses by prioritizing vulnerabilities and risk reduction strategies through stakeholder engagement. Unfortunately, with a change in administration, the results of this effort were not quantified and the program was terminated (Cutter et al., 2008). However, similar efforts also were building globally, as 1990 began the International Decade for


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Natural Disaster Reduction. World leaders met in 1994 for a conference in Yokohama, Japan, which was the first international event recognizing social vulnerability in the context of risk reduction. More recently, the Hyogo World Conference on Disaster Reduction addressed resiliency’s role in hazard mitigation and ways to build more resilient communities (Cutter et al., 2008). Resilience is an important concept as it is linked to risk reduction and sustainability.
The concept of resiliency originated in the ecological field and is now being applied in the context of social vulnerability. However, there is not clear consensus on the definition of the term or how it can be operationally applied (Klein, Nicholls, & Thomalla, 2003). The term generally describes resilience as a system’s ability to recover and return to a state or multiple states that existed before undergoing stress. The concept also takes into consideration a system’s ability to advance through adaptation to a disturbance (Cutter et al., 2008; Holling, 1973; Klein, Nicholls, & Thomalla, 2003). When applied specifically in the social (human) context the term describes how communities, or an individual, cope or adapts to stress (Blakie, Cannon, Davis, & Wisner, 2014; Pelling, 2003). Social resilience is measured through parameters such as economic structure and property rights, and is often related to individual or community stability (Klein, Nicholls, & Thomalla, 2003). When analyzing resilience, Cutter et al. (2008) acknowledge that resilience can be an outcome (i.e., ability to cope) or a process (i.e., continual learning to increase capacity) depending on how it is defined and applied. Their research finds that there are two qualities defining resilience, the first is inherent, which measures periods without disaster, and the second is adaptive, which measures flexibility in response during a crisis. These measures are used to perform assessments on economic and social systems, infrastructure, and organizations, to name a few (Cutter et al., 2008). Though resilience is a prominent focus of


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vulnerability assessment and risk reduction, research is lacking on how resilience can be achieved (Cutter et al., 2008; Klein, Nicholls, & Thomalla, 2003; Tobin, 1999).
Resiliency within the Context of Risk Reduction and Hazard Mitigation
US agencies are incorporating resiliency principles into risk reduction (Cutter et al.,
2008). This has proven beneficial, as findings show that more proactive communities take steps to increase resiliency before disasters strike through planning and action, and are more adaptive (Cutter et al., 2008; Dovers & Handmer, 1992). When discussing the ways in which communities or individuals can adapt, the hazard literature focuses on preparedness and mitigation. Actions being taken by practitioners on behalf of communities align with the transition of research from the dominant to the social vulnerability paradigm, as both are promoting action before a disaster strikes, rather than focusing on solutions after the event. Though mitigation planning and action can enhance society’s resilience, there is discussion surrounding the challenges and risks associated with planning for unknown future conditions (i.e., climate change) and the promotion of an ambiguous (not uniformly applied) concept of resiliency (Bruneau et al., 2003; Burby et al., 2000; Klein, Nicholls, & Thomalla, 2003).
Decision-Support Tools
One way communities can plan for hazards is through the use of decision-support tools. These tools help to communicate risks, which are an integral part of taking action as they help shape and influence risk perception (Komedantova et al., 2014). “The successful implementation of disaster risk reduction options and strategies demand not only comprehensive risk assessment schemes, but also an appropriate mechanism to communicate and transfer knowledge on risk and its underlying drivers to the various stakeholders involved in the decision-making process” (Komendantova et al., 2014, p.51). FEMA’S NRI map viewer is an example of a decision-


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support tool; decision-support tools include map viewers, software and online platforms. These tools help to inform stakeholders on hazard probability and associated impacts (Komendatova et al., 2014). Komendatova et al. (2014) find that stakeholders are interested in multi-risk assessment but are unable to gain a full understanding of hazards because of the complex processes involved with existing tools. They find that user understanding of decision-support tools are limited and that stakeholder feedback is valuable in designing tools that are usable, which further helps to make implementation achievable. However, stakeholder participation and integration of stakeholder feedback into multi-risk assessment tool development is absent from the existing literature. These findings help to inform the simplicity, usefulness, and knowledge variables being tested in this study (see Methodology section).
Federal Emergency Management Agency (FEMA) and the National Risk Index
FEMA was established in 1979 under Executive Order 12127, and in 2003 became part of the Department of Homeland Security. It is a US Federal Government entity which operates under a hierarchical structure. The agency has ten regional offices throughout the US. Region VIII is located in Denver, Colorado and serves Colorado, Montana, North Dakota, South Dakota, Utah, and Wyoming. FEMA has been active in implementing resilient actions through mitigation and preparedness initiatives since 1994 when the agency created its National Mitigation Strategy (Cutter et al., 2008). This initiative worked to reduce disaster losses through public-private partnerships and incentivized stakeholder engagement. Continuing forward, FEMA embeds resiliency concepts within other FEMA initiatives including the Whole Community Framework, created in 2011 and the National Mitigation Framework, published in 2013 and revised in 2016. The Whole Community Framework focuses on preparedness actions and calls upon the involvement of all stakeholders, in an effort to promote national safety and greater resiliency


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(FEMA, 2011). The National Mitigation Framework seeks to reduce disaster impacts through prevention, further decreasing response needs and easing recovery (FEMA, 2016b). Ultimately, actions outlined in the framework seek to establish better-prepared and resilient communities. The National Risk Index
FEMA continues to strive for communities that are more resilient and in October 2017, the Natural Hazards Risk Assessment Program with regional office support developed a National Risk Index (NRI), beta published October 2017 - http://tiny.cc/NRI_beta. The NRI incorporates a national set of 18 natural hazard-specific indices, applied independently or combined as a composite multi-hazard risk index (FEMA, PowerPoint slides, December 16, 2016). The NRI conceptualizes natural hazard risk as the interaction between hazard likelihood, social vulnerability, built environment, and resilience and defines risk as “the intersection of hazards and consequences” (FEMA, PowerPoint slides, December 16, 2016, slide 13). TheNRFs public facing map viewer tool allows users to view a baseline risk index or calculate a custom risk score using components of the baseline for a defined geographic region, as well as the ability to generate maps and reports, and export geospatial data for all components. The NRI further promotes FEMA initiatives including preparedness, mitigation, and resilience at the local level. Literature Review Findings
Overall, the literature suggests that communities with a better understanding of their hazard risk, social vulnerability, and resilience are able to prepare for and prioritize the mitigation of risks. This allows communities to return to their former state and reduce disasters and losses from happening, especially to those most susceptible to hazards at the individual and community level. One way to inform decision makers about risks in their communities is through decision-support tools. However, gaps in the existing literature suggest that stakeholder feedback


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on existing decision-support tools is lacking and that tools need to be usable and simple to understand to better disseminate information and increase successful implementation of mitigation actions. The research being performed through this capstone project seeks to better understand how the NRI can help emergency and floodplain managers inform risk related decisions. Understanding this will allow FEMA to determine if the online viewer is likely to be adopted at the local level. This research also provides insight into the ways the tool can be modified to better serve the needs of practitioners.
Methodology
This study answers the research questions listed below. Hypotheses and variables for each research question are outlined as follows.
Research Questions and Hypotheses
This study answers the research question: what is the likeliness of emergency and floodplain managers in Colorado will adopt the National Risk Index’s interactive online viewer?
Hypotheses and variables measured are listed below and a detailed measurement table outlining the hypotheses, variables and measures can be found in Table 1 of Appendix B.
1. What is the likelihood of emergency and floodplain managers in Colorado indicating they
are interested in adopting the National Risk Index’s interactive online viewer?
• Hypothesis 1-1: If emergency and floodplain managers find the online map viewer useful, the likelihood of adoption in decision-making in turn increases.
• Null Hypothesis 1-1: Usefulness does not impact the likelihood of tool adoption.
• Variables 1-1: Usefulness (independent variable) and adoptability (dependent variable).


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• Hypothesis 1-2: The simplicity of the online viewer will increase an emergency or floodplain manager’s potential to adopt it in decision-making.
• Null Hypothesis 1-2: Simplicity does not affect tool adoptability.
• Variables 1-2: Simplicity (independent variable) and adoptability (dependent variable).
• Hypothesis 1-3: Knowledge of the information being provided by the tool will increase the likeliness of emergency and floodplain managers to adopt its use in decision-making.
• Null Hypothesis 1-3: Knowledge of the information being provided by the tool does not affect adoptability.
• Variables 1-3: Knowledge of the information being provided by the tool (independent variable) and adoptability (dependent variable).
2. Are emergency and floodplain managers in counties with disaster histories more likely to
indicate they are interested in adopting the tool?
• Hypothesis 2-1: Counties with greater disaster histories (documented as a federally declared disaster) will be more likely to show interest in adopting the tool.
• Null Hypothesis 2-1: Disaster history does not affect tool adoption.
• Variables 2-1: Disaster history (independent variable) and tool adoption (dependent variable).
3. Are emergency and floodplain managers in communities with greater staff capacity more
likely to indicate interest in adopting the map viewer?
• Hypothesis 3-1: Communities with greater staff capacity will be more likely to adopt
the tool.


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• Null Hypothesis 3-1: Staff capacity does not affect tool adoption.
• Variables 3-1: Staff capacity (independent variable) and tool adoption (dependent variable).
Measurement and Data Collection
The study takes a mixed methods approach to collect and analyze primary data. Secondary data was also collected to provide information on federally declared disasters for counties within Colorado. This information was cross-referenced from the following source: https://www.fema.gov/media-library/assets/documents/106308. The approach uses a 15-question survey (see Appendix C), completed by emergency and floodplain managers throughout Colorado, in addition to four interviews with emergency managers representative of different areas of the state. The interview protocol can be found in Appendix D.
Sampling Plan
The study collects information from surveys and interviews. The unit of analysis is emergency and floodplain managers. Emergency managers are responsible for identifying and analyzing potential impacts of hazards; conducting emergency management activities; performing risk and hazard assessments; and coordinating planning processes through collaboration with other government agencies and organizations (FEMA, 2017a). Floodplain managers are responsible for administering local floodplain ordinances; processing floodplain development permits; keeping records of floodplain development; correcting violations and taking enforcement action; and coordinating with other programs and agencies, all in an effort to reduce risks from floods (FEMA, 2017b). Emergency and floodplain managers were selected for this study because they are involved in local mitigation and preparedness and help guide


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decision-making specific to natural hazard management at the local level. Mitigation planners were not engaged in this study because they are not present in every community.
The population from which the sample is drawn is communities throughout Colorado.
The sampling technique is purposive and solicits responses from 93 emergency managers, representing 88 communities, and 244 floodplain managers, representing 241 communities. Only emergency and floodplain managers with email information were contacted. There were 9 emergency and 2 floodplain managers not included because their email information was not available. Prior to emailing the surveys, 64 emergency managers were notified by phone call or voice message and 222 floodplain managers received a postcard in the mail notifying them about the upcoming survey. Only emergency managers with phone numbers listed or voice mailboxes set up received phone calls/voice messages, and only floodplain managers with listed physical addresses received a postcard (See Appendix E for postcard announcement).
Surveys were sent to emergency and floodplain managers via email and interviews were conducted via telephone. The number of interviews and communities selected for interview were decided through conversations with the client; communities were selected because they represent different areas of the state (i.e., hazards, population size, governance, etc.). Emergency and floodplain managers were assured that their responses are confidential and were provided with a written letter that included assurances, more information on the study, and contact information for follow-up questions. The survey included instructions on how to use the NRI viewer, which prompted users to explore certain aspects of the tool, and answer questions testing the three constructs: simplicity; usefulness; and knowledge.
For the purpose of this study, constructs (latent) are conceptualized as follows: simplicity is the ease of accessing the tool, navigating through it and generating results; usefulness is


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information that is helpful and able to be used in planning, mitigation, and overall risk decisionmaking; and knowledge is how well the user understands the information being provided. Survey questions 6 and 7 test for simplicity, 8 and 9 test for usefulness, 10 and 11 test for knowledge, and 12 tests for adoptability. These measures are operationalized using the Likert Scale, which provides insight on the test population’s attitudes. Interviews were used to collect more robust data to inform the study and further measure variables.
Validity and Reliability
The proposed measures should be considered internally valid or accurate because they seek to understand the relationship between usefulness, simplicity, knowledge, staff capacity, and disaster history on adopting the NRI’s online viewer in local decision-making. The variables reflect the meaning of the concept under consideration and are relevant, as they intend to predict future behavior. To maximize validity, researchers and practitioners that are intimately familiar with the research topic were solicited to conduct a review to assess the research methods and processes used to confirm their validity. The research design can be considered reliable or replicable because it is being conducted systematically by distributing identical surveys and disclosing the same information to all parties. The research attempts to maximize reliability by clearly defining constructs in a way that is both exhaustive and mutually exclusive. However, it is expected that there is bias within the sample because though surveys attempt to identify and include a complete population, not everyone solicited participated (non-response bias). External validity is maximized by conducting additional interviews in particular regions of the state. Results are not intended to be generalizable as they are being generated on behalf of FEMA.


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Data Analysis
This study employs descriptive and correlational analysis methods to measure the data collected and test hypotheses. To test Hypotheses 1-1 through 1-3, descriptive statistics are used, in addition to correlational analysis, using crosstab tables to measure the Chi-Square and p-Value of each variable. The p-Value, or probability, tests whether or not the relationship between the variables is truly non-random. The level of significance used for the correlational tests is 0.05. P-values less than or equal to 0.05 are considered statistically significant. A crosstab analysis is used because the relationship between ordinal variables is being measured. Hypotheses 2-1 and 3-1 also use descriptive statistical methods, in addition to a correlational test, which measures the Spearman’s rho. The Spearman’s rho coefficient provides information about the strength and direction of the relationship between the variables being tested. A Spearman’s rho test is used because the relationship is being measured between an ordinal variable and ratio variables.
Results
Of the 338 emails sent to emergency and floodplain managers, 25 emails failed and 10 bounced. A total of 50 surveys were completed. Of the emails that were delivered (303), 16.5% of the sample group responded. Four interviews were requested and conducted with emergency managers (100% response rate). Interviews provided more in-depth information about communities, their emergency management programs, and their experiences when testing the NRI map viewer tool. For an in depth review of the research questions, hypotheses, results and recommendations, see Appendix G.
Survey Findings
Demographic information. Demographic findings reveal that a greater percentage of survey respondents were male (56%, n=50) (See Appendix F for all statistical outputs). A higher


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percentage of respondents were age 45-54 (35.3%, n=51) and ages 55-64 (33.3%, n=51). An equal percentage (35.3%, n=51) of survey takers hold a college degree or have a graduate degree or higher and the majority of respondents are white/Caucasian at 88% (n=50).
Testing hypotheses 1-1,1-2, and 1-3. Descriptive statistics show that a greater percentage of respondents are likely to use the map viewer in future decision-making because it was easy to navigate (31.4% moderately or slightly likely, n=51) (simplicity: question 6). Of respondents, 56.9% (n= 51) believe that turning the map layers on and off and viewing community specific information was simple to do (simplicity: question 7). Of the survey participants, 35.3% (n=51) are moderately likely and 29.4% (n=51) of respondents are slightly likely to use the information provided by the map viewer in future planning or mitigation efforts (usefulness: question 8). A greater percentage of survey takers found the information a little (30%, n=50) or a moderate amount (30%, n=50) helpful in planning and mitigation related decisions at the local level (usefulness: question 9). A higher percentage of respondents (41.2%, n=51) had a good understanding of the input parameters the map tool uses, where only 5.9% (n=51) understood the parameters a great deal and 9.8% (n=51) did not understand the parameters at all (knowledge: question 10). Of the respondents, 52.9% (n=51) understood the information the map viewer provided a moderate amount and 21.6% (n=51) understood the information a little (knowledge: question 11). Of participants, 33.3% (n=51) are moderately likely and 25.5% (n=51) are slightly likely to use the map viewer when performing future hazard mitigation planning and implementation activities (adoptability: question 12).
The Chi-Square test statistic has a value of 62.46 and is statistically significant at a 0.05 level significance (p=0.00). A statically significant relationship was found between adoption likelihood and simplicity and adoption likelihood and usefulness because the p-value is lower


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than the level of significance; both questions testing simplicity (questions 6 and 7) were found statistically significant, and both questions testing for usefulness (questions 8 and 9) were found statistically significant. This indicates that the relationship of the variables sampled is caused by actual differences in the population rather than by chance. The Chi-Square for potential adoptability compared to simplicity in question 6 is 74.34 and in question 7 is 60.73. Both are statistically significant at a 0.05 level significance (p=0.00 and p=0.01 respectively). The Chi-Square for adoptability compared to usefulness in question 8 is 109.84 and in question 9 is 52.91. Both are statistically significant at a 0.05 level (p=0.00 and p=0.00 respectively). The relationship between likelihood of adoptability and knowledge is not found to be statistically significant (questions 10 and 11).
Testing hypotheses 2-1 and 3-1. A Spearman’s rho correlation was calculated for the relationship between map viewer adoption potential and county disaster history. A weak negative correlation was found {rho (20) = -0.049). A Spearman’s rho analysis was calculated for the relationship between map viewer adoption potential and staff capacity (i.e., full-time employees). A weak positive correlation was found {rho (49) = 0.007) (Orcher, 2016).
Interview Findings
Community information and emergency management programs. Communities represented in the four interviews have annual operating budgets greater than $100,000 on average. Two communities believe their emergency management budgets are well funded, while one believes it is not well funded and the other believes it is adequately funded. The communities had varied levels of staff capacity ranging from 1, 2, 5 and 10 full-time staff members and all but one of the emergency managers interviewed focus full-time on emergency management responsibilities. All but one of the emergency managers interviewed have 10 years


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of experience or greater. All communities identify natural hazards as a high concern; specifically, all identify floods, three out of four identify wildfire, and three out of four identify winter weather as a high concern. Three out of four communities have experienced what they would consider a natural disaster in the last five years (2013, 2015 and 2017). Three of the four communities participate in a community specific hazard mitigation plan, and one participates in a regional hazard mitigation plan. Three out of the four communities hire a contractor to complete their hazard mitigation plans, and the fourth community hires a contractor to develop some parts of the plan and completes some in-house. All communities use some type of decision-support tool to carry out their emergency management responsibilities.
Map viewer findings. In regard to the NRI map viewer, all emergency managers interviewed believe the map viewer is easy to use (simplicity) and all believe the tool provided moderately useful information (usefulness). Two believe the map viewer can help to guide hazard related decision-making, while two communities believe it can somewhat help (usefulness). Two communities understand the input parameters (Hazards, Built Environment, Social Vulnerability and Resilience) and the information generated by turning on and off the map layers, one somewhat understands and one does not understand (knowledge). Time spent using the map viewer varied between communities from 15, 30, 60 and 180 minutes. Communities’ views on how the tool can be used in future decision-making differed though two communities found the tool helped to provide a snapshot of the geographic area susceptible to specific hazards and believed it could be used to support the prioritization of where mitigation is needed. General feedback from communities called for more robust or complete data sets, increased community-specific data (many expressed their community’s risk was shown as a composite or as the same score for all of county, which may not be the case in reality), and greater explanations of the data


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sources used to create map layers (i.e., metadata) and calculate outputs (i.e., risk scores). It was noted that information being provided did not match data available locally. Communities expressed interest in using the map viewer, though two acknowledged that this is just one of many tools in the toolbox, and it is important to use it alongside of the others at their disposal.
Discussion
The survey results help to answer the research question: What is the likelihood of emergency and floodplain managers in Colorado to adopt the National Risk Index’s interactive online viewer? and further help to test hypotheses 1-1, 1-2, 1-3, 2-1 and 3-1. The statistical outputs from the crosstab tables show that hypotheses 1-1 and 1-2 can be accepted as there is a statistically significant relationship between simplicity and usefulness on the likeliness of adopting the NRI map viewer. A greater percentage of respondents found the viewer simple to use and the information useful in decision-making. The more simple and useful the tool, the more likely an emergency or floodplain manager is to adopt the tool in future decision-making. A lesser number of respondents found the information provided by the tool or the input parameters used to generate results understandable. The relationship between adoptability potential and knowledge was not statistically significant but it can be inferred from these results that if the information provided was better understood, a greater number of respondents would be likely to use the map viewer in future-decision-making.
Hypotheses 2-1 and 3-1 should be rejected as there is a weak relationship between potential adoptability of the map viewer and disaster history and staff capacity. When linking interview findings to survey responses, the statistically insignificant relationship between staff capacity and potential adoptability may be further explained, as the majority of emergency managers interviewed outsource or use contract mechanisms to develop hazard mitigation plans.


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Limitations
Although there was a weak relationship between potential adoptability of the map viewer and disaster history (Hypothesis 2-1), it should be noted that the sample size for disaster history in the Spearman’s rho correlation is low because the decision to declare a federal disaster occurs at the county level, and therefore data are only collected at this level. This is a limitation to the study because mid- or small-scale disasters are not considered.
Although 50 emergency and floodplain managers completed the survey, it would be beneficial to increase the response rate. Emails sent directly from Qualtrics (the web-based program used to conduct surveys and send emails) may be delivered to recipients’ spam folders. The number of interviews conducted was also a limitation, as collecting information for only four Colorado communities does not present a holistic representation of all demographics, hazard types and other variables of Colorado communities.
Survey questions did not track if a respondent was an emergency or a floodplain manager, or if they fill both emergency and floodplain manager roles for their community. This is valuable information to consider because it provides more insight on if being responsible for one hazard (i.e., floodplain manager) or multiple hazards (i.e., emergency manager) has an impact on potential viewer adoption. Not collecting this information is a study limitation. Recommendations
Open-ended survey questions and interview responses helped to provide specific recommendations on increasing map viewer adoptability in decision-making. Prominent themes include being able to upload local Geographic Information System (GIS) layers or download GIS layers from the map viewer for local use; the need for metadata with descriptions of the data layers (including scales) and data sources; increasing community specific datasets (i.e., critical


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facilities, transportation routes, infrastructure, etc.); using better symbology (i.e., make hazard indicators more intuitive: red equates to high hazards, etc. and use symbology that allows two or more layers to be viewed at once); and, including more floodplain specific hazard information. Providing metadata and more detailed descriptions of data sources used and how measures are calculated, in addition to making symbology more user-friendly can make the information being provided by the map viewer more understandable. In turn, this can increase user’s knowledge of the information and increase the potential for the tool to be adopted by local officials in future decision-making.
Moving forward, if the map viewer were to generate a report, emergency managers would like to see it include hazards by location (county, municipality, and census track); outputs with vulnerable populations and socioeconomic data that are not readily available (i.e., individual risk score for vulnerable population for specific demographics); statistical outputs; historical data; a map summary for each of the map layers; number of structures in an area or addresses at risk; customizable maps with legends; and qualifying information to support grant requests (i.e., dollar impacts over time caused by a certain hazard). FEMA should consider these recommendations when working to enhance future iterations of the map viewer and if/when creating report generating capability.
It is recommended that FEMA take actions to make the map viewer more understandable since it can be inferred that more practitioners would use the map viewer tool in future decisionmaking if the information being provided was better understood. In addition to the fact sheet already in circulation, it is recommended FEMA create a data dictionary and metadata for the map layers available on the viewer. Additionally, FEMA should consider facilitating training opportunities such as a webinar, which explains how the tool can be used. Making webinar slides


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available for download on FEMA’s website or providing a tutorial document for those not interested in attending a training, may also be helpful. Keeping stakeholders involved throughout the decision-support tool development process is also recommended. This allows stakeholders to be engaged and help shape and enhance the tools available to them (i.e., make the tool more simple, usable, understandable), which can also increase their potential to adopt it.
Another recommendation for FEMA to consider is collecting additional disaster data at the local level (i.e., city or town). It is recommended that FEMA work with the state to track these data. Currently, more localized disaster information can be found within the Colorado State Hazard Mitigation Plan. However, it is not in a unified format and it would be beneficial to catalog these data in a standardized data base. An overview of high-priority recommendations can be found as an infographic in Appendix H.
Research Next Steps
Moving forward, it is recommended that more interviews are conducted with communities throughout Colorado whose demographics were not considered in this study. Increasing the number of interviews conducted would also enhance research findings. This is because, increasing the number of interviews could help to validate responses, generate more robust data, and retest variables. Additionally, future studies should consider re-distributing the survey and revising the survey questions. When sending surveys in the future, it may be beneficial to send the survey web link from a personal email rather than from Qualtrics. The unit of analysis (respondents) should be increased to include hazard mitigation planning contractors, community planners, mitigation officers and other elected officials involved with planning for hazards at the local level. Contractors should be considered as stakeholders because they may be the primary users of decision-support tools. A survey question should be added to track if emergency


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managers are responsible for hazard mitigation planning activities in their community, if not, they should record who is responsible for this activity. This will help increase FEMA’s understanding of who in certain communities is most likely to use decision-support tools. If retesting the same unit of analysis (only emergency and floodplain managers), include another question to track if a respondent is an emergency or a floodplain manager, or if they fill both emergency and floodplain manager roles for their community. To gain better insight on if disaster history impacts map viewer adoption, more localized data in addition to federally declared disaster information should be collected and analyzed. It would also be beneficial to collect information on which communities have hazard mitigation plans.
Conclusion
The results of this study help to inform FEMA on the ways the NRI map viewer can be improved to better serve the needs of practitioners in Colorado. Past research finds that decision-support tools are often not well understood by the stakeholders intended to use them. Vetting the beta version of the map viewer through emergency and floodplain managers presents a unique opportunity; it allows those on-the-ground to help shape the decision-support tools available to them. Study results find a statistically significant relationship between the potential to adopt the map viewer and simplicity and usefulness. Therefore, users are more likely to adopt the viewer in decision-support because it is perceived to be simple to use and provide useful information.
Increasing the tool’s use can enable communities to better measure risks contributed by the built environment, natural hazards, social vulnerability and resilience. Operationalizing these factors can offer communities a more informed understanding of their risks and further take action to protect vulnerable populations and mitigate impacts. Taking action and increasing mitigation at the community level aligns with FEMA goals, which aim to enhance community


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resilience. Using the information provided by the map viewer alongside of other decision-support tools available to communities can help to inform mitigation actions and prevent disasters before they occur.


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References
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Blaikie, P., Cannon, T., Davis, I., & Wisner, B. (1994). At risk: natural hazards, people’s vidnerability and disasters. Routledge.
Birkmann, J., & Birkmann, J. (2006). Measuring vidnerability to natural hazards: towards disaster resilient societies (No. Sirsi) i9789280811353).
Bruneau, M., Chang, S. E., Eguchi, R. T., Lee, G. C., O’Rourke, T. D., Reinhom, A. M.,. & Von Winterfeldt, D. (2003). A framework to quantitatively assess and enhance the seismic resilience of communities. Earthquake spectra, 19(4), 733-752.
Burby, R. J., Deyle, R. E., Godschalk, D. R., & Olshansky, R. B. (2000). Creating hazard
resilient communities through land-use planning. Natural hazards review, 1(2), 99-106.
Cutter, S. L. (1996). Vulnerability to environmental hazards. Progress in human geography, 20(4), 529-539.
Cutter, S. L., Boruff, B. J., & Shirley, W. L. (2003). Social vulnerability to environmental hazards. Social science quarterly, 84(2), 242-261.
Cutter, S. L., Barnes, L., Berry, M., Burton, C., Evans, E., Tate, E., & Webb, J. (2008). A place-based model for understanding community resilience to natural disasters. Global environmental change, 18(4), 598-606.
Dovers, S. R., & Handmer, J. W. (1992). Uncertainty, sustainability and change. Global Environmental Change, 2(4), 262-276.


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FEMA. (2011). Whole Community. Retrieved from https://www.fema.gov/whole-communitv FEMA. (2016). FEMA National Risk Index [PowerPoint slides], December 19, 2016.
FEMA. (2016b). National Migitigation Framework, Second Edition. June 2016. Retrieved from https://www.fema.gov/media-library/assets/documents/117787 FEMA. (2017). About the Agency. Retrieved from https://www.fema.gov/about-agencv FEMA. (2017a). Roles of Local Emergency Managers. Retrieved from https://emilms.fema.gov/IS0230d/FEM0104Q40text.htm FEMA. (2017b). Floodplain Management Requirements: Unit 7: Ordinance Administration.
Retrieved from https://www.fema.gov/floodplain-management-requirements Hewitt, K. (1983). Interpretations of calamity from the viewpoint of human ecology Holling, C. S. (1973). Resilience and stability of ecological systems. Annual review of ecology and 3 lystematic, 4{ 1), 1-23.
Kasperson, J. X., Kasperson, R. E., & Turner, B. L. (1995). Regions at risk. United Nations University Press.
Klein, R. J., Nicholls, R. J., & Thomalla, F. (2003). Resilience to natural hazards: How useful is this concept? Global Environmental Change Part B: Environmental Hazards, 5(1), 35-45.
Komendantova, N., Mrzyglocki, R., Mignan, A., Khazai, B., Wenzel, F., Patt, A., & Fleming, K. (2014). Multi-hazard and multi-risk decision-support tools as a part of participatory risk governance: feedback from civil protection stakeholders. International Journal of disaster risk reduction, 8, 50-67.
Mileti, D. (1999). Disasters by design: A reassessment of natural hazards in the United States. Joseph Henry Press.


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Orcher, L. T. (2016). Conducting research: Social and behavioral science methods. Routledge.
Pelling, M. (2003). The vidnerability of cities: natural disasters and social resilience. Earthscan.
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planning? Global Environmental Change Part B: Environmental Hazards, 7(1), 13-25.
Thomas, D. S., Phillips, B. D., Lovekamp, W. E., & Fothergill, A. (Eds.). (2013). Social vidnerability to disasters. CRC Press.
Thomas, S.K, Daly, K., Nyanza, E.C., Ngallaba, S.E., & Bull, S.(nd). Health Worker Acceptability of an mHealth Solution for PMTCT in Tanzania. Unpublished.


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Appendix A
Models Used to Measure Social Vulnerability Pressure and Release Model
One popular model used to operationalize vulnerability is the Pressure and Release (PAR) Model created by Blaikie, Cannon, Davis, and Wisner in 1994. This model seeks to measure how natural hazards affect vulnerable populations. PAR calculates risk by multiplying hazards and vulnerability. Root causes (limited access to power and ideologies), dynamic pressures, and unsafe conditions make up the progressive level of vulnerability (See Appendix A). This framework is dynamic, which makes quantifying elements of vulnerability difficult. This is because “in multicausal situations and a dynamic environment, it is hard to differentiate between the causal links of different dynamic pressures on unsafe conditions and the impact of root causes on dynamic pressures” (Birkman & Birkman, 2006, p. 31). The model is often used to determine underlying factors contributing to vulnerability, though it does not provide insight into the relationship between proximity to environmental hazards and humans (Cutter et al., 2008).
Figure 1. Pressure and Release Model


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THf PROGRESSION OF VULNERABILITY
COOT CAUSES
DYNAMIC
PRESSURES
UNSAFE
CONDITIONS
Umited (KUIt tO
• fW
• S»rucJuf#t
• fttiouran
Ideologies
• Politico!
• Econo«r»c system*
lock of
• local nritofont
• Tfptnmg
• Appropriate sk-tt*
• local •'witmmnh
• locol market*
• Press freedom
• EfKicoi »kK)dofdi in public kfw
Macro'forces
• Ropid population growth
• Ropid urbanisation
• Arms expenditure
• Defer repayment tchecMos
• Deforestation
• Decline in soJ productivity
Fragile
physical
environment
• Dongerous locations
• Unprotected buildings and infrastructure
Fragile locol economy
• Livelihoods at risk
• low income levels
Vulnerable
society
• Special groups at rnk
• Lack of local inslAitiofts
Public octioas
• lack of disaster preparedness
• hevoJonce of endom»c d-soas#
DISASTER
RISK s Hazard ♦ Vulnerability
R = H ♦ V
HAZARDS
Earthquake
High winds (cyclone/ hurricane/ typhoon!
Flooding
Vblconk
eruption
landslide
Drought
Virus and pests
Blaikie, P., Cannon, T., Davis, I., & Wisner, B. (1994). At risk: natural hazards, people’s vidnerability and disasters. Routledge.
Social Vulnerability Index
Building on the vulnerability research, Cutter, Boruff, and Shirley developed the Social Vulnerability Index (SoVI) in 2003. This index uses socioeconomic and demographic data to measure vulnerability at the county level and takes into consideration 11 independent social factors. The index was used to measure the SoVI scores for all counties in the US. The lowest vulnerability score was -9.6 and the highest 49.51, with a mean score of 1.54. More vulnerable populations (lower scores) were found in the southern portion of the country. Counties with more vulnerable populations on average had racial inequalities, rapid population growth, large populations of Hispanic and/or Native American residents, or were highly urbanized. There were 393 counties found to be vulnerable, with the lowest score applied to Manhattan Borough in New


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York City (Cutter, Boruff, & Shirley, 2003). Some of the least vulnerable populations were found in New England and in the Great Lakes states, with the least vulnerable population being in Yellowstone, Montana. The model works well to explain statistical variance across the US, though fails to incorporate all elements of the models that came before it The Hazards-of-Place Model of Vulnerability
The Hazards-of-Place Model was created by Cutter in 1996 and helps to quantify social vulnerability in the context of place. The model measures place vulnerability by combining mitigation and risk to generate hazard potential. The hazard potential is then filtered through the following vulnerability elements: geographic context, social fabric, biophysical vulnerability, and social vulnerability (See Appendix B). A place’s vulnerability can be altered over time as mitigation and risks change. The model considers social vulnerability in the context of exposure (proximity to place) and provides insights into the characteristics that allow communities to respond and recover from hazards (Cutter, Boruff, & Shirley, 2003). However, it does not account for the root causes, which are better represented in the Pressure and Release Model (Cutter et al., 2008).
Figure 2. The Hazards-of-Place Model


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Cutter, S. L. (1996). Vulnerability to environmental hazards. Progress in human geography, 20(4), 529-539.
The Disaster Resilience of Place Model
In an effort to operationalize resilience, Cutter et al. (2008) created the Disaster Resilience of Place (DROP) model (Appendix C). This framework identifies metrics and standards for measuring disaster resilience and performs comparative assessments at the community/local level that helps conceptualize resilience. “This model is designed to present the relationship between vulnerability and resilience; one that is theoretically grounded, amenable to quantification; and one that can be readily applied to address real problems in real places”
(Cutter et al., 2008, p. 602). The model assesses the social resilience of places (neighborhood, census tract, city, or county), though it recognizes the interconnectedness between social and natural systems and the built environment (i.e., human actions affect the environment). Negative actions can degrade natural systems, which results in less environmental protection from hazards. To represent the interconnected nature between society, the DROP considers resilience as both


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antecedent and inherent conditions. In addition to these place-based factors, the impact of external factors on the community are also considered (e.g., federal policies, state regulations).
Figure 3. The Disaster Resilience of Place Model

Cutter, S. L., Barnes, L., Berry, M., Burton, C., Evans, E., Tate, E., & Webb, J. (2008). A place-based model for understanding community resilience to natural disasters. Global environmental change, 75(4), 598-606.


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Appendix B Measurement Table
Table 1. Measurement Table
Variable Measure Level of measurement Data source
Hypothesis 1-1: If e they will be more li mergency and floodplain managers find the online map viewer useful cely to use it in decision-making.
DV: Adoption Likert scale Ordinal Survey questions and interview findings (self-reported).
IV: Usefulness of online map viewer Likert scale Ordinal Survey questions and interview findings (self-reported).
Hypothesis 1-2: The simplicity of the online viewer wi floodplain manager’s potential to adopt it in decision- 1 increase an emergency and making.
DV: Adoptability See above under Hypothesis (1-1)
IV: Simplicity of online map viewer See above under Hypothesis (1-1)
Hypothesis 1-3: Knowledge of the information being provided by the tool will increase the likeliness of emergency and floodplain managers to adopt its use in decision-making.
DV: Adoptability See above under Hypothesis (1-1)
IV: Knowledge of information provided See above under Hypothesis (1-1)
Hypothesis 2-1: Communities with greater disaster histories will be more likely to show interest in tool adoption.
DV: Adoptability See above under Hypothesis (1-1)
IV: Disaster history Total number of federally declared disasters Ratio FEMA database {Summary of Disaster Declarations and Grants) '. https://www.fema.gov/media-library/assets/documents/106308
Hypothesis 3-1: Co interest in adopting mmunities with greater staff capacity will be more likely to indicate the tool.
DV: Adoptability See above under Hypothesis (1-1)
IV: Staff capacity Number of staff working on emergency management issues Ratio Survey questions and interview findings (self-reported).


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Appendix C Survey Questions
Please answer all questions to the best of your knowledge by selecting one answer for each multiple choice question and fill in each open-ended response.
A. Demographic Information
1. What is your highest level of education completed?
• High school, GED, or below
• Some college (two-year or four-year)
• College degree
• Some graduate school
• Graduate degree or higher
2. What is your gender?
Female Male Other
3. Which age range best describes you?
• 18-24 years
• 25-34 years
• 35-44 years
• 45-54 years
• 55-64 years
• Age 65 or older
4. Which race/ethnicity best describes you?
• American Indian or Alaskan Native
• Asian/Pacific Islander
• Black or African American
• Hispanic American
• White/Caucasian
• Multiple ethnicities/Other
5. How many full-time employees work on emergency of floodplain management related issues within your community?
B. Getting to the know the Map Viewer Tool


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For the remainder of the questions you will be prompted to use the map viewer (http://tiny.cc/NRI_beta). This will give you the opportunity to see how the viewer operates and provide feedback on its functions.
Before getting started please review the two page fact sheet (linked at the end of email text). Next, open the viewer in your web browser by clicking on or copying and pasting the link into your browser: http://tinv.cc/NRI beta
The browser will open to the NRI welcome screen, where users can learn more about the NRI project. To navigate to the map interface, click the “OK” button in the lower right.
In the mapping interface, search for your community using the search bar in the upper left (see images below). The default view is the baseline risk score for the US. Feel free to click on the Hazards, Social Vulnerability, Built Environment, and Community Resilience tabs to toggle on and off the different layers to understand your community’s base risk for natural hazards (see images below). Please feel free to turn on and off layers to get a feel for the tool Now you are ready to answer the following questions!


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Operational layers
â–¼ Q FEMA National Risk Index {dick to expand) â–º 0 National Risk Index â–¼ |___| Individual Hazard Risk Scores
>i Select Layers !Ss|—' to turn them.
► □ Avalanche Risk Index {NRI-H*V«-B-R) Otl
â–º â–¡ Coastal Flood Risk Index{NRI=H+V-*-B-R)
â–º â–¡ Cold Wave Risk Index {NRI-H-V-B-R)
â–º | | Drought Risk Index {NRI-H-*-V-*-B-R)
â–º â–¡ Earthquake Risk Index {NRI=H-V+B-R)
No Hazard | Very Low Low___________
Hail Risk Index {NRI-H-V-B-R)
6. Are you likely to use the tool in future decision-making because it was easy to navigate?
Extremely Moderately Slightly Neither Moderately Extremely
likely likely likely likely unlikely unlikely
nor
unlikely
7. Turning layers on and off and viewing community specific information was simple to do
Strongly Agree Somewhat Neither Somewhat Disagree Strongly
agree agree agree nor disagree Disagree
disagree
8. Does the map viewer provide you with information that you are likely to use in future planning or mitigation efforts?
Extremely Moderately Slightly Neither Moderately Extremely
likely likely likely likely unlikely unlikely
nor
unlikely
9. How helpful is this information in planning and mitigation related decisions at the local level?
A great deal A lot Some A little None at all
10. How well do you understand the input parameters (Hazards, Social Vulnerability, Built Environment, and Community Resilience)?
A great deal A lot Some A little None at all


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11. How well do you understand the information the map viewer provided?
A great deal A lot Some A little None at all
12. How likely are you to use the map viewer when performing future hazard mitigation planning and implementation activities?
Extremely Moderately Slightly Neither Moderately Extremely
likely likely likely likely unlikely unlikely
nor
unlikely
13. How long did you use the map viewer for and what did you use it to do?
14. What could be changed to make you more likely to adopt the map viewer for use in future emergency and floodplain management decisions?
15. If the map viewer included a report feature, what information from the viewer would be helpful to generate into a report?
16. Enter your email below for a chance to win a $50 Amazon gift


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Appendix D Interview Protocol
Introduction and Purpose
Thank you so much for agreeing to meet with me. I’m Stephanie DiBetitto and as you know I am conducting a project on the adoptability of FEMA’s National Risk Index map viewer tool for my Capstone Project with the University of Colorado Denver. Today, I would like to talk to you about your career and experience as an emergency manager to help inform my study. I will be tape recording this interview, but it will only be heard by me and your comments will be anonymous, as no names will be used in this report. I anticipate our interview will take a little less than one hour. Do you have any questions about the interview or the research before I begin?
Questions
A. Experience
1. What is your official job title?
2. Is emergency manager your full time position or do you have other responsibilities?
3. How many years have you been in an emergency management related field?
4. What are your responsibilities as an emergency manager?
5. Are you the only person with the community that works on emergency management related tasks?
6. Are emergency management tasks and positions well funded in your community? Do you know how much on average is allocated towards your community’s emergency management budget?
7. Are natural hazards a high concern for your community? Which hazard in particular?
8. When and how severe was the last disaster experienced? What type of disaster was this?
B. Planning and Mitigation
9. What type of planning and mitigation actions are you involved with in your community. For example, are you involved in hazard mitigation planning? Comprehensive planning? Etc.
10. Do you participate in a regional mitigation plan or one specific to your community? Do you prepare the planning document in house or through a contract mechanism?
11. Do you use any decision-support tools to inform emergency management decisions specifically related to natural hazards? Any web based tools like GIS or ArcGIS Online? If yes, please explain.
C. NRI Assessment
12. Was the NRI map viewer easy to use? Was it easy to navigate through and generate information? If yes, why? If no, why?
13. How useful is the information provided by the map viewer tool? Do you believe it can help to guide hazard related decisions? If so, in what ways?


ADOPTABILITY OF NRFS MAP VIEWER
44
14. Do you understand the input parameters the tool uses (Hazards, Social Vulnerability,
Built Environment, and Community Resilience)? Do you understand the information provided by turning on and off the map layers?
15. In what ways can you use the tool in future decision-making?
16. How long did you use the map viewer for? What did you use it to do?
17. If the map viewer were to generate a report, what information would be helpful to include in the report?
18. What do you like and dislike about the tool?
19. What changes to the tool would make you more likely to adopt its use in future decisionmaking? Planning? Implementation?


ADOPTABILITY OF NRFS MAP VIEWER
45
Appendix E Postcard Notification
Postcards were sent out via mail to Colorado floodplain managers a week prior to survey distribution via email.
Figure 4. Front of Postcare
Figure 5. Back of Postcard
Need Your Help!
Stephanie DiBetitto 303-656-0136
University of Colorado Denver - School of Public Affairs
Stephanie.dibetitto(u ucdenver.edu
US
Postage
PAID
My nome is Stephonie DiBetiHo ond I'm o graduate student in the School of Public Affairs ot the University of Colorado Denver. Currently, I'm working to complete my capstone project, which seeks to assess the adaptability of a newly released Notionol Risk Index ond its map viewer tool. The intended use of the viewer is to inform emergency monogers, local officials and community plonners on natural hazard risks.
In the next week you will be receiving an email from me that contains o link to o survey about the mop viewer tool. The survey will take about 25 minutes to complete. Your participation is completely voluntary and responses are completely confidential.
John Smith
123 Main Street
Small Town. USA 00000-0000
Survey responses are highly encouragedl Please help a desperate graduate student out by completing the survey and enter to win a $50 Amazon gift card.
IIIiiiIIIiiiIIIiiiIIIiiiIIIiiiIIIimIIIiiiIIIiiiIIImiIIImiIIIiiiIIImiII
Front Side | Back Side


ADOPTABILITY OF NRFS MAP VIEWER
46
Appendix F
Statistical Outputs
Crosstab Outputs and Chi-Square Values
Are you likety to use the tool in future decision making because it was easy to navigate? Turning layers on and off and viewing community specific information was simple to do Does the map viewer provide you with information that you are likety to use in future planning or... How helpful is this information in planning and mitigation related decisions at the local level? How well do you understand the input parameters (Hazards, Social Vulnerability. Built Environment... How well do you understand the information the map viewer provided?
How likely are you to use the map viewer when performing future hazard mitigation planning and im... Chi Square 74.43* 60.73* 109.84* 52.91* 25.30* 25.64*
Degrees of Freedom 36 36 36 24 24 24
p-value 0.00 0.01 0.00 0.00 0.39 0.37
Note: The Chi-Square approximation may be inaccurate - expected frequency less than 5.
Are you likely to use the tool in future decision making because it was easy to navigate? Turning layers on and off and viewing community specific information was simple to do Does the map viewer provide you with information that you are likely to use in future planning or... How helpful is this information in planning and mitigation related decisions at the local level? How well do you understand the input parameters (Hazards, Social Vulnerability, Built Environment... How well do you understand the information the map viewer provided? How likely are you to use the map viewer when performing future hazard mitigation planning and im...
What is your highest level of education completed? Chi Square 34.65* 18.07* 11.76* 7.19* 15.72* 17.72* 19.13*
Degrees of Freedom 24 24 24 16 16 16 24
p-value 0.07 0.80 0.98 0.97 0.47 0.34 0.75
‘Note: The Chi-Square approximation may be inaccurate - expected frequency less than 5.
Are you likely to use the tool in future decision making because it was easy to navigate? Turning layers on and off and viewing community specific information was simple to do Does the map viewer provide you with information that you are likely to use in future planning or... How helpful is this information in planning and mitigation related decisions at the local level? How well do you understand the input parameters (Hazards, Social Vulnerability. Built Environment... How well do you understand the information the map viewer provided? How likely are you to use the map viewer when performing future hazard mitigation planning and im...
What is your gender? Chi Square 1.91* 8.98* 7.15* 2.94* 1.89* 6.44* 4.09*
Degrees of Freedom 12 12 12 8 8 8 12
p-value 1.00 0.70 0.85 0.94 0.98 0.60 0.98
‘Note: The Chi-Square approximation may be inaccurate - expected frequency less than 5.
Are you likely to use the tool in future decision making because it was easy to navigate? Turning layers on and off and viewing community specific information was simple to do Does the map viewer provide you with information that you are likely to use in future planning or... How helpful is this information in planning and mitigation related decisions at the local level? How well do you understand the input parameters (Hazards, Social Vulnerability. Built Environment... How well do you understand the information the map viewer provided? How likely are you to use the map viewer when performing future hazard mitigation planning and im...
What age range best describes you? Chi Square 21.78* 62.46* 30.57* 15.07* 12.46* 20.20* 27.32*
Degrees of Freedom 30 30 30 20 20 20 30
p-value 0.86 0.00 0.44 0.77 0.90 0.45 0.61
‘Note: The Chi-Square approximation may be inaccurate - expected frequency less than 5.
Are you likely to use the tool in future decision making because it was easy to navigate? Turning layers on and off and viewing community specific information was simple to do Does the map viewer provide you with information that you are likely to use in future planning or... How helpful is this information in planning and mitigation related decisions at the local level? How well do you understand the input parameters (Hazards. Social Vulnerability, Built Environment... How well do you understand the information the map viewer provided? How likely are you to use the map viewer when performing future hazard mitigation planning and im...
Which race/ethnicrty best describes you? Chi Square 19.46* 1120* 7.99* 10.24* 10.76* 6.12* 6.60*
Degrees of Freedom 30 30 30 20 20 20 30
p-value 0.93 1.00 1.00 0.96 0.95 1.00 1.00
*Note: The Chi-Square approximation may be inaccurate - expected frequency less than 5.
Spearman’s rho Generated by SPSS
Correlations
fed dis fte time adopt
Spearman's rho fed dis Correlation Coefficient 1.000 .345 .518 -.049
Sig. (2-tailed) .116 .058 .830
N 22 22 14 22


ADOPTABILITY OF NRFS MAP VIEWER
47
fte Correlation Coefficient .345 1.000 -.129 .007
Sig. (2-tailed) .116 .458 .962
N 22 51 35 51
time Correlation Coefficient .518 -.129 1.000 .103
Sig. (2-tailed) .058 .458 .556
N 14 35 35 35
adopt Correlation Coefficient -.049 .007 .103 1.000
Sig. (2-tailed) .830 .962 .556
N 22 51 35 51
Descriptive Statistics Generated by SPSS
Descriptive Statistics
N Minimum Maximum Mean Std. Deviation
fte 51 .0 20.0 3.394 4.2178
fed dis 22 3 13 6.95 2.591
time 35 1.0 60.0 15.243 11.7576
Valid N (listwise) 14


ADOPTABILITY OF NRFS MAP VIEWER
48
Measures of Central Tendency Generated by Qualtrics
What is your highest level of education completed?
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Value Percent Count Percent
1 High School, GED, or below - 4 7.8%
2 Some college (two-year or 8 15.7%
four-year)
3 College degree 18 35.3%
4 Some graduate school â–  3 5.9%
5 Graduate degree or 18 35.3%
higher
- Total 51 100.0%
Minimum Maximum Mean Variance Std. Dev. Respondents
1 5 3.45 1.77 1.33 51


ADOPTABILITY OF NRFS MAP VIEWER
49
What is your gender?
Value Percent Count Percent
1 Male 56.0%
2 Female 44.0%
3 Other 0 0.0%
Total 100.0%
Minimum Maximum Mean Variance Std. Dev. Respondents
1 2 1.44 0.25 0.5 50


ADOPTABILITY OF NRFS MAP VIEWER
50
What age range best describes you?
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Value Percent Count Percent
1 18-24 years 0 0.0%
2 25-34 years 5 9.8%
3 35-44 years 8 15.7%
4 45-54 years 18 35.3%
5 55-64 years 17 33.3%
6 65 years or older â–  3 5.9%
- Total 51 100.0%
Minimum Maximum Mean Variance Std. Dev. Respondents
2 6 4.1 1.13 1.06 51


ADOPTABILITY OF NRFS MAP VIEWER
51
Which race/ethnicity best describes you?
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2 Asian/Pacific Islander 0 0.0%
3 Black or African American 0 0.0%
4 Hispanic American 5 10.0%
5 White/Caucasian 44 88.0%
6 Multiple ethnicities/Other 1 1 2.0%
- Total 50 100.0%
Minimum Maximum Mean Variance Std. Dev. Respondents
4 6 4.92 0.12 0.34 50


ADOPTABILITY OF NRFS MAP VIEWER
52
Are you likely to use the tool in future decision making because it was easy to navigate?
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Value Percent Count Percent
1 Extremely likely 6 11.8%
2 Moderately likely 16 31.4%
3 Slightly likely 16 31.4%
4 Neither likely nor unlikely 5 9.8%
5 Slightly unlikely â–  2 3.9%
6 Moderately unlikely â–  3 5.9%
7 Extremely unlikely â–  3 5.9%
- Total 51 100.0%
Minimum Maximum Mean Variance Std. Dev. Respondents
1 7 3.04 2.56 1.6 51


ADOPTABILITY OF NRFS MAP VIEWER
53
Turning layers on and off and viewing community specific information was simple to do
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1 7 2.47 1.85 1.36 51


ADOPTABILITY OF NRFS MAP VIEWER
54
Does the map viewer provide you with information that you are likely to use in future planning or mitigation efforts?
Value Percent Count Percent
1 Extremely likely 1 1 2.0%
2 Moderately likely 18 35.3%
3 Slightly likely 15 29.4%
4 Neither likely nor unlikely â–  2 3.9%
5 Slightly unlikely â–  2 3.9%
6 Moderately unlikely 6 11.8%
7 Extremely unlikely â–  7 13.7%
- Total 51 100.0%
Minimum Maximum Mean Variance Std. Dev. Respondents
1 7 3.63 3.56 1.89 51


ADOPTABILITY OF NRFS MAP VIEWER
55
How helpful is this information in planning and mitigation related decisions at the local level?
20
18
16
14
12
10
8
Value Percent Count Percent
1 A great deal â–  2 4.0%
2 A lot 11 22.0%
3 A moderate amount 15 30.0%
4 A little 15 30.0%
5 None at all 7 14.0%
. Total 50 100.0%
Minimum Maximum Mean Variance Std. Dev. Respondents
1 5 3.28 1.19 1.09 50


ADOPTABILITY OF NRFS MAP VIEWER
56
How well do you understand the Input parameters (Hazards, Social Vulnerability, Built Environment, and Community Resilience)?
50
45
40
35
30
25
20
15
10
5
0


Value Percent Count Percent
1 A great deal â–  3 5.9%
2 A lot 6 11.8%
3 A moderate amount 21 41.2%
4 A little 16 31.4%
5 None at all â– â–  5 9.8%
. Total 51 100.0%
Minimum Maximum Mean Variance Std. Dev. Respondents
1 5 3.27 1 1 51


ADOPTABILITY OF NRFS MAP VIEWER
57
How well do you understand the information the map viewer provided?
50
45
40
35
30
25
20
Value Percent Count Percent
1 A great deal â–  3 5.9%
2 A lot 9 17.6%
3 27 52.9%
4 A little 11 21.6%
5 None at all | 1 2.0%
. 51 100.0%
Minimum Maximum Mean Variance Std. Dev. Respondents
1 5 2.96 0.72 0.85 51


ADOPTABILITY OF NRFS MAP VIEWER
58
How likely are you to use the map viewer when performing future hazard mitigation planning and implementation activities?
20
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2 Moderately likely
3 Slightly likely
4 Neither likely nor unlikely
5 Slightly unlikely
6 Moderately unlikely
7 Extremely unlikely
- Total
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2 3.9%
17 33.3%
13 25.5%
5 9.8%
3 5.9%
7 13.7%
4 7.8%
51 100.0%
Minimum Maximum Mean Variance Std. Dev. Respondents
1 7 3.53 3.09 1.76 51


Running head: ADOPTABILITY OF NRFS MAP VIEWER
59
Appendix G
Research Questions, Hypotheses, Results, and Recommendations Table
Table 2. Research Questions, Hypotheses, Results, and Recommendations
Hypothesis Corresponding Survey or Interview Question Results Recommendations
Research Question 1: What is the likeliness of emergency managers in Colorado to indicate they are interested in adopting the National Risk Index’s interactive online viewer?
Hypothesis 1-1: If emergency and floodplain managers find the online map viewer useful, the likelihood of adoption in decision-making will increase. Survey Question 8: Does the map viewer provide you with information that you are likely to use in future planning or mitigation efforts? Survey Question 9: How helpful is this information in planning and mitigation related decisions at the local level? Survey Question 12: How likely are you to use the map viewer when performing future hazard mitigation planning and implementation activities? Interview Questions 13: How useful is the information provided by the map viewer tool? Do you believe it can help to guide hazard related decisions? If so, in what ways? Interview Question 15: In what ways can you use the tool in future decision-making? • A correlational analysis was performed using a crosstab table to measure the Chi-Square and p-Value of adoptability (survey question 12) compared to usefulness in survey question 8 (Chi-Square=109.84; p=0.00). The p-Value is statistically significant at a 0.05 level. • A correlational analysis was performed using crosstab tables to measure the Chi-Square and p-Value of adoptability (survey question 12) compared to usefulness in question 9 (Chi-Square=52.91; p=0.00). The p-Value is statistically significant at a 0.05 level. • Make Geographic Information System (GIS) layers downloadable from the map viewer • Allow for local GIS files to be uploaded to the map viewer • Increase community specific datasets • Include more floodplain specific hazard layers
Hypothesis 1-2: The simplicity of the online viewer will increase an emergency or floodplain manager’s potential to adopt it in decisionmaking. Survey Question 6: Are you likely to use the tool in future decision-making because it was easy to navigate? Survey Question 7: Turning layers on and off and viewing community specific information was simple to do • A correlational analysis was performed using a crosstab table to measure the Chi-Square and p-Value of adoptability (survey question 12) compared to simplicity in survey question 6 (Chi-Square=74.34; p=0.00). The No recommendation.


ADOPTABILITY OF NRFS MAP VIEWER
60
Survey Question 12: How likely are you to use the map viewer when performing future hazard mitigation planning and implementation activities? Interview Question 12: Was the NRI map viewer easy to use? Was it easy to navigate through and generate information? If yes, why? If no, why? Interview Question 15: In what ways can you use the tool in future decision-making? p-Value is statistically significant at a 0.05 level. • A correlational analysis was performed using a crosstab table to measure the Chi-Square and p-Value of adoptability (survey question 12) compared to simplicity in survey question 7 (Chi-Square=60.73; p=0.01). The p-Value is statistically significant at a 0.05 level.
Hypothesis 1-3: Knowledge of the information being provided by the tool will increase the likelihood of emergency and floodplain managers to adopt its use in decisionmaking. Survey Question 10: How well do you understand the input parameters (Hazards, Social Vulnerability, Built Environment, and Community Resilience)? Survey Question 11: How well do you understand the information the map viewer provided? Interview Question 15: In what ways can you use the tool in future decision-making? Survey Question 12: How likely are you to use the map viewer when performing future hazard mitigation planning and implementation activities? Interview Question 14: Do you understand the input paramaters the tool uses (Hazards, Social Vulnerability, Built Environment, and Community Resilience)? Do you understand the information provided by turning on and off the map layers? Interview Question 15: In what ways can you use the tool in future decision-making? • A correlational analysis was performed using a crosstab table to measure the Chi-Square and p-Value of adoptability (survey question 12) compared to knowledge in survey question 10 (Chi-Square=25.30; p=0.39). The relationship between likelihood of adoptability and knowledge is not found to be statistically significant. • A correlational analysis was performed using a crosstab table to measure the Chi-Square and p-Value of adoptability (survey question 12) compared to knowledge in survey question 11 (Chi-Square=26.34; p=0.37). The relationship between likelihood of adoptability and knowledge is not found to be statistically significant. • Create a data dictionary and metadata for the map layers on the viewer. • Facilitate training opportunities such as a webinar. • Make webinar slides available for download on FEMA’s website. • Provide a tutorial document available for download on FEMA’s website. • Use more intuitive symbology • Use symbology that allows for more layers to be visible at once. • Involve Stakeholders throughout the decision-support tool development process.


ADOPTABILITY OF NRFS MAP VIEWER
61
Research Question 2: Are emergency and floodplain managers in counties with disaster histories more likely to indicate they are interested in adopting the tool?
Hypothesis 2-1: Counties with greater disaster histories (documented as a federally declared disaster) will be more likely to show interest in adopting the tool. Secondary Data: FEMA Database (Summary of Disaster Declarations and Grants)'. https://www.fema.gov/media- library/assets/documents/106308 Survey Question 12: How likely are you to use the map viewer when performing future hazard mitigation planning and implementation activities? • A Spearman’s rho correlational test was calculated to measure the strength of the relationship between counties with disaster histories (federally declared disasters from FEMA Database) and potential tool adoption (survey question 12). • A weak negative correlation was found (rho (20) = -0.049) for the relationship between map viewer adoption potential and county disaster history. • FEMA should work with the state to track localized disaster data.
Research Question 3: Are emergency and floodplain managers in communities with greater staff capacity more li adopting the map viewer? cely to indicate interest in
Hypothesis 3-1: Communities with greater staff capacity will be more likely to adopt the tool. Survey Question 5: How many full-time employees work on emergency or floodplain management related issues within your community? Survey Question 12: How likely are you to use the map viewer when performing future hazard mitigation planning and implementation activities? • A Spearman’s rho correlational test was calculated to measure the strength of the relationship between community staff capacity and potential tool adoption (survey question 12). • A weak positive correlation was found (rho (49) = 0.007) for the relationship between map viewer adoption potential (question 2) and staff capacity (question 5). • Include a question in the survey to track if a respondent is an emergency or a floodplain manager, or if they fill both emergency and floodplain manager roles for their community.


Running head: ADOPTABILITY OF NRFS MAP VIEWER
62
Appendix H
High-Priority Recommendations Infographic
Programmatic
Recommendations
FEMA should continue to promote resiliency and create guidance to help communities measure and achieve it.
Resiliency can help to decrease social vulnerability and reduce damages caused by natural disasters, however iterature suggests how it is operationalized is not well understood.
Continue to engage stakeholders in FEMA programs and involve them in the development of decision-support tools.
Engaging stakeholders allows stakeholders to shape the tools available to them and can further increase a tool's, understandability, usability, and simplicity.
Tool Enhancement Recommendations
Increase user knowledge about the information the interactive map viewer provides. Ways to accomplish this include: Creating a data dictionary and metadata for the map layers on the viewer.
Facilitating training opportunities such as a webinar.
Developing a tutorial document available for download on FEMA's website.
It can be inferred from the study that if the information provided by the viewer was better understood, a greater number of respondents would be likely to use the map viewer in future-decision-making. Increase community specific datasets.
Interviews revealed emergency managers felt the data provided is too general. Increasing community specific data may enhance the tool's usability.
High-Priority Recommendations:
Adoptability of FEMA's NRI Interactive Map Viewer

University of Colorado Denver
Research
Recommendations
FEMA
Increase the number of interviews conducted and the sample population to include hazard mitigation planning contractors, community planners, mitigation officers and other elected officials involved with planning for hazards at the local level.
Increasing the number of interviews and the sample population can help to validate responses, generate more robust data, and retest variables.
Add a survey question that tracks who is responsible for hazard mitigation planning activities within a community.
This will help increase FEMA's understanding of who in communities is most likely to use decision-support tools.
FEMA should consider collecting disaster data at the local level (i.e., city or town) and work with the state to track these data.
The decision to declare a Federal disaster occurs at the County level.
FEMA tracks this data, though it would be beneficial if they worked with the State to collect localized disaster information in a unified format (e.g., standardized data base). This would help to capture mid- or small-sized disasters and better understand if they have an impact on the decision to adopt decision-support tools at the local level.
Stephanie DiBetitto


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Digital Library Program is a nonprofit center responsible for the collection and preservation of digital resources for education.The capstone project, protected by your copyright, and/or created under the supervision of the client has been identified as important to the educational mission of the University of Colorado Denver and Auraria Library.The University of Colorado Denver and Auraria Library respectfully requests non-exclusive rights to digitize the capstone project for Internet distribution in image and text formats for an unlimited term. Digitized versions will be made available via the Internet, for on- and off-line educational use, with a statement identifying your rights as copyright holder and the terms of the grant of permissions.Please review, sign and return the follow Grant of Permissions. Please do not hesitate to call me or email your questions.Sincerely,Matthew C. MarinerAuraria LibraryDigital Collections ManagerMatthew.mariner@ucdenver.edu303.556.5817
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Full Text

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Running head: ADOPTABILITY OF NR 1 Adoptability of the Fede ral Emergency Management Agency Online Map Viewer Stephanie DiBetitto University of Colorado Denver School of Public Affairs

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2 Capstone Project Disclosure This client based project was completed on behalf of the Federal Emergency Management Agency and supervised by PUAD 5361 Capstone course instructor Wendy L. Bolyard, PhD and second faculty reader De borah Thomas, PhD . This project does not necessarily reflect the views of the School of Public Affairs or the faculty readers. Raw data were not inc luded in this document, rather relevant materials were provided directly to the client. Permissions to include this project in the Auraria Library Digital Repository are found in the final appendix . Questions about this capstone project should be directed to the student author.

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3 Table of Contents Executive Summary ................................ ................................ ................................ ....................... 4 Literature Review and Statement of Purpose ................................ ................................ ............. 7 Measuring Risk ................................ ................................ ................................ ............................ 7 Social Vulnerability ................................ ................................ ................................ ...................... 8 Resiliency within the Context of Vulnerability ................................ ................................ ............ 9 Resiliency within the Context of Risk Reduction and Hazard Mitigation ................................ . 12 Decision Support Tools ................................ ................................ ................................ .............. 12 Federal Emergency Management Agency (FEMA) and the National Risk Index ..................... 1 3 The National Risk Index ................................ ................................ ................................ ............ 14 Literature Review Findings ................................ ................................ ................................ ........ 14 Methodology ................................ ................................ ................................ ................................ . 1 5 Research Questions and Hypotheses ................................ ................................ .......................... 1 5 Measurement and Data Collection ................................ ................................ ............................. 1 7 Sampling Pla n ................................ ................................ ................................ ............................ 1 7 Validity and Reliability ................................ ................................ ................................ .............. 1 9 Data Analysis ................................ ................................ ................................ ............................. 20 Results ................................ ................................ ................................ ................................ ........... 20 Survey Findings ................................ ................................ ................................ .......................... 21 Demographic information. ................................ ................................ ................................ ..... 21 Testing hypotheses 1 1, 1 2, and 1 3.. ................................ ................................ .................... 21 Testing hypotheses 2 1 and 3 1. ................................ ................................ ............................. 2 2 Interview Findings ................................ ................................ ................................ ...................... 22 Community information and emergency management. ................................ ......................... 2 2 Map viewer findings. ................................ ................................ ................................ .............. 23 Discussion ................................ ................................ ................................ ................................ ..... 2 4 Limitations. ................................ ................................ ................................ ............................. 25 Recommendations . ................................ ................................ ................................ ................. 2 5 Research Next Steps . ................................ ................................ ................................ .............. 2 7 Conclusion ................................ ................................ ................................ ................................ .... 2 8 References ................................ ................................ ................................ ................................ ..... 29

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4 Appendix A: Models Used to Measure Social Vulnerability ................................ ................... 33 Pressure and Release Model ................................ ................................ ................................ ....... 33 Social Vulnerability Index ................................ ................................ ................................ ......... 3 4 The Hazards of Place Model of Vulnerability ................................ ................................ ........... 3 5 The Disaster Resilience of Place Model ................................ ................................ .................... 3 6 Appendix B: Measurement Table ................................ ................................ ............................... 3 8 Appendix C: Survey Questions ................................ ................................ ................................ ... 3 9 Appendix D: Interview Protocol ................................ ................................ ................................ . 43 Appendix E: Postcard Notification ................................ ................................ ............................ 4 5 Appendix F: Statistical Outputs ................................ ................................ ................................ . 4 6 Crosstab Outputs and Chi Square Values ................................ ................................ .................. 4 6 rho Generated by SPSS ................................ ................................ .......................... 4 6 Descriptive Statistics Generated by SPSS ................................ ................................ .................. 4 7 Measures of Central Tendency Generated by Qualtrics ................................ ............................. 4 8 Appendix G: Research Questions, Hypotheses, Results and Recommendations ................... 5 9 Appendix H : High Priority Recommendations Infographics ................................ ................. 62

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5 Executive Summary The Federal Emergency Management Agency has created a National Risk Index (NRI). The NRI is unique, as it incorporates resilience, which is often excluded from historic hazard risk frameworks. One component of the NRI is a public facing interactive online map viewer. The map viewer is an example of a decision support tool; decision support tools include map viewers, software and online platforms. These tools help to info rm stakeholders on hazard probability and associated impacts. The intended use of the interface is to inform emergency managers, local officials and community planners on their natural hazard risk. Though the interactive online viewer is primarily for use by public entities, it has not been tested at the community level. To understand the role the interactive online viewer can play in local community decision making adoptability by emergency and fl oodplain managers throughout Colorado. Survey s and interviews were used to collect data. Responses reveal that there is a statistically significant relationship between map viewer adoption potential and simplicity, as well as between map viewer adoption potential and usefulness. Therefore, emergency and floodplain managers that find the interactive map viewer simple to use and the information provided useful will be more likely to indicate interest in adopting it to aid future decisions (i.e. , mitigation activities) . This research provides insight into the ways the tool can be modified to better serve th e needs of practitioners. This is important information to gather because the intent is to offer users a tool that will enhance resilience in decision support.

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Running head: ADOPTABILITY OF NR 5 Adoptability of the Online Map Viewer There are many ways to measure risks associated with natural hazards. Existing models and frameworks associated with natural hazards vary in methodologies and theoretical frameworks. Traditional risk models focus on physical rather than social vulnerabilities associated with natural risks. However, as models and research evolve, researchers shi ft their focus towards measuring social vulnerability to understand the root causes of post disaster casualties and pre disaster risk reduction (Klein, Nicholls, & Thomalla, 2003; Strategy, 1994; Thomas, Phillips, Lovekamp, & Fothergill, 2013). Recent rese arch suggests that studying physical hazards without incorporating social vulnerability does not provide a full understanding of risk. Thomas, Phillips, Lovekamp, and Fothergi l l, (2013) find that Researchers have studied and written about human vulnerabil ity to disasters for decades. Yet, far too frequently, effort s to reduce vulnerability occur only after a major event has claimed lives and destroyed individual and community assets, including homes, businesses, and savings. Measures to reduce vulnerability tend to rely on established practices, analyzing current policies and revising already existing plans, but recent research on vulnerability has much to offer managers and practitioners in disaster risk reduction . (p. 2) Measuring and underst anding social vulnerability is an important component because it provides insight into the ways risks can be reduced and life and property at the individual and community level can be protected (Thomas, Phillips, Lovekamp, & Fothergill, 2013). This is beca use natural hazards are unavoidable, though disasters and damages can be prevented. As the body of knowledge surrounding social vulnerability grows, i t becomes clear that human actions risk; therefore, models continue to develop and incorporate more progressive metrics. As a resu lt, models are now

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6 starting to e , providing a more informed understanding of ri sk. Natural Hazards Risk Assessment Program with regional office support has created a National Risk Index (NRI) , beta published Octobe r 2017 http://tin y.cc/NRI_beta . The NRI is unique, as it incorporates resilience, which is often excluded from historic hazard risk frameworks and is an emerging concept within social vulnerability research . One component of the NRI is a public facing interactive online viewer. The intended use of the interface is to inform emergency managers, local officials , and community planners on their natural hazard risk, generate baseline and custom risk reports , and display visual products t o inform hazard mitigation plans (state and local), promote risk awareness, engage community members, provide a baseline risk assessment, guide hazard mitigation grant funding prioritization, and reduce future risk, among other uses . The NRI and its output s work to strengthen FEMA initiatives (FEMA, PowerPoint slides, December 16, 2016). Though the interactive online viewer is primarily for use by local governments , it has not been tested at the community practitioner level. To understand the role the inter active online viewer can play in local community decision making , this study perform s a mixed methods and floodplain managers throughout Colorado. The research question asks : W hat is the likelihood of emerge ncy and floodplain managers in Colorado will tool can help emergency and floodplain managers inform risk related decisions will allow FEMA to determine if the online viewer is successfully designed and includes relevant and understandable information . This research provide s insight into the ways the tool can be

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7 modified to better serve the needs of practitioners. This is important information to gather for FEMA because the ir intent is to give users a tool that can inform decision making and risk reducing action, which can further enhance resilience by decreasing vulnerability. Users include practitioners making on the ground emergency management decisions (i.e. , emergency man agers, mitigation and operational planners, floodplain managers). This report includes the following sections: literature review and statement of purpose; methodology; results; discussion and recommendations; conclusion; references; and appendices. Liter ature Review and Statement of Purpose This section outline s the major themes present in the literature and provide s readers with a more in depth understanding of the importance of measuring social vulnerability and resilience within the context of risks caused by natural hazards. Furthermore, it explains the use of decision support tools to measure risks, which can help to increase mitigation and resiliency. Measuring Risk Measuring risk in the context of natural hazards varies by frameworks used and how researchers define terms. To understand the fundamentals of how risks are measured, it is important to understand the two paradigms of disaster. Thomas, Phillips, Lovekamp, and Fothergill (2013) explain the two paradigms in contrast to one another, as the first focuses on ph ysical vulnerabilities and the second focuses on social vulnerabilities. The measure of physical risks is the dominant paradigm, which portrays nature as the primary cause of disasters. Solutions within the dominant paradigm avoid disasters through hierarc hies that apply technology, science, and engineering. This approach promotes conquering rather than co existing with natural systems (Hewitt 1983; Strategy, 1994; Thomas, Phillips, Lovekamp, & Fothergill, 2013). Measuring risk through the lens of the domin ant paradigm may inform the existent

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8 literature as to why risks occur but does not explain why disasters result . Recent research finds that risks are natural, though disasters are a result of human action and exposure to risk (Blankoff, 2006; Mileti, 1999) . For these reasons, the dominant paradigm has many shortcomings, including its lack of a full consideration of all forms of disaster, its command and control approach to management, and its inability to acknowledge social systems (Thomas, Phillips, Loveka mp, & Fothergill, 2013). understanding of the causes of, and solutions to, disasters and fails, in particular, to recognize the true nature of vulnerability and the capacity that related populations bring to bear on their own risk as well as that of the large society (Thomas, Phillips, Lovekamp, & Fothergill, 2013, p.10 ) . A b etter understanding of social vulnerability within the context of risk provides greater insight into the causes of disasters (Blaiki e, Cannon, Davis, & Wisner, 1994). Social Vulnerability The vulnerability paradigm incorporates social characteristics, which take into consideration why certain groups of people are more susceptible to risk. This paradigm incorporates political and socioeconomic influences into measurements. Metrics used to measure social vulnerability include class, gender, age, ethnicity, and disabilities, to name a few. Solutions to reduce social vulnerability include grassroots initiatives, prioritized by the com munity, that work with nature (Blankoff, 2006; Thomas, Phillips, Lovekamp, & Fothergill, 2013). It is critical that social vulnerabilities are incorporated into risk management and reduction, as structural fixes may only stand until the next event and cont inue to keep those most susceptible to risk vulnerable. Furthermore, rebuilding in ways that reduce social vulnerability and mitigate future disasters promotes community resilience (Mileti, 1999).

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9 Social vulnerability is measured to gain insight into a co mmunity's potential for loss post disaster and as a way to guide mitigation strategies (Cutter, 19 96 ). Adger (2006) finds studying powerlessness, and marginali ty of both physical and social systems, and for guiding normative analysis of actions to enhance well being through reduction of risk (p. 268). For example, when reflecting on Hurricane Katrina in New Orleans, Louisiana in 2005, 70 % of those who perished were elderly (65 years of age or older) and African American (Thomas, Phillips, Lovekamp, & Fothergill, 2013). Measuring vulnerability allows researchers and practitioners to analyze and predict what causes loss and makes individuals and communities suscep tible to risk. Though there is no standard set of metrics used to measure vulnerability, Cutter, Boruff, and Shirley (2003) explain: There is a general consensus within the social science community about some of the major factors that influence social vuln erability. These include: lack of access to resources (including information, knowledge, and technology); limited access to political power and representation; social capital, including social networks and connections; beliefs and customs; building stock a nd age; frail and physically limited individuals; and type and density of infrastructure and lifelines . (p. 245) Vulnerability theory focuses on three themes , which include the measure of vulnerability from exposure, societal resilience or resistance, or place (Burton, Kates, & White, 1993; Blaikie, Cannon, Davis, & Wisner, 1994; Cutter, 1996; Cutter, Boruff, & Shirley, 2003; Hewitt 1997; Kasperson, Kasperson, & Turner, 1995). Within the existent literature, there are varying definitions of the term vulne rability, which complicates how it is operationalize d . However, several models are commonly used to measure social vulnerability (See Appendix A) . Resiliency within the Context of Vulnerability As research in the social vulnerability field progresses , there is greater attention on the multiple stressors and multiple pathways contributing to vulnerability (Adger, 2006). New

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10 research focuses on the integration of social social ecological system reflects the idea that human action and social structures are integral to natur e and hence any distinction between social and natural systems is arbitrary (Adger, 2006, p. 268). Resiliency fits into this body of knowledge, as it helps to explain the extent of dis turbance a system can absorb before the state of that system is significantly altered. It also explains the capacity a social ecological system has to self organize or adapt to an altered condition (Adger, 2006). Additionally, it can be posited from Klein, Nicholls, and (2003) research that there is an inverse relationship between resilience and vulnerability: as resilience increases, vulnerability decreases. Resilience came to the forefront as a way to reduce risk as unsustainable technological solutions were not reducing dollar losses resulting from natural hazards (Mileti, 1999). Moving away from structural or technological solutions towards resiliency was spurred by an assessment completed by Mileti (1999). This assessment was the second of t wo studies completed 25 years apart and commissioned by the US government as a way to determine why losses from natural disasters continued to increase. Research f in d s that disaster resistant communities avoid greater ed the interactive nature of natural and human systems, the buil t environment, and the role of human agency in producing hazards and disasters (acts of people, not acts of God) (Cutter et al . , 2008). The US government through FEMA was in support of developing disaster resilient communities. In 1994 , the National Mitigation Strategy was created as a way to decrease losses by prioritizing vulnerabilities and risk reduction strategies through stakeholder engagement. Unfortunately, with a change in admin istration, the results of this effort were not quantified and the program was terminated ( Cutter et al . , 2008 ). However, similar efforts also were building globally, as 1990 began the International Decade for

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11 Natural Disaster Reduction. World leaders met i n 1994 for a conference in Yokohama, Japan, which was the first international event recognizing social vulnerability in the context of risk reduction. More recently, the Hyogo World Conference on Disaster Reduction addressed igation and ways to build more resilient communities ( Cutter et al . , 2008 ). Resilience is an important concept as it is linked to risk reduction and sustainability. The concept of resiliency originated in the ecological field and is now being applied in t he context of social vulnerability. However, there is not clear consensus on the definition of the term or how it can be operationally applied (Klein, Nicholls, & Thomalla, 2003). The term return to a state or multiple ability to advance through adaptation to a disturbance (Cutter et al., 2008; Holling, 1973; Klein, Nicholls, & Thomalla, 2003) . When applied specifically in the social (human) context the term describes how communities , or an individual , cope or adapts to stress (Blakie, Cannon, Davis, & Wisner, 2014; Pelling, 2003). Social resilience is measured through parameters such as econom ic structure and property rights, and is often related to individual or community stability (Klein, Nicholls, & Thomalla, 2003). When analyzing resilience, Cutter et al. (2008) acknowledge that resilience can be an outcome (i.e. , ability to cope) or a proc ess (i.e. , continual learning to increase capacity) depending on how it is defined and applied. Their research finds that there are two qualities defining resilience, the first is inherent, which measures periods without disaster, and the second is adaptiv e, which measures flexibility in response during a crisis. These measures are used to perform assessments on economic and social systems, infrastructure, and organizations, to name a few (Cutter et al., 2008). Though resilience is a prominent focus of

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12 vuln erability assessment and risk reduction, research is lacking on how resilience can be achieved (Cutter et al., 2008; Klein, Nicholls, & Thomalla, 2003; Tobin, 1999). Resiliency within the Context of Risk Reduction and Hazard Mitigation US agencies are incorporating resiliency principles into risk reduction (Cutter et al., 2008). This has proven beneficial, as findings show that more p roactive communities take steps to increase resiliency before disasters strike through planning and action, and are more adaptive (Cutter et al., 2008; Dovers & Handmer, 1992). When discussing the ways in which communities or individuals can adapt, the hazard literature focuses on preparedness and mitigation. Actions being taken by practitioner s on behalf of communities alig n with the transition of research from the dominant to the social vulnerability paradigm, as both are promoting action before a disaster strikes, rather than focusing on solutions after the event. Though mitigation planning and action resilience, there is discussion surrounding the challenges and risks associated with planning for unknown future conditions (i.e. , climate change) and the promotion of an ambiguous (not uniformly applied) concept of resiliency ( Bruneau et al., 2003; Burby et al., 2000; Klein, Nicholls, & Thomalla, 2003). Decision Support Tools One way communities can plan for hazards is through the use of decision support tools. These tools help to communicate risks , which are an integral part of taking action as they he lp shape and influence risk perception (Komedantova et al., 2014) of disaster risk reduction options and st rategies demand not only compre hensive risk assessment schemes, but also an appropriate mechanism to communicate and transfer knowledge on risk and its underlying drivers to the various stakeholders involved in the decision making process ( Komendantova et al., 2014, p. 51). NRI map viewer is an example of a decision -

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13 support tool ; decision s upport tools include map viewers, software and online platforms . These tools help to inform stakeholders on hazard probability and associated impacts (Komendatova et al., 2014). Komendatova et al. (2014) find that stakeholders are interested in mult i risk assessment but are unab le to gain a full understanding of hazards because of the complex processes involved with existing tools. They find that user understanding of dec ision support tools are limited and that stakeholder feedback is valuable in designing tools that are usable , which further help s to make implementation achievable. However, stakeholder participation and integration of stakeholder feedback into multi risk assessment tool development is absent from the existing literature. These finding s help to inform the simplic ity, usefulness, and knowledge variables being tested in this study (see Methodology section). Federal Emergency Management Agency (FEMA) and the National Risk Index FEMA was established in 1979 under Executive Order 12127, and i n 2003 became part of the Department of Homeland Security. It is a US Federal G overnment entity which operates under a hierarchical structure. The agency has ten regional offices throughout the US . Region VIII is located in Denver, Colorado and serves Colorado, Montana, North D akota, South Dakota, Utah, and Wyoming. FEMA has been active in implementing resilient actions through mitigation and preparedness initiatives since 1994 when the agency created its National Mitigation Strategy (Cutter et al., 2008). This initiative worked to reduce disaster losses through public private partnerships and incentivized stakeholder engagement. Continuing forward, FEMA embed s resiliency concepts within other FEMA initiatives including the Whole Community Framework, created in 2011 and the Natio nal Mitigation Framework, published in 2013 and revised in 2016. The Whole Community Framework focuses on preparedness actions and calls upon the involvement of all stakeholders , in an effort to promote national safety and greater resiliency

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14 (FEMA, 2011). The National Mitigation Framework seeks to reduce disaster impacts through prevention, further decreasing response needs and easing recovery (FEMA, 2016b). Ultimately, actions outlined in the framework seek to establish better prepared and resilient commun ities. The National Risk Index FEMA continues to strive for communities that are more resilient and in October 2017, the Natural Hazards Risk Assessment Program with regional office support developed a National Risk Index (NRI), beta published October 201 7 http://tiny.cc/NRI_beta . The NRI incorporates a national set of 18 natural hazard specific indices, applied independently or combined as a composite multi hazard risk index (FEMA, PowerPoint slides, December 16, 2016). The NRI conceptualizes natural hazard risk as the interaction between hazard likelihood, social vulnerability, built environment, and resilience and hazards and consequences (FEMA, PowerPoint slides, December 16, 2016, slide 13). Th e public facing map viewer tool allows users to view a baseline risk index or calculate a custom risk score using components of the baseline for a defined geographic region, as well as the ability to generate maps and reports, and export geospatial data for all components. The NRI further promotes FEMA initiatives including preparedness, mitigation , and resilience at the local level. Literature Review Findings Overall , the literature suggests that communities with a better understanding of their hazard risk, social vulnerability , and resilience are able to prepare for and prioritize the mitigation of risks . This al lows communities to return to their former state and reduce disasters and losses from happening , especially to those most susceptible to hazards at the individual and community level . One way to inform decision makers about risks in their communities is through decision support tools. However, gaps in the ex isting literature suggest that stakeholder feedback

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15 on existing decision support tools is lacking and that tools need to be usable and simple to understand to better disseminate information and incre ase successful implementation of mitigation actions. The research being performed through this capstone project seeks to better understand how the NRI can help emergency and floodplain managers inform risk related decisions. Understanding this will allow FEMA to determine if the online viewer is likely to be ado pted at the local level . This research also provides insight into the ways the tool can be modified to better serve the needs of practitioners. Methodology This study answer s the research questions listed below. Hypotheses and variables for each research question are outlined as follows. Research Question s and Hypotheses This study answer s the research question: what is the likeliness of emergency and floodplain managers in Colorado will ? H ypotheses and variables measured are listed below and a detailed m easurement table outlining the hypotheses, variables and measures can be found in Table 1 of Appendix B. 1. What is the likeli hood of emergency and floodplain managers in Colorado indicat ing they are interest ed in adopt ing Hypothesis 1 1: If emergency and floodplain managers find the online map viewer useful , the likelihood of adoption in decision making in turn increases. Null Hypothesis 1 1: Usefulness does not impact the likelihood of tool adoption. Variables 1 1: Usefulness (ind ependent variable) and adoptability (dependent variable).

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16 Hypothesis 1 2 : The simplicity of the online viewer will increase an emergency or floodplain potential to adopt it in decision making . Null Hypothesis 1 2 : Simplicity does not affect tool adoptability . Variables 1 2 : Simplicity (ind ependent variable) and adoptability (dependent variable). Hypothesis 1 3 : Knowledge of the information being provi ded by the tool will increase the likeliness of emergency and floodplain managers to adopt its use in decision making . Null Hypothesis 1 3 : Knowledge of the information being provided by the tool does not affect adoptability. Variables 1 3 : Knowledge of t he information being provided by the tool (independent variable) and adoptability (dependent variable). 2. Are emergency and floodplain managers in co un ties with disaster histories more likely to indicate they are interested in adopt ing the tool? Hypothesis 2 1: Co un ties with greater disaster histories (documented as a federally declared disaster) will be more likely to show interest in adopt ing the tool. Null Hypothesis 2 1: Disaster history does not affect tool adoption. Variables 2 1 : Disaster history (independent variable) and tool adoption (dependent variable). 3. Are emergency and floodplain managers in communities with greater staff capacity more likely to indicate interest in adopting the map viewer? Hypothesis 3 1: Communities w ith greater staff capacity will be more likely to adopt the tool.

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17 Null Hypothesis 3 1: Staff capacity does not affect tool adoption. Variables 3 1 : Staff capacity (independent variable) and tool adoption (dependent variable). Measurement and Data Collecti on The study takes a mixed methods approach to collect and analyze primary data. Secondary data was also collected to provide information on f ederally declared disasters for counties within Colorado . This information was cross referenced from the following source: https://www.fema.gov/media library/assets/documents/106308 . The approach uses a 1 5 question survey (see Appendix C ) , completed by emergency and floodplain managers throughout Colorado, in addition to four interviews with emergency managers representative of different areas of the state. The interview protocol can be found in Appendix D . Sampling Plan The study collect s information from surveys and interviews. The unit of analysis is emergency and floodplain managers . Emergency managers are responsible for identifying and analyzing potential impacts of hazards; conducting emergency management activities; performing risk and hazard assessments; and coordinating planning processes through collaboration with other government agencies and organizations (FEMA, 2017a). Flood plain managers are responsible for administering local floodplain ordinances; process ing floodplain development permits; keep ing records of floodplain development; correct ing violations and taking enforcement action; and coordinat ing with other programs and agencies, all in an effort to reduce risks from floods (FEMA, 2017b). Emergency and fl oodplain managers were selected for this study because they are involved in local mitigation and preparedness and help guide

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18 decision making specific to natural hazard management at the local level . Mitigation planners were not engaged in this study becaus e they are not present i n every community. The population from which the sample is drawn is communities throughout Colorado. The sampling technique is purposive and solicits responses from 93 emergency managers , representing 88 communities , and 24 4 floodp lain managers , representing 241 communities . Only emergency and floodplain managers with email information were contacted. There were 9 emergency and 2 floodplain managers not included because their email information was not available . Prior to emailing th e surveys , 64 emergency managers were notified by phone call or voice message and 222 floodplain managers received a postcard in the mail notifying them about the upcoming survey. Only emergency managers with phone numbers listed or voice mailboxes set up received phone call s / voice message s , and only floodplain managers with listed physical addresses receive d a postcard (See Appendix E for postcard announcement) . Surveys were sent to emergency and floodplain managers via email and interviews w e re conducted via telephone. The number of interviews and communities selected for interview were decided through conversation s with the client ; communities were selected because they represent diffe rent areas of the state (i.e., hazards , population size, governance, e tc.). Emergency and floodplain managers were assured that their responses are confidential and were provided with a written letter that included assurances, more information on the study, and contact information for follow up questions. The survey include d instructions on how to use the NRI viewer , which prompt ed users to explore certain aspects of the tool, and answer questions test ing the three construct s: simplicity; usefulness; and knowledge. For the purpose of this study , constructs (latent) are conceptualized as follows : simplicity is the ease of accessing the tool, navigating through it and generating results; u sefulness is

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19 information that is helpful and able to be used in planning, mitiga tion, and overall risk decision making ; and k nowledge i s how well the user understands the information being provided . Survey questions 6 and 7 test for simplicity, 8 and 9 test for usefulness, 10 and 11 test for knowledge, and 12 test s for adoptability. These m easures are operationalized using the Likert Scal e, which Interviews were used to collect more robust data to inform the study and further measure variables. Validity and Reliability The proposed measures should be considered internally valid or accurate because they seek to understand the relationship between usefulness, simplicity, knowledge, staff capacity, and disaster history on adopting the decision making . The variables reflect the meaning of the concept under consideration and are relevant, as they intend to predict future behavior. To maximize validity, researchers and practitioners that are intimately familiar with the research topic were solicited to con duct a review to assess the research methods and processes used to confirm their validity . The research design can be considered reliable or replicable because it is being conducted systematically by distributing identical surveys and disclosing the same information to all parties. The research attempts to maximize reliability by clearly defining constructs in a way that is both exhaustive and mutually exclusive . However, it is expected that there is bias within the sample because though surveys attempt to identify and include a complete population, not everyone solicited participate d (non response bias). External validity is maximized by conducting additional interviews in particular regions of the state. R esults are not intended to be generalizable as the y are being generated on behalf of FEMA.

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20 Data Analysis Th is study employ s descriptive and correlational analysis methods to measure the data collected and test hypotheses. To test Hypotheses 1 1 through 1 3 , descriptive statistics are used, i n addition to correlational analysis , using crosstab table s to measure the Chi Square and p Value of each variable. The p Value , or probability , tests whether or not the relationship between the variables is truly non random. The level of significance used f or the correlational tests is 0.05. P values less than or equal to 0.05 are considered statistically significant. A crosstab analysis is used because the relationship between ordinal variables is being measured . Hypothes e s 2 1 and 3 1 also use descriptive statistical methods, in addition to a correlational test , which measure s the Spearman r ho . rho coefficient provides information about the strength and direction of the relationship between the variables being tested. A rho test is used because the relationship is being measured between an ordinal variable and ratio variables. Results Of the 338 emails sent to emergency and floodplain managers, 25 emails failed and 10 bounced. A total of 50 surveys were completed. Of the emails that were delivered (303), 16.5% of the sample group responded. Four interviews were requested and conducted w ith emergency managers (100% response rate). Interviews provided more in depth information about communities, their emergency management programs, and their experiences when testing the NRI map viewer tool. For an in depth review of the research questions, hypotheses, results and recommendations, see Appendix G. Survey Findings Demographic i nformation . Demographic findings reveal that a greater percentage of survey respondents were male (56%, n=50) (See Appendix F for all statistical outputs). A higher

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21 per centage of respondents were age 45 54 (35.3% , n=51 ) and ages 55 64 (33.3% , n=51 ). An equal percentage (35.3% , n=51 ) of survey takers hold a college degree or have a graduate degree or higher and the majority of respondents are white/Caucasian at 88% (n=50) . Testing h ypotheses 1 1, 1 2, and 1 3 . Descriptive statistics show that a greater percentage of respondents are likely to use the map viewer in future decision making because it was easy to navigate (31.4% moderately or slightly likely , n=51 ) (simplicity: question 6) . Of respondents, 56.9% (n= 51) believe that turning the map layers on and off and viewing community specific information was simple to do (simplicity: question 7). Of the survey participants , 35.3% (n=51) are moderately likely and 29.4% (n=51) of respondents are slightly likely to use the information provided by the map viewer in future planning or mitigation efforts (usefulness: question 8). A greater percentage of survey takers found the information a little (30% , n=50 ) or a moderate amount ( 30% , n=50 ) helpful in planning and mitigation related decisions at the local level (usefulness: question 9). A higher percentage of respondents (41.2% , n=51 ) had a good understanding of the input parameters the map tool uses, where only 5.9% (n=51) underst ood the parameters a great deal and 9.8% (n=51) did not understand the parameters at all (knowledge: question 10). Of the respondents, 52.9% (n=51) understood the information the map viewer provided a moderate amount and 21.6% (n=51) understood the information a little (knowledge: question 11). Of participants, 33.3% (n=51) are moderately likely and 25.5% (n=51) are slightly likely to use the map viewer when performing future hazard mitigation planning and implementation activities (ad optability: question 12). The Chi Square test statistic has a value of 62.46 and is statistically significant at a 0.05 level significance (p=0.00). A statically significant relationship was found between adoption likelihood and simplicity and adoption li kelihood and usefulness because the p value is lower

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22 than the level of significance; both questions testing simplicity (questions 6 and 7) were found statistically significant, and both questions testing for usefulness (questions 8 and 9) were found statis tically significant. This indicates that the relationship of the variables sampled is caused by actual differences in the population rather than by chance. The Chi Square for potential adoptability compared to simplicity in question 6 is 74.34 and in quest ion 7 is 60.73. Both are statistically significant at a 0.05 level significance (p=0.00 and p=0.01 respectively). The Chi Square for adoptability compared to usefulness in question 8 is 109.84 and in question 9 is 52.91. Both are statistically significant at a 0.05 level (p=0.00 and p=0.00 respectively). The relationship between likelihood of adoptability and knowledge is not found to be statistically significant (questions 10 and 11). Testing h ypotheses 2 1 and 3 1 . A rho correlation was calc ulated for the relationship between map viewer adoption potential and county disaster history . A weak negative correlation was found ( rho (20) = rho analysis was calculated for the relationship between map viewer adoption potential an d staff capacity (i.e. , full time employees). A weak positive correlation was found ( rho (49) = 0.007) (Orcher, 2016) . Interview Findings Community i nformation and e mergency m anagement p rograms . C ommunities represented in the four interviews have annual operating budgets greater than $100,000 on average. Two communities believe their emergency management budgets are well funded, while one believes it is not well funded and the other believes it is adequately funded. The communities had varied levels of st aff capacity ranging from 1, 2, 5 and 10 full time staff members and all but one of the emergency managers interviewed focus full time on emergency management responsibilities. All but one of the emergency managers interviewed have 10 years

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23 of experience o r greater. All communities identify natural hazards as a high concern; s pecifically, all identify floods, three out of four identify wildfire, and three out of four identify winter weather as a high concern. Three out of four communities have experienced w hat they would consider a natural disaster in the last five years (2013, 2015 and 2017). Three of the four communities participate in a community specific hazard mitigation plan, and one participates in a regional hazard mitigation plan. Three out of the f our communities hire a contractor to complete their hazard mitigation plans , and the fourth community hires a contractor to develop some parts of the plan and completes some in house . All communities use some type of decision support tool to carry out thei r emergency management responsibilities. Map v iewer f indings . In regard to the NRI map viewer, a ll emergency managers interviewed believe the map view er is easy to use (simplicity) and a ll believe the tool provided moderately useful information (usefulness ). Two believe t he map viewer can help to guide hazard related decision making, while two communities believe it can somewhat help (usefulness) . Two communities understand the input parameters (Hazards, Built Environment, Social Vulnerability and Resilienc e) and the information generated by turning on and off the map layers, one somewhat understands and one does not understand (knowledge). Time spent using the map viewer varied between communities from 15, 30, 60 and 180 minutes. views on how t he tool can be used in future decision making differed though two communities found the tool helped to provide a snapshot of the geographic area susceptible to specific hazards and believed it could be used to support the prioritization of where mitigation is needed. General feedback from communities called for more robust or complete data sets, increased community specific data (many expressed risk was shown as a composite or as the same score for all of county , which may not be the case in reality ) , and greater explanation s of the data

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24 sources used to create map layers (i.e. , metadata) and calculate outputs (i.e. , risk scores) . It was noted that information being provided did not match data available locally. Communities expressed interest in using the map viewer, though two acknowledged that this is just one of many tools in the toolbox , and it is important to use it alongside of the others at their disposal. Discussion The survey results help to answer the research question: What is the likelihood of emergency viewer? a nd further help to test hypotheses 1 1, 1 2, 1 3, 2 1 and 3 1. The statistical outputs from the cross tab tables show that hypotheses 1 1 and 1 2 can be accepted as there is a statistically significant relationship between simplicity and usefulness on the likeliness of adopting the NRI map viewer. A greater percentage of respondents found the viewer simple to use and the information useful in decision making. The more simple and useful the tool , the more likely a n emergency or floodplain manager is to adopt the tool in future decision making. A lesser number of respondents found the information provided by the tool or the input parameters used to generate results understandable. The relationship between adoptability potential and knowledge was not statistically significant but i t can be inferred from these results that if the information provided was better understood, a greater number of respondents would be likely to use the map viewer in future decision making . Hypotheses 2 1 and 3 1 should be rejected as there is a weak relationship between potential adoptability of the map viewer and disaster history an d staff capacity. When linking interview findings to survey responses, t he statistically insignificant relationship between staff capacity and potential adoptability may be further explained , as the majority of emergency managers interviewed outsource or use contract mechanisms to develop hazard mitigation plans.

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25 Limitations Although there was a weak relationship between potential adoptability of the map viewer and disaster history (Hypothesis 2 1), it should be noted that the sample size f or disaster history in rho correlation is low because the decision to declare a federal disaster occurs at the county level, and therefore data are only collected at th is level. This is a limitation to the study because mid or small scale d isasters are not considered . Although 50 emergency and floodplain managers completed the survey, it would be beneficial to increase the response rate. E mails sent directly from Qualtrics (the web based program used to conduct surveys and send emails) may be delivered to s . The number of interviews conducted was also a limitation , as collecting information for only four Colorado communities does not present a holistic representation of all demographics, hazard types and other variable s of Colorado communities. Survey questions did not track if a respondent was an emergency or a floodplain manager, or if they fill both emergency and floodplain manager roles for their community. This is valuable information to consider because it prov ides more insight on if being responsible for one hazard (i.e., floodplain manager) or multiple hazards (i.e., emergency manager) has an impact on potential viewer adoption. Not collecting this information is a study limitation. Recommendations Open ended survey questions and interview responses helped to provide specific recommendations on increasing map viewer adoptability in decision making. Prominent themes include being able to upload local Geographic Information System (GIS) layers or download GIS la yers from the map viewer for local use; the need for metadata with descriptions of the data layers (including scales) and data sources; increasing community specific datasets (i.e., critical

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26 facilities, transportation routes, infrastructure, etc.); using b etter symbology (i.e., make hazard indicators more intuitive: red equates to high hazards, etc. and use symbology that allows two or more layers to be viewed at once); and, including more floodplain specific hazard information. Providing metadata and more detailed descriptions of data sources used and how measures are calculated, in addition to maki ng symbology more user friendly can make the information being the information and increase the potential for the tool to be adopted by local officials in future decision making. Moving forward, if the map viewer were to generate a report, emergency managers would like to see it include hazards by location (county, muni cipality, and census track); outputs with vulnerable populations and socioeconomic data that are not readily available (i.e., individual risk score for vulnerable population for specific demographics); statistical outputs; historical data; a map summary fo r each of the map layers; number of structures in an area or addresses at risk; customizable maps with legends; and qualifying information to support grant requests (i.e., dollar impacts over time caused by a certain hazard). FEMA should consider these re commendations when working to enhance future iterations of the map viewer and if/when creating report generating capability. I t is recommended that FEMA take action s to make the map viewer more understandable since it can be inferre d that more practitione rs would use the map viewer tool in future decision making if the information being provided was better understood . In addition to the fact sheet already in circulation, it is recommended FEMA create a data dictionary and metadata for the map layers availa ble on the viewer. Additionally, FEMA should consider facilitating training opportunities such as a webinar, which explains how the tool can be used. Making webinar slides

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27 not interested in attending a training , may also be helpful. Keeping stakeholders involved throughout the decision support tool development process is also recommended . This allows stakeholders to be engaged and help shape and enhance the tools available to them (i.e., make the tool more simple, usable, understandable), which can also increase their potential to adopt it. Another recommendation for FEMA to consider is collecting additional disaster data at the local level (i.e., city or town) . It is recomm ended that FEMA work with the state to track th ese data. Currently, m ore localized disaster information can be found within the Colorado State Hazard Mitigation Plan. However, it is not in a unified format and it would be beneficial to catalog th ese data i n a standardized data base. An overview of high priority recommendations can be found as an infographic in Appendix H. Research Next Steps Moving forward, it is recommended that more interviews are conducted with communities throughout Colorado whose demographics were not considered in this study. Increasing the number of interviews conducted would also enhance research findings. This is because, increasing the number of interviews could help to validate responses, generate more robust data, and retest variables. Additionally, future studies should consider re distributing the survey and revising the survey questions. When sending surveys in the future, it may be beneficial to send the survey web link from a personal email rather than from Qualtrics. The unit of analysis (respondents) should be increased to include h azard mitigation planning contractors , community planners, mitigation officers and other elected officials involved with planning for hazards at the local level. Contractors should be cons idered as stakeholders because they may be the primary users of decision support tool s . A survey question should be added to track i f emergency

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28 managers are r esponsible for hazard mitigation planning activities in their community, if not, they shoul d recor d who is responsible for this activity. This will help increase FEMA understanding of who in certain communities is most likely to use decision support tools. If re testing the same unit of analysis (only emergency and floodplain managers), include anoth er question to track if a respondent is an emergency or a floodplain manager, or if they fill both emergency and floodplain manager roles for their community. To gain better insight on if disaster history impacts map viewer adoption, more localized data in addition to federally declared disaster information should be collected and analyzed. It would also be beneficial to collect information on which communities have hazard mitigation plans. Conclusion The results of this study help to inform FEMA on the ways the NRI map viewer can be improved to better serve the needs of practitioners in Colorado. Past research finds that decision support tools are often not well understood by the stakeholders intended to use them . Vetting the beta version of the map viewer through emergency and floodplain managers presents a unique opportunity ; it allow s those on the ground to help shape the decision support tools available to them. Study results find a statistically si gnificant relationship between the potential to adopt the map viewer and simplicity and usefulness. Therefore, users are more likely to adopt the viewer in decision support because it i s perceived to be simple to use and provide useful information. Incre can enable communities to better measure risks contributed by the built environment, natural hazards, social vulnerability and resilience. Operationalizing these factors can offer communities a more informed understanding of their risk s and further take action to protect vulnerable populations and mitigate impacts . Taking action and increasing mitigation at the community level aligns with FEMA goals, which aim to enhance community

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29 resilience . Using th e information provided by the map vi ewer alongside of other decision support tools available to communities can help to inform mitigation actions and prevent disasters before they occur.

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30 References Adger, W. N. (2006). Vulnerability. Global environmental change , 16 (3), 268 281. Bankoff, G. (2006). The tale of the three pigs: Taking another look at vulnerability in the light of the Indian Ocean tsunami and Hurricane Katrina . Retrieved from http://understandingkatrina.ss rc.org/Bankoff/ Blaikie, P., Cannon, T., Davis, I., & Wisner, B. (1994). At risk: natural hazards, people s vulnerability and disasters . Routledge. Birkmann, J., & Birkmann, J. (2006). Measuring vulnerability to natural hazards: towards disaster resilient societies (No. Sirsi) i9789280811353). Winterfeldt, D. (2003). A framework to quantitatively assess and enhance the seismic resilience of communities. Earthquake spectra , 19 (4), 733 752. Burby, R. J., Deyle, R. E., Godschalk, D. R., & Olshansky, R. B. (2000). Creating hazard resilient communities through land use planning. Natural hazards review , 1 (2), 99 106. Cutter, S. L. (1996). Vulnerability to environmental hazards. Progress in human geography , 20 (4), 529 539. Cutter, S. L., Boruff, B. J., & Shirley, W. L. (2003). Social vulnerability to environmental hazards. Social science quarterly , 84 (2), 242 261. Cutter, S. L., Barnes, L., Berry, M., Burton, C., Evans, E ., Tate, E., & Webb, J. (2008). A place based model for understanding community resilience to natural disasters. Global environmental change , 18 (4), 598 606. Dovers, S. R., & Handmer, J. W. (1992). Uncertainty, sustainability and change. Global Environment al Change , 2 (4), 262 276.

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31 FEMA. (2011). Whole Community . Retrieved from https://www.fema.gov/whole community FEMA. (2016). FEMA National Risk Index [PowerPoint slides]. December 19, 2016. FEMA. (2016b). National Migitigation Framework, Second Edition. June 2016. Retrieved from https://www.fema.gov/media library/assets/documents/117787 FEMA. (2017). About the Agency. Retrieved from https://www.fema.gov/about agency FEMA. (2017a). Roles of Local Emergency Managers. Retrieved from https://emilms.fema.gov/ IS0230d/FEM0104040text.htm FEMA. (2017b). Floodplain Management Requirements: Unit 7: Ordinance Administration. Retrieved from https://www.fema.gov/floodplain management requirements Hewitt, K. (1983). Interpretations of calamity from the viewpoint of huma n ecology Holling, C. S. (1973). Resilience and stability of ecological systems. Annual review of ecology and 31 ystematic , 4 (1), 1 23. Kasperson, J. X., Kasperson, R. E., & Turner, B. L. (1995). Regions at risk . United Nations University Press. Klein, R. J., Nicholls, R. J., & Thomalla, F. (2003). Resilience to natural hazards: How useful is this concept? Global Environmental Change Part B: Environmental Hazards , 5 (1), 35 45. Komendantova, N., Mrzyglocki, R., Mignan, A., Khazai, B., Wenzel, F., P att, A., & Fleming, K. (2014). Multi hazard and multi risk decision support tools as a part of participatory risk governance: feedback from civil protection stakeholders. International Journal of disaster risk reduction, 8, 50 67. Mileti, D. (1999). Disast ers by design: A reassessment of natural hazards in the United States . Joseph Henry Press.

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32 Orcher, L. T. (2016). Conducting research: Social and behavioral science methods . Routledge. Pelling, M. (2003). The vulnerability of cities: natural disasters and social resilience . Earthscan. Strategy, Y. (1994, May). Plan of Action for a Safer World; Guidelines for Natural Disaster Prevention, Preparedness and Mitigation. In World Conference on Natural Disaster Reduction, Yokohama, Japan (pp. 23 27). Tobin, G. A. (1999). Sustainability and community resilience: the holy grail of hazards planning? Global Environmental Change Part B: Environmental Hazards , 1 (1), 13 25. Thomas, D. S., Phillips, B. D., Lovekamp, W. E., & Fothergill, A. (Eds.). (2013). Social vulnerabil ity to disasters . CRC Press. Thomas, S.K, Daly, K., Nyanza, E.C., Ngallaba, S.E., & Bull, S.(nd). Health Worker Acceptability of an mHealth Solution for PMTCT in Tanzania. Unpublished.

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33 Appendix A Models Used to Measure Social Vulnerability Pressu re and Release Model One popular model used to operationalize vulnerability is the Pressure and Release (PAR) Model created by Blaikie, Cannon, Davis, and Wisner in 1994. This model seeks to measure how natural hazards affect vulnerable populations. PAR calculates risk by multiplying hazards and vulnerability. Root causes (limited access to power and ideologies), dynamic pressures, and unsafe conditions make up the progressive level of vulnerability (See Appendix A). This framework is dynamic, which makes quantifying elements of vulnerability difficult. between the causal links of different dynamic pressures on unsafe conditions and the impact of root causes o The model is often used to determine underlying factors contributing to vulnerability, though it does not provide insight into the relationship between proximity to environmental hazards and humans (C utter et al., 2008). Figure 1. Pressure and Release Model

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34 Blaikie, P., Cannon, T., Davis, I., & Wisner, B. (1994). vulnerability and disasters . Routledge. Social Vulnerability Index Building on the vulnerability research, Cutter, Boruff, and Shirley developed the Social Vulnerability Index (SoVI) in 2003. This index uses socioeconomic and demographic data to measure vulnerability at the county level and takes into consideration 11 independent social factors. The i ndex was used to measure the SoVI scores for all counties in the US. The lowest vulnerability score was 9.6 and the highest 49.51, with a mean score of 1.54. More vulnerable populations (lower scores) were found in the southern portion of the country. Cou nties with more vulnerable populations on average had racial inequalities, rapid population growth, large populations of Hispanic and/or Native American residents, or were highly urbanized. There were 393 counties found to be vulnerable, with the lowest sc ore applied to Manhattan Borough in New

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35 York City (Cutter, Boruff, & Shirley, 2003). Some of the least vulnerable populations were found in New England and in the Great Lakes states, with the least vulnerable population being in Yellowstone, Montana. The m odel works well to explain statistical variance across the US, though fails to incorporate all elements of the models that came before it The Hazards of Place Model of Vulnerability The Hazards of Place Model was created by Cutter in 1996 and helps to quantify social vulnerability in the context of place. The model measures place vulnerability by combining mitigation and risk to generate hazard potential. The hazard potential is then filtered through the following vulnerability elements: geographic con text, social fabric, biophysical vulnerability, mitigation and risks change. The model considers social vulnerability in the context of exposure (proximity to pl ace) and provides insights into the characteristics that allow communities to respond and recover from hazards (Cutter, Boruff, & Shirley, 2003). However, it does not account for the root causes, which are better represented in the Pressure and Release Mod el (Cutter et al., 2008). Figure 2. The Hazards of Place Model

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36 Cutter, S. L. (1996). Vulnerability to environmental hazards. Progress in human geography , 20 (4), 529 539. The Disaster Resilience of Place Model In an effort to operationalize resilience, Cutter et al. (2008) created the Disaster Resilience of Place (DROP) model (Appendix C). This framework identifies metrics and standards for measuring disaster resilience and performs comparative assessments at th e relationship between vulnerability and resilience; one that is theoretically grounded, amenable to quantification; and one that can be readily applied to ad (Cutter et al., 2008, p. 602). The model assesses the social resilience of places (neighborhood, census tract, city, or county), though it recognizes the interconnectedness between social and natural systems and the buil t environment (i.e., human actions affect the environment). Negative actions can degrade natural systems, which results in less environmental protection from hazards. To represent the interconnected nature between society, the DROP considers resilience as both

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37 antecedent and inherent conditions. In addition to these place based factors, the impact of external factors on the community are also considered (e.g., federal policies, state regulations). Figure 3. The Disaster Resilience of Place Model Cutter, S. L., Barnes, L., Berry, M., Burton, C., Evans, E., Tate, E., & Webb, J. (2008). A place based model for understanding community resilience to natural disasters. Global environmental change , 18 (4), 598 606.

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Running head: ADOPTABILITY OF NR 38 Appendix B Measurement Ta ble Table 1. Measurement Table Variable Measure Level of m easurement Data source Hypothesis 1 1: If emergency and floodplain managers find the online map viewer useful they will be more likely to use it in decision making . DV : Adoption Likert scale Ordinal Survey questions and interview findings (self reported). IV: Usefulness of online map viewer Likert scale Ordinal Survey questions and interview findings (self reported). Hypothesis 1 2: The simplicity of the online viewer will increase an emergency and floodplain making. DV: Adoptability See above under Hypothesis (1 1) I V : Simplicity of online map viewer See above under Hypothesis (1 1) Hypothesis 1 3: Knowledge of the information being provided by the tool will increase the likeliness of emergency and floodplain managers to adopt its use in decision making. DV: Adoptability See above under Hypothesis (1 1) I V : Knowledge of information provided See above under Hypothesis (1 1) Hypothesis 2 1: Communities with greater disaster histories will be more likely to show interest in tool adoption. DV: Adoptability See above under Hypothesis (1 1) I V : Disaster history Total number of federally declared disasters Ratio FEMA database ( Summary of Disaster Declarations and Grants ) : https://www.fema.gov/media library/assets/documents/106308 Hypothesis 3 1: Communities with greater staff capacity will be more likely to indicate interest in adopting the tool. DV: Adoptability See above under Hypothesis (1 1) I V : Staff capacity Number of staff working on emergency management issues Ratio Survey questions and interview findings (self reported).

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39 Appendix C Survey Questions Please answer all questions to the best of your knowledge by selecting one answer for each multiple choice question and fill in each open ended response . A. Demographic Information 1. What is your highest level of education completed? High school, GED , or below Some c ollege (two year or four year) College degree Some g raduate school Graduate degree or higher 2. What is your gender? Female Male Other 3. Wh ich age range best describes you? 18 24 years 25 34 years 35 44 years 45 54 years 55 64 years Age 65 or older 4. Which race/ethnicity best describes you? American Indian or Alaskan Native Asian/Pacific Islander Black or African American Hispanic American White/Caucasian Multiple ethnicities/ Other 5. How many full time employees work on emergency of floodplain management related issues within your community ? ____________ B. Getting to the know the Map Viewer Tool

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40 For the remainder of the questions you will be prompted to use the map viewer (http://tiny.cc/NRI_beta). This will give you the opportunity to see how the viewer operates and provide feedback on its functions. Before getting started please review the two page fact sheet (linked at the end of email t ext). Next, open the viewer in your web browser by clicking on or copying and pasting the link into your browser: http://tiny.cc/NRI_beta The browser will open to the NRI welcome screen, where users can learn more about the NRI project. In the mapping interface, search for your community using the search bar in the upper left (see images below). The default view is the baseline risk score for the US. Feel free to click on the Hazards, Social Vulnerability, Built Environment, and Community Resilience tabs to hazards (see images below). Please feel f ree to turn on and off layers to get a feel for the tool. Now you are ready to answer the following questions!

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41 6. Are you likely to use the tool in future decision making because it was easy to navigate? Extremely likely Moderately likely S lightly likely Neither likely nor unlikely Moderately unlikely Extremely unlikely 7. Turning layers on and off and viewing community specific information was simple to do 8. Does the map viewer provide you with information that you are likely to use in future planning or mitigation efforts? Extremely likely Moderately likely S lightly likely Neither likely nor unlikely Moderately unlikely Extremely unlikely 9. How helpful is this information in planning and mitigation related decisions at the local level? A great deal A lot Some A little None at all 10. How well do you understand the input parameters ( Hazards, Social Vulnerability, Built Environment, and Community Resilience) ? A great deal A lot Some A little None at all Strongly agree Agree Somewhat agree Neither agree nor disagree Somewhat disagree Disagree Strongly Disagree

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42 11. How well do you understand the information the map viewer provided? A great deal A lot Some A little None at all 12. How likely are you to use the map viewer when performing future hazard mitigation planning and implementation activities? Extremely likely Moderately likely S lightly likely Neither likely nor unlikely Moderately unlikely Extremely unlikely 13. How long did you use the map viewer for and what did you use it to do? _____________ ___________________________________________________________ 14. What could be changed to make you more likely to adopt the map viewer for use in future emergency and floodpla in management decisions? _____________ ___________________________________________________________ 15. If the map viewer included a report feature, what information from the viewer would be helpful to generate into a report? _____________ ___________________________________________________________ 16. Enter your email below for a chance to win a $50 Amazon gift ________________________________________________________________________

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Running head: ADOPTABILITY OF NR 43 Appendix D Interview Protocol Introduction an d Purpose Capstone Project with the University of Colorado Denver. To day, I would like to talk to you about your career and experience as an emergency manager to help inform my study. I will be tape recording this interview, but it will only be heard by me and your comments will be anonymous, as no names will be used in thi s report. I anticipate our interview will take a little less than one hour. Do you have any questions about the interview or the research before I begin? Questions A. Experience 1. What is your official job title? 2. Is emergency manager your full time position or do you have other responsibilities? 3. How many years have you been in an emergency management related field? 4. What are your responsibilities as an emergency manager? 5. Are you the only person with the community that works on emergency management related ta sks? 6. Are emergency management tasks and positions well funded in your community? Do you management budget? 7. Are natural hazards a high concern for your community? Which hazard in par ticular? 8. When and how severe was the last disaster experienced? What type of disaster was this? B. Planning and Mitigation 9. What type of planning and mitigation actions are you involved with in your community. For example, are you involved in hazard mitigati on planning? Comprehensive planning? Etc. 10. Do you participate in a regional mitigation plan or one specific to your community? Do you prepare the planning document in house or through a contract mechanism? 11. Do you use any decision support tools to inform e mergency management decisions specifically related to natural hazards? Any web based tools like GIS or ArcGIS Online? If yes, please explain. C. NRI Assessment 12. Was the NRI map viewer easy to use? Was it easy to navigate through and generate information? If yes, why? If no, why? 13. How useful is the information provided by the map viewer tool? Do you believe it can help to guide hazard related decisions? If so, in what ways?

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44 14. Do you understand the input parameters the tool uses ( Hazards, Social Vulnerability, B uilt Environment, and Community Resilience ) ? Do you understand the information provided by turning on and off the map layers? 15. In what ways can you use the tool in future decision making? 16. How long did you use the map viewer for? What did you use it to do? 17. If the map viewer were to generate a report, what information would be helpful to include in the report? 18. What do you like and dislike about the tool? 19. What changes to the tool would make you more likely to adopt its use in future decision making? Planning? Implementation?

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45 Appendix E Postcard Notification Postcards were sent out via mail to Colorado floodplain managers a week prior to survey distribution via email. Figure 4. Front of Postcare Figure 5. Back of Postcard

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46 Appendix F Statistical Outputs Crosstab Outputs and Chi Square Values rho Generated by SPSS Correlations fed_dis fte time adopt Spearman's rho fed_dis Correlation Coefficient 1.000 .345 .518 .049 Sig. (2 tailed) . .116 .058 .830 N 22 22 14 22

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47 fte Correlation Coefficient .345 1.000 .129 .007 Sig. (2 tailed) .116 . .458 .962 N 22 51 35 51 time Correlation Coefficient .518 .129 1.000 .103 Sig. (2 tailed) .058 .458 . .556 N 14 35 35 35 adopt Correlation Coefficient .049 .007 .103 1.000 Sig. (2 tailed) .830 .962 .556 . N 22 51 35 51 Descriptive Statistics Generated by SPSS Descriptive Statistics N Minimum Maximum Mean Std. Deviation fte 51 .0 20.0 3.394 4.2178 fed_dis 22 3 13 6.95 2.591 time 35 1.0 60.0 15.243 11.7576 Valid N (listwise) 14

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48 Measures of Central Tendency Generated by Qualtrics

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Running head: ADOPTABILITY OF NR 59 Appendix G Research Questions, Hypotheses, Results , and Recommendations Table Table 2. Research Questions, Hypotheses, Results , and Recommendations Hypothesis Corresponding Survey or Interview Question Results Recommendations Research Question 1: s interactive online viewer? Hypothesis 1 1: If emergency and floodplain managers find the online map viewer useful, the likelihood of adoption in decision making will increase. Survey Question 8: Does the map viewer provide you with information that you are likely to use in future planning or mitigation efforts? Survey Question 9: How helpful is this information in planning and mitigation related decisions at the local level? Survey Question 12: How likely are you to use the map viewer when performing future hazard mitigation plann ing and implementation activities? Interview Questions 13: How useful is the information provided by the map viewer tool? Do you believe it can help to guide hazard related decisions? If so, in what ways? Interview Question 15 : In what ways can you use th e tool in future decision making? A c orrelational analysis was performed using a crosstab table t o measure the Chi Square and p V alue of adoptability (survey question 12) compared to usefulness in survey question 8 (Chi Square=109.84; p=0.00) . The p Value is s tatistically significant at a 0.05 level. A correlational analysis was performed using crosstab tables to measure the Chi Square and p Value of adoptability (survey question 12) compared to usefulness in question 9 (Chi Square=52.91; p=0.00). The p Val ue is statistically significant at a 0.05 level. Make Geographic Information System (GIS) layers downloadable from the map viewer Allow for local GIS files to be uploaded to the map viewer Increase community specific datasets Include more floodplain specific hazard layers Hypothesis 1 2 : The simplicity of the online viewer will increase an emergency or floodplain adopt it in decision making. Survey Question 6: Are you likely to use the tool in future decision making because it was easy to navigate? Survey Question 7: Turning layers on and off and viewing community specific information was simple to do A correlational analysis was performed using a crosstab table to measure the Chi Square and p Value of adoptability (survey question 12) compared to simplicity in survey question 6 (Chi Square=74.34; p=0.00) . The No recommendation.

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60 Survey Question 12: How likely are you to use the map viewer when performing future hazard mitigation planning and implementation activities? Interview Question 12: Was the NRI map viewer easy to use? Was it easy to navigate through and generate information? If yes, why? If no, why? Interview Question 15 : In wha t ways can you use the tool in future decision making? p Value is statistically significant at a 0.05 level. A correlational analysis was performed using a crosstab table to measure the Chi Square and p Value of adoptability (survey question 1 2) compared to simplicity in survey question 7 (Chi Square=60.73; p=0.01). The p Value is statistically significant at a 0.05 level. Hypothesis 1 3 : Knowledge of the information being provided by the tool will increase the likelihood of emergency and floodplain managers to adopt its use in decision making. Survey Question 10: How well do you understand the input parameters (Hazards, Social Vulnerability, Built Environment, and Community Resilience)? Survey Question 11: How well do you understand the information the map viewer provided? Interview Question 15 : In what ways can you use the tool in future decision making? Survey Question 12: How likely are you to use the map viewer when performing future hazard mitigation planning and impl ementation activities? Interview Question 14: Do you understand the input paramaters the tool uses (Hazards, Social Vulnerability, Built Environment, and Community Resilience)? Do you understand the information provided by turning on and off the map layers? Interview Question 15 : In what ways can you use the tool in future decision making? A correlational analysis was performed using a crosstab table to measure the Chi Square and p Value of ad optability (survey question 12) compared to knowledge in survey question 10 (Chi Square=25.30; p=0.39). The re lationship between likelihood of adoptability and knowledge is not found to be statistically significant. A correlational analysis was performed using a crosstab table to measure the Chi Square and p Value of adoptability (survey question 12) compared to k nowledge in survey question 11 (Chi Square=26.34; p=0.37). The relationship between likelihood of adoptability and knowledge is not found to be statistically significant. Create a data dictionary and metadata for the map layers on the viewer. Facilitate tr aining opportunities such as a webinar. Make webinar slides available for download Provide a tutorial document available for website. Use more intuitive symbology Use symbology that allows for more layers to be visib le at once . Involve Stakeholders throughout the decision support tool development process.

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61 Research Question 2: Are emergency and floodplain managers in counties with disaster histories more likely to indicate they a re interested in adopting the tool? Hypothesis 2 1 : Counties with greater disaster histories (documented as a federally declared disaster) will be more likely to show interest in adopting the tool. Secondary Data: FEMA Database ( Summary of Disaster Declarations and Grants ): https://www.fema.gov/media library/assets/documents/106308 Survey Question 12: How likely are you to use the map viewer when performing future hazard mitigation planning and implementation activiti es? A rho co rrelational test was calculated to measure the strength of the relationship between counties with disaster histories (federally declared disasters from FEMA Database) and potential tool adoption (survey question 12) . A weak negative correlation was found ( rho (20) = 0.049) for the relationship between map viewer adoption potential and county disaster history. FEMA should work with the state to track localized disaster data. Research Question 3: Are emergency and floodplain managers in communities with greater staff capacity more likely to indicate interest in adopting the map viewer? Hypothesis 3 1 : Communities with greater staff capacity will be more likely to adopt the tool. Survey Question 5: How many full time employees work on emergency or floodplain management related issues within your community? Survey Question 12: How likely are you to use t he map viewer when performing future hazard mitigation planning and implementation activities? rho correlational test was calculated to measure the strength of the relationship between community staff capacity and potential tool adoption (sur vey question 12). A weak positive correlation was found ( rho (49) = 0.007) for the relationship between map viewer adoption potential (question 2) and staff capacity (question 5) . Include a question in the survey to track if a respondent is an emergency or a floodplain manager, or if they fill both emergency and floodplain manager roles for their community.

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Running head: ADOPTABILITY OF NR 62 Appendix H High Priority Recommendations Infographic

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Form Name: capstone repository permission Submission Time: December 8, 2017 12:30 pm Browser: Firefox 57.0 / Windows 7 IP Address: 216.81.94.69 Unique ID: 370110867 Location: 38.874900817871, -77.032501220703 Description Area SCHOOL OF PUBLIC AFFAIRS ELECTRONIC CAPSTONE REPOSITORY Description Area Dear Capstone Author and Capstone Client:The Auraria Library Digital Library Program is a nonprofit center responsible for the collection and preservation of digital resources for education.The capstone project, protected by your copyright, and/or created under the supervision of the client has been identified as important to the educational mission of the University of Colorado Denver and Auraria Library.The University of Colorado Denver and Auraria Library respectfully requests non-exclusive rights to digitize the capstone project for Internet distribution in image and text formats for an unlimited term. Digitized versions will be made available via the Internet, for onand off-line educational use, with a statement identifying your rights as copyright holder and the terms of the grant of permissions.Please review, sign and return the follow Grant of Permissions. Please do not hesitate to call me or email your questions.Sincerely,Matthew C. MarinerAuraria LibraryDigital Collections ManagerMatthew.mariner@ucdenver.edu303.556.5817 Grant of Permissions Description Area In reference to the following title(s): Author (Student Name) Stephanie DiBetitto Title (Capstone Project Title) Adoptability of the Federal Emergency Management Agency National Risk Index's Interactive Online Map Viewer Publication Date December 1, 2017 I am the: Client Description Area As client of the copyright holder affirm that the content submitted is identical to that which was originally supervised and that the content is suitable for publication in the Auraria Library Digital Collections.

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Description Area This is a non-exclusive grant of permissions for on-line and off-line use for an indefinite term. Off-line uses shall be consistent either for educational uses, with the terms of U.S. copyright legislation's "fair use" provisions or, by the University of Colorado Denver and/or Auraria Library, with the maintenance and preservation of an archival copy. Digitization allows the University of Colorado Denver and/or Auraria Library to generate imageand text-based versions as appropriate and to provide and enhance access using search software. This grant of permissions prohibits use of the digitized versions for commercial use or profit. Signature Your Name Casey Zuzak Date December 8, 2017 Email Address ATTENTION Description Area Grant of Permissions is provided to: Auraria Digital Library Program / Matthew C. MarinerAuraria Library1100 Lawrence | Denver, CO 80204matthew.mariner@ucdenver.edu303-556-5817

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Form Name: capstone repository permission Submission Time: December 5, 2017 11:11 am Browser: Chrome 62.0.3202.94 / Windows 7 IP Address: 165.127.23.2 Unique ID: 369279360 Location: 39.676200866699, -104.8870010376 Description Area SCHOOL OF PUBLIC AFFAIRS ELECTRONIC CAPSTONE REPOSITORY Description Area Dear Capstone Author and Capstone Client:The Auraria Library Digital Library Program is a nonprofit center responsible for the collection and preservation of digital resources for education.The capstone project, protected by your copyright, and/or created under the supervision of the client has been identified as important to the educational mission of the University of Colorado Denver and Auraria Library.The University of Colorado Denver and Auraria Library respectfully requests non-exclusive rights to digitize the capstone project for Internet distribution in image and text formats for an unlimited term. Digitized versions will be made available via the Internet, for onand off-line educational use, with a statement identifying your rights as copyright holder and the terms of the grant of permissions.Please review, sign and return the follow Grant of Permissions. Please do not hesitate to call me or email your questions.Sincerely,Matthew C. MarinerAuraria LibraryDigital Collections ManagerMatthew.mariner@ucdenver.edu303.556.5817 Grant of Permissions Description Area In reference to the following title(s): Author (Student Name) Stephanie DiBetitto Title (Capstone Project Title) Adoptability of the Federal Emergency Management Agency National Risk Index's Interactive Online Map Viewer Publication Date 12/4/2017 I am the: Author (student) Description Area As copyright holder or licensee with the authority to grant copyright permissions for the aforementioned title(s), I hereby authorize Auraria Library and University of Colorado Denver to digitize, distribute, and archive the title(s) for nonprofit, educational purposes via the Internet or successive technologies.

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Description Area This is a non-exclusive grant of permissions for on-line and off-line use for an indefinite term. Off-line uses shall be consistent either for educational uses, with the terms of U.S. copyright legislation's "fair use" provisions or, by the University of Colorado Denver and/or Auraria Library, with the maintenance and preservation of an archival copy. Digitization allows the University of Colorado Denver and/or Auraria Library to generate imageand text-based versions as appropriate and to provide and enhance access using search software. This grant of permissions prohibits use of the digitized versions for commercial use or profit. Signature Your Name Stephanie DiBetitto Date 12/5/2017 Email Address ATTENTION Description Area Grant of Permissions is provided to: Auraria Digital Library Program / Matthew C. MarinerAuraria Library1100 Lawrence | Denver, CO 80204matthew.mariner@ucdenver.edu303-556-5817