This dataset provides a thorough exploration of the global demographic landscape, offering a detailed overview of population statistics, geographical area, and population density for countries worldwide. With meticulously curated data, this resource enables in-depth analyses and insights into the dynamic interplay between population distribution and geographic characteristics on a global scale. Researchers, policymakers, and analysts can leverage this dataset to examine trends, make informed decisions, and gain a nuanced understanding of the intricate patterns shaping the demographics of nations in the contemporary era.

Traffic analytics, rankings, and competitive metrics for worldpopulationreview.com as of September 2025

Context

The world's population has undergone remarkable growth, exceeding 7.5 billion by mid-2019 and continuing to surge beyond previous estimates. Notably, China and India stand as the two most populous countries, with China's population potentially facing a decline while India's trajectory hints at surpassing it by 2030. This significant demographic shift is just one facet of a global landscape where countries like the United States, Indonesia, Brazil, Nigeria, and others, each with populations surpassing 100 million, play pivotal roles.

The steady decrease in growth rates, though, is reshaping projections. While the world's population is expected to exceed 8 billion by 2030, growth will notably decelerate compared to previous decades. Specific countries like India, Nigeria, and several African nations will notably contribute to this growth, potentially doubling their populations before rates plateau.

Content

This dataset provides comprehensive historical population data for countries and territories globally, offering insights into various parameters such as area size, continent, population growth rates, rankings, and world population percentages. Spanning from 1970 to 2023, it includes population figures for different years, enabling a detailed examination of demographic trends and changes over time.

Dataset

Structured with meticulous detail, this dataset offers a wide array of information in a format conducive to analysis and exploration. Featuring parameters like population by year, country rankings, geographical details, and growth rates, it serves as a valuable resource for researchers, policymakers, and analysts. Additionally, the inclusion of growth rates and world population percentages provides a nuanced understanding of how countries contribute to global demographic shifts.

This dataset is invaluable for those interested in understanding historical population trends, predicting future demographic patterns, and conducting in-depth analyses to inform policies across various sectors such as economics, urban planning, public health, and more.

Structure

This dataset (world_population_data.csv) covering from 1970 up to 2023 includes the following columns:

Acknowledgment

The primary dataset was retrieved from the World Population Review. I sincerely thank the team for providing the core data used in this dataset.

© Image credit: Freepik

Globally, about 25 percent of the population is under 15 years of age and 10 percent is over 65 years of age. Africa has the youngest population worldwide. In Sub-Saharan Africa, more than 40 percent of the population is below 15 years, and only three percent are above 65, indicating the low life expectancy in several of the countries. In Europe, on the other hand, a higher share of the population is above 65 years than the population under 15 years. Fertility rates The high share of children and youth in Africa is connected to the high fertility rates on the continent. For instance, South Sudan and Niger have the highest population growth rates globally. However, about 50 percent of the world’s population live in countries with low fertility, where women have less than 2.1 children. Some countries in Europe, like Latvia and Lithuania, have experienced a population decline of one percent, and in the Cook Islands, it is even above two percent. In Europe, the majority of the population was previously working-aged adults with few dependents, but this trend is expected to reverse soon, and it is predicted that by 2050, the older population will outnumber the young in many developed countries. Growing global population As of 2025, there are 8.1 billion people living on the planet, and this is expected to reach more than nine billion before 2040. Moreover, the global population is expected to reach 10 billions around 2060, before slowing and then even falling slightly by 2100. As the population growth rates indicate, a significant share of the population increase will happen in Africa.

In the middle of 2023, about 60 percent of the global population was living in Asia.The total world population amounted to 8.1 billion people on the planet. In other words 4.7 billion people were living in Asia as of 2023. Global populationDue to medical advances, better living conditions and the increase of agricultural productivity, the world population increased rapidly over the past century, and is expected to continue to grow. After reaching eight billion in 2023, the global population is estimated to pass 10 billion by 2060. Africa expected to drive population increase Most of the future population increase is expected to happen in Africa. The countries with the highest population growth rate in 2024 were mostly African countries. While around 1.47 billion people live on the continent as of 2024, this is forecast to grow to 3.9 billion by 2100. This is underlined by the fact that most of the countries wit the highest population growth rate are found in Africa. The growing population, in combination with climate change, puts increasing pressure on the world's resources.

Context

The outbreak of COVID19 pushed Kaggle to launch several competitions to better understand how the new virus spreads.

The data provided by this competition is not only divided by nation (China, US, Canada...), but also sometimes by province/state/dependency/territory (California, Hubei, French Guiana, Saskatchewan...).

Although there are already several Kaggle datasets that provide population estimates by nation, I couldn't find any that provided a population estimate for each one of the constituent states ("provinces/states") included in the data for the 2nd week COVID19 Global Forecasting competition. So here they are, packaged in a super simple two-column CSV file.

Content

Each row in this dataset is a rough estimate of the population in each of the constituent states that appear in the COVID19 Global Forecasting competition. Each row is, of course, one of these inner states. By "constituent state" I mean one of: - the 54 United States of America - the 33 Chinese provinces - 10 Canadian provinces (plus a territory, Northwest Territories) - 11 French overseas territories - 10 British overseas territories - 6 Australian states (plus 2 internal territories) - 5 constituent countries of the Kingdom of the Netherlands - 2 autonomous Danish territories (Faroe Islands and Greenland)

In total, 134 states are listed.

Acknowledgements

The population estimates were collected from the following sources: - Australia: Wikipedia - Canada: worldpopulationreview.com - China: another Kaggle dataset - Denmark: worldpopulationreview.com - France: worldometers.info (retrieved 2020-04-02, 18:00 UTC) - Netherlands: worldometers.info (retrieved 2020-04-02, 18:00 UTC) - US: worldpopulationreview.com - Guam: worldpopulationreview.com - UK: worldometers.info (retrieved 2020-04-02, 18:00 UTC)

Actual value and historical data chart for World Population Total

Context

While working on the gun violence data set, i wanted to normalize the number of incidents because some states are more populous than others so normalizing the gun incidents per million people gave me a different outlook towards the data. The source of this data is unofficial as the last numbers from US census bureau were available only from 2010. I just wanted to get a quick unofficial source of this data and stumbled upon this site

http://worldpopulationreview.com/states/

Content

Simple two columns - state and population as of 2018

Acknowledgements

http://worldpopulationreview.com/states/

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

This dataset contains the rankings of the happiest countries in the world for the year 2024, sourced from World Population Review. The rankings are based on various indicators of well-being such as income, social support, life expectancy, freedom to make life choices, generosity, and perceptions of corruption. The data reflects the global rankings of countries by their happiness index in 2024, providing insights into the factors contributing to national well-being. Original Dataset Link: https://worldpopulationreview.com/country-rankings/happiest-countries-in-the-world

Note that COVID-19 testing data will not be updated; however, COVID-19 infections and deaths from the Johns Hopkins dataset will be updated every few days.

Combines the Johns Hopkins COVID-19 data with several other public datasets

2018 GDP https://data.worldbank.org/indicator/NY.GDP.MKTP.CD

Crime and Population https://worldpopulationreview.com/countries/crime-rate-by-country/

Smoking rate https://ourworldindata.org/smoking#prevalence-of-smoking-across-the-world

Sex (% Female) https://data.worldbank.org/indicator/SP.POP.TOTL.FE.ZS

Median Age https://worldpopulationreview.com/countries/median-age/

Also includes COVID-19 specific data from @koryto https://www.kaggle.com/koryto/countryinfo

Mbouda Population 2023
This dataset falls under the category Traffic Generating Parameters Population.
It contains the following data:
This dataset was scouted on 2022-02-14 as part of a data sourcing project conducted by TUMI. License information might be outdated: Check original source for current licensing. The data can be accessed using the following URL / API Endpoint: https://worldpopulationreview.com/world-cities/mbouda-population

Associated with manuscript titled: Fifty Muslim-majority countries have fewer COVID-19 cases and deaths than the 50 richest non-Muslim countriesThe objective of this research was to determine the difference in the total number of COVID-19 cases and deaths between Muslim-majority and non-Muslim countries, and investigate reasons for the disparities. Methods: The 50 Muslim-majority countries had more than 50.0% Muslims with an average of 87.5%. The non-Muslim country sample consisted of 50 countries with the highest GDP while omitting any Muslim-majority countries listed. The non-Muslim countries’ average percentage of Muslims was 4.7%. Data pulled on September 18, 2020 included the percentage of Muslim population per country by World Population Review15 and GDP per country, population count, and total number of COVID-19 cases and deaths by Worldometers.16 The data set was transferred via an Excel spreadsheet on September 23, 2020 and analyzed. To measure COVID-19’s incidence in the countries, three different Average Treatment Methods (ATE) were used to validate the results. Results published as a preprint at https://doi.org/10.31235/osf.io/84zq5(15) Muslim Majority Countries 2020 [Internet]. Walnut (CA): World Population Review. 2020- [Cited 2020 Sept 28]. Available from: http://worldpopulationreview.com/country-rankings/muslim-majority-countries (16) Worldometers.info. Worldometer. Dover (DE): Worldometer; 2020 [cited 2020 Sept 28]. Available from: http://worldometers.info

Clinical studies, especially randomized controlled trials, are essential for generating evidence for clinical practice. However, generalizability is a long-standing concern when applying trial results to real-world patients. Generalizability assessment is thus important, nevertheless, not consistently practiced. We performed a systematic scoping review to understand the practice of generalizability assessment. We identified 187 relevant papers and systematically organized these studies in a taxonomy with three dimensions: (1) data availability (i.e., before or after trial [a priori vs a posteriori generalizability]), (2) result outputs (i.e., score vs non-score), and (3) populations of interest. We further reported disease areas, underrepresented subgroups, and types of data used to profile target populations. We observed an increasing trend of generalizability assessments, but less than 30% of studies reported positive generalizability results. As a priori generalizability can be assessed using only study design information (primarily eligibility criteria), it gives investigators a golden opportunity to adjust the study design before the trial starts. Nevertheless, less than 40% of the studies in our review assessed a priori generalizability. With the wide adoption of electronic health records systems, rich real-world patient databases are increasingly available for generalizability assessment; however, informatics tools are lacking to support the adoption of generalizability assessment practice.

Methods We performed the literature search over the following 4 databases: MEDLINE, Cochrane, PychINFO, and CINAHL. Following the Institute of Medicine’s standards for systematic review and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), we conducted the scoping review in the following six steps: 1) gaining an initial understanding about clinical trial generalizability assessment, population representativeness, internal validity, and external validity, 2) identifying relevant keywords, 3) formulating four search queries to identify relevant articles in the 4 databases, 4) screening the articles by reviewing titles and abstracts, 5) reviewing articles’ full-text to further filter out irrelevant ones based on inclusion and exclusion criteria, and 6) coding the articles for data extraction.

Study selection and screening process

We used an iterative process to identify and refine the search keywords and search strategies. We identified 5,352 articles as of February 2019 from MEDLINE, CINAHL, PychINFO, and Cochrane. After removing duplicates, 3,569 records were assessed for relevancy by two researchers (ZH and XT) through reviewing the titles and abstracts against the inclusion and exclusion criteria. Conflicts were resolved with a third reviewer (JB). During the screening process, we also iteratively refined the inclusion and exclusion criteria. Out of the 3,569 articles, 3,275 were excluded through the title and abstract screening process. Subsequently, we reviewed the full texts of 294 articles, among which 106 articles were further excluded based on the exclusion criteria. The inter-rater reliability of the full-text review between the two annotators is 0.901 (i.e., Cohen’s kappa, p < .001). 187 articles were included in the final scoping review.

Data extraction and reporting

We coded and extracted data from the 187 eligible articles according to the following aspects: (1) whether the study performed an a priori generalizability assessment or a posteriori generalizability assessment or both; (2) the compared populations and the conclusions of the assessment; (3) the outputs of the results (e.g., generalizability scores, descriptive comparison); (4) whether the study focused on a specific disease. If so, we extracted the disease and disease category; (5) whether the study focused on a particular population subgroup (e.g., elderly). If so, we extracted the specific population subgroup; (6) the type(s) of the real-world patient data used to profile the target population (i.e., trial data, hospital data, regional data, national data, and international data). Note that trial data can also be regional, national, or even international, depending on the scale of the trial. Regardless, we considered them in the category of “trial data” as the study population of a trial is typically small compared to observational cohorts or real-world data. For observational cohorts or real-world data (e.g., EHRs), we extracted the specific scale of the database (i.e., regional, national, and international). For the studies that compared the characteristics of different populations to indicate generalizability issues, we further coded the populations that were compared (e.g., enrolled patients, eligible patients, general population, ineligible patients), and the types of characteristics that were compared (i.e., demographic information, clinical attributes and comorbidities, treatment outcomes, and adverse events). We then used Fisher’s exact test to assess whether there is a difference in the types of characteristics compared between a priori and a posteriori generalizability assessment studies.

Mombasa Population 2022
This dataset falls under the category Traffic Generating Parameters Population.
It contains the following data: Mombasa Population 2022
This dataset was scouted on 2022-02-13 as part of a data sourcing project conducted by TUMI. License information might be outdated: Check original source for current licensing. The data can be accessed using the following URL / API Endpoint: https://worldpopulationreview.com/world-cities/mombasa-population

Context

This file contains an estimate of the world's population and consumer price index by country.

Content

It only has four columns with the country column representing the name of a specific country, country code identifing a particular country, the population representing the estimated population size of a country as of 2018 September, and the Consumer_price_index representing the estimated consumer price index for every country. Some countries may be missing or may be under a different name.

Acknowledgements

Credit to http://worldpopulationreview.com/countries

https://tradingeconomics.com/country-list/consumer-price-index-cpi

This is dataset which you can find population of Ecuadorian cities in 2022 . The data downloaded from this website. In my case, I utilize this data for making choropleth map for analyzing data of "Store Sales - Time Series Forecasting" data and please freely utilize this data for such use. (Thank you very much for "World Population Review"!)

Abstract

Different countries have different health outcomes that are in part due to the way respective health systems perform. Regardless of the type of health system, individuals will have health and non-health expectations in terms of how the institution responds to their needs. In many countries, however, health systems do not perform effectively and this is in part due to lack of information on health system performance, and on the different service providers.

The aim of the WHO World Health Survey is to provide empirical data to the national health information systems so that there is a better monitoring of health of the people, responsiveness of health systems and measurement of health-related parameters.

The overall aims of the survey is to examine the way populations report their health, understand how people value health states, measure the performance of health systems in relation to responsiveness and gather information on modes and extents of payment for health encounters through a nationally representative population based community survey. In addition, it addresses various areas such as health care expenditures, adult mortality, birth history, various risk factors, assessment of main chronic health conditions and the coverage of health interventions, in specific additional modules.

The objectives of the survey programme are to: 1. develop a means of providing valid, reliable and comparable information, at low cost, to supplement the information provided by routine health information systems. 2. build the evidence base necessary for policy-makers to monitor if health systems are achieving the desired goals, and to assess if additional investment in health is achieving the desired outcomes. 3. provide policy-makers with the evidence they need to adjust their policies, strategies and programmes as necessary.

Geographic coverage

The survey sampling frame must cover 100% of the country's eligible population, meaning that the entire national territory must be included. This does not mean that every province or territory need be represented in the survey sample but, rather, that all must have a chance (known probability) of being included in the survey sample.

There may be exceptional circumstances that preclude 100% national coverage. Certain areas in certain countries may be impossible to include due to reasons such as accessibility or conflict. All such exceptions must be discussed with WHO sampling experts. If any region must be excluded, it must constitute a coherent area, such as a particular province or region. For example if ¾ of region D in country X is not accessible due to war, the entire region D will be excluded from analysis.

Analysis unit

Households and individuals

Universe

The WHS will include all male and female adults (18 years of age and older) who are not out of the country during the survey period. It should be noted that this includes the population who may be institutionalized for health reasons at the time of the survey: all persons who would have fit the definition of household member at the time of their institutionalisation are included in the eligible population.

If the randomly selected individual is institutionalized short-term (e.g. a 3-day stay at a hospital) the interviewer must return to the household when the individual will have come back to interview him/her. If the randomly selected individual is institutionalized long term (e.g. has been in a nursing home the last 8 years), the interviewer must travel to that institution to interview him/her.

The target population includes any adult, male or female age 18 or over living in private households. Populations in group quarters, on military reservations, or in other non-household living arrangements will not be eligible for the study. People who are in an institution due to a health condition (such as a hospital, hospice, nursing home, home for the aged, etc.) at the time of the visit to the household are interviewed either in the institution or upon their return to their household if this is within a period of two weeks from the first visit to the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLING GUIDELINES FOR WHS

Surveys in the WHS program must employ a probability sampling design. This means that every single individual in the sampling frame has a known and non-zero chance of being selected into the survey sample. While a Single Stage Random Sample is ideal if feasible, it is recognized that most sites will carry out Multi-stage Cluster Sampling.

The WHS sampling frame should cover 100% of the eligible population in the surveyed country. This means that every eligible person in the country has a chance of being included in the survey sample. It also means that particular ethnic groups or geographical areas may not be excluded from the sampling frame.

The sample size of the WHS in each country is 5000 persons (exceptions considered on a by-country basis). An adequate number of persons must be drawn from the sampling frame to account for an estimated amount of non-response (refusal to participate, empty houses etc.). The highest estimate of potential non-response and empty households should be used to ensure that the desired sample size is reached at the end of the survey period. This is very important because if, at the end of data collection, the required sample size of 5000 has not been reached additional persons must be selected randomly into the survey sample from the sampling frame. This is both costly and technically complicated (if this situation is to occur, consult WHO sampling experts for assistance), and best avoided by proper planning before data collection begins.

All steps of sampling, including justification for stratification, cluster sizes, probabilities of selection, weights at each stage of selection, and the computer program used for randomization must be communicated to WHO

STRATIFICATION

Stratification is the process by which the population is divided into subgroups. Sampling will then be conducted separately in each subgroup. Strata or subgroups are chosen because evidence is available that they are related to the outcome (e.g. health, responsiveness, mortality, coverage etc.). The strata chosen will vary by country and reflect local conditions. Some examples of factors that can be stratified on are geography (e.g. North, Central, South), level of urbanization (e.g. urban, rural), socio-economic zones, provinces (especially if health administration is primarily under the jurisdiction of provincial authorities), or presence of health facility in area. Strata to be used must be identified by each country and the reasons for selection explicitly justified.

Stratification is strongly recommended at the first stage of sampling. Once the strata have been chosen and justified, all stages of selection will be conducted separately in each stratum. We recommend stratifying on 3-5 factors. It is optimum to have half as many strata (note the difference between stratifying variables, which may be such variables as gender, socio-economic status, province/region etc. and strata, which are the combination of variable categories, for example Male, High socio-economic status, Xingtao Province would be a stratum).

Strata should be as homogenous as possible within and as heterogeneous as possible between. This means that strata should be formulated in such a way that individuals belonging to a stratum should be as similar to each other with respect to key variables as possible and as different as possible from individuals belonging to a different stratum. This maximises the efficiency of stratification in reducing sampling variance.

MULTI-STAGE CLUSTER SELECTION

A cluster is a naturally occurring unit or grouping within the population (e.g. enumeration areas, cities, universities, provinces, hospitals etc.); it is a unit for which the administrative level has clear, nonoverlapping boundaries. Cluster sampling is useful because it avoids having to compile exhaustive lists of every single person in the population. Clusters should be as heterogeneous as possible within and as homogenous as possible between (note that this is the opposite criterion as that for strata). Clusters should be as small as possible (i.e. large administrative units such as Provinces or States are not good clusters) but not so small as to be homogenous.

In cluster sampling, a number of clusters are randomly selected from a list of clusters. Then, either all members of the chosen cluster or a random selection from among them are included in the sample. Multistage sampling is an extension of cluster sampling where a hierarchy of clusters are chosen going from larger to smaller.

In order to carry out multi-stage sampling, one needs to know only the population sizes of the sampling units. For the smallest sampling unit above the elementary unit however, a complete list of all elementary units (households) is needed; in order to be able to randomly select among all households in the TSU, a list of all those households is required. This information may be available from the most recent population census. If the last census was >3 years ago or the information furnished by it was of poor quality or unreliable, the survey staff will have the task of enumerating all households in the smallest randomly selected sampling unit. It is very important to budget for this step if it is necessary and ensure that all households are properly enumerated in order that a representative sample is obtained.

It is always best to have as many clusters in the PSU as possible. The reason for this is that the fewer the number of respondents in each PSU, the lower will be the clustering effect which

Psychological and brain science explore human behavior and the human brain by studying volunteers who participate in these studies. Given that the mind and behavior of participants are influenced by their own biological and social factors, the generalizability of findings in these fields largely depends on the representativeness of samples. However, the representativeness of samples in psychological and brain science has long been criticized as “WEIRD” (Western, Educated, Industrialized, Rich, and Democratic). In recent years, several meta-researches have surveyed the representativeness of samples in published studies from different sub-fields, but an overall understanding of the representativeness of samples in psychological and brain science is lacking. In this review, we analyze these meta-researches to provide a comprehensive perspective on the current state of sample representativeness. Two common issues emerged across these meta-researches. Firstly, the demographics of participants were incomplete in most of the published studies. Most psychological and brain science studies reported participants' gender, age, and country, but participants' race/ethnicity, education level, and socioeconomic status were far less reported. Other important demographics, such as rural/urban division, were not reported at all. Additionally, the reporting of these demographics has increased only slightly in recent years compared to decades ago. Thus, the under-reporting of demographic information in literature was largely unchanged. Secondly, based on the reported demographics, we found that samples in the field are far from being representative of the world population: most participants are young, highly educated Caucasian females in Western countries; middle-aged and older, less educated, colored people in and outside Western countries are less likely to be studied. In terms of countries, Southeast Asian, African, Latin American, and Middle Eastern countries appear fewer in psychological and brain science research.These two issues may be due to the following reasons: convenience sampling dominates psychological and brain science; Western researchers dominate the field of psychology and brain science, with most of the editors-in-chief, editorial board members, and authors coming from Europe and America; psychology and brain science undervalued the effect of socioeconomic and cultural factors; and researchers mistakenly believe that findings from Western participants can be generalized to all human beings. Addressing the issue of sample representativeness in psychological and brain sciences requires a concerted effort by researchers, academic societies, journals, and funding agencies: Researchers should collect and report detailed demographic information about participants, state the limitations of generalizability, and use sampling methods that can increase representativeness whenever possible (e.g., probability sampling); academic societies should pay attention to the representativeness issues by organizing more academic symposium or workshops on this topic; journals should increase the representativeness of editorial board members and encourage more rigorous research with samples from underrepresented groups or studies that examine the generalizability of important findings; funding agencies can encourage researchers to pay more attention to study groups from underrepresented countries, and provide financial support for studying hard-to-research population. Improving sample representativeness will enhance the value of applying psychological and brain science knowledge in real-life settings and promote the building of a community with a shared future for mankind.

This dataset is the Supplementary Material for a review of uncertainty in petrel population estimates. It contains raw data from the literature review, source code for the full analysis, and additional text accompanying the manuscript.

Raw data were extracted from a literature review of petrel population estimates on islands. References were sourced from the Web of Science bibliographic index searched on 20 January 2020 using the search terms "burrowing seabird" OR "burrow-nesting seabird" OR "burrow-nesting petrel" OR "burrowing petrel" OR “scientific name” OR “common name” (taxonomy followed HBW and BirdLife International, 2018) for all species in the families Procellariidae, Hydrobatidae and Oceanitidae, AND “abundance” OR “population” in the title, abstract or keywords.

The data contain the original reference with metadata on year, journal, species studied, island studied, motivations for the study. We extracted published population estimates reported in each paper. Most represented a mean, but where only minima or maxima were reported we used this as the estimate, and where only minima and maxima were reported we used their average as the estimate. To allow comparison between studies we extracted basic dispersion statistics and manipulated them to approximate confidence intervals (see paper for methods).

The full dataset includes: 1. data.csv - the raw data from the literature review including information for 60 variables.

1. supplementary_code.rmd - full code for the analysis.

2. Supplementary material.docx - supporting text including methods, results and references.

BackgroundFatigue is one of the most common subjective symptoms that impairs daily life and predict health-related events. This study aimed to estimate the prevalence of fatigue in the global population.MethodsPubMed and the Cochrane Library were used to search for relevant articles from inception to December 31, 2021. Studies with prevalence data of fatigue in the general population were selected and reviewed by three authors independently and cross-checked. Regarding subgroups, adults (≥18 years), minors (