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 Seattle. The dataset can be utilized to gain insights into gender-based income distribution within the Seattle population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Seattle, among individuals aged 15 years and older with income, there were 312.52 thousand men and 289.09 thousand women in the workforce. Among them, 189,091 men were engaged in full-time, year-round employment, while 140,215 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 2.74% fell within the income range of under $24,999, while 3.56% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 57.49% of men in full-time roles earned incomes exceeding $100,000, while 45.01% 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 Seattle median household income by race. You can refer the same here

Abstract: Census tract-based race and ethnicity data aggregated to City of Seattle Community Reporting Areas (CRAs) from the 1990 and 2010 Brown University Longitudinal Database (LTDB), 2010 decennial census and the 2014-2018 5-year American Community Survey (ACS). Brown University researchers created the LTDB to allow for comparing census data over time (see https://s4.ad.brown.edu/projects/diversity/Researcher/Bridging.htm). The race and ethnicity categories in the 2010 LTDB have been modified from those in the 2010 census to more closely match the 1990 race categories. (Before 2000, census questionnaires allowed respondents to identify as one race only. The LTDB allocates mixed-race people in post-1990 census estimates to non-white categories.) Please remember that the ACS data carry margins of error, and for small racial/ethnic groups they can be significant. The numeric and percentage changes overtime are also included. There is also a polygon representation for the City of Seattle as a whole.Purpose: Census data of racial and ethnic categories from 1990 and 2010 Brown University LTDB, 2010 decennial and 2018 American Community Survey (ACS). Data is for the City of Seattle Community Reporting Areas as well as a polygon representation for the City of Seattle as a whole. Numeric and percentage changes over time are also included.

This dataset tracks annual diversity score from 1991 to 2023 for Seattle School District No. 1 vs. Washington

About this dataset

Context

The dataset tabulates the Seattle 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 Seattle 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 Seattle was 755,078, a 0.79% increase year-by-year from 2022. Previously, in 2022, Seattle population was 749,134, an increase of 2.37% compared to a population of 731,757 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Seattle increased by 190,969. In this period, the peak population was 755,078 in the year 2023. The numbers suggest that the population has not reached its peak yet and is showing a trend of further growth. 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 Seattle is shown in this column.
• Year on Year Change: This column displays the change in Seattle 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 Seattle Population by Year. 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

This dataset tracks annual diversity score from 1991 to 2023 for Nova High School vs. Washington and Seattle School District No. 1

This dataset tracks annual diversity score from 1991 to 2023 for West Seattle Elementary School vs. Washington and Seattle School District No. 1

This dataset tracks annual diversity score from 2001 to 2004 for Interagency King County Jail vs. Washington and Seattle School District No. 1

About this dataset

Context

The dataset tabulates the Seattle 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 Seattle 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 2022, the population of Seattle was 749,256, a 2.43% increase year-by-year from 2021. Previously, in 2021, Seattle population was 731,507, a decline of 1.22% compared to a population of 740,520 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Seattle increased by 185,147. In this period, the peak population was 753,291 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 2022

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2022)
• Population: The population for the specific year for the Seattle is shown in this column.
• Year on Year Change: This column displays the change in Seattle 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 Seattle Population by Year. You can refer the same here

Table from the American Community Survey (ACS) 5-year series for King County and City of Seattle median values for a variety of topics including age, gross rent, monthly owner costs, family and nonfamily incomes, earnings. Includes the margin of error for the values.Table created for and used in the Neighborhood Profiles application.Vintages: 2010, 2015, 2020, 2023ACS Table(s): B01002, B25064, B25088, B19013, B19113, B19202, B20017Data downloaded from: Census Bureau's Explore Census Data The United States Census Bureau's American Community Survey (ACS):<a hre

This dataset tracks annual diversity score from 1991 to 2023 for Graham Hill Elementary School vs. Washington and Seattle School District No. 1

This dataset tracks annual diversity score from 2015 to 2023 for Ryther Center vs. Washington and Seattle School District No. 1

This dataset tracks annual diversity score from 2016 to 2023 for Bridges Transition vs. Washington and Seattle School District No. 1

About this dataset

Context

The dataset tabulates the Seattle population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Seattle. The dataset can be utilized to understand the population distribution of Seattle by age. For example, using this dataset, we can identify the largest age group in Seattle.

Key observations

The largest age group in Seattle, WA was for the group of age 25 to 29 years years with a population of 94,551 (12.75%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Seattle, WA was the 80 to 84 years years with a population of 10,073 (1.36%). 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

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Seattle is shown in this column.
• % of Total Population: This column displays the population of each age group 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 Age. You can refer the same here

Like other urban green spaces, urban community gardens can act as biodiversity refugees, especially for small organisms like arthropods. In turn, arthropods can provide important ecosystem pest control services to these agroecosystems. Thus, an often-asked question among urban gardeners is how to improve gardens and surrounding areas for natural enemies and associated pest control services. We examine how local vegetation and garden characteristics, as well as the surrounding landscape composition affect ground-dwelling beetles (Coleoptera: Carabidae and Staphylinidae), spiders (Aranea), opilionids (Opilionida), and ladybird beetles (Coleoptera: Coccinellidae), all of which are important predators. In the summer 2019, we collected predators, vegetation, ground cover, and garden and landscape characteristic data of ten community gardens in the city of Seattle, Washington. We found that different groups of natural enemies are associated with different environmental variables and at different scales; probably related to differences in their dispersal capabilities, habits, and diets. Floral variables (# of flowers, # of species in flower) had a negative effect on non-flying natural enemies (spiders, opilionids, and ground-dwelling beetles), but not on flying ones (ladybird beetles). The only taxa that was significantly affected by a landscape-scale variable was Opilionida, the only group examined that exclusively disperses by ground. Our results show contrasting results to similar studies in different regions and highlight the need to expand the taxa and regions of study. Methods Study site We conducted the study in the city of Seattle, Washington, located in the U.S. Pacific Northwest (47.6062° N, 122.3321° W). Seattle's population in 2020 was estimated to be 737,015 in an area of 83 square miles (Office of Planning and Community Development 2023). While Seattle is among the fastest growing cities in the US, the city is committed to protecting urban biodiversity in its various green-spaces (City of Seattle 2018) and has an increasing demand for urban agriculture. The Community Garden program alone oversees 89 community gardens throughout the city. These gardens occupy about 10 hectares where food is grown for gardeners and for the general public City of Seattle 2023).
Our study took place in 10 of these urban community gardens. The gardens are managed in an allotment style where households rent and cultivate individual plots within the garden. The chosen gardens range in size from 240 to 16,187 m2, housing 21 to 259 individual plots, have been in operation from 5 to 46 years, and are >2km from each other. All selected gardens are administered by Seattle Department of Neighborhoods' P-Patch Program which requires use of organic gardening inputs and methods (Seattle Department of Neighborhoods, 2020). Thus, no synthetic chemicals including pesticides, insecticides, herbicides, weed killers, and fertilizers are allowed anywhere in the gardens. To standardize the sampling area of our study sites, we established a 20 x 20 m plot in the center of each garden. Our samplings and observations were limited to these areas for the duration of our study. Landscape-scale variables We used land-cover data from the 2011 National Land Cover Database (NLCD, 30-m resolution (Homer et al. 2015) and calculated the percentage of land-cover types in 500-m buffers from the center of each garden. The 500m buffer has been used to study landscape effects of many taxa (Schmidt et al. 2008, Concepción et al. 2008, Batáry et al. 2012, Otoshi et al. 2015). We used five land-cover categories established by the National Land Cover Database (NLCD): developed open, developed low, developed medium/high (we combined the NLCD categories of “developed, medium intensity” and “developed, high intensity into one category), and natural/semi-natural (which included deciduous forest, evergreen forest, mixed forest, shrub/scrub, herbaceous, hay/pasture), and agricultural (listed in the NLCD as “cultivated crops”) (Multi-Resolution Land Characteristics 2023). In addition, we calculated the proportion of urban parks in the 500m buffers using the City of Seattle parks map available through the King County GIS website (https://kingcounty.gov/services/gis.aspx). These parks are managed by the city and have a variety of uses and characteristics. We included urban parks as one of our landscape variables because from studies in rural agricultural systems, we know that farms embedded in landscapes with a higher proportion of natural habitats (i.e. forests, wetlands, grasslands) support higher local density and diversity of beneficial arthropods, even in fields with low local vegetation diversity (Tscharntke et al. 2005, Bianchi et al. 2006, Chaplin‐Kramer et al. 2011). In cities, especially rapidly expanding ones like Seattle, nearby ‘natural’ or ‘semi-natural’ areas consist largely of urban parks and reserves— habitats which may be vital to connect apparently isolated urban green-spaces (Langellotto et al. 2018). Much like fragments of forests, grasslands, and wetlands in rural agricultural landscapes (Landis et al. 2000, Schellhorn et al. 2014), urban parks may provide alternative resources, prey and shelter, thus enhancing natural enemy abundance and diversity in nearby urban agroecosystems. Garden-scale variables Vegetation was sampled three times between June and August 2019, approximately a month in between sampling periods. Vegetation was sampled within the same standardized 20 x 20 m plot in each garden. Canopy cover was measured using a concave spherical densitometer at the center of each plot in addition to 10 m to the North, South, East and West of the center. Inside each of the 20 x 20 m plots, we counted and identified all trees and shrubs (woody vegetation). We also recorded the number of trees and shrubs in flower. Within the 20 x 20 m plot, we then selected eight locations to place 1 x 1 m plots. To randomly select each of the eight locations, we first marked four 5 x 20 m strips within the 20 x 20 m. For each strip, using a random number table from 0-20, we chose two random numbers (which represented, in meters, the distance from 0 to 20 m from the beginning to the end of the length of the strip). We then walked along the edge the strip until reaching the randomly chosen distances and then used a second random number table from 0-5 (which represented, in meters, the distance from 0 to 5 m from one edge to other of the width of the strip) to choose the location of the plot. We repeated this procedure for the four 5 x 20 m strips for a total of eight randomly chosen plots. Within each of these plots, we measured the height of the tallest herbaceous vegetation, and counted the total number of flowers and total number of crops and ornamentals in flower. We identified each plant species and estimated the percentage of cover of each plant type (crop, grass, ornamental, weed, herbaceous). Within each of these 1 x 1m plots, we also estimated the percentage of ground-cover make-up of bare soil, mulch/wood chips, straw and leaf litter. In addition, we obtained information on garden size (garden area in m2, and number of individual plots), and garden age (years since establishment) from the city of Seattle community garden information website (City of Seattle 2023). Natural enemies At each garden site we conducted three rounds of natural enemies sampling. This included sampling ground-dwelling beetles (Carabidae and Staphylinidae), spiders (Aranea) and opilionids (Opilionida), and ladybird beetles (Coleoptera: Coccinellidae). We sampled natural enemies three times between June and August, 2019. The first round of sampling occurred between June 24th - 26th, the second round between July 17th - 19th, and the final round between August 12th - 13th. Natural enemies were sampled using a combination of visual and trapping sampling methods (see below). We estimated total abundance across all sampling methods and sampling periods for the focus natural enemies (ground-dwelling beetles, spiders, opilionids, and ladybird beetles) (see data analysis). We lumped Carabidae and Staphylinidae into one category—ground-dwelling beetles—and estimated abundance for all. Per time limitations, we only were able to further identify spiders (to family) and ladybird beetles (to species). Thus, in addition to abundance, for spiders we also estimated family richness and for ladybird beetles, species richness across all sampling methods and periods. Visual Sampling Using the same randomized methodology described for the vegetative sampling, eight 0.5 x 0.5 m quadrants within each garden’s 20 x 20 m plot were selected. In each of these 0.5 x 0.5 m plots, one person visually searched in the vegetation for ten minutes for ladybird beetles, spiders, opilionids and ground beetles. All specimens were collected and preserved in vials with alcohol (with the exception of minimal escaped specimens we were unable to collect; we ID’d these specimens visually in the field to family for spiders and morphospecies for ground and ladybird beetles). We recorded the number of individuals (for all), family (spiders), and species (ladybird beetles). Traps Four random trap locations were selected in each 20 x 20 m plot using the aforementioned randomization methodology. At each location, four 7.62 cm x 12.7 cm yellow sticky cards (BioQuip Products Inc., Compton, CA, USA) on 20cm wire stakes were placed in each corner of a 0.5 x 0.5 m quadrant. A pitfall trap was placed in the middle of the quadrant flush with the ground, filled up one third with water and dish soap. After 24 hours the traps were retrieved and the specimens were identified. Data analysis For abundances of spiders, opilionids, and ground beetles, we summed the total number of individuals from both the pitfalls and visuals (none were found in sticky cards) and across the three sampling periods.

This dataset tracks annual diversity score from 1999 to 2023 for Interagency Programs vs. Washington and Seattle School District No. 1

This dataset tracks annual diversity score from 1991 to 2023 for Maple Elementary School vs. Washington and Seattle School District No. 1

In the aftermath of the attacks on September 11, 2001, and subsequent terrorist attacks elsewhere around the world, a key counterterrorism concern was the possible radicalization of Muslims living in the United States. The purpose of the study was to examine and identify characteristics and practices of four American Muslim communities that have experienced varying levels of radicalization. The communities were selected because they were home to Muslim-Americans that had experienced isolated instances of radicalization. They were located in four distinct regions of the United States, and they each had distinctive histories and patterns of ethnic diversity. This objective was mainly pursued through interviews of over 120 Muslims located within four different Muslim-American communities across the country (Buffalo, New York; Houston, Texas; Seattle, Washington; and Raleigh-Durham, North Carolina), a comprehensive review of studies an literature on Muslim-American communities, a review of websites and publications of Muslim-American organizations and a compilation of data on prosecutions of Muslim-Americans on violent terrorism-related offenses.

This dataset tracks annual diversity score from 1991 to 2023 for Olympic Hills Elementary School vs. Washington and Seattle School District No. 1

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