FEMA Community Resilience 

Challenges Index 

Annual Update of Indicator Tables and Correlation 

Analysis

May 2023
FEMA Community Resilience Challenges Index: Annual Update of Indicator Tables and Correlation Analysis

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FEMA Community Resilience Challenges Index: Annual Update of Indicator Tables and Correlation Analysis

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Table of Contents

List of 2022 Analysis of Community Resilience Indicators: Updated Census Data........................ 2

Indicator Binning Methodology ....................................................................................................... 3

Population Characteristics: 3 Indicators ......................................................................................... 5

Household Characteristics: 4 Indicators ......................................................................................... 8

Housing: 2 Indicators ................................................................................................................... 12

Healthcare: 3 Indicators ............................................................................................................... 14

Economic: 6 Indicators ................................................................................................................. 17

Connection to Community: 4 Indicators ....................................................................................... 23

Correlation Analysis ..................................................................................................................... 29
FEMA Community Resilience Challenges Index: Annual Update of Indicator Tables and Correlation Analysis

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List of 2022 Analysis of Community Resilience Indicators: 

Updated Census Data

The charts in this document provide details about each of the 22 indicators identified through the 2022 

analysis of community resilience indicators and available in the Resilience Analysis and Planning Tool 

(RAPT). A majority of the 22 indicators use data from the American Community Survey 5-year estimates 

2017-2021. This summary is designed to provide transparency to users and includes details about how 

each indicator was calculated in RAPT, and what data is available for each indicator. RAPT enables 

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

resilience by county, tribe and census tract. More information on RAPT can be found on the RAPT 

resource hub here: https://rapt-fema.hub.arcgis.com/ . 

Reference notes (lowercase letters) in the “Author rationale for including this indicator” sections 

indicate which of the resilience assessment methodologies identified in the analysis provided the 

explanation for why the indicator is an effective measure of community resilience. A key for the 

references (a through n) follows at the end of this document. A description of binning methods used in 

the analysis is also included.

For each indicator, the tables below include:

 Indicator metric;

 Data source;

 Calculation (numerator and denominator);

 National average;

 Binning methods; 

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

 Methodologies referencing this indicator; and

 Author rationale for including this indicator.

Each table notes which of the following methodologies used each indicator:

 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

 Community Resilience Index (CRI2) e

 Comprehensive Disaster Resilience Index (CDRI2) f

 Disaster Resilience of Place (DROP) g

 Fraser h

 Nursey-Brey (N-B) i

 Resilience Capacity Index (RCI) j
FEMA Community Resilience Challenges Index: Annual Update of Indicator Tables and Correlation Analysis

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 Regional Climate Resilience Index (RCRI) k

 Social Vulnerability Index (SoVI) l

 Social Vulnerability Index (SVI) m

 The Composite Resilience Index (TCRI) n

Indicator Binning Methodology

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. 

The binning methods for the 22 commonly used 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.1

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

1 Jiang, B., 2013, Head/tail Breaks: A New Classification Scheme for Data with a Heavy-tailed Distribution. The Professional Geographer, 65, 

482-494.
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o Income: A convention for displaying income data already exists: $0–20,000, $20,001–

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

o 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.2 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)

2 U.S. Census Bureau. https://www.census.gov/glossary/#term_Netmigration?term=Net+migration accessed March 20, 2023. 
FEMA Community Resilience Challenges Index: Annual Update of Indicator Tables and Correlation Analysis

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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

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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
FEMA Community Resilience Challenges Index: Annual Update of Indicator Tables and Correlation Analysis

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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

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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
FEMA Community Resilience Challenges Index: Annual Update of Indicator Tables and Correlation Analysis

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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

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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.
FEMA Community Resilience Challenges Index: Annual Update of Indicator Tables and Correlation Analysis

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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

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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
FEMA Community Resilience Challenges Index: Annual Update of Indicator Tables and Correlation Analysis

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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).
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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

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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
FEMA Community Resilience Challenges Index: Annual Update of Indicator Tables and Correlation Analysis

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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

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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
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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

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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
FEMA Community Resilience Challenges Index: Annual Update of Indicator Tables and Correlation Analysis

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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

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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
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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

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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
FEMA Community Resilience Challenges Index: Annual Update of Indicator Tables and Correlation Analysis

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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

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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
FEMA Community Resilience Challenges Index: Annual Update of Indicator Tables and Correlation Analysis

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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

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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
FEMA Community Resilience Challenges Index: Annual Update of Indicator Tables and Correlation Analysis

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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

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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
FEMA Community Resilience Challenges Index: Annual Update of Indicator Tables and Correlation Analysis

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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

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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.
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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

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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
FEMA Community Resilience Challenges Index: Annual Update of Indicator Tables and Correlation Analysis

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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

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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
FEMA Community Resilience Challenges Index: Annual Update of Indicator Tables and Correlation Analysis

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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

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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.
FEMA Community Resilience Challenges Index: Annual Update of Indicator Tables and Correlation Analysis

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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

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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
FEMA Community Resilience Challenges Index: Annual Update of Indicator Tables and Correlation Analysis

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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 

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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.
FEMA Community Resilience Challenges Index: Annual Update of Indicator Tables and Correlation Analysis

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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

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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
FEMA Community Resilience Challenges Index: Annual Update of Indicator Tables and Correlation Analysis

25 

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
FEMA Community Resilience Challenges Index: Annual Update of Indicator Tables and Correlation Analysis

26 

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
FEMA Community Resilience Challenges Index: Annual Update of Indicator Tables and Correlation Analysis

27 

Key for Methodologies Cited under “Author Rationale for Including This 

Indicator” 

a 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/hazard-resilience/251 , accessed Match 20, 2023. 

b 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.

c 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 20, 2023. 

d 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, Available at https://www.researchgate.net/profile/WalterPeacock/publication/346474442_Advancing_the_Resilience_of_Coastal_Localities_Developing_Im

plementing_and_Sustaining_the_Use_of_Coastal_Resilience_Indicators_A_Final_Report/links/5fc43

376458515b79788e5cd/Advancing-the-Resilience-of-Coastal-Localities-Developing-Implementingand-Sustaining-the-Use-of-Coastal-Resilience-Indicators-A-Final-Report.pdf, accessed March 20, 

2023. 

e 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 20, 

2023. 

f CRI2: Kathleen Sherrieb, Fran H. Norris and Sandro Galea, 2010, “Measuring Capacities for

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

g 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 

http://resiliencesystem.com/sites/default/files/Cutter_jhsem.2010.7.1.1732.pdf, accessed March 

20, 2023. 

h 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=oaC86lYRuw

gAAAAA:ChyrqLcLG- 4TT_ZqxEMMDP9oFyRMJODxQ6To9x5yfaLmZxYOMUb4qc3UIx1UdteBCftuEd7d, 

accessed March 20, 2023. 

i 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=ofxgFiTUUE0

AAAAA:qsHc0N1BtTDG NR4w5Phl6g9B_QGfpCj1y-GaF1CottH2i3eLEsQzPKLGC40C39LABoed8qmK, 

accessed March 20, 2023.

j 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, 38. Available at 
FEMA Community Resilience Challenges Index: Annual Update of Indicator Tables and Correlation Analysis

28 

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

March 20, 2023. 

k 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=_VRVTAEajgU

AAAAA:pTCr0FbuAU7 Y7mjURGNV44_JYPRbhjy2cqxNXdiDcGhwt6SE-IUfzKFQQopJ0pKyZ2wwwTYB

accessed March 20, 2023.

l 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/abs/10.1111/1540-6237.8402002, accessed March 20, 2023. 

m SVI: 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%20

Management.pdf, accessed March 20, 2023. 

n TCRI: T. Perfrement and T. Lloyd, 2015, “The 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 20, 2023. 
Community Resilience Indicator Analysis: 2022 Update

29

Correlation Analysis

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 1: 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

30

Indicator Positively Correlates With Negatively Correlates With

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)
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.

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 

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

Unemployme

nt 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

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

Single-Parent 

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

31
Community Resilience Indicator Analysis: 2022 Update 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 

Population Change

Inactive Voters

Unemployed 

Women

Employment in 

Dominant Sector

No Smartphone

Poverty

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 

Professional 

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 

Organizations

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

Unemployed 

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

Employment 

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

No 

Smartphone 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

32
Community Resilience Indicator Analysis: 2022 Update

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 

Number of 

Hospitals

No Affiliation with a 

Religion

Presence of Civic 

and Social 

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

33
Community Resilience Indicator Analysis: 2022 Update 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 

Number of 

Hospitals

No Affiliation with a 

Religion

Presence of Civic 

and Social 

Population Change

Inactive Voters

Unemployed 

Women

Employment in 

Dominant Sector

No Smartphone

Poverty

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

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

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