Contained within the 5th Edition (1978 to 1995) of the National Atlas of Canada is a map that shows distribution of Indians and Inuit using several types of symbols to represent population in 1976.

About this dataset

Context

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

Key observations

The percent distribution of Canadian population by race (across all racial categories recognized by the U.S. Census Bureau): 87.26% are white, 3.30% are American Indian and Alaska Native and 9.43% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Canadian
• Population: The population of the racial category (excluding ethnicity) in the Canadian is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Canadian total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Canadian Population by Race & Ethnicity. You can refer the same here

The dataset is based on Statistics Canada census data spanning four census periods (2001, 2006, 2016, and 2021). The dataset captures population statistics disaggregated by ethnicity at the Dissemination Area (DA) level—the smallest standard geographic unit for census data dissemination, covering approximately 400-700 people per unit. For Toronto, this encompasses approximately 3,700 DAs, providing high spatial resolution for analyzing urban dynamics. The dataset includes detailed population counts for the five largest ethnic groups in Toronto: China, India, Philippines, Portugal, and Sri Lanka. The features are also extracted from census datasets and 298 socioeconomic and demographic features from the census data, organized into 12 categories:Demographics: Population age structure, household composition, and family sizeHousing: Dwelling types, ownership status, housing values, and maintenance needsFamily Structure: Marriage patterns, presence of children, household typesIncome: Median household and individual income, income sourcesEmployment: Labor force participation, employment/unemployment ratesMobility & Migration: Internal and external migration patterns, non-permanent residentsVisible Minorities: Population distribution by visible minority statusLanguage: Official language use, mother tongue, and multilingual capabilitiesOccupation: Employment categories across economic sectorsReligion: Religious affiliations and practicesIndustry: Distribution across industry sectorsPlace of Birth: Country of origin information

Data on immigrant status and period of immigration by place of birth, citizenship, age and gender for the population in private households in Canada, provinces and territories, census metropolitan areas and parts.

Temporary residents who are in Canada on a study permit in the observed calendar year. Datasets include study permit holders by year in which permit(s) became effective or with a valid permit in a calendar year or on December 31st. Please note that in these datasets, the figures have been suppressed or rounded to prevent the identification of individuals when the datasets are compiled and compared with other publicly available statistics. Values between 0 and 5 are shown as “--“ and all other values are rounded to the nearest multiple of 5. This may result to the sum of the figures not equating to the totals indicated.

Census data reveals that population density varies noticeably from area to area. Small area census data do a better job depicting where the crowded neighborhoods are. In this map, the yellow areas of highest density range from 30,000 to 150,000 persons per square kilometer. In those areas, if the people were spread out evenly across the area, there would be just 4 to 9 meters between them. Very high density areas exceed 7,000 persons per square kilometer. High density areas exceed 5,200 persons per square kilometer. The last categories break at 3,330 persons per square kilometer, and 1,500 persons per square kilometer.This dataset is comprised of multiple sources. All of the demographic data are from Michael Bauer Research with the exception of the following countries:Australia: Esri Australia and MapData ServicesCanada: Esri Canada and EnvironicsFrance: Esri FranceGermany: Esri Germany and NexigaIndia: Esri India and IndicusJapan: Esri JapanSouth Korea: Esri Korea and OPENmateSpain: Esri España and AISUnited States: Esri Demographics

This map shows where senior populations are found throughout the world. Areas with more than 10% seniors are highlighted with a dark red shading while a dot representation reveals the number of seniors and their distribution in bright red.This dataset is comprised of multiple sources. All of the demographic data are from Michael Bauer Research with the exception of the following countries:Australia: Esri Australia and MapData ServicesCanada: Esri Canada and EnvironicsFrance: Esri FranceGermany: Esri Germany and NexigaIndia: Esri India and IndicusJapan: Esri JapanSouth Korea: Esri Korea and OPENmateSpain: Esri España and AISUnited States: Esri Demographics

Data on the immigrant population by place of birth, period of immigration, gender and age for the population in private households in Canada.

Examining international student demographics helps educational institutions better understand the diverse backgrounds and requirements of their global student community. This dataset provides insights into a variety of aspects including, gender, marital status, Visa type, origin of country, academic level, and much more.

For use case and analysis reference, please take a look at this notebook "https://www.kaggle.com/code/webdevbadger/international-student-demographics-analysis">International Student Demographics Analysis .

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16711385%2Fefde694297c2830c0058032eae820358%2Ftop-countries.png?generation=1704958145733418&alt=media" alt="">

Feature Descriptions

academic.csv

• year: The year. The format is YYYY/YY.
• students: The number of students.
• us_students: The number of non-international students.
• undergraduate: The number of undergraduate students.
• graduate: The number of graduate students.
• non_degree: The number of non-degree students.
• opt: The number of OPT students. OPT stands for Optional Practical Training.

academic_detail.csv

• year: The year. The format is YYYY/YY.
• academic_type: The academic type. One of ["Undergraduate", "Graduate", "Non-Degree", "OPT"],
• academic_level: The academic level. One of ["Associate's", "Bachelor's", "Master's", 'Doctoral', "Professional", "Graduate, Unspecified", "Non-Degree, Intensive English", "Non-Degree, Other", "OPT"].
• students: The number of students.

field_of_study.csv

• year: The year. The format is YYYY/YY.
• field_of_study: The field of the study.
• major: The major of the study.
• students: The number of students.

origin.csv

• year: The year. The format is YYYY/YY.
• origin_region: The region of origin, such as Asia, Europe, and North America.
• origin: The origin, such as Canada, China, and India.
• academic_type: The academic type. One of ["Undergraduate", "Graduate", "Non-Degree", "OPT"].
• students: The number of students.

source_of_fund.csv

• year: The year. The format is YYYY/YY.
• academic_type: The academic type. One of ["Undergraduate", "Graduate", "Non-Degree", "OPT"].
• source_type: The fund source type. One of ["International", "U.S.", "Other"].
• source_of_fund: The source of fund. One of [ "Personal and Family", "Foreign Government or University", "Foreign Private Sponsor", "International Organization", "Current Employment", "U.S. College or University", "U.S. Government", "U.S. Private Sponsor", "Other Sources"].
• students: The number of students.

status.csv

• year: The year. The format is YYYY/YY.
• female: The number of female students.
• male: The number of male students.
• single: The number of non-married students.
• married: The number of married students.
• full_time: The number of full-time students.
• part_time: The number of part-time students.
• visa_f: The number of students with F Visa.
• visa_j: The number of students with J Visa.
• visa_other: The number of students with other types of Visas.

Acknowledgement

OpenDoorsData.org

This dataset contains information about annual estimates of fine particulate matter (PM2.5) concentrations and trends beginning in 1998. PM2.5 refers to airborne particulate matter less than 2.5 µm in diameter; comprises several chemical and particulate constituents, including nitrate, ammonium, elemental carbons, organic carbons, silicon and sodium ions and dust, and originates from a variety of sources, including vehicle exhaust, forest fires, and industrial processes. Exposure to PM2.5 is a leading environmental risk factor for mortality and the global burden of disease.

Global and regional PM2.5 concentrations are estimated using a combination of satellite observations, chemical transport modeling, and ground-based monitoring. Annual and coarse-resolution averages correspond to a simple mean of within-grid values. Gridded datasets are provided to allow users to agglomerate data as best meets their particular needs.

Annual and monthly datasets are provided in NetCDF [.nc] format, with naming convention V6GL02.02.CNNPM25.REGION.YYYYMM_START-YYYYMM_END.nc. REGION refers to the file region (e.g. ‘Global’). YYYYMM_START and YYYYMM_END refer to the numeric start and end date of the file (e.g. for annual mean PM2.5 for 2015, YYYYMM_START is 201501 and YYYYMM_END is 201512). Gridded files use the WGS84 projection.

Variable names within these files include "lat" (latitude coordinate centers of the PM2.5 grid, "lon" (longitude coordinates centers of the PM2.5 grid), and "PM25" (gridded mean PM2.5 concentrations).

Processed summary files are available for annual global country-level means, Canada provincial-level means, China and India regional-level means, and US state-level means. Population-weighted estimates and total population describe only those people covered by the V6.GL.02.02 dataset and are provided by Gridded Population of the World, version 4 (GPWv4). Country borders are defined following the Database of Global Administrative Areas, version 3.6 (GAD3.6).

Abstract

This study provides an update on measures of educational attainment for a broad cross section of countries. In our previous work (Barro and Lee, 1993), we constructed estimates of educational attainment by sex for persons aged 25 and over. The values applied to 129 countries over a five-year intervals from 1960 to 1985.

The present study adds census information for 1985 and 1990 and updates the estimates of educational attainment to 1990. We also have been able to add a few countries, notably China, which were previously omitted because of missing data.

Dataset:

Educational attainment at various levels for the male and female population. The data set includes estimates of educational attainment for the population by age - over age 15 and over age 25 - for 126 countries in the world. (see Barro, Robert and J.W. Lee, "International Measures of Schooling Years and Schooling Quality, AER, Papers and Proceedings, 86(2), pp. 218-223 and also see "International Data on Education", manuscipt.) Data are presented quinquennially for the years 1960-1990;

Educational quality across countries. Table 1 presents data on measures of schooling inputs at five-year intervals from 1960 to 1990. Table 2 contains the data on average test scores for the students of the different age groups for the various subjects.Please see Jong-Wha Lee and Robert J. Barro, "Schooling Quality in a Cross-Section of Countries," (NBER Working Paper No.w6198, September 1997) for more detailed explanation and sources of data.

Geographic coverage

The data set cobvers the following countries: - Afghanistan - Albania - Algeria - Angola - Argentina - Australia - Austria - Bahamas, The - Bahrain - Bangladesh - Barbados - Belgium - Benin - Bolivia - Botswana - Brazil - Bulgaria - Burkina Faso - Burundi - Cameroon - Canada - Cape verde - Central African Rep. - Chad - Chile - China - Colombia - Comoros - Congo - Costa Rica - Cote d'Ivoire - Cuba - Cyprus - Czechoslovakia - Denmark - Dominica - Dominican Rep. - Ecuador - Egypt - El Salvador - Ethiopia - Fiji - Finland - France - Gabon - Gambia - Germany, East - Germany, West - Ghana - Greece - Grenada - Guatemala - Guinea - Guinea-Bissau - Guyana - Haiti - Honduras - Hong Kong - Hungary - Iceland - India - Indonesia - Iran, I.R. of - Iraq - Ireland - Israel - Italy - Jamaica - Japan - Jordan - Kenya - Korea - Kuwait - Lesotho - Liberia - Luxembourg - Madagascar - Malawi - Malaysia - Mali - Malta - Mauritania - Mauritius - Mexico - Morocco - Mozambique - Myanmar (Burma) - Nepal - Netherlands - New Zealand - Nicaragua - Niger - Nigeria - Norway - Oman - Pakistan - Panama - Papua New Guinea - Paraguay - Peru - Philippines - Poland - Portugal - Romania - Rwanda - Saudi Arabia - Senegal - Seychelles - Sierra Leone - Singapore - Solomon Islands - Somalia - South africa - Spain - Sri Lanka - St.Lucia - St.Vincent & Grens. - Sudan - Suriname - Swaziland - Sweden - Switzerland - Syria - Taiwan - Tanzania - Thailand - Togo - Tonga - Trinidad & Tobago - Tunisia - Turkey - U.S.S.R. - Uganda - United Arab Emirates - United Kingdom - United States - Uruguay - Vanuatu - Venezuela - Western Samoa - Yemen, N.Arab - Yugoslavia - Zaire - Zambia - Zimbabwe

Context

This data set come from UCI Machine Learning Repository at https://archive.ics.uci.edu/ml/datasets/census+income

Data Set Information:

Prediction task is to determine whether a person makes over 50K a year from the analysis of 13 predictors.

Content

age: continuous. workclass: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked. fnlwgt: continuous. education: Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool. education-num: continuous. marital-status: Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse. occupation: Tech-support, Craft-repair, Other-service, Sales, Exec-managerial, Prof-specialty, Handlers-cleaners, Machine-op-inspct, Adm-clerical, Farming-fishing, Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces. relationship: Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried. race: White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black. sex: Female, Male. capital-gain: continuous. capital-loss: continuous. hours-per-week: continuous. native-country: United-States, Cambodia, England, Puerto-Rico, Canada, Germany, Outlying-US(Guam-USVI-etc), India, Japan, Greece, South, China, Cuba, Iran, Honduras, Philippines, Italy, Poland, Jamaica, Vietnam, Mexico, Portugal, Ireland, France, Dominican-Republic, Laos, Ecuador, Taiwan, Haiti, Columbia, Hungary, Guatemala, Nicaragua, Scotland, Thailand, Yugoslavia, El-Salvador, Trinadad&Tobago, Peru, Hong, Holand-Netherlands.

Acknowledgements

Extraction was done by Barry Becker from the 1994 Census database. A set of reasonably clean records was extracted using the following conditions: ((AAGE>16) && (AGI>100) && (AFNLWGT>1)&& (HRSWK>0))

Description of fnlwgt (final weight)

The weights on the CPS files are controlled to independent estimates of the civilian non-institutional population of the US. These are prepared monthly for us by Population Division here at the Census Bureau. We use 3 sets of controls.

These are:

1. A single cell estimate of the population 16+ for each state.
2. Controls for Hispanic Origin by age and sex.
3. Controls by Race, age and sex.

We use all three sets of controls in our weighting program and "rake" through them 6 times so that by the end we come back to all the controls we used.

The term estimate refers to population totals derived from CPS by creating "weighted tallies" of any specified socio-economic characteristics of the population.

People with similar demographic characteristics should have similar weights. There is one important caveat to remember about this statement. That is that since the CPS sample is actually a collection of 51 state samples, each with its own probability of selection, the statement only applies within state.

Summary

Data Set Characteristics: Multivariate Area: Social Attribute Characteristics: Categorical, Integer Number of Attributes: 14 Date Donated: 1996-05-01 Associated Tasks: Classification Missing Values? Yes

This table provides the number of temporary foreign workers in Canada and in provinces by their country of citizenship.

This table contains 25 series, with data for years 1955 - 2013 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...) Last permanent residence (25 items: Total immigrants; France; Great Britain; Total Europe ...).

This map shows where youth populations are found throughout the world. Areas with more than 33% youth are highlighted with a dark red shading while a dot representation reveals the number of seniors and their distribution in bright red.This dataset is comprised of multiple sources. All of the demographic data are from Michael Bauer Research with the exception of the following countries:Australia: Esri Australia and MapData ServicesCanada: Esri Canada and EnvironicsFrance: Esri FranceGermany: Esri Germany and NexigaIndia: Esri India and IndicusJapan: Esri JapanSouth Korea: Esri Korea and OPENmateSpain: Esri España and AISUnited States: Esri Demographics

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