The statistic shows the development of the world population from 1950 to 2050. The world population was around 7.38 billion people in 2015.

The global population

As shown above, the total number of people living on Earth has more than doubled since the 1950s, and continues to increase. A look at the development of the world population since the beginning of the Common Era shows that such a surge in numbers is unprecedented. The first significant rise in population occurred during the 14th century, after the Black Death had killed approximately 25 million people worldwide. Subsequently, the global population increased slowly but steadily until it reached record numbers between 1950 and 2000.

The majority of the global population lives on the Asian continent, as a statistic of the world population by continent shows. In around 100 years, it is estimated that population levels on the African continent will have reached similar levels to those we see in Asia today. As for a forecast of the development of the world population, the figures are estimated to have reached more than 10 billion by the 22nd century.

Growing population numbers pose an increasing risk to the planet, since rocketing numbers equal increased consumption of food and resources. Scientists worry that natural resources, such as oil, and food resources will become scarce, endangering the human race and, even more so, the world’s ecosystem. Nowadays, the number of undernourished / starving people worldwide has decreased slightly, but forecasts paint a darker picture.

Until the 1800s, population growth was incredibly slow on a global level. The global population was estimated to have been around 188 million people in the year 1CE, and did not reach one billion until around 1803. However, since the 1800s, a phenomenon known as the demographic transition has seen population growth skyrocket, reaching eight billion people in 2023, and this is expected to peak at over 10 billion in the 2080s.

Whereas the population is expected to decrease somewhat until 2100 in Asia, Europe, and South America, it is predicted to grow significantly in Africa. While there were 1.55 billion inhabitants on the continent at the beginning of 2025, the number of inhabitants is expected to reach 3.81 billion by 2100. In total, the global population is expected to reach nearly 10.18 billion by 2100. Worldwide population In the United States, the total population is expected to steadily increase over the next couple of years. In 2024, Asia held over half of the global population and is expected to have the highest number of people living in urban areas in 2050. Asia is home to the two most populous countries, India and China, both with a population of over one billion people. However, the small country of Monaco had the highest population density worldwide in 2024. Effects of overpopulation Alongside the growing worldwide population, there are negative effects of overpopulation. The increasing population puts a higher pressure on existing resources and contributes to pollution. As the population grows, the demand for food grows, which requires more water, which in turn takes away from the freshwater available. Concurrently, food needs to be transported through different mechanisms, which contributes to air pollution. Not every resource is renewable, meaning the world is using up limited resources that will eventually run out. Furthermore, more species will become extinct which harms the ecosystem and food chain. Overpopulation was considered to be one of the most important environmental issues worldwide in 2020.

This database presents population and other demographic estimates and projections from 1960 to 2050. They are disaggregated by age-group and gender and cover approximately 200 economies.

This dataset was kindly made available by the World Bank.

Population:😮

• A population is a distinct group of individuals, whether that group comprises a nation or a group of people with a common characteristic. In statistics, a population is the pool of individuals from which a statistical sample is drawn for a study. What is the types of population?
• And while every population pyramid is unique, most can be categorized into three prototypical shapes: expansive (young and growing), constrictive (elderly and shrinking), and stationary (little or no population growth). Let's take a deeper dive into the trends these three shapes reveal about a population and its needs.
• The global growth rate in absolute numbers accelerated to a peak of 92.9 million in 1988, but has declined to 81.3 million in 2020. Long-term projections indicate that the growth rate of the human population of this planet will continue to decline, and that by the end of the 21st century, it will reach zero.

Before 2025, the world's total population is expected to reach eight billion. Furthermore, it is predicted to reach over 10 billion in 2060, before slowing again as global birth rates are expected to decrease. Moreover, it is still unclear to what extent global warming will have an impact on population development. A high share of the population increase is expected to happen on the African continent.

Content

The current population of the United States of America is 334,464,117 as of Saturday, April 16, 2022, based on Worldometer elaboration of the latest United Nations data. This three datasets contain population data of USA (2020 and histIndiaorical), population forecast and population in major cities.

Attribute Information

• Year - Years from 2020-1955
• Population - Population in the respective year
• Yearly % Change - Percentage Yearly Change in Population
• Yearly Change - Yearly Change in Population
• Migrants (net) - Total number of migrants
• Median Age - Median age of the population
• Fertility Rate - Fertility rate
• Density (P/Km²)- Population density (population per square km)
• Urban Pop %- Percentage of urban population
• Urban Population- Urban population
• Country's Share of World Pop - Population share
• World Population - World Population in the respective year
• India Global Rank - Global Rank in Population

Source

Link : https://www.worldometers.info/world-population/us-population/

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Link : https://www.kaggle.com/anandhuh/datasets

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The West Africa Coastal Vulnerability Mapping: Population Projections, 2030 and 2050 data set is based on an unreleased working version of the Gridded Population of the World (GPW), Version 4, year 2010 population count raster but at a coarser 5 arc-minute resolution. Bryan Jones of Baruch College produced country-level projections based on the Shared Socioeconomic Pathway 4 (SSP4). SSP4 reflects a divided world where cities that have relatively high standards of living, are attractive to internal and international migrants. In low income countries, rapidly growing rural populations live on shrinking areas of arable land due to both high population pressure and expansion of large-scale mechanized farming by international agricultural firms. This pressure induces large migration flow to the cities, contributing to fast urbanization, although urban areas do not provide many opportunities for the poor and there is a massive expansion of slums and squatter settlements. This scenario may not be the most likely for the West Africa region, but it has internal coherence and is at least plausible. To provide areas in West Africa that may be particularly exposed to climate stressors owing to future high population growth.

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

There are 5 datasets:

1. World_population(1955-2023).csv
2. World_population(1951-2020).csv
3. World Population Projections(2020 -2100).csv
4. World Population by Region(2020).csv
5. World Population Forecast (2020-2050).csv

1. World_population(1955-2023).csv:

This dataset consists of World Population from 1955 to 2023

Year: Year is from 1955 to 2023 Population: Count of the world's population Yearly % Change: Percentage of yearly change in population Yearly Change: Increase in Yearly Change in Population Median Age: Median Age of Population Fertility Rate: Fertility Rate from 1955 to 2015 with an interval of 5 years and from 2015 to 2020 yearly. Density: Population Density is in (P/Km²)

2. World_population(1951-2020).csv:

This dataset consists of World Population from 1951 to 2020

Year: Year is from 1951 to 2020 World Population: Count of the world population Yearly Change: Percentage of yearly change in population Net Change: Change in Population Density: Population Density is in (P/Km²) Urban Pop: Count of Urban Population count Median Age: Median Age of Population Urban Pop%: percentage of Urban Population% Fertility Rate: Fertility Rate from 1955 to 2015 with an interval of 5 years and from 2015 to 2020 yearly.

3.World Population Projections(2020 -2100).csv

This dataset contains world population projections from 2020-2100 Year: From 2020-2100 World Population: Count of World Population Yearly Change(%): Percentage yearly change Net Change: Net change in population Density(P/Km²): Population Density is in (P/Km²)

4. World Population by Region(2020).csv

This dataset contains the Population across regions in the Year 2020 Region: Name of Region Population(2020): Population in 2020 Yearly Change(%): Percentage yearly change Net Change: Net change in population Density(P/Km²): Population Density is in (P/Km²) Land Area(Km²): Land Area of Region in Km² Migrants(net): The count of Migrants, has a negative value which indicates the count of people who migrated from that region to another region. Fert.Rate: Fertility Rate Med.Age: Median Age of Population Urban Pop %: Urban Population Percentage World Share: World Share of Population

5. World Population Forecast (2020-2050).csv

This dataset contains the population forecasts from 2020-2050 with an interval of 5 years. Year (July 1): Year Population: Total count of the population Yearly % Change: Percentage Change in population yearly Yearly Change: Yearly change in population Median Age: Median Age of Population Fertility Rate: Fertility Rate Density (P/Km²): Population Density is in (P/Km²)

This statistic shows the twenty countries with the projected largest urban populations worldwide in 2050. Forecasts estimate that the urban population of China will be **** billion people in 2050.

This database presents population and other demographic estimates and projections from 1960 to 2050, covering more than 200 economies. It includes population data by various age groups, sex, urban/rural; fertility data; mortality data; and migration data.

This is a hybrid gridded dataset of demographic data for the world, given as 5-year population bands at a 0.5 degree grid resolution.

This dataset combines the NASA SEDAC Gridded Population of the World version 4 (GPWv4) with the ISIMIP Histsoc gridded population data and the United Nations World Population Program (WPP) demographic modelling data.

Demographic fractions are given for the time period covered by the UN WPP model (1950-2050) while demographic totals are given for the time period covered by the combination of GPWv4 and Histsoc (1950-2020)

Method - demographic fractions

Demographic breakdown of country population by grid cell is calculated by combining the GPWv4 demographic data given for 2010 with the yearly country breakdowns from the UN WPP. This combines the spatial distribution of demographics from GPWv4 with the temporal trends from the UN WPP. This makes it possible to calculate exposure trends from 1980 to the present day.

To combine the UN WPP demographics with the GPWv4 demographics, we calculate for each country the proportional change in fraction of demographic in each age band relative to 2010 as:

\(\delta_{year,\ country,age}^{\text{wpp}} = f_{year,\ country,age}^{\text{wpp}}/f_{2010,country,age}^{\text{wpp}}\)

Where:

- \(\delta_{year,\ country,age}^{\text{wpp}}\) is the ratio of change in demographic for a given age and and country from the UN WPP dataset.

- \(f_{year,\ country,age}^{\text{wpp}}\) is the fraction of population in the UN WPP dataset for a given age band, country, and year.

- \(f_{2010,country,age}^{\text{wpp}}\) is the fraction of population in the UN WPP dataset for a given age band, country for the year 2020.

The gridded demographic fraction is then calculated relative to the 2010 demographic data given by GPWv4.

For each subset of cells corresponding to a given country c, the fraction of population in a given age band is calculated as:

\(f_{year,c,age}^{\text{gpw}} = \delta_{year,\ country,age}^{\text{wpp}}*f_{2010,c,\text{age}}^{\text{gpw}}\)

Where:

- \(f_{year,c,age}^{\text{gpw}}\) is the fraction of the population in a given age band for given year, for the grid cell c.

- \(f_{2010,c,age}^{\text{gpw}}\) is the fraction of the population in a given age band for 2010, for the grid cell c.

The matching between grid cells and country codes is performed using the GPWv4 gridded country code lookup data and country name lookup table. The final dataset is assembled by combining the cells from all countries into a single gridded time series. This time series covers the whole period from 1950-2050, corresponding to the data available in the UN WPP model.

Method - demographic totals

Total population data from 1950 to 1999 is drawn from ISIMIP Histsoc, while data from 2000-2020 is drawn from GPWv4. These two gridded time series are simply joined at the cut-over date to give a single dataset covering 1950-2020.

The total population per age band per cell is calculated by multiplying the population fractions by the population totals per grid cell.

Note that as the total population data only covers until 2020, the time span covered by the demographic population totals data is 1950-2020 (not 1950-2050).

Disclaimer

This dataset is a hybrid of different datasets with independent methodologies. No guarantees are made about the spatial or temporal consistency across dataset boundaries. The dataset may contain outlier points (e.g single cells with demographic fractions >1). This dataset is produced on a 'best effort' basis and has been found to be broadly consistent with other approaches, but may contain inconsistencies which not been identified.

In 2050, the three East Asian countries Hong Kong (SAR of China), South Korea, and Japan are forecasted to have the highest share of people aged 65 years or more. Except for Kuwait, all the countries on the list are either in Europe or East Asia. By 2050, 22 percent of the world's population is expected to be above 60 years.

A computerized data set of demographic, economic and social data for 227 countries of the world. Information presented includes population, health, nutrition, mortality, fertility, family planning and contraceptive use, literacy, housing, and economic activity data. Tabular data are broken down by such variables as age, sex, and urban/rural residence. Data are organized as a series of statistical tables identified by country and table number. Each record consists of the data values associated with a single row of a given table. There are 105 tables with data for 208 countries. The second file is a note file, containing text of notes associated with various tables. These notes provide information such as definitions of categories (i.e. urban/rural) and how various values were calculated. The IDB was created in the U.S. Census Bureau''s International Programs Center (IPC) to help IPC staff meet the needs of organizations that sponsor IPC research. The IDB provides quick access to specialized information, with emphasis on demographic measures, for individual countries or groups of countries. The IDB combines data from country sources (typically censuses and surveys) with IPC estimates and projections to provide information dating back as far as 1950 and as far ahead as 2050. Because the IDB is maintained as a research tool for IPC sponsor requirements, the amount of information available may vary by country. As funding and research activity permit, the IPC updates and expands the data base content. Types of data include: * Population by age and sex * Vital rates, infant mortality, and life tables * Fertility and child survivorship * Migration * Marital status * Family planning Data characteristics: * Temporal: Selected years, 1950present, projected demographic data to 2050. * Spatial: 227 countries and areas. * Resolution: National population, selected data by urban/rural * residence, selected data by age and sex. Sources of data include: * U.S. Census Bureau * International projects (e.g., the Demographic and Health Survey) * United Nations agencies Links: * ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/08490

This comprehensive dataset, derived from the United Nations World Urbanization Prospects 2018, provides detailed insights into the global demographic shifts from 1950 to 2050. It covers a wide range of data points including total, urban, and rural populations, alongside growth rates and urbanization trends across different regions, subregions, and countries.

Dataset Files WUP2018-F01-Total_Urban_Rural.xls: Population counts for urban and rural areas as of mid-2018, including percentages. WUP2018-F02-Proportion_Urban.xls: Historical and projected percentages of urban populations from 1950 to 2050. WUP2018-F03-Urban_Population.xls: Urban population figures from 1950 to 2050. WUP2018-F04-Rural_Population.xls: Rural population figures from 1950 to 2050. WUP2018-F05-Total_Population.xls: Total population figures from 1950 to 2050. WUP2018-F06-Urban_Growth_Rate.xls: Annual urban population growth rates from 1950 to 2050. WUP2018-F07-Rural_Growth_Rate.xls: Annual rural population growth rates from 1950 to 2050. WUP2018-F08-Total_Growth_Rate.xls: Total population growth rates from 1950 to 2000. WUP2018-F09-Urbanization_Rate.xls: Changes in the rate of urbanization from 1950 to 2050. WUP2018-F10-Rate_Proportion_Rural.xls: Changes in the proportion of rural populations from 1950 to 2050. WUP2018-F18-Total_Population_Annual.xls: Detailed annual total population data from 1950 to 2050. WUP2018-F19-Urban_Population_Annual.xls: Detailed annual urban population data from 1950 to 2050. WUP2018-F20-Rural_Population_Annual.xls: Detailed annual rural population data from 1950 to 2050. WUP2018-F21-Proportion_Urban_Annual.xls: Detailed annual urban population percentages from 1950 to 2050. Potential Uses This dataset is invaluable for researchers, policy makers, urban planners, and sociologists interested in understanding the dynamics of urbanization and its impacts on global development. The data can be used for:

Analyzing trends in urban and rural growth. Forecasting future demographic shifts. Planning for infrastructure, services, and resources in rapidly urbanizing regions. Studying regional differences in development and urbanization.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F11723377%2F59bc70fb3d13d9f954e317aacbfd2bd6%2FPopulation.png?generation=1681981140865261&alt=media" alt="">

The population data from the United Nations is a dataset that contains information on the estimated population of each country in the world for various years between 1980 and 2050. The dataset includes the following columns:

• place: Name of the country or region
• pop1980: Estimated population for the year 1980
• pop2000: Estimated population for the year 2000
• pop2010: Estimated population for the year 2010
• pop2022: Estimated population for the year 2022
• pop2023: Estimated population for the year 2023
• pop2030: Estimated population for the year 2030
• pop2050: Estimated population for the year 2050
• country: ISO 3166-1 alpha-3 code of the country
• area: Total land and water area of the country (in square kilometers)
• landAreaKm: Land area of the country (in square kilometers)
• cca2: ISO 3166-1 alpha-2 code of the country
• cca3: ISO 3166-1 alpha-3 code of the country
• netChange: Annual net change in population (in thousands)
• growthRate: Annual population growth rate (as a percentage)
• worldPercentage: Percentage of world population
• density: Population density (in persons per square kilometer)
• densityMi: Population density (in persons per square mile)
• rank: Rank of the country by population

The dataset provides a comprehensive overview of the population of each country over time and can be used to analyze population trends, make population projections, and compare the population of different countries. The dataset can also be used in combination with other data sources to explore correlations between population and various social and economic indicators.

The past century of increases in human population and resource consumption has produced some undesirable effects, ranging from environmental degradation to climate change to political unrest. We are accustomed to seeing these dependent variables charted with time on the x-axis. But this study presents metrics of biodiversity, consumption, and pollution and their extremely strong correlations when charted against human population size. Then we suggest that a more rapid yet non-coercive lowering of global Total Fertility Rates to 1.75 by 2050, and holding there, will produce many benefits for current and future generations of our own species and for nature. Among these benefits are reduced CO2 emissions, habitat recovery, protection of wild species, and reduced conflict over scarce resources.

World population density 1950-2050

Spain Demographic Trends

Population, Migration, and Age Trends from 1955 to 2050

By [source]

About this dataset

This dataset provides an in-depth look into the demographic information for Spain. It includes trends in population, migration, and age from 1955 to 2050. This dataset can provide understanding into the growth of Spain which has been marked as one of the fastest-growth developing countries. It reveals important statistics such as population numbers, yearly change percentages, fertility rate figures, density of people per square kilometer and more across all ages over a considerable period of time. Furthermore, it also outlines aspects such World Population Total and Country’s Share of World Pop with each country’s global rank among other nations. It will be useful for those wanting to gain insight into specific populations numbers that shape the Spanish culture today

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How to use the dataset

This dataset provides comprehensive information about the demographic trends in Spain from 1955 to 2050, including population, migration, urbanization, age and fertility rates. This data can be utilized to gain a better understanding of population structure changes of Spain over time and helps answer some important questions such as: What is the overall trend in population growth? How has migration affected population change? How is the median age changing?

To make the most effective use of this dataset you should begin by exploring each column one by one. You can see an overview of each year's data using summary statistics like mean, median or mode which can help you identify any interesting trends that might exist among these metrics. Next investigate how each statistic has changed over time by creating a line graph for each of them. These visualizations will help you compare different variables side-by-side and better understand their relationships with one another. Finally, analyze all observations together to form your conclusions about demographic patterns in Spain from 1955 to 2050 and how they have impacted its overall population makeup

Research Ideas

• To calculate the rate of population growth over the years and predict future population levels in Spain.
• To analyze migration trends of people from abroad moving to Spain and compare it to those of Spanish citizens leaving or entering the country.
• To study age trends in Spain, including median age for both general population and specific regions within the country, as well as fertility rates/birth rates for each demographic group/region

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: Population_of_Spain_Historical.csv | Column name | Description | |:---------------------------------|:------------------------------------------------------------------------------| | Year | Year of the data point. (Integer) | | Population | Total population of Spain in a given year. (Integer) | | Yearly% Change | Percentage change in population from the previous year. (Float) | | Yearly Change | Change in population from the previous year. (Integer) | | Migrants (net) | Net migration rate of Spain in a given year. (Integer) | | Median Age | Median age of the population in a given year. (Float) | | Fertiliy Rate | Fertility rate of citizens in a given year. (Float) | | Density(/km2) | Population density of Spain in a given year. (Float) | | Urban Pop | Percentage of population living in urban areas in a given year. (Float) | | Urban Population | Population living in urban areas in a given year. (Integer) | | Country's Share of World Pop | Percentage ...