Community Resilience 

Challenges Index

Identifying Commonly Used Indicators from Peer-Reviewed 

Research: Updated for Research Published 2003-2021

May 2023
Community Resilience Indicator Analysis: 2022 Update

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Community Resilience Indicator Analysis: 2022 Update

Table of Contents

1. Overview.........................................................................................................................................................2

2. 2018 CRCI Summary.....................................................................................................................................3

3. 2022 CRCI Summary.....................................................................................................................................5

3.1. Literature Review and Selected Methodologies .............................................................................5

3.2. Commonly Used Indicators and Data Sources................................................................................5

3.3. Data Binning .....................................................................................................................................8

3.4. Correlation Analysis ..........................................................................................................................9

3.5. FEMA Community Resilience Challenges Index (CRCI).................................................................10

3.6. Limitations and Benefits................................................................................................................11

3.7. Future Research .............................................................................................................................13

Appendix A: Methodologies from the Literature Review Research Published.........................................14

Appendix B: Methodologies Meeting CRCI Inclusion Criteria....................................................................35

Appendix C: List of Commonly Used Indicators from Selected Methodologies .......................................38

Appendix D: CRCI Correlation Matrix ..........................................................................................................61

i
Community Resilience Indicator Analysis: 2022 Update

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1. Overview

In 2017, FEMA’s National Integration Center (NIC) Technical Assistance (TA) Branch identified a need 

to establish a data-driven basis for prioritizing locations for TA investment and guiding local 

emergency management planning. To achieve this goal, FEMA tasked Argonne National Laboratory 

(Argonne) with identifying commonly used indicators of community resilience across the landscape of 

published peer-reviewed research.

FEMA and Argonne completed the first analysis of community resilience indicators in 2018 and 

repeated the process in 2022. The analysis process begins with a literature review and cataloguing 

of published peer-reviewed assessment methodologies on social vulnerability and community 

resilience. The literature review findings are then filtered by inclusion criteria established by the

research team to ensure the methodologies are:

• Quantitative,

• Data and methodology are publicly available,

• Calculated at the county level or lower,

• Examine generalized hazard risk (rather than a singular hazard), and

• Focused on pre-disaster community conditions.

After this, the research team identifies the commonly used indicators across these methodologies 

and selects the best data source for each indicator. Finally, the research team bins the data for 

visual display, conducts a correlation analysis and creates a composite index, the FEMA Community 

Resilience Challenges Index (FEMA CRCI). 

In 2018, this analysis identified eight resilience and vulnerability assessment methodologies and 20 

commonly used indicators (indicators used in three or more of the eight methodologies). The FEMA 

CRCI in 2018 was created from these 20 indicators and was produced at the county level. In 2022, 

the FEMA and Argonne research team updated the literature review which expanded the list of

methodologies examined to 14 methodologies published between 2003 and 2021. Examining the 

indicators used in these methodologies, the research team identified 22 indicators1 as commonly 

used (indicators used in five or more of the 14 methodologies). In 2022, the research team 

produced the FEMA CRCI at the county and the census tract levels.

1 Five indicators were added to the list in 2022, and three indicators were retired from the 2018 list.
Community Resilience Indicator Analysis: 2022 Update

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To make the CRCI data more accessible and more actionable, each individual indicator and the 

FEMA CRCI is binned and included in FEMA’s Resilience Analysis and Planning Tool (RAPT).2 RAPT 

enables emergency managers and community partners to quickly visualize relative differences in 

potential resilience by county, tribe and census tract.

By reviewing the data for each of these 22 indicators individually, emergency managers can gain 

insights for targeted outreach strategies, planning, mitigation investments and response and 

recovery operations. Communities, regional governments and others can use this data to better 

understand potential challenges to resilience. As the social science field of examining and validating 

indicators of resilience evolves, FEMA will update RAPT to provide emergency managers and 

community partners with additional data and tools to inform planning, mitigation, response and 

recovery.

It is important to understand that the role of the emergency manager is not to change or to

“improve” the data, but to plan appropriately for the community characteristics reflected in the data. 

These datasets are community characteristics that researchers have identified as important 

considerations for resilience. For example, people with disabilities may have greater challenges to be 

resilient to disasters. If a community has a high population of people with disabilities, the emergency 

manager(s) may need to create tailored preparedness outreach programs and strategies to ensure 

those residents have support if evacuation is necessary.

Rather than label the CRCI and the underlying indicators as an absolute measure of resilience, FEMA 

considers this data as “potential challenges to resilience.” Everyone is vulnerable to disasters. While

scholars theorize that certain characteristics may make an individual or a household less resilient (or 

more socially vulnerable), the data does not reflect measures that individuals and/or communities 

have taken to address potential challenges, such as emergency management planning and outreach 

or household preparedness measures. To aid emergency managers in understanding how to use

these indicators, calling them potential challenges to resilience supports a more positive and 

strategic application of the data in all phases of emergency management.

2. 2018 CRCI Summary

To identify the set of methodologies for the first analysis of community resilience indicators, Argonne 

catalogued peer-reviewed methodologies cited in meta-analyses of community resilience published

between 2013-2017. The research team then selected the methodologies that met the following 

inclusion criteria.

 Quantitative measures: To ensure that indicators could be easily compared across 

methodologies, the team only included methodologies that used exclusively quantitative 

measures.

2 RAPT is a free, online, geographic information system (GIS) tool with data layers on population characteristics, 

infrastructure and hazards. RAPT is designed to support all phases of emergency management. Access RAPT here: 

www.fema.gov/rapt.
Community Resilience Indicator Analysis: 2022 Update

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 Publicly available methodology: For the analysis and findings to be transparent, the team only 

included methodologies that were publicly available.

 Public data source: To ensure transparency, replicability and updates over time, indicator data 

had to be from publicly available secondary sources, such as the U.S. Census and the Bureau of 

Labor Statistics.

 At least county-level unit of analysis: The team only included studies where the unit of analysis 

was, or could be easily adapted to, a U.S. County. Although more granularity offers greater clarity, 

many studies do not include data below the county level.

 Generalized risk: Because the NIC provides technical assistance relative to a wide range of 

hazards, the inclusion criteria retained methodologies that applied to multiple hazards. 

Methodologies that focused on one specific risk (such as earthquakes, food security, poverty or 

public health) were included in the CRIA analysis. Although these studies offer insights for these 

topic areas, studies with an all-hazards perspective were more appropriate for comparative 

purposes.

 Pre-disaster conditions: As NIC TA supports communities to build resilience prior to a disaster, 

the research team focused on community characteristics present before an incident occurred.

Using these inclusion criteria as a filter, the research team identified eight methodologies for further 

analysis in 2018. The names of some methodologies highlight social vulnerability and some highlight 

community resilience. While community resilience encompasses more than population 

characteristics, the research team drew from methodologies with either focus to capture the 

broadest research set. The research team also included methodologies from developed countries 

other than the United States.

After cataloguing more than 100 unique indicators used across the eight methodologies, the team 

identified 20 indicators that were used in three or more of these methodologies. Identifying 

indicators used in multiple methodologies suggests researcher agreement on the importance of 

these indicators.

Argonne then selected U.S. datasets for each of the 20 indicators (from the U.S. Census American 

Community Survey five-year estimates and other public data sources), binned the data and created

GIS data layers to include in RAPT. The research team used the American Community Survey (ACS)

five-year estimates because it provides greater statistical reliability of population and community 

characteristics than one-year data, especially for smaller geographic areas. In addition to the 

individual data layers, FEMA and Argonne created the FEMA CRCI at the county level using standard 

deviation to calculate the spread of values for the country.

The first report on this analysis, released in December 2018, used data from the 2012-2016 ACS 

5-year estimates. FEMA updated the indicator data with the ACS 5-year estimates from 2013-

2017 in the November 2019 RAPT release. In March 2020, FEMA updated RAPT with ACS 5-year

estimates for 2015-2019.
Community Resilience Indicator Analysis: 2022 Update

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3. 2022 CRCI Summary

3.1. Literature Review and Selected Methodologies

To capture the most current community resilience research, FEMA conducted another literature 

review in 2021 to identify peer-reviewed articles published from 2018-2021.

The literature review was conducted via a Web of Science library search using the search terms: 

“resilience” and “index, methodology(ies), or indicator(s)” and “community or disaster.” The 2021 

literature search identified 2,151 records. Argonne reviewed the abstracts for all records and 

selected articles that discussed resilience assessment methodologies not included in the 2018 

analysis. The team then compared the resulting 17 articles against the analysis inclusion criteria to 

identify additional methodologies for analysis.

Appendix A includes the comprehensive list of over 90 methodologies identified from the 2018 and 

2021 literature reviews. The table includes the methodologies names, dates of publication, a link to 

the methodology reports or developers and a determination for each inclusion criterion.

Combining the findings from both literature reviews, 14 methodologies met all of the inclusion 

criteria. Full citations for these methodologies are included in Appendix B.

The methodologies met the inclusion criteria from the 2022 literature review:

 Fraser: Japanese Social Capital and Social Vulnerability Indices

 Nursey-Bray: Indicators for Adaptive Capacity

 Regional Climate Resilience Index (RCRI)

 Composite Community Disaster Resilience Index (CCDRI)

 Comprehensive Disaster Resilience Index (CDRI2)

 Social Vulnerability Index (SoVI)

The following methodologies met the inclusion criteria from the 2018 literature review:

 Australian National Disaster Resilience Index (ANDRI)

 Baseline Resilience Indicators for Communities (BRIC)

 Community Disaster Resilience Index (CDRI)

 Community Resilience Index (CRI2)

 Disaster Resilience of Place (DROP)

 Resilient Capacity Index (RCI)

 Social Vulnerability Index (SVI)

 The Composite Resilience Index (TCRI)

3.2. Commonly Used Indicators and Data Sources

To identify the commonly used indicators across the 2022 CRIA set of 14 methodologies, the team 

first documented all the indicators used in each of the six newly identified methodologies (157 in
Community Resilience Indicator Analysis: 2022 Update

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total) and added them to the list of the indicators from the 2018 methodologies. The research team 

then established the threshold of commonly used indicators as those used in five or more of the 14 

methodologies (in at least 35% of the methodoloiges). This analysis resulted in identifying 22 

common indicators. The research team also documented citations from the methodologies’ authors

that explained their reasoning for using the indicators.

The following table lists the 22 indicators and references how many of the 14 methodologies used 

that indicator. Five indicators that were not on the 2018 list have been added: inactive voters, 

unemployed women, workforce employed in the dominant sector, households with a smartphone 

and population below poverty level. Three indicators were retired from the list because they were 

not used in at least five of the new set of 14 methodologies (listed in blue italics): public school

capacity, rental property capacity and hotel/motel capacity.
Community Resilience Indicator Analysis: 2022 Update

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Table 1: 2022 CRCI Indicators

Commonly Used Indicators 

Number of 

Methodologies 

Using this 

Indicator (of 14) 

Unemployed labor force 13

Population without a high school diploma 11

Percent of inactive voters* 10

Households without a vehicle 9

Number of hospitals 9

Population age 65 and older 9

Medical professional capacity 8

Population with a disability 7

Households with limited English 7

Single-parent households 7

Population without health insurance 7

Unemployed women labor force* 7

Presence of civic and social organizations 6

Median household income 6

Income inequality 6

Population change 6

Population without religious affiliation 6

Mobile homes as percentage of housing 6

Owner-occupied housing 6

Workforce in predominant sector* 5

Households without a smartphone* 5

Population below poverty level* 5

Public school capacity 4

Rental property capacity 4

Hotel/motel capacity 3

Indicators added in 2022 are marked with an asterisk “*”

Indicators in blue italics were included in the 2018 analysis but are not used in at least five of the 

2022 methodologies. 

After identifying the 22 commonly used indicators, the research team selected the most authoritative 

data source for each indicator metric. Most of the indicators related to population and community 

characteristics are available from the U.S. Census Bureau. When available, the research team 

selected datasets for U.S. counties, census tracts and tribes.
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To assist emergency managers with analyzing their community, the research team grouped the 22 

2022 CRCI Indicators into six categories:

Population Characteristics Economic

 Population without a High School Education  Population Below Poverty Level

 Population 65 and Older  Median Household Income

 Population with a Disability  Unemployed Labor Force

 Unemployed Women Labor Force

Household Characteristics  Income Inequality+

 Workforce in Predominant Sector  Households without a Vehicle

 Households with Limited English Connection to Community  Single-Parent Households

 Households without a Smartphone  Presence of Civic and Social Organizations*

 Population with Religious Affiliation*

Housing  Percent of Inactive Voters*

 Population Change*  Mobile Homes as Percentage of Housing

 Owner-Occupied Housing

Healthcare

Census tract data is available for all indicators, except:  Number of Hospitals* * Indicates County level data only,  Medical Professional Capacity* + Indicates County and Tribal level data only  Population without Health Insurance

3.3. Data Binning

With such large datasets, binning the data and assigning consistent color ramps for the bins 

provides a visual cue to quickly grasp a data range. While the specific datapoint for the geography 

(county, census tract or tribe) is also available, the bins provide a more immediate high-level 

understanding of a geographic area’s characteristics.

To bin each dataset for mapping, Argonne used the Python Spatial Analysis Library, PySAL, and its 

Exploratory Spatial Data Analysis sub-package. Python is an open-source, high-level programming 

language that is used in social science research. The package includes nine binning methods.

Rather than make arbitrary “breaks” in the data, these binning methods allowed the research team 

to use the best binning method that would group data that are close in value to each other and 

maximize the variance between bins.

The team evaluated which of the nine binning methods 1) best fit the relationships of the breaks to 

each dataset’s means and medians and 2) could be consistently replicated. This analysis identified 

four binning methods as the best fit for most datasets. For the county-level datasets, the research 

team binned the dataset into five bins. For the indicators with census tract data, the research team 

binned the dataset into seven bins, allowing greater differentiation with these substantially larger 

datasets.
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The binning methods for the 22 CRCI indicators are:

 Fisher–Jenks Breaks: This method aims to return class breaks such that classes are internally 

homogenous while assuring heterogeneity among classes. The Python toolkit calculates squared 

deviations against class means.

 Jenks–Caspall Breaks: This method aims to minimize the absolute deviation from within-class 

medians. Python’s calculation focuses on within-class absolute deviations from the median.

 Head/Tail Breaks: Algorithmically optimal breaks and the number of classes are based on the 

dataset itself. The Head/Tails Breaks method works well with heavily tailed datasets, iterating 

through the data to minimize around the mean.3

 Other: In specific cases, the team used alternative criteria to select binning methodologies.

‒ Income: A convention for displaying income data already exists: $0–20,000, $20,001– 

$40,000, etc. (an intuitive methodology similar to equal intervals).

‒ Population Change: The population change dataset is provided by the U.S. Census as “net 

migration,” which provides a positive (increase in population) or negative (decrease in 

population) number.4 Large population changes in either direction could cause challenges to 

resilience. The team chose to represent the population change data as standard deviations 

from zero, where less change is preferred to more change (regardless of whether the change 

is positive or negative).

Appendix C provides the indicator, data source, national average, binning method and other 

information about each of the 22 indicators. In the 2022 release of RAPT, all datasets drawing from 

the U.S. Census are from the 2016-2020 ACS 5-year estimates and the census tract boundaries are 

the updated boundaries from the 2020 Decennial Census. FEMA will update RAPT with new ACS 

data annually as new U.S. Census data is released.

3.4. Correlation Analysis

The research team conducted a correlation analysis to measure and describe the strength and 

direction of the relationships among the 22 CRCI indicators. The correlation analysis shows how 

individual indicators may be related to each other. Understanding these correlations helps

communities design resilience strategies that take these relationships into account.

The Pearson Correlation Coefficient is a numerical measure of linear correlation from −1 to 1.

3 Jiang, B., 2013, Head/tail Breaks: A New Classification Scheme for Data with a Heavy-tailed Distribution. The Professional 

Geographer, 65, 482-494. 

4 U.S. Census Bureau. https://www.census.gov/glossary/#term_Netmigration, accessed March 28, 2022.
Community Resilience Indicator Analysis: 2022 Update

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 A coefficient closer to 1 indicates a positive correlation (variable A increases as variable B

increases).

 A coefficient of 0 indicates no correlation.

 A coefficient closer to −1 indicates a negative correlation (variable A increases as variable B

decreases).

As jurisdictions consider strategies to address indicator metrics that reveal challenges to resilience, 

the correlation analysis helps identify populations that may face multiple challenges concurrently. 

For example, there is a high correlation between individuals that are unemployed and those that are 

more likely to speak a language other than English and be without access to a vehicle. Outreach to 

these populations should consider all three of these characteristics.

The full chart of Pearson Correlation Coefficients can be found in Appendix D. 

3.5. FEMA Community Resilience Challenges Index (CRCI)

The research team developed a process to create a composite index comprised of the 22 commonly 

used indicators, the FEMA CRCI. This index provides a relative composite value by county and by 

census tract, measured as an average of counts of standard deviations from the national mean for 

each indicator. The 2023 update to the FEMA CRCI uses the most currently available census data, 

the 2017-2021 ACS 5-year estimates, and will be updated annually.

To produce the CRCI, the team first oriented all the datasets in the same direction (higher numbers 

or percentages represent higher resilience challenges) and then converted each county and census 

tract’s data point to a standardized score value based on standard deviations above or below the 

indicator’s national mean (except for population change calculated as standard deviations from 

zero). The team then averaged the 22 standardized score values for each county and census tract to

create the FEMA CRCI value. Because there is no validated method for weighting resilience 

indicators, the research team did not weight individual indicators in developing the FEMA CRCI.

 County CRCI: When data for an indicator was not available for a given county, the team used the 

national mean for that indicator; this approach does not artificially push an aggregate indicator 

more positive or more negative. Though the team examined linear and non-linear models, they 

determined that using the national mean for missing county data, the simplest solution, was the 

best solution.

 Census Tract CRCI: When data for an indicator was not available at the census tract level,

the research team imputed the county data for the census tract calculation.

The FEMA CRCI process produces a numerical standard deviation data point for each county and

each census tract. As with the indicator datasets, the FEMA CRCI is binned into five bins for the 

county and seven bins for the census tract and are included in RAPT. Including the CRCI data in RAPT 

allows users to view both the composite index and datapoints for each indicator comprising the 

index to better understand the drivers behind the composite index values.
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3.6. Limitations and Benefits

Limitations 

Incomplete national datasets. The U.S. Census’s primary datasets do not include information for 

many of the U.S. territories, including Guam, the U.S. Virgin Islands, American Samoa, and the 

Commonwealth of the Northern Mariana Islands. Territories other than Puerto Rico may face some of 

the highest challenges to resilience in the U.S. but have not been assessed in this report because 

the data are not included in national datasets. 

Some data unavailable at census tract level. Some CRCI indicators have data available at the county 

level but not at the census tract level (Presence of Civic and Social Organizations, Inactive Voters, 

Number of Hospitals, Religious Affiliation and Population Change). When developing the census tract 

CRI, the research team imputed the county data for the census tract CRCI calculation.

Universal application of indicators. With the exception of not including the Households with Limited 

English indicator for CRCI in Puerto Rico, the CRCI for all other jurisdictions includes all 22 indicators. 

The CRCI does not factor in potential differences for challenges to resilience based on rural, suburban, or urban locations. 

No assessment of hazard risk or community capacity. This analysis identifies potential challenges to 

community resilience to help communities plan, prepare, and build resilience. This analysis does not 

capture specific hazard risk nor ways that a community may have developed the capacity to address 

these challenges. For example, communities with relatively lower levels of hospitals or medical 

professions per capita may have developed surge capacity support for medical services by training 

the public, supporting volunteer programs, or investing in mobile clinics. 

Benefits 

Peer-reviewed research. The analysis in this paper draws exclusively from the current body of peerreviewed research on community resilience and social vulnerability in disasters. The peer review 

process helps to ensure that the research methodologies have been subjected to the scrutiny of 

experts in related academic fields. 

Commonly used indicators suggest research agreement. This analysis identifies specific, individual 

indicators used in multiple methodologies (at least five methodologies out of 14 methodologies 

meeting the inclusion criteria). This approach captures multiple research perspectives to identify 

important resilience indicators rather than choosing one methodology among many. 

Both an index and data for individual indicators. In addition to producing a composite index, this 

analysis also provides county and census tract datapoints for each indicator. Many research 

methodologies produce an index to support high-level relative values and, in some cases, sub-
Community Resilience Indicator Analysis: 2022 Update

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indices, without providing the underlaying data for each indicator. By providing data for each 

indicator, this approach may help pinpoint specific areas to develop community capacity. 

Open-source data. The research team used only publicly available data to ensure that this analysis 

was fully transparent and could be replicated at no cost. The appendix outlines the source data for 

each indicator.

Broad application of findings. In addition to helping to prioritize locations for FEMA technical 

assistance, this analysis is useful for all phases of emergency management and can be used by 

many FEMA program areas and State, Local, Tribal and Territorial partners.
Community Resilience Indicator Analysis: 2022 Update

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3.7. Future Research

As the social science of community resilience continues to evolve, additional analysis could evaluate 

the usefulness of weighting the indicators, validating the indicators, examining specific indicators by 

risk and adding or retiring indicators. Principal component analysis, factor analysis, regression 

analysis, or structured sensitivity analysis could provide findings on the relative importance and 

weight of an indicator’s contribution to overall resilience. It may also be of interest to examine how 

these indicators may have different impacts in rural versus urban areas or differences by region.

The researchers involved in developing the CRCI process, and RAPT continue to look for 

opportunities to evaluate the validity and usefulness of this set of indicators with the goal of 

ensuring that the data is easily accessible and a useful focusing tool for initial situational awareness 

for preparedness, mitigation, response and recovery efforts.
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Appendix A: Methodologies from the Literature 

Review

Name Date 

Published

Author/ 

Developer

Title Unit of 

Analysis

Area of 

Focus Risk Focus

Pre or 

Post 

Disaster

Quantitative?Public 

Domain?

Public 

Data 

Source?

Hazus

Frequent 

Version 

Updates

Federal 

Emergency 

Management 

Agency

(FEMA)

Hazus

Methodology Community United 

States

Earthquake, 

Flood, 

Hurricane, 

Tsunami

Post Yes Yes Yes

Open 

Resilience 

Index (ORI)

2021 Feldmeyer, 

D; et. al.

An open

resilience index:

Crowdsourced

indicators

empirically

developed from

natural hazard

and climatic

event data

National Global All-hazards Pre Yes Yes Yes

Japanese 

Social 

Capital and 

Social 

Vulnerability 

Indices

2021 Fraser, T 

Japanese Social

Capital and

Social

Vulnerability

Indices:

Measuring

Drivers of

Community

Local Japan All-hazards Pre Yes Yes Yes
Community Resilience Indicator Analysis: 2022 Update

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Name Date 

Published

Author/ 

Developer

Title Unit of 

Analysis

Area of 

Focus Risk Focus

Pre or 

Post 

Disaster

Quantitative?Public 

Domain?

Public 

Data 

Source?

Resilience 2000-

2017

Conceptual 

Framework 

for Social 

Vulnerability

2021 Mason, K; et. 

al.

Social

Vulnerability

Indicators for

Flooding in

Aotearoa New

Zealand

Local

New 

Zealand

Flood Pre Yes Yes Yes

2021 Nursey-Bray, 

et. al.

Developing

indicators for

adaptive capacity

for multiple use

coastal regions:

Insights from the

Spencer Gulf,

South Australia

Local Australia All-hazards Pre Yes Yes Yes

Australian 

Disaster 

Resilience 

Index (ADRI)

2021 Parsons, M; 

et. al.

Disaster

resilience in

Australia: A

geographic

assessment

using an index of

coping and

adaptive capacity

National Australia All-hazards Pre Yes Yes No
Community Resilience Indicator Analysis: 2022 Update

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Name Date 

Published

Author/ 

Developer

Title Unit of 

Analysis

Area of 

Focus Risk Focus

Pre or 

Post 

Disaster

Quantitative?Public 

Domain?

Public 

Data 

Source?

2021

Zarghami, 

SA; Dumrak, 

J 

A system

dynamics model

for social

vulnerability to

natural disasters:

Disaster risk

assessment of an

Australian city

Local Australia All-hazards Pre Yes No Yes

Composite 

Community 

Disaster 

Resilience 

Index 

(CCDRI)

2020

Al Rifat, SA; 

Liu, WB

Measuring

Community

Disaster

Resilience in the

Conterminous

Coastal United

States

County United 

States

All-hazards Pre Yes Yes Yes

Regional 

Climate 

Resilience 

Index (RCRI)

2020 Feldmeyer, 

D; et. al.

Regional Climate

Resilience Index:

A Novel

multimethod

Comparative

Approach for

Indicator

Development,

Empirical

Validation and

Implementation

County Germany All-hazards Pre Yes Yes Yes
Community Resilience Indicator Analysis: 2022 Update

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Name Date 

Published

Author/ 

Developer

Title Unit of 

Analysis

Area of 

Focus Risk Focus

Pre or 

Post 

Disaster

Quantitative?Public 

Domain?

Public 

Data 

Source?

2020 Yang, YF; et. 

al.

A Federated PreEvent Community

Resilience

Approach for

Assessing

Physical and

Social SubSystems: an

Extreme Rainfall

Case In Hong

Kong

Urban Hong Kong All-hazards Pre and 

Post

Yes Yes No

2020

Zobel, CW; 

Baghersad, 

M 

Analytically

comparing

disaster

resilience across

multiple

dimensions

Local

United 

States All-hazards Post Yes Yes No

2019

GillespieMarthaler, L; 

et. al.

Selecting

Indicators for

Assessing

Community

Sustainable

Resilience

Local

United 

States

All-hazards Pre Mixed Yes Yes
Community Resilience Indicator Analysis: 2022 Update

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Name Date 

Published

Author/ 

Developer

Title Unit of 

Analysis

Area of 

Focus Risk Focus

Pre or 

Post 

Disaster

Quantitative?Public 

Domain?

Public 

Data 

Source?

DRIFT 2019

Manyena, B; 

Machingura, 

F; O'Keefe, P 

Disaster

Resilience

Integrated

Framework for

Transformation

(DRIFT): A new

approach to

theorizing and

operationalizing

resilience

Local Global All-hazards Pre Yes Yes No

Comprehensi 

ve Disaster 

Resilience 

Index

(CDRI2)

2019 Marzi, S; et. 

al.

Constructing a

Comprehensive

Disaster

Resilience Index:

The case of Italy

Local Italy All-hazards Pre Yes Yes Yes

2019

Nicholson, D; 

Vanli, OA; 

Jung, S; 

Ozguven, EE

A spatial

regression and

clustering

method for

developing placespecific social

vulnerability

indices using

census and

social media data

Local

United 

States

All-hazards Post Yes Yes No
Community Resilience Indicator Analysis: 2022 Update

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Name Date 

Published

Author/ 

Developer

Title Unit of 

Analysis

Area of 

Focus Risk Focus

Pre or 

Post 

Disaster

Quantitative?Public 

Domain?

Public 

Data 

Source?

2018 Rohith, VR; 

et. al.

Disaster

Preparedness

Index: A Valid and

Reliable Tool to

Comprehend

Disaster

Preparedness in

India

Local India All-hazards Pre No Yes No

5S Social 

Resilience 

Framework

2018 Saja, AMA; 

et. al.

An inclusive and

adaptive

framework for

measuring social

resilience to

disasters

Local Global All-hazards Pre No Yes No

NaHRSI 2018 Summers, 

JK; et. al.

Measuring

Community

Resilience to

Natural Hazards:

The Natural

Hazard

Resilience

Screening Index

(NaHRSI) -

Development and

Application to the

United States

County United 

States

All-hazards Pre Yes No No
Community Resilience Indicator Analysis: 2022 Update

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Name Date 

Published

Author/ 

Developer

Title Unit of 

Analysis

Area of 

Focus Risk Focus

Pre or 

Post 

Disaster

Quantitative?Public 

Domain?

Public 

Data 

Source?

ANDRI2 2017 P. Morley et. 

al.

The Australian

Natural Disaster

Resilience Index

(ANDRI2): A

System for

Assessing the

Resilience of

Australian

Communities to

Natural Hazards

Community Australia Natural Pre Mixed Yes Yes

GRI 2017 FM Global 2018 FM Global

Resilience Index

(GRI)

Country Global Multiple Pre Yes Yes No

CREAT 2016

U.S.

Environment 

al Protection 

Agency

Climate Resilience

Evaluation and

Awareness Tool

(CREAT)

Water 

Utilities

United 

States Climate Risk Pre Mixed No No

2016

National 

Institute of 

Standards 

and 

Technology 

(NIST)

Community

Resilience

Planning Guide for

Building and

Infrastructure

Systems (Volumes

1 and 2)

Community Kenya/ 

Uganda Infrastructure Pre No Yes No
Community Resilience Indicator Analysis: 2022 Update

21

Name Date 

Published

Author/ 

Developer

Title Unit of 

Analysis

Area of 

Focus Risk Focus

Pre or 

Post 

Disaster

Quantitative?Public 

Domain?

Public 

Data 

Source?

RIM 2016 N.S. Lam et 

al.

Resilience

Inference

Measurement

(RIM): Measuring

Community

Resilience to

Coastal Hazards

along the

Northern Gulf of

Mexico

County United 

States

Coastal 

Hazards Post Yes Yes Yes

WRI 2016

Institute for 

Environment 

and Human 

Security of 

the

United Natio

ns

World Risk Index

(WRI) Country Global Multiple Pre Yes Yes Yes

AGIR 2015 European 

Commission

Measuring and

Monitoring

Progress on

Resilience

Building for Food

and Nutrition

Security

Country West Africa Food Security Pre Mixed No Yes

CDR 2015

D. Keun et 

al.

A Measurement of

Community

Disaster

Resilience (CDR)

in Korea

Community South 

Korea Natural Pre Yes Yes Yes
Community Resilience Indicator Analysis: 2022 Update

22

Name Date 

Published

Author/ 

Developer

Title Unit of 

Analysis

Area of 

Focus Risk Focus

Pre or 

Post 

Disaster

Quantitative?Public 

Domain?

Public 

Data 

Source?

CDRST 2015

Torrens 

Resilience 

Institute

Developing a

Model and Tool to

Measure

Community

Disaster

Resilience

Community Australia Multiple Pre Mix Yes Mixed

CRDSA 2015 S.A. Alshehri 

et al.

Disaster

Community

Resilience

Assessment

Method: A

Consensus based

Delphi and AHP

Approach

Community Saudi 

Arabia Multiple Pre Mix No No

CR-E 2015 Nasrullah et 

al.

Status of

Community

Resilience in

Disaster Prone

Districts of

Pakistan

District Pakistan Earthquake Pre Yes Yes No

CRF 2015

The 

Rockefeller 

Foundation,

Arup

City Resilience

Framework (CRF)

and City

Resilience Index

City Global Multiple Pre No Yes No

FSRI 2015

New

Economics 

Foundation

Financial System

Resilience Index

(FSRI)

Country Global Financial 

System Pre Yes No No

RELi 2015

Capital 

Markets

Partnership

RELi Resilience

Action Checklist Community United 

States Infrastructure Pre No Yes No

TCRI 2015

T.

Perfrement 

and T. Lloyd

The Composite

Resilience Index

(TCRI)

Community Australia Natural Pre Yes Yes Yes
Community Resilience Indicator Analysis: 2022 Update

23

Name Date 

Published

Author/ 

Developer

Title Unit of 

Analysis

Area of 

Focus Risk Focus

Pre or 

Post 

Disaster

Quantitative?Public 

Domain?

Public 

Data 

Source?

TNC Coastal 

Resilience 2015

The Nature

Conservancy 

(TNC)

Coastal Resilience

Mapping Tool Community Global Coastal 

Hazards Pre Yes No Yes

UDRI 2015

Earthquakes 

and 

Megacities 

Initiative

A Guide to

Measuring Urban

Risk Resilience –

the Urban

Disaster Risk

Index (UDRI)

City Global Natural Post Mixed Yes No

Spatially 

Explicit 

Resilience 

Vulnerability 

Model 

(SERV)

2014

Tim G. 

Frazier, et. 

al.

A framework for

the development

of the SERV

model: A Spatially

Explicit

ResilienceVulnerability

model

Local United 

States Flood Pre Yes No No

ASPIRE 2014

The World 

Bank

The Atlas of Social

Protection

Indicators of

Resilience and

Equity (ASPIRE)

Country Global Poverty Pre Yes Yes Yes

BRIC 2014

Susan Cutter 

et al.

Baseline

Resilience

Indicators for

Communities

(BRIC),

The Geographies

of Community

Disaster

Resilience

County United 

States Multiple Pre Yes Yes Yes
Community Resilience Indicator Analysis: 2022 Update

24

Name Date 

Published

Author/ 

Developer

Title Unit of 

Analysis

Area of 

Focus Risk Focus

Pre or 

Post 

Disaster

Quantitative?Public 

Domain?

Public 

Data 

Source?

CoBRA 2014

United 

Nations 

Development 

Programme 

(UNDP)/Dryla 

nds 

Development

Centre

Community Based

Resilience

Analysis (CoBRA)

Community Kenya, 

Uganda Drought Pre No Yes No

CRS 2014

Community 

and Regional 

Resilience 

Institute, 

Meridien

A Practical

Approach to

Building

Resilience;

Community

Resilience System

(CRS)

Community United 

States Multiple Pre Yes No No

FCR 2014

International 

Federation of 

Red Cross

(IFRC)

IFRC Framework

for Community

Resilience (FCR)

Community Global Multiple Pre Mixed Yes No

Grosvenor 2014 Grosvenor Resilient Cities

Research Report City Global Multiple Pre Mixed No N/A

RCI 2014 Foster, K.A.

Resilience

Capacity Index

(RCI)

Metropolitan 

Statistical

Area

United 

States Multiple Pre Yes Yes Yes

RRI – Rural 2014

Rural 

Disaster 

Resilience

Project

Rural Resilience

Index (RRI)

Community 

– Rural Global Multiple Pre No No N/A
Community Resilience Indicator Analysis: 2022 Update

25

Name Date 

Published

Author/ 

Developer

Title Unit of 

Analysis

Area of 

Focus Risk Focus

Pre or 

Post 

Disaster

Quantitative?Public 

Domain?

Public 

Data 

Source?

UCR 2014 Rockefeller 

Foundation

Urban Climate

Resilience (UCR):

A Review of

Methodologies

Adopted under the

ACCCRN Initiative

in Indian Cities

City India Natural Pre No No No

United 

Nations 

International 

Strategy for 

Disaster 

Reduction

(UNISDR)

2014 UNISDR

Disaster

Resilience

Scorecard for

Cities

City Global Multiple Pre No Yes No

WISC 2014

Well-being, 

Identity, 

Services and 

Capitals 

(WISC)

Theorizing

Community

Resilience to

Improve

Computational

Modeling

Community United 

States Multiple Pre Yes No Yes

CCRAM 2013 D. Leykin et 

al.

Conjoint

Community

Resilience

Assessment

Measure (CCRAM)

Community Global Multiple Pre and 

post

Mixed No No

CRR 2013

World 

Economic

Forum

Global Risks 2013 Country Global Multiple Pre Yes Yes Yes
Community Resilience Indicator Analysis: 2022 Update

26

Name Date 

Published

Author/ 

Developer

Title Unit of 

Analysis

Area of 

Focus Risk Focus

Pre or 

Post 

Disaster

Quantitative?Public 

Domain?

Public 

Data 

Source?

CV 2013

Texas A&M 

University, 

Hazard 

Reduction 

and 

Recovery

Center

Status and Trends

of Coastal

Vulnerability (CV)

to Natural

Hazards Project

County United 

States

Coastal 

Hazards Pre Yes Yes Yes

IDRI 2013

United 

Nations 

Development

Programme

Indonesia Disaster

Recovery Index

(IDRI)

Community Indonesia Volcano/ 

Flood Post Mixed No Yes

IDS 2013

Institute of 

Development 

Studies (IDS)

Towards a

Quantifiable

Measure of

Resilience

Multi-level Global Food Security Pre Yes Yes N/A

LDRI 2013 P.M. Orencio 

and M. Fujii

Localized

DisasterResilience Index

(LDRI)

Community Philippines Coastal 

Hazards Pre Mixed No No

ODI 2013 NIST

Overseas

Development

Institute (ODI),

Disaster Risk

Management

Potential Targets

and Indicators

Community Global Multiple Pre and 

Post Yes No N/A
Community Resilience Indicator Analysis: 2022 Update

27

Name Date 

Published

Author/ 

Developer

Title Unit of 

Analysis

Area of 

Focus Risk Focus

Pre or 

Post 

Disaster

Quantitative?Public 

Domain?

Public 

Data 

Source?

ORP 2013

Oregon 

Seismic 

Safety Policy 

Advisory 

Commission

The Oregon

Resilience Plan

(ORP)

Reducing Risk

and Improving

Recovery for the

Next Cascadia

Earthquake and

Tsunami

Regional Oregon Infrastructure Post Mixed Yes No

OXFAM 2013 OXFAM

A

Multidimensional

Approach to

Measuring

Resilience

Community Global Humanitarian Pre Mixed No No

RMI 2013

Argonne 

National 

Laboratory

Resilience

Measurement

Index (RMI):

Indicator of

Critical

Infrastructure

Resilience

Facility United 

States Infrastructure Pre Mixed No Mixed

RRI2 2013 DARA Risk Reduction

Index (RRI2)

Territorial

Units West Africa Multiple Pre No Yes No

SERI 2013 Verisk

Maplecroft

Socio-economic

Risk Index (SERI) Country Global Multiple Pre Yes No N/A

Surging Seas 2013 Climate

Central

Surging Seas Risk

Finder Community U.S. Coast StormSurge/Flood Pre Yes Yes Yes
Community Resilience Indicator Analysis: 2022 Update

28

Name Date 

Published

Author/ 

Developer

Title Unit of 

Analysis

Area of 

Focus Risk Focus

Pre or 

Post 

Disaster

Quantitative?Public 

Domain?

Public 

Data 

Source?

US Agency

for 

International 

Development 

(USAID)

2013 Feed the 

Future

Community

Resilience:

Conceptual

Framework and

Measurement –

Feed the Future

Learning Agenda

Community Global Poverty Pre Yes No No

CART 2012

R.L.

Pfefferbaum 

et al.

Communities

Advancing

Resilience Toolkit

(CART)

Community United 

States Multiple Pre No Yes No

DRLA 2012

Disaster 

Resilience 

Leadership 

Academy 

(DRLA),

Tulane

University

Haiti

Humanitarian

Assistance

Evaluation:

Resilience

Perspective

Household Haiti Natural Pre Mixed Yes No

GFM 2012

UN Office for 

the 

Coordination 

of 

Humanitaria 

n Affairs 

(OCHA) and

Maplecroft

Global Focus

Model (GFM) Country Global Multiple Pre Yes No Mixed

ICBRR 2012 Canadian 

Red Cross

Measuring

Disaster-Resilient

Communities;

Integrated

Community Based

Risk Reduction

(ICBRR)

Coastal 

Community Indonesia Coastal Hazards Pre Mixed No No
Community Resilience Indicator Analysis: 2022 Update

29

Name Date 

Published

Author/ 

Developer

Title Unit of 

Analysis

Area of 

Focus Risk Focus

Pre or 

Post 

Disaster

Quantitative?Public 

Domain?

Public 

Data 

Source?

LCOT 2012 Tufts 

University

Livelihoods

Change Over Time

(LCOT)

Household

Sudan,

Ethiopia, 

Haiti

Multiple Post Yes Yes Yes

BCRD 2011 RAND

Building

Community

Resilience to

Disasters (BCRD)–

A Way Forward to

Enhance National

Health Security

Community United 

States Health Pre Mixed No Mixed

PVI 2011

InterAmerican 

Development 

Bank

Indicators of

Disaster Risk and

Risk

Management;

Prevalent

Vulnerability Index

(PVI)

Country 

and Subnational

Latin 

America Multiple Pre Yes No Yes

ResilUS 2011

U.S.

Resilience 

Institute, 

Western 

Washington

University

U.S. Resilience 

Institute (ResilUS) Community United States Earthquake Post Yes No Yes

SVI 2011

Agency for 

Toxic 

Substances 

& Disease

Registry

Social

Vulnerability Index

(SVI)

County United 

States Multiple Pre Yes Yes Yes
Community Resilience Indicator Analysis: 2022 Update

30

Name Date 

Published

Author/ 

Developer

Title Unit of 

Analysis

Area of 

Focus Risk Focus

Pre or 

Post 

Disaster

Quantitative?Public 

Domain?

Public 

Data 

Source?

Community 

Disaster 

Resilience 

Index (CDRI)

2010

W. G.

Peacock, et. 

al.

Advancing

Resilience of

Coastal Localities:

Developing,

Implementing and

Sustaining the

Use of Coastal

Resilience

Indicators

Coastal U.S.

Coastal Multiple Pre Mixed Yes Yes

CDRI3 2010

Kyoto 

University, 

UNISDR

Climate and

Disaster

Resilience

Initiative (CDRI3);

Capacity Building

Program

City Southeast 

Asia Multiple Pre Mixed Yes No

CERI 2010

Advantage 

West 

Midlands

Community

Economic

Resilience Index

(CERI)

Community U.K. Recession Pre Yes Yes Yes

CRI 2010

MississippiAlabama Sea 

Grant

Consortium

Coastal Resilience

Index (CRI): A

Community SelfAssessment

Community

United 

States – 

Coastal

Coastal 

Hazards Post No Yes No

CRI2 2010

K. Sherrieb 

et al.

Measuring

Capacities for

Community

Resilience,

Community

Resilience Index

(CRI2)

County United 

States Multiple Pre Yes No Yes
Community Resilience Indicator Analysis: 2022 Update

31

Name Date 

Published

Author/ 

Developer

Title Unit of 

Analysis

Area of 

Focus Risk Focus

Pre or 

Post 

Disaster

Quantitative?Public 

Domain?

Public 

Data 

Source?

DROP 2010 S. Cutter et 

al.

Disaster

Resilience of

Place (DROP),

Disaster

Resilience

Indicators for

Benchmarking

Baseline

Conditions

County

United 

States – 

Southeast

None Pre Yes Yes Yes

FAO 2010

Food and 

Agriculture 

Organization 

(FAO) of the 

United

Nations (UN)

FAO Resilience

Tool Community Global Food Security Pre Yes Yes Yes

FAOLivelihoods 2010

L. Alinovi et 

al., European

Report on 

Development

Livelihoods

Strategy and

Household

Resilience to Food

Insecurity

Country Kenya Food Security Pre Yes Yes No

PEOPLES 2010

NIST, 

MCEER:

University of

Buffalo

PEOPLES

Resilience

Framework

Community United 

States Multiple Pre Mixed No Yes

CRT 2009 Bay Localize

Community

Resilience Toolkit

(CRT): Workshop

Guide

City or 

County

United 

States

Climate 

Change Pre No Yes No
Community Resilience Indicator Analysis: 2022 Update

32

Name Date 

Published

Author/ 

Developer

Title Unit of 

Analysis

Area of 

Focus Risk Focus

Pre or 

Post 

Disaster

Quantitative?Public 

Domain?

Public 

Data 

Source?

DFID 2009

DFID

Disaster Risk 

Reduction 

Interagency 

Coordination 

Group

Characteristics of

a DisasterResilient

Community

Community Global Multiple Pre Mixed Yes No

SPUR 2009

San 

Francisco 

Planning + 

Urban 

Research 

Association

(SPUR)

The Resilient City:

Defining What San

Francisco Needs

from its Seismic

Mitigation Policies

Community United 

States

Earthquake/ 

Infrastructure Post Yes No No

CARRI 2008

Oak Ridge 

National 

Laboratory

Community and

Regional

Resilience

Initiative (CARRI)

Community United 

States Multiple Pre Yes Yes

Not 

Identifie 

d 

Hyogo 2008

International 

Strategy for 

Disaster 

Reduction

Indicators of

Progress:

Guidance on

Measuring the

Reduction of

Disaster Risks and

the

Implementation of

the Hyogo

Framework for

Action

City Global Natural Pre and 

Post Mixed Yes No
Community Resilience Indicator Analysis: 2022 Update

33

Name Date 

Published

Author/ 

Developer

Title Unit of 

Analysis

Area of 

Focus Risk Focus

Pre or 

Post 

Disaster

Quantitative?Public 

Domain?

Public 

Data 

Source?

RASA 2008

B. Maguire 

and S.

Cartwright

Assessing a

Community’s

Capacity to

Manage Change:

A Resilience

Approach to

Social

Assessment

(RASA)

Community Australia 

(rural) Water Scarcity Pre No Yes No

Resilient 

Capacity 

Index (RCI2) 

– Regions

2008

Berkeley 

Institute of 

Urban and 

Regional

Development

Resilience and

Regions: Building

Understanding of

the Metaphor

Metro 

Regions Global Multiple Pre N/A Yes N/A

CCR/ 

IOTWS 2007

USAID-Asia 

Community 

Coastal 

Resilience 

(CCR)

A Guide for

Evaluating Coastal

Community

Resilience to

Tsunami/Other

Hazards

Community Southeast 

Asia Tsunami Pre No Yes No

MCEER R4 2007

Multidisciplin 

ary Center

for 

Earthquake 

Engineering 

Research 

(MCEER),

University of

Buffalo

Conceptualizing

and Measuring

Resilience

Community Global Infrastructure Pre N/A Yes N/A
Community Resilience Indicator Analysis: 2022 Update

34

Name Date 

Published

Author/ 

Developer

Title Unit of 

Analysis

Area of 

Focus Risk Focus

Pre or 

Post 

Disaster

Quantitative?Public 

Domain?

Public 

Data 

Source?

TRIAMS 2006 World Health 

Organization

Tsunami Recovery

Impact

Assessment and

Monitoring System

Risk Reduction

Indicators

(TRIAMS)

Community Indian 

Ocean Tsunami Post Mixed Yes No

THRIVE 2004 Prevention 

Institute

Tool for Health &

Resilience in

Vulnerable

Environments

(THRIVE)

Community United 

States

Health 

Disparity Pre Mixed Yes No

Social 

Vulnerability 

Index (SoVI)

2003

Cutter, SL; 

Boruff, BJ; 

Shirley, WL

Social

Vulnerability to

Environmental

Hazards

County United 

States

All-hazards Pre Yes Yes Yes
Community Resilience Indicator Analysis: 2022 Update

35

Appendix B: Methodologies 

Meeting CRCI Inclusion Criteria

* Indicates a methodology in the 2018 CRIA

^ Indicates an international methodology

Australian Natural Disaster Resilience Index (ANDRI)*^

Phil Morley, Melissa Parsons and Sarb Johal, 2017, “The Australian Natural Disaster Resilience 

Index: A System for Assessing the Resilience of Australian Communities to Natural Hazards,” 

Bushfire & Natural Hazards CRC. Available at https://www.bnhcrc.com.au/research/hazardresilience/251, accessed March 20, 2023. 

Baseline Resilience Indicators for Communities (BRIC)*

Susan L. Cutter, Kevin D. Ash and Christopher T. Emrich, 2014, “Baseline Resilience Indicators for 

Communities, the Geographies of Community Disaster Resilience,” Global Environmental Change 

29, 65–77. Available at 

https://www.sciencedirect.com/science/article/pii/S0959378014001459?casa_token=3O407z10

Qm0AAAAA:Y5ulORVy-s9vrNcwASxOb28AD15MgS35Urfa1VCQ1n7Hae3Mt3oR6yKjes9Y7K_f1HQiOYB, accessed March 30, 2023. 

Composite Community Disaster Resilience Index (CCDRI)

Rifat, S. A. A., & Liu, W., 2020, “Measuring Community Disaster Resilience in the Conterminous 

Coastal United States.” ISPRS International Journal of Geo-Information. Available at 

https://www.mdpi.com/2220-9964/9/8/469/pdf accessed March 30, 2023. 

Community Disaster Resilience Index (CDRI)*

Walter Gillis Peacock, et al., 2010, “Advancing Resilience of Coastal Localities: Developing, 

Implementing, and Sustaining the Use of Coastal Resilience Indicators: A Final Report,” Hazard 

Reduction and Recovery Center, December. Available at 

https://www.researchgate.net/profile/Walter_Peacock/publication/254862206_Final_Report_Adva

ncing_the_Resilience_of_Coastal_Localities_10-02R/links/00b7d51feb3e3d0d4a000000.pdf,

accessed March 30, 2023. 

Comprehensive Disaster Resilience Index (CDRI2)^

Marzi, S., Mysiak, J., Essenfelder, A. H., Amadio, M., Giove, S., & Fekete, A., 2019, “Constructing a 

Comprehensive Disaster Resilience Index: The Case of Italy.” PloS one. Available at 

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0221585, accessed March 30, 

2023. 
Community Resilience Indicator Analysis: 2022 Update

36

Community Resilience Index (CRI2)*

Kathleen Sherrieb, Fran H. Norris and Sandro Galea, 2010, “Measuring Capacities for Community 

Resilience,” Social Indicators Research 99: 227–247. Available at 

https://link.springer.com/article/10.1007/s11205-010-9576-9, accessed March 30, 2023. 

Disaster Resilience of Place (DROP)*

Susan L. Cutter, Christopher G. Burton and Christopher T. Emrich, 2010, “Disaster Resilience of 

Place, Disaster Resilience Indicators for Benchmarking Baseline Conditions,” Journal of Homeland 

Security and Emergency Management 7. Available at 

https://www.degruyter.com/abstract/j/jhsem.2010.7.1/jhsem.2010.7.1.1732/jhsem.2010.7.1.17

32.xml, accessed March 30, 2023. 

Fraser^

Fraser, T., 2021, “Japanese Social Capital and Social Vulnerability Indices: Measuring Drivers of 

Community Resilience 2000–2017.” International Journal of Disaster Risk Reduction. Available at 

https://www.sciencedirect.com/science/article/pii/S2212420920314679?casa_token=oaC86lYRu

wgAAAAA:ChyrqLcLG-4TT_ZqxEMMDP9oFyRMJODxQ6To9x5yfaLmZxYOMUb4qc3UIx1UdteBCftuEd7d,

accessed March 30, 2023. 

Nursey-Bray^

Nursey-Bray, M., Gillanders, B., & Maher, J. A., 2021, “Developing Indicators for Adaptive Capacity for 

Multiple Use Coastal Regions: Insights from the Spencer Gulf, South Australia.” Ocean & Coastal 

Management. Available at 

https://www.sciencedirect.com/science/article/pii/S0964569121002118?casa_token=ofxgFiTUUE

0AAAAA:qsHc0N1BtTDGNR4w5Phl6g9B_QGfpCj1y-GaF1CottH2i3eLEsQzPKLGC40C39LABoed8qmK,

accessed March 30, 2023. 

Resilience Capacity Index (RCI)*

Kathryn A. Foster, 2014, “Resilience Capacity Index: Disaster Resilience Measurements: Stocktaking 

of Ongoing Efforts in Developing Systems for Measuring Resilience, United Nations Development 

Programme, February, p. 38. Available at 

https://www.preventionweb.net/files/37916_disasterresiliencemeasurementsundpt.pdf, accessed 

March 30, 2023. 

Regional Climate Resilience Index (RCRI)^

Feldmeyer, D., Wilden, D., Jamshed, A., & Birkmann, J., 2020, “Regional Climate Resilience Index: A 

Novel Multimethod Comparative Approach for Indicator Development, Empirical Validation and 

Implementation.” Ecological indicators. Available at 

https://www.sciencedirect.com/science/article/pii/S1470160X20307998?casa_token=_VRVTAEaj

gUAAAAA:pTCr0FbuAU7Y7mjURGNV44_JYPRbhjy2cqxNXdiDcGhwt6SE-IUfzKFQQopJ0pKyZ2wwwTYB,

accessed March 30, 2023.
Community Resilience Indicator Analysis: 2022 Update

37

Social Vulnerability Index (SoVI)

Cutter, Susan L., Bryan J. Boruff and W. Lynn Shirley, 2003, "Social Vulnerability to Environmental 

Hazards." Social Science Quarterly 84.2. Available at 

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/1540-

6237.8402002?casa_token=IUAXvoqNhUUAAAAA:SAFjsqpLlMmcdZHyB0n4s6DyUlw65VVv0u9XKwX

4nRakED59dUqGHEW5FKDXDDRjiSTKIvmNzhM1Iw, accessed March 30, 2023. 

Social Vulnerability Index (SVI)*1

Barry E. Flanagan, et al., 2011, “A Social Vulnerability Index for Disaster Management,” Journal of 

Homeland Security and Emergency Management 8. Available at 

https://svi.cdc.gov/Documents/Data/A%20Social%20Vulnerability%20Index%20for%20Disaster%2

0Management.pdf, March 30, 2023. 

The Composite Resilience Index (TCRI)*

T. Perfrement and T. Lloyd, 2015, “The Composite Resilience Index: The Modelling Tool to Measure 

and Improve Community Resilience to Natural Hazards,” The Resilience Index. Available at 

https://theresilienceindex.weebly.com/our-solution.html, accessed March 30, 2023.

1 The CRCI literature review was conducted in 2021. The SVI has since been updated as of December 2022.
Community Resilience Indicator Analysis: 2022 Update

38

Appendix C: List of Commonly 

Used Indicators from Selected 

Methodologies

This appendix provides details about each of the 22 indicators identified through the 2022 analysis 

process. For each indicator, the tables below include:

 Indicator metric;

 Data source;

 National average;

 Binning method used;

 Data geography (available at county, census tract, tribal, Puerto Rico and other);

 Methodologies using this indicator; and

 Author rationale for including this indicator.

Each table notes which of the following methodologies are used each indicator. Below are the 

methodologies used in the CRCI, their abbreviations (as applicable), and the reference letter that 

corresponds to the citations found in the “author rationale for including this indicator” section in the 

tables below. Full citations for these methodologies are found in Appendix B.

 Australian Disaster Resilience Index (ANDRI) a

 Baseline Resilience Indicators for Communities (BRIC) b

 Composite Community Disaster Resilience Index (CCDRI) c

 Community Disaster Resilience Index (CDRI) d

 Comprehensive Disaster Resilience Index (CDRI2) e

 Disaster Resilience of Place (DROP) f

 Fraser g

 Nursey-Bray h

 Resilience Capacity Index (RCI) i

 Regional Climate Resilience Index (RCRI) j

 Social Vulnerability Index (SoVI) k

 Social Vulnerability Index (SVI) l

 The Composite Resilience Index (TCRI) m
Community Resilience Indicator Analysis: 2022 Update

39

Population Characteristics: 3 Indicators

Population without High School Diploma

Metric Data Source

Percentage of population over age 25 without a high 

school diploma or General Educational

Development (GED)

American Community Survey (ACS) 2017–2021 

five-year estimates, Table S1501

National Average Binning Methods

11.1% of the population over age 25 do not have a 

high school diploma or GED.

Census Tract: Jenks 

Caspall

County: Jenks Caspall

Data Geography

Data is available at the Census tract, county and Tribal levels. Puerto Rico is included.

Methodologies Using This Indicator

# 

of

14

ANDRI BRIC CDRI CRI2 DROP RCI SoVI SVI TCRI N-B CCDRI RCRI CDRI2 Fraser

11 X X X X X X X X X X X 

Author Rationale for Including This Indicator

Higher levels of education are associated with health, as well as an improved ability to communicate 

and comprehend information. b,m

Education is included as an input to economic resilience as higher levels of education is a 

characteristic of a strong labor force and supports individuals’ ability to access community resources. d,j

Higher levels of education can improve the capacity to prepare for, and respond to, the stress of 

disasters. a,g,n

For individuals with lower levels of education, the practical and bureaucratic hurdles to assist in coping 

with, and recovering from, a disaster are much more difficult to navigate. m
Community Resilience Indicator Analysis: 2022 Update

40

Population Age 65 and Older

Metric Data Source

Percentage of the population age 65 and older ACS 2017–2021 five-year estimates, 

Table S0101

National Average Binning Methods

16.0% of the U.S. population is age 65 and older. Census Tract: Fisher 

Jenks

County: Jenks Caspall

Data Geography

Data is available at the Census tract, county and Tribal levels. Puerto Rico is included.

Methodologies Using This Indicator

# 

of

14

ANDRI BRIC CDRI CRI2 DROP RCI SoVI SVI TCRI N-B CCDRI RCRI CDRI2 Fraser

9 X X X X X X X X X 

Author Rationale for Including This Indicator

Several methodologies noted that the percentage of elderly adults in the population could affect 

resilience. a,b,g

Those over 65 tend to be less mobile. n

Those over 65 may find it more difficult to prepare for disasters and adapt to extreme circumstances. n

Many people over 65 require assistance from family, neighbors and others, which might not be 

available during a disaster. m
Community Resilience Indicator Analysis: 2022 Update

41

Population with a Disability

Metric Data Source

Percentage of the population with a disability5 ACS 2017–2021 five-year estimates, 

Table S1810

National Average Binning Methods

12.7% of the U.S. population has a disability Census Tract: Fisher 

Jenks

County: Jenks Caspall

Data Geography

Data is available at the Census tract, county and Tribal level. Puerto Rico is included.

Methodologies Using This Indicator

# 

of

14

ANDRI BRIC CDRI CRI2 DROP RCI SoVI SVI TCRI N-B CCDRI RCRI CDRI2 Fraser

7 X X X X X X X 

Author Rational for Including This Indicator

Individuals with disabilities tend to be more vulnerable to physical, social and economic challenges. b,j

Having functional, mobility, or access needs can make responding to disasters more challenging, 

including adapting to extreme circumstances and dealing with the increased stress. a,j,n

During an emergency, family members, neighbors, or a caretaker may be less able to provide support 

to individuals with special needs that require the assistance of others. m

5 Per the ACS question wording, this definition would include individuals with the following conditions: serious difficulty 

hearing, seeing, walking and/or dressing; serious difficulty because of a physical, mental or emotional condition; serious 

difficulty concentrating, remembering, making decisions, or doing errands alone.
Community Resilience Indicator Analysis: 2022 Update

42

Household Characteristics: 4 Indicators

Households Without a Vehicle

Metric Data Source

Percentage of occupied housing units with no 

vehicles available

ACS 2017–2021 five-year estimates, 

Table B08201

National Average Binning Methods

8.3% of households are without a vehicle. Census Tract: Jenks 

Caspall

County: Head Tail 

Breaks

Data Geography

Data is available at the Census tract, county and Tribal levels. Puerto Rico is included.

Methodologies Using This Indicator

# 

of

14

ANDRI BRIC CDRI CRI2 DROP RCI SoVI SVI TCRI N-B CCDRI RCRI CDRI2 Fraser

9 X X X X X X X X X 

Author Rationale for Including This Indicator

Access to transportation helps individuals support their livelihoods and provides critical mobility to 

adapt to the extreme circumstances of a disaster. d,g,n

Communities where fewer individuals have access to a vehicle may have less resilience to a disaster. b

Lack of access to vehicle can be especially problematic in terms of evacuation in urban areas where 

automobile ownership is lower, especially among inner city poor populations. m
Community Resilience Indicator Analysis: 2022 Update

43

Households with Limited English

Metric Data Source

Percentage of households in which everyone 14 and 

older has difficulty speaking English.6

ACS 2017–2021 five-year estimates, 

Table S1602

National Average Binning Methods

4.2% of U.S. households are limited Englishspeaking households where all members 14 or 

older have difficulty speaking English.

Census Tract: Fisher 

Jenks

County: Jenks Caspall

Data Geography

Data is available at the Census tract, county and Tribal levels. Puerto Rico is included.

Methodologies Using This Indicator

# of 14 ANDRI BRIC CDRI CRI2 DROP RCI SoVI SVI TCRI N-B CCDRI RCRI CDRI2 Fraser

7 X X X X X X X 

Author Rationale for Including This Indicator

Proficiency in English supports community resilience because of improved ability to communicate 

between individuals, as well as allowing individuals to better access community resources. a,d,m

Greater numbers of proficient English speakers can be vital for effective communication interactions in 

the event of a disaster. b,n

In communities where the first language is neither English nor Spanish, accurate translations of 

advisories may be scarce. m

Communities with fewer English-speaking residents may demonstrate lower levels of resilience. g

6 A “limited English-speaking household” is one in which no member 14 years and older speaks only English or speaks a 

non-English language and speaks English “very well.” In other words, all members 14 years and older have at least 

some difficulty with English (https://census.gov/library/visualizations/2017/comm/english-speaking.html.html,

accessed August 7, 2018).
Community Resilience Indicator Analysis: 2022 Update

44

Single-Parent Households

Metric Data Source

Percentage of households with single parents of 

children under 18 (no spouse/partner present) 

ACS 2017–2021 five-year estimates, 

Table B09005

National Average Binning Method

25.1% of U.S. family households are single parent 

households.

Census Tract: Jenks 

Caspall

County: Jenks Caspall

Data Geography

Data is available at the Census tract, county and Tribal levels. Puerto Rico is included.

Methodologies Using This Indicator

# 

of

14

ANDRI BRIC CDRI CRI2 DROP RCI SoVI SVI TCRI N-B CCDRI RCRI CDRI2 Fraser

7 X X X X X X X 

Author Rationale for Including This Indicator

Single-parent households are more vulnerable to a disaster because they tend to have lower 

socioeconomic status and fewer sources of social support than that of two-parent families. f,m

Single-parent households are also vulnerable as all daily responsibilities fall to one parent, making 

recovery more difficult. m
Community Resilience Indicator Analysis: 2022 Update

45

Households without a Smartphone

Metric Data Source

Percent of households without a smartphone ACS 2017–2021 5-year estimates, Table S2801

National Average Binning Method

13.5% of U.S. households do not have a 

smartphone.

Census Tract: Jenks 

Caspall

County: Jenks Caspall

Data Geography

Data is available at the Census tract, county and Tribal levels. Puerto Rico is included.

Methodologies Using This Indicator

# 

of

14

ANDRI BRIC CDRI CRI2 DROP RCI SoVI SVI TCRI N-B CCDRI RCRI CDRI2 Fraser

5 X X X X X 

Author Rationale for Including This Indicator

Access to telephones enables communication which is vital during disaster events. b

Communities with more access to telephone services will be better prepared for and will respond 

better before and during a disaster. c

Availability and accessibility of natural hazard information and community engagement encourages risk 

awareness. a
Community Resilience Indicator Analysis: 2022 Update

46

Housing: 2 Indicators

Mobile Homes as A Percentage of Housing Units

Measure Data Source

Percentage of housing units that are mobile 

homes

U.S. Census American Community Survey (ACS) 

2017–2021 five-year estimates, Table DP04

National Average Binning Methods

5.9% of housing units in the U.S. are mobile homes. Census Tract: Fisher 

Jenks

County: Fisher Jenks

Data Geography

Data is available at the Census tract, county and Tribal levels. Puerto Rico is included.

Methodologies Using This Indicator

# 

of

14

ANDRI BRIC CDRI CRI2 DROP RCI SoVI SVI TCRI N-B CCDRI RCRI CDRI2 Fraser

6 X X X X X X 

Author Rationale for Including This Indicator

Higher numbers of mobile homes in a community are related to lower levels of resilience because of 

the lower-quality construction of these homes and lack of basements, which makes them particularly 

susceptible to damage from hazards. b,g,m

Mobile homes are frequently found outside of metropolitan areas that may not be readily accessible by 

interstate highways or public transportation. m
Community Resilience Indicator Analysis: 2022 Update

47

Owner-Occupied Housing

Metric Data Source

Percentage of housing units that are owneroccupiedACS 2017–2021 five-year estimates, 

Table DP04

National Average Binning Methods

57.4% of housing units in the U.S. are owneroccupied.Census Tract: Jenks 

Caspall

County: Fisher Jenks

Data Geography

Data is available at the Census tract, county and Tribal levels. Puerto Rico is included.

Methodologies Using This Indicator

# 

of

14

ANDRI BRIC CDRI CRI2 DROP RCI SoVI SVI TCRI N-B CCDRI RCRI CDRI2 Fraser

6 X X X X X X 

Author Rationale for Including This Indicator

Home ownership is often included as a measure of a community’s economic strength and thus is a 

marker of community resilience. b,d,g,n

Home ownership is also used to reflect residents’ levels of place attachment to their communities. d,j

Low levels of home ownership can indicate a community with a faltering economy and a population 

with less long-term commitment to the community, which could hamper both individual and community 

mitigation actions to prepare for disaster as well as recovery efforts. a,j
Community Resilience Indicator Analysis: 2022 Update

48

Healthcare: 3 Indicators

Number of Hospitals

Metric Data Source

The number of hospitals per 10,000 people U.S. Census Bureau, 2020 County Business

Patterns, Table 00A1, NAICS code 622110

National Average Binning Method

There are .17 hospitals per 10,000 people in the 

U.S.

Census Tract: Fisher 

Jenks

County: Head Tail 

Breaks

Data Geography

Data is available at the county level. Puerto Rico is included.

Methodologies Using This Indicator

# 

of

14

ANDRI BRIC CDRI CRI2 DROP RCI SoVI SVI TCRI N-B CCDRI RCRI CDRI2 Fraser

9 X X X X X X X X X 

Author Rationale for Including This Indicator

This measure represents essential community infrastructure, both because it represents the capacity 

of the healthcare system to support residents’ overall health and to provide critical emergency medical 

care. a,b,d,g,n 

Lack of this critical capacity negatively affects a community’s ability to respond to and recover from 

disasters. d
Community Resilience Indicator Analysis: 2022 Update

49

Medical Professional Capacity

Metric Data Source

The number of health-diagnosing and treating 

practitioners per 1,000 population 

ACS 2017–2021 five-year estimates, Table 

S2401

National Average Binning Methods

There are 20.2 health diagnosing and treating 

practitioners per 1,000 population in the U.S. 

Census Tract: Jenks 

Caspall

County: Fisher Jenks

Data Geography

Data is available at the county level. Puerto Rico is included.

Methodologies Using This Indicator

# 

of

14

ANDRI BRIC CDRI CRI2 DROP RCI SoVI SVI TCRI N-B CCDRI RCRI CDRI2 Fraser

8 X X X X X X X X 

Author Rationale for Including This Indicator

Availability of physicians is linked with the overall physical and mental health of community residents.

b,d,f,g

Lack of access to physicians is related to lower levels of overall community resilience as indicated by 

low birthweight and premature mortality. f

Physicians are a critical emergency resource in the response to and recovery from a disaster. a
Community Resilience Indicator Analysis: 2022 Update

50

Population without Health Insurance

Metric Data Source

Percentage of the population without health 

insurance

ACS 2017–2021 5-year estimates, 

Table S2701

National Average Binning Methods

8.8% of the U.S. population does not have health 

insurance.

Census Tract: Jenks 

Caspall

County: Fisher Jenks

Data Geography

Data is available at the Census tract, county and tribal levels. Puerto Rico is included.

Methodologies Using This Indicator

# 

of

14

ANDRI BRIC CDRI CRI2 DROP RCI SoVI SVI TCRI N-B CCDRI RCRI CDRI2 Fraser

7 X X X X X X X 

Author Rationale for Including This Indicator

Health is a critical component of community well-being. An unhealthy population has more difficulty 

accessing community support or engaging in the process of building disaster resilience. d,g

Communities with more individuals covered by health insurance tend to have higher measures of 

physical and mental health. b,g

Health insurance coverage is one indication of individuals’ capacity to effectively respond to and 

recover from a crisis, both mentally and physically. j

Communities with lower percentages of individuals with health insurance may have lower levels of 

resilience. g
Community Resilience Indicator Analysis: 2022 Update

51

Economic: 6 Indicators

Unemployed Labor Force

Metric Data Source

Percentage of the civilian labor force age 16 and 

over who are unemployed

ACS 2017–2021 five-year estimates, Table 

DP03

National Average Binning Methods

5.5% of the civilian labor force age 16 and over are 

unemployed.

Census Tract: Jenks 

Caspall

County: Fisher Jenks

Data Geography

Data is available at the Census tract, county and tribal levels. Puerto Rico is included.

Methodologies Using This Indicator

# 

of

14

ANDRI BRIC CDRI CRI2 DROP RCI SoVI SVI TCRI N-B CCDRI RCRI CDRI2 Fraser

13 X X X X X X X X X X X X X 

Author Rationale for Including This Indicator

High levels of employment contribute to a healthy community economy, which supports community 

resilience. a,b,f,g,n

Employment also provides residents with financial resources that contribute to their livelihoods. d

Unemployed persons do not have the employee benefit plans that provide income and health cost 

assistance in the event of injury or death. m

Counties with higher levels of unemployment may have fewer community resources to support 

residents’ needs and a population that is both less prepared for a disaster and less able to cope with 

the aftermath. n
Community Resilience Indicator Analysis: 2022 Update

52

Income Inequality

Metric Data Source

Gini Index of income distribution across a 

population; the closer to 1, the greater the income 

inequality.7

ACS 2017–2021 five-year estimates, 

Table B19083

National Average Binning Method

The average Gini Index in the U.S. is .48. Census Tract: Jenks 

Caspall

County: Fisher Jenks

Data Geography

Data is available at the county and Tribal levels. Puerto Rico is included.

Methodologies Using This Indicator

# 

of

14

ANDRI BRIC CDRI CRI2 DROP RCI SoVI SVI TCRI N-B CCDRI RCRI CDRI2 Fraser

10 X X X X X X 

Author Rationale for Including This Indicator

The economic environment is a major factor in a community’s resilience; and when income inequality is 

present, earnings tend to be distributed in a way that does not support broader community goals. b,f,g

A skewed distribution of economic resources may negatively affect the cohesiveness of the residents’ 

response to a disaster. j

7 The Gini Index or coefficient uses a scale of 0–1 to measure the difference between the ideal distribution of income 

(perfect equality [0] where 50 percent of the population would receive 50 percent of the available income) and the actual 

distribution. The closer the number is to 1, the greater the income inequality.
Community Resilience Indicator Analysis: 2022 Update

53

Median Household Income

Metric Data Source

Median household income ACS 2017–2021 five-year estimates, 

Table S1903

National Average Binning Methods

The median household income in the U.S. is

$69,021. 

Census Tract: Manual County: Manual

Data Geography

Data is available at the Census tract, county and tribal levels. Puerto Rico is included.

Methodologies Using This Indicator

# 

of

14

ANDRI BRIC CDRI CRI2 DROP RCI SoVI SVI TCRI N-B CCDRI RCRI CDRI2 Fraser

6 X X X X X X 

Author Rationale for Including This Indicator

There is a strong relationship between individuals’ financial resources and their resilience to a 

disaster. b,d

Low-income households are at greater risk because they tend to live in lower-quality housing situated 

in higher risk areas, are less likely to have prepared for a disaster and have fewer resources to support 

recovery. d

The median household income of a community may also reflect its economic resilience and the 

community resources available to support recovery. n
Community Resilience Indicator Analysis: 2022 Update

54

Unemployed Women in the Labor Force

Metric Data Source

Percent of women in the civilian work force age 

16 and over who are unemployed

ACS 2017–2021 5-year estimates, Table DP03

National Average Binning Method

5.6% of women in the workforce age 16 and over 

are unemployed.

Census Tract: Jenks 

Caspall

County: Fisher Jenks

Data Geography

Data is available at the Census tract, county and Tribal levels. Puerto Rico is included.

Methodologies Using This Indicator

# 

of

14

ANDRI BRIC CDRI CRI2 DROP RCI SoVI SVI TCRI N-B CCDRI RCRI CDRI2 Fraser

6 X X X X X X 

Author Rationale for Including This Indicator

Communities enhance disaster resilience through nondiscriminatory wage policies, ensuring that all 

groups have fair access to resources. b

Economic stability at the community level, particularly the stability of livelihoods is an indicator of 

resilience. g
Community Resilience Indicator Analysis: 2022 Update

55

Population Below Poverty Level

Metric Data Source

Population below U.S. Census poverty level in 

past 12 months8

ACS 2017–2021 5-year estimates, Table S1701

National Average Binning Method

12.6% of the U.S. population lives below the 

poverty level.

Census Tract: Jenks 

Caspall

County: Jenks Caspall

Data Geography

Data is available at the Census tract, county and Tribal levels. Puerto Rico is included.

Methodologies Using This Methodology

# 

of

14

ANDRI BRIC CDRI CRI2 DROP RCI SoVI SVI TCRI N-B CCDRI RCRI CDRI2 Fraser

5 X X X X X 

Author Rationale for Including This Indicator

Economic resources play an important role in boosting resilience and adaptive capacity. d

Economically disadvantaged populations are disproportionately affected by disasters. The poor are less 

likely to have the income or assets needed to prepare for a possible disaster or to recover after a 

disaster. m

8 For more on how the Census defines poverty see: https://www.census.gov/topics/income-poverty/poverty/guidance/povertymeasures.html.
Community Resilience Indicator Analysis: 2022 Update

56

Workforce Employed in Predominant Sector

Metric Data Source

Percent of workforce employed in the predominant 

sector

ACS 2017–2021 5-year estimates, Table DP03

National Average Binning Method

24.6% of the workforce is employed in the 

dominant sector of their county.

Census Tract: Fisher 

Jenks

County: Fisher Jenks

Data Geography

Data is available at the Census tract, county and tribal levels. Puerto Rico is included.

Methodologies Using This Indicator

# 

of

14

ANDRI BRIC CDRI CRI2 DROP RCI SoVI SVI TCRI N-B CCDRI RCRI CDRI2 Fraser

5 X X X X X 

Author Rationale for Including This Indicator

Diversity is important for long term economic resilience; the local economy should not be overly 

dependent on continuing success in just one sector. b

In a diversified environment, if one industry weakens or fails, there are others that can provide 

employment and sustain the regional economy. f
Community Resilience Indicator Analysis: 2022 Update

57

Connection to Community: 4 Indicators

Percent of Inactive Voters

Metric Data Source

Percent of inactive voters (defined differently by 

state) 9

2020 U.S. Election Assistance Commission - 

Election Administration and Voting Survey

National Average Binning Method

9.0% of registered voters in the U.S. are 

inactive.10

Census Tract: Fisher 

Jenks

County: Fisher Jenks

Data Geography

Data is available at the county level. Alaska, Puerto Rico and territorial data were provided at a 

State/Territorial level only so the data for counties within those areas were imputed from the 

State/Territorial number.11

Methodologies Using This Indicator

# of 

14

ANDRI BRIC CDRI CRI2 DROP RCI SoVI SVI TCRI N-B CCDRI RCRI CDRI2 Fraser

10 X X X X X X X X X X 

Author Rationale for Including This Indicator

An active voting population is an indicator of having a community that is engaged, enhancing overall 

community resilience. c

Participation in elections increases social and political trust. d

Civic engagement, including voting, is an important form of bridging social capital. h

9 Inactive voter is defined by each State. For more information see: 

https://www.eac.gov/sites/default/files/eac_assets/1/1/2014_Statutory_Overview_Final-2015-03-09.pdf.

10 For more information on the Election Administration and Voting Survey 2020 Comprehensive Report see: 

https://www.eac.gov/sites/default/files/document_library/files/2020_EAVS_Report_Final_508c.pdf.

11 For more information on the Election Administration and Voting Survey 2020 Comprehensive Report see: 

https://www.eac.gov/sites/default/files/document_library/files/2020_EAVS_Report_Final_508c.pdf.
Community Resilience Indicator Analysis: 2022 Update

58

Presence of Civic and Social Organizations

Metric Data Source

Number of civic and social organizations per 

10,000 people

U.S. Census Bureau, 2020 County Business

Patterns, Table 00A1, NAICS Code 8134

National Average Binning Method

There are .77 civic and social organizations per 

10,000 people

Census Tract: Jenks 

Caspall

County: Head Tail 

Breaks

Data Geography

Data is available at the county level. Puerto Rico is included.

Methodologies Using This Indicator

# 

of

14

ANDRI BRIC CDRI CRI2 DROP RCI SoVI SVI TCRI N-B CCDRI RCRI CDRI2 Fraser

6 X X X X X X 

Author Rationale for Including This Indicator

This measure indicates the level of community engagement by looking at the level of civic 

infrastructure through which residents support their communities. b,f,g,j

Participation in civic organizations provides a mechanism for residents to invest in and take from their 

community and also increases networking and trusted relationships. d,j

The availability of formal social networks can be critical during response and recovery to quickly 

mobilize resources and disseminate information. b,d,f

Residents who participate in local civic organizations can use them for help and provide mutually 

beneficial cooperation during a crisis. b,f
Community Resilience Indicator Analysis: 2022 Update

59

Population without Religious Affiliation

Metric Data Source

Percentage of the population that do not affiliate 

with a religion

Association of Statisticians of American Religious 

Bodies. 2020 U.S. Religion Census. 

http://www.usreligioncensus.org/index.php

National Average Binning Method

48.8 % of the U.S. population are not religious 

adherents.

Census Tract: Jenks 

Caspall

County: Jenks Caspall

Data Geography

Data is available at the county level.

Methodologies Using This Indicator

# 

of

14

ANDRI BRIC CDRI CRI2 DROP RCI SoVI SVI TCRI N-B CCDRI RCRI CDRI2 Fraser

6 X X X X X X 

Author Rationale for Including This Indicator

Affiliation with a religious organization or civic organization can be used as a proxy measure for social 

connectedness, and how much a community may be able to rely on the good will of other local citizens, 

leading to reciprocity and mutually beneficial cooperation. b,f,g

Religious adherents can access additional support beyond their family and neighbors. Religious 

organizations are often organized to actively provide physical and social support to their congregations 

and communities during times of individual and community crisis. b,d,f
Community Resilience Indicator Analysis: 2022 Update

60

Population Change

Metric Data Source

Net change in population from people moving in or 

out of the county relative to the U.S. mean.

U.S. Census Bureau, Population Division. Table: 

Cumulative Estimate of the Components of 

Resident Population Change (PEPTCOMP): 2017–

2021 

National Average Binning Method

Not Applicable Census Tract: Standard 

Deviation

County: Standard 

Deviation

Data Geography

Data is available at the county level.

Methodologies Using This Indicator

# 

of

14

ANDRI BRIC CDRI CRI2 DROP RCI SoVI SVI TCRI N-B CCDRI RCRI CDRI2 Fraser

6 X X X X X X 

Author Rationale for Including This Indicator

Communities where large numbers of residents have lived for extended periods are likely to have 

strong place attachment, be invested in the well-being of the community before a disaster and willing 

to respond to revitalize a community after a disaster. b,j

Familiarity can help individuals navigate a community during an acute crisis, as well as know how to 

access services after the crisis has passed. j

A rapid influx of new residents may result in lower levels of attachment to the community, less 

familiarity with local hazards and how to prepare for them and fewer community connections that can 

provide support during a crisis. b,f,j

A reduction in population will reduce local tax income and community resources to respond to a 

disaster. b
Community Resilience Indicator Analysis: 2022 Update

61

Appendix D: CRCI Correlation Matrix

The research team conducted a correlation analysis to measure and describe the strength and 

direction of the relationships among the 22 commonly used community resilience indicators. 

Correlation analysis shows how individual indicators may be related to each other. Understanding 

these correlations will help communities design resilience strategies that take these relationships 

into account.

The Pearson Correlation Coefficient12 is a numerical measure of linear correlation from −1 to 1.

 A coefficient closer to 1 indicates a positive correlation (variable A increases as variable B 

increases).

 A coefficient of 0 indicates no correlation.

 A coefficient closer to −1 indicates a negative correlation (variable A increases as variable B 

decreases).

As jurisdictions consider strategies to address those indicators that reveal challenges to resilience, 

they should consider relationships between indicators signifying populations that may face multiple 

challenges. For example, campaigns focusing on individuals that are unemployed should also 

consider that they are more likely to be single-parent households, have difficulty speaking English, 

lack a high school diploma and be without access to a vehicle.

Table 1 summarizes some highlights of the county correlation analysis.

 

Table 2: Highlighted County Correlation Relationships

Indicator Positively Correlates With Negatively Correlates With

Age (adults over 65)  No smartphone (r = 0.45)  Limited English Speaking (r = -0.26) 

 Household Income (r = -0.26)

 

Low Educational 

Attainment

 Poverty (r = 0.62) 

 No health insurance (r = 0.46) 

 Household Income (r = -0.56)

 Medical Professional Capacity

(r = -0.45) (access to healthcare)

 

 

Disability

 No smartphone (r = 0.53) 

 Presence of mobile homes (r = 

0.45) 

 Poverty (r = 0.46) 

 Household Income (r = -0.64)

 Medical Professional Capacity

(r = -0.32) (access to healthcare)

 

 

12 Stangroom, J. “Pearson Correlation Coefficient Calculator.” Social Science Statistics. 

http://www.socscistatistics.com/tests/pearson/.
Community Resilience Indicator Analysis: 2022 Update

62

Limited English 

Speaking

 Low educational attainment (r = 

0.43)

 Age over 65 (r = -0.26)

No Health Insurance

 Low educational attainment 

(r = 0.46)

 Presence of mobile homes (r = 0.35)

 Medical Professional Capacity

(r = -0.36) (access to healthcare)

 Home Ownership (r= -0.26)

 Household Income (r= -0.27)

No Vehicle

 Poverty (r = 0.46)

 Unemployment rate (r = 0.45)

 Single parent household (r = 0.40)

 Home ownership (r = -0.34)

 Household income (r = -0.28)

 Population change (r = -0.28)

Unemployment Rate

 Unemployed women (r = 0.87)

 Poverty (r = 0.66)

 Single parent household (r = 0.51)

 Household Income (r = -0.44)

 Home ownership (r = -0.27)

Household Income

 Medical Professional Capacity (r = 

0.37) (access to healthcare)

 No smartphone (r = 0.65)

 Disability (r = -0.64)

 Low Educational Attainment (r = -0.56)

Single-Parent 

Household

 Poverty (r = 0.62)

 Unemployment rate (r = 0.51)

 Income inequality (r = 0.45)

 Unemployed women (r = 0.46)

 Household Income (r = -0.48)

 Home Ownership (r = -0.32)

Presence of Mobile 

Homes

 Low educational attainment 

(r = 0.43)

 Disability (r = 0.45)

 Household income (r = -0.42)

 Medical professional capacity

(r = -0.38) (access to healthcare)

Unemployed Women

 Unemployment rate (r = 0.88)

 Poverty (r = 0.62)

 Household income (r = -0.40)

 Medical professional capacity

(r = -0.22) (access to healthcare)

No Smartphone

 Disability (r = 0.53)

 Poverty (r = 0.49)

 Age over 65 (r = 0.45)

 Household income (r = 0.65)

 Medical professional capacity

(r = -0.33) (access to healthcare)

Poverty

 Low educational attainment

 (r = 0.62)

 Unemployment rate (r = 0.66)

 Single parent household (r = 0.62)

 Unemployed women (r = 0.59)

 Household income (r = -0.74)

 Homeownership (r = -0.37)
63

Community Resilience Indicator Analysis: 2022 Update

In the tables below, the positive correlations have green shading, and the negative correlations have blue. Values that are too small to have statistical 

significance are marked with an asterisk.

Table 3: Correlation Analysis - County

Age over 65

Low Educational 

Attainment

Disability

Limited English 

Speaking

No Health 

Insurance

No Vehicle

Unemployment 

Rate

Household Income

Income Inequality

Home Ownership

Single-Parent 

Household

Presence of Mobile 

Homes

Medical 

Professional 

Capacity

Number of 

Hospitals

No Affiliation with 

a Religion

Presence of Civic 

and Social 

Organizations

Population Change

Inactive Voters

Unemployed 

Women

Employment in 

Dominant Sector

No Smartphone

Poverty

Age over 

65 1.00 -0.13 0.39 -0.26 -0.16 -0.13 -0.05 -0.26 0.03* -0.11 -0.10 0.12 -0.08 -0.02* 0.03* 0.03* 0.20 -0.07 -0.07 -0.07 0.45 -0.04

Low 

Educational 

Attainment

-0.13 1.00 0.40 0.43 0.46 0.30 0.42 -0.56 0.29 -0.23 0.44 0.43 -0.45 -0.04 -0.14 -0.26 -0.22 0.08 0.38 -

0.02* 0.40 0.62

Disability 0.39 0.40 1.00 -0.21 0.08 0.15 0.36 -0.64 0.23 -0.18 0.31 0.45 -0.32 -0.05 0.02* -0.14 0* 0.10 0.30 -

0.03* 0.53 0.46

Limited 

English 

Speaking

-0.26 0.43 -0.21 1.00 0.32 0.10 0.02* 0.08 0.05 -0.15 0.03* -0.04 -0.13 0.01* -0.13 -0.05 -0.16 0.08 0.04 0.04 -0.15 0.04

No Health 

Insurance -0.16 0.46 0.08 0.32 1.00 0.09 0.12 -0.27 0.14 -0.26 0.23 0.35 -0.36 -0.04 -0.18 -0.21 -0.06 0.06 0.09 -0.07 0.08 0.23

No Vehicle -0.13 0.30 0.15 0.10 0.09 1.00 0.45 -0.28 0.34 -0.34 0.40 -0.04 -0.09 0.05 0* 0.04 -0.28 0.09 0.38 0.18 0.22 0.46

Unemploy

ment Rate -0.05 0.42 0.36 0.02* 0.12 0.45 1.00 -0.43 0.36 -0.27 0.51 0.15 -0.23 0.03* 0.09 -0.10 -0.10 0.12 0.87 0.02* 0.26 0.66

Household 

Income -0.26 -0.56 -0.64 0.08 -0.27 -0.28 -0.43 1.00 -0.38 0.37 -0.48 -0.42 0.42 0.03* 0.11 0.14 0.21 -0.10 -0.38 -0.09 -0.65 -0.74

Income 

Inequality 0.03* 0.29 0.23 0.05 0.14 0.34 0.36 -0.38 1.00 -0.34 0.45 0.15 -0.02* 0.02* -0.10 -0.10 -0.07 0.09 0.33 0.09 0.20 0.53
64

Age over 65

Low Educational 

Attainment

Disability

Limited English 

Speaking

No Health 

Insurance

No Vehicle

Unemployment 

Rate

Household Income

Income Inequality

Home Ownership

Single-Parent 

Household

Presence of Mobile 

Homes

Medical 

Professional 

Capacity

Number of 

Hospitals

No Affiliation with 

a Religion

Presence of Civic 

and Social 

Organizations

Population Change

Inactive Voters

Unemployed 

Women

Employment in 

Dominant Sector

No Smartphone

Poverty

Home 

Ownership -0.11 -0.23 -0.18 -0.15 -0.26 -0.34 -0.27 0.37 -0.34 1.00 -0.32 -0.10 0.27 -0.01* 0.03* 0.03* 0.20 -0.14 -0.23 -0.14 -0.21 -0.37

SingleParent 

Household

-0.10 0.44 0.31 0.03* 0.23 0.40 0.51 -0.48 0.45 -0.32 1.00 0.27 -0.22 0.04 -0.09 -0.12 -0.20 0.09 0.46 0.02* 0.24 0.62

Presence of 

Mobile 

Homes

0.12 0.43 0.45 -0.04 0.35 -0.04 0.15 -0.42 0.15 -0.10 0.27 1.00 -0.38 -0.06 0.03* -0.22 0.03* 0.01* 0.13 -

0.02* 0.33 0.26

Medical 

Professiona

l Capacity

-0.08 -0.45 -0.32 -0.13 -0.36 -0.09 -0.23 0.42 -

0.02* 0.27 -0.22 -0.38 1.00 0.07 0.02* 0.17 0.10 -0.04 -0.20 0.17 -0.33 -0.33

Number of 

Hospitals

-

0.02* -0.04 -0.05 0.01* -0.04 0.05 0.03* 0.03* 0.02* -0.01* 0.04 -0.06 0.07 -0.02* 0.07 -0.02* 0.02* 0.02* 0.04 -0.02* 0*

No 

Affiliation 

with a 

Religion

0.03* -0.14 0.02* -0.13 -0.18 0* 0.09 0.11 -0.10 0.03* -0.09 0.03* 0.02* -

0.02* 1.00 0.08 0.24 0.06 0.05 0.01* 0* -0.05

Presence of 

Civic and 

Social 

Organizatio

ns

0.03* -0.26 -0.14 -0.05 -0.21 0.04 -0.10 0.14 -0.10 0.03* -0.12 -0.22 0.17 0.07 0.08 1.00 -0.05 -0.04 -0.11 0.10 -0.07 -0.18

Population 

Change 0.20 -0.22 0* -0.16 -0.06 -0.28 -0.10 0.21 -0.07 0.20 -0.20 0.03* 0.10 -0.02* 0.24 -0.05 1.00 -0.07 -0.07 -0.25 -0.20 -0.20

Inactive 

Voters -0.07 0.08 0.10 0.08 0.06 0.09 0.12 -0.10 0.09 -0.14 0.09 0.01* -0.04 0.02* 0.06 -0.04 -0.07 1.00 0.10 0.03* -0.01* 0.11

Unemploye

d Women -0.07 0.38 0.30 0.04 0.09 0.38 0.87 -0.38 0.33 -0.23 0.46 0.13 -0.20 0.02* 0.05 -0.11 -0.07 0.10 1.00 -0.02* 0.20 0.59

Employme

nt in 

Dominant 

Sector

-0.07 -

0.02*

-

0.03* 0.04 -0.07 0.18 0.02* -0.09 0.09 -0.14 0.02* -0.02* 0.17 0.04 0.01* 0.10 -0.25 0.03* -0.02* 1.00 0.12 0.11
65

Age over 65

Low Educational 

Attainment

Disability

Limited English 

Speaking

No Health 

Insurance

No Vehicle

Unemployment 

Rate

Household Income

Income Inequality

Home Ownership

Single-Parent 

Household

Presence of Mobile 

Homes

Medical 

Professional 

Capacity

Number of 

Hospitals

No Affiliation with 

a Religion

Presence of Civic 

and Social 

Organizations

Population Change

Inactive Voters

Unemployed 

Women

Employment in 

Dominant Sector

No Smartphone

Poverty

No 

Smartphon

e 

0.45 0.40 0.53 -0.15 0.08 0.22 0.26 -0.65 0.20 -0.21 0.24 0.33 -0.33 -

0.02* 0* -0.07 -0.20 -0.01* 0.20 0.12 1.00 0.49

Poverty -0.04 0.62 0.46 0.04 0.23 0.46 0.66 -0.74 0.53 -0.37 0.62 0.26 -0.33 0* -0.05 -0.18 -0.20 0.11 0.59 0.11 0.49 1.00

*Not statistically significant 

Positive relationships have green shading 

Negative relationships have blue shading
66

Table 4: Correlation Analysis - Census Tract

Age over 65

Low Educational 

Attainment

Disability

Limited English 

Speaking

No Health 

Insurance

No Vehicle

Unemployment 

Rate

Household Income

Income Inequality

Home Ownership

Single-Parent 

Household

Presence of Mobile 

Homes

Medical 

Professional 

Capacity

Number of 

Hospitals

No Affiliation with a 

Religion

Presence of Civic 

and Social 

Organizations

Population Change

Inactive Voters

Unemployed 

Women

Employment in 

Dominant Sector

No Smartphone

Poverty

Age over 65 1.00 -0.17 0.40 -0.17 -0.20 -0.07 -0.06 0* 0.15 0.25 -0.09 0.15 0.09 0.01 0.08 0.07 0.16 -0.08 -0.07 0.07 0.36 -0.15

Low Educational 

Attainment -0.17 1.00 0.25 0.56 0.56 0.29 0.32 -0.48 0.11 -0.29 0.32 0.19 -0.39 -0.02 -0.10 -0.14 -0.15 0.12 0.28 -0.03 0.32 0.52

Disability 0.40 0.25 1.00 -0.09 0.12 0.19 0.28 -0.50 0.20 -0.08 0.31 0.30 -0.22 0* 0.02 0.04 0.07 -0.03 0.19 0.08 0.57 0.39

Limited English 

Speaking -0.17 0.56 -0.09 1.00 0.39 0.30 0.12 -0.16 0.08 -0.30 0.11 -0.08 -0.17 0.01* -0.08 -0.13 -0.22 0.13 0.14 0* 0* 0.24

No Health Insurance -0.20 0.56 0.12 0.39 1.00 0.09 0.17 -0.41 0.03 -0.25 0.25 0.20 -0.28 -0.02 -0.17 -0.16 0.06 -0.01 0.16 -0.06 0.14 0.35

No Vehicle -0.07 0.29 0.19 0.30 0.09 1.00 0.30 -0.28 0.35 -0.51 0.36 -0.14 -0.13 0.04 -0.05 0.05 -0.34 0.06 0.23 0.19 0.24 0.45

Unemployment Rate -0.06 0.32 0.28 0.12 0.17 0.30 1.00 -0.32 0.19 -0.24 0.35 0.02 -0.22 0.04 -0.02 -0.05 -0.12 0.07 0.78 0.11 0.23 0.49

Household Income 0* -0.48 -0.50 -0.16 -0.41 -0.28 -0.32 1.00 -0.24 0.48 -0.48 -0.24 0.40 -0.01 0.05 -0.02 -0.02 0.03 -0.24 0.01* -0.50 -0.62

Income Inequality 0.15 0.11 0.20 0.08 0.03 0.35 0.19 -0.24 1.00 -0.32 0.21 0.03 0* 0.03 -0.06 0.02 -0.12 0.04 0.14 0.11 0.24 0.40

Home Ownership 0.25 -0.29 -0.08 -0.30 -0.25 -0.51 -0.24 0.48 -0.32 1.00 -0.38 0.12 0.24 -0.05 0.04 -0.01 0.19 -0.12 -0.20 -0.03 -0.08 -0.49

Single-Parent 

Household -0.09 0.32 0.31 0.11 0.25 0.36 0.35 -0.48 0.21 -0.38 1.00 0* -0.22 0.04 -0.10 -0.02 -0.11 0* 0.27 0.09 0.25 0.54

Presence of Mobile 

Homes 0.15 0.19 0.30 -0.08 0.20 -0.14 0.02 -0.24 0.03 0.12 0* 1.00 -0.16 -0.06 0.04 -0.08 0.18 -0.04 0.02 -0.03 0.27 0.10

Medical Professional 

Capacity 0.09 -0.39 -0.22 -0.17 -0.28 -0.13 -0.22 0.40 0* 0.24 -0.22 -0.16 1.00 0.04 -0.02 0.06 0* -0.03 -0.19 0.23 -0.23 -0.32

Number of Hospitals 0.01 -0.02 0* 0.01* -0.02 0.04 0.04 -0.01 0.03 -0.05 0.04 -0.06 0.04 1.00 -0.05 0.13 -0.07 0.04 0.02 0.03 0.01 0.04
67

Age over 65

Low Educational 

Attainment

Disability

Limited English 

Speaking

No Health 

Insurance

No Vehicle

Unemployment 

Rate

Household Income

Income Inequality

Home Ownership

Single-Parent 

Household

Presence of Mobile 

Homes

Medical 

Professional 

Capacity

Number of 

Hospitals

No Affiliation with a 

Religion

Presence of Civic 

and Social 

Organizations

Population Change

Inactive Voters

Unemployed 

Women

Employment in 

Dominant Sector

No Smartphone

Poverty

No Affiliation with a 

Religion 0.08 -0.10 0.02 -0.08 -0.17 -0.05 -0.02 0.05 -0.06 0.04 -0.10 0.04 -0.02 -0.05 1.00 0.09 0.22 0.06 -0.02 -0.01 -0.01 -0.08

Presence of Civic and 

Social Organizations 0.07 -0.14 0.04 -0.13 -0.16 0.05 -0.05 -0.02 0.02 -0.01 -0.02 -0.08 0.06 0.13 0.09 1.00 -0.10 -0.03 -0.06 0.07 0.10 -0.04

Population Change 0.16 -0.15 0.07 -0.22 0.06 -0.34 -0.12 -0.02 -0.12 0.19 -0.11 0.18 0* -0.07 0.22 -0.10 1.00 -0.19 -0.08 -0.12 -0.05 -0.12

Inactive Voters -0.08 0.12 -0.03 0.13 -0.01 0.06 0.07 0.03 0.04 -0.12 0* -0.04 -0.03 0.04 0.06 -0.03 -0.19 0.06 0* -0.08 0.05

Unemployed Women -0.07 0.28 0.19 0.14 0.16 0.23 0.78 -0.24 0.14 -0.20 0.27 0.02 -0.19 0.02 -0.02 -0.06 -0.08 0.06 1.00 0.06 0.16 0.39

Employment in 

Dominant Sector 0.07 -0.03 0.08 0* -0.06 0.19 0.11 0.01* 0.11 -0.03 0.09 -0.03 0.23 0.03 -0.01 0.07 -0.12 0* 0.06 1.00 0.10 0.15

No Smartphone 0.36 0.32 0.57 0* 0.14 0.24 0.23 -0.50 0.24 -0.08 0.25 0.27 -0.23 0.01 -0.01 0.10 -0.05 -0.08 0.16 0.10 1.00 0.39

Poverty -0.15 0.52 0.39 0.24 0.35 0.45 0.49 -0.62 0.40 -0.49 0.54 0.10 -0.32 0.04 -0.08 -0.04 -0.12 0.05 0.39 0.15 0.39 1.00

*Not statistically significant 

Positive relationships have green shading 

Negative relationships have blue shading