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

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

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

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)

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

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?

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

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

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

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"!)

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.

Context

After observing many naive conversations about COVID-19, claiming that the pandemic can be blamed on just a few factors, I decided to create a data set, to map a number of different data points to every U.S. state (including D.C. and Puerto Rico).

Content

This data set contains basic COVID-19 information about each state, such as total population, total COVID-19 cases, cases per capita, COVID-19 deaths and death rate, Mask mandate start, and end dates, mask mandate duration (in days), and vaccination rates.

However, when evaluating a pandemic (specifically a respiratory virus) it would be wise to also explore the population density of each state, which is also included. For those interested, I also included political party affiliation for each state ("D" for Democrat, "R" for Republican, and "I" for Puerto Rico). Vaccination rates are split into 1-dose and 2-dose rates.

Also included is data ranking the Well-Being Index and Social Determinantes of Health Index for each state (2019). There are also several other columns that "rank" states, such as ranking total cases per state (ascending), total cases per capita per state (ascending), population density rank (ascending), and 2-dose vaccine rate rank (ascending). There are also columns that compare deviation between columns: case count rank vs population density rank (negative numbers indicate that a state has more COVID-19 cases, despite being lower in population density, while positive numbers indicate the opposite), as well as per-capita case count vs density.

Acknowledgements

Several Statista Sources: * COVID-19 Cases in the US * Population Density of US States * COVID-19 Cases in the US per-capita * COVID-19 Vaccination Rates by State

Other sources I'd like to acknowledge: * Ballotpedia * DC Policy Center * Sharecare Well-Being Index * USA Facts * World Population Overview

Inspiration

I would like to see if any new insights could be made about this pandemic, where states failed, or if these case numbers are 100% expected for each state.

Context

This ranking was created by aggregating data from 14 websites and counting how many times each country was mentioned in the top 3, top 5, and top 10 places. There is no official measures or rankings for a countries education system.

Content

The 14 web sources are as follows: https://worldpopulationreview.com/country-rankings/education-rankings-by-country https://worldtop20.org/worldbesteducationsystem https://www.currentschoolnews.com/education-news/best-educational-system-in-the-world/ https://www.edsys.in/best-education-system-in-the-world/ https://www.indiaeducation.net/studyabroad/articles/countries-with-the-best-higher-education-system.html http://blog.mpanchang.com/10-best-education-systems-in-the-world/ https://admission.buddy4study.com/study-abroad/best-education-systems-in-world https://www.usnews.com/news/best-countries/best-education https://www.theedadvocate.org/the-edvocates-list-of-the-20-best-education-systems-in-the-world/ https://www.worldatlas.com/articles/10-countries-with-the-best-education-systems.html https://ceoworld.biz/2020/05/10/ranked-worlds-best-countries-for-education-system-2020/ https://www.independent.co.uk/news/education/11-best-school-systems-world-a7425391.html https://naijaquest.com/best-education-system-in-the-world/ https://mintbook.com/blog/best-educational-systems-in-the-world/

Acknowledgements

Created for BAD 52 - Human Relations in Organizations from the Santa Rosa Junior College in Fall 2020.

Greetings everyone! I hope you find this dataset valuable for your COVID-19 models. It is aligned with SRK's Novel Corona Virus dataset. Feel free to upvote if you use it!

This dataset contains what I find as essential demographic information for every country specified in the submission COVID-19 competition file. Moreover, there is additional data which is critical in my point of view in order to predict the infection rate and mortality rate per country such as the number of COVID detection tests, detection date of 'patient zero' and initial restrictions dates. Please look at the columns description for the comprehensive explanation.

Major Insights:

1. I've seen that there are some pretty clear distinctions between female and male mortality rate as men tend to develop more severe symptoms. Therefore, I added some variables which represent the sex ratio (amount of males per female) in each country, with separation by age groups & total. Moreover, I added lung disease data (death rate per 100k people) in each country with separation by sex as well.
2. The average amount of children per woman has a quite high p-value when trying to analyze the trend of the confirmed cases. Especially when it comes in interaction with 'density' and school restrictions.

Citations and Data Gathering

1. https://www.worldometers.info/ - Population, Density, Median Age, Urban Population, Fertility Rate, Patient Zero Detection Date, Confirmed Cases, New Cases, Total Deaths, Total Recovered, Critical Cases.
2. @benhamner 's link (see acknowledgements section below) - Restrictions Initial dates.
3. https://worldpopulationreview.com/countries/smoking-rates-by-country/ - % of smokers by country.
4. https://data.worldbank.org/indicator/SH.MED.BEDS.ZS - Hospital beds per 1000 citizens.
5. https://en.wikipedia.org/wiki/List_of_countries_by_sex_ratio - Sex ratio by age.
6. https://www.worldlifeexpectancy.com/cause-of-death/lung-disease/by-country/ - Lung diseases death rate.
7. https://en.wikipedia.org/wiki/COVID-19_testing - COVID-19 Tests
8. https://www.worldbank.org/ - GDP 2019, Health Expenses (Whatever was missing was filled with information from Wikipedia)
9. https://en.climate-data.org/ - Temperature and Humidity raw data.

Acknowledgements

1. Restrictions are taken from here. Thanks to Ben Hamner for sharing this link!
2. Special thanks to @diamondsnake for the idea of collecting the average temperature and humidity.

Good luck trying to learn more about the virus, feel free to comment and collaborate in order to collect more relevant data!

My

Context

This dataset include happiness index for the year 2021 according to UN. For more information, you can visit here

Content

It contains columns which are 1. happiness index(lower the value, better the happiness index) 2. happiness score(higher the value, better the happiness score. Ranges from 0-8) 3. Population of that particular country.

Inspiration

Take a deep analysis of the happiness index and check if population of the country affects the happiness score.

This dataset is a per-state amalgamation of demographic, public health and other relevant predictors for COVID-19.

Deaths, Infections and Tests by State

The COVID Tracking Project: https://covidtracking.com/data/api

Used positive, death and totalTestResults from the API for, respectively, Infected, Deaths and Tested in this dataset. Please read the documentation of the API for more context on those columns

Predictor Data and Sources

Population (2020)

Density is people per meter squared https://worldpopulationreview.com/states/

ICU Beds and Age 60+

https://khn.org/news/as-coronavirus-spreads-widely-millions-of-older-americans-live-in-counties-with-no-icu-beds/

GDP

https://worldpopulationreview.com/states/gdp-by-state/

Income per capita (2018)

https://worldpopulationreview.com/states/per-capita-income-by-state/

Gini

https://en.wikipedia.org/wiki/List_of_U.S._states_by_Gini_coefficient

Unemployment (2020)

Rates from Feb 2020 and are percentage of labor force
https://www.bls.gov/web/laus/laumstrk.htm

Sex (2017)

Ratio is Male / Female
https://www.kff.org/other/state-indicator/distribution-by-gender/

Smoking Percentage (2020)

https://worldpopulationreview.com/states/smoking-rates-by-state/

Influenza and Pneumonia Death Rate (2018)

Death rate per 100,000 people
https://www.cdc.gov/nchs/pressroom/sosmap/flu_pneumonia_mortality/flu_pneumonia.htm

Chronic Lower Respiratory Disease Death Rate (2018)

Death rate per 100,000 people
https://www.cdc.gov/nchs/pressroom/sosmap/lung_disease_mortality/lung_disease.htm

Active Physicians (2019)

https://www.kff.org/other/state-indicator/total-active-physicians/

Hospitals (2018)

https://www.kff.org/other/state-indicator/total-hospitals

Health spending per capita

Includes spending for all health care services and products by state of residence. Hospital spending is included and reflects the total net revenue. Costs such as insurance, administration, research, and construction expenses are not included.
https://www.kff.org/other/state-indicator/avg-annual-growth-per-capita/

Pollution (2019)

Pollution: Average exposure of the general public to particulate matter of 2.5 microns or less (PM2.5) measured in micrograms per cubic meter (3-year estimate)
https://www.americashealthrankings.org/explore/annual/measure/air/state/ALL

Medium and Large Airports

For each state, number of medium and large airports https://en.wikipedia.org/wiki/List_of_the_busiest_airports_in_the_United_States

Temperature (2019)

Note that FL was incorrect in the table, but is corrected in the Hottest States paragraph
https://worldpopulationreview.com/states/average-temperatures-by-state/
District of Columbia temperature computed as the average of Maryland and Virginia

Urbanization (2010)

Urbanization as a percentage of the population https://www.icip.iastate.edu/tables/population/urban-pct-states

Age Groups (2018)

https://www.kff.org/other/state-indicator/distribution-by-age/

School Closure Dates

Schools that haven't closed are marked NaN https://www.edweek.org/ew/section/multimedia/map-coronavirus-and-school-closures.html

Note that some datasets above did not contain data for District of Columbia, this missing data was found via Google searches manually entered.

Context

Dataset aims to facilitate a state by state comparison of potential risk factors that may heighten Covid 19 transmission rates or deaths. It includes state by state estimates of: covid 19 positives/deaths, flu/pneumonia deaths, major city population densities, available hospital resources, high risk health condition prevalance, population over 60, means of work transportation rates, housing characteristics (ie number of large apartment complexes/seniors living alone), and industry information.

Content

The Data Includes:

1) Covid 19 Outcome Stats:

Covid_Death : Covid Deaths by State

Covid_Positive : Covid Positive Tests by State

2) US Major City Population Density by State: CBSA_Major_City_max_weighted_density

3) KFF Estimates of Total Hospital Beds by State:

Kaiser_Total_Hospital_Beds

4) 2018 Season Flu and Pneumonia Death Stats:

FLUVIEW_TOTAL_PNEUMONIA_DEATHS_Season_2018

FLUVIEW_TOTAL_INFLUENZA_DEATHS_Season_2018

5)US Total Rates of Flu Hospitalization by Underlying Condition:

Fluview_US_FLU_Hospitalization_Rate_....

6) State by State BRFSS Prevalance Rates of Conditions Associated with Higher Flu Hospitalization Rates

BRFSS_Diabetes_Prevalance BRFSS_Asthma_Prevalance BRFSS_COPD_Prevalance
BRFSS_Obesity BMI Prevalance BRFSS_Other_Cancer_Prevalance BRFSS_Kidney_Disease_Prevalance BRFSS_Obesity BMI Prevalance BRFSS_2017_High_Cholestoral_Prevalance BRFSS_2017_High_Blood_Pressure_Prevalance Census_Population_Over_60

7)State by state breakdown of Means of Work Transpotation:

COMMUTE_Census_Worker_Public_Transportation_Rate

8) State by state breakdown of Housing Characteristics

9) State by State breakdown of Industry Information

Acknowledgements

Links to data sources:

https://worldpopulationreview.com/states/

https://covidtracking.com/data/

https://gis.cdc.gov/GRASP/Fluview/FluHospRates.html https://www.kff.org/health-costs/issue-brief/state-data-and-policy-actions-to-address-coronavirus/#stateleveldata

https://data.census.gov/cedsci/table?q=United%20States&tid=ACSDP1Y2018.DP05&hidePreview=true&vintage=2018&layer=VT_2018_040_00_PY_D1&cid=S0103_C01_001E

Census Tables: ACSST1Y2018.S1811 ACSST1Y2018.S0102 ACSST1Y2018.S2403 ACSST1Y2018.S2501 ACSST1Y2018.S2504

https://www.census.gov/library/visualizations/2012/dec/c2010sr-01-density.html

https://gis.cdc.gov/grasp/fluview/mortality.html

Inspiration

I hope to show the existence of correlations that warrant a deeper county by county analysis to identify areas of increased risk requiring increased resource allocation or increased attention to preventative measures.

Source -> https://worldpopulationreview.com/country-rankings/olympic-medals-by-country

Description of each column : Country -> Name of the Country Total Medal -> Total medals for the respective country (includng gold , silver and Bronze ) gold -> Number of gold Medals silver -> Number of silver medals Bronze -> Number of bronze medals Youth Total -> Number of youth medals

The dataset that I have created through web scraping using BeautifulSoup library in Python provides a comprehensive overview of the legality of firearms across various countries. It contains detailed information on the laws and regulations governing firearms possession, use, and ownership. The dataset also includes data on the number of deaths resulting from firearm incidents, including suicides, accidents, and police shootings. In addition, the dataset provides insights into the number of firearms owned by citizens, whether they are registered or unregistered. The information is compiled from reliable sources such as Wikipedia, Wisevoter, GunPolicy, and WorldPopulationReview, ensuring that the dataset is both comprehensive and accurate. This dataset is an invaluable resource for researchers, policymakers, and others who are interested in studying the prevalence and impact of firearms on society. With its comprehensive coverage of firearm laws and incidents across various countries, this dataset offers valuable insights into the complex issue of gun control and can be used to inform policy decisions aimed at reducing the negative impact of firearms on individuals and communities.

The dataset I have created can be used for various technical applications such as machine learning and data analytics. For example, researchers and developers can use this dataset to train machine learning algorithms to identify patterns and correlations between firearm laws and incidents. This can help in developing predictive models to forecast firearm-related incidents and aid in policymaking. Data analytics techniques can also be applied to the dataset to identify trends and patterns in the data, helping researchers to gain a better understanding of the complex issues surrounding firearms. Overall, the dataset I have created offers a wealth of information on firearms laws and incidents, and its potential applications extend beyond research to include policy and decision-making in various fields.

*******Links used:******* - Wikipedia - WiseVoter - GunPolicy - WorldPopulationReview