About this dataset

Context

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

Key observations

The percent distribution of Seattle population by race (across all racial categories recognized by the U.S. Census Bureau): 64.94% are white, 6.79% are Black or African American, 0.54% are American Indian and Alaska Native, 16.32% are Asian, 0.23% are Native Hawaiian and other Pacific Islander, 2.42% are some other race and 8.77% are multiracial.

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Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 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 Seattle
• Population: The population of the racial category (excluding ethnicity) in the Seattle is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Seattle 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 Seattle Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset presents the median household income across different racial categories in Seattle. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to gain insights into economic disparities and trends and explore the variations in median houshold income for diverse racial categories.

Key observations

Based on our analysis of the distribution of Seattle population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 61.84% of the total residents in Seattle. Notably, the median household income for White households is $130,622. Interestingly, despite the White population being the most populous, it is worth noting that Asian households actually reports the highest median household income, with a median income of $133,340. This reveals that, while Whites may be the most numerous in Seattle, Asian households experience greater economic prosperity in terms of median household income.

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 of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in Seattle.
• Median household income: Median household income, adjusting for inflation, presented in 2023-inflation-adjusted dollars

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 Seattle median household income by race. You can refer the same here

About this dataset

Context

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

Key observations

The percent distribution of Seattle population by race (across all racial categories recognized by the U.S. Census Bureau): 61.84% are white, 6.60% are Black or African American, 0.57% are American Indian and Alaska Native, 17.17% are Asian, 0.26% are Native Hawaiian and other Pacific Islander, 3.03% are some other race and 10.54% 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 Seattle
• Population: The population of the racial category (excluding ethnicity) in the Seattle is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Seattle 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 Seattle Population by Race & Ethnicity. You can refer the same here

Graph and download economic data for Population Estimate, Total, Not Hispanic or Latino, Two or More Races, Two Races Including Some Other Race (5-year estimate) in King County, WA (B03002010E053033) from 2009 to 2023 about King County, WA; Seattle; WA; non-hispanic; estimate; persons; 5-year; population; and USA.

Graph and download economic data for Population Estimate, Total, Hispanic or Latino, Some Other Race Alone (5-year estimate) in Pierce County, WA (B03002018E053053) from 2009 to 2023 about Pierce County, WA; Seattle; WA; latino; hispanic; estimate; persons; 5-year; population; and USA.

Two or more races Poverty Rate Statistics for 2023. This is part of a larger dataset covering poverty in Seattle, Washington by age, education, race, gender, work experience and more.

About this dataset

Context

The dataset tabulates the Seattle Hispanic or Latino population. It includes the distribution of the Hispanic or Latino population, of Seattle, by their ancestries, as identified by the Census Bureau. The dataset can be utilized to understand the origin of the Hispanic or Latino population of Seattle.

Key observations

Among the Hispanic population in Seattle, regardless of the race, the largest group is of Mexican origin, with a population of 35,380 (58.25% of the total Hispanic population).

Content

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

Origin for Hispanic or Latino population include:

• Mexican
• Puerto Rican
• Cuban
• Other Hispanic or Latino

Variables / Data Columns

• Origin: This column displays the origin for Hispanic or Latino population for the Seattle
• Population: The population of the specific origin for Hispanic or Latino population in the Seattle is shown in this column.
• % of Total Hispanic Population: This column displays the percentage distribution of each Hispanic origin as a proportion of Seattle total Hispanic or Latino 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 Seattle Population by Race & Ethnicity. You can refer the same here

Table from the American Community Survey (ACS) 5-year series on race and ethnicity related topics for City of Seattle Council Districts, Comprehensive Plan Growth Areas and Community Reporting Areas. Table includes B03002 Hispanic or Latino Origin by Race, B02008-B02013 Race Alone or in Combination with One or More. Data is pulled from block group tables for the most recent ACS vintage and summarized to the neighborhoods based on block group assignment.Table created for and used in the Neighborhood Profiles application.Vintages: 2023ACS Table(s): B03002, B02008, B02009, B02010, B02011, B02012, B02013Data downloaded from: Census Bureau's Explore Census Data The United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2020 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

Graph and download economic data for Population Estimate, Total, Hispanic or Latino, Some Other Race Alone (5-year estimate) in Snohomish County, WA (B03002018E053061) from 2009 to 2023 about Snohomish County, WA; Seattle; WA; latino; hispanic; estimate; persons; 5-year; population; and USA.

About this dataset

Context

The dataset presents the median household incomes over the past decade across various racial categories identified by the U.S. Census Bureau in Seattle. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. It also showcases the annual income trends, between 2013 and 2023, providing insights into the economic shifts within diverse racial communities.The dataset can be utilized to gain insights into income disparities and variations across racial categories, aiding in data analysis and decision-making..

Key observations

• White: In Seattle, the median household income for the households where the householder is White increased by $35,437(37.23%), between 2013 and 2023. The median household income, in 2023 inflation-adjusted dollars, was $95,185 in 2013 and $130,622 in 2023.
• Black or African American: In Seattle, the median household income for the households where the householder is Black or African American increased by $15,578(38.88%), between 2013 and 2023. The median household income, in 2023 inflation-adjusted dollars, was $40,064 in 2013 and $55,642 in 2023.
• Refer to the research insights for more key observations on American Indian and Alaska Native, Asian, Native Hawaiian and Other Pacific Islander, Some other race and Two or more races (multiracial) households

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 of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in Seattle.
• 2010: 2010 median household income
• 2011: 2011 median household income
• 2012: 2012 median household income
• 2013: 2013 median household income
• 2014: 2014 median household income
• 2015: 2015 median household income
• 2016: 2016 median household income
• 2017: 2017 median household income
• 2018: 2018 median household income
• 2019: 2019 median household income
• 2020: 2020 median household income
• 2021: 2021 median household income
• 2022: 2022 median household income
• 2023: 2023 median household income
• Please note: All incomes have been adjusted for inflation and are presented in 2023-inflation-adjusted dollars.

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 Seattle median household income by race. You can refer the same here

!!PLEASE NOTE!! When downloading the data, please select "File Geodatabase" to preserve long field names. Shapefile will truncate field names to 10 characters.This version of the Racial and Social Equity Index indexes all tracts in the remainder of King County against tracts in the city of Seattle. This index should only be used in direct consultation with the Office of Planning and Community Development, and is intended to be of use for comparing tracts in the remainder of King County within the context of percentiles set by tracts within the city of Seattle.Version: CurrentThe Racial and Social Equity Index combines information on race, ethnicity, and related demographics with data on socioeconomic and health disadvantages to identify where priority populations make up relatively large proportions of neighborhood residents. Click here for a User Guide.See the City of Seattle RSE Index in action in the Racial and Social Equity ViewerThe Composite Index includes sub-indices of: Race, English Language Learners, and Origins Index ranks census tracts by an index of three measures weighted as follows: Persons of color (weight: 1.0) English language learner (weight: 0.5) Foreign born (weight: 0.5)Socioeconomic Disadvantage Index ranks census tracts by an index of two equally weighted measures: Income below 200% of poverty level Educational attainment less than a bachelor’s degreeHealth Disadvantage Index ranks census tracts by an index of seven equally weighted measures: No leisure-time physical activity Diagnosed diabetes Obesity Mental health not good AsthmaLow life expectancy at birth DisabilityThe index does not reflect population densities, nor does it show variation within census tracts which can be important considerations at a local level.Sources are as indicated below.Produced by City of Seattle Office of Planning & Community Development. For more information on the indices, including guidance for use, contact Diana Canzoneri (diana.canzoneri@seattle.gov).Sources: 2017-2021 Five-Year American Community Survey Estimates, U.S. Census Bureau; 2020 Decennial Census, U.S. Census Bureau; estimates from the Centers for Disease Control’ Behavioral Risk Factor Surveillance System (BRFSS) published in the “The 500 Cities Project,”; Washington State Department of Health’s Washington Tracking Network (WTN);, and estimates from the Public Health – Seattle & King County (based on the Community Health Assessment Tool).Language is for population age 5 and older. Educational attainment is for the population age 25 and over.Life expectancy is life expectancy at birth.Other health measures based on percentages of the adult population.

!!PLEASE NOTE!! When downloading the data, please select "File Geodatabase" to preserve long field names. Shapefile will truncate field names to 10 characters.Version: CurrentThe Racial and Social Equity Index combines information on race, ethnicity, and related demographics with data on socioeconomic and health disadvantages to identify where priority populations make up relatively large proportions of neighborhood residents. Click here for a User Guide.See the layer in action in the Racial and Social Equity ViewerClick here for an 11x17 printable pdf version of the map.The Composite Index includes sub-indices of: Race, English Language Learners, and Origins Index ranks census tracts by an index of three measures weighted as follows: Persons of color (weight: 1.0) English language learner (weight: 0.5) Foreign born (weight: 0.5)Socioeconomic Disadvantage Index ranks census tracts by an index of two equally weighted measures:Income below 200% of poverty level Educational attainment less than a bachelor’s degreeHealth Disadvantage Index ranks census tracts by an index of seven equally weighted measures:No leisure-time physical activityDiagnosed diabetes ObesityMental health not good AsthmaLow life expectancy at birthDisabilityThe index does not reflect population densities, nor does it show variation within census tracts which can be important considerations at a local level.Sources are as indicated below.Produced by City of Seattle Office of Planning & Community Development. For more information on the indices, including guidance for use, contact Diana Canzoneri (diana.canzoneri@seattle.gov).Sources: 2017-2021 Five-Year American Community Survey Estimates, U.S. Census Bureau; 2020 Decennial Census, U.S. Census Bureau; estimates from the Centers for Disease Control’ Behavioral Risk Factor Surveillance System (BRFSS) published in the “The 500 Cities Project,”; Washington State Department of Health’s Washington Tracking Network (WTN);, and estimates from the Public Health – Seattle & King County (based on the Community Health Assessment Tool).Language is for population age 5 and older. Educational attainment is for the population age 25 and over.Life expectancy is life expectancy at birth.Other health measures based on percentages of the adult population.

A comparison of the race and ethnicity of highly capable students compared to the same demographic groups in the general student population. Comparisons are also included for the Low-Income, English Language Learners, Students with Disabilities, Section 504, Homeless, and Highly Mobile student groups. Data is aggregated by school, district, and state level.

Black or African American Poverty Rate Statistics for 2023. This is part of a larger dataset covering poverty in Seattle, Washington by age, education, race, gender, work experience and more.

Hispanic Poverty Rate Statistics for 2023. This is part of a larger dataset covering poverty in Seattle, Washington by age, education, race, gender, work experience and more.

Two or more races Health Insurance Coverage Statistics for 2023. This is part of a larger dataset covering consumer health insurance coverage rates in Seattle, Washington by age, education, race, gender, work experience and more.

Background Rural U.S. communities are at risk from COVID-19 due to advanced age and limited access to acute care. Recognizing this, the Vashon Medical Reserve Corps (VMRC) in King County, Washington, implemented an all-volunteer, community-based COVID-19 response program. This program integrated public engagement, SARS-CoV-2 testing, contact tracing, vaccination, and material community support, and was associated with the lowest cumulative COVID-19 case rate in King County. This study aimed to investigate the contributions of demographics, geography and public health interventions to Vashon’s low COVID-19 rates. Methods This observational cross-sectional study compares cumulative COVID-19 rates and success of public health interventions from February 2020 through November 2021 for Vashon Island with King County (including metropolitan Seattle) and Whidbey Island, located ~50 km north of Vashon. To evaluate the role of demography, we developed multiple linear regression models of COVID-19 rates using metrics of age, race/ethnicity, wealth and educational attainment across 77 King County zip codes. To investigate the role of remote geography we expanded the regression models to include North, Central and South Whidbey, similarly remote island communities with varying demographic features. To evaluate the effectiveness of VMRC’s community-based public health measures, we directly compared Vashon’s success of vaccination and contact tracing with that of King County and South Whidbey, the Whidbey community most similar to Vashon. Results Vashon’s cumulative COVID-19 case rate was 29% that of King County overall (22.2 vs 76.8 cases/K). A multiple linear regression model based on King County demographics found educational attainment to be a major correlate of COVID-19 rates, and Vashon’s cumulative case rate was just 38% of predicted (p<.05), so demographics alone do not explain Vashon’s low COVID-19 case rate. Inclusion of Whidbey communities in the model identified a major effect of remote geography (-49 cases/K, p<.001), such that observed COVID-19 rates for all remote communities fell within the model’s 95% prediction interval. VMRC’s vaccination effort was highly effective, reaching a vaccination rate of 1500 doses/K four months before South Whidbey and King County and maintaining a cumulative vaccination rate 200 doses/K higher throughout the latter half of 2021 (p<.001). Including vaccination rates in the model reduced the effect of remote geography to -41 cases/K (p<.001). VMRC case investigation was also highly effective, interviewing 96% of referred cases in an average of 1.7 days compared with 69% in 3.7 days for Washington Department of Health investigating South Whidbey cases and 80% in 3.4 days for Public Health–Seattle & King County (both p<0.001). VMRC’s public health interventions were associated with a 30% lower case rate (p<0.001) and 55% lower hospitalization rate (p=0.056) than South Whidbey. Conclusion While the overall magnitude of the pre-Omicron COVID-19 pandemic in rural and urban U.S. communities was similar, we show that island communities in the Puget Sound region were substantially protected from COVID-19 by their geography. We further show that a volunteer community-based COVID-19 response program was highly effective in the Vashon community, augmenting the protective effect of geography. We suggest that Medical Reserve Corps should be an important element of future pandemic planning. The study period extended from the pandemic onset in February 2020 through November 2021. Daily COVID-19 cases, hospitalizations, deaths and test numbers for King County as a whole and by zip code were downloaded from the King County COVID-19 dashboard (Feb 22, 2022 update). Population data for King County and Vashon are from the April 2020 US Census. Zip code level population data are the average of two zip code tabulation area estimates from the WA Office of Financial Management and Cubit (a commercial data vendor providing access to US Census information). The Asset Limited, Income Constrained, and Employed (ALICE) metric, a measure of the working poor, was obtained from United Way.