The Military Bases dataset was last updated on October 23, 2024 and are defined by Fiscal Year 2023 data, from the Office of the Assistant Secretary of Defense for Energy, Installations, and Environment and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The dataset depicts the authoritative locations of the most commonly known Department of Defense (DoD) sites, installations, ranges, and training areas world-wide. These sites encompass land which is federally owned or otherwise managed. This dataset was created from source data provided by the four Military Service Component headquarters and was compiled by the Defense Installation Spatial Data Infrastructure (DISDI) Program within the Office of the Assistant Secretary of Defense for Energy, Installations, and Environment. Only sites reported in the BSR or released in a map supplementing the Foreign Investment Risk Review Modernization Act of 2018 (FIRRMA) Real Estate Regulation (31 CFR Part 802) were considered for inclusion. This list does not necessarily represent a comprehensive collection of all Department of Defense facilities. For inventory purposes, installations are comprised of sites, where a site is defined as a specific geographic location of federally owned or managed land and is assigned to military installation. DoD installations are commonly referred to as a base, camp, post, station, yard, center, homeport facility for any ship, or other activity under the jurisdiction, custody, control of the DoD. While every attempt has been made to provide the best available data quality, this data set is intended for use at mapping scales between 1:50,000 and 1:3,000,000. For this reason, boundaries in this data set may not perfectly align with DoD site boundaries depicted in other federal data sources. Maps produced at a scale of 1:50,000 or smaller which otherwise comply with National Map Accuracy Standards, will remain compliant when this data is incorporated. Boundary data is most suitable for larger scale maps; point locations are better suited for mapping scales between 1:250,000 and 1:3,000,000. If a site is part of a Joint Base (effective/designated on 1 October, 2010) as established under the 2005 Base Realignment and Closure process, it is attributed with the name of the Joint Base. All sites comprising a Joint Base are also attributed to the responsible DoD Component, which is not necessarily the pre-2005 Component responsible for the site. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529039

The world has become much more peaceful, and yet, even after adjusting for inflation, global military spending is now three times greater than at the height of the Cold War. These developments have motivated a renewed interest from both policy makers and scholars about the drivers of military spending and the implications that follow. Existing findings on the relationship between threat and arming and arms races and war hinge on the completeness and accuracy of existing military spending data. Moreover, data on military spending is used to measure important concepts from international relations such as the distribution of power, balancing, the severity of states’ military burdens, and arms races. Everything we know about which states are most powerful, whether nations are balancing, and whether military burdens and arms races are growing more or less severe rests on the accuracy of existing military spending estimates.

Analysis of ‘world military power 2020’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/mingookkim/world-military-power-2020 on 14 February 2022.

--- Dataset description provided by original source is as follows ---

I found this data on a site called data.world. It is a data material published as a dataset created by vizzup.

This is a data that allows you to see the world military rankings in 2020 and numerical status such as the army, navy, and air force.

In addition, some related data such as population and economy related to military power are also included.

Please refer to data analysis as a good data to compare military power.

Original Source : globalfirepower.com on 1st may 2020

--- Original source retains full ownership of the source dataset ---

United States US: Military Expenditure data was reported at 609.758 USD bn in 2017. This records an increase from the previous number of 600.106 USD bn for 2016. United States US: Military Expenditure data is updated yearly, averaging 277.591 USD bn from Sep 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 711.338 USD bn in 2011 and a record low of 45.380 USD bn in 1960. United States US: Military Expenditure data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Defense and Official Development Assistance. Military expenditures data from SIPRI are derived from the NATO definition, which includes all current and capital expenditures on the armed forces, including peacekeeping forces; defense ministries and other government agencies engaged in defense projects; paramilitary forces, if these are judged to be trained and equipped for military operations; and military space activities. Such expenditures include military and civil personnel, including retirement pensions of military personnel and social services for personnel; operation and maintenance; procurement; military research and development; and military aid (in the military expenditures of the donor country). Excluded are civil defense and current expenditures for previous military activities, such as for veterans' benefits, demobilization, conversion, and destruction of weapons. This definition cannot be applied for all countries, however, since that would require much more detailed information than is available about what is included in military budgets and off-budget military expenditure items. (For example, military budgets might or might not cover civil defense, reserves and auxiliary forces, police and paramilitary forces, dual-purpose forces such as military and civilian police, military grants in kind, pensions for military personnel, and social security contributions paid by one part of government to another.); ; Stockholm International Peace Research Institute (SIPRI), Yearbook: Armaments, Disarmament and International Security.; ; Data for some countries are based on partial or uncertain data or rough estimates. For additional details please refer to the military expenditure database on the SIPRI website: https://sipri.org/databases/milex

This project updates INTERNATIONAL MILITARY INTERVENTION (IMI), 1946-1988 (ICPSR 6035), compiled by Frederic S. Pearson and Robert A. Baumann (1993). This newer study documents 447 intervention events from 1989 to 2005. To ensure consistency across the full 1946-2005 time span, Pearson and Baumann's coding procedures were followed. The data collection thus "documents all cases of military intervention across international boundaries by regular armed forces of independent states" in the international system (Pearson and Baumann, 1993). "Military interventions are defined operationally in this collection as the movement of regular troops or forces (airborne, seaborne, shelling, etc.) of one country inside another, in the context of some political issue or dispute" (Pearson and Baumann, 1993). As with the original IMI (OIMI) collection, the 1989-2005 dataset includes information on actor and target states, as well as starting and ending dates. It also includes a categorical variable describing the direction of the intervention, i.e., whether it was launched in support of the target government, in opposition to the target government, or against some third party actor within the target state's borders. The intensity of the military intervention is captured in ordinal variables that document the scale of the actor's involvement, "ranging from minor engagement such as evacuation, to patrols, act of intimidation, and actual firing, shelling or bombing" (Pearson and Baumann, 1993). Casualties that are a direct result of the military intervention are coded as well. A novel aspect of IMI is the inclusion of a series of variables designed to ascertain the motivations or issues that prompted the actor to intervene, including to take sides in a domestic dispute in the target state, to affect target state policy, to protect a socio-ethnic or minority group, to attack rebels in sanctuaries in the target state, to protect economic or resource interests, to intervene for strategic purposes, to lend humanitarian aid, to acquire territory or to dispute its ownership, and to protect its own military/diplomatic interests. There are three main differences between OIMI and this update. First, the variable, civilian casualties, which complements IMI's information on the casualties suffered by actor and target military personnel has been added. Second, OIMI variables on colonial history, previous intervention, alliance partners, alignment of the target, power size of the intervener, and power size of the target have been deleted. The Web-based resources available today, such as the CIA World Fact Book, make information on the colonial history between actor and target readily available. Statistical programs allow researchers to generate all previous interventions by the actor into the target state. Since competing measures and data collections are used for alliances and state power, it was thought best to allow analysts who use IMI the freedom to choose the variables or dataset that measure the phenomena of their choice. Third, the data collection techniques differ from OIMI. OIMI relied on the scouring of printed news sources such as the New York Times Index, Facts on File, and Keesing's to collect information on international military interventions, whereas the computer-based search engine, Lexis-Nexis Academic, was used as the foundation for the new study's data search. Lexis-Nexis Academic includes print sources as well as news wire reports and many others. After Lexis-Nexis searches were conducted for each year in the update by at least four different investigators, regional sources, the United Nations Web site, and secondary works were consulted.

The dataset depicts the authoritative locations of the most commonly known Department of Defense (DoD) sites, installations, ranges, and training areas world-wide. These sites encompass land which is federally owned or otherwise managed. This dataset was created from source data provided by the four Military Service Component headquarters and was compiled by the Defense Installation Spatial Data Infrastructure (DISDI) Program within the Office of the Assistant Secretary of Defense for Energy, Installations, and Environment. Only sites reported in the BSR or released in a map supplementing the Foreign Investment Risk Review Modernization Act of 2018 (FIRRMA) Real Estate Regulation (31 CFR Part 802) were considered for inclusion. This list does not necessarily represent a comprehensive collection of all Department of Defense facilities. For inventory purposes, installations are comprised of sites, where a site is defined as a specific geographic location of federally owned or managed land and is assigned to military installation. DoD installations are commonly referred to as a base, camp, post, station, yard, center, homeport facility for any ship, or other activity under the jurisdiction, custody, control of the DoD.While every attempt has been made to provide the best available data quality, this data set is intended for use at mapping scales between 1:50,000 and 1:3,000,000. For this reason, boundaries in this data set may not perfectly align with DoD site boundaries depicted in other federal data sources. Maps produced at a scale of 1:50,000 or smaller which otherwise comply with National Map Accuracy Standards, will remain compliant when this data is incorporated. Boundary data is most suitable for larger scale maps; point locations are better suited for mapping scales between 1:250,000 and 1:3,000,000.If a site is part of a Joint Base (effective/designated on 1 October, 2010) as established under the 2005 Base Realignment and Closure process, it is attributed with the name of the Joint Base. All sites comprising a Joint Base are also attributed to the responsible DoD Component, which is not necessarily the pre-2005 Component responsible for the site.

Here are a few use cases for this project:

1. Military Intelligence and Threat Analysis: Utilize the "Army_type" computer vision model to analyze satellite images, drone feeds, or ground-level reconnaissance imagery to identify and monitor movement or positions of various military units. This information can assist in assessing potential threats, providing valuable insights for decision-making in national defense operations.

2. Geopolitical Conflict Monitoring: The model can be employed in monitoring and tracking the movements of military assets and troops in regions of geopolitical tensions or disputed territories. Understanding the distribution of military forces can help predict potential conflict flashpoints and help in implementing diplomatic solutions before any escalation.

3. Search and Rescue Operations: In disaster scenarios or during military operations, the "Army_type" model can aid in identifying locations of specific military units or assets that might require immediate assistance. The information gathered from real-time analysis can help coordinate search and rescue efforts or assist in evacuating military personnel from high-threat areas.

4. Military Base Planning and Security: The model can contribute to the efficient base planning for armed forces. By analyzing surrounding areas, the model can identify potential threats or vulnerabilities, enabling authorities to make informed decisions on base fortifications and resource allocation to guarantee the safety of military personnel and assets.

5. Training and Simulation Exercises: The "Army_type" computer vision model can be integrated into military training programs, providing realistic simulations to prepare military personnel for real-world scenarios. The model can help create objective evaluations of exercises by continuously monitoring and identifying individual units, allowing for more focused feedback and better understanding of potential strengths and weaknesses in strategic maneuvers.

The World War II Enlistment and Casualty Records data set contains individual-level information on soldiers who were drafted or volunteered for service in the U.S. armed forces during World War II. The repository consists of three files: The digitized list of fallen soldiers who served in the U.S. Army or Army Air Force by name, state, and county of residence (300,131 observations) The digitized list of fallen soldiers who served in the U.S. Navy, Marine Corps, or Coast Guard by name, state, and county of residence (65,507 observations) The World War II Army and Army Air Force Enlistment records which were merged with the list of fallen soldiers (8,293,187 observations)

United States US: Military Expenditure: % of GDP data was reported at 3.149 % in 2017. This records a decrease from the previous number of 3.222 % for 2016. United States US: Military Expenditure: % of GDP data is updated yearly, averaging 4.864 % from Sep 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 9.063 % in 1967 and a record low of 2.908 % in 1999. United States US: Military Expenditure: % of GDP data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Defense and Official Development Assistance. Military expenditures data from SIPRI are derived from the NATO definition, which includes all current and capital expenditures on the armed forces, including peacekeeping forces; defense ministries and other government agencies engaged in defense projects; paramilitary forces, if these are judged to be trained and equipped for military operations; and military space activities. Such expenditures include military and civil personnel, including retirement pensions of military personnel and social services for personnel; operation and maintenance; procurement; military research and development; and military aid (in the military expenditures of the donor country). Excluded are civil defense and current expenditures for previous military activities, such as for veterans' benefits, demobilization, conversion, and destruction of weapons. This definition cannot be applied for all countries, however, since that would require much more detailed information than is available about what is included in military budgets and off-budget military expenditure items. (For example, military budgets might or might not cover civil defense, reserves and auxiliary forces, police and paramilitary forces, dual-purpose forces such as military and civilian police, military grants in kind, pensions for military personnel, and social security contributions paid by one part of government to another.); ; Stockholm International Peace Research Institute (SIPRI), Yearbook: Armaments, Disarmament and International Security.; Weighted average; Data for some countries are based on partial or uncertain data or rough estimates.

This updated version of a global dataset covers the time period 1800-2019, with yearly observations for all countries that have been independent at any point in time since WWII. Within the category of democracies, we first make a distinction between republics and monarchies. Republics are then classified into presidential, semi-presidential, and parliamentary systems. Within the category of monarchies, most systems are parliamentary but a few countries are conferred to the category semi-monarchies. Autocratic countries are classified into the following main categories: absolute monarchy, military rule, party-based rule, personalist rule, and oligarchy. Within the categories party-based rule and oligarchy a number of subcategories are also identified.

Description

The Correlates of War project hosts a variety of datasets related to the study of inter-state conflict.

Details

As of 2007-09-22 the following datasets were listed:

State System Membership (v2004.1): This data set records the fluctuating composition of the state system since 1816. It also identifies countries corresponding to the standard Correlates of War country codes. Access the system membership data here.

Inter-, Extra- and Intra-State War (v3.0): War takes many forms in the contemporary era, including serious military conflicts between states (inter-state war), between states and non-state actors (extra-state war), and within states (intra-state war). This data set records such events over the 1816-1997 period. Access the Interstate War data here. Access the Extrastate War data here. Access the Intrastate War data here.

Militarized Interstate Disputes (v3.02): This data set records all instances of when one state threatened, displayed, or used force against another. Version 3.0 covers the 1816-2001 period, and can be downloaded from this page.

National Material Capabilities (v3.02): Power is considered by many to be a central concept in explaining conflict, and six indicators—military expenditure, military personnel, energy consumption, iron and steel production, urban population, and total population—are included in this data set. It serves as the basis for the most widely used indicator of national capability, CINC (Composite Indicator of National Capability) and covers the period 1816-2001. Access the capabilities data here.

Formal Alliances (v3.03): Alliances have been credited with preventing wars and provoking wars, and they have been important instruments of statecraft for centuries. This data set records all formal alliances among states between 1816 and 2000, including mutual defense pacts, non-aggression treaties, and ententes. This data set is hosted by Douglas Gibler, University of Kentucky. It may be downloaded here.

Territorial Change (v3.0): Territory has played an important role in interstate conflict, and this data set records all peaceful and violent changes of territory from 1816-2000. This data set is hosted by Paul Diehl, University of Illinois. Access the territorial change data here.

Direct Contiguity (v3.0): Geographic factors are known to play an important role in conflict. The Direct Contiguity data set registers the land and sea borders of all states since the Congress of Vienna, and covers 1816-2000. This data set is hosted by Paul Diehl, University of Illinois. Access the direct contiguity data here.

Colonial/Dependency Contiguity (v3.0): The Colonial/Dependency Contiguity data set registers contiguity relationships between the colonies/dependencies of states (by land and by sea up to 400 miles) from 1816-2002. Access the colonial/dependency contiguity data here.

Intergovernmental Organizations (v2.1): Although the number of intergovernmental organizations (IGOs) grew dramatically during the late 20th century, they have been part of the world scene for much longer. This data set tracks the status and membership of such organizations from 1815-2000. Access information about this data here. This data set is hosted by Timothy Nordstrom, University of Mississippi, and John Pevehouse, University of Wisconsin.

Diplomatic Exchange (v2006.1): The Diplomatic Exchange data set tracks diplomatic representation at the level of chargé d'affaires, minister, and ambassador between states from 1817-2005. Access information about this data here. This data set is hosted by Reşat Bayer, Koç University.

Bilateral Trade: Trade is considered by many to have a pacifying effect on the relations of states. This collection of bilateral trade data begins in 1870 and covers most members of the interstate system. Access trade data here.

Openness: Open (?)

• Access: all datasets available for direct download in convenient formats (e.g. zipped csv)
• License: no license specified but does state on the datasets page: "The data sets listed on this page are all available for download." Some datasets require citation as a condition of use (with the exact form stated on the website).

Historical chart and dataset showing China military size by year from 1985 to 2020.

The data were originally collected by Ruth Leger Sivard and were made available by the Inter-university Consortium for Political and Social Research. This dataset contains descriptive information on national ersources and social military expenditures in 1978-1980 for 141 countries on 55 variables. The data consist of both raw and per capital values, and ranks of values, for variable measuring military strength, general economic conditions, and human resources (including education, health, and general welfare). Although the statistical tables were largely prepared in 1982, the latest year for which adequate worldwide coverage was possible for many of the social statistics was 1979 and for some it was 1978. Projections to 1977, therefore were sometimes used for social statistics while military expenditures, armed forces as well as GNP were generally available to 1980.

Historical chart and dataset showing Jordan military size by year from 1985 to 2020.

SELECTED SOCIAL CHARACTERISTICS IN THE UNITED STATES VETERAN STATUS - DP02 Universe - Civilian population 18 Year and over Survey-Program - American Community Survey 5-year estimates Years - 2020, 2021, 2022 Veteran status is used to identify people with active duty military service and service in the military Reserves and the National Guard. Veterans are men and women who have served (even for a short time), but are not currently serving, on active duty in the U.S. Army, Navy, Air Force, Marine Corps, or the Coast Guard, or who served in the U.S. Merchant Marine during World War II. People who served in the National Guard or Reserves are classified as veterans only if they were ever called or ordered to active duty, not counting the 4-6 months for initial training or yearly summer camps.

This data collection, which focuses on military spending and arms transfers, supplies information on 166 developed and developing countries of the world. Data are provided in four tables. Table I (Part 1) consists of military expenditures, armed forces, Gross National Product, central government expenditures, and population by region, organization, and country for 1983-1993. Table II (Part 2) includes arms transfer deliveries and total trade by region, organization, and country for 1983-1993. Table III (Part 3) provides cumulative information for 1991-1993 on arms transfer deliveries by major supplier and recipient country. Table IV (Part 4) contains arms transfer deliveries and agreements for 1983-1993 by supplier and recipient region. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR06516.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

The CenSoc WWII Army Enlistment Dataset is a cleaned and harmonized version of the National Archives and Records Administration’s Electronic Army Serial Number Merged File, ca. 1938 - 1946 (2002). It contains enlistment records for over 9 million men and women who served in the United States Army, including the Army Air Corps, Women's Army Auxiliary Corps, and Enlisted Reserve Corps. We publish links between men in the CenSoc WWII Army Enlistment Dataset, Social Security Administration mortality data, and the 1940 Census. The CenSoc Enlistment-Census-1940 file links these enlistment records to the complete 1940 Census, and may be merged with IPUMS-USA census data using the HISTID identifier variable. The CenSoc Enlistment-Numident file links enlistment records to the Berkley Unified Numident Mortality Database (BUNMD), and the CenSoc Enlistment-DMF file links enlistment records to the Social Security Death Master File. For enlistment records in the Enlistment-Numident and Enlistment-DMF datasets that have been independently and additionally linked to the 1940 Census, we include the HISTID identifier variable that can be used to merge the data with IPUMS census data.

This data collection, which focuses on military spending and arms transfers, supplies information on 144 developed and developing countries of the world for which information was available. Data are provided in five tables. Table I (Part 1) consists of military expenditures, armed forces, Gross National Product, central government expenditures, and population by region, organization, and country for 1981-1991. Table II (Part 2) includes arms transfer deliveries and total trade by region, organization, and country for 1981-1991. Table III (Part 3) provides cumulative information for 1987-1991 on arms transfer deliveries by major supplier and recipient country. Table IV (Part 4) contains arms transfer deliveries and agreements for 1981-1991 by supplier and recipient region. Table V (included in the documentation) supplies cumulative information for 1987-1991 on number of arms delivered by selected supplier, recipient developing region, and major weapon type. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR06364.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

Project WARLUX - Soldiers and their communities in WWII: The impact and legacy of war experiences in Luxembourg is a research project based at the Luxembourg Centre for Contemporary and Digital History (C²DH) (University of Luxembourg). The projects focuses on the war experiences of male Luxembourgers born between 1920 and 1927 who were recruited and conscripted into Nazi German services (Reichsarbeitsdienst (RAD) and Wehrmacht) under the Nazi occupation in Luxembourg during the Second World War.

Data Sample

While over 12,000 men and women were affected by the conscription, Project WARLUX focuses on a case study of 304 recruits from Schifflange and their families. In total, the data sample includes around 1200 persons, recruits and their family members.

Origin of the data

The dataset primarily consists of compiled archival documentation, including organizational and official documents, statistics, and standardized fiches and cards. These sources are primarily sourced from the Luxembourgish National Archives and other relevant repositories.

In addition to basic information such as name, birth date, and residence, the (internal) dataset also incorporates military records sourced from German archives. Furthermore, supplementary information related to captivity, repatriation, and compensation was collected in the post-war period. The surveys and statistics conducted by the Luxembourgish state provide valuable insights into the experiences and trajectories of the war-affected generation.

It is important to note that the dataset is a composite of multiple heterogeneous sources, reflecting its diverse origins.

Database

The researchers involved in the WARLUX project opted for the utilization of a relational database, nodegoat.

The WARLUX project adheres to an object-oriented approach, which is reflected in the core functionalities provided by nodegoat. Given the project's specific focus on the war experiences of recruited Luxembourgers within Nazi services such as the Wehrmacht and RAD, the included data model (warlux data model file) represents only a partial depiction of the comprehensive nodegoat environment employed in the WARLUX project. Within this data model, the interconnected objects and their respective sub-objects are presented, with particular emphasis placed on the individual profiles of recruits and their involvement in military service.

As the data can not be published due to restriction, the team provides a pseudonymized dataset as an example of the data structure.

The provided dataset shows the male recruits (and conscripts) of the Case Study Schifflange (born between 1920 and 1927). It includes

• nodegoat ID
• their birthdate
• information on death if it occurred during the war
• whereabout after the war (unknown, missing, KIA, returned etc.)

The dataset also includes references to their recruitment into

• the Wehrmacht and/or the RAD as well as their subsequent activities such as
• being captured as a Prisoner of War (POW)
• serving for the Allied Forces
• desertion, or
• draft evasion (réfractaire).

The access to the WARLUX nodegoat database, on recruits of Schifflange/Luxembourg is restricted due to sensitive data. For further questions please contact warlux@uni.lu

The project is funded by the Fond National de la Recherche Luxembourg (FNR).

Description

This comprehensive dataset provides a wealth of information about all countries worldwide, covering a wide range of indicators and attributes. It encompasses demographic statistics, economic indicators, environmental factors, healthcare metrics, education statistics, and much more. With every country represented, this dataset offers a complete global perspective on various aspects of nations, enabling in-depth analyses and cross-country comparisons.

Key Features

Country: Name of the country.

Density (P/Km2): Population density measured in persons per square kilometer.

Abbreviation: Abbreviation or code representing the country.

Agricultural Land (%): Percentage of land area used for agricultural purposes.

Land Area (Km2): Total land area of the country in square kilometers.

Armed Forces Size: Size of the armed forces in the country.

Birth Rate: Number of births per 1,000 population per year.

Calling Code: International calling code for the country.

Capital/Major City: Name of the capital or major city.

CO2 Emissions: Carbon dioxide emissions in tons.

CPI: Consumer Price Index, a measure of inflation and purchasing power.

CPI Change (%): Percentage change in the Consumer Price Index compared to the previous year.

Currency_Code: Currency code used in the country.

Fertility Rate: Average number of children born to a woman during her lifetime.

Forested Area (%): Percentage of land area covered by forests.

Gasoline_Price: Price of gasoline per liter in local currency.

GDP: Gross Domestic Product, the total value of goods and services produced in the country.

Gross Primary Education Enrollment (%): Gross enrollment ratio for primary education.

Gross Tertiary Education Enrollment (%): Gross enrollment ratio for tertiary education.

Infant Mortality: Number of deaths per 1,000 live births before reaching one year of age.

Largest City: Name of the country's largest city.

Life Expectancy: Average number of years a newborn is expected to live.

Maternal Mortality Ratio: Number of maternal deaths per 100,000 live births.

Minimum Wage: Minimum wage level in local currency.

Official Language: Official language(s) spoken in the country.

Out of Pocket Health Expenditure (%): Percentage of total health expenditure paid out-of-pocket by individuals.

Physicians per Thousand: Number of physicians per thousand people.

Population: Total population of the country.

Population: Labor Force Participation (%): Percentage of the population that is part of the labor force.

Tax Revenue (%): Tax revenue as a percentage of GDP.

Total Tax Rate: Overall tax burden as a percentage of commercial profits.

Unemployment Rate: Percentage of the labor force that is unemployed.

Urban Population: Percentage of the population living in urban areas.

Latitude: Latitude coordinate of the country's location.

Longitude: Longitude coordinate of the country's location.

Potential Use Cases

Analyze population density and land area to study spatial distribution patterns.

Investigate the relationship between agricultural land and food security.

Examine carbon dioxide emissions and their impact on climate change.

Explore correlations between economic indicators such as GDP and various socio-economic factors.

Investigate educational enrollment rates and their implications for human capital development.

Analyze healthcare metrics such as infant mortality and life expectancy to assess overall well-being.

Study labor market dynamics through indicators such as labor force participation and unemployment rates.

Investigate the role of taxation and its impact on economic development.

Explore urbanization trends and their social and environmental consequences.