About this dataset

Context

The dataset tabulates the Maui County population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Maui County across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Maui County was 164,183, a 0.11% decrease year-by-year from 2022. Previously, in 2022, Maui County population was 164,359, a decline of 0.27% compared to a population of 164,797 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Maui County increased by 35,390. In this period, the peak population was 167,682 in the year 2019. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Maui County is shown in this column.
• Year on Year Change: This column displays the change in Maui County population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Maui County Population by Year. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Maui County by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Maui County across both sexes and to determine which sex constitutes the majority.

Key observations

There is a slight majority of female population, with 50.13% of total population being female. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Maui County is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Maui County total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Maui County Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the Maui County population by age. The dataset can be utilized to understand the age distribution and demographics of Maui County.

Content

The dataset constitues the following three datasets

• Maui County, HI Age Group Population Dataset: A complete breakdown of Maui County age demographics from 0 to 85 years, distributed across 18 age groups
• Maui County, HI Age Cohorts Dataset: Children, Working Adults, and Seniors in Maui County - Population and Percentage Analysis
• Maui County, HI Population Pyramid Dataset: Age Groups, Male and Female Population, and Total Population for Demographics Analysis

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

About this dataset

Context

The dataset tabulates the population of Maui County by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Maui County. The dataset can be utilized to understand the population distribution of Maui County by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Maui County. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Maui County.

Key observations

Largest age group (population): Male # 55-59 years (6,108) | Female # 60-64 years (6,004). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Maui County population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Maui County is shown in the following column.
• Population (Female): The female population in the Maui County is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Maui County for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Maui County Population by Gender. You can refer the same here

A network of citizen science volunteers periodically monitored water quality at several beaches across the island of Maui in the State of Hawaii during the years 2010-2016. This community-based monitoring effort provided valuable data for resource management purposes. Informed volunteer networks can serve as a community's "eyes and ears" and will often provide the first indications of changes to a system. In addition to the value of early detection, it is important to maintain ongoing monitoring efforts to compile data and document resource conditions. In addition to water temperature, salinity, turbidity (water clarity), and pH, sampling sites were also monitored for harmful bacteria levels of Enterococcus through 2014-06-26. This indicator bacteria has been correlated with the presence of human pathogens (disease-causing organisms) and therefore with human illnesses such as gastroenteritis, diarrhea, and various infections in epidemiological studies. As such, it is commonly measured in beach water quality monitoring programs. For reference, the U.S. Environmental Protection Agency (EPA) 1986 Ambient Water Quality Criteria for Bacteria recommends that a water quality alert is posted if either: (a.) the latest water sample exceeds 104 CFU/100 mL of Enterococcus, or (b.) the geometric mean of the past 30 days of water samples exceeds 35 CFU/100 mL. One of the limitations of all available and EPA-approved test methods is that the sample must be incubated for about 24 hours. Data were managed through an online repository, the Coral Reef Monitoring Data Portal (now defunct), developed and coordinated by the Coral Reef Alliance (CORAL) and its partners with funding provided by the Hawaii Tourism Authority (HTA) and the National Oceanic and Atmospheric Administration (NOAA). Data collection was coordinated by the Hawaii Division of Aquatic Resources (DAR), the Hawaiian Islands Humpback Whale National Marine Sanctuary (HIHWNMS), Aquanimity Now, the Digital Bus, Save Honolua Coalition, Project S.E.A.-Link, and other local organizations and agencies. Data are publicly distributed by the Pacific Islands Ocean Observing System (PacIOOS).

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The Address Ranges Relationship File (ADDR.dbf) contains the attributes of each address range. Each address range applies to a single edge and has a unique address range identifier (ARID) value. The edge to which an address range applies can be determined by linking the address range to the All Lines Shapefile (EDGES.shp) using the permanent topological edge identifier (TLID) attribute. Multiple address ranges can apply to the same edge since an edge can have multiple address ranges. Note that the most inclusive address range associated with each side of a street edge already appears in the All Lines Shapefile (EDGES.shp). The TIGER/Line Files contain potential address ranges, not individual addresses. The term "address range" refers to the collection of all possible structure numbers from the first structure number to the last structure number and all numbers of a specified parity in between along an edge side relative to the direction in which the edge is coded. The address ranges in the TIGER/Line Files are potential ranges that include the full range of possible structure numbers even though the actual structures may not exist.

[Metadata] Description: Maui Nui Critical Habitat Ecosystem: These data identify, in general, the areas where final critical habitat occurs for 135 species known historically from Maui Nui, on the islands of Maui, Molokai and/or Kahoolawe. A total of approximately 63,537 hectares (ha) (157,002 ac)) of land on Maui Nui fall within the boundaries of the critical habitat units designated for the 135 species. This critical habitat designation requires the Service to consult under section 7 of the Act with regard to actions carried out, funded, or authorized by a Federal agency. Section 4 of the Act requires us to consider economic and other relevant impacts when specifying any particular area as critical habitat. This rule also determines that designating critical habitat would not be prudent for seven species. We solicited data and comments from the public on all aspects of the proposed rule, including data on economic and other impacts of the designation.August, 2017: Reloaded all critical habitat layers to reflect current/final schema from USFWS. Boundaries were unchanged.For additional information, please refer to complete metadata at https://files.hawaii.gov/dbedt/op/gis/data/criticalhab.pdf or contact Hawaii Statewide GIS Program, Office of Planning and Sustainable Development, State of Hawaii; PO Box 2359, Honolulu, HI 96804; (808) 587-2846; email: gis@hawaii.gov; Website: https://planning.hawaii.gov/gis.

This resource is a member of a series. The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The All Roads Shapefile includes all features within the MTDB Super Class "Road/Path Features" distinguished where the MAF/TIGER Feature Classification Code (MTFCC) for the feature in MTDB that begins with "S". This includes all primary, secondary, local neighborhood, and rural roads, city streets, vehicular trails (4wd), ramps, service drives, alleys, parking lot roads, private roads for service vehicles (logging, oil fields, ranches, etc.), bike paths or trails, bridle/horse paths, walkways/pedestrian trails, and stairways.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national filewith no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independentdata set, or they can be combined to cover the entire nation. Linear Water Features includes single-line drainage water features and artificial path features that run through double-line drainage features such as rivers and streams, and serve as a linear representation of these features. The artificial path features may correspond to those in the USGS National Hydrographic Dataset (NHD). However, in many cases the features do not match NHD equivalent feature and will not carry the NHD metadata codes. These features have a MAF/TIGER Feature Classification Code (MTFCC) beginning with an "H" to indicate the super class of Hydrographic Features.

This data set provides the locations and corresponding elevations where insect collection, acoustic recording, or Hawaiian hoary bat (Lasiurus cinereus semotus) captures occurred as part of a study of bat activity, diet, and prey availability in the Waihou Mitigation Area, Maui. To fulfill requirements for mitigating bat fatalities under it's approved incidental take permit, Auwahi Wind Energy LLC (Auwahi Wind) provided funding for research of Hawaiian hoary bat ecology. Data from this study were collected to assess bat habitat use, diet, prey availability and foraging ecology in a restoration area created for bat habitat mitigation. We surveyed bat acoustic activity and foraging activity over a three-year period for a baseline observation of bat detection frequency and seasonal patterns. Insects were collected in two different seasons to describe the abundance and diversity of potential bat prey items in the restoration area. Bats were captured during the same periods to collect guano for dietary analyses. Please reference the associated metadata and datasets that correspond to this publication for more information on how these locations were used.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national filewith no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independentdata set, or they can be combined to cover the entire nation. Linear Water Features includes single-line drainage water features and artificial path features that run through double-line drainage features such as rivers and streams, and serve as a linear representation of these features. The artificial path features may correspond to those in the USGS National Hydrographic Dataset (NHD). However, in many cases the features do not match NHD equivalent feature and will not carry the NHD metadata codes. These features have a MAF/TIGER Feature Classification Code (MTFCC) beginning with an "H" to indicate the super class of Hydrographic Features.

Simulating WAves Nearshore (SWAN) regional wave model 7-day output with a 5-day hourly forecast for the Hawaiian islands of Maui County (Maui, Molokai, Lanai, and Kahoolawe) at approximately 500-m resolution. This high-resolution model is utilized to capture shallow water effects and nearshore coastal dynamics such as refracting, shoaling, and smaller scale shadowing. It is run directly after the Hawaii regional WaveWatch III (WW3) wave model (ww3_hawaii) has completed. Please note that some of the nested model setup is still in the testing and validation phase. While considerable effort has been made to implement all model components in a thorough, correct, and accurate manner, numerous sources of error are possible. As such, please use these data with the caution appropriate for any ocean related activity.

In this project, we use the Light Detection and Ranging (LiDAR) data to create the Digital Elevation Model (DEM). The LiDAR data can be downloaded through the Data Access Viewer of NOAA ( https://coast.noaa.gov/dataviewer/#/lidar/search/). For Molokai, the majority of the DEM is created using the data of 2013 U.S. Army Corps of Engineers (USACE) National Coastal Mapping Program (NCMP) Topobathy LiDAR – Local Mean Sea Level (LMSL). For some areas not covered by this data set, we use the LiDAR data from 2006 FEMA LiDAR: Hawaiian Islands and 2007 JALBTCX Hawaii LiDAR: North Coasts of Hawaii (Big Island), Kauai, Maui, Molokai, Oahu, which are accessed in the Data Access Viewer of NOAA. Please read “Description of Digital Elevation Model (DEM) for Molokai, Hawaii.docx” for detailed information.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The Address Ranges Feature Shapefile (ADDRFEAT.dbf) contains the geospatial edge geometry and attributes of all unsuppressed address ranges for a county or county equivalent area. The term "address range" refers to the collection of all possible structure numbers from the first structure number to the last structure number and all numbers of a specified parity in between along an edge side relative to the direction in which the edge is coded. Single-address address ranges have been suppressed to maintain the confidentiality of the addresses they describe. Multiple coincident address range feature edge records are represented in the shapefile if more than one left or right address ranges are associated to the edge. The ADDRFEAT shapefile contains a record for each address range to street name combination. Address range associated to more than one street name are also represented by multiple coincident address range feature edge records. Note that the ADDRFEAT shapefile includes all unsuppressed address ranges compared to the All Lines Shapefile (EDGES.shp) which only includes the most inclusive address range associated with each side of a street edge. The TIGER/Line shapefile contain potential address ranges, not individual addresses. The address ranges in the TIGER/Line Files are potential ranges that include the full range of possible structure numbers even though the actual structures may not exist.

About this dataset

Context

The dataset tabulates the population of Maui County by race. It includes the population of Maui County across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Maui County across relevant racial categories.

Key observations

The percent distribution of Maui County population by race (across all racial categories recognized by the U.S. Census Bureau): 30.98% are white, 0.76% are Black or African American, 0.35% are American Indian and Alaska Native, 28.29% are Asian, 10.45% are Native Hawaiian and other Pacific Islander, 1.97% are some other race and 27.21% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Maui County
• Population: The population of the racial category (excluding ethnicity) in the Maui County is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Maui County total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Maui County Population by Race & Ethnicity. You can refer the same here

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The Address Range / Feature Name Relationship File (ADDRFN.dbf) contains a record for each address range / linear feature name relationship. The purpose of this relationship file is to identify all street names associated with each address range. An edge can have several feature names; an address range located on an edge can be associated with one or any combination of the available feature names (an address range can be linked to multiple feature names). The address range is identified by the address range identifier (ARID) attribute that can be used to link to the Address Ranges Relationship File (ADDR.dbf). The linear feature name is identified by the linear feature identifier (LINEARID) attribute that can be used to link to the Feature Names Relationship File (FEATNAMES.dbf).

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within Maui County. The dataset can be utilized to gain insights into gender-based income distribution within the Maui County population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Maui County, among individuals aged 15 years and older with income, there were 60,571 men and 60,407 women in the workforce. Among them, 29,924 men were engaged in full-time, year-round employment, while 26,224 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 7.77% fell within the income range of under $24,999, while 9.43% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 24.89% of men in full-time roles earned incomes exceeding $100,000, while 15.96% of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Maui County median household income by race. You can refer the same here

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national filewith no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independentdata set, or they can be combined to cover the entire nation. The Address Range / Feature Name Relationship File (ADDRFN.dbf) contains a record for each address range / linear feature name relationship. The purpose of this relationship file is to identify all street names associated with each address range. An edge can have several feature names; an address range located on an edge can be associated with one or any combination of the available feature names (an address range can be linked to multiple feature names). The address range is identified by the address range identifier (ARID) attribute that can be used to link to the Address Ranges Relationship File (ADDR.dbf). The linear feature name is identified by the linear feature identifier (LINEARID) attribute that can be used to link to the Feature Names Relationship File (FEATNAMES.dbf).

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in Maui County. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in Maui County. It showcases how household incomes typically rise as the head of the household gets older. The dataset can be utilized to gain insights into age-based household income trends and explore the variations in incomes across households.

Key observations: Insights from 2023

In terms of income distribution across age cohorts, in Maui County, householders within the 45 to 64 years age group have the highest median household income at $111,870, followed by those in the 25 to 44 years age group with an income of $95,828. Meanwhile householders within the 65 years and over age group report the second lowest median household income of $80,179. Notably, householders within the under 25 years age group, had the lowest median household income at $67,977.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Age groups classifications include:

• Under 25 years
• 25 to 44 years
• 45 to 64 years
• 65 years and over

Variables / Data Columns

• Age Of The Head Of Household: This column presents the age of the head of household
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific age group

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Maui County median household income by age. You can refer the same here