Context

The United States Census Bureau’s international dataset provides estimates of country populations since 1950 and projections through 2050. Specifically, the dataset includes midyear population figures broken down by age and gender assignment at birth. Additionally, time-series data is provided for attributes including fertility rates, birth rates, death rates, and migration rates.

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.census_bureau_international.

Sample Query 1

What countries have the longest life expectancy? In this query, 2016 census information is retrieved by joining the mortality_life_expectancy and country_names_area tables for countries larger than 25,000 km2. Without the size constraint, Monaco is the top result with an average life expectancy of over 89 years!

standardSQL

SELECT age.country_name, age.life_expectancy, size.country_area FROM ( SELECT country_name, life_expectancy FROM bigquery-public-data.census_bureau_international.mortality_life_expectancy WHERE year = 2016) age INNER JOIN ( SELECT country_name, country_area FROM bigquery-public-data.census_bureau_international.country_names_area where country_area > 25000) size ON age.country_name = size.country_name ORDER BY 2 DESC /* Limit removed for Data Studio Visualization */ LIMIT 10

Sample Query 2

Which countries have the largest proportion of their population under 25? Over 40% of the world’s population is under 25 and greater than 50% of the world’s population is under 30! This query retrieves the countries with the largest proportion of young people by joining the age-specific population table with the midyear (total) population table.

standardSQL

SELECT age.country_name, SUM(age.population) AS under_25, pop.midyear_population AS total, ROUND((SUM(age.population) / pop.midyear_population) * 100,2) AS pct_under_25 FROM ( SELECT country_name, population, country_code FROM bigquery-public-data.census_bureau_international.midyear_population_agespecific WHERE year =2017 AND age < 25) age INNER JOIN ( SELECT midyear_population, country_code FROM bigquery-public-data.census_bureau_international.midyear_population WHERE year = 2017) pop ON age.country_code = pop.country_code GROUP BY 1, 3 ORDER BY 4 DESC /* Remove limit for visualization*/ LIMIT 10

Sample Query 3

The International Census dataset contains growth information in the form of birth rates, death rates, and migration rates. Net migration is the net number of migrants per 1,000 population, an important component of total population and one that often drives the work of the United Nations Refugee Agency. This query joins the growth rate table with the area table to retrieve 2017 data for countries greater than 500 km2.

SELECT growth.country_name, growth.net_migration, CAST(area.country_area AS INT64) AS country_area FROM ( SELECT country_name, net_migration, country_code FROM bigquery-public-data.census_bureau_international.birth_death_growth_rates WHERE year = 2017) growth INNER JOIN ( SELECT country_area, country_code FROM bigquery-public-data.census_bureau_international.country_names_area

Update frequency

Historic (none)

Dataset source

United States Census Bureau

Terms of use: This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

See the GCP Marketplace listing for more details and sample queries: https://console.cloud.google.com/marketplace/details/united-states-census-bureau/international-census-data

Context

The United States Census Bureau’s International Dataset provides estimates of country populations since 1950 and projections through 2050.

Content

The U.S. Census Bureau provides estimates and projections for countries and areas that are recognized by the U.S. Department of State that have a population of at least 5,000. Specifically, the data set includes midyear population figures broken down by age and gender assignment at birth. Additionally, they provide time-series data for attributes including fertility rates, birth rates, death rates, and migration rates.

Fork this kernel to get started.

Acknowledgements

https://bigquery.cloud.google.com/dataset/bigquery-public-data:census_bureau_international

https://cloud.google.com/bigquery/public-data/international-census

Dataset Source: www.census.gov

This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source -http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Banner Photo by Steve Richey from Unsplash.

Inspiration

What countries have the longest life expectancy?

Which countries have the largest proportion of their population under 25?

Which countries are seeing the largest net migration?

Malawi MW: Life Expectancy at Birth: Total data was reported at 63.223 Year in 2016. This records an increase from the previous number of 62.661 Year for 2015. Malawi MW: Life Expectancy at Birth: Total data is updated yearly, averaging 46.355 Year from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 63.223 Year in 2016 and a record low of 37.805 Year in 1960. Malawi MW: Life Expectancy at Birth: Total data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Malawi – Table MW.World Bank: Health Statistics. Life expectancy at birth indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life.; ; (1) United Nations Population Division. World Population Prospects: 2017 Revision, or derived from male and female life expectancy at birth from sources such as: (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme.; Weighted average;

Forecast value for employment rate using ARIMA (1,2,1).

This dataset provides a comprehensive panel data structure covering 27 EU member states from 2007 to 2020. It includes economic, social, governance, and development indicators alongside data on EU funds allocated as a percentage of GDP. The primary objective of this dataset is to facilitate the analysis of the impact of EU funding on socio-economic progress across different countries and periods.The time period covered: 2007–2020Geographical coverage: 27 EU member statesTemporal resolution: Annual observationsThe dataset contains yearly observations for each country, with the following key indicators:EU Funds (EUfunds) – Ratio of annual EU funds paid to member states as a percentage of GDP (European Commission).Human Development Index (HDI) – Composite index of life expectancy, education, and income (UNDP).GINI Index (GINI) – Measure of income inequality (0 = perfect equality, 100 = maximum inequality, World Bank).Economic Sentiment Indicator (ESI ave.) – Business and consumer confidence indicator (European Commission).Economic Complexity Index (ECI) – Measure of a country’s ability to produce knowledge-intensive goods (Harvard Growth Lab).Educational Attainment (Educ.att.) – Percentage of the population with secondary or higher education (World Bank).Gender Parity Index (GPI) – Ratio of female-to-male school enrollment rates (World Bank).Index of Economic Freedom – Score based on property rights, trade freedom, and government intervention (Heritage Foundation).Governance Indicators (World Bank Governance Indicators):Control of Corruption (Contr.of.Corr.)Rule of Law (Rule.of.Law)Regulatory Quality (Regul.Qual.)Political Stability and Absence of Violence (Pol.stab.and.Abs.viol.)Voice and Accountability (Voice.and.Account.)Government Effectiveness (Gov.effect.)Sustainable Development Goals (SDG Index) – Composite measure of progress toward the UN SDGs (Sustainable Development Solutions Network).This dataset provides a rich foundation for empirical analysis, policy evaluation, and academic research on the role of EU funding in economic and social development.

Canada is an aging society. In 2006, 13.7% of the total population of Canada was 65 years and over. This proportion was 9.7% only twenty five years ago in 1981. During the same period, the proportion of the population that was very old increased at a more rapid pace. For example, between 1981 and 2006 the proportion of the population that was 80 years and over rose from 1.7% to 3.7%. The number of people in this age group topped the 1 million mark (at 1.2 million) for the first time in 2006. In 2006, the population of Saskatchewan was the oldest in the country with 15.4% of the population 65 years and over. It also had the largest proportion of the oldest old, where one out of every 20 Saskatchewan residents was 80 years of age and over. The national average was one in 27. Saskatchewan's situation is unique, in that it has both the largest proportion of seniors and one of the largest proportions of children among the provinces. This is attributable to several factors: higher fertility compared to any other Canadian province due to a large Aboriginal population; a life expectancy that was, until quite recently, one of the highest in the country; and substantial losses of young adults migrating to Alberta to find employment. In general, Atlantic Canada (Newfoundland, and Labrador, Prince Edward Island, Nova Scotia, and New Brunswick) and British Columbia had an older age structure population (14-15% in the age group 65 and over) compared with the national average, once again a reflection of their lower fertility rates.