Applying National Community Social 

Vulnerability Indicators to Fishing 

Communities in the Pacific Island Region

Danika Kleiber

Dawn Kotowicz

Justin Hospital

U.S. DEPARTMENT OF COMMERCE

National Oceanic and Atmospheric Administration

National Marine Fisheries Service

Pacific Islands Fisheries Science Center 

NOAA Technical Memorandum NMFS-PIFSC-65

https://doi.org/10.7289/V5/TM-PIFSC-65

January 2018
Applying National Community Social 

Vulnerability Indicators to Fishing 

Communities in the Pacific Island Region 

Danika Kleiber1

Dawn Kotowicz2

Justin Hospital3

1Joint Institute for Marine and Atmospheric Research

University of Hawaii

1000 Pope Road

Honolulu, Hawaii 96822

2Coastal Institute

University of Rhode Island

Kingston, Rhode Island, 02881

3Pacific Islands Fisheries Science Center

National Marine Fisheries Service

1845 Wasp Boulevard

Honolulu, Hawaii 96818

NOAA Technical Memorandum NMFS-PIFSC-65

January 2018 

U.S. Department of Commerce

Wilbur L. Ross, Jr., Secretary

National Oceanic and Atmospheric Administration

Benjamin Friedman, Acting NOAA Administrator

National Marine Fisheries Service

Chris Oliver, Assistant Administrator for Fisheries
Recommended citation: 

Kleiber, D., D. Kotowicz, and J. Hospital. 2018. Applying national community social 

vulnerability indicators to fishing communities in the Pacific Island Region. NOAA Tech. 

Memo. NMFS-PIFSC-65, 63 p. 

Copies of this report are available from:

Science Operations Division

Pacific Islands Fisheries Science Center

National Marine Fisheries Service

National Oceanic and Atmospheric Administration

1845 Wasp Boulevard, Building #176 

Honolulu, Hawaii 96818 

Or online at:

https://www.pifsc.noaa.gov/library/

Cover: Photo courtesy of (Clockwise from top left) Grace-McCaskey, Levine, Pan, and GraceMcCaskey

iii

Table of Contents

Table of Contents ........................................................................................................................... iii

List of Tables ................................................................................................................................. iv

List of Figures ................................................................................................................................. v

I. INTRODUCTION .................................................................................................................. 1

Defining Fishing Communities ................................................................................................... 1

Developing Social Indicators ...................................................................................................... 2

Social Indicators in Fishing Communities .................................................................................. 3

Social Vulnerability .................................................................................................................... 4

Gentrification Pressure ............................................................................................................... 4

Fisheries Engagement and Reliance ........................................................................................... 5

Natural Hazard Risk .................................................................................................................... 5

II. METHODS................................................................................................................................ 5

Assessing Community Scales ..................................................................................................... 5

Secondary Data Gathering .......................................................................................................... 6

Statistical Analysis ...................................................................................................................... 7

Metadata and Data Access .......................................................................................................... 9

III. RESULTS ................................................................................................................................. 9

Scale of Community ................................................................................................................... 9

Community Outliers ................................................................................................................. 10

Indices ....................................................................................................................................... 11

Social Vulnerability .................................................................................................................. 14

Commercial Fishing Vulnerability ........................................................................................... 28

Natural Hazard Vulnerability .................................................................................................... 31

IV. DISCUSSION ........................................................................................................................ 33

Constraints of the Data ............................................................................................................. 33

Integrating the Data into Socio-ecological Assessments .......................................................... 35

Next Steps ................................................................................................................................. 37

V. CONCLUSION ....................................................................................................................... 37

VI. REFERENCES ....................................................................................................................... 38

VII. APPENDICES ....................................................................................................................... 43

Appendix A. Variables.............................................................................................................. 43

Appendix B. Secondary Data Collection .................................................................................. 48

Appendix C. Index Scores ........................................................................................................ 52
iv

List of Tables

Table 1. Indicators and indices ....................................................................................................... 4

Table 2. Community Scale comparison for island communities in the western Pacific ............... 10

Table 3. Vulnerability Indices results ........................................................................................... 13

Table 4. Housing Characteristics community examples ............................................................... 14

Table 5. Labor Force community examples ................................................................................. 17

Table 6. Personal Disruption community examples ..................................................................... 20

Table 7. Population Composition community examples .............................................................. 22

Table 8. Poverty community examples ......................................................................................... 24

Table 9. Occupational Diversity community examples ................................................................ 26

Table 10. Fishing engagement community examples ................................................................... 28

Table 11. Fishing reliance community examples ......................................................................... 29
v 

List of Figures

Figure 1. Vulnerability level based on Housing Characteristics .................................................. 15

Figure 2. Vulnerability level based on Labor Force ..................................................................... 18

Figure 3. Subsistence activity ....................................................................................................... 19

Figure 4. Vulnerability level based on Personal Disruption ......................................................... 21

Figure 5. Vulnerability level based on Population Composition ...................................................... 24

Figure 6. Vulnerability level based on Poverty ............................................................................ 25

Figure 7. Shannon Index of Occupational Diversity ..................................................................... 27

Figure 8. Fishing Engagement and Reliance indices ..................................................................... 25

Figure 9. Natural Hazards ............................................................................................................. 32

Figure 10. Localized Poverty index scores ................................................................................... 36
vi
1

I. INTRODUCTION

The Magnuson-Stevens Fishery Conservation and Management Act (16 U.S.C. § 1801 - 1891 

(d)) requires regional fisheries management councils and the National Marine Fisheries Service 

(NMFS), in the context of proposed management measures, to consider potential effects of 

proposed management measures upon fishermen and fishing communities and:

“[T]ake into account the importance of fishery resources to fishing communities by 

utilizing economic and social data...to provide for the sustained participation of such 

communities, and...to the extent practicable, minimize adverse economic impacts on 

such communities” (16 U.S.C. § 1851(a)(8)). 

Fishery management plans must also consider fairness and equity in conservation and 

management measures such that “no particular individual, corporation, or other entity acquires 

an excessive share of [fishing] privileges” or impacts (16 U.S.C. § 1851(a) (4)). 

Social impact assessment is one tool used to compare management measures and inform 

decisions about fisheries management; however, this type of assessment addresses only the issue 

being examined and commonly misses “a nuanced representation of fishery systems’ dynamics, 

including how impacts arise from multiple stresses and pressures” (Tuler et al., 2008). Social 

indicators derived from secondary data can complement social impact analysis and provide a 

broader measurement of a community that may be affected by changing conditions in social and 

ecological systems connected to those communities. 

Defining Fishing Communities

The Magnuson-Stevens Fishery Conservation and Management Act (MSA) defines a “fishing 

community” as:

“[A] community which is substantially dependent on or substantially engaged in the 

harvest or processing of fishery resources to meet social and economic needs, and 

includes fishing vessel owners, operators, and crew and United States fish processors that 

are based in such community” (16 U.S.C. § 1802 (17)). 

Using this definition to identify fishing communities can highlight the “awkward incongruities 

between the anthropological emphasis on situational meaning and legal demands for exactness”

(Clay and Olson, 2007). In anthropological contexts community can include shared geographies, 

shared culture, experiences, and histories. When applying the MSA definition to the Pacific 

Islands Region, The Western Pacific Regional Fishery Management Council (WPRFMC) 

observed that:

“[A] large proportion of the people living in the Western Pacific region observe and 

interact daily with the ocean for food, income and recreation...fishing also continues to 

contribute to the cultural integrity and social cohesion of island communities...These 

individuals are not set apart...from island populations as a whole” (Western Pacific 

Regional Fishery Management Council, 1998). 
2

The emphasis on the ubiquitous and shared economic, subsistence, and cultural ties to fisheries 

led to broadly defined fishing communities. Guam, the Northern Mariana Islands, and American 

Samoa were each recognized as a fishing community without further delineation within those 

territories. It was also initially proposed that the State of Hawai‘i be defined as a single fishing 

community, but this was later revised to seven fishing communities representing the main 

inhabited islands including Kauai, Ni‘ihau, O‘ahu, Maui, Molokai, Lanai and the island of 

Hawai‘i (Western Pacific Regional Fishery Management Council, 2002).

While the WPRFMC definitions of fishing communities were purposefully broad, this analysis 

uses geographic distinctions at the Census County Division (CCD) level. Analysis of 

communities at the CCD level was chosen to provide geographical boundaries that capture 

census-designated geography. This differs from fishing community Social Vulnerability Index 

development done in other U.S. regions that used a Census Designated Place scale (Jepson and 

Colburn, 2013). This will be discussed further in the results.

Developing Social Indicators 

The impetus for this research comes from a need to evaluate social impacts of proposed fisheries 

management measures on fishing communities and to assess potential differential impacts of 

proposed measures on communities. This analysis could supplement social impact analyses, 

providing a broader view of community dynamics when assessing the impacts of changes in a 

fishery or fishing community. There is also a need to predict or evaluate impacts of 

environmental, economic or social disruptions such as from a natural hazard, on fishing 

communities. The framework presented here could lay a foundation to predict vulnerabilities.

Indicators are quantitative or qualitative factors or variables that provide simple, valid and 

reliable means to track changes over time. Similar to ecological indicators that may be used to 

measure the structure, function, and composition of an ecological system, social indicators can 

track aspects of a social system, to identify changes over time, response to disruptions, and 

inform policy decisions. 

This work draws on social indicators research investigating the different social aspects of human 

communities in different geographies and its application to social policy related to vulnerability 

(Smith, 1981). Social vulnerability measures the relative ability of people, communities or 

institutions to endure stress. This stress could be social such as political unrest, or natural such as 

disasters. Hence social vulnerability indices are used to compare the relative vulnerability of 

different groups. These groups could be at the individual, household, community, country or 

regional scale depending on the scale of data aggregation. 

Smith (1981) notes the difficulty of developing valid and reliable indices to measure aspects of 

society identified as needing monitoring. Social indices, such as the United Nations 

Development Program’s Human Development Index have been developed internationally to 

examine human health and access to knowledge and resources, and include variables such life 

expectancy, and the number of years children go to school (United Nations, 1990). Other 

international examples include the World Bank’s World Development Indicators which are 

based on global development and poverty data (World Bank, 2017). International indicators are 
3

often measured at the country level to allow intergovernmental agencies to make country 

comparisons, and direct policy and resources where they are most needed.

Social aspects of vulnerability have also been developed for disaster studies where researchers 

acknowledge that social factors are often as important as physical and biological characteristics 

of a place (Cutter, Mitchell, and Scott, 2000; Jacob et al., 2010; Fatemi et al., 2017). For example 

Cutter et al. (2003) developed social indicators at a county level from a number of social 

indicators including variables related to population composition, infrastructure, and occupation. 

These factors allowed for county level comparative analysis of social vulnerability to natural 

hazards throughout the United States. Similarly in a review of social indicators for natural 

disaster vulnerability, Fatemi et al. (2017) found that variables relating to gender, public health, 

infrastructure and migration patterns were the most commonly used.

Boyd and Charles (2006) detailed the need for community-level social indicators to monitor 

fishing community sustainability but recognized the difficulty of finding reliable and available 

data needed to develop relevant and appropriate indicators. Although there has been excellent 

work to help identify and collect indicator variables in coastal communities (see

Wongbusarakum and Pomeroy, 2000), Boyd and Charles (2006) noted that when indicators were 

identified, ecological measures were more readily available than those social measures, making 

them more likely to be considered in management decisions. Jacob and Jepson (2009) proposed a 

community-level index of fish stock sustainability using local landings data and acknowledge 

that this index would need to be supplemented by other indicators or with local community 

knowledge to inform policy and management decisions (Jacob and Jepson, 2009). Jacob et al. 

(2013, 2010) then created more robust social indicators for fishing communities using secondary 

census and fisheries data and verified their results with on-site observations. 

National Marine Fisheries Service Southeastern Regional Office (SERO) and Northeast Fisheries 

Science Center (NEFSC) have expanded on this work, having developed their own set of social 

indicators for fishing communities in their regions respectively, and then collectively, to be used 

as part of the process to assess social impacts upon fishing communities (Jepson and Colburn,

2013). This has also be more recently updated and made publically available through the NOAA 

website (NOAA, 2017).

Social Indicators in Fishing Communities

In this study we closely follow the indicator framework developed by Jepson and Colburn 

(2013), and the more recent updates available on the project website (NOAA, 2017). They 

identified broad indicator categories to examine fishing communities: 1) Social Vulnerability, 

2) Gentrification Pressure, and 3) Fishing Engagement and Reliance (Table 1). These indicators 

have been developed in the U.S. Gulf region (Gulf & South Atlantic Fisheries Foundation, Inc., 

2010; Jacob et al., 2013, 2010), and expanded to include the Northeast region (Colburn and 

Jepson, 2012; Jepson and Colburn, 2013). To these indicators we will also include: 4) Natural 

Hazards Risk index. These four aspects of vulnerability can be explored using regularly collected 

secondary quantitative data (Table 1).
4 

Table 1. Indicators and indices. 

Indicator Index Coverage

1. Social Vulnerability Housing Regional

Labor Force Regional

Personal Disruption Regional

Population Composition Regional

Poverty Regional

Occupational Diversity Regional

2. Gentrification Pressure Housing Disruption Regional

Retiree Migration Regional

Urban Sprawl Regional

3. Fishing Engagement and Reliance Commercial Fishing Engagement Hawai‘i

Commercial Fishing Reliance Hawai‘i

4. Natural Hazards Risk Natural Hazards Hawai‘i

Social Vulnerability

Work on social vulnerability is based on the premise that there are different capacities to deal 

with disasters, or adapt to incremental yet relentless change, and that these differences can be 

influenced by social variables. As Morrow points out: “The effect on any particular 

household...results from a complex set of interacting conditions, some having to do with 

geography and location, some with the dwelling, and still others with the social and economic 

characteristics of the people living there” (1999, p. 2). The most recent work by Jepson and

Colburn identified 5 indices that describe different aspects of social vulnerability. These include: 

1) Labor Force, 2) Housing Characteristics, 3) Poverty, 4) Population Composition, and 5) 

Personal disruption (NOAA, 2017). To this we also added the Occupational Diversity Index 

(Table 1). Variables related to education, age, household composition, race and ethnicity, 

income, public assistance, housing, and employment all contribute to the development of these 

indices.

Gentrification Pressure

Gentrification is an external factor that can increase the vulnerability of fisheries communities 

and threaten the viability of commercial working waterfronts (Colburn and Jepson, 2012). 

Gentrification can include changing demographics of a coastal population and in particular shifts 

from a working population to a retired population. Gentrification can also include measures of 

urbanization. The latest indices identified by Jepson and Colburn include: 1) Housing Disruption, 

2) Retiree Migration, and 3) Urban Sprawl (NOAA, 2017). Variables used to develop these 

indices include metrics of age, employment, income assistance, population density, housing 

availability, and marine access availability. 
5

Fisheries Engagement and Reliance

Measures of fisheries – total and per capita of commercial and recreational fisheries – can be 

used to indicate engagement in and reliance on fisheries at a community level (Jepson and

Colburn, 2013). Variables could include a count of fishing licenses, fishing pressure, amount of 

landings, value of landings, and number of dealers. Vulnerability in fisheries has been researched 

extensively, identifying aspects of individual fishermen and fishing communities associated with

increased sensitivity to changes and disruptions in their social and economic conditions as a 

result of environmental and management changes in fisheries and fish stocks on which they rely 

(Colburn and Jepson, 2012; Jacob et al., 2013, 2010). Again, diversity has been found to 

decrease vulnerability from the individual fisherman who is skilled at targeting several types of 

fish, to a fishing community that relies on many different species of fish harvested from various 

parts of the marine environment using several gear types (Pollnac et al., 2006; Jacob et al., 2013). 

Communities with alternative livelihood options may also be more resilient to changes to 

availability or access to fisheries resources (Boyd and Charles, 2006). Diversity of economic 

engagement in this case is measured by a Shannon Index (Jepson and Colburn, 2013; NOAA,

2017).

Natural Hazard Risk 

Risks from natural hazards are particularly concerning for island communities that must be selfsufficient in the event that a hazard cuts off access for incoming supplies, food or other goods 

(Mileti, 1999). A given natural hazard affects the population, economy, infrastructure and 

fisheries in the community where it strikes, so natural hazards vulnerability is heavily 

intertwined with other types of social vulnerability. Hazards affecting coastal natural and built 

environments and infrastructure are important to consider for island communities since there is 

not as much space inland for relocating communities and their associated built environment 

(Cutter, Boruff, and Shirley, 2003). Natural hazards are also an important concern for fishing 

communities due to their proximity to the ocean, necessary access to the coast and ocean, and the 

tendency to locate fisheries-dependent infrastructure (such as ports and processing facilities) 

close to the ocean for ease of access (Bigford, 1991). Measures of Hazard Risk could include 

storms, earthquakes, tsunamis, and volcanic activities.

II. METHODS

Assessing Community Scales

For this analysis we chose to use the Census County Division (CCD) scale, however 

vulnerability indices developed for the Northeast and Southeast regions used Census Designated 

Place (CDP) scale (Jepson and Colburn, 2013). Census data is also available at the even finer 

scale of Census Block Group (CBG). To further complicate the choices the WPRFMC defines 

community at the island or island grouping scale. It is important to consider issues of evenness of 

representation, ability to perform statistical analyses, scale at which it is meaningful to integrate 

with ecological vulnerability models, and the scale at which management measures occur. In the 

results we outline the strengths and limitations of four different scales of community designation.
6

Secondary Data Gathering 

Variables used to examine social vulnerability in fishing communities in Hawai‘i, Guam, 

Commonwealth of the Northern Mariana Islands (CNMI), and American Samoa are based on 

those used for Jepson and Colburn’s (2013) and Jacob et al. (2010) analyses, with a few 

exceptions where comparable data are not available. Additional variables describing risk of 

natural hazards and more detailed commercial fisheries data were added to the analysis for 

Hawai‘i fishing communities, but were not available for the territories (Table 1). The census 

recognizes 44 CCDs in Hawai‘i, 19 in Guam, 8 in CNMI, and 16 in American Samoa. However, 

because this study focuses only on inhabited communities, a small number of CCDs were 

removed before the analysis. These include Pu‘unene in Hawai‘i, the Northern Islands in CNMI, 

and Rose Island in American Samoa. Ni‘ihau is inhabited but lacks social data so it was also 

removed.

The database used for this analysis includes demographic and fisheries data. All variables are 

secondary data derived from both government and non-governmental sources. The majority of 

the demographic data were sourced from the U.S. Census Bureau’s American Community 

Survey (ACS) using 5-year summary files (ACS 2006-2010) at the CCD level in Hawai‘i, and 

decadal 2010 data for the territories. Data were obtained using the online American Factfinder 

tool (U.S. Census Bureau 2017a). These data included variables related to population structure, 

housing, resident transiency, education, employment, and household income (Appendix A. 

Variables). 

A natural hazard risk score was first attempted using a principle components analysis and 

weather variables found in Jepson and Colburn (2013). However, these variables – particularly 

hail, tornados, and wind – were not relevant in Hawai‘i. Therefore the Natural Hazards Risk 

Index was derived from the overall hazards risks from the Atlas of Natural Hazards in the 

Hawaiian Coastal Zone’s technical hazards maps (Fletcher et al., 2002). The Index is a measure 

of “overall hazards” which includes tsunami, stream flooding, high waves, storms, erosion, sea 

level rise, and volcanic/seismic hazard estimates (Fletcher et al., 2002). The Natural Hazards 

maps of the Atlas of Natural Hazards in the Hawaiian Coastal Zones are color coded for each 

risk variable along the coastline on a 4-level scale from low to high risk. To attain our CCD 

variable values for overall hazards we joined the CCD (State of Hawaii, 2017) and Natural 

Hazard shapefiles (“Atlas of Natural Hazards in the Hawaiian Coastal Zone | USGS I Map 

2761”, 2017) exporting the database files of joined layers each segment of coastline was checked 

to see that it was correctly assigned to a CCD (coastline segments did not necessarily align with 

CCD boundaries and some are much longer than others). Any segments of coastline that fell in 

two CCD boundaries were treated as a separate segment in each CCD. The average hazard score 

for all segments of coastline in a CCD was calculated to obtain the final CCD hazard score. The 

natural hazard variables were limited to Hawai‘i.

Fisheries variables used in this analysis were gathered from three data sources: (1) State of 

Hawai‘i Division of Aquatic Resources (HDAR) Fisherman’s Reporting System (FRS); (2) 

HDAR marine dealer data; and (3) Hawai‘i longline federal logbook data. These data includes all 

commercial fisheries including charter fishers. These data are only available for Hawai‘i (Table 

1). The FRS represents monthly catch reports from commercially-licensed fishermen and is the 

source of all commercial landing data for the State of Hawai‘i except for longline catch data. 
7

Fishermen are required to report all commercial fishing activity on FRS forms provided by 

HDAR including information about species, gear, location of catch, and landing port. Dealer data 

consists of daily sales of fish purchased by fish dealers1 from the Hawai‘i-based commercial 

fishing fleets and includes information about the sale such as species, number of pieces, pounds 

sold, price per pound (estimated); and information about the boat such as the vessel name and 

captain name (Lowe et al., 2016, p. 159). Longline data in Hawai‘i is collected through the 

mandatory federal longline logbook program supported by NMFS. The logbooks are daily setand-haul logs submitted at the end of each trip consisting of catch data by number of fish kept 

and released. These data also include the species, condition at time of haul, and location of 

catch2. 

For this analysis, communities are defined as CCDs, a geographical designation used by the U.S. 

Census Bureau for statistical purposes. Variables used in this analysis were either directly 

sourced at the CCD level or when not available at this level in Hawai‘i indirectly sourced at the 

zip code level and then aggregated to their corresponding CCD. Inherent in aggregating zip 

codes to CCD we find that there are instances of zip codes stretching across multiple CCDs. To 

avoid double counting the values were recalculated using allocation assumptions based on visual 

examination of overlap on Google map, as well as CDP population distributions within CCDs. 

See Appendix 2 for a listing of Hawai‘i zip codes and their corresponding CCDs (Appendix B. 

Secondary Data Collection).

Statistical Analysis

We used variables and methods in Jepson and Colburn (2013) in an attempt to apply their index 

structure to the Pacific Island Region. In total, 105 variables were gathered, describing 

socioeconomic and fisheries aspects of the 83 populated CCDs of Hawai‘i, Guam, 

Commonwealth of the Northern Mariana Islands, and American Samoa (Appendix A. Variables). 

Social vulnerability variables were divided into five index categories: 1) Labor Force, 2) 

Housing Characteristics, 3) Poverty, 4) Population Composition, and 5) Personal disruption 

(NOAA, 2017). The variables used in these categories were available throughout the region. 

Variables were also grouped into three Gentrification Pressure index categories: 1) Housing 

Disruption, 2) Retiree Migration, and 3) Urban Sprawl. 

For the Social Vulnerability, Gentrification Pressure and Fishing indicators we used a factor 

analysis to create indices with single factor solutions. Principle component analyses were used to 

reduce these variables into single factor solutions using the “prcomp” function in the R stats 

package (R Core Team, 2017). For the principal component and factor analyses, several tests 

were used to determine if the sample was suitable for the analysis: (1) Kaiser-Meyer-Olkin 

(KMO) measure; (2) Bartlett’s test of sphericity; (3) Armor’s reliability theta; and (4) the 

determinant. The KMO measures the size of the sample to determine if it is large enough and if 

there is sufficient variation in the data; values greater than 0.5 indicate an adequate sample 

 

1 Fish dealers in Hawaii are not licensed at the state or federal level. They are required to report to the state, but it is 

effectively voluntary. 2 Longline logbooks have latitude and longitude by set, and these are typically aggregated to 5 or 10 mi2 grids for 

reporting purposes.
8

(Kaiser, 1974). Bartlett’s test is used to evaluate the data for homoscedasticity or homogeneity of 

variances (Bartlett, 1937). In this test the null hypothesis is that the variables are orthogonal. If 

the test statistic is ≤ 0.05 significance level then the correlation matrix can be said to be 

significantly different than the identify matrix and there is enough variation to analyze for 

underlying factors or components. Armor’s reliability theta is a measure that evaluates the 

internal consistency of the items in the first factor scale derived from a principal component 

analysis (Armor, 1973). It is a coefficient of the correlation of the resulting scale with the 

hypothetical scale that measures the actual scale, therefore, the closer theta is to 1, the higher the 

internal consistency of the factor. Finally, the determinant is assessed to ensure that the matrix 

for analysis is not an identity matrix and does not demonstrate multicollinearity. The matrix is 

suitable for analysis as long as the determinant is greater than zero. 

To begin developing indices multicollinearity among variables within each index was examined 

and variables with very low multicollinearity were removed. If variables were perfectly 

correlated redundancies were removed. We then performed a varimax rotation and following 

Kaiser’s rule included the number of principle components with an eigenvalue > 1. We followed 

the methods of Jepson and Colburn (2013) to examine which variables loaded highest onto the 

factor, and thereby have a list of likely substitute variables if we were not able to reach a single 

factor solution. Variables were chosen to represent a diversity of measures that a clear 

relationship to vulnerability. Whenever possible we selected variables used by Jepson and 

Colburn (2013).

A Shannon Index to measure Occupational Diversity was calculated following the methods 

developed by Jepson and Colburn (2013). In each community the percent of people in each of 

five possible occupation categories (Appendix A. Variables) were inputted into a Shannon Index 

calculation (Equation 1). The assumption of this index is that as the proportional abundance 

becomes more equal, the overall diversity of occupations increases (Jepson and Colburn, 2013). 

Because this is calculated differently from the other indices the scores range will also differ.

Equation 1. Calculation for the Shannon Index of Occupational Diversity3

H = the Shannon occupational diversity index

Pi = fraction of the entire population made up of occupation i

S = number of occupation categories encountered

Σ = sum from occupation 1 to occupation S

Natural Hazard Risk was initially calculated using a principle components analysis. However a 

single factor solution could not be reached because only one risk variable (hurricanes) occurred 

in all communities. Hail and wind were not found in any communities (a score of zero for each 

 

3 Jepson and Colburn, 2013, p 71.

H = �− (Pi ∗ llll Pi)

SS

ii=1
9

community), and tornados risk scores above zero were only found in five communities. Instead 

we used the natural hazard risks outlined in the “Atlas of Natural Hazards in the Hawaiian 

Coastal Zone” (Fletcher et al., 2002). We used the “Overall Risk” measure that is based on the 

combined risk factors of seven variables (Appendix A. Variables). Both the Shannon Index and 

Natural Hazard Risk Index were transformed by centering on the mean.

For the purposes of data visualization the vulnerability index scores were categorized as “High” 

(more than one standard deviation above the mean), “Med high” (between 0.5 and 0.99 standard 

deviation above the mean), “Med” (between 0 and 0.49 standard deviation above the mean), or 

“Low” (below the mean). The indices were plotted using ggplot2 package in R (Wickham,

2009).

Metadata and Data Access

Full metadata of this project is available through the NMFS InPort enterprise management 

system (PIFSC, 2018). The metadata record will provide variable definitions for all data utilized 

in this analysis and links to access the source data.

III. RESULTS

Scale of Community

Island

This definition of fishing community follows the mandate from the WPRFMC definition which 

contends that: 

“A large portion of the people living in the Western Pacific region observe and interact 

daily with the ocean for food, income and recreation...fishing also continues to 

contribute to the cultural integrity and social cohesion of island communities...These 

individuals are not set apart...from island populations as a whole.” (Western Pacific 

Regional Fishery Management Council, 1998).

This means that all communities in the islands should be considered fishing communities. Under 

this definition there are 10 communities under U.S. jurisdiction in the western Pacific: American 

Samoa, Guam, Commonwealth of the Northern Marinas Islands, and 7 islands of Hawai‘i (Table

2). While this scale acknowledges that all people on the islands are connected to fishing, it may 

not capture important differences among communities within islands and island groups. The 

population size of the communities also varies quite a bit, which would lead to uneven 

representation, while at the same time the number of communities is quite small making 

statistical analysis difficult. An added difficulty is that census data does not come at this scale 

and would have to be aggregated.

Census County Division (CCD)

A CCD scale allows for a finer scale analysis for comparisons of communities within islands. 

The population mean and median of each CCD is smaller than the island level (Table 2). The 
10

scale allows for statistical analysis on most indices and meaningful integration into biological 

vulnerability models (Oliver, pers. comm. 2017). 

Census Designated Place (CDP)

A CDP scale is even finer (Table 2), but geographically it only covers inhabited land areas. 

There were concerns that it may miss portions of the population, but that appears not to be the 

case as the total population calculation is very similar to CCD (Table 2). In American Samoa the 

CDP scale is designated at the village level. While the increase in samples could help with index 

development, the finer scale may not allow for meaningful integration into biological 

vulnerability models. 

Census Block Group (CBG)

A CBG scale is even finer than the CDP scale and is being considered as a community scale for 

future vulnerability index development. This scale provides greater flexibility of use because 

new data can be aggregated to a CCD or CDP level.

Table 2. Community scale comparison for island communities in the western Pacific.

Scale

Number of 

Communities

Population size

Mean Median Total

Island* 10 154,892 59,276 1,548,923

Census County Division (CCD) 83 17,805 5,371 1,549,001

Census Designated Place (CDP) 420 3,733 886 1,567,715

Census Block Group** 1,190 1,398 1,235 1,629,061

*Islands include: Kauai, Ni‘ihau, O‘ahu, Molokai, Lanai, Hawai‘i, Guam, CNMI and American Samoa.

**Removed Block Group 0s from the analysis (water cover).

Community Outliers

In the analysis a small number of communities were often listed as having very high or very low 

vulnerability. In most cases this may be caused by very small population sizes which overinflate 

proportional data. Of the four communities identified as outliers, only Ni‘ihau was excluded 

because the ACS did not provide demographic data for this community.

Ni‘ihau, Hawai‘i (population 170)

A small population of 170 was estimated in 2010, but the data from the American FactFinder 

(2005–2009) give a population size of zero and contain no other demographic information. 

Hence Ni‘ihau was excluded from this analysis.

Kalawao, Hawai‘i (population 74)
11

Located on the island of Molokai, this small community was once a forced quarantine area for 

people with Hansen’s disease, and is now encompassed by Kalaupapa National Park. The 

population consists of residents who have decided to remain in the community. There are no 

children in this community (median age = 62.5 in 2010).

Sprecklesville, Hawai‘i (population 280)

The Maui Country Club is found here and the community is home to many vacation rentals.

Swains Island, American Samoa (population 17)

The community consists of 17 people of Tokelauan descent that harvest coconuts for copra.

Indices

We were able to replicate a single-factor solution for 5 Social Vulnerability indices and 2

Fisheries indices (Table 3 and Appendix A. Variables). A single-factor solution could not be 

reached for the 3 Gentrification Vulnerability indices. A limited number of variables could be 

substituted, often because variables that were available for Hawai‘i (such as the Cost of Living 

Index) were not available in the territories. There were also some inconsistencies over time for 

some variables measured in Hawai‘i and the territories. Finally certain variables had

inconsistencies suggesting problems with data collection. In the future, developing these indices 

will require finding other reliable and widely available variables.

For most of the indices developed for this region, a single factor solution required at least some 

alternative variables from Jepson and Colburn (2013). While the Commercial Fishing 

Engagement index used all the same variables (Table 3, compared to Table 3 in Jepson and 

Colburn 2013, 19), several other indices shared one to three variables in the index score 

calculation. 

Community index scores are visualized (Figs. 1 and 2, and Figs. 4 – 10) by descending order of 

vulnerability within island groups in the Commonwealth (Northern Mariana Islands) and 

Territories (Guam and American Samoa). In Hawai‘i the communities are grouped by county, 

but named by island. Vulnerability index scores were categorized as “High” (more than one 

standard deviation above the mean), “Med high” (between 0.5 and 0.99 standard deviation above 

the mean), “Med” (between 0 and 0.49 standard deviation above the mean), or “Low” (below the 

mean). The indices were plotted using ggplot2 package in R (Wickham, 2009).In each 

visualization the index score mean (centered at zero) is marked by a dotted line and one standard 

deviation above and below the mean is marked with grey shading. 

For each index the variable value and index score for four communities are displayed in tables 4 

to 9 to show the connection between the measures of vulnerability and the variables. One

community from each island group was selected to represent the range of vulnerability (Olosega 

in American Samoa, Hagåtña in Guam, Rota in CNMI, and Lahaina in Hawai‘i). The exception 

is the Occupation Diversity Index where North Hilo was selected as the community from 

Hawai‘i because it was exceptional in its low levels of occupational diversity. For the fishing 

indices data was only available for the Hawaiian Islands, hence four communities from different 
12

islands were selected as examples (Honolulu on O‘ahu, North Kona on the island of Hawai‘i, 

Kalawao on the island of Molokai, and Hana on the island of Maui). 
13

Table 3. Vulnerability Indices Results 

Index 

Category Index Variable Factor Loadings

% 

Variance KMO Bartlett Armor Determinate

Social 

Vulnerability

Housinga median rentc 0.61 0.66 0.65 < 2e-16 0.75 0.48

median number of roomsc 0.52

% households lacking plumbingb -0.60

Labor Forcea % females employedc 0.62 0.69 0.65 0.005 0.77 0.42

% households with income < $10,000b -0.53

% in arts, entertainment & recreationc 0.59

Personal 

Disruption

% no high school diplomab 0.59 0.63 0.67 < 2e-16 0.71 0.57

% population in povertyb 0.59

% unemployedb 0.55

Population 

Composition

% female single headed householdsb 0.53 0.60 0.60 < 4e-14 0.67 0.61

% Bachelor's degreec -0.56

% population age under 5b 0.64

Povertya % individuals in poverty under 18b -0.57 0.71 0.63 0.01 0.87 0.04

% families with children < 5 in povertyb -0.56

% female headed families in povertyb -0.41

% population in povertyb -0.44

Commercial 

Fishing

Engagement # commercial permitsb -0.47 0.83 0.58 < 2e-16 0.93 > 0.00

# dealersb -0.50

pounds of landingsb -0.52

value of landings (USD)b -0.51

Reliancea commercial permits per capita (1000)b 0.57 0.51 0.66 < 2e-16 0.68 0.49

dealers per capita (1000)b 0.43

% in agriculture, forestry, fishing, & 

huntingb

0.46

pounds of landings per capita (1000)b 0.53

a Index scores reversed so that high factor scores equate to higher levels of vulnerability

b Increase in variable equates with greater vulnerability

c Increase in variable equates with lower vulnerability
14

Social Vulnerability

In the following section we detail the results of the social vulnerability indices. It should be 

noted that these indices were developed for fishing communities in the East coast of the United 

States. Differences in urbanization, history, and culture of fishing communities in the Pacific 

region may make some of these indices inappropriate for modeling vulnerability. We discuss the 

possible strengths and limitations of each index in turn in the following section, and then explore 

the issue it more generally in the discussion. It should also be noted that while we present the 

vulnerability of communities by individual indices, vulnerability is a composite of many indices, 

so no one result should be taken as evidence of high or low vulnerability. Furthermore this 

assessment is a preliminary analysis and will require a ground truth exercise to verify the 

findings. 

Housing Index

The Housing Characteristics Index indicates greater vulnerability in communities with lower 

rent, fewer rooms per housing unit, and less plumbing facilities (Table 4). The index was 

calculated and the communities were listed in order of vulnerability by island grouping (Figure

1). The index scores were reversed so that higher index scores are associated with higher 

vulnerability. 

Lower rent, fewer rooms, and less plumbing are correlated with higher proportions of people in 

poverty, and households making less than $10,000 per year, and hence associated with higher 

vulnerability (Table 4). For example the island community of Rota has a lower median rent and a 

higher percentage of houses without plumbing facilities. These housing characteristics would be 

associated with higher vulnerability when compared with the higher rent and plumbing facilities 

in Lahaina (Table 4). 

It is important to note that the housing characteristics index may not be meaningful across all the 

island groups. While the connection between rent and vulnerability in Hawai‘i would be fairly 

robust, the traditional land tenure system that is found throughout American Samoa would make 

the measure less meaningful. Under the land tenure system 98% of land in American Samoa is 

shared at a community level (Levine and Allen, 2009). In practical terms this means that people 

do not individually own their homes or pay rent, as can be seen Olosega where the median rent is 

zero (Table 4).

Table 4. Housing Characteristics Community Examples

Variable Olosega Rota Hagåtña Lahaina

median rent 0 297 752 1231

median number of rooms 4.5 3.7 3.7 3.1

% households lacking plumbing 31.1% 7.4% 7.8% 0.0%

Index score 2.07 1.27 0.66 0.12
15

Figure 1. Vulnerability level based on housing characteristics.
16

Labor Force Index

The Labor Force Index indicates greater vulnerability in communities with fewer women 

employed, fewer people employed in the arts, entertainment, or recreation industry, and more 

households with yearly income under $10,000 (Table 3). The index was calculated and the 

communities were listed in order of vulnerability by island grouping (Figure 2). The index scores 

were reversed so that higher index scores are associated with higher vulnerability. 

Communities associated with low income, indicated by a higher percentage of households with 

income under $10,000 per year, are likely to exhibit low levels of adaptive capacity in times of 

crisis or change (Cutter, Boruff, and Shirley, 2003). Conversely, higher levels of women’s 

employment have been associated with lower levels of poverty, and greater income distribution 

(Kasearu, Maestripieri, and Ranci, 2017).

More affluent communities have lower vulnerability index scores while less affluent 

communities have higher index scores4 (Table 5). However measures such as yearly income 

under $10,000, may not but universally applicable as a proxy of vulnerability across the region. 

While this may be a good indication of poverty within communities in Hawai‘i, it may not be an 

indicator of poverty in American Samoa or the other territories. Many of the communities with 

low vulnerability, such as Lahaina, are centers of tourism, which could explain the higher rates 

of employment in the arts, entertainment, and recreation industry. These service industry jobs 

also correlate with women’s employment. Because women’s employment is also a variable in 

this index, gender equality as it relates to employment opportunities for women may also be at 

play when examining the differences between the high vulnerability scores of American Samoa, 

and the lower vulnerability scores of the other island groups. The high labor force index scores 

of American Samoa communities should be viewed in the context of a greater prevalence of 

subsistence livelihoods (Fig. 3)5. A greater proportion of people engaged in subsistence 

livelihoods may mean that people do not rely on jobs or the market economy for their wellbeing, or community stability and adaptive capacity. In these cases economic measures as a 

proxy for vulnerability are not as relevant. Census measures of subsistence activities are only 

available in the territories.

 

4 However this is not a universal finding. In a study of perceived vulnerability to climate change in American 

Samoa, in some cases below average income households were less likely to report high vulnerability than above 

average income households (Wongbusarakum, 2009). 5 People engaged in subsistence activities “mainly produced goods for his or her own or family’s use and needs”. 

This includes farming, fishing, and other activities (United States Census, 2014).
17

Table 5. Labor Force community examples.

Variable Olosega Hagåtña Rota Lahaina

% females employed 37 51 65 66

% households with income < $10,000 28.9 11.9 18.9 2.6

% in arts, entertainment & recreation 0.0 18.1 17.8 35.8

Index score 3.20 0.07 -0.25 -2.58
18

Figure 2. Vulnerability level based on Labor Force.
19

Figure 3. Subsistence activity.
20

Personal Disruption Index

The Personal Disruption Index indicates greater vulnerability in communities with more people 

without a high school diploma, more people in poverty, and more unemployed people (Fig. 4). 

Higher index scores are associated with higher vulnerability.

The variables used in this index overlap with other indices (Labor Force, Population 

Composition, Poverty), but allow for a singular focus on factors that add to community 

disruption. The index developed by Jepson and Colburn (2013) included a crime index which are 

based on FBI data of personal crime rates. This index has yet to be developed for the territories 

at a CCD level. 

There were higher scores for Personal Disruption in the territories than Hawai‘i. For example 

Lahaina had lower rates of poverty, unemployment, and people without a high school diploma 

than Olosega, Hagåtña, and Rota (Table 6). As with other indices, measures of poverty and 

unemployment that indicate high levels of vulnerability in American Samoa, should be viewed 

within the context of high levels of subsistence activities (Fig. 3), and strong cultural support 

networks (Levine and Allen, 2009). Measures of poverty may be particularly problematic, and 

this will be discussed more fully in the Poverty Index section.

Table 6. Personal Disruption community examples.

Variable Olosega Hagåtña Rota Lahaina

% no high school diploma 7.3% 14.6% 7.0% 6.7%

% population in poverty 55.9% 27.2% 44.2% 6.4%

% unemployed 11.3% 5.2% 5.8% 4.5%

Index score 2.39 1.00 0.53 -0.98
21

Figure 4. Vulnerability level based on Personal Disruption. 
22

Population Composition Index

The Population Composition Index indicates greater vulnerability in communities with more 

families headed by a single woman, more people under the age of 5, and fewer people with a 

Bachelor’s degree (Table 3). The index was calculated and the communities were listed in order 

of vulnerability by island grouping (Fig. 5). Higher index scores are associated with higher 

vulnerability.

The number of children can influence the vulnerability of families. Small children can be a 

significant financial burden, and may need additional assistance in times of crises or adapting to 

changes over time. Likewise single-parent households, and particularly single female-headed 

households in contexts where women are less likely to find stable or well-paid employment, are 

often less able to adapt to changing circumstances (Morrow 1999; Cutter, Boruff, and Shirley,

2003). Education on the other hand can offer higher earning potential, and more employment 

choices. Lower levels of education have also been associated with increased vulnerability to 

many different types of change and disruption. Less formal education often means these 

individuals are less aware of how to handle disruptions and have fewer resources to rely upon in 

times of need (Cutter, Boruff, and Shirley, 2003). However, in a study of two communities in 

American Samoa, higher education corresponded with lower perception of household 

vulnerability to specific natural hazards, but also were more likely to perceive lower household 

resilience (Wongbusarakum, 2009).

As with other indices, communities in Hawai‘i exhibit less vulnerability than communities in the 

territories. All but two communities in American Samoa are listed as having high vulnerability. 

Lack of access to secondary education may be one factor (but see Wongbusarakum, 2009). For 

example in Olosega only 1.2% of the population has a bachelor’s degree, while the number is 

much higher in Hagåtña, Rota, and Lahaina (Table 7). American Samoa also had a higher 

proportion of children under 5 years old. On the other hand Guam had the highest prevalence of 

single female-headed households. 

Table 7. Population Composition community examples.

Variable Olosega Hagåtña Rota Lahaina

% female single headed households 16.7 26.6 18.1 9.4

% Bachelor's degree 1.2 8.8 13.9 18.2

% population age under 5 10.7 7.6 8.0 6.5

Index score 1.74 1.16 0.07 -1.43
23

Figure 5. Vulnerability level based on Population Composition.
24

Poverty Index

The Poverty Index indicates greater vulnerability in communities with more people in poverty6, 

more people under 18 in poverty, more families with children under 5 in poverty, and more 

families headed by a single woman in poverty (Table 3). The index was calculated and the 

communities were listed in order of vulnerability by island grouping (Fig. 6). The index scores 

were reversed so that higher index scores are associated with higher vulnerability. 

Overall, economic affluence is associated with greater access to resources which can be used to 

adapt to changing conditions in social, economic, or ecological systems associated with a 

community (Cutter, Boruff, and Shirley, 2003; Tuler et al., 2008). Furthermore, at the family 

level, single-parent households have been found to be more vulnerable than those with two 

adults (Oliver-Smith, 1996).

Overall rates of poverty were lower in Hawai‘i than in the territories as exemplified by Lahaina 

when compared to Hagåtña, Olosega, and Rota (Table 8). However the measure of poverty by 

the ACS relies on a national average (U.S. Census Bureau, 2017b), which does not take into 

account regional differences. This is problematic when comparing areas with different levels of 

reliance on a market based economy. In the case of American Samoa subsistence activities and 

reliance on support from social network could alleviate many of the vulnerabilities associated 

with poverty such as food insecurity. Furthermore the land tenure system in American Samoa 

could also alleviate poverty related housing issues such as a lack of affordable housing (Levine 

and Allen, 2009).

Table 8. Poverty community examples.

Variable Hagåtña Olosega Rota Lahaina

% individuals in poverty under 18 53.7 45.5 32.7 3.4

% families with children < 5 in poverty 55.3 38.1 37.0 1.4

% female headed families in poverty 42.9 19.0 29.6 8.8

% population in poverty 17.8 55.9 44.2 6.4

Index score 1.36 1.32 0.91 -2.40

 

6 The U.S. Census defines poverty in the following way: "the Census Bureau uses a set of money income thresholds 

that vary by family size and composition to determine who is in poverty. If a family's total income is less than the 

family's threshold, then that family and every individual in it is considered in poverty. The official poverty 

thresholds do not vary geographically, but they are updated for inflation using the Consumer Price Index (CPI-U). 

The official poverty definition uses money income before taxes and does not include capital gains or noncash 

benefits (such as public housing, Medicaid, and food stamps)" (U.S. Census Bureau, 2017b) . In 2010 the annual 

income threshold for a family of 2 with no children was $14,602. Because they are using a threshold related to a 

national average, the measure may not be relevant to many areas of the Pacific.
25

Figure 6. Vulnerability-level based on poverty.
26

Occupational Diversity Vulnerability

The Occupational Diversity Vulnerability index indicates greater vulnerability in communities 

with fewer occupation options. The index was calculated and the communities were listed in 

order of vulnerability by island grouping (Fig. 7). Diverse economies have been shown to adapt 

more effectively and more completely to disruptions than those reliant on fewer industries or 

inputs, due to their ability to rely on alternative economic sectors during disruptions or changes 

(Mileti, 1999).

The index is a measure of the spread of the workforce across five labor categories recorded by 

the ACS. If workers are concentrated in one or two categories, the occupational diversity goes 

down and the vulnerability increases. For example North Hilo has high vulnerability in this 

index. While the percentage of workers in management and sales (Table 9) is comparable to the 

state averages (29.9% and 23.5% respectively), this community has a much higher than the state 

average service employment (25.1%), and lower than average employment in natural resources 

(13.3%) and production (8.2%). Different examples are also apparent throughout the territories. 

While Olosega’s workers are mostly concentrated in management and natural resources, labor in 

Rota and Hagåtña are more evenly spread across the five different occupations (Table 9). 

Although most communities were not identified as being highly vulnerable, four communities 

were one standard deviation above the mean. Two of those communities (Swains Island in 

American Samoa and Kalawao in Hawai‘i) have very small populations, and also may not 

engage in a market economy to the same degree as other fishing communities in North America. 

It is also worth noting that in communities such as Olosega, while the economic labor 

opportunities may be limited, over 60% of the community engages in subsistence activities, 

suggesting robust engagement in the non-market economy which could mitigate vulnerability 

(Table 3).

Table 9. Occupational Diversity community examples.

Variable

North Hilo

(%)

Olosega

(%)

Rota

(%)

Hagåtña

(%)

Management, business, science, and 

arts occupations 29.2 40.7 27.3 28.7

Service occupations 38.4 16.7 28.9 23.0

Sales and office occupations 25.0 5.6 17.9 20.8

Natural resources, construction, and 

maintenance occupations 2.6 31.5 20.2 14.9

Production, transportation, and 

material moving occupations 4.8 5.6 5.7 12.6

Index score 17.96 14.69 -1.38 -7.38
27

Figure 7. Shannon Index of Occupational Diversity
28

Commercial Fishing Vulnerability

Commercial Fishing Engagement

The Commercial Fishing Engagement Index indicates greater vulnerability in communities with 

more commercial permits, more dealers, larger catch volume, and larger catch value (Table 3).

The index was calculated and the communities were listed in order of vulnerability by island 

grouping (Fig. 8A).

The communities scoring highly on the Fishing Engagement index are sites of high levels of 

commercial fishing, often with ports located within their geographic boundaries. They are also 

communities with the highest population numbers and include Honolulu, ‘Ewa, and North Kona 

(Fig. 8A). Communities scoring low on this component have smaller populations and lower 

levels of commercial fishing such as Kalawao where there are no commercial permits (Table 10). 

Scores on this component suggest higher levels of vulnerability to changes in fishery 

management or ecological conditions, but changes affecting different fisheries or types of fishing 

would affect certain communities more than others. For example, charter fishing is a large part of 

fishing in North Kona and would be severely affected by changes in charter fishing regulations 

while changes affecting the longline fishery would primarily affect Honolulu. 

Table 10. Fishing Engagement community examples.

Variable Honolulu North Kona Hana Kalawao

Commercial Permits 305 327 15 0

Dealers 21 13 2 0

Landings (lbs.) 18,000,000 1,100,000 78,000 0

Value (USD) 44,000,000 1,700,000 130,000 0

Index score 10.27 3.10 -0.58 -0.94

Commercial Fishing Reliance

The Commercial Fishing Reliance Index indicates greater vulnerability in communities with 

more per capita commercial permits, more per capita dealers, larger per capita catch volume, and 

a larger proportion of the population employed in fishing and agriculture (Table 3). The index 

was calculated and the communities were listed in order of vulnerability by island grouping 

(Figure 8B). The index scores were reversed so that higher index scores are associated with 

higher vulnerability.

Commercial Fishing Reliance variables are almost all calculated per capita and therefore they are 

normalized for population size. Low index scores indicate communities with high levels of 

fisheries engagement by proportion of overall population, for example in Hana (Table 11). Some 

communities, such as Honolulu, while scoring highly in terms of engagement, were not listed as 

highly vulnerable in terms of reliance, while North Kona is listed as highly vulnerable in both 

indices (Tables 10 and 11). Very large populations could be more resilient to disruptions in 

commercial fisheries since fishing is only a small portion of the broader economy. The inclusion 
29

of data from dealer reports could mean that these indices underestimate communities with high 

proportions of commercial fishing but no dealers. 

Table 11. Fishing Reliance community examples.

Variable Hana North Kona Honolulu Kalawao

Commercial Permits / 1000 capita 6.53 8.68 0.81 0

Dealers per capita / 1000 capita 0.88 0.34 0.06 0

Landings (lbs.) / 1000 capita 34,406 30,217 47,174 0

Fishing & agriculture employment (%) 18.1 2.6 0.5 0

Index score 4.40 2.28 0.25 -2.14

Also significant to note is that all of the fisheries data used for this analysis is based on reports of 

commercial fishing. The lack of non-commercial fishing indices is due to difficulties in the 

extrapolation of non-commercial fishing data to the general population. The data collected is site 

specific, but given current data collection methods we are unable to estimate the total numbers of 

non-commercial fishers or their catch. However, non-commercial fishing is a large part of many 

communities in Hawai‘i both for food and cultural practices and, therefore, these indices cannot 

be assumed to represent overall fishing engagement in Hawai‘i. This would also be true of the 

other island groups in this region.
30

Figure 8. Fishing Engagement and Reliance indices.
31

Natural Hazard Vulnerability

The Natural Hazard Vulnerability Index indicates greater vulnerability in communities with 

greater risk of tsunamis, stream flooding, high waves, storms, erosion, sea level, and volcanic 

activity (Fletcher et al., 2002; Appendix A. Variables) The communities were listed in order of 

vulnerability by island grouping (Fig. 9).

Fishing communities are likely to be more severely affected by coastal hazards such tsunamis, 

high waves, erosion and sea level rise risk (Pomeroy et al., 2006). Risk from storms and flooding 

streams may affect coastal and terrestrial areas and are common throughout Hawai‘i, posing a 

high likelihood of affecting communities. 

On this index, Keaau-Mountain View and Hilo on Hawai‘i Island are the most vulnerable, and 

Waialua on O‘ahu is the least vulnerable to natural hazards. Hilo, with the second highest score 

has experienced severe damage from tsunamis in the past and is therefore likely to be affected by 

a tsunami in the future. On the other end of the index, Wahiawa does not have any coastal area 

within its geographic boundaries and since three of the seven factors included in overall risk 

primarily affect coastal areas (tsunamis, high waves and sea-level rise), Wahiawa would be less 

vulnerable. 
32

Figure 9. Natural hazards.
33

IV. DISCUSSION

This research used factor analysis and principal components to identify indices that describe 

different aspects of vulnerability in fishing communities in Hawai‘i, Guam, the Commonwealth 

of the Northern Marianas Islands, and American Samoa. While it is clear that many of the 

indicators that were developed for the North and South east coast fishing communities are either 

not available in or relevant to the Pacific region, this process was still an important first step. 

These underlying concepts, when developed for the Pacific context and verified through field 

work, could provide information about relative vulnerability of fishing communities to a given 

threat, disruption or proposed policy change.

Constraints of the Data

Vulnerability indices for fisheries communities in the United States, including Hawai‘i, are 

currently calculated and mapped, and available to the public (NOAA, 2017). However, the 

indices were developed for a Northeast and Southeast coast fishing community context. To 

examine if these measures were relevant and replicable in the Pacific region we opted to 

replicate the Jepson and Colburn (2013) index development. We did this at a regional scale, 

which in our case includes the state of Hawai‘i, and the western Pacific Territories of Guam, the 

Commonwealth of the Northern Mariana Islands, and American Samoa. By adding the territories 

we increased the number of communities and thereby improved the subject-to-item ratios which 

increased the ability to find single factor solutions in PCA analysis. We also broadened the 

application of the indices so they could be incorporated into region-wide models of ecological 

and social vulnerabilities. However this also adds a wider range of socioeconomic profiles, 

making certain variables more or less relevant.

Missing Variables

The first and most obvious limitation when including the territories in a region wide assessment 

is that certain variables are not available in the territories. The crime index and cost of living 

index, both of which were found to be robust additions to social vulnerability indices in the 

Northeast and Southeast (Jepson and Colburn, 2013), were not available in territories. The crime 

index data is collected, but would require working with local police departments to localize the 

incidents for index calculation at a CCD level. New data sets purchased by the national socioeconomic group for more recent years are similarly limited to the U.S. states. However, even if 

we are able to calculate crime rates at a CCD level we would have to contend with rate inflation 

for small populations. 

Decennial data are available in the territories, but direct comparisons are also limited by several 

issues. The first is that decennial data are only collected in the territories every 10 years. This 

would limit examination of finer scale temporal changes in vulnerability. There were also 

differences in how certain questions were asked which makes direct comparison impossible. For 

example in the States the measure “Speaks English less than well” is used, while in the territories 

the measure is “Does not speak English”. In other cases measures of wealth are different. In 

Hawai‘i the first categorization of annual income is $5,000 or below, whereas in the territories it 

is $2,500 or below. These data limitations meant that we were unable to develop certain indices 
34

(such as Housing Disruption). Finding new sources of data would be important for being able to 

develop these indices as a regional level.

Irrelevant Variables and Indices

It is also important to recognize that many of the measures developed for the Northeast and 

Southeast regions are not universally applicable within this region. For example measures related 

to a commercial economy may be a good indicator of vulnerability in some communities, but be 

less indicative of vulnerability in non-market based economies. This could include the indices 

that describe housing characteristics, housing disruption, poverty, and for some variable used to 

describe labor force characteristics (Appendix A. Variables). This could include variables related 

to income, poverty, rent, mortgage, housing values, or rates of employment.

In other cases measures of infrastructure or access to urban areas are also problematic. For 

example the number of mobile homes is not a good variable because they are uncommon enough

in this region to be a good variable. Another is example is the distance to the nearest city with a 

50k+ population is less meaningful for in the Pacific where access to air travel, rather than 

driving distance, is the main barrier. 

Ethnicity, race, and language are all important variables when it comes to vulnerability (Cutter, 

Boruff, and Shirley, 2003). While these variables are important and available in the Hawai‘i and 

the western Pacific territories, we were unable to find a single variable of ethnicity or race that 

could be easily attributed to vulnerability in all the island groupings. Similarly the lack of 

English speaking skills may be a limitation in some contexts, but not others because it is not the 

predominant language in many western Pacific communities.

Variables that Could be Considered 

As stated before, one of the limitations of the region wide analysis is the differences between 

island groups. In particular Hawai‘i may be closer in terms of urbanization and market economy 

to mainland U.S. communities, than the communities in Guam, CNMI, or American Samoa. 

For example, when measuring poverty, a global poverty threshold may be more relevant than a 

U.S. poverty threshold. Another alternative would be to include variables such as subsistence

practices which are collected by the U.S. Census in the territories. However, data available 

through the U.S. Census may not be the best measure of vulnerability, in which case 

Wongbusarakum (pers. comm., 2017) recommends measures related to access to resources 

where livelihood can be depended on, skills and knowledge to make use of the resource for 

livelihood purposes, and social networks that support those livelihoods. The limiting factor 

would be being able to collect this data broadly and consistently.

It may also be worth going back to the indicator literature more broadly to examine other 

vulnerability indicators being used. For example in a review of social vulnerability indicators in 

disasters, Fatemi et al. (2017) found four main indices used in the literature: gender, public health 

conditions, public infrastructures, and migration. While these do not match well with the indices 

developed for fishing communities in the United States (see Jepson and Colburn, 2013; Jacob et 

al., 2013), they may be more relevant to fishing communities in the western Pacific.
35

Integrating the Data into Socio-ecological Assessments

The social vulnerability indices developed during this process were used in island reports on 

coral reef resilience and social vulnerability (Oliver et al., in review). Measures of vulnerability 

were mapped onto the land, and measures of coral resilience were mapped on corresponding 

water areas. The purpose of this visual juxtaposition is to allow managers to integrate social and 

biological information into spatial planning. The process of including social vulnerability indices 

also brought up some important considerations for future use of this data.

The main issue raised was the utility of using region wide vulnerability scores when reporting to 

individual island groups. In particular the high levels of overall vulnerability in American Samoa 

and low levels in Hawai‘i was a concern. The project lead felt that local collaborators in 

American Samoa would dispute the suggestion that their communities are socially vulnerable. 

This is not an unfounded response. Throughout this report we have also highlighted ways in 

which these measures of social vulnerability may not be appropriate when applied to the nonmarket based economy and land tenure traditions of American Samoa. Furthermore, when index 

scores are presented at the regional scale, variation among communities within American Samoa, 

and within Hawai‘i is lost in the simplified four categories of vulnerability. 

To present data that is relevant for the local context, the index scores were re-centered at the 

island group level. In this case the grouping was American Samoa, Hawai‘i, and Guam and 

CMNI (Fig. 10). The outlier communities of Swain’s Island in American Samoa, and Kalawao in 

Hawai‘i were also removed. The result is more localized measures of vulnerability. For example 

when examining the poverty index at the regional scale over half of communities in American 

Samoa would be considered highly vulnerable (an index score of more than one SD above the 

$83,037, in American Samoa it was $29,213, and hence what it means to live in poverty could be 

quite different. In the localized score only one community fits that category (Fig. 10). 

Conversely regional scores showed not highly vulnerable Hawaiian communities (Fig. 6),

whereas localized scores found eight highly vulnerable (Fig. 10). 
36

Figure 10. Localized poverty index scores.
37

Next Steps

Considering the constraints of the data (i.e., a small subject sample with low subject-to-item 

ratios) the results of this analysis should be tested with confirmatory factor analysis. More recent 

secondary data has been collected for Hawai‘i communities to include all ACS 5-year average 

data series between 2005–2009 and 2012–2016. These additional data could be used in 

confirmatory factor analysis to assess the validity of the factors produced in this research, and to 

derive time-series indicator values to understand the temporal scale of community dynamics and 

vulnerabilities. Secondly, these results should be validated using qualitative data from select 

communities to compare with results of this analysis (e.g., Jacob et al., 2013). Finally the index 

scores generated from the national data set should be compared with the regionally developed 

indices. This comparison could only be done in Hawai‘i communities.

Given the problems with variation in applicability of specific variables and indices, future 

analyses should separate the territories and Hawai‘i. This would allow for a greater number of 

variables to be used in Hawai‘i that aren’t available in the territories (such as crime, or cost of 

living indices). It would also mean that variables that could be very relevant to Hawai‘i, such as 

rent costs, would not be misapplied to context where they are not relevant. It would be important 

to develop new relevant indices that could capture vulnerability in the rural and non-market 

economies of the Pacific territories.

One noted area lacking data in this analysis is non-commercial (or recreational) fishing. Fisheries 

catch-and-effort information is collected for Hawai‘i from non-commercial fishers both in person 

and by phone, but has not yet been integrated. Efforts are being made to find ways to integrate 

these data to support noncommercial fishing engagement and reliance indicators. Additional 

applications in the future could be to explore fishery-specific analyses to determine community 

engagement and reliance on fisheries or species of management interest. This framework could 

also be integrated with species vulnerability assessments or with forecasts of climate change 

(Colburn et al., 2016).

V. CONCLUSION

The results presented above are meant as a first step in establishing social vulnerability indicators 

for fishing communities in Hawai‘i, Guam, the Commonwealth of the Northern Marianas 

Islands, and American Samoa. This analysis draws on Jepson and Colburn’s (2013) work 

identifying social indicators for fishing communities in the northeast and southeast regions. If 

developed further, and with specific attention to using relevant variables and indices in the 

western Pacific territories, social indicators could allow us to compare vulnerability levels of 

different communities within this region. These indices describe factors of social cohesion in 

communities that have been associated with greater adaptive capacity to abrupt or gradual 

changes (Cutter, Boruff, and Shirley, 2003). However, some of the data used in that analysis is 

unavailable, of questionable validity, or not applicable for this region. With more data and more 

iterations of this analysis, these indicators can be further refined and improved over time.
38

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43

VII. APPENDICES 

Appendix A. Variables

Table A. Full list of variables used in indices.

Vulnerability 

Indicator Index Variable Name Variable Description Source

Fishing Commercial 

Engagement

COMMPMTa # commercial permits Fisher Report

DEALERNUMa # dealers Dealer Report

POUNDSa pounds of landings Fisher Report

VALUEa value of landings (USD) Dealer Report

Commercial 

Reliance

COMMPRPOPa commercial permits per capita (1000) Fisher Report

DEALERNUMPOPa dealers per capita (1000) Dealer Report

PCTAGRFRFSHa % in agriculture, forestry, fishing, hunting industries ACS

PNDSPRPOPa pounds of landings per capita (1000) Fisher Report

VALUPRPOP value of landings per capita (1000) Dealer Report

Social Housing HUMNRa median # of rooms ACS

MED_GRRNTa median gross rent ACS

PCTNOPLBGa % of households lacking complete plumbing 

facilities 

ACS

MEDDWELLAGE median year structure built ACS

HMOWNVAC homeowner vacancy rate ACS

HOUSUNITNUM_OWNOCC total owner-occupied housing units ACS

MDYRSRESID median years in residence ACS

MED_MTMRG median monthly owner costs ACS

PCTGRPQRTRS_NONINST % non-institutionalized people living in group 

quarters

ACS

PCTGRPQRTRS_TOTAL % all living in group quarters ACS

PCTHASMORTGAGE % housing units with a mortgage ACS

PCTHSMORTGAGELON % housing units with a second mortgage and home 

equity loan

ACS

PCTMBLHM % mobile homes ACS
44

Vulnerability 

Indicator Index Variable Name Variable Description Source

PCTNOVHCL % households with no cars ACS

PCTOWNER % owner ACS

PCTRENTER % renter ACS

TOTHU total housing units ACS

Labor Force PCTEMPFEMAa % females employed ACS

PCTHHHUNDR10Ka % households with income under $10,000 ACS

PCTSERVINDa % in arts, entertainment and recreation industries ACS

MEANHHINC mean household income ACS

MEANTRAVTIM mean travel time to work ACS

MEDHHINC median household income ACS

PCTCNSTRCT % in construction, extraction, installation, 

maintenance, and repair

ACS

PCTEMPLOYED % in employed the civilian labor force ACS

PCTHH100K % household with income $100,000+ ACS

PCTLABFEMALE % females in the civilian labor force ACS

PCTSERVOCCb % in service occupations ACS

PCTSLFEMP % self employed ACS

Personal 

Disruption

PCTNODIPLOMAa % 9th to 12th no diploma ACS

PCTUNEMPLDa % unemployed ACS

PCTPOVac % population in poverty ACS

PCTMALESEPARATD % males separated ACS

PCTCHGUNEM % change in % unemployed 2000-2010 ACS

PCTFEMALEDIVORCD % females divorced ACS

PCTFEMALESEPARATD % females separated ACS

PCTLIVESMHS % lived in same house 1 yr ago ACS

PCTMALEDIVORCD % males divorced ACS
45

Vulnerability 

Indicator Index Variable Name Variable Description Source

PCTPOP9THGRD % less than 9th grade ACS

Population 

Composition

PCTBATCHLRSa % Bachelor's degree ACS

PCTFEMHEADHOUSEa % female single headed households ACS

POP0_5PCTa % age 5 or under ACS

AVGHHSZE average household size ACS

MEDAGE median age ACS

PCTFEMALE % females ACS

PCTFORBRN % foreign born ACS

PCTHHUNDR18 % households with children under 18 ACS

PCTMALES % males ACS

PCTNATBORN % born in the state or territory they currently live in ACS

PCTNOSPKENG % does not speak English (or speaks "less than 

well")

ACS

PCTPOPHSGRD % high school grad ACS

POP85PCT % age 85 plus ACS

POPAAPCT % Asian alone ACS

POPBAPCT % Black alone ACS

POPCHPCT % population change ACS

POPHSPCT % Hispanic ACS

POPPAPCT % Pacific alone ACS

POPWAPCT % White alone ACS

Poverty PCTCHLDPOVa % in poverty under 18 ACS

PCTFMPOV5a % of families with children under 5 in poverty ACS

PCTFMPOVFEMHHa % of female headed families in poverty ACS

PCFMINPOV % of families below poverty level ACS

PCT65POV % of individuals in poverty 65 plus ACS
46

Vulnerability 

Indicator Index Variable Name Variable Description Source

PCTPOVc % population in poverty ACS

PCTRECSSI % households receiving SSI ACS

Occupational 

Diversity

PCTMNGOCC % in management, business, science, and arts 

occupations

ACS

PCTSERVOCCb % in service occupations ACS

PCTSALESOCC % in sales and office occupations ACS

PCTCONSOCC % in natural resources, construction, and 

maintenance occupations

ACS

PCTPRODOCC % in production, transportation, and material 

moving occupations

ACS

Gentrification Housing 

Disruption

PCTCHGMRG % change median mortgage 2000-2010 ACS

PCTCHGRENTER % change in # of renters 2000-2010 ACS

PCTCHGRNT % change median rent 2000-2010 ACS

PCTCHNGHOMVAL % change in median home values 2000-2010 ACS

PCTRENTRMTHLYCST % renters with costs 35% plus of household 

income

ACS

Retiree 

Migration

MEANRETINC mean household retirement income ACS

PCTHHUOVER65 % households with ages 65 plus ACS

PCTLABFORCE % in the civilian labor force ACS

PCTRECRET % households with retirement income ACS

PCTRECSOC % households with social security ACS

PCTSERVOCCb % in service occupations ACS

Urban 

Sprawl

LANDAREA land area (in sq. miles) ACS

MEDHOMVAL median home value ACS

POPDENS population density ACS

Natural 

Hazard

Risk HAILRSKAVG Damaging Hail Risk Average moving.com

HURRSKAVG Damaging Hurricanes Risk Average moving.com

TORNRSKAVG Damaging Tornadoes Risk Average moving.com

WNDRSKAVG Damaging Winds Risk Average moving.com
47

Vulnerability 

Indicator Index Variable Name Variable Description Source

TSUNAMI_USGS Tsunami Risk USGS

STREAMFLOOD_USGS Streamflood Risk USGS

HIGHWAVES_USGS Highwaves Risk USGS

STORMS_USGS Storm Risk USGS

EROSION_USGS Erosion Risk USGS

SEALEVEL_USGS Sea Level Risk USGS

VOLSEIS_USGS Volcanic Risk USGS

aUsed in the Index Score Calculation

bUsed as Labor Force, Retiree Migration, and Shannon Index variable

cUsed as Personal Disruption and Poverty Index variable

 
48

Appendix B. Secondary Data Collection

Table B. Hawai‘i zip code and CCD relationships and allocation.

Zip Code CCD % 

Allocation Rationale

96720 Hilo, Hawai‘i 100

96721 Hilo, Hawai‘i 100

96727 Honokaa-Kukuihaele, Hawai‘i 90 Google Map

96718 Kau, Hawai‘i 100

96737 Kau, Hawai‘i 100

96772 Kau, Hawai‘i 100

96777 Kau, Hawai‘i 100

96749 Keaau-Mountain View, 

Hawai‘i 100

96760 Keaau-Mountain View, 

Hawai‘i 100

96771 Keaau-Mountain View, 

Hawai‘i 100

96785 Keaau-Mountain View, 

Hawai‘i 100

96710 North Hilo, Hawai‘i 49 Google Map

96764 North Hilo, Hawai‘i 100

96773 North Hilo, Hawai‘i 100

96780 North Hilo, Hawai‘i 100

96719 North Kohala, Hawai‘i 100

96743 North Kohala, Hawai‘i 10 CDP population distribution: Waimea, Puako and Google Map

96755 North Kohala, Hawai‘i 100

96725 North Kona, Hawai‘i 100

96739 North Kona, Hawai‘i 100

96740 North Kona, Hawai‘i 100

96745 North Kona, Hawai‘i 100

96750 North Kona, Hawai‘i 100

96727 Paauhau-Paauilo, Hawai‘i 10 Google Map

96743 Paauhau-Paauilo, Hawai‘i 10 CDP population distribution: Waimea, Puako and Google Map

96774 Paauhau-Paauilo, Hawai‘i 100

96775 Paauhau-Paauilo, Hawai‘i 100

96776 Paauhau-Paauilo, Hawai‘i 100

96778 Pahoa-Kalapana, Hawai‘i 100

96710 Papaikou-Wailea, Hawai‘i 51 Google Map

96728 Papaikou-Wailea, Hawai‘i 100

96781 Papaikou-Wailea, Hawai‘i 100

96783 Papaikou-Wailea, Hawai‘i 100

96738 South Kohala, Hawai‘i 100

96743 South Kohala, Hawai‘i 80 CDP population distribution: Waimea, Puako and Google Map

96704 South Kona, Hawai‘i 100

96726 South Kona, Hawai‘i 100
49

Zip Code CCD % 

Allocation Rational

e

96701 Ewa, O‘ahu 100

96706 Ewa, O‘ahu 100

96707 Ewa, O‘ahu 100

96709 Ewa, O‘ahu 100

96759 Ewa, O‘ahu 100

96782 Ewa, O‘ahu 100

96789 Ewa, O‘ahu 100

96797 Ewa, O‘ahu 100

96853 Ewa, O‘ahu 100

96860 Ewa, O‘ahu 100

96861 Ewa, O‘ahu 100

96801 Honolulu, O‘ahu 100

96802 Honolulu, O‘ahu 100

96804 Honolulu, O‘ahu 100

96805 Honolulu, O‘ahu 100

96806 Honolulu, O‘ahu 100

96807 Honolulu, O‘ahu 100

96808 Honolulu, O‘ahu 100

96809 Honolulu, O‘ahu 100

96810 Honolulu, O‘ahu 100

96811 Honolulu, O‘ahu 100

96812 Honolulu, O‘ahu 100

96813 Honolulu, O‘ahu 100

96814 Honolulu, O‘ahu 100

96815 Honolulu, O‘ahu 100

96816 Honolulu, O‘ahu 100

96817 Honolulu, O‘ahu 100

96818 Honolulu, O‘ahu 100

96819 Honolulu, O‘ahu 100

96820 Honolulu, O‘ahu 100

96821 Honolulu, O‘ahu 100

96822 Honolulu, O‘ahu 100

96823 Honolulu, O‘ahu 100

96824 Honolulu, O‘ahu 100

96825 Honolulu, O‘ahu 100

96826 Honolulu, O‘ahu 100

96827 Honolulu, O‘ahu 100

96828 Honolulu, O‘ahu 100

96830 Honolulu, O‘ahu 100

96835 Honolulu, O‘ahu 100

96836 Honolulu, O‘ahu 100

96837 Honolulu, O‘ahu 100

96838 Honolulu, O‘ahu 100
50

Zip Code CCD % 

Allocation Rationale

96839 Honolulu, O‘ahu 100

96840 Honolulu, O‘ahu 100

96841 Honolulu, O‘ahu 100

96843 Honolulu, O‘ahu 100

96844 Honolulu, O‘ahu 100

96846 Honolulu, O‘ahu 100

96847 Honolulu, O‘ahu 100

96848 Honolulu, O‘ahu 100

96849 Honolulu, O‘ahu 100

96850 Honolulu, O‘ahu 100

96859 Honolulu, O‘ahu 100

96712 Koolauloa, O‘ahu 53.4 CDP population distribution: Haleiwa, Pupukea

96730 Koolauloa, O‘ahu 100

96731 Koolauloa, O‘ahu 100

96762 Koolauloa, O‘ahu 100

96717 Koolaupoko, O‘ahu 100

96734 Koolaupoko, O‘ahu 100

96744 Koolaupoko, O‘ahu 100

96795 Koolaupoko, O‘ahu 100

96863 Koolaupoko, O‘ahu 100

96786 Wahiawa, O‘ahu 100 Google Map – Overlap with Waialua, but population in Wahiawa 

96854 Wahiawa, O‘ahu 100

96857 Wahiawa, O‘ahu 100

96712 Waialua, O‘ahu 46.6 CDP population distribution: Haleiwa, Pupukea

96791 Waialua, O‘ahu 100

96792 Waianae, O‘ahu 100

96742 Kalawao, Molokai 100

96705 Eleele-Kalaheo, Kauai 100

96741 Eleele-Kalaheo, Kauai 100

96714 Hanalei, Kauai 100

96722 Hanalei, Kauai 100

96754 Hanalei, Kauai 100

96746 Kapaa, Kauai 90 CDP population distribution: Kapaa, Wailua-Anahola

96716 Kaumakani-Hanapepe, Kauai 100

96747 Kaumakani-Hanapepe, Kauai 100

96769 Kaumakani-Hanapepe, Kauai 100 No CML fishermen reporting on Ni‘ihau

96752 Kekaha-Waimea, Kauai 100

96796 Kekaha-Waimea, Kauai 100

96756 Koloa-Poipu, Kauai 100

96765 Koloa-Poipu, Kauai 100

96766 Lihue, Kauai 85 CDP population distribution: Lihue, Puhi

96769 Ni‘ihau, Kauai 0 No CML fishermen reporting on Ni‘ihau

96715 Puhi-Hanamaulu, Kauai 100
51

Zip Code CCD % 

Allocation Rationale

96766 Puhi-Hanamaulu, Kauai 15 CDP population distribution: Lihue, Puhi

96703 Wailua-Anahola, Kauai 100

96746 Wailua-Anahola, Kauai 10 CDP population distribution: Kapaa, Wailua-Anahola

96751 Wailua-Anahola, Kauai 100

96748 East Molokai, Molokai 100

96708 Haiku-Pauwela, Maui 95 Google Map

96708 Hana, Maui 5 Google Map

96713 Hana, Maui 100

96732 Kahului, Maui 98 Population Distribution: Kahului, Sprecklesville

96733 Kahului, Maui 100

96753 Kihei, Maui 100

96790 Kula, Maui 100

96761 Lahaina, Maui 100

96767 Lahaina, Maui 100

96763 Lanai, Lanai 100

96768 Makawao-Paia, Maui 100

96779 Makawao-Paia, Maui 100

96784 Makawao-Paia, Maui 100 No residents in Puunene, assigned to neighboring Makawao-Paia

96788 Makawao-Paia, Maui 100

96732 Puunene, Maui 0 No residents in Puunene

96784 Puunene, Maui 0 No residents in Puunene, assigned to neighboring Makawao-Paia

96732 Spreckelsville, Maui 2 Population Distribution: Kahului, Sprecklesville

96793 Waihee-Waikapu, Maui 32.6 CDP Population Distribution: Waihee-Waikapu, Wailuku

96793 Wailuku, Maui 67.4 CDP Population Distribution: Waihee-Waikapu, Wailuku

96729 West Molokai, Molokai 100

96757 West Molokai, Molokai 100

96770 West Molokai, Molokai 100
52

Appendix C. Index Scores

Table C. Index score values.

Community Island 

Group

Housing 

Char.

Labor 

Force

Personal 

Disruption

Population 

Comp. Poverty Occupation Diversity

Fishing 

Engagement

Fishing 

Reliance

Natural 

Hazard 

Risk

Hilo, Hawai‘i HI -0.74 0.24 -0.48 -1.29 -0.23 -3.22 0.98 -0.67 1.17

Honokaa-Kukuihaele, Hawai‘i HI -0.66 -1.52 -0.34 -0.68 -2.15 -5.08 -0.75 -0.80 -0.33

Kau, Hawai‘i HI -0.23 0.26 0.13 0.47 0.63 5.93 -0.36 3.25 0.67

Keaau-Mountain View, Hawai‘i HI -0.33 0.03 -0.05 -0.62 0.14 5.04 -0.27 -0.34 1.67

North Hilo, Hawai‘i HI -0.61 -0.39 -0.68 -2.32 -1.88 -17.96 -0.91 -1.08 -0.33

North Kohala, Hawai‘i HI -1.20 -1.95 -1.50 -1.05 -1.59 0.05 -0.59 1.30 0.33

North Kona, Hawai‘i HI -0.70 -1.35 -0.96 -1.66 -1.65 4.30 3.09 2.28 0.87

Paauhau-Paauilo, Hawai‘i HI -0.65 -1.85 -1.21 -1.41 -1.30 -2.14 -0.87 -0.52 -0.33

Pahoa-Kalapana, Hawai‘i HI -0.49 1.04 -0.21 -0.20 0.04 7.11 -0.69 0.43 0.67

Papaikou-Wailea, Hawai‘i HI -0.63 0.27 -0.62 -0.84 -0.10 1.17 -0.88 -0.43 -0.33

South Kohala, Hawai‘i HI -1.32 -2.11 -1.52 -1.06 -1.88 -4.39 -0.04 -0.18 0.67

South Kona, Hawai‘i HI -0.28 -0.53 -1.09 -1.82 -1.47 1.72 0.03 3.14 0.07

Ewa, O‘ahu HI -1.97 -0.59 -1.70 -1.03 -2.07 1.85 2.38 -1.57 0.00

Honolulu, O‘ahu HI -0.43 -0.41 -1.59 -1.94 -1.61 -7.57 10.27 0.25 0.00

Koolauloa, O‘ahu HI -1.46 -1.23 -1.55 -1.34 -1.93 -0.97 -0.63 -1.46 0.67

Koolaupoko, O‘ahu HI -2.47 -0.42 -1.68 -1.13 -1.79 -5.41 0.91 -0.88 0.42

Wahiawa, O‘ahu HI -1.96 0.57 -0.88 0.44 -0.54 6.23 -0.42 -1.02 -4.33

Waialua, O‘ahu HI -1.75 -0.54 -0.77 0.23 -1.28 1.85 -0.55 -0.39 1.17

Waianae, O‘ahu HI -0.99 0.17 0.96 1.61 0.43 8.91 0.18 -0.64 0.00

Kalawao, Molokai HI 1.94 -1.96 -2.49 -2.63 -2.84 -34.21 -0.94 -2.14 NA

Eleele-Kalaheo, Kauai HI -1.16 -1.72 -1.72 -0.49 -2.07 -1.04 -0.70 -0.57 -0.33

Hanalei, Kauai HI -1.10 -0.78 -1.36 -1.48 -1.53 -5.05 -0.05 1.20 -0.08

Kapaa, Kauai HI -0.78 -0.68 -1.28 -0.24 -1.14 3.92 -0.26 1.02 0.67

Kaumakani-Hanapepe, Kauai HI -0.37 -1.92 -1.31 -1.43 -2.74 10.18 -0.82 1.02 -0.33

Kekaha-Waimea, Kauai HI -1.07 -0.96 -1.53 -0.99 -1.04 2.53 -0.53 1.49 -0.33
53

Community Island 

Group

Housing 

Char.

Labor 

Force

Personal 

Disruption

Population 

Comp. Poverty Occupation Diversity

Fishing 

Engagement

Fishing 

Reliance

Natural 

Hazard 

Risk

Koloa-Poipu, Kauai HI -0.60 -1.31 -1.33 -0.87 -1.93 -0.18 0.09 1.36 -1.33

Lihue, Kauai HI -0.60 0.26 -1.28 -1.39 -0.81 -0.17 -0.04 0.89 -0.33

Puhi-Hanamaulu, Kauai HI -1.16 -1.45 -1.10 -1.13 -1.94 1.83 -0.70 -1.43 0.17

Wailua-Anahola, Kauai HI -1.48 -1.01 -1.05 -1.91 -1.42 1.36 -0.86 -1.69 -0.33

East Molokai, Molokai HI -0.10 0.44 1.31 -1.21 0.61 3.88 -0.56 0.60 -0.83

Haiku-Pauwela, Maui HI -1.11 -1.22 -0.78 -0.29 -1.88 -3.01 -0.77 -0.91 -0.33

Hana, Maui HI 0.10 -1.67 -1.04 -0.14 -2.73 -1.36 -0.57 4.40 -0.47

Kahului, Maui HI -0.53 -0.94 -0.99 -0.42 -1.52 2.43 0.25 -0.68 0.67

Kihei, Maui HI -0.37 -2.17 -1.25 -1.54 -1.42 -3.27 -0.25 -1.12 0.67

Kula, Maui HI -1.07 -0.58 -1.65 -3.02 -0.79 -6.51 -0.77 -1.18 -0.58

Lahaina, Maui HI 0.11 -2.58 -0.98 -1.43 -2.40 -0.82 -0.10 -0.91 0.47

Lanai, Lanai HI -0.41 -3.09 -1.43 -0.93 -2.80 -4.88 -0.56 1.06 -1.21

Makawao-Paia, Maui HI -1.35 -0.84 -0.84 -1.04 -1.84 1.83 -0.13 -0.63 0.67

Spreckelsville, Maui HI -2.20 -0.90 -1.95 -2.02 -2.93 1.83 -0.93 -0.89 0.67

Waihee-Waikapu, Maui HI -1.68 -1.52 -1.48 -1.34 -2.45 -3.64 -0.58 0.22 0.33

Wailuku, Maui HI -0.84 -1.17 -0.60 -0.24 -1.96 1.97 -0.21 -1.16 0.67

West Molokai, Molokai HI -0.17 -0.20 -0.20 -0.67 0.17 -0.30 -0.88 -0.63 -1.19

Agana Heights, Guam GU 0.01 -0.18 0.20 0.87 1.62 -1.55 NA NA NA

Agat, Guam GU 0.42 0.91 1.88 1.38 1.80 4.54 NA NA NA

Asan, Guam GU -0.51 -0.05 -0.12 0.61 1.73 -0.52 NA NA NA

Barrigada, Guam GU -0.44 -0.06 0.02 0.49 1.53 2.49 NA NA NA

Chalan Pago-Ordot, Guam GU -0.29 0.03 0.29 0.79 1.18 1.37 NA NA NA

Dededo, Guam GU 0.16 -0.43 0.90 0.54 1.54 5.63 NA NA NA

Hagåtña, Guam GU 0.65 0.06 1.00 1.16 1.35 7.38 NA NA NA

Inarajan, Guam GU 0.53 0.27 0.70 1.11 1.84 4.65 NA NA NA

Mangilao, Guam GU 0.10 -0.08 0.86 0.75 1.60 3.82 NA NA NA

Merizo, Guam GU -0.06 0.30 2.07 1.89 1.96 2.10 NA NA NA

Mongmong-Toto-Maite, Guam GU 0.07 0.27 1.51 1.77 1.99 4.58 NA NA NA

Piti, Guam GU -1.58 -0.13 -0.61 0.31 1.42 -5.52 NA NA NA

Santa Rita, Guam GU -1.20 0.58 -0.80 -0.26 1.25 -0.75 NA NA NA
54

Community Island 

Group

Housing 

Char.

Labor 

Force

Personal 

Disruption

Population 

Comp. Poverty Occupation Diversity

Fishing 

Engagement

Fishing 

Reliance

Natural 

Hazard 

Risk

Sinajana, Guam GU -0.13 0.21 0.74 1.22 1.52 3.92 NA NA NA

Talofofo, Guam GU -0.83 0.04 0.43 0.47 1.41 2.37 NA NA NA

Tamuning, Guam GU 0.12 -0.50 -0.40 -0.44 1.02 3.51 NA NA NA

Umatac, Guam GU 0.42 1.18 2.52 2.82 2.45 5.80 NA NA NA

Yigo, Guam GU 0.06 -0.18 0.54 0.40 1.44 6.78 NA NA NA

Yona, Guam GU -0.49 0.23 0.99 1.27 2.09 -0.83 NA NA NA

Rota MP 1.27 -0.25 0.53 0.07 0.91 1.37 NA NA NA

Saipan (1) MP 1.75 1.04 2.49 0.75 1.36 5.71 NA NA NA

Saipan (2) MP 2.06 0.68 2.32 0.71 1.48 6.06 NA NA NA

Saipan (3) MP 1.96 0.01 1.05 0.12 1.22 0.36 NA NA NA

Saipan (4) MP 1.17 -0.14 1.90 0.42 1.04 1.68 NA NA NA

Saipan (5) MP 1.31 0.60 2.12 0.60 1.25 0.97 NA NA NA

Tinian MP 2.01 -1.70 1.11 0.44 1.21 -6.18 NA NA NA

Ituau, Tutuila AS 1.14 1.83 1.26 1.86 1.67 9.11 NA NA NA

Ma'oputasi, Tutuila AS 1.26 2.05 1.51 2.03 1.93 10.64 NA NA NA

Sa'ole, Tutuila AS 1.62 2.12 1.61 1.90 1.89 9.56 NA NA NA

Sua, Tutuila AS 0.92 1.87 0.41 1.66 1.50 7.87 NA NA NA

Vaifanua, Tutuila AS 2.01 2.04 1.09 1.92 1.65 6.66 NA NA NA

Faleasao, Manu'a AS 1.70 2.18 0.54 2.05 1.28 1.06 NA NA NA

Fitiuta, Manu'a AS 1.81 3.31 2.53 1.40 1.73 3.21 NA NA NA

Ofu, Manu'a AS 1.91 2.42 1.65 1.03 0.29 -0.80 NA NA NA

Olosega, Manu'a AS 2.07 3.20 2.39 1.74 1.32 -14.69 NA NA NA

Ta'u, Manu'a AS -0.65 3.58 0.92 2.07 2.13 -4.73 NA NA NA

Swains Island AS 7.25 3.62 -3.02 -1.98 -2.67 -85.06 NA NA NA

Lealataua, Tutuila AS 1.22 1.82 1.54 1.55 1.75 8.74 NA NA NA

Leasina, Tutuila AS 1.43 1.95 2.74 1.82 2.08 8.19 NA NA NA

Tualatai, Tutuila AS 1.52 1.93 1.68 1.63 1.99 8.25 NA NA NA

Tualauta, Tutuila AS 1.12 1.61 0.97 1.55 1.89 7.47 NA NA NA
55

Availability of NOAA Technical Memorandum NMFS

Copies of this and other documents in the NOAA Technical Memorandum NMFS series issued 

by the Pacific Islands Fisheries Science Center are available online at the PIFSC Web site 

http://www.pifsc.noaa.gov in PDF format. In addition, this series and a wide range of other 

NOAA documents are available in various formats from the National Technical Information 

Service, 5285 Port Royal Road, Springfield, VA 22161, U.S.A. [Tel: (703)-605-6000]; URL: 

http://www.ntis.gov. A fee may be charged.

Recent issues of NOAA Technical Memorandum NMFS–PIFSC are listed below:

NOAA-TM-NMFS-PIFSC-64 A survey design performance analysis examining linkages 

between reef fish assemblages and benthic morphologies in the 

main Hawaiian Islands.

K. D. GOROSPE and T. S. ACOBA

(November 2017)

63 Economic and social characteristics of the Hawaii small boat

fishery 2014.

H. I. CHAN and M. PAN

(May 2017)

62 Injury determinations for marine mammals observed interacting 

with Hawaii and America Samoa longline fisheries during 2010-

2014.

A. L. BRADFORD and K. A. FORNEY

(March 2017)