This is a Dataset of the World Population Consisting of Each and Every Country. I have attempted to analyze the same data to bring some insights out of it. The dataset consists of 234 rows and 17 columns. I will analyze the same data and bring the below pieces of information regarding the same.

1. Continent Population Characteristics Analysis.
2. Analysis of Countries.
   • Top 10 Most Populated and Least Populated Countries
   • Top 10 Largest and Smallest Countries as per Area
   • Population Growth From 1970 to 2020 (50 Years)
3. Countries Represent % Of World Population.
   • Countries that represent below 0.1% of the World Population.
   • Countries that represent above 2% of the world Population
   • Top 10 Over Populated Countries based on Density Per Sq KM.
   • Top 10 Least Populated Countries based on Density Per Sq KM.

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.

Amazonia holds the largest continuous area of tropical forests with intense land use change dynamics inducing water, carbon, and energy feedbacks with regional and global impacts. Much of our knowledge of land use change in Amazonia comes from studies of the Brazilian Amazon, which accounts for two thirds of the region. Amazonia outside of Brazil has received less attention because of the difficulty of acquiring consistent data across countries. We present here an agricultural statistics database of the entire Amazonia region, with a harmonized description of crops and pastures in geospatial format, based on administrative boundary data at the municipality level. The spatial coverage includes countries within Amazonia and spans censuses and surveys from 1950 to 2012. Harmonized crop and pasture types are explored by grouping annual and perennial cropping systems, C3 and C4 photosynthetic pathways, planted and natural pastures, and main crops. Our analysis examined the spatial pattern of ratios between classes of the groups and their correlation with the agricultural extent of crops and pastures within administrative units of the Amazon, by country, and census/survey dates. Significant correlations were found between all ratios and the fraction of agricultural lands of each administrative unit, with the exception of planted to natural pastures ratio and pasture lands extent. Brazil and Peru in most cases have significant correlations for all ratios analyzed even for specific census and survey dates. Results suggested improvements, and potential applications of the database for carbon, water, climate, and land use change studies are discussed. The database presented here provides an Amazon-wide improved data set on agricultural dynamics with expanded temporal and spatial coverage

Even though Canada is the second largest country in the world in terms of land area, it ranks 33rd in terms of population. Almost all of Canada’s population is concentrated in a narrow band along the country’s southern edge. Nearly 80% of the total population lives within the 25 major metropolitan areas, which represent only 0.79% of the total area of the country.

The QoG Institute is an independent research institute within the Department of Political Science at the University of Gothenburg. Overall 30 researchers conduct and promote research on the causes, consequences and nature of Good Governance and the Quality of Government - that is, trustworthy, reliable, impartial, uncorrupted and competent government institutions. The main objective of our research is to address the theoretical and empirical problem of how political institutions of high quality can be created and maintained. A second objective is to study the effects of Quality of Government on a number of policy areas, such as health, the environment, social policy, and poverty. QoG Standard Dataset is our largest data set consisting of more than 2,000 variables from sources related to the Quality of Government. In the QoG Standard CS dataset, data from and around 2018 is included. Data from 2018 is prioritized, however, if no data is available for a country for 2018, data for 2019 is included. If no data exists for 2019, data for 2017 is included, and so on up to a maximum of +/- 3 years. In the QoG Standard TS dataset, data from 1946 to 2021 is included and the unit of analysis is country-year (e.g., Sweden-1946, Sweden-1947, etc.). QoG-institutet är ett oberoende forskningsinstitut som tillhör Statsvetenskapliga institutionen vid Göteborgs universitet. Sammanlagt är det ungefär 30 forskare som bedriver internationell forskning om orsaker till och konsekvenserna av korruption och samhällsstyrningens kvalitet. Forskningen fokuserar på det teoretiska och empiriska problemet hur politiska institutioner av hög kvalitet kan skapas och upprätthållas, samt studerar effekterna av samhällsstyrningens kvalitet på ett antal olika politikområden, som exempelvis hälsa, miljö, socialpolitik och fattigdom. QoG Standard Dataset är vår största datauppsättning som består av mer än 2 000 variabler från källor relaterade till konceptet Quality of Government. I QoG Standard CS dataset ingår data från omkring 2018. Data från 2018 är prioriterat, men där inga uppgifter finns tillgängliga för 2018 för ett specifikt land så ingår data för 2019. Om inga uppgifter finns tillgängliga för 2019 så ingår data för 2017 och så vidare upp till max +/- 3 år. I QoG Standard TS dataset ingår data från 1946 till 2021 och analysenheten är land-år (t.ex. Sverige-1946, Sverige-1947, etc.). In the QoG Standard CS dataset, data from and around 2018 is included. Data from 2018 is prioritized, however, if no data is available for a country for 2018, data for 2019 is included. If no data exists for 2019, data for 2017 is included, and so on up to a maximum of +/- 3 years. Time-series dataset: 194 countries which are members of the United Nations well as previous members of the UN provided that their de facto sovereignty has not changed substantially since they were members. Plus an addition of 17 historical countries. A total of 211 nations. Cross-sectional dataset: 194 countries which are members of the United Nations well as previous members of the UN provided that their de facto sovereignty has not changed substantially since they were members. Tidsseriedataset: 194 länder som är medlemmar i FN eller som tidigare varit medlemmar och vars suveränitet inte förändrats sedan medlemskapet. Samt 17 nationer som upphört att existera. Totalt 211 nationer. Tvärsnittsdataset: 194 länder som är medlemmar i FN eller som tidigare varit medlemmar och vars suveränitet inte förändrats sedan medlemskapet.

The majority of the Canadian population, about 60% is concentrated within a thin belt of land representing 2.2% of the land between Windsor, Ontario and Quebec City. Even though Canada is the second largest country in the world in terms of land area, it only ranks 33rd in terms of population. The agricultural areas in the Prairies and eastern Canada have higher population densities than the sparsely populated North, but not as high as southern Ontario or southern Quebec.

The national inventory of land purchases and leases in Lao PDR is unique in providing comprehensive in-depth analysis of the extent and impacts of large scale land acquisitions across the country. It represents a major contribution to achieving greater transparency in what has previously been a very opaque field of business, and could serve as a model for other countries. Its major asset is the systematic and spatially-referenced compilation of data on the location, extent and implementation status of land-based investments.

Context

This is a dataset comprising of the 380 municipalities in the Netherlands, which is a country in Western Europe with a population of approximately 17 million people. While Holland's population size is comparable to a country like Chile, the area that the Dutchmen have to share only comprises of 41,543 square kilometers, of which more than 18% consists of water (Chile: 756,096 square kilometers). Nonetheless, the Netherlands is the world's second-largest exporter of food and agricultural products.

'The Netherlands' literally translates to: 'lower countries', strongly influenced by the flat geography that characterises its lands. It is widely known as a peaceful and tolerant place to live and work, consequently ranking high in international indexes. According to the UN, the Netherlands is the sixth-happiest country in the world (United Nations World Happiness Report, 2017).

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The municipality of Amsterdam.

Content

Rows (380):

• The 380 different municipalities in the Netherlands.

Columns (30):

• municipality: the name of the municipality.
• province: the name of the province that the municipality lies in.
• latitude: the latitude of the municipality.
• longitude: the longitude of the municipality.
• surface_km2: the surface (total area) of the municipality (land & water) in surface kilometers.
• surface_land_km2: the surface (total area) of the municipality (land only) in surface kilometers.
• surface_water_km2: the surface (total area) of the municipality (water only) in surface kilometers.
• population: the total population of the municipality.
• avg_household_income_2012: the average household income within the municipality in 2012 (€).
• avg_woz_2014: the average house value within the municipality in 2014 (€).
• ww_ratio_2014: the ratio of people that received unemployment benefits within the municipality in 2014.
• murders_2014: the amount of murders that took place within the municipality in 2014.
• university: a binary variable that indicates whether a Government Supported University lies within the borders of the municipality.
• asylum_migrants_1999: the amount of asylum migrants that settled in the municipality in 1999.
• asylum_migrants_2000: the amount of asylum migrants that settled in the municipality in 2000.
• asylum_migrants_2001: the amount of asylum migrants that settled in the municipality in 2001.
• asylum_migrants_2002: the amount of asylum migrants that settled in the municipality in 2002.
• asylum_migrants_2003: the amount of asylum migrants that settled in the municipality in 2003.
• asylum_migrants_2004: the amount of asylum migrants that settled in the municipality in 2004.
• asylum_migrants_2005: the amount of asylum migrants that settled in the municipality in 2005.
• asylum_migrants_2006: the amount of asylum migrants that settled in the municipality in 2006.
• asylum_migrants_2007: the amount of asylum migrants that settled in the municipality in 2007.
• asylum_migrants_2008: the amount of asylum migrants that settled in the municipality in 2008.
• asylum_migrants_2009: the amount of asylum migrants that settled in the municipality in 2009.
• asylum_migrants_2010: the amount of asylum migrants that settled in the municipality in 2010.
• asylum_migrants_2011: the amount of asylum migrants that settled in the municipality in 2011.
• asylum_migrants_2012: the amount of asylum migrants that settled in the municipality in 2012.
• asylum_migrants_2013: the amount of asylum migrants that settled in the municipality in 2013.
• asylum_migrants_2014: the amount of asylum migrants that settled in the municipality in 2014.
• asylum_migrants_2015: the amount of asylum migrants that settled in the municipality in 2015.

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The municipality of Rotterdam.

The table Global Temperatures by Major City is part of the dataset Climate Change: Earth Surface Temperature Data, available at https://columbia.redivis.com/datasets/1e0a-f4931vvyg. It contains 239177 rows across 7 variables.

Australia Population Density: People per Square Km data was reported at 3.382 Person/sq km in 2022. This records an increase from the previous number of 3.339 Person/sq km for 2021. Australia Population Density: People per Square Km data is updated yearly, averaging 2.263 Person/sq km from Dec 1961 (Median) to 2022, with 62 observations. The data reached an all-time high of 3.382 Person/sq km in 2022 and a record low of 1.365 Person/sq km in 1961. Australia Population Density: People per Square Km data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Australia – Table AU.World Bank.WDI: Population and Urbanization Statistics. Population density is midyear population divided by land area in square kilometers. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship--except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. Land area is a country's total area, excluding area under inland water bodies, national claims to continental shelf, and exclusive economic zones. In most cases the definition of inland water bodies includes major rivers and lakes.;Food and Agriculture Organization and World Bank population estimates.;Weighted average;