In 2023, the population of the Seattle-Tacoma-Bellevue metropolitan area in the United States was about 4.04 million people. This was a slight decrease from the previous year, when the population was about 4.03 million.

Graph and download economic data for Resident Population in Seattle-Tacoma-Bellevue, WA (MSA) (STWPOP) from 2000 to 2024 about Seattle, WA, residents, population, and USA.

Historical dataset of population level and growth rate for the Seattle metro area from 1950 to 2025.

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

In 2023, the GDP of the Seatle-Tacoma-Bellevue metro area amounted to ****** billion U.S. dollars, an increase from the previous year. The GDP of the United States since 1990 can be accessed here. Seattle metro area The Seattle metropolitan area in the U.S. state of Washington includes the city of Seattle, King County, Snohomish County, and Pierce County within the Puget Sound region. About **** million people were living in the Seattle metro area, which is more than half of Washington's total population in 2021 (about **** million people). This makes the Seattle metro area the **** largest metropolitan area in the United States, by population. However, Seattle is in fourth place among the 20 largest metro areas in terms of household income, which stood at ****** U.S. dollars in 2019. This is by far more than the average household income in the United States. Household income in Washington is on a similar high level. In 2021, the federal state of Washington was ranked **** in terms of household income among the states of the U.S. The city of Seattle is the largest city in the Pacific Northwest region of North America. It has about ******* residents and is among the ** largest cities in the United States. Seattle has always been an important coastal seaport city and a gateway to Alaska. The importance of the city and metro area is also due to fact that some of the biggest companies worldwide started in Seattle during the 1980s. Companies like Amazon and Microsoft are still based in the Seattle area in the state of Washington.

2020 census geography including tracts for the city of Seattle, King County, Washington. Excludes partial tracts with very small populations within the city limits along the southern border of the city.Includes assignment of Seattle Community Reporting Areas (CRA-53), Community Reporting Area Groups (neighborhood roll up-13), Council Districts (7-assigned to the tract with the majority of the population based on the distribution of the component census blocks), and Urban Village Demographic Areas (UVDA). UVDA assignments subject to change based on future planning areas.

Census 2020 blocks with the Washington State Office of Financial Management Small Area Estimates Program (SAEP) estimates. Enhanced with City of Seattle council districts and growth management areas.PLEASE BE AWARE, the urban village and comprehensive plan area designations are subject to change annually.Estimates are annual April, 1 for the 2010-202X with the most current year added Q4 of that year.(SAEP) estimates are meant to provide a consistent set of small area population and housing data for statewide applications. SAEP estimates are generated by the Washington State Office of Financial Management for census areas and other areas of statewide significance.Before using the SAEP estimates, please see the SAEP User Guide to gain a better understanding of the data and methods behind the estimates as well as limitations in their use. For more specific information about the 2020 data release, please see the User Notes and Errata document.Please note that SAEP estimates are NOT the official state population estimates used for revenue distribution and program administration related to cities and counties. Users interested in city and county estimates should see the state's official April 1 population estimates program.

Annual April, 1 Small Area Estimates Program (SAEP) estimates provide a consistent set of small area population and housing data at the census block (vintage 2020). This table summarized to the City of Seattle growth management areas.Estimates are annual April, 1 for the 2010-202X with the most current year added Q4 of that year.(SAEP) estimates are meant to provide a consistent set of small area population and housing data for statewide applications. SAEP estimates are generated by the Washington State Office of Financial Management for census areas and other areas of statewide significance.Before using the SAEP estimates, please see the SAEP User Guide to gain a better understanding of the data and methods behind the estimates as well as limitations in their use. For more specific information about the 2020 data release, please see the User Notes and Errata document.Please note that SAEP estimates are NOT the official state population estimates used for revenue distribution and program administration related to cities and counties. Users interested in city and county estimates should see the state's official April 1 population estimates program.

Tabular data that powers basic monitoring dashboards for the total population, housing and jobs for the City of Seattle. Each record represents the totals for each year since 2000 (and 1995) through the most recently available data. Includes the change from the previous year.

City of Seattle neighborhood boundaries with American Community Survey (ACS) 5-year series data of frequently requested topics. Data is pulled from block group tables for the most recent ACS vintage and summarized to the neighborhoods based on block group assignment. Seattle neighborhood geography of Council Districts, Comprehensive Plan Growth Areas are included.The census block groups have been assigned to a neighborhood based on the distribution of the total population from the 2020 decennial census for the component census blocks. If the majority of the population in the block group were inside the boundaries of the neighborhood, the block group was assigned wholly to that neighborhood.Feature layer created for and used in the Neighborhood Profiles application.The attribute data associated with this map is updated annually to contain the most currently released American Community Survey (ACS) 5-year data and contains estimates and margins of error. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. <div style='font-family:"Avenir Next W01", "Avenir Next W00", "Avenir Next&qu

2020 census blocks for the City of Seattle.Includes assignment of Seattle Community Reporting Areas (CRA-53), Community Reporting Area Groups (neighborhood roll up-13), Council Districts (7) for both the boundaries 2013-2023 and current 2024 boundaries, and Urban Villages. Urban Village assignments subject to change based on future planning areas.Water blocks are included due to population and housing in over-water structures including houseboats and live-a-boards.

A dataset listing Washington cities by population for 2024.

Comprehensive demographic dataset for Columbia City, Seattle, WA, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.

As cities grow, lakes are often assumed to suffer from increasing nonpoint pollution. Many waterbodies have become more eutrophic in recent decades, as expected, but many others have become less eutrophic, especially in urban/suburban areas. What policies, practices, and ecosystem processes have helped some lakes stay stable or become less eutrophic even in a growing city? Identifying and understanding success stories is important to continue protecting these lakes and improving other urban/suburban lakes. We found one such success story when we examined water-quality trends over the past 25 years (1998–2022) in Lake Washington, a well-studied large lake in the Seattle metro area. The watershed population grew rapidly during that time (34% from 2000–2020), yet Lake Washington became substantially less eutrophic, and indicators of development impacts stabilized or decreased. Chlorophyll concentrations during the main spring bloom decreased sharply ( 25%/decade), and water clarity and nea..., , # Data from: Cities can grow without harming lakes: Lake Washington has become less eutrophic despite rapid population growth

Dataset DOI: 10.5061/dryad.dfn2z35fj

Description of the data and file structure

This contains the water-quality data associated with the following manuscript:

Nidzgorski, Daniel A. and DeGasperi, Curtis L. (2025). Cities can grow without harming lakes: Lake Washington has become less eutrophic despite rapid population growth. Ecosphere.

For questions or requests, please contact Daniel Nidzgorski at dnidz@civiceco.org, and we would appreciate it if you cited.

Files and variables

File: LakeWashingtonWaterQuality1998-2022.csv

Description: These water-quality data were collected from Lake Washington (Seattle area, USA; 47.6° N, 122.3° W) by King County's long-term monitoring program. This dataset includes data from 1998-2022 from the Madison Park 0852 monitoring location at one of the dee...,

Dashboard displaying population, housing and jobs data for the City of Seattle since 2000 through the most recently available data. Includes totals and change by year.Sources include: For population and housing the April 1 official population estimates are produced by the Washington State Office of Financial Management (OFM). OFM population estimates are cited in numerous statutes using population as criteria for fund allocations, program eligibility, or program operations, and as criteria for determining county participation under the Growth Management Act.For jobs the Washington State Employment Security Department, Quarterly Census of Employment and Wages (QCEW) is a federal/state cooperative program that measures employment and wages in industries covered by unemployment insurance. Data are available by industry and county and used to evaluate labor trends, monitor major industry developments and develop training programs. These job estimates are from the March dataset from each year (chosen as a representative month when seasonal fluctuations are minimized). The unit of measurement is jobs, rather than working persons or proportional full-time employment equivalents. Employment by census tract totals are broken down by major sector only. To provide more accurate workplace reporting, the Puget Sound Regional Council gathers supplemental data from the Boeing Company, the Office of Washington Superintendent of Public Instruction (OSPI), and governmental units throughout the central Puget Sound region.

In 2022, San Francisco had the highest median household income of cities ranking within the top 25 in terms of population, with a median household income in of 136,692 U.S. dollars. In that year, San Jose in California was ranked second, and Seattle, Washington third.

Following a fall after the great recession, median household income in the United States has been increasing in recent years. As of 2022, median household income by state was highest in Maryland, Washington, D.C., Utah, and Massachusetts. It was lowest in Mississippi, West Virginia, and Arkansas. Families with an annual income of 25,000 and 49,999 U.S. dollars made up the largest income bracket in America, with about 25.26 million households.

Data on median household income can be compared to statistics on personal income in the U.S. released by the Bureau of Economic Analysis. Personal income rose to around 21.8 trillion U.S. dollars in 2022, the highest value recorded. Personal income is a measure of the total income received by persons from all sources, while median household income is “the amount with divides the income distribution into two equal groups,” according to the U.S. Census Bureau. Half of the population in question lives above median income and half lives below. Though total personal income has increased in recent years, this wealth is not distributed throughout the population. In practical terms, income of most households has decreased. One additional statistic illustrates this disparity: for the lowest quintile of workers, mean household income has remained more or less steady for the past decade at about 13 to 16 thousand constant U.S. dollars annually. Meanwhile, income for the top five percent of workers has actually risen from about 285,000 U.S. dollars in 1990 to about 499,900 U.S. dollars in 2020.

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.

Table from the American Community Survey (ACS) C16001 of language spoken at home for the population 5 years and over. These are multiple, nonoverlapping vintages of the 5-year ACS estimates of population and housing attributes starting in 2010 shown by the corresponding census tract vintage. Also includes the most recent release annually.King County, Washington census tracts with nonoverlapping vintages of the 5-year American Community Survey (ACS) estimates starting in 2010. Vintage identified in the "ACS Vintage" field.The census tract boundaries match the vintage of the ACS data (currently 2010 and 2020) so please note the geographic changes between the decades. Tracts have been coded as being within the City of Seattle as well as assigned to neighborhood groups called "Community Reporting Areas". These areas were created after the 2000 census to provide geographically consistent neighborhoods through time for reporting U.S. Census Bureau data. This is not an attempt to identify neighborhood boundaries as defined by neighborhoods themselves.Vintages: 2010, 2015, 2020, 2021, 2022, 2023ACS Table(s): C16001Data 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.