Chart and table of population level and growth rate for the Vancouver, Canada metro area from 1950 to 2025.

The composite layer in this map shows total population counts by sex, age, and race groups data from the 2020 Census Demographic and Housing Characteristics. This is shown by Nation, Consolidated City, Census Designated Place, Incorporated Place boundaries. Each geography layer contains a common set of Census counts based on available attributes from the U.S. Census Bureau. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis.   By default, only the City of Vancouver "Incorporated Place" layer is visible and filtered to Vancouver; you may remove the filter and/or reveal other geographies using Map Browser settings. Vintage of boundaries and attributes: 2020 Demographic and Housing Characteristics Table(s): P1, H1, H3, P2, P3, P5, P12, P13, P17, PCT12 (Not all lines of these DHC tables are available in this feature layer.)Data downloaded from: U.S. Census Bureau’s data.census.gov siteDate the Data was Downloaded: May 25, 2023Geography Levels included: Nation, Consolidated City, Census Designated Place, Incorporated PlaceNational Figures: included in Nation layer The United States Census Bureau Demographic and Housing Characteristics: 2020 Census Results 2020 Census Data Quality Geography & 2020 Census Technical Documentation Data Table Guide: includes the final list of tables, lowest level of geography by table and table shells for the Demographic Profile and Demographic and Housing Characteristics.News & Updates

About this dataset

Context

The dataset tabulates the Vancouver 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 Vancouver 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 Vancouver was 196,442, a 1% increase year-by-year from 2022. Previously, in 2022, Vancouver population was 194,500, an increase of 0.90% compared to a population of 192,770 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Vancouver increased by 51,483. In this period, the peak population was 196,442 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 Vancouver is shown in this column.
• Year on Year Change: This column displays the change in Vancouver 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 Vancouver Population by Year. You can refer the same here

About this dataset

Context

The dataset tabulates the Vancouver 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 Vancouver 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 Vancouver was 194,512, a 0.91% increase year-by-year from 2021. Previously, in 2021, Vancouver population was 192,759, an increase of 0.78% compared to a population of 191,259 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Vancouver increased by 49,553. In this period, the peak population was 194,512 in the year 2022. 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 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 Vancouver is shown in this column.
• Year on Year Change: This column displays the change in Vancouver 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 Vancouver Population by Year. You can refer the same here

About this dataset

Context

The dataset tabulates the Vancouver population by year. The dataset can be utilized to understand the population trend of Vancouver.

Content

The dataset constitues the following datasets

• Vancouver, WA Population Dataset: Yearly Figures, Population Change, and Percent Change 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/.

Estimated number of persons by quarter of a year and by year, Canada, provinces and territories.

This feature layer shows total population counts by sex, age, and race groups data from the 2020 Census Demographic and Housing Characteristics. This is shown by Incorporated Place boundaries. Each geography layer contains a common set of Census counts based on available attributes from the U.S. Census Bureau. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis.   Vintage of boundaries and attributes: 2020 Demographic and Housing Characteristics Table(s): P1, H1, H3, P2, P3, P5, P12, P13, P17, PCT12 (Not all lines of these DHC tables are available in this file.)Data downloaded from: U.S. Census Bureau’s data.census.gov siteDate the Data was Downloaded: May 25, 2023Geography Levels included: Incorporated Place The United States Census Bureau Demographic and Housing Characteristics: 2020 Census Results 2020 Census Data Quality Geography & 2020 Census Technical Documentation Data Table Guide: includes the final list of tables, lowest level of geography by table and table shells for the Demographic Profile and Demographic and Housing Characteristics.News & Updates Data Processing Notes: These 2020 Census boundaries come from the US Census TIGER geodatabases. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For Census tracts and block groups, 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 and block group 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 unchanged and available as attributes within the data table (units are square meters).  The layer contains all US states, Washington D.C., and Puerto Rico. Census tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99). Block groups that fall within the same criteria (Block Group denoted as 0 with no area land) have also been removed.Percentages and derived counts, are calculated values (that can be identified by the "_calc_" stub in the field name). Field alias names were created based on the Table Shells file available from the Data Table Guide for the Demographic Profile and Demographic and Housing Characteristics. Not all lines of all tables listed above are included in this layer. Duplicative counts were dropped. For example, P0030001 was dropped, as it is duplicative of P0010001.To protect the privacy and confidentiality of respondents, their data has been protected using differential privacy techniques by the U.S. Census Bureau.

About this dataset

Context

The dataset tabulates the data for the Vancouver, WA population pyramid, which represents the Vancouver population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Vancouver, WA, is 26.7.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Vancouver, WA, is 24.2.
• Total dependency ratio for Vancouver, WA is 50.9.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Vancouver, WA is 4.1.

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 for the Vancouver population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Vancouver for the selected age group is shown in the following column.
• Population (Female): The female population in the Vancouver for the selected age group is shown in the following column.
• Total Population: The total population of the Vancouver for the selected age group is shown in the following column.

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 Vancouver Population by Age. You can refer the same here

This CSV file shows total population counts by sex, age, and race groupsdata from the2020 CensusDemographic andHousing Characteristics. Thisisshown by Nation, Consolidated City, Census Designated Place, Incorporated Placeboundaries. Eachgeographylayercontainsa common set of Census countsbased on available attributes from the U.S. Census Bureau. There are alsoadditionalcalculated attributes related to this topic, which can be mapped or used within analysis.  Vintageof boundaries and attributes:2020Demographic andHousing CharacteristicsTable(s): P1, H1, H3, P2, P3, P5, P12, P13, P17, PCT12 (Not all lines of these DHC tables are available in this file.)Data downloaded from: U.S. Census Bureau’s data.census.gov siteDatethe Data was Downloaded: May 25, 2023Geography Levels included: Nation, Consolidated City, Census Designated Place, Incorporated PlaceNational Figures: included in Nation layerThe United States Census BureauDemographic andHousing Characteristics:2020 Census Results2020 Census Data QualityGeography &2020 CensusTechnical DocumentationData Table Guide: includes the final list of tables, lowest level of geography by table and table shells for the Demographic Profile and Demographic and Housing Characteristics.News & UpdatesData Processing Notes:These 2020 Census boundaries come from the US Census TIGER geodatabases.These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. ForCensustractsand block groups, 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 metersor larger (mid tolarge sizedwater bodies) are erased from the tractand block groupboundaries, 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 tomore accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are unchanged and available as attributes within the data table (units are square meters). The layercontainsall US states, Washington D.C., and Puerto Rico.Census tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Block groups that fall within the same criteria (Block Group denoted as 0 with no area land) have also been removed.Percentages and derived counts, are calculated values (that can beidentifiedby the "_calc_" stub in the field name).Field alias names were created based on the Table Shells file available from the Data Table Guide for theDemographic Profile and Demographic andHousing Characteristics.Not all lines of all tables listed above are included in this layer. Duplicative counts were dropped. For example, P0030001 was dropped, as it is duplicative of P0010001.To protect the privacy and confidentiality of respondents, their data has been protected usingdifferential privacy techniquesby the U.S. Census Bureau.

Annual population estimates as of July 1st, by census metropolitan area and census agglomeration, single year of age, five-year age group and gender, based on the Standard Geographical Classification (SGC) 2021.

An update to the original web layer referenced above; this layer is meant to provide easier access to attributes that determined the overall indexing categories when interacted with--particularly the following: adults w/o a 4-year degree, households with limited English proficiency, population below 200% of the federal poverty level, population identifying as people of color, population with a disability, renter households, age 65 or older, youth under age 18. These changes are meant to highlight the maps purpose as a measure of social and economic vulnerability.

In 2048, the population in Manitoba is projected to reach about 1.84 million people. This is compared to a population of 1.46 million people in 2024.

Graph and download economic data for Resident Population in Clark County, WA (WACLAR1POP) from 1970 to 2024 about Clark County, WA; Portland; WA; residents; population; and USA.

U.S. Census Bureau's year 2020 block boundaries for the Portland-Vancouver-Salem, OR-WA Combined Statistical Area (CSA). Includes population, race and ethnicity, and housing characteristics. Source: Census, 2020. Date of last data update: 2024-01-26 This is official RLIS data. Contact Person: Joe Gordon joe.gordon@oregonmetro.gov 503-797-1587 RLIS Metadata Viewer: https://gis.oregonmetro.gov/rlis-metadata/#/details/3803 RLIS Terms of Use: https://rlisdiscovery.oregonmetro.gov/pages/terms-of-use

This dataset includes six tables which were custom ordered from Statistics Canada. All tables include commuting characteristics (mode of commuting, duration/distance), labour characteristics (employment income groups in 2015, Industry by the North American Industry Classification System 2012), and visible minority groups. The dataset is in Beyond 20/20 (.ivt) format. The Beyond 20/20 browser is required in order to open it. This software can be freely downloaded from the Statistics Canada website: https://www.statcan.gc.ca/eng/public/beyond20-20 (Windows only). For information on how to use Beyond 20/20, please see: http://odesi2.scholarsportal.info/documentation/Beyond2020/beyond20-quickstart.pdf https://wiki.ubc.ca/Library:Beyond_20/20_Guide Custom order from Statistics Canada includes the following dimensions and variables: Geography: Place of Work (POW), Census Tract (CT) within CMA Vancouver. The global non-response rate (GNR) is an important measure of census data quality. It combines total non-response (households) and partial non-response (questions). A lower GNR indicates a lower risk of non-response bias and, as a result, a lower risk of inaccuracy. The counts and estimates for geographic areas with a GNR equal to or greater than 50% are not published in the standard products. The counts and estimates for these areas have a high risk of non-response bias, and in most cases, should not be released. However, it will be provided upon request. GNR values for POR and POW are different for each geography. Universe: The Employed Labour Force having a usual place of work for the population aged 15 years and over in private households that are rented (Tenure rented), full year-full time workers (40-52weeks) Variables: Visible minority (15) 1. Total - Visible minority 2. Total visible minority population 3. South Asian 4. Chinese 5. Black 6. Filipino 7. Latin American 8. Arab 9. Southeast Asian 10. West Asian 11. Korean 12. Japanese 13. Visible minority, n.i.e. 14. Multiple visible minorities 15. Not a visible minority Commuting duration and distance (18) 1. Total - Commuting duration 2. Less than 15 minutes 3. 15 to 29 minutes 4. 30 to 44 minutes 5. 45 to 59 minutes 6. 60 minutes and over 7. Total - Commuting distance 8. Less than 1 km 9. 1 to 2.9 km 10. 3 to 4.9 km 11. 5 to 6.9 km 12. 7 to 9.9 km 13. 10 to 14.9 km 14. 15 to 19.9 km 15. 20 to 24.9 Km 16. 25 to 29.9 km 17. 30 to 34.9 km 18. 35 km or more Main mode of commuting (7) 1. Total - Main mode of commuting 2. Driver, alone 3. 2 or more persons shared the ride to work 4. Public transit 5. Walked 6. Bicycle 7. Other method Employment income groups in 2015 (39) 1. Total – Total Employment income groups in 2015 2. Without employment income 3. With employment income 4. Less than $30,000 (including loss) 5. $30,000 to $79,999 6. $30,000 to $39,999 7. $40,000 to $49,999 8. $50,000 to $59,999 9. $60,000 to $69,999 10. $70,000 to $79,999 11. $80,000 and above 12. Median employment income ($) 13. Average employment income ($) 14. Total – Male Employment income groups in 2015 15. Without employment income 16. With employment income 17. Less than $30,000 (including loss) 18. $30,000 to $79,999 19. $30,000 to $39,999 20. $40,000 to $49,999 21. $50,000 to $59,999 22. $60,000 to $69,999 23. $70,000 to $79,999 24. $80,000 and above 25. Median employment income ($) 26. Average employment income ($) 27. Total – Female Employment income groups in 2015 28. Without employment income 29. With employment income 30. Less than $30,000 (including loss) 31. $30,000 to $79,999 32. $30,000 to $39,999 33. $40,000 to $49,999 34. $50,000 to $59,999 35. $60,000 to $69,999 36. $70,000 to $79,999 37. $80,000 and above 38. Median employment income ($) 39. Average employment income ($) Industry - North American Industry Classification System (NAICS) 2012 (54) 1. Total - Industry - North American Industry Classification System (NAICS) 2012 2. 11 Agriculture, forestry, fishing and hunting 3. 21 Mining, quarrying, and oil and gas extraction 4. 22 Utilities 5. 23 Construction 6. 236 Construction of buildings 7. 237 Heavy and civil engineering construction 8. 238 Specialty trade contractors 9. 31-33 Manufacturing 10. 311 Food manufacturing 11. 41 Wholesale trade 12. 44-45 Retail trade 13. 441 Motor vehicle and parts dealers 14. 442 Furniture and home furnishings stores 15. 443 Electronics and appliance stores 16. 444 Building material and garden equipment and supplies dealers 17. 445 Food and beverage stores 18. 446 Health and personal care stores 19. 447 Gasoline stations 20. 448 Clothing and clothing accessories stores 21. 451 Sporting goods, hobby, book and music stores 22. 452 General merchandise stores 23. 453 Miscellaneous store retailers 24. 454 Non-store retailers 25. 48-49 Transportation and warehousing 26. 481 Air transportation 27. 482 Rail transportation 28. 483 Water...

Canada's largest metropolitan area is Toronto, in Ontario. In 2022. Over 6.6 million people were living in the Toronto metropolitan area. Montréal, in Quebec, followed with about 4.4 million inhabitants, while Vancouver, in Britsh Columbia, counted 2.8 million people as of 2022.

BackgroundThe COVID-19 pandemic has highlighted health disparities, especially among specific population groups. This study examines the spatial relationship between the proportion of visible minorities (VM), occupation types and COVID-19 infection in the Greater Vancouver region of British Columbia, Canada.MethodsProvincial COVID-19 case data between June 24, 2020, and November 7, 2020, were aggregated by census dissemination area and linked with sociodemographic data from the Canadian 2016 census. Bayesian spatial Poisson regression models were used to examine the association between proportion of visible minorities, occupation types and COVID-19 infection. Models were adjusted for COVID-19 testing rates and other sociodemographic factors. Relative risk (RR) and 95% Credible Intervals (95% CrI) were calculated.ResultsWe found an inverse relationship between the proportion of the Chinese population and risk of COVID-19 infection (RR = 0.98 95% CrI = 0.96, 0.99), whereas an increased risk was observed for the proportions of the South Asian group (RR = 1.10, 95% CrI = 1.08, 1.12), and Other Visible Minority group (RR = 1.06, 95% CrI = 1.04, 1.08). Similarly, a higher proportion of frontline workers (RR = 1.05, 95% CrI = 1.04, 1.07) was associated with higher infection risk compared to non-frontline.ConclusionDespite adjustments for testing, housing, occupation, and other social economic status variables, there is still a substantial association between the proportion of visible minorities, occupation types, and the risk of acquiring COVID-19 infection in British Columbia. This ecological analysis highlights the existing disparities in the burden of diseases among different visible minority populations and occupation types.

U.S. Census Bureau's year 2020 tract boundaries for the Portland-Vancouver-Salem, OR-WA Combined Statistical Area (CSA). Includes population, race and ethnicity, and housing characteristics. Source: Census, 2020. Date of last data update: 2021-10-27 This is official RLIS data. Contact Person: Joe Gordon joe.gordon@oregonmetro.gov 503-797-1587 RLIS Metadata Viewer: https://gis.oregonmetro.gov/rlis-metadata/#/details/3723 RLIS Terms of Use: https://rlisdiscovery.oregonmetro.gov/pages/terms-of-use

The population of Metro Vancouver (20110729Regional Growth Strategy Projections Population, Housing and Employment 2006 – 2041 File) will have increased greatly by 2040, and finding a new source of reservoirs for drinking water (2015_ Water Consumption_ Statistics File) will be essential. This issue of drinking water needs to be optimized and estimated (Data Mining file) with the aim of developing the region. Three current sources of water reservoirs for Metro Vancouver are Capilano, Seymour, and Coquitlam, in which the treated water is being supplied to the customer. The linear optimization (LP) model (Optimization, Sensitivity Report File) illustrates the amount of drinking water for each reservoir and region. In fact, the B.C. government has a specific strategy for the growing population till 2040, which leads them toward their goal. In addition, another factor is the new water source for drinking water that needs to be estimated and monitored to anticipate the feasible water source (wells) until 2040. As such, the government will have to make a decision on how much groundwater is used. The goal of the project is two steps: (1) an optimization model for three water reservoirs, and (2) estimating the new source of water to 2040. The process of data analysis for the project includes: the data is analyzed with six software—Trifacta Wrangler, AMPL, Excel Solver, Arc GIS, and SQL—and is visualized in Tableau. 1. Trifacta Wrangler Software clean data (Data Mining file). 2. AMPL and Solver Excel Software optimize drinking water consumption for Metro Vancouver (data in the Optimization and Sensitivity Report file). 3. ArcMap collaborates the raw data and result of the optimization water reservoir and estimating population till 2040 with the ArcGIS software (GIS Map for Tableau file). 4. Visualizing, estimating, and optimizing the source of drinking water for Metro Vancouver until 2040 with SQL software in Tableau (export tableau data file).

Between 2001 and 2023, the population of Toronto, in the Canadian province of Ontario, increased by around ** percent. Indeed, the metropolis's inhabitants were about *** million in 2001, and more than *** million two decades later.In 2023, Toronto was the largest metropolitan area in Canada in terms of population, ahead of Montreal and Vancouver.