This dataset comprises 348 files, each representing a unique economic indicator for the BRICS nations—Brazil, Russia, India, China, and South Africa—spanning from 1970 to 2020. The dataset includes a wide array of economic metrics such as government consumption expenditure, GDP growth, adjusted savings, and various other national accounts data. This comprehensive dataset is ideal for economic research, financial analysis, and policy evaluation, offering a robust foundation for exploring economic trends and making data-driven decisions.

Key Features: - Diversity of Indicators: Covers a wide range of economic indicators, including net national income, government expenditure, GDP, and more. - Historical Coverage: Provides data spanning five decades, enabling both historical trend analysis and long-term forecasting. - Country Focus: Specifically tailored to the BRICS nations, offering insights into some of the world’s most influential emerging economies.

Usage

This dataset can be utilized for various purposes, such as: - Economic Analysis: Researchers can use the dataset to study economic trends and performance in BRICS countries. - Machine Learning: Data scientists can train models to predict future economic indicators or identify patterns in the data. - Policy Development: Policymakers can analyze the data to develop informed strategies for economic development.

Example Use Case: Suppose you want to analyze the trend in GDP per capita growth across BRICS nations. You could load the relevant files, clean the data, and use statistical tools or machine learning models to study the trend and make predictions.

System

This dataset is self-contained and can be integrated into broader economic research systems. The data files are in CSV format, making them easy to load and manipulate with standard data analysis tools like Python, R, and Excel.

Integration: While the dataset is standalone, it can be combined with other datasets or models for more complex analyses, such as predicting future economic performance or simulating policy impacts.

Data Provenance

The dataset is sourced from the World Bank’s BRICS Economic Indicators, a trusted and comprehensive source of economic data. The data was compiled, cleaned, and structured to facilitate easy analysis and integration into various analytical workflows.

Source: Kaggle - BRICS World Bank Indicators Dataset Coverage: The dataset includes data from Brazil, Russia, India, China, and South Africa, from 1970 to 2020.

Data Preprocessing: Each file was cleaned to remove inconsistencies, and missing values were handled appropriately to ensure the quality and reliability of the data.

Data Overview

The dataset is organized into 348 CSV files, each focusing on a specific economic indicator. Examples include: - GDP per Capita (Constant 2010 US$): Tracks the GDP per capita adjusted for inflation. - Government Final Consumption Expenditure (% of GDP): Measures government spending as a percentage of GDP. - Adjusted Net Savings: Accounts for environmental depletion and degradation in national savings.

Each file contains the following columns: - SeriesName: Describes the economic indicator. - CountryName: The name of the BRICS country. - Year: The year the data was recorded. - Value: The numerical value of the indicator for that year.

This dataset provides a rich resource for anyone looking to delve into the economic history and performance of BRICS countries, offering the data necessary to explore past trends and project future developments.

Graph and download economic data for Coincident Economic Activity Index for the United States (USPHCI) from Jan 1979 to Aug 2025 about coincident economic activity, indexes, and USA.

The Economic Decline Indicator considers factors related to economic decline within a country. For example, the Indicator looks at patterns of progressive economic decline of the society as a whole as measured by per capita income, Gross National Product, unemployment rates, inflation, productivity, debt, poverty levels, or business failures. It also takes into account sudden drops in commodity prices, trade revenue, or foreign investment, and any collapse or devaluation of the national currency. The Economic Decline Indicator further considers the responses to economic conditions and their consequences, such as extreme social hardship imposed by economic austerity programs, or perceived increasing group inequalities. The Economic Decline Indicator is focused on the formal economy – as well as illicit trade, including the drug and human trafficking, and capital flight, or levels of corruption and illicit transactions such as money laundering or embezzlement. Quality/Lineage: The data is downloaded from the above link http://fundforpeace.org/fsi/indicators/e1/ and manipulated only table format keeping the value same for all the countries as the requirement of the Strive database. The map is created based on the values of the country using rworldmap package in R.

The Uneven Economic Development Indicator considers inequality within the economy, irrespective of the actual performance of an economy. For example, the Indicator looks at structural inequality that is based on group (such as racial, ethnic, religious, or other identity group) or based on education, economic status, or region (such as urban-rural divide). The Indicator considers not only actual inequality, but also perceptions of inequality, recognizing that perceptions of economic inequality can fuel grievance as much as real inequality, and can reinforce communal tensions or nationalistic rhetoric. Further to measuring economic inequality, the Indicator also takes into account the opportunities for groups to improve their economic status, such as through access to employment, education, or job training such that even if there is economic inequality present, to what degree it is structural and reinforcing. Quality/Lineage: The data is downloaded from the above link http://fundforpeace.org/fsi/indicators/e2/ and manipulated only table format keeping the value same for all the countries as the requirement of the Strive database. The map is created based on the values of the country using rworldmap package in R.

The OECD organizes the MEI database using a hierarchical four level subject system of increasing detail, for example: · Production / Commodity Output / Crude Steel / Total Some series are defined just by three levels or fewer, for example: · Interest rates / 90 day rates / Interbank rate The topics listed below correspond to the first OECD subject level. - leading indicators (indicators that run 6-12 months ahead of GNP cycle) - national accounts - national income - production - business tendency surveys - consumer surveys - manufacturing - construction - domestic demand - employment - unemployment - other labour market indicators - labor compensation - producer prices - consumer price index - other prices - monetary aggregates and their components - domestic credit and debt - interest rates - security issues - share prices - currency conversions - external finance - foreign trade - balance of payments - capital and financial accounts - financial accounts - net errors and omissions - world trade

Monthly and long-term United States economic indicators data: historical series and analyst forecasts curated by FocusEconomics.

Monthly and long-term Chad economic indicators data: historical series and analyst forecasts curated by FocusEconomics.

Nighttime Lights Economic Indicators Market Outlook

According to our latest research, the global Nighttime Lights Economic Indicators market size was valued at USD 1.84 billion in 2024, reflecting robust adoption across multiple sectors. The market is expected to grow at a CAGR of 14.2% from 2025 to 2033, reaching USD 5.31 billion by 2033. This growth is primarily driven by increased reliance on satellite and remote sensing data for real-time economic analysis, urban planning, and disaster management, as organizations worldwide seek more accurate, timely, and granular economic indicators beyond traditional data sources.

One of the primary growth factors for the Nighttime Lights Economic Indicators market is the increasing demand for high-resolution, real-time economic data to support decision-making in both the public and private sectors. Traditional economic indicators often suffer from time lags, limited spatial granularity, and potential biases, making them less suitable for rapid response or localized analysis. Nighttime lights data, captured via satellite and aerial imagery, offers a dynamic and unbiased proxy for economic activity, urbanization, and infrastructure development. This capability is particularly valuable for tracking economic growth in regions with limited statistical infrastructure or where ground-based data collection is challenging. The proliferation of advanced remote sensing technologies and the decreasing cost of satellite imagery acquisition have further democratized access to these data sources, enabling a broader range of stakeholders to leverage nighttime lights as a reliable economic indicator.

Another significant driver is the integration of advanced analytics, such as machine learning and geospatial information systems (GIS), with nighttime lights data to extract actionable insights. These technologies allow for the automated processing and interpretation of vast amounts of imagery, transforming raw visual data into meaningful economic metrics. For instance, machine learning algorithms can identify patterns in light intensity that correlate with economic output, infrastructure expansion, or disaster impact. This analytical capability is crucial for applications such as urban planning, disaster management, and environmental monitoring, where timely and precise information is essential for effective intervention. The growing sophistication of these analytical tools is expanding the utility of nighttime lights data, making it a cornerstone of data-driven policy and business strategies.

The expanding application landscape also contributes to the market’s growth trajectory. Beyond economic forecasting and urban planning, nighttime lights data is increasingly used for disaster response, environmental monitoring, and infrastructure development. Governments and humanitarian organizations, for example, utilize changes in nighttime illumination to assess the impact of natural disasters or conflicts, enabling rapid resource allocation and recovery planning. Similarly, environmental agencies monitor light pollution and its effects on ecosystems, while infrastructure developers assess growth patterns to guide investment decisions. The versatility of nighttime lights data, coupled with its global coverage, positions it as a critical resource for a wide array of stakeholders seeking to enhance situational awareness and optimize resource allocation.

Regionally, the market exhibits strong growth in Asia Pacific and North America, driven by robust investments in space technology, urbanization, and digital infrastructure. Asia Pacific, in particular, is witnessing accelerated adoption due to rapid urban expansion in countries such as China and India, where traditional economic data collection faces significant challenges. North America benefits from advanced satellite networks and a mature ecosystem of analytics providers, supporting widespread integration of nighttime lights data across sectors. Europe follows closely, leveraging the data for sustainable development and climate monitoring initiatives. Meanwhile, Latin America and the Middle East & Africa are gradually increasing their adoption, supported by international collaborations and technology transfer initiatives. These regional dynamics highlight the global relevance and transformative potential of nighttime lights economic indicators in shaping the future of economic analysis and planning.

The data set records the main economic indicators of the establishment of modern enterprise system by key enterprises in Qinghai Province from 2001 to 2006, and the data are divided by different enterprise names. The data are collected from the statistical yearbook of Qinghai Province issued by the Bureau of statistics of Qinghai Province. The data set contains five data tables, which are: the main economic indicators of key enterprises establishing modern enterprise system in 2001.xls, the main economic indicators of key enterprises establishing modern enterprise system in 2002.xls, the main economic indicators of key enterprises establishing modern enterprise system in 2003.xls, the main economic indicators of key enterprises establishing modern enterprise system in 2004.xls, and the main economic indicators of key enterprises establishing modern enterprise system in 2004.xls The main economic indicators of 2006.xls. The data table structure is the same. For example, the data table in 2001 has 19 fields: Field 1: indicator name Field 2: number of enterprises Field 3: total assets Field 4: cumulative foreign investment Field 5: average annual balance of current assets Field 6: total liabilities Field 7: current liabilities Field 8: end of year shareholder (owner) Equity Field 9: Equity Field 10: main business income Field 11: main business cost Field 12: investment income Field 13: total profit Field 14: Investment completion amount of fixed assets Field 15: R & D expenses Field 16: year end number of employees Field 17: employees on duty Field 18: other employees Field 19: remuneration of employees

General Abstract/Purpose (70 words): Data were collected to assist in cost-benefit analysis of flood mitigation actions that could be taken by the U.S. and Canada to prevent structural damage and associated costs and losses in future flood conditions, including conditions worse than the historical record flooding in spring of 2011. Data were commissioned to revise or fill gaps in estimates from structural damage modeling software commonly used for depth-damage economic assessments of flood impacts. The Summary text that immediately follows this introductory sentence offers overview information, but also includes context and detail that is not present in the Word document ("Principal Indicator Combo SET - REVIEW FINAL v2.docx") that constitutes the main body of this data release, supported by Excel files (that are copied without formatting in csv files for each Excel tab). Lake Champlain is a relatively large lake bordered by New York on the western side and Vermont on the eastern side, whose uppermost region spans the U.S.-Canadian border. The 436 mi^2 (1,130 km^2) lake sits within a 9,277 mi^2 (23,900 km^2) basin, and Champlain’s only drainage point is north into Canada via the Richelieu River into the province of Quebec. About 75% of the Lake Champlain shoreline of New York is within Adirondack State Park, covering all or part of Clinton, Essex, and Washington counties. Of Vermont’s 14 counties, Franklin, Chittenden, and Addison Counties border Lake Champlain, while Grand Isle is surrounded by Champlain and at its northern edge the Canadian border. Development and anthropogenic modifications, especially over the last 50 years, have converted wetlands, changed the timing and flows of water, and increased impervious surface area including new residences in floodplains on both sides of the border. Occasionally there is damaging flooding, with significant economic damages in New York, Vermont, and Quebec. With flood stage at 99.57’ (30.35m) and major flooding from 101.07’ (30.81m) over sea level, a 101.4’ (30.91m) flood in 1993 broke the previous recorded high flood in 1869. Following the third heaviest recorded snow, almost no seasonal snowmelt, then heavy rains, the spring of 2011 brought record flooding more than one foot over the 1993 record to 102.77’ (31.32m), expanding the lake’s area by 66 mi^2 (106.2 km^2, or about 5.8%). From reaching flood stage to peak and then returning to a lake level below flood stage took around six weeks. Wind-to-wave-driven erosion was up to 5 feet (1.5m) above static lake elevation in some areas. The record flood height (102.77’) is often reported as 103.07’ or 103.27’ in Burlington, owing to different vertical and horizontal datums and digital elevation models (DEMs), and some wave action. In a 1976 flood the U.S. side incurred more than 50% of the economic damages, but in 2011, Quebec experienced some 80% of structural and economic damages estimated at $82 million. Tropical Storm Irene hit the area in August of 2011 and did far more damage on the American side, for example spurring $29 million in home and business repair loans for damage across 12 of Vermont’s 14 counties. Co-reporting across the two events for 2011 confounded some data, making it impossible to separately identify spring flooding numbers. Following the Boundary Waters Treaty between the U.S. and Canada in 1909, from 1912 the International Joint Commission (IJC) handles boundary water issues between the two countries. The IJC Lake Champlain Richelieu River (LCRR) Study Project is a bi-national (U.S., Canada) multi-agency effort to assess flood risk and flood mitigation options as they affect potential structural damages and wider non-structural damages that include secondary economic, community, and psychological effects. Key economic parts of the report to the IJC LCRR Study Board are calculated using a new tool developed for the study project, an Integrated Socio-Economic-Environmental (ISEE) model, with forecasting for damages up to 105.57’ flood (105.9’, or 106’ [32.3m] for short, by alternative datum and DEMs, as apply in some of the modeling and estimations herein). There is also a Collaborative Decision Support Tool (CDST) that also processes non-structural economic damages, costs, or losses as inputs. CDST is a pared-down version of ISEE that applies historical estimates but does not project outcomes for higher floods in the future. Outputs from this data release are inputs to the ISEE or the CDST for calculations of the benefit-to-cost ratios projected to follow different structural interventions. For example adding a weir in the Richelieu River yielded a greater-than-one benefit-to-cost ratio in late-stage modeling, whereas a dam on either side, or an entirely new canal on the Canadian side, were never entertained as cost feasible or even appropriate. USGS economists were contracted to supply economic “principal indicators” for potential U.S.-side depth-damage effects from lake-rise flooding. The scope of this analysis is limited by several factors associated with the objectives of the IJC LCRR Study Board. Damages from tributary flooding were defined out of a project focused on joint-management options for mitigating flood effects, as tributary flows would be managed only by the U.S. Uncommonly low Lake Champlain levels were also ultimately considered as a stakeholder concern (the weir option also addressed this concern). It is standard to model economic damages to structures and related economic costs due to flooding using the FEMA-designed Hazus®-MH (Multi-Hazard) Flood Model of structural damages (https://www.fema.gov/flood-maps/products-tools/hazus; the Hazus-MH Technical Manual, 2011, 569pp, which explains definitions and parameterization of the tool rather than use of the tool itself, is a frequently referred source here). “Hazus” (tool) modeling is used in the LCRR Study Board research to estimate structural damages at different flood depths, and the primary work presented in this data release estimates depth-damage values for “Principal Indicators” (PIs) that were defined to supplement or alternatively estimate results from applying Hazus, where gaps exist or where straight Hazus values may be questionable in the LCRR context. A number of Principal Indicators were estimated on the Canadian and U.S. sides, where no PIs include any estimates for repair of structural damage, as those calculations are done separately using the Hazus tool (or the ISEE model application with Hazus outputs as inputs). In the final list, the USGS team produced estimates for six PIs: temporary lodging costs, residential debris clean-up and disposal, damage to roads and bridges, damage to water treatment facilities, income loss from industrial or commercial properties, and separately and specifically recreation sector income loss. So associated with residential damage, the costs of securing emergency and longer-term lodging when a household is displaced by lake-rise flooding are estimated, and the costs of cleaning up and removing and disposing of debris from residential property damage are estimated. In the public sector, costs of clean up and repair of damages to roads and bridges from lake-rise flooding are calculated, as are damages and potential revenue losses from flood mitigation measures and service reductions where public or private water utilities are inundated by lake-rise flooding. In the commercial sector, revenue losses from being closed for business due to flooding are calculated outside of the recreation sector, and then also for the recreation sector as lakeside campgrounds, marinas, and ferry services (where the last is also used for local commercial traffic). All of these PIs are characterized by being little-discussed in the literature. To derive information necessary to bound economic estimates for each of the 6 PIs, consultation with subject-matter experts in New York and Vermont (or at agencies covering these areas) was employed more often than anything in peer-reviewed literature specifically applied. Depth-damage functions that result are not formal mathematical functions, and across the six PIs calculations and results tend to be in increments of one foot or more. Results thus suggest magnitudes of costs that comply with reasonable scenario assumptions for a small but fairly consistent set of flood depths from 99.57’ to 105.57’, where the latter value is almost three feet (1m) above the historic maximum flood. Nothing reported in these estimates is empirically deterministic, or capable of including probabilistic error margins. Simplifying assumptions serve first to actually simplify the calculations and legibility of estimated results, and second to avoid the impression that specifically calibrated empirical estimations are being conducted. This effort offers plausible, logical, reliable, and reproducible magnitudes for estimates, using a method that can be easily modified if better information becomes available for future estimations. Certain worksheets and specific results are withheld to avoid the outright identification of specific businesses (or homes). Facts in this abstract generally attribute to: International Lake Champlain-Richelieu River Study Board, 2019. The Causes and Impacts of Past Floods in the Lake Champlain-Richelieu River Basin – Historical Information on Flooding, A Report to the International Joint Commission, 108pp (https://ijc.org/en/lcrr). Some supplemental factual support is from: Lake Champlain Basin Program, 2013. Flood Resilience in the Lake Champlain Basin and Upper Richelieu River, 93 pp (https://ijc.org/en/lcrr).

US County Demographics

Social, Health, and Economic Indicators

By Danny [source]

About this dataset

This dataset contains US county-level demographic data from 2016, giving insight into the health and economic conditions of counties in the United States. Aggregated and filtered from various sources such as the US Census Small Area Income and Poverty Estimates (SAIPE) Program, American Community Survey, CDC National Center for Health Statistics, and more, this comprehensive dataset provides information on population as well as desert population for each county. Additionally, data is split between metropolitan and nonmetropolitan areas according to the Office of Management and Budget's 2013 classification scheme. Valuable information pertaining to infant mortality rates and total population are also included in this detailed set of data. Use this dataset to gain a better understanding of one of our nation's most essential regions

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How to use the dataset

• Look at the information within the 'About this Dataset' section to have an understanding of what data sources were used to create this dataset as well as any transformations that may have been done while creating it.
• Familiarize yourself with the columns provided in the data set to understand what information is available for each county such as total population (totpop), parental education level (educationLvl), median household income (medianIncome), etc.,
• Use a combination of filtering and sorting techniques to narrow down results and focus in on more specific county demographics that you are looking for such as total households living below poverty line by state or median household income per capita between two counties etc.,
• Keep in mind any additional transformations/simplifications/aggregations done during step 2 when using your data for analysis. For example, if certain variables were pivoted during step two from being rows into columns because it was easier to work with multiple years of income levels by having them all consolidated into one column then be aware that some states may not appear in all records due to those transformations being applied differently between regions which could result in missing values or other inconsistencies when doing downstream analysis on your selected variables.
• Utilize resources such as Wikipedia and government census estimates if you need more detailed information surrounding these demographic characteristics beyond what's available within our current dataset – these can be helpful when conducting further research outside of solely relying on our provided spreadsheet values alone!

Research Ideas

• Creating a US county-level heat map of infant mortality rates, offering insight into which areas are most at risk for poor health outcomes.
• Generating predictive models from the population data to anticipate and prepare for future population trends in different states or regions.
• Developing an interactive web-based tool for school districts to explore potential impacts of student mobility on their area's population stability and diversity

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: Food Desert.csv | Column name | Description | |:--------------------|:----------------------------------------------------------------------------------| | year | The year the data was collected. (Integer) | | fips | The Federal Information Processing Standard (FIPS) code for the county. (Integer) | | state_fips | The FIPS code for the state. (Integer) | | county_fips | The FIPS code for the county. (Integer)...

(StatCan Product) This survey is conducted to release the official statistics which report on the operation of the Employment Insurance Program and to provide complementary labour market statistics, for example, for areas not covered by other Statistics Canada surveys (e.g. small geographic areas for the Yukon, Northwest Territories and Nunavut). Customization details: This information product has been customized to present employment insurance statistics for Alberta's 14 Regional Economic Indicator Regions annually from 2006 to 2011. For more information on these regions see: http://www.albertacanada.com/about-alberta/regional-economic-indicators.html The two types of beneficiaries presented are: - Total of income beneficiaries - Beneficiaries with regular benefits

Coliforms: Southeast: Rio de Janeiro data was reported at 98.810 % in 2022. This records a decrease from the previous number of 105.830 % for 2021. Coliforms: Southeast: Rio de Janeiro data is updated yearly, averaging 95.040 % from Dec 2012 (Median) to 2022, with 11 observations. The data reached an all-time high of 105.830 % in 2021 and a record low of 83.060 % in 2013. Coliforms: Southeast: Rio de Janeiro data remains active status in CEIC and is reported by Ministry of Cities. The data is categorized under Brazil Premium Database’s Environmental, Social and Governance Sector – Table BR.EVB014: Quality Indicators: Sample Quantity Compliance Index.

Coliforms: Southeast: Minas Gerais data was reported at 122.360 % in 2022. This records an increase from the previous number of 121.040 % for 2021. Coliforms: Southeast: Minas Gerais data is updated yearly, averaging 92.480 % from Dec 2012 (Median) to 2022, with 11 observations. The data reached an all-time high of 122.360 % in 2022 and a record low of 66.510 % in 2013. Coliforms: Southeast: Minas Gerais data remains active status in CEIC and is reported by Ministry of Cities. The data is categorized under Brazil Premium Database’s Environmental, Social and Governance Sector – Table BR.EVB014: Quality Indicators: Sample Quantity Compliance Index.

Coliforms: Central West: Distrito Federal data was reported at 132.340 % in 2022. This records an increase from the previous number of 122.720 % for 2021. Coliforms: Central West: Distrito Federal data is updated yearly, averaging 114.645 % from Dec 2014 (Median) to 2022, with 8 observations. The data reached an all-time high of 132.340 % in 2022 and a record low of 103.330 % in 2020. Coliforms: Central West: Distrito Federal data remains active status in CEIC and is reported by Ministry of Cities. The data is categorized under Brazil Premium Database’s Environmental, Social and Governance Sector – Table BR.EVB014: Quality Indicators: Sample Quantity Compliance Index.

Monthly and long-term Portugal economic indicators data: historical series and analyst forecasts curated by FocusEconomics.

This dataset simulates a set of key economic, social, and environmental indicators for 20 countries over the period from 2010 to 2019. The dataset is designed to reflect typical World Bank metrics, which are used for analysis, policy-making, and forecasting. It includes the following variables:

Country Name: The country for which the data is recorded. Year: The specific year of the observation (from 2010 to 2019). GDP (USD): Gross Domestic Product in billions of US dollars, indicating the economic output of a country. Population: The total population of the country in millions. Life Expectancy (in years): The average life expectancy at birth for the country’s population. Unemployment Rate (%): The percentage of the total labor force that is unemployed but actively seeking employment. CO2 Emissions (metric tons per capita): The per capita carbon dioxide emissions, reflecting environmental impact. Access to Electricity (% of population): The percentage of the population with access to electricity, representing infrastructure development. Country:

Description: Name of the country for which the data is recorded. Data Type: String Example: "United States", "India", "Brazil" Year:

Description: The year in which the data is observed. Data Type: Integer Range: 2010 to 2019 Example: 2012, 2015 GDP (USD):

Description: The Gross Domestic Product of the country in billions of US dollars, indicating the economic output. Data Type: Float (billions of USD) Example: 14200.56 (represents 14,200.56 billion USD) Population:

Description: The total population of the country in millions. Data Type: Float (millions of people) Example: 331.42 (represents 331.42 million people) Life Expectancy (in years):

Description: The average number of years a newborn is expected to live, assuming that current mortality rates remain constant throughout their life. Data Type: Float (years) Range: Typically between 50 and 85 years Example: 78.5 years Unemployment Rate (%):

Description: The percentage of the total labor force that is unemployed but actively seeking employment. Data Type: Float (percentage) Range: Typically between 2% and 25% Example: 6.25% CO2 Emissions (metric tons per capita):

Description: The amount of carbon dioxide emissions per person in the country, measured in metric tons. Data Type: Float (metric tons) Range: Typically between 0.5 and 20 metric tons per capita Example: 4.32 metric tons per capita Access to Electricity (%):

Description: The percentage of the population with access to electricity. Data Type: Float (percentage) Range: Typically between 50% and 100% Example: 95.7%

Interpretation of individual indicators.

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.

Data Source: This dataset was compiled from multiple data sources

If this was helpful, a vote is appreciated ❤️ Thank you 🙂

Coliforms: South: Rio Grande do Sul data was reported at 103.790 % in 2022. This records a decrease from the previous number of 110.240 % for 2021. Coliforms: South: Rio Grande do Sul data is updated yearly, averaging 103.790 % from Dec 2012 (Median) to 2022, with 11 observations. The data reached an all-time high of 110.240 % in 2021 and a record low of 99.450 % in 2019. Coliforms: South: Rio Grande do Sul data remains active status in CEIC and is reported by Ministry of Cities. The data is categorized under Brazil Premium Database’s Environmental, Social and Governance Sector – Table BR.EVB014: Quality Indicators: Sample Quantity Compliance Index.