🧾 About Dataset

🌍 Global GDP by Country — 2024 Edition

📖 Overview

The Global GDP by Country (2024) dataset provides an up-to-date snapshot of worldwide economic performance, summarizing each country’s nominal GDP, growth rate, population, and global economic contribution.

This dataset is ideal for economic analysis, data visualization, policy modeling, and machine learning applications related to global development and financial forecasting.

📊 Dataset Information

• Total Records: 181 countries
• Time Period: 2024 (latest available global data)
• Geographic Coverage: Worldwide
• File Format: CSV
• File Size: ~10 KB
• Missing Values: None (100% complete dataset)

🎯 Target Use-Cases:
- Economic growth trend analysis
- GDP-based country clustering
- Per capita wealth comparison
- Share of world economy visualization

🧩 Key Features

📈 Statistical Summary

Population Overview

• Mean Population: 43.6 million
• Standard Deviation: 155.5 million
• Minimum Population: 9,816 (small island nations)
• Median Population: 9.1 million
• Maximum Population: 1.43 billion (China)

🌟 Highlights

💰 Top Economies (Nominal GDP):
United States, China, Japan, Germany, India

📈 Fastest Growing Economies:
India, Bangladesh, Vietnam, and Rwanda

🌐 Global Insights:
- The dataset covers 181 countries representing 100% of global GDP.
- Suitable for data visualization dashboards, AI-driven economic forecasting, and educational research.

💡 Example Use-Cases

• Build a choropleth map showing GDP distribution across continents.
• Train a regression model to predict GDP per capita based on population and growth.
• Compare economic inequality using population vs GDP share.

📚 Dataset Citation

Source: Worldometers — GDP by Country (2024)
Dataset compiled and cleaned by: Asadullah Shehbaz
For open research and data analysis.

In 2025, the United States had the largest economy in the world, with a gross domestic product of over 30 trillion U.S. dollars. China had the second largest economy, at around 19.23 trillion U.S. dollars. Recent adjustments in the list have seen Germany's economy overtake Japan's to become the third-largest in the world in 2023, while Brazil's economy moved ahead of Russia's in 2024. Global gross domestic product Global gross domestic product amounts to almost 110 trillion U.S. dollars, with the United States making up more than one-quarter of this figure alone. The 12 largest economies in the world include all Group of Seven (G7) economies, as well as the four largest BRICS economies. The U.S. has consistently had the world's largest economy since the interwar period, and while previous reports estimated it would be overtaken by China in the 2020s, more recent projections estimate the U.S. economy will remain the largest by a considerable margin going into the 2030s.The gross domestic product of a country is calculated by taking spending and trade into account, to show how much the country can produce in a certain amount of time, usually per year. It represents the value of all goods and services produced during that year. Those countries considered to have emerging or developing economies account for almost 60 percent of global gross domestic product, while advanced economies make up over 40 percent.

The Global 15x15 Minute Grids of the Downscaled GDP Based on the Special Report on Emissions Scenarios (SRES) B2 Scenario, 1990 and 2025, are geospatial distributions of Gross Domestic Product (GDP) per Unit area (GDP densities). These global grids were generated using the Country-level GDP and Downscaled Projections Based on the SRES B2 Scenario, 1990-2100 data set, and CIESIN's Gridded Population of World, Version 2 (GPWv2) data set as the base map. First, the GDP per capita was developed at a country-level for 1990 and 2025. Then the gridded GDP was developed within each country by applying the GDP per capita to each grid cell of the GPW, under the assumption that the GDP per capita was uniform within a country. This data set is produced and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).

This dataset provides annual GDP data for all recognized countries from 2015 to 2025, sourced from the IMF. Figures for 2015–2024 are reported values, while 2025 contains projections as the year is not yet complete. For some countries (such as Afghanistan and a few others), certain years are missing due to data not being reported. The dataset is well-suited for: 1) Trend analysis – Study global GDP growth patterns over the past decade. 2) Forecasting models – Train machine learning models to predict future GDP values. 3) Country comparisons – Compare economic performance between countries or regions. 4) Time-series learning – Practice ARIMA, Prophet, LSTM, and other forecasting techniques. 5) Impact studies – Analyze the impact of global events (e.g., COVID-19) on GDP. 6) Correlation analysis – Link GDP with other indicators (population, inflation, CO₂ emissions). 7) Regional studies – Examine differences between continents or economic blocs (EU, ASEAN, G7, BRICS). 8) Inequality measurement – Compare GDP distribution across developed vs. developing economies. 9) Visualization projects – Create dashboards, heatmaps, or choropleth maps of GDP data. 10) Educational use – Use the dataset in economics, finance, or data science courses as a teaching resource.

This dataset provides values for GDP reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

This dataset represents Exclusive Economic Zones (EEZ) of the world. Up to now, there was no global public domain cover available.
Therefore, the Flanders Marine Institute decided to develop its own database. The database includes two global GIS-layers: one contains polylines that represent the maritime boundaries of the world countries, the other one is a polygon layer representing the Exclusive Economic Zone of countries. The database also contains digital information about treaties. Please note that the EEZ shapefile also includes the internal waters of each country.

The Global 15x15 Minute Grids of the Downscaled GDP Based on the Special Report on Emissions Scenarios (SRES) B2 Scenario, 1990 and 2025, are geospatial distributions of Gross Domestic Product (GDP) per Unit area (GDP densities). These global grids were generated using the Country-level GDP and Downscaled Projections Based on the SRES B2 Scenario, 1990-2100 data set, and CIESIN's Gridded Population of World, Version 2 (GPWv2) data set as the base map. First, the GDP per capita was developed at a country-level for 1990 and 2025. Then the gridded GDP was developed within each country by applying the GDP per capita to each grid cell of the GPW, under the assumption that the GDP per capita was uniform within a country. This data set is produced and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).

**🌍 World Countries Dataset This World Countries Dataset contains detailed information about countries across the globe, offering insights into their geographic, demographic, and economic characteristics.

It includes various features such as population, area, GDP, languages, and regional classifications. This dataset is ideal for projects related to data visualization, statistical analysis, geographical studies, or machine learning applications such as clustering or classification of countries.

This dataset was manually compiled/collected from reliable open data sources (e.g., Wikipedia, World Bank, or other governmental datasets).

**🔍 Sample Questions Explored Using Python: - Q. 1) Which countries have the highest and lowest population? - Q. 2) What is the average area (in sq. km) of countries in each region? - Q. 3) Which countries have more than 100 million population and GDP above $1 trillion? - Q. 4) Which languages are most commonly spoken across countries? - Q. 5) Show a bar graph comparing GDPs of G7 nations. - Q. 6) How many countries are there in each continent or region? - Q. 7) Which countries have both a high population density and low GDP per capita? - Q. 8) Create a world map visualization of population or GDP distribution. - Q. 9) What are the top 10 most densely populated countries? - Q. 10) How many landlocked countries are there in the world?

**🧾 Features / Columns in the Dataset: - Country: The name of the country (e.g., "Pakistan", "France").

• Capital: The capital city of the country.

• Region: Broad geographical region (e.g., "Asia", "Europe").

• Subregion: More specific geographical grouping (e.g., "Southern Asia").

• Population: Total population of the country.

• Area (sq. km): Total land area in square kilometers.

• Population Density: Number of people per square kilometer.

• GDP (USD): Gross Domestic Product (in U.S. dollars).

• GDP per Capita: GDP divided by the population.

• Official Languages: Officially recognized language(s) spoken.

• Currency: Name of the currency used.

• Timezones: Timezones in which the country falls.

• Borders: List of bordering countries (if any).

• Landlocked: Whether the country is landlocked (Yes/No).

• Latitude / Longitude: Coordinates for geographical plotting.

This map contains Gross Domestic Product - the total value of goods produced and services provided - by country, per capita in 2016, expressed in 2016 US Dollars. Expressing the GDP in "per capita" terms allows for better comparisons across countries. Total GDP is available in an accompanying map. GDP as a measure has been largely criticized as an incomplete measure of productivity and wealth, as it does not take into account production in the informal economy, quality of life, degradation to the environment, or income distribution. However, GDP is an internationally comparable measure, used in everything from banks setting interest rates to political campaign speeches.Source: World Bank, World Development Indicators.

An exclusive economic zone (EEZ) is a sea zone prescribed by the United Nations Convention on the Law of the Sea over which a sovereign state has special rights over the exploration and use of marine resources, including energy production from water and wind. This maritime boundary is designed to be used with other marine boundaries in order to help determine areas of trade, commerce and transportation. The 200 NM zone is measured, country-by-country, from another maritime boundary, the baseline (usually but not in all cases the mean low-water mark, used is not the same thing as the coast line. For each country, obtain the official list of the baseline points from the United Nations under Maritime Space.The exclusive economic zone stretches much further into sea than the territorial waters, which end at 12 NM (22 km) from the coastal baseline (if following the rules set out in the UN Convention on the Law of the Sea). Thus, the EEZ includes the contiguous zone. States also have rights to the seabed of what is called the continental shelf up to 350 NM (648 km) from the coastal baseline, beyond the EEZ, but such areas are not part of their EEZ. The legal definition of the continental shelf does not directly correspond to the geological meaning of the term, as it also includes the continental rise and slope, and the entire seabed within the EEZ. The chart below diagrams the overlapping jurisdictions which are part of the EEZ. When the (EEZ) boundary is between countries which are separated by less than 200NM is settled by international tribunals at any arbitrary line. Many countries are still in the process of extending their EEZs beyond 200NM using criteria defined in the United Nations Convention on the Law of the Sea. Dataset Summary The data for this layer were obtained from https://www.marineregions.org/published here. Link to source metadata.Preferred Citation: Flanders Marine Institute (2023). Maritime Boundaries Geodatabase: Maritime Boundaries and Exclusive Economic Zones (200NM), version 12. Available online at https://www.marineregions.org/. https://doi.org/10.14284/632This layer is a feature service, which means it can be used for visualization and analysis throughout the ArcGIS Platform. This layer is not editable.

https://upload.wikimedia.org/wikipedia/commons/a/aa/World_Map.jpg" alt="World map graphic">

Image credit: DATABASE – Computer Owner, “World Map,” Wikimedia Commons, CC BY 3.0.

Overview

Key macro indicators for 10 economies from 2015 to 2024. Countries: USA, CHN, JPN, DEU, IND, GBR, FRA, BRA, CAN, KOR.

• Rows: 500
• Columns: 4
• Missing cells: 12 total in the value column
• Format: long table by country, indicator, year

File

• global_economic_indicators_2015_2024.csv

Indicators

• NY.GDP.MKTP.CD - GDP in current USD
• SP.POP.TOTL - Population total
• SL.UEM.TOTL.ZS - Unemployment rate percent
• FP.CPI.TOTL.ZG - Inflation rate percent
• IT.NET.USER.ZS - Internet users percent

Columns

• country - ISO3 code
• indicator - readable name as above
• year - calendar year
• value - numeric value, may be missing for some country-year cells

Quick facts from EDA

• Minimal missingness concentrated in value
• Year range 2015 to 2024
• Mixed scales: GDP is large in USD, other indicators are percentages

The Gross Domestic Product per capita (gross domestic product divided by mid-year population converted to international dollars, using purchasing power parity rates) has been identified as an important determinant of susceptibility and vulnerability by different authors and used in the Disaster Risk Index 2004 (Peduzzi et al. 2009, Schneiderbauer 2007, UNDP 2004) and is commonly used as an indicator for a country’s economic development (e.g. Human Development Index). Despite some criticisms (Brooks et al. 2005) it is still considered useful to estimate a population’s susceptibility to harm, as limited monetary resources are seen as an important factor of vulnerability. However, collection of data on economic variables, especially sub-national income levels, is problematic, due to various shortcomings in the data collection process. Additionally, the informal economy is often excluded from official statistics. Night time lights satellite imagery of NOAA grid provides an alternative means for measuring economic activity. NOAA scientists developed a model for creating a world map of estimated total (formal plus informal) economic activity. Regression models were developed to calibrate the sum of lights to official measures of economic activity at the sub-national level for some target Country and at the national level for other countries of the world, and subsequently regression coefficients were derived. Multiplying the regression coefficients with the sum of lights provided estimates of total economic activity, which were spatially distributed to generate a 30 arc-second map of total economic activity (see Ghosh, T., Powell, R., Elvidge, C. D., Baugh, K. E., Sutton, P. C., & Anderson, S. (2010).Shedding light on the global distribution of economic activity. The Open Geography Journal (3), 148-161). We adjusted the GDP to the total national GDPppp amount as recorded by IMF (International Monetary Fund) for 2010 and we divided it by the population layer from Worldpop Project. Further, we ran a focal statistics analysis to determine mean values within 10 cell (5 arc-minute, about 10 Km) of each grid cell. This had a smoothing effect and represents some of the extended influence of intense economic activity for local people. Finally we apply a mask to remove the area with population below 1 people per square Km. This dataset has been produced in the framework of the "Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)" project, Work Package 4 (WP4). More information on ClimAfrica project is provided in the Supplemental Information section of this metadata.

Mapping Our World Using GIS is a 1:1 set of instructional materials for teaching basic concepts found in middle school world geography. Each module consists of multiple files.

        Economists generally classify a country as “developing” or “developed” by determining the percentage

of gross domestic product (GDP) engaged in each of three sectors of the economy — agriculture, industry, and services. A country with a high percentage of its GDP in agriculture is categorized as developing, while a country with a high percentage of its GDP in services and industry is categorized as developed.

        In this activity, you will use maps of percentages of GDP in the three sectors to explore patterns of

development around the world. You will also examine two other economic indicators — energy use and GDP per capita — and compare the maps of GDP in economic sectors to the maps of GDP per capita and energy use. You will evaluate whether or not the economic sector criteria are good indicators of a country’s economic status.

  The Mapping Our World collection is at: http://esriurl.com/MOW. All Esri GeoInquiries can be found at: http://www.esri.com/geoinquiries

Consolidated Open Source Global Development Dataset (COSGDD)

Executive Summary

Motivation

Welcome to the Consolidated Open Source Global Development Dataset (COSGDD)!

The Consolidated Open Source Global Development Dataset (COSGDD) was created to address the growing need for accessible, consolidated, and diverse global datasets for education, research, and policy-making. By combining data from publicly available, open-source datasets, COSGDD provides a one-stop resource for analyzing key socio-economic, environmental, and governance indicators across the globe.

Streamlit Dashboard Link (The LIME explanation graph will take time to load) - https://cosgdd.streamlit.app/ Github Code Repo Link - https://github.com/AkhilByteWrangler/Consolidated-Open-Source-Global-Development-Dataset

Overview

Imagine having a magical map of the world that shows you not just the roads and mountains but also how happy people are, how much money they make, how clean the air is, and how fair their governments are. This dataset is that magical map - but in the form of organized data!

It combines facts and figures from trusted sources to help researchers, governments, companies, and YOU understand how the world works and how to make it better.

Why Does This Dataset Exist?

The world is complicated. Happiness doesn’t depend on just one thing like money; it’s also about health, fairness, relationships, and even how clean the air is. But these pieces of the puzzle are scattered across many places. This dataset brings everything together in one place, making it easier to:
- Answer big questions like:
- What makes people happy?
- Is wealth or freedom more important for well-being?
- How does urbanization affect happiness?
- Find patterns and trends across countries.
- Make smart decisions based on real-world data.

Who Should Use This Dataset?

This dataset is for anyone curious about the world, including:
- Researchers: Study connections between happiness, governance, and sustainability.
- Policy Makers: Design better policies to improve quality of life.
- Data Enthusiasts: Explore trends and patterns using statistics or machine learning.
- Businesses: Understand societal needs to improve Corporate Social Responsibility (CSR).

Description of Data

This dataset consolidates data from well-established sources such as the World Happiness Report, The Economist Democracy Index, environmental databases, and more. It includes engineered features to deepen understanding of well-being and sustainability.

Core Features

• Happiness Metrics:
  • Life Ladder: Self-reported happiness scores.
• Economic Indicators:
  • Log GDP per capita: Log-transformed measure of wealth.
  • Tax Revenue: Government revenue as a share of GDP.
• Social Indicators:
  • Social support: Proportion of people with reliable social networks.
  • Freedom to make life choices: Self-reported freedom levels.
• Environmental Metrics:
  • Total Emissions: Aggregated greenhouse gas emissions.
  • Renewables Production: Share of renewable energy production.
• Governance Indicators:
  • Democracy_Index: Quantitative measure of democratic governance.
  • Rule_of_Law_Index: Assessment of the legal system’s strength.
• Engineered Features:
  • Freedom_Index: Combines wealth and freedom.
  • Generosity_Per_Dollar: Normalized generosity against GDP.
  • Environmental_Bonus: Evaluates environmental efficiency relative to economic output.
  • See full documentation for more.

Core Columns

1. Country

• Unit: Country name as a string.
• Source: Sourced from all contributing datasets (e.g., World Happiness Report, UN datasets).
• Significance:
  Identifies the geographic region for the data. Essential for country-specific analyses, comparisons, and aggregations.

2. Year

• Unit: Year as an integer (e.g., 2024).
• Source: Included across all datasets.
• Significance:
  Indicates the time frame of the data. Vital for studying trends, changes over time, and time-series modeling.

Happiness Metrics

3. Life Ladder

• Unit: Scale from 0 (worst possible life) to 10 (best possible life).
• Source: World Happiness Report.
• Significance:
  Captures subjective well-being based on self-reported happiness. A central measure for studying the quality of life globally.

4. Log GDP per Capita

• Unit: Logarithmic transformation of GDP per capita in constant international dollars.
• Source: World Happiness Report, based on World Bank data.
• Significance:
  Provides a no...

Snapshot img

Digital Map Market Size 2025-2029

The digital map market size is forecast to increase by USD 31.95 billion at a CAGR of 31.3% between 2024 and 2029.

The market is driven by the increasing adoption of intelligent Personal Digital Assistants (PDAs) and the availability of location-based services. PDAs, such as smartphones and smartwatches, are becoming increasingly integrated with digital map technologies, enabling users to navigate and access real-time information on-the-go. The integration of Internet of Things (IoT) enables remote monitoring of cars and theft recovery. Location-based services, including mapping and navigation apps, are a crucial component of this trend, offering users personalized and convenient solutions for travel and exploration. However, the market also faces significant challenges.
Ensuring the protection of sensitive user information is essential for companies operating in this market, as trust and data security are key factors in driving user adoption and retention. Additionally, the competition in the market is intense, with numerous players vying for market share. Companies must differentiate themselves through innovative features, user experience, and strong branding to stand out in this competitive landscape. Security and privacy concerns continue to be a major obstacle, as the collection and use of location data raises valid concerns among consumers.

What will be the Size of the Digital Map Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free Sample

In the market, cartographic generalization and thematic mapping techniques are utilized to convey complex spatial information, transforming raw data into insightful visualizations. Choropleth maps and dot density maps illustrate distribution patterns of environmental data, economic data, and demographic data, while spatial interpolation and predictive modeling enable the estimation of hydrographic data and terrain data in areas with limited information. Urban planning and land use planning benefit from these tools, facilitating network modeling and location intelligence for public safety and emergency management.

Spatial regression and spatial autocorrelation analyses provide valuable insights into urban development trends and patterns. Network analysis and shortest path algorithms optimize transportation planning and logistics management, enhancing marketing analytics and sales territory optimization. Decision support systems and fleet management incorporate 3D building models and real-time data from street view imagery, enabling effective resource management and disaster response. The market in the US is experiencing robust growth, driven by the integration of Geographic Information Systems (GIS), Global Positioning Systems (GPS), and advanced computer technology into various industries.

How is this Digital Map Industry segmented?

The digital map industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Application

  Navigation
  Geocoders
  Others


Type

  Outdoor
  Indoor


Solution

  Software
  Services


Deployment

  On-premises
  Cloud


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    Indonesia
    Japan
    South Korea


  Rest of World (ROW)

By Application Insights

The navigation segment is estimated to witness significant growth during the forecast period. Digital maps play a pivotal role in various industries, particularly in automotive applications for driver assistance systems. These maps encompass raster data, aerial photography, government data, and commercial data, among others. Open-source data and proprietary data are integrated to ensure map accuracy and up-to-date information. Map production involves the use of GPS technology, map projections, and GIS software, while map maintenance and quality control ensure map accuracy. Location-based services (LBS) and route optimization are integral parts of digital maps, enabling real-time navigation and traffic data.

Data validation and map tiles ensure data security. Cloud computing facilitates map distribution and map customization, allowing users to access maps on various devices, including mobile mapping and indoor mapping. Map design, map printing, and reverse geocoding further enhance the user experience. Spatial analysis and data modeling are essential for data warehousing and real-time navigation. The automotive industry's increasing adoption of connected cars and long-term evolution (LTE) technologies have fueled the demand for digital maps. These maps enable driver assistance applications,

The gross domestic product (GDP) of California was about 4.1 trillion U.S. dollars in 2024, meaning that it contributed the most out of any state to the country’s GDP in that year. In contrast, Vermont had the lowest GDP in the United States, with 45.71 billion U.S. dollars. What is GDP? Gross domestic product, or GDP, is the total monetary value of all goods and services produced by an economy within a certain time period. GDP is used by economists to determine the economic health of an area, as well as to determine the size of the economy. GDP can be determined for countries, states and provinces, and metropolitan areas. While GDP is a good measure of the absolute size of a country's economy and economic activity, it does account for many other factors, making it a poor indicator for measuring the cost or standard of living in a country, or for making cross-country comparisons. GDP of the United States The United States has the largest gross domestic product in the world as of 2023, with China, Japan, Germany, and India rounding out the top five. The GDP of the United States has almost quadrupled since 1990, when it was about 5.9 trillion U.S. dollars, to about 25.46 trillion U.S. dollars in 2022.

Poverty headcount ratio at $2.15 a day (2017 PPP) (% of population)

Poverty headcount ratio at national poverty lines (% of population)

Multidimensional poverty headcount ratio (% of total population)

The Aqueduct Global Flood Risk Maps provide current and future river flood risk estimates in urban damage, affected GDP, and affected population by country, river basin, and state. The datasets in these maps include current and future river flood risk estimates in urban damage, affected GDP, and affected population by country, river basin, and state.

Nighttime lights satellite imagery and the LandScan population grid provide an alternative means for measuring economic activity. This data is based on a scientific model for creating a disaggregated map of estimated total (formal plus informal) economic activity for countries and states of the world. Regression models were developed to calibrate the sum of lights to official measures of economic activity at the sub-national level for China, India, Mexico, and the United States and at the national level for other countries of the world, and subsequently unique coefficients were derived. Multiplying the unique coefficients with the sum of lights provided estimates of total economic activity, which were spatially distributed to generate a spatially disaggregated 1 km2 map of total economic activity. The vintage date of this dataset is 2010. Email: ncei.info@noaa.gov

United States GDP: PCE: 1996p: DG: Others: Books & Maps data was reported at 36.437 USD bn in Oct 2003. This records an increase from the previous number of 36.225 USD bn for Sep 2003. United States GDP: PCE: 1996p: DG: Others: Books & Maps data is updated monthly, averaging 14.997 USD bn from Jan 1967 (Median) to Oct 2003, with 442 observations. The data reached an all-time high of 38.497 USD bn in Jan 2002 and a record low of 8.736 USD bn in Feb 1977. United States GDP: PCE: 1996p: DG: Others: Books & Maps data remains active status in CEIC and is reported by Bureau of Economic Analysis. The data is categorized under Global Database’s USA – Table US.A203: NIPA 1999: Personal Consumption Expenditure.