As of July 1, 2022, there were approximately 13.87 million males and 14.13 million females living in metropolitan areas across Canada. This statistic breaks down this figure by metropolitan area. Toronto was the most populated city in the country, counting over 3.2 million males and 3.4 million females.

About this dataset

Context

The dataset tabulates the Toronto 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 Toronto 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 Toronto was 196, a 1.51% decrease year-by-year from 2022. Previously, in 2022, Toronto population was 199, an increase of 1.53% compared to a population of 196 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Toronto decreased by 0. In this period, the peak population was 212 in the year 2012. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

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

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Toronto is shown in this column.
• Year on Year Change: This column displays the change in Toronto 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 Toronto Population by Year. You can refer the same here

Estimated number of persons on July 1, by 5-year age groups and gender, and median age, for Canada, provinces and territories.

Between July 1, 2021 and June 30, 2022, more than 70 percent of the immigrants arriving in the city of Toronto, in the Canadian province of Ontario, were between the ages of 20 and 44. People between the ages of 25 and 29, in particular, were the most numerous among the immigrant population, at 43,955. During the same period, there were 12,625 immigrants arriving in Toronto.

Between 2001/2002 and 2007/2008, the number of births in Toronto, in the Canadian province of Ontario, increased at a relatively steady rate for both sexes. Since then, this number has fluctuated, but overall decreased, especially between 2019 and 2021. In 2019/2020, 32,858 males and 31,334 females were born, and the following year there were only 30,890 male births and 29,314 female births.

Statistics Canada publishes monthly labour force statistics for all Canadian Census Metropolitan Areas (CMAs) and provinces. In addition, the City of Toronto purchases a special run from Statistics Canada of Labour Force Survey (LFS) data for city of Toronto residents (i.e. separate from the rest of the Toronto CMA). LFS data are collected by place of residence, and therefore city of Toronto's "employment" represents "employed residents" and not "jobs" in the city of Toronto. There are more jobs in the city of Toronto than employed city of Toronto residents. In this LFS database, you will find 22 monthly tables and 28 annual tables. Most of the tables contain data for five geographies: city of Toronto, Toronto CMA, Toronto/Hamilton/Oshawa CMAs, Ontario and Canada ( see attachment Table of Contents below a full description ). LFS data in the IVT tables are not seasonally adjusted. Top level seasonally adjusted LFS data are available in our monthly Toronto Economic Bulletin on Open Data. LFS is based on a monthly sample of approximately 2,800 households in the Toronto CMA, about half of the sample is from the city of Toronto; therefore, estimates will vary from the results of a complete census. LFS follows a rotating panel sample design, in which households remain in the sample for six consecutive months. The total sample consists of six representative sub-samples of panels, and each month a panel is replaced after completing its six month stay in the survey. Outgoing households are replaced by households in the same or similar area. This results in a five-sixths month-to-month sample overlap, which makes the design efficient for estimating month-to-month changes. The rotation after six months prevents undue respondent burden for households that are selected for the survey ( see attachment Guide to the Labour Force Survey for more information). Upon reviewing the data, you will see that at least some cells in the IVT tables have been suppressed. For confidentiality reasons, Statistics Canada suppresses Labour Force Survey data for any cell that corresponds to less than 1,500 persons. At the beginning of 2015, Statistics Canada substantially changed the methodology used to produce LFS population estimates for the city of Toronto. These changes have resulted in large and inexplicable swings in population and related counts, which are not real. However, the unemployment and participation rates for city residents showed very little change in this revision. The red dots in the chart above represents Statistics Canada's Annual Demographics estimates for the populations of the city of Toronto, age 15 and over. These are only estimates, but they are generally accepted as the most accurate estimates for the city's population. (Source: https://www150.statcan.gc.ca/n1/pub/91-214-x/91-214-x2018000-eng.htm). The most recent Statistics Canada population estimate for the city of Toronto is for July 1, 2015; therefore, we have to use projections thereafter. There are several population projections for the city. The projection that EDC staff has chosen to use for rebasing city of Toronto LFS data is the Ontario Ministry of Finance Population Projections 2017-2041 and downloaded June, 2017 from http://www.fin.gov.on.ca/en/economy/demographics/projections/ Please see attachment Rebased Labour Force Survey for City of Toronto below for annual adjustment factors, monthly adjustment factors and an example of how to rebase the absolute numbers for the city of Toronto.

Between 2006 and 2023, the level of employment in Toronto, in the province of Ontario, Canada, has fluctuated, but overall increased. There were approximately 2,765,600 people employed in 2006, compared to 3,674,500 17 years later. By 2023, the industry that employed the most people in the metropolitan area of Toronto was wholesale and retail trade, with over 532,000 people employed.

In 2022, Canada had a population density of about 4.43 people per square kilometer. The country has one of the lowest population densities in the world, as the total population is very small in relation to the dimensions of the land. Canada has a relatively stable population size, consistently with a growth of around one percent compared to the previous year. A small population in a large territory In terms of total area, Canada is the second largest country in the world. Its ten provinces and three territories extend from the Pacific to the Atlantic and northward to the Arctic Ocean, and this in total covers about 9.9 million square miles. The most densely populated area of Canada is what’s known as the Quebec City-Windsor Corridor in the provinces of Quebec and Ontario. Canada has a degree of urbanization of around 81 percent, because most Canadians prefer to live in cities where opportunities for work and leisure are in close proximity to each other and conditions are less rough.

ObjectiveThis work aims to study the disproportionate impact of the COVID-19 pandemic on the Jane and Finch community, one of the socially vulnerable neighborhoods in the Greater Toronto Area (GTA), Ontario, Canada, in terms of morbidity, mortality, and healthcare services.MethodologyA dataset provided by the Black Creek Community Health Centre (BCCHC), gathered from different health-related portals, covering various health statistics during COVID-19, namely, COVID-19 number of cases, hospitalizations, deaths, percentage of vaccination with one-, two-, and three-dose(s), Primary and Preventive Care (PPC) visits which include fecal and pap-smear cancer tests, and percentage of completed Imaging, Procedures, and Surgeries (IPS) which include the number of patients waiting for surgery were studied using statistical analysis. Underserved communities in the Peel, York, and City of Toronto regions were recognized using the Ontario Marginalized Index (ON-Marg). The Jane and Finch community was selected from the fifth quintile of the ON-Marg index and compared with the remaining locations (first to fourth ON-Marg quantiles) using Kruskal-Wallis, Mann–Whitney u, and t-tests. The Gini index was used to understand the inequality of the health parameters among the selected neighborhoods. Local Indicator of Spatial Association (LISA) was used to detect the neighborhoods with significantly higher numbers of COVID-19 cases, hospitalizations, and mortalities.ResultsThe Jane and Finch community had a significantly (p

Canada CA: Prevalence of Severe Food Insecurity in the Population: % of population data was reported at 1.000 % in 2020. This records an increase from the previous number of 0.900 % for 2019. Canada CA: Prevalence of Severe Food Insecurity in the Population: % of population data is updated yearly, averaging 0.650 % from Dec 2015 (Median) to 2020, with 6 observations. The data reached an all-time high of 1.000 % in 2020 and a record low of 0.600 % in 2017. Canada CA: Prevalence of Severe Food Insecurity in the Population: % of population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Canada – Table CA.World Bank.WDI: Social: Health Statistics. The percentage of people in the population who live in households classified as severely food insecure. A household is classified as severely food insecure when at least one adult in the household has reported to have been exposed, at times during the year, to several of the most severe experiences described in the FIES questions, such as to have been forced to reduce the quantity of the food, to have skipped meals, having gone hungry, or having to go for a whole day without eating because of a lack of money or other resources.;Food and Agriculture Organization of the United Nations (FAO);;

Related article: Bergroth, C., Järv, O., Tenkanen, H., Manninen, M., Toivonen, T., 2022. A 24-hour population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland. Scientific Data 9, 39.

In this dataset:

We present temporally dynamic population distribution data from the Helsinki Metropolitan Area, Finland, at the level of 250 m by 250 m statistical grid cells. Three hourly population distribution datasets are provided for regular workdays (Mon – Thu), Saturdays and Sundays. The data are based on aggregated mobile phone data collected by the biggest mobile network operator in Finland. Mobile phone data are assigned to statistical grid cells using an advanced dasymetric interpolation method based on ancillary data about land cover, buildings and a time use survey. The data were validated by comparing population register data from Statistics Finland for night-time hours and a daytime workplace registry. The resulting 24-hour population data can be used to reveal the temporal dynamics of the city and examine population variations relevant to for instance spatial accessibility analyses, crisis management and planning.

Please cite this dataset as:

Bergroth, C., Järv, O., Tenkanen, H., Manninen, M., Toivonen, T., 2022. A 24-hour population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland. Scientific Data 9, 39. https://doi.org/10.1038/s41597-021-01113-4

Organization of data

The dataset is packaged into a single Zipfile Helsinki_dynpop_matrix.zip which contains following files:

HMA_Dynamic_population_24H_workdays.csv represents the dynamic population for average workday in the study area.

HMA_Dynamic_population_24H_sat.csv represents the dynamic population for average saturday in the study area.

HMA_Dynamic_population_24H_sun.csv represents the dynamic population for average sunday in the study area.

target_zones_grid250m_EPSG3067.geojson represents the statistical grid in ETRS89/ETRS-TM35FIN projection that can be used to visualize the data on a map using e.g. QGIS.

Column names

YKR_ID : a unique identifier for each statistical grid cell (n=13,231). The identifier is compatible with the statistical YKR grid cell data by Statistics Finland and Finnish Environment Institute.

H0, H1 ... H23 : Each field represents the proportional distribution of the total population in the study area between grid cells during a one-hour period. In total, 24 fields are formatted as “Hx”, where x stands for the hour of the day (values ranging from 0-23). For example, H0 stands for the first hour of the day: 00:00 - 00:59. The sum of all cell values for each field equals to 100 (i.e. 100% of total population for each one-hour period)

In order to visualize the data on a map, the result tables can be joined with the target_zones_grid250m_EPSG3067.geojson data. The data can be joined by using the field YKR_ID as a common key between the datasets.

License Creative Commons Attribution 4.0 International.

Related datasets

Järv, Olle; Tenkanen, Henrikki & Toivonen, Tuuli. (2017). Multi-temporal function-based dasymetric interpolation tool for mobile phone data. Zenodo. https://doi.org/10.5281/zenodo.252612

Tenkanen, Henrikki, & Toivonen, Tuuli. (2019). Helsinki Region Travel Time Matrix [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3247564

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units and the group quarters population for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2019-2023 American Community Survey 5-Year Estimates.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year..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 roughly 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 ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..The 60 years and over column of data refers to the age of the householder for the estimates of households, occupied housing units, owner-occupied housing units, and renter-occupied housing units lines..The age specified on the population 15 years and over, population 25 years and over, population 30 years and over, civilian population 18 years and over, civilian population 5 years and over, population 1 years and over, population 5 years and over, and population 16 years and over lines refer to the data shown in the "Total" column while the second column is limited to the population 60 years and over..Telephone service data are not available for certain geographic areas due to problems with data collection of this question that occurred in 2019. Both ACS 1-year and ACS 5-year files were affected. It may take several years in the ACS 5-year files until the estimates are available for the geographic areas affected..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be...

Abstract

The 1980 South African Population Census was a count of all persons present on Republic of South African territory during census night (i.e. at midnight between 6 and 7 May 1980). The purpose of the population census was to collect, process and disseminate detailed statistics on population size, composition and distribution at small area level. The 1980 South African Population Census contains data collected on HOUSEHOLDS: household goods and dwelling characteristics as well as employment of domestic workers; INDIVIDUALS: population group, citizenship/nationality, marital status, fertility and infant mortality, education, employment, religion, language and disabilities, as well as mode of transport used and participation in sport and other recreational activities

Geographic coverage

The 1980 census covered the so-called white areas of South Africa, i.e. the areas in the former four provinces of the Cape, the Orange Free State, Transvaal, and Natal. It also covered areas in the following so-called National States of Ciskei, KwaZulu, Gazankulu, Lebowa, Qwaqwa, Kangwane, and Kwandebele. The 1980 South African census excluded the areas of the Transkei and Bophuthatswana. A census data file for Bophuthatswana was released with the final South African Census 1980 dataset.

Analysis unit

The units of analysis of the 1980 census includes households, individuals and institutions

Universe

The 1980 South African census covered all household members (usual residents).

The 1980 South African Population Census was enumerated on a de facto basis, that is, according to the place where persons were located during the census. All persons who were present on Republic of South African territory during census night (i.e. at midnight between 6 and 7 May 1980) were enumerated and included in the data. Visitors from abroad who were present in the RSA on holiday or business on the night of the census, as well as foreigners (and their families) who were studying or economically active, were not enumerated and included in the figures. Likewise, members of the Diplomatic and Consular Corps of foreign countries were not included. However, the South African personnel linked to the foreign missions including domestic workers were enumerated. Crews and passengers of ships were also not enumerated, unless they were normally resident in the Republic of South Africa. Residents of the RSA who were absent from the night were as far as possible enumerated on their return and included in the region where they normally resided. Personnel of the South African Government stationed abroad and their families were, however enumerated. Such persons were included in the Transvaal (Pretoria).

Kind of data

Census/enumeration data [cen]

Mode of data collection

Face-to-face [f2f]

Research instrument

The 1980 Population Census questionnaire was administered to all household members and covered household goods and dwelling characteristics, and employment of domestic workers. Questions concerning individuals included those on citizenship/nationality, marital status, fertility and infant mortality, education, employment, religion, language and disabilities, as well as mode of transport used and participation in sport and other recreational activities.

The following questions appear in the questionnaire but the corresponding data has not been included in the data set: PART C: PARTICULARS OF DWELLING: 2. How many separate families (i) Number of families (ii) Number of non-family persons (iii) total number of occupants [i.e. persons in families shown against (i) plus persons shown against 3. Persons employed by household Full-time, Part-time (a) How many persons are employed as domestics by you? (Include garden workers) (b) Total cash wages paid to above –mentioned persons for April 1980 4. Ownership – Do not answer this question if your dwelling is on a farm. (i) Own dwelling – (Including hire-purchase, sectional title property or property of wife): (a) Is the dwelling Fully paid Partly paid-off (b) If partly paid-off, state monthly repayment (include housing subsidy, but exclude insurance. (ii) Rented or occupied free dwelling : (a) Is the dwelling occupied free, rented furnished, rented unfurnished (b) If rented, state monthly rent (c) Is the dwelling owned by the employer? (d) Does it belong to the state, SA Railways, a provincial administration, a divisional council, or a municipality or other local authority? PART D: PARTICULARS OF THE FAMILY 1. Number of members in the family 2. Occupation. (Nature of work done) (a) Head of family (b) Wife 3. Annual income of head of family and wife. Annual income of: Head, Wife (if applicable)

Footnotes: 1 Population estimates based on the Standard Geographical Classification (SGC) 2016 as delineated in the 2016 Census. 2 A census metropolitan area (CMA) or a census agglomeration (CA) is formed by one or more adjacent municipalities centred on a population centre (known as the core). A CMA must have a total population of at least 100,000 of which 50,000 or more must live in the core based on adjusted data from the previous Census of Population Program. A CA must have a core population of at least 10,000 also based on data from the previous Census of Population Program. To be included in the CMA or CA, other adjacent municipalities must have a high degree of integration with the core, as measured by commuting flows derived from data on place of work from the previous Census Program. If the population of the core of a CA falls below 10,000, the CA is retired from the next census. However, once an area becomes a CMA, it is retained as a CMA even if its total population declines below 100,000 or the population of its core falls below 50,000. All areas inside the CMA or CA that are not population centres are rural areas. When a CA has a core of at least 50,000, based on data from the previous Census of Population, it is subdivided into census tracts. Census tracts are maintained for the CA even if the population of the core subsequently falls below 50,000. All CMAs are subdivided into census tracts (2016 Census Dictionary, catalogue number 98-301-X2016001). 3 An area outside census metropolitan areas and census agglomerations is made up of all areas (within a province or territory) unallocated to a census metropolitan area (CMA) or census agglomeration (CA). 4 The population growth, which is used to calculate population estimates of census metropolitan areas and census agglomerations (table 17100135), is comprised of the components of population growth (table 17100136). 5 This table replaces table 17100079. 6 The components of population growth for census metropolitan areas (CMAs) and census agglomerations (CAs) sometimes had to be calculated using information at the census division level, using the geographic conversion method. This method involves using the population component calculated at the level of the CD(s) in which the CMA or CA is located and applying a ratio corresponding to the proportion of the CMA or CA population included in the corresponding CD(s). For periods prior to 2005/2006, all demographic components for all CMAs and CAs were calculated using geographic conversions. For the periods from 2005/2006 to 2010/2011 inclusively, emigration and internal migration components for areas that were not CMAs according to the 2011 SGC were calculated using geographic conversions. For the periods 2011/2012 to 2015/2016 inclusively, the emigration and internal migration components of regions that were not CMAs or CAs according to the 2011 SGC were calculated using geographic conversions. For the relevant demographic components, trends should be interpreted with caution where the method of calculation has changed over time. This caveat applies particularly to the intraprovincial migration component, for which the assumptions of the geographic conversion method are more at risk of not being met. 7 Period from July 1 to June 30. 8 Age on July 1. 9 The estimates for net interprovincial migration are preliminary for 2020/2021 and final up to 2019/2020.

Reporting of Aggregate Case and Death Count data was discontinued May 11, 2023, with the expiration of the COVID-19 public health emergency declaration. Although these data will continue to be publicly available, this dataset will no longer be updated.

This archived public use dataset has 11 data elements reflecting United States COVID-19 community levels for all available counties.

The COVID-19 community levels were developed using a combination of three metrics — new COVID-19 admissions per 100,000 population in the past 7 days, the percent of staffed inpatient beds occupied by COVID-19 patients, and total new COVID-19 cases per 100,000 population in the past 7 days. The COVID-19 community level was determined by the higher of the new admissions and inpatient beds metrics, based on the current level of new cases per 100,000 population in the past 7 days. New COVID-19 admissions and the percent of staffed inpatient beds occupied represent the current potential for strain on the health system. Data on new cases acts as an early warning indicator of potential increases in health system strain in the event of a COVID-19 surge.

Using these data, the COVID-19 community level was classified as low, medium, or high.

COVID-19 Community Levels were used to help communities and individuals make decisions based on their local context and their unique needs. Community vaccination coverage and other local information, like early alerts from surveillance, such as through wastewater or the number of emergency department visits for COVID-19, when available, can also inform decision making for health officials and individuals.

For the most accurate and up-to-date data for any county or state, visit the relevant health department website. COVID Data Tracker may display data that differ from state and local websites. This can be due to differences in how data were collected, how metrics were calculated, or the timing of web updates.

Archived Data Notes:

This dataset was renamed from "United States COVID-19 Community Levels by County as Originally Posted" to "United States COVID-19 Community Levels by County" on March 31, 2022.

March 31, 2022: Column name for county population was changed to “county_population”. No change was made to the data points previous released.

March 31, 2022: New column, “health_service_area_population”, was added to the dataset to denote the total population in the designated Health Service Area based on 2019 Census estimate.

March 31, 2022: FIPS codes for territories American Samoa, Guam, Commonwealth of the Northern Mariana Islands, and United States Virgin Islands were re-formatted to 5-digit numeric for records released on 3/3/2022 to be consistent with other records in the dataset.

March 31, 2022: Changes were made to the text fields in variables “county”, “state”, and “health_service_area” so the formats are consistent across releases.

March 31, 2022: The “%” sign was removed from the text field in column “covid_inpatient_bed_utilization”. No change was made to the data. As indicated in the column description, values in this column represent the percentage of staffed inpatient beds occupied by COVID-19 patients (7-day average).

March 31, 2022: Data values for columns, “county_population”, “health_service_area_number”, and “health_service_area” were backfilled for records released on 2/24/2022. These columns were added since the week of 3/3/2022, thus the values were previously missing for records released the week prior.

April 7, 2022: Updates made to data released on 3/24/2022 for Guam, Commonwealth of the Northern Mariana Islands, and United States Virgin Islands to correct a data mapping error.

April 21, 2022: COVID-19 Community Level (CCL) data released for counties in Nebraska for the week of April 21, 2022 have 3 counties identified in the high category and 37 in the medium category. CDC has been working with state officials t

Key Table Information.Table Title.Allocation of Usual Hours Worked per Week in the Past 12 Months for the Population 16 Years and Over.Table ID.ACSDT1Y2024.B99233.Survey/Program.American Community Survey.Year.2024.Dataset.ACS 1-Year Estimates Detailed Tables.Source.U.S. Census Bureau, 2024 American Community Survey, 1-Year Estimates.Dataset Universe.The dataset universe of the American Community Survey (ACS) is the U.S. resident population and housing. For more information about ACS residence rules, see the ACS Design and Methodology Report. Note that each table describes the specific universe of interest for that set of estimates..Methodology.Unit(s) of Observation.American Community Survey (ACS) data are collected from individuals living in housing units and group quarters, and about housing units whether occupied or vacant. For more information about ACS sampling and data collection, see the ACS Design and Methodology Report..Geography Coverage.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year.Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Sampling.The ACS consists of two separate samples: housing unit addresses and group quarters facilities. Independent housing unit address samples are selected for each county or county-equivalent in the U.S. and Puerto Rico, with sampling rates depending on a measure of size for the area. For more information on sampling in the ACS, see the Accuracy of the Data document..Confidentiality.The Census Bureau has modified or suppressed some estimates in ACS data products to protect respondents' confidentiality. Title 13 United States Code, Section 9, prohibits the Census Bureau from publishing results in which an individual's data can be identified. For more information on confidentiality protection in the ACS, see the Accuracy of the Data document..Technical Documentation/Methodology.Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section.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 roughly 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 ACS Technical Documentation). The effect of nonsampling error is not represented in these tables.Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Weights.ACS estimates are obtained from a raking ratio estimation procedure that results in the assignment of two sets of weights: a weight to each sample person record and a weight to each sample housing unit record. Estimates of person characteristics are based on the person weight. Estimates of family, household, and housing unit characteristics are based on the housing unit weight. For any given geographic area, a characteristic total is estimated by summing the weights assigned to the persons, households, families or housing units possessing the characteristic in the geographic area. For more information on weighting and estimation in the ACS, see the Accuracy of the Data document.Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the populati...

Key Table Information.Table Title.Selected Characteristics of the Foreign-Born Population by Region of Birth: Latin America.Table ID.ACSST1Y2024.S0506.Survey/Program.American Community Survey.Year.2024.Dataset.ACS 1-Year Estimates Subject Tables.Source.U.S. Census Bureau, 2024 American Community Survey, 1-Year Estimates.Dataset Universe.The dataset universe of the American Community Survey (ACS) is the U.S. resident population and housing. For more information about ACS residence rules, see the ACS Design and Methodology Report. Note that each table describes the specific universe of interest for that set of estimates..Methodology.Unit(s) of Observation.American Community Survey (ACS) data are collected from individuals living in housing units and group quarters, and about housing units whether occupied or vacant. For more information about ACS sampling and data collection, see the ACS Design and Methodology Report..Geography Coverage.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year.Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Sampling.The ACS consists of two separate samples: housing unit addresses and group quarters facilities. Independent housing unit address samples are selected for each county or county-equivalent in the U.S. and Puerto Rico, with sampling rates depending on a measure of size for the area. For more information on sampling in the ACS, see the Accuracy of the Data document..Confidentiality.The Census Bureau has modified or suppressed some estimates in ACS data products to protect respondents' confidentiality. Title 13 United States Code, Section 9, prohibits the Census Bureau from publishing results in which an individual's data can be identified. For more information on confidentiality protection in the ACS, see the Accuracy of the Data document..Technical Documentation/Methodology.Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section.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 roughly 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 ACS Technical Documentation). The effect of nonsampling error is not represented in these tables.Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Weights.ACS estimates are obtained from a raking ratio estimation procedure that results in the assignment of two sets of weights: a weight to each sample person record and a weight to each sample housing unit record. Estimates of person characteristics are based on the person weight. Estimates of family, household, and housing unit characteristics are based on the housing unit weight. For any given geographic area, a characteristic total is estimated by summing the weights assigned to the persons, households, families or housing units possessing the characteristic in the geographic area. For more information on weighting and estimation in the ACS, see the Accuracy of the Data document.Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the na...

Key Table Information.Table Title.Place of Birth (Hispanic or Latino) in the United States.Table ID.ACSDT1Y2024.B06004I.Survey/Program.American Community Survey.Year.2024.Dataset.ACS 1-Year Estimates Detailed Tables.Source.U.S. Census Bureau, 2024 American Community Survey, 1-Year Estimates.Dataset Universe.The dataset universe of the American Community Survey (ACS) is the U.S. resident population and housing. For more information about ACS residence rules, see the ACS Design and Methodology Report. Note that each table describes the specific universe of interest for that set of estimates..Methodology.Unit(s) of Observation.American Community Survey (ACS) data are collected from individuals living in housing units and group quarters, and about housing units whether occupied or vacant. For more information about ACS sampling and data collection, see the ACS Design and Methodology Report..Geography Coverage.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year.Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Sampling.The ACS consists of two separate samples: housing unit addresses and group quarters facilities. Independent housing unit address samples are selected for each county or county-equivalent in the U.S. and Puerto Rico, with sampling rates depending on a measure of size for the area. For more information on sampling in the ACS, see the Accuracy of the Data document..Confidentiality.The Census Bureau has modified or suppressed some estimates in ACS data products to protect respondents' confidentiality. Title 13 United States Code, Section 9, prohibits the Census Bureau from publishing results in which an individual's data can be identified. For more information on confidentiality protection in the ACS, see the Accuracy of the Data document..Technical Documentation/Methodology.Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section.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 roughly 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 ACS Technical Documentation). The effect of nonsampling error is not represented in these tables.Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Weights.ACS estimates are obtained from a raking ratio estimation procedure that results in the assignment of two sets of weights: a weight to each sample person record and a weight to each sample housing unit record. Estimates of person characteristics are based on the person weight. Estimates of family, household, and housing unit characteristics are based on the housing unit weight. For any given geographic area, a characteristic total is estimated by summing the weights assigned to the persons, households, families or housing units possessing the characteristic in the geographic area. For more information on weighting and estimation in the ACS, see the Accuracy of the Data document.Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities...

Educational attainment in the population aged 25 to 64, off-reserve Indigenous, non-Indigenous and total population, Canada and jurisdictions. This table is included in Section D: Postsecondary education: Educational attainment of the population aged 25 to 64 of the Pan Canadian Education Indicators Program (PCEIP). PCEIP draws from a wide variety of data sources to provide information on the school-age population, elementary, secondary and postsecondary education, transitions, and labour market outcomes. The program presents indicators for all of Canada, the provinces, the territories, as well as selected international comparisons and comparisons over time. PCEIP is an ongoing initiative of the Canadian Education Statistics Council, a partnership between Statistics Canada and the Council of Ministers of Education, Canada that provides a set of statistical measures on education systems in Canada.

There are approximately 8.16 billion people living in the world today, a figure that shows a dramatic increase since the beginning of the Common Era. Since the 1970s, the global population has also more than doubled in size. It is estimated that the world's population will reach and surpass 10 billion people by 2060 and plateau at around 10.3 billion in the 2080s, before it then begins to fall. Asia When it comes to number of inhabitants per continent, Asia is the most populous continent in the world by a significant margin, with roughly 60 percent of the world's population living there. Similar to other global regions, a quarter of inhabitants in Asia are under 15 years of age. The most populous nations in the world are India and China respectively; each inhabit more than three times the amount of people than the third-ranked United States. 10 of the 20 most populous countries in the world are found in Asia. Africa Interestingly, the top 20 countries with highest population growth rate are mainly countries in Africa. This is due to the present stage of Sub-Saharan Africa's demographic transition, where mortality rates are falling significantly, although fertility rates are yet to drop and match this. As much of Asia is nearing the end of its demographic transition, population growth is predicted to be much slower in this century than in the previous; in contrast, Africa's population is expected to reach almost four billion by the year 2100. Unlike demographic transitions in other continents, Africa's population development is being influenced by climate change on a scale unseen by most other global regions. Rising temperatures are exacerbating challenges such as poor sanitation, lack of infrastructure, and political instability, which have historically hindered societal progress. It remains to be seen how Africa and the world at large adapts to this crisis as it continues to cause drought, desertification, natural disasters, and climate migration across the region.