Source: Our World in Data Exported: 18/05/2022, 1:57 PM (MESZ)

https://ourworldindata.org/grapher/life-expectancy-vs-gdp-per-capita

In 2025, Luxembourg was the country with the highest gross domestic product per capita in the world. Of the 20 listed countries, 13 are in Europe and five are in Asia, alongside the U.S. and Australia. There are no African or Latin American countries among the top 20. Correlation with high living standards While GDP is a useful indicator for measuring the size or strength of an economy, GDP per capita is much more reflective of living standards. For example, when compared to life expectancy or indices such as the Human Development Index or the World Happiness Report, there is a strong overlap - 14 of the 20 countries on this list are also ranked among the 20 happiest countries in 2024, and all 20 have "very high" HDIs. Misleading metrics? GDP per capita figures, however, can be misleading, and to paint a fuller picture of a country's living standards then one must look at multiple metrics. GDP per capita figures can be skewed by inequalities in wealth distribution, and in countries such as those in the Middle East, a relatively large share of the population lives in poverty while a smaller number live affluent lifestyles.

Context

Life expectancy at birth is defined as the average number of years that a newborn could expect to live if he or she were to pass through life subject to the age-specific mortality rates of a given period. The years are from 1950 to 2018.

Content

For regional- and global-level data pre-1950, data from a study by Riley was used, which draws from over 700 sources to estimate life expectancy at birth from 1800 to 2001.

Riley estimated life expectancy before 1800, which he calls "the pre-health transition period". "Health transitions began in different countries in different periods, as early as the 1770s in Denmark and as late as the 1970s in some countries of sub-Saharan Africa". As such, for the sake of consistency, we have assigned the period before the health transition to the year 1770. "The life expectancy values employed are averages of estimates for the period before the beginning of the transitions for countries within that region. ... This period has presumably the weakest basis, the largest margin of error, and the simplest method of deriving an estimate."

For country-level data pre-1950, Clio Infra's dataset was used, compiled by Zijdeman and Ribeira da Silva (2015).

For country-, regional- and global-level data post-1950, data published by the United Nations Population Division was used, since they are updated every year. This is possible because Riley writes that "for 1950-2001, I have drawn life expectancy estimates chiefly from various sources provided by the United Nations, the World Bank’s World Development Indicators, and the Human Mortality Database".

For the Americas from 1950-2015, the population-weighted average of Northern America and Latin America and the Caribbean was taken, using UN Population Division estimates of population size.

Acknowledgements

Life expectancy:

Data publisher's source: https://www.lifetable.de/RileyBib.pdf Data published by: James C. Riley (2005) – Estimates of Regional and Global Life Expectancy, 1800–2001. Issue Population and Development Review. Population and Development Review. Volume 31, Issue 3, pages 537–543, September 2005., Zijdeman, Richard; Ribeira da Silva, Filipa, 2015, "Life Expectancy at Birth (Total)", http://hdl.handle.net/10622/LKYT53, IISH Dataverse, V1, and UN Population Division (2019) Link: https://datasets.socialhistory.org/dataset.xhtml?persistentId=hdl:10622/LKYT53, http://onlinelibrary.wiley.com/doi/10.1111/j.1728-4457.2005.00083.x/epdf, https://population.un.org/wpp/Download/Standard/Population/ Dataset: https://ourworldindata.org/life-expectancy

GDP per capita:

Data publisher's source: The Maddison Project Database is based on the work of many researchers that have produced estimates of economic growth for individual countries. Data published by: Bolt, Jutta and Jan Luiten van Zanden (2020), “Maddison style estimates of the evolution of the world economy. A new 2020 update”. Link: https://www.rug.nl/ggdc/historicaldevelopment/maddison/releases/maddison-project-database-2020 Dataset: https://ourworldindata.org/life-expectancy

Inspiration

The life expectancy vs GDP per capita analysis.

This dataset supports a reproducible, exploratory analysis of global economic growth and environmental impact. It includes harmonized GDP, CO₂ emissions, and population data across 180+ countries from 1960–2022, cleaned and merged to enable structured analysis of climate equity, development trends, and sustainability policy.

Key features:

• Country-level annual and cumulative CO₂ emissions (Our World in Data)
• GDP, GDP per capita, and percent growth (World Bank)
• CO₂ per capita and CO₂ intensity metrics
• Country classifications by region and income group
• Emissions and economic bands (e.g. high emitters, low income)

Transformations include ISO harmonization, missing value handling, log transformations, and custom classification labels. The final dataset is designed to support policy research, machine learning experiments, or exploratory visualization.

Inspiration

Can economic growth coexist with sustainability? This dataset was born from curiosity about that question. It reflects a methodical journey through public data to shed light on global emissions, income inequality, and the pathways different countries take in balancing development with environmental impact.

Usability Notes

Final file: gdp_co2_by_country_final.csv includes all merged and processed variables.

Variables are clearly labeled; numeric and transformed fields are included.

Suitable for time-series analysis, forecasting, or visual storytelling.

Acknowledgments

Data sourced from:

• Our World in Data
• World Bank Open Data
• Custom metadata files for region/income classification

Project repository: GitHub - GDP-sustainability

Image credit: Marc Manhart @ Pixabay

Energy Consumption and Mix Dataset by Our World in Data

Dataset Description

This dataset is a comprehensive collection of key metrics related to energy consumption and energy mix, maintained by Our World in Data. It includes global, regional, and country-level data on primary energy consumption, energy mix, electricity mix, fossil fuel production, and related energy metrics.

Key Metrics

The dataset contains several important metrics related to global energy:

• Energy Consumption (primary energy, per capita consumption, growth rates)
• Energy Mix (share of renewables, fossil fuels, etc.)
• Electricity Mix (sources of electricity generation such as coal, hydro, wind, solar)
• Fossil Fuel Production
• Primary Energy Consumption
• Per Capita and Per GDP Energy Indicators
• Yearly data by country and global aggregates

Possible Analyses

The "Energy Consumption and Mix" dataset offers a wide range of opportunities for analysis. Here are some examples of what can be done with this dataset:

1. Global Energy Consumption Trends

• Objective: Examine how global primary energy consumption has evolved over time.
• Approach:
  • Plot global energy consumption year by year, broken down by energy source (e.g., coal, oil, natural gas, renewables).
  • Analyze growth rates in total energy consumption.
  • Investigate how the share of renewables has changed in the global energy mix.
• Potential Insights: This analysis can provide insight into which energy sources are becoming more dominant globally and how the energy landscape has shifted.

2. Energy Mix by Country

• Objective: Compare the energy mix of different countries.
• Approach:
  • For each country, visualize the breakdown of energy sources (e.g., renewables, fossil fuels) over time.
  • Analyze which countries have transitioned towards cleaner energy sources.
  • Compare the energy mix of developed vs. developing countries.
• Potential Insights: This could show which countries are leading in the transition to renewable energy and which are still reliant on fossil fuels.

3. Electricity Generation Sources by Region

• Objective: Explore regional differences in electricity generation.
• Approach:
  • Compare regions based on the percentage of electricity generated from different sources (e.g., coal, hydro, wind, solar).
  • Analyze trends in renewable electricity generation by region.
• Potential Insights: This analysis can identify regions that have made the most progress in transitioning to cleaner electricity sources.

4. Energy Consumption Per Capita

• Objective: Investigate energy consumption on a per capita basis.
• Approach:
  • Calculate the per capita energy consumption for different countries and regions.
  • Compare energy consumption per capita over time.
  • Identify which countries have the highest and lowest per capita energy consumption.
• Potential Insights: This can help uncover disparities in energy access and usage between different countries or regions.

5. Energy Consumption and Economic Growth

• Objective: Analyze the relationship between energy consumption and GDP.
• Approach:
  • Plot energy consumption per capita vs. GDP per capita for different countries and regions.
  • Look for correlations between energy consumption growth and economic growth.
  • Explore how energy consumption patterns differ between high-income and low-income countries.
• Potential Insights: This analysis can highlight the role of energy in driving economic development and the efficiency of energy usage across income levels.

6. Carbon Emissions from Energy Sources

• Objective: Assess the impact of different energy sources on carbon emissions.
• Approach:
  • Calculate emissions based on the share of fossil fuels in the energy mix.
  • Analyze trends in carbon emissions as countries transition to cleaner energy sources.
  • Compare countries or regions with high fossil fuel dependency to those with higher renewable energy shares.
• Potential Insights: This analysis could highlight the environmental impact of different energy sources and track progress towards emissions reductions.

Citation

Hannah Ritchie, Pablo Rosado and Max Roser (2023) - “Energy” Published online at OurWorldinData.org. Retrieved from: https://ourworldindata.org/energy [Online Resource]

Dataset built merging different datasets. The goal of the dataset is to be useful for data analysis.

Columns explanation: - Country: 119 countries names. The list is available here. - Year: from 2000 to 2015 (both included). Longitudinal format. - Continent: names of the different continents (6 continets: Europe, Asia, Africa, North America, South America, Oceania). Data taken from this link. - Least Developed: if the value is TRUE, the country is classified as "Least Developed", if it is FALSE, the country isn't classified as "Least Developed". Data of Least Developed countries taken from this link. - Life Expectancy: data taken from this link. - Population: data taken from this link. - CO2 emissions: data taken from this link. - Health expenditure: data taken from this link. - Electric power consumption: data taken from this link. - Forest area: data taken from this link. - GDP per capita: data taken from this link. - Individuals using the Internet: data taken from this link. - Military expenditure: data taken from this link. - People practicing open defecation: data taken from this link. - People using at least basic drinking water services: data taken from this link. - Obesity among adults: data taken from this link. - Beer consumption per capita: data taken from this link.

Dataset Description This dataset contains annual time series data for India from 2002 to 2022, compiled to study the relationship between economic growth, environmental quality, and the development of the renewable energy (RE) sector in capital markets.

Variables Included The dataset includes the following variables: CO₂ Emissions (tons per capita) Gross Domestic Product (GDP) – measured in Real GDP per capita (in constant 2015 USD) GDP Squared (GDP²) – to capture potential non-linear effects Traded Value of Renewable Energy Equities – annual traded value of RE firms' equities, calculated as the product of the traded share quantities and their respective market prices. Then, those values are normalized by dividing with constant real GDP of India of that year.
Greenhouse Gas (GHG) Emissions – included as an alternative measure of environmental quality

Data Sources Data on CO₂ emissions is sourced from the International Energy Agency (IEA, 2025), whereas GHG emissions data is obtained from the website of Our World in Data. GDP and GDP²: Obtained from the World Development Indicators (WDI) database Traded Value of RE Equities: Collected from Centre for Monitoring Indian Economy (CMIE) Prowess IQ database for renewable energy companies listed at NSE India Ltd., in India. Values were aggregated annually and deflated to constant prices.

How to Interpret the Data All variables are organized as annual time series. Log transformation is applied to continuous variables (e.g., CO₂, GDP, RE traded value) to ensure scale consistency and facilitate regression analysis. GDP² is computed from the log-transformed GDP to allow analysis of potential non-linear relationships with emissions. Each row represents a single year, and each column corresponds to one of the variables listed above.

This dataset can be used for: Time series econometric modeling Policy analysis on green finance, emissions, and sustainable growth Replication studies or extended analyses in environmental economics or financial development research

It is provided in Excel format and ready for use in statistical software such as R, Stata, EViews, or Python.

Description

This is a cross-sectional dataset for anemia prevalence, median income and GDP PPP per capita (per thousand) in 2019.

It is cleaned and joined from three different sources.

• Anemia prevalence is the percentage of the estimated population that suffers from anemia.
• Daily median income is the estimated median income earned daily, measured in international $.
• GDP PPP per capita (per thousand) is the gross domestic production of the country/region, adjusted for purchasing power, measured in international $.

An international dollar is defined by the World Bank as a dollar that "would buy in the cited country a comparable amount of goods and services a U.S. dollar would buy in the United States."

Data is joined based on common columns among the three. Null values have been dropped. Anemia: 192 observations Median Income: 174 GDP: 266

Discussion

I have used this dataset to analyze the correlations among the three, and how much median income and GDP could predict anemia prevalence using a linear regression model. Please note that causation cannot be proven solely by this.

Acknowledgement

The original data was very dirty. Some effort was required to clean it. Although it is based on public data, you would be deeply appreciated for giving attribute to me, Takemi Kuroki.

Sources

• Anemia prevalence data (ane): World Health Organization (WHO)
• Daily median income data (medinc): Our World in Data
• GDP PPP per capita data (gdp): World Bank

标题：全球GDP人均数据分析报告 数据内容： 该数据集包含了全球多个国家和地区的GDP人均数据，涵盖了2019年的数据，并结合了世界银行和Pennworld Table的统计结果。数据经过通货膨胀和生活成本差异的调整，确保了数据的可比性和准确性。数据内容包括以下字段： - Entity：国家或地区名称。 - Code：国家或地区的编码。 - Year：年份。 - GDP per capita, PPP (constant 2017 international $)：以购买力平价（PPP）衡量的GDP人均值，以2017年国际美元为基准。 - GDP per capita (output, multiple price benchmarks)：基于多种价格基准的GDP人均值。 - Population (historical)：历史人口数据。 - World regions according to OWID：根据OWID（Our World in Data）划分的全球区域分类。 数据来源： 互联网公开数据 数据用途： 该数据集适用于多个行业的分析和研究，具体包括： 1. 经济学研究：用于分析全球GDP人均水平的差异及其驱动因素。 2. 政策制定：帮助政府和国际组织制定经济政策和发展规划。 3. 学术研究：为学者和研究人员提供可靠的数据支持，用于论文撰写和学术探讨。 4. 商业分析：帮助企业评估不同地区的经济潜力，支持投资决策。 统计信息分析： - Entity：共有303种不同的国家或地区名称，覆盖范围广泛。 - Code：包含261种不同的编码，可能对应不同的国家或地区标识符。 - Year：时间跨度较大，共有265种不同的年份值。 - GDP per capita, PPP (constant 2017 international $)：共有6563种不同的数值，反映了不同国家或地区在PPP调整下的GDP人均水平。 - GDP per capita (output, multiple price benchmarks)：包含10109种不同的数值，表明在不同价格基准下，GDP人均值的差异较大。 - Population (historical)：历史人口数据覆盖范围广，共有54150种不同的值。 - World regions according to OWID：共有7种不同的全球区域分类，表明数据覆盖了主要的地理区域。 标签：GDP人均, 经济分析, 全球数据, 地区分类, 统计信息, 人口数据, 经济趋势, 投资决策, 政策制定, 数据科学 行业分类： - 经济学 - 公共政策 - 商业分析 - 数据科学 - 国际发展

Context

Total general government expenditure on education (all levels of government and all levels of education), given as a share of GDP. The last two decades have seen a small but general increase in the share of income that countries devote to education.

Content

Although the data is highly irregular due to missing observations for many countries, we can still observe a broad upward trend for the majority of countries. Specifically, it can be checked that of the 88 countries with available data for 2000/2010, three-fourths increased education spending as a share of GDP within this decade. As incomes – measured by GDP per capita – are generally increasing around the world, this means that the total amount of global resources spent on education is also increasing in absolute terms.

The reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).

Acknowledgements

Data publisher's source: UNESCO Institute for Statistics.
Published by: World Development Indicators - World Bank (2021.07.30). Link: http://data.worldbank.org/data-catalog/world-development-indicators Dataset: https://ourworldindata.org/global-rise-of-education

Inspiration

Which countries have the biggest expenditures when it comes to education?

Total alcohol per capita consumption is defined as the total (sum of recorded and unrecorded alcohol) amount of alcohol consumed per person (15 years of age or older) over a calendar year, in litres of pure alcohol, adjusted for tourist consumption.

Statistical concept and methodology: The estimates for the total alcohol consumption are produced by summing up the 3-year average per capita (15+) recorded alcohol consumption and an estimate of per capita (15+) unrecorded alcohol consumption for a calendar year. Tourist consumption takes into account tourists visiting the country and inhabitants visiting other countries.

Variable time span 2000 – 2018

Link: https://ourworldindata.org/alcohol-consumption

🌍 Global Happiness, Wellbeing & Development Indicators (2002–2024)

Integrated OWID datasets on happiness, GDP, governance, corruption, freedom, HDI, and life expectancy.

⭐ Overview

This dataset is a curated integration of multiple global indicators related to happiness, wellbeing, governance, corruption, freedom, gender rights, economic prosperity, and demographic development. All files are sourced from Our World in Data (OWID) and represent harmonized, research-grade metrics used by economists, policy analysts, academics, and data professionals around the world.

The goal of this dataset is to provide a single, easy-to-use resource for exploring the key factors that influence national wellbeing and quality of life across more than 170 countries from 2002 to 2024.

⭐ What’s Included

This dataset brings together multiple dimensions of global wellbeing:

- Happiness & Life Satisfaction - Cantril Ladder (0–10 life evaluation) - Share of people reporting happiness - Time-series emotional wellbeing indicators

- Economic Indicators - GDP per capita (PPP, constant 2021$) - Historical population estimates

- Governance & Institutional Quality - Corruption Perception Index (CPI) - Freedom House civil & political liberties - Women’s civil rights index

- Human Development & Health - Augmented Human Development Index (AHDI) - Life expectancy at birth

Each file has been kept exactly as published by OWID, with no modification of values, ensuring full transparency and reproducibility.

⭐ Why This Dataset Matters

Understanding what drives national wellbeing is a central question in modern economics, social science, and development policy. This dataset enables powerful analysis such as:

• Which countries achieve high happiness at lower income levels?
• Does economic growth lead to improvements in wellbeing?
• How do corruption, freedom, and gender rights influence happiness?
• Which regions show consistent improvement in human development?
• How do life expectancy and governance interact with wellbeing trends?

This collection is ideal for:

• Exploratory Data Analysis (EDA)
• Cross-country comparisons
• Regression modelling & causal inference
• Time-series and panel-data analytics
• Dashboard development
• Machine learning experiments
• Academic and applied research

⭐ Data Source

All datasets are published by Our World in Data (OWID), using original data from:

• Gallup World Poll
• World Bank
• Transparency International
• UN Population Division
• V-Dem Institute
• Freedom House
• International survey archives

Licensed under Creative Commons BY 4.0.

⭐ Temporal & Geospatial Coverage

• Years: 2002–2024
• Countries: 170+
• Regions: All OWID regions (Africa, Asia, Americas, Europe, Oceania)

⭐ Intended Audience

• Data Analysts
• Data Scientists
• Policy Researchers
• Social Scientists
• Students & educators
• Kaggle learners
• Anyone interested in global wellbeing

⭐ Summary

This dataset provides a comprehensive, ready-to-use foundation for studying global happiness, development, and governance. Whether you’re building visualizations, statistical models, dashboards, or research papers, this single dataset gives you everything you need to explore what shapes human wellbeing around the world.

标题：全球国民收入与国内生产总值数据集 数据内容： 该数据集包含全球多个国家和地区在不同年份的国民收入（GNI）和国内生产总值（GDP）相关数据。具体数据元素包括： 1. Entity：国家或地区的名称。 2. Code：国家或地区的编码。 3. Year：数据对应的年份。 4. GNI per capita, PPP (constant 2021 international $)：按购买力平价计算的人均国民收入（GNI），单位为2021年国际美元。 5. GDP per capita, PPP (constant 2021 international $)：按购买力平价计算的人均国内生产总值（GDP），单位为2021年国际美元。 6. Population：人口数量。 7. World regions according to OWID：根据OWID（Our World in Data）划分的全球区域分类。 数据来源： 互联网公开数据 数据用途： 该数据集可用于多个行业的研究和分析，例如： 1. 经济学：研究国家或地区的经济发展水平、收入分配、购买力平价等。 2. 公共政策：评估政策对国民收入和GDP的影响，支持政策制定和优化。 3. 金融与投资：分析国家或地区的经济潜力，支持跨国投资决策。 4. 社会学：研究人口与经济发展的关系，评估社会福利和生活质量。 5. 宏观经济研究：比较不同国家或地区的经济表现，分析全球经济发展趋势。 标签：国民收入, 国内生产总值, 购买力平价, 人口统计, 数据分析, 经济研究, 全球经济, 统计数据, 宏观经济, 世界银行数据 行业分类： 1. 经济学：研究国民收入与GDP的关系，分析经济发展趋势。 2. 公共政策：评估政策对经济的影响，支持政策优化。 3. 金融与投资：分析经济潜力，支持投资决策。 4. 社会学：研究人口与经济发展的关系，评估社会福利。 5. 宏观经济：比较不同国家或地区的经济表现，分析全球经济趋势。

标题：全球蛋白质供应与经济发展的关联分析 数据内容： 该数据集包含了全球范围内不同国家或地区的动物食品蛋白质供应与人均GDP的关联数据。数据内容包括： - 国家或地区（Entity）：标识数据来源的国家或地区。 - 编码（Code）：国家或地区的唯一标识符。 - 年份（Year）：数据采集的年份。 - 蛋白质供应量（Animal Products | 00002941 || Food available for consumption | 0674pc || grams of protein per day per capita）：以克/天/人为单位测量的动物食品蛋白质供应量。 - 人均GDP（GDP per capita, PPP (constant 2021 international $)）：根据购买力平价（PPP）调整后的人均GDP数据。 - 地区分类（World regions according to OWID）：根据Our World in Data（OWID）标准划分的全球地区分类。 数据来源： 互联网公开数据 数据用途： 该数据集可用于研究蛋白质供应与经济发展的关联性，适用于以下行业的分析： - 农业与食品工业：研究蛋白质消费与农业生产的关系，评估食品供应链的效率。 - 公共卫生与营养学：分析蛋白质摄入量与居民健康状况的关联，支持营养政策制定。 - 经济发展与政策研究：研究人均GDP与蛋白质消费的相互作用，评估经济发展对居民生活水平的影响。 - 环境与可持续发展：分析蛋白质消费与资源利用的平衡，支持可持续发展目标的制定。 标签：蛋白质供应, 人均GDP, 动物食品, 经济发展, 全球数据, 营养健康, 购买力平价, 统计分析, 地区分类, 可持续发展， 行业分类： - 农业 - 食品工业 - 公共卫生 - 经济发展 - 环境保护 - 政策研究 统计信息分析： - 数据集涵盖了325种不同的国家或地区，覆盖范围广泛。 - 时间跨度为63个年份，数据具有一定的历史连续性。 - 蛋白质供应量的统计值为12156种不同值，表明蛋白质消费存在显著的地域差异。 - 人均GDP的统计值为7062种不同值，反映了不同国家或地区之间的经济差异。 - 地区分类分为7种，便于从区域角度分析数据。 - 数据集的丰富性和多样性使其适用于多维度的分析和研究。

标题：人均煤炭能源消耗与人均GDP数据分析报告 数据内容： 该数据集记录了全球多个国家和地区在不同年份的人均煤炭能源消耗量（以千瓦时为单位）以及人均国内生产总值（GDP）数据。数据集包含以下字段： 1. 实体（Entity）：表示国家或地区名称。 2. 代码（Code）：表示国家或地区的编码。 3. 年份（Year）：表示数据记录的年份。 4. 人均煤炭消耗量（Coal per capita (kWh)）：表示人均每年消耗的煤炭能源量。 5. 人均GDP（GDP per capita）：表示人均国内生产总值，通常用于衡量经济发展水平。 6. 世界地区分类（World regions according to OWID）：表示根据Our World in Data（OWID）划分的地理区域。 数据来源： 互联网公开数据 数据用途： 该数据集可以用于以下行业的研究和分析： 1. 能源行业：研究煤炭消耗与经济发展的关系，评估能源使用效率。 2. 经济研究：分析人均GDP与能源消耗之间的关联，探讨经济增长与能源需求的关系。 3. 环境科学：研究煤炭消耗对碳排放和环境的影响，支持可持续发展政策的制定。 4. 政策制定：为各国政府提供数据支持，帮助制定能源转型和经济发展的政策。 行业分类： 1. 能源行业 2. 经济研究 3. 环境科学 4. 政策制定 标签：人均煤炭消耗，人均GDP，能源与经济，数据分析，可持续发展，碳排放，能源政策，经济增长，地区分类，能源转型

标题：全球贫困与经济发展的关联分析 数据内容： 该数据集包含以下数据元素： 1. 国家或地区（Entity）：记录数据所涉及的国家或地区名称。 2. 国家代码（Code）：用于唯一标识国家或地区的编码。 3. 年份（Year）：记录数据的时间维度，涵盖多个年份。 4. 最贫穷的10％收入或消费门槛（Poorest decile - Threshold）：反映一个国家或地区中最贫穷的10％人口的收入或消费水平。 5. GDP平价购买力（PPP）（GDP per capita, PPP (constant 2017 international $))：以PPP调整后的人均GDP数据，用于跨国家比较经济水平。 6. 人口数量（Population）：记录国家或地区的总人口数。 7. 世界地区分类（World regions according to OWID）：根据OWID（Our World in Data）的标准，对国家或地区进行地理区域分类。 数据来源： 该数据集来源于互联网公开数据。 数据用途： 该数据集可用于以下行业的研究和分析： 1. 经济学：研究贫困与经济发展的关系，分析贫困水平与人均GDP的关联。 2. 社会学：探讨社会不平等现象，评估政策对贫困的影响。 3. 公共政策：为政府制定减贫政策和经济规划提供数据支持。 4. 发展援助：帮助国际组织和非政府组织识别需要援助的国家或地区。 5. 数据分析与研究：用于学术研究，分析全球贫困与经济发展的趋势和模式。 标签：贫困分析, 人均GDP, 经济发展, 全球数据, 经济政策, 社会不平等, 国际援助, 统计分析, 数据研究, 经济指标 行业分类： 1. 经济学 2. 社会学 3. 公共政策 4. 发展援助 5. 数据分析与研究

The dataset contains general information about world countries as well as information about their flags, economy, and geographical location.

File description:

world_flags_2024.csv - dataset data_description.txt - full description of each column.

Source:

The dataset contains 41 columns: 8 of them are numeric-valued, others are either Boolean or nominal-valued. In the CSV file fields are separated by commas.

Note: Possible errors or inaccuracies in the interpretation of blazon images or other symbols on flags are not intentional, but arise from a lack of awareness on the part of the author.

Data fields:

Country - Names of all sovereign states as of 2024.

FlagUrl - Link to country's flag on Flagpedia.net.

AspectRatio - Aspect ration of the flag. Format: Height:Width.

LatestAdoption - Year of the last changes in the flag design.

White - 1 if white color present in the flag, 0 otherwise.

Red - 1 if red color present in the flag, 0 otherwise.

Blue - 1 if blue color present in the flag, 0 otherwise.

Black - 1 if black color present in the flag, 0 otherwise.

Yellow - 1 if yellow color present in the flag, 0 otherwise.

Green - 1 if green color present in the flag, 0 otherwise.

Orange - 1 if orange color present in the flag, 0 otherwise.

OtherColor - 1 if any other color present in the flag, 0 otherwise.

StripesEqual - 1 if all the stripes that make up the flag have equal width, 0 otherwise.

StripesVertical - 1 if stripes are arranged vertically, 0 otherwise.

StripesHorizontal - 1 if stripes are arranged horizontally, 0 otherwise.

StripesDiagonal - 1 if stripes are arranged diagonally, 0 otherwise.

StripesOther - 1 if the direction of stripes is mixed, 0 otherwise.

SingleColor - 1 if the flag is single color, i.e. there is no stripes, 0 otherwise.

LeftTriangle - 1 if there is a triangle on the left hand side of the flag, 0 otherwise.

Canton - 1 if there is an insert with an image in the top-left corner of the flag, 0 otherwise.

Cross - 1 if the flag contains a cross, 0 otherwise.

Crescent - 1 if the flag contains a crescent, 0 otherwise.

Sun - 1 if the flag contains the sun, 0 otherwise.

Bird - 1 if the flag contains a bird, 0 otherwise.

Stars - Number of stars on the flag.

Circle - 1 if the flag contains a circle, 0 otherwise.

BlazonOrOther - 1 if the flag contains a blazon or any other symbol, 0 otherwise.

Continent - Continent where the country is located. Note: Some countries have their parts located on multiple continents. For those countries the continent where the majority of its territory is located is chosen. Example: Russian Federation and Turkey.

Landlocked - 1 if the country has no direct access to an ocean, 0 otherwise.

TotalArea - Area of the country in km^2.

Population - Population of the country as of 2024.

Capital - Name of the capital of the country.

CapitalPopulation - Population of the capital.

HighestPoint - The highest point of the country.

LowestPoint - The lowest point of the country.

Religion - Dominant religion. If multiple, the most popular is chosen.

Currency - Name of the currency of the country.

CallingCode - Calling code of the country.

GDPPerCapita - GDP per capita in USD as of 2022. Zero if unknown.

HDI - Human Development Index as of 2022.

Gini - Income inequality: Gini coefficient as of 2023.

References:

1. https://www.un.org/dgacm/sites/www.un.org.dgacm/files/Documents_Protocol/officialnamesofcountries.pdf
2. https://flagpedia.net
3. https://www.worldometers.info
4. https://www.britannica.com
5. https://ourworldindata.org
6. https://www.jetpunk.com/info/capital-cities-by-population
7. https://wisevoter.com/country-rankings/religion-by-country
8. https://assets.pewresearch.org/wp-content/uploads/sites/11/2012/12/globalReligion-tables.pdf
9. Header image generated by leonardo.ai

Inspired by:

https://www.kaggle.com/datasets/edoardoba/world-flags https://www.kaggle.com/code/mscgeorges/country-flags-analysis

标题：全球灾害影响与经济指标分析数据集 数据内容： 该数据集包含了2021年全球范围内受灾难影响的人口数据和人均GDP数据。数据内容包括以下字段： 1. Entity：表示国家或地区，共有292种不同值。 2. Code：表示国家或地区的编码，共有259种不同值。 3. Year：表示年份，共有125种不同值。 4. Total affected per 100,000 people - All disasters excluding extreme temperature：表示每10万人中因灾害（不包括极端温度）影响的总人数，共有5858种不同值。 5. GDP per capita, PPP (constant 2021 international $)：表示按购买力平价（PPP）计算的人均GDP，单位为2021年国际元，共有7062种不同值。 6. World regions according to OWID：表示根据OWID（Our World in Data）划分的全球区域，共有7种不同值。 数据来源： 互联网公开数据 数据用途： 该数据集可用于多个行业的研究与分析： - 应急管理：用于评估灾害对人口的影响，制定灾害响应和救援计划。 - 经济学研究：分析灾害对经济（如人均GDP）的影响，研究灾害与经济发展的关系。 - 保险行业：评估灾害风险，制定保险政策。 - 城市规划：优化灾害易发地区的基础设施和城市规划。 - 环境保护：研究灾害频率与经济发展之间的关系，制定减灾和环境保护策略。 标签：灾害影响, 人均GDP, 全球数据, 经济指标, 区域分析, 应急管理, 灾害风险, 购买力平价， 行业分类： - 应急管理 - 经济学研究 - 保险行业 - 城市规划 - 环境保护

标题：航空旅行与经济发展的关联分析数据集基于2019年全球数据 数据内容： 该数据集包含了全球多个国家和地区在2019年的航空旅行数量与人均国内生产总值（GDP）的相关数据。具体包括以下数据元素： 1. 实体（Entity）：代表国家或地区的名称。 2. 代码（Code）：国家或地区的标准代码。 3. 年份（Year）：数据记录的年份，主要是2019年。 4. 人均航空旅行次数（Air travel trips per capita）：每年人均乘坐飞机的次数。 5. 人均GDP（PPP）（GDP per capita, PPP (constant 2021 international $)）：按购买力平价计算的人均国内生产总值，单位为2021年国际美元。 6. 人口（Population）：国家或地区的历史人口数据。 7. 世界地区分类（World regions according to OWID）：根据OWID（Our World in Data）定义的世界地区分类。 数据来源： 互联网公开数据 数据用途： 该数据集可以用于分析航空旅行与经济发展之间的关系，适用于以下行业和应用场景： 1. 经济学：研究人均GDP与航空旅行需求之间的相关性。 2. 旅行与旅游业：分析航空旅行市场潜力，制定市场扩展策略。 3. 政策制定：为政府和相关机构提供数据支持，优化航空运输政策。 4. 市场营销：用于航空公司的市场细分和目标客户定位。 标签：航空旅行, 人均GDP, PPP, 国家分类, 人口统计, 经济分析, 旅行需求, 数据关联, 世界地区分类, 2019年数据， 行业分类： 1. 经济学与金融 2. 旅游业与酒店业 3. 政策与公共管理 4. 市场研究与分析 5. 运输与物流 统计分析： 该数据集涵盖了全球306个不同的实体，每个实体都有对应的标准代码。数据记录的时间跨度覆盖了265个不同的年份，其中2019年是主要关注年份。人均航空旅行次数有185种不同的值，表明不同国家和地区在航空旅行需求上的显著差异。人均GDP（PPP）的值范围非常广，共有7062种不同的值，这反映了不同国家和地区在经济水平上的巨大差异。人口数据涵盖了54150种不同的值，进一步说明了数据集的广泛覆盖范围。世界地区分类共有7种不同的值，表明数据集根据OWID的标准将全球划分为7个主要地区。这些统计信息为研究航空旅行与经济发展的关系提供了坚实的基础。

Data published by Our World in Data based on EM-DAT, CRED / UCLouvain, Brussels, Belgium – www.emdat.be (D. Guha-Sapir)

Variable time span 1900 – 2010

This dataset has been calculated and compiled by Our World in Data based on raw disaster data published by EM-DAT, CRED / UCLouvain, Brussels, Belgium – www.emdat.be (D. Guha-Sapir). EM-DAT publishes comprehensive, global data on each individual disaster event – estimating the number of deaths; people affected; and economic damages, from UN reports; government records; expert opinion; and additional sources. Our World in Data has calculated annual aggregates, and decadal averages, for each country based on this raw event-by-event dataset. Decadal figures are measured as the annual average over the subsequent ten-year period. This means figures for ‘1900’ represent the average from 1900 to 1909; ‘1910’ is the average from 1910 to 1919 etc. We have calculated per capita rates using population figures from Gapminder (gapminder.org) and the UN World Population Prospects (https://population.un.org/wpp/). Economic damages data is provided by EM-DAT in concurrent US$. We have calculated this as a share of gross domestic product (GDP) using the World Bank’s GDP figures (also in current US$) (https://data.worldbank.org/indicator). Definitions of specific metrics are as follows: – ‘All disasters’ includes all geophysical, meteorological, and climate events including earthquakes, volcanic activity, landslides, drought, wildfires, storms, and flooding. – People affected are those requiring immediate assistance during an emergency situation. – The total number of people affected is the sum of injured, affected, and homeless.Link www.emdat.be