Russia is the largest country in the world by far, with a total area of just over 17 million square kilometers. After Antarctica, the next three countries are Canada, the U.S., and China; all between 9.5 and 10 million square kilometers. The figures given include internal water surface area (such as lakes or rivers) - if the figures were for land surface only then China would be the second largest country in the world, the U.S. third, and Canada (the country with more lakes than the rest of the world combined) fourth. Russia Russia has a population of around 145 million people, putting it in the top ten most populous countries in the world, and making it the most populous in Europe. However, it's vast size gives it a very low population density, ranked among the bottom 20 countries. Most of Russia's population is concentrated in the west, with around 75 percent of the population living in the European part, while around 75 percent of Russia's territory is in Asia; the Ural Mountains are considered the continental border. Elsewhere in the world Beyond Russia, the world's largest countries all have distinctive topographies and climates setting them apart. The United States, for example, has climates ranging from tundra in Alaska to tropical forests in Florida, with various mountain ranges, deserts, plains, and forests in between. Populations in these countries are often concentrated in urban areas, and are not evenly distributed across the country. For example, around 85 percent of Canada's population lives within 100 miles of the U.S. border; around 95 percent of China lives east of the Heihe–Tengchong Line that splits the country; and the majority of populations in large countries such as Australia or Brazil live near the coast.

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.

This list includes both countries and dependent territories. Data based on the latest United Nations Population Division estimates.

• Country - Name of countries and dependent territories.
• Population2023 - Population in the year 2023
• YearlyChange - Percentage Yearly Change in Population
• NetChange - Net Change in Population
• Density(P/Km²)- Population density (population per square km)
• Land Area(Km²) - Land area of countries / dependent territories.
• Migrants(net) - Total number of migrants
• Fert.Rate - Fertility rate
• Med.Age - Median age of the population
• UrbanPop%- Percentage of urban population
• WorldShare - Population share

In 2023, China's cities made up one-fourth of the global land area, consisting of built-up urban areas with populations of 500,000 and more. India's urban areas made up more than ** percent of the land area classified as large urban cities worldwide.

Global Arable Land Area Share by Country (Hectares), 2023 Discover more data with ReportLinker!

Global Arable Land Area by Country, 2023 Discover more data with ReportLinker!

In 2023, the area of organic agricultural land in Europe is largest in Spain at over **** million hectares, followed by France and Italy with approximately **** and *** million hectares, respectively. In the United Kingdom (UK) there were approximately *** thousand hectares of organic agricultural land.

Forest area (% of land area) in South Asia was reported at 25.51 % in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. South Asia - Forest area (% of land area) - actual values, historical data, forecasts and projections were sourced from the World Bank on August of 2025.

Global Land Area Equipped for Irrigation by Country, 2023 Discover more data with ReportLinker!

Forest area (% of land area) in Norway was reported at 33.48 % in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. Norway - Forest area (% of land area) - actual values, historical data, forecasts and projections were sourced from the World Bank on August of 2025.

The Rural Access Index (RAI) is a measure of access, developed by the World Bank in 2006. It was adopted as Sustainable Development Goal (SDG) indicator 9.1.1 in 2015, to measure the accessibility of rural populations. It is currently the only indicator for the SDGs that directly measures rural access.The RAI measures the proportion of the rural population that lives within 2 km of an all-season road. An all-season road is one that is motorable all year, but may be temporarily unavailable during inclement weather (Roberts, Shyam, & Rastogi, 2006). This dataset implements and expands on the most recent official methodology put forward by the World Bank, ReCAP's 2019 RAI Supplemental Guidelines. This is, to date, the only publicly available application of this method at a global scale.MethodologyReCAP's methodology provided new insight on what makes a road all-season and how this data should be handled: instead of removing unpaved roads from the network, the ones that are classified as unpaved are to be intersected with topographic and climatic conditions and, whenever there’s an overlap with excess precipitation and slope, a multiplying factor ranging from 0% to 100% is applied to the population that would access to that road. This present dataset developed by SDSN's SDG Transformation Centre proposes that authorities ability to maintain and remediate road conditions also be taken into account.Data sourcesThe indicator relies on four major items of geospatial data: land cover (rural or urban), population distribution, road network extent and the “all-season” status of those roads.Land cover data (urban/rural distinction)Since the indicator measures the acess rural populations, it's necessary to define what is and what isn't rural. This dataset uses the DegUrba Methodology, proposed by the United Nations Expert Group on Statistical Methodology for Delineating Cities and Rural Areas (United Nations Expert Group, 2019). This approach has been developed by the European Commission Global Human Settlement Layer (GHSL-SMOD) project, and is designed to instil some consistency into the definitions based on population density on a 1-km grid, but adjusted for local situations.Population distributionThe source for population distribution data is WorldPop. This uses national census data, projections and other ancillary data from countries to produce aggregated, 100 m2 population data. Road extentTwo widely recognized road datasets are used: the real-time updated crowd-sourced OpenStreetMap (OSM) or the GLOBIO’s 2018 GRIP database, which draws data from official national sources. The reasons for picking the latter are mostly related to its ability to provide information on the surface (pavement) of these roads, to the detriment of the timeliness of the data, which is restrained to the year 2018. Additionally, data from Microsoft Bing's recent Road Detection project is used to ensure completeness. This dataset is completely derived from machine learning methods applied over satellite imagery, and detected 1,165 km of roads missing from OSM.Roads’ all-season statusThe World Bank's original 2006 methodology defines the term all-season as “… a road that is motorable all year round by the prevailing means of rural transport, allowing for occasional interruptions of short duration”. ReCAP's 2019 methodology makes a case for passability equating to the all-season status of a road, along with the assumption that typically the wet season is when roads become impassable, especially so in steep roads that are more exposed to landslides.This dataset follows the ReCAP methodology by creating an passability index. The proposed use of passability factors relies on the following three aspects:• Surface type. Many rural roads in LICs (and even in large high-income countries including the USA and Australia) are unpaved. As mentioned before, unpaved roads deteriorate rapidly and in a different way to paved roads. They are very susceptible to water ingress to the surface, which softens the materials and makes them very vulnerable to the action of traffic. So, when a road surface becomes saturated and is subject to traffic, the deterioration is accelerated. • Climate. Precipitation has a significant effect on the condition of a road, especially on unpaved roads, which predominate in LICs and provide much of the extended connectivity to rural and poor areas. As mentioned above, the rainfall on a road is a significant factor in its deterioration, but the extent depends on the type of rainfall in terms of duration and intensity, and how well the roadside drainage copes with this. While ReCAP suggested the use of general climate zones, we argue that better spatial and temporal resolutions can be acquired through the Copernicus Programme precipitation data, which is made available freely at ~30km pixel size for each month of the year.• Terrain. The gradient and altitude of roads also has an effect on their accessibility. Steep roads become impassable more easily due to the potential for scour during heavy rainfall, and also due to slipperiness as a result of the road surface materials used. Here this is drawn from slope calculated from SRTM Digital Terrain data.• Road maintenance. The ability of local authorities to remediate damaged caused by precipitation and landslides is proposed as a correcting factor to the previous ones. Ideally this would be measured by the % of GDP invested in road construction and maintenance, but this isn't available for all countries. For this reason, GDP per capita is adopted as a proxy instead. The data range is normalized in such a way that a road maxed out in terms of precipitation and slope (accessibility score of 0.25) in a country at the top of the GDP per capita range is brought back at to the higher end of the accessibility score (0.95), while the accessibility score of a road meeting the same passability conditions in a country which GDP per capita is towards the lower end is kept unchanged.Data processingThe roads from the three aforementioned datasets (Bing, GRIP and OSM) are merged together to them is applied a 2km buffer. The populations falling exclusively on unpaved road buffers are multiplied by the resulting passability index, which is defined as the normalized sum of the aforementioned components, ranging from 0.25 to. 0.9, with 0.95 meaning 95% probability that the road is all-season. The index applied to the population data, so, when calculated, the RAI includes the probability that the roads which people are using in each area will be all-season or not. For example, an unpaved road in a flat area with low rainfall would have an accessibility factor of 0.95, as this road is designed to be accessible all year round and the environmental effects on its impassability are minimal.The code for generating this dataset is available on Github at: https://github.com/sdsna/rai

Graph and download economic data for Consumer Price Index: Harmonised Prices: All Items Less Food, Energy, Tobacco, Alcohol: Total for the Euro Area (19 Countries) (CPHPLA01EZM661N) from Jan 1996 to Jan 2023 about alcohol, tobacco, core, harmonized, Euro Area, Europe, all items, CPI, inflation, price index, indexes, and price.

Terrestrial protected areas (% of total land area) in Costa Rica was reported at 26.5 % in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. Costa Rica - Terrestrial protected areas (% of total land area) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Total outbound internationally mobile tertiary students studying abroad, all countries, both sexes (number) in Brazil was reported at 67183 Persons in 2018, according to the World Bank collection of development indicators, compiled from officially recognized sources. Brazil - Total outbound internationally mobile tertiary students studying abroad, all countries, both sexes - actual values, historical data, forecasts and projections were sourced from the World Bank on August of 2025.

Total outbound internationally mobile tertiary students studying abroad, all countries, both sexes (number) in Vietnam was reported at 108527 Persons in 2018, according to the World Bank collection of development indicators, compiled from officially recognized sources. Vietnam - Total outbound internationally mobile tertiary students studying abroad, all countries, both sexes - actual values, historical data, forecasts and projections were sourced from the World Bank on August of 2025.

The average for 2023 based on 189 countries was 0.529 percent. The highest value was in China: 33.017 percent and the lowest value was in the Central African Republic: 0 percent. The indicator is available from 1980 to 2023. Below is a chart for all countries where data are available.

Overview

This dataset is the repository for the following paper submitted to Data in Brief:

Kempf, M. A dataset to model Levantine landcover and land-use change connected to climate change, the Arab Spring and COVID-19. Data in Brief (submitted: December 2023).

The Data in Brief article contains the supplement information and is the related data paper to:

Kempf, M. Climate change, the Arab Spring, and COVID-19 - Impacts on landcover transformations in the Levant. Journal of Arid Environments (revision submitted: December 2023).

Description/abstract

The Levant region is highly vulnerable to climate change, experiencing prolonged heat waves that have led to societal crises and population displacement. Since 2010, the area has been marked by socio-political turmoil, including the Syrian civil war and currently the escalation of the so-called Israeli-Palestinian Conflict, which strained neighbouring countries like Jordan due to the influx of Syrian refugees and increases population vulnerability to governmental decision-making. Jordan, in particular, has seen rapid population growth and significant changes in land-use and infrastructure, leading to over-exploitation of the landscape through irrigation and construction. This dataset uses climate data, satellite imagery, and land cover information to illustrate the substantial increase in construction activity and highlights the intricate relationship between climate change predictions and current socio-political developments in the Levant.

Folder structure

The main folder after download contains all data, in which the following subfolders are stored are stored as zipped files:

“code” stores the above described 9 code chunks to read, extract, process, analyse, and visualize the data.

“MODIS_merged” contains the 16-days, 250 m resolution NDVI imagery merged from three tiles (h20v05, h21v05, h21v06) and cropped to the study area, n=510, covering January 2001 to December 2022 and including January and February 2023.

“mask” contains a single shapefile, which is the merged product of administrative boundaries, including Jordan, Lebanon, Israel, Syria, and Palestine (“MERGED_LEVANT.shp”).

“yield_productivity” contains .csv files of yield information for all countries listed above.

“population” contains two files with the same name but different format. The .csv file is for processing and plotting in R. The .ods file is for enhanced visualization of population dynamics in the Levant (Socio_cultural_political_development_database_FAO2023.ods).

“GLDAS” stores the raw data of the NASA Global Land Data Assimilation System datasets that can be read, extracted (variable name), and processed using code “8_GLDAS_read_extract_trend” from the respective folder. One folder contains data from 1975-2022 and a second the additional January and February 2023 data.

“built_up” contains the landcover and built-up change data from 1975 to 2022. This folder is subdivided into two subfolder which contain the raw data and the already processed data. “raw_data” contains the unprocessed datasets and “derived_data” stores the cropped built_up datasets at 5 year intervals, e.g., “Levant_built_up_1975.tif”.

Code structure

1_MODIS_NDVI_hdf_file_extraction.R

This is the first code chunk that refers to the extraction of MODIS data from .hdf file format. The following packages must be installed and the raw data must be downloaded using a simple mass downloader, e.g., from google chrome. Packages: terra. Download MODIS data from after registration from: https://lpdaac.usgs.gov/products/mod13q1v061/ or https://search.earthdata.nasa.gov/search (MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V061, last accessed, 09th of October 2023). The code reads a list of files, extracts the NDVI, and saves each file to a single .tif-file with the indication “NDVI”. Because the study area is quite large, we have to load three different (spatially) time series and merge them later. Note that the time series are temporally consistent.

2_MERGE_MODIS_tiles.R

In this code, we load and merge the three different stacks to produce large and consistent time series of NDVI imagery across the study area. We further use the package gtools to load the files in (1, 2, 3, 4, 5, 6, etc.). Here, we have three stacks from which we merge the first two (stack 1, stack 2) and store them. We then merge this stack with stack 3. We produce single files named NDVI_final_*consecutivenumber*.tif. Before saving the final output of single merged files, create a folder called “merged” and set the working directory to this folder, e.g., setwd("your directory_MODIS/merged").

3_CROP_MODIS_merged_tiles.R

Now we want to crop the derived MODIS tiles to our study area. We are using a mask, which is provided as .shp file in the repository, named "MERGED_LEVANT.shp". We load the merged .tif files and crop the stack with the vector. Saving to individual files, we name them “NDVI_merged_clip_*consecutivenumber*.tif. We now produced single cropped NDVI time series data from MODIS.
The repository provides the already clipped and merged NDVI datasets.

4_TREND_analysis_NDVI.R

Now, we want to perform trend analysis from the derived data. The data we load is tricky as it contains 16-days return period across a year for the period of 22 years. Growing season sums contain MAM (March-May), JJA (June-August), and SON (September-November). December is represented as a single file, which means that the period DJF (December-February) is represented by 5 images instead of 6. For the last DJF period (December 2022), the data from January and February 2023 can be added. The code selects the respective images from the stack, depending on which period is under consideration. From these stacks, individual annually resolved growing season sums are generated and the slope is calculated. We can then extract the p-values of the trend and characterize all values with high confidence level (0.05). Using the ggplot2 package and the melt function from reshape2 package, we can create a plot of the reclassified NDVI trends together with a local smoother (LOESS) of value 0.3.
To increase comparability and understand the amplitude of the trends, z-scores were calculated and plotted, which show the deviation of the values from the mean. This has been done for the NDVI values as well as the GLDAS climate variables as a normalization technique.

5_BUILT_UP_change_raster.R

Let us look at the landcover changes now. We are working with the terra package and get raster data from here: https://ghsl.jrc.ec.europa.eu/download.php?ds=bu (last accessed 03. March 2023, 100 m resolution, global coverage). Here, one can download the temporal coverage that is aimed for and reclassify it using the code after cropping to the individual study area. Here, I summed up different raster to characterize the built-up change in continuous values between 1975 and 2022.

6_POPULATION_numbers_plot.R

For this plot, one needs to load the .csv-file “Socio_cultural_political_development_database_FAO2023.csv” from the repository. The ggplot script provided produces the desired plot with all countries under consideration.

7_YIELD_plot.R

In this section, we are using the country productivity from the supplement in the repository “yield_productivity” (e.g., "Jordan_yield.csv". Each of the single country yield datasets is plotted in a ggplot and combined using the patchwork package in R.

8_GLDAS_read_extract_trend

The last code provides the basis for the trend analysis of the climate variables used in the paper. The raw data can be accessed https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS%20Noah%20Land%20Surface%20Model%20L4%20monthly&page=1 (last accessed 9th of October 2023). The raw data comes in .nc file format and various variables can be extracted using the [“^a variable name”] command from the spatraster collection. Each time you run the code, this variable name must be adjusted to meet the requirements for the variables (see this link for abbreviations: https://disc.gsfc.nasa.gov/datasets/GLDAS_CLSM025_D_2.0/summary, last accessed 09th of October 2023; or the respective code chunk when reading a .nc file with the ncdf4 package in R) or run print(nc) from the code or use names(the spatraster collection).
Choosing one variable, the code uses the MERGED_LEVANT.shp mask from the repository to crop and mask the data to the outline of the study area.
From the processed data, trend analysis are conducted and z-scores were calculated following the code described above. However, annual trends require the frequency of the time series analysis to be set to value = 12. Regarding, e.g., rainfall, which is measured as annual sums and not means, the chunk r.sum=r.sum/12 has to be removed or set to r.sum=r.sum/1 to avoid calculating annual mean values (see other variables). Seasonal subset can be calculated as described in the code. Here, 3-month subsets were chosen for growing seasons, e.g. March-May (MAM), June-July (JJA), September-November (SON), and DJF (December-February, including Jan/Feb of the consecutive year).
From the data, mean values of 48 consecutive years are calculated and trend analysis are performed as describe above. In the same way, p-values are extracted and 95 % confidence level values are marked with dots on the raster plot. This analysis can be performed with a much longer time series, other variables, ad different spatial extent across the globe due to the availability of the GLDAS variables.

Graph and download economic data for Consumer Price Index: All Items: Total: Total for the Euro Area (19 Countries) (EA19CPALTT01IXOBM) from Jan 1990 to Jan 2023 about Euro Area, Europe, all items, CPI, price index, indexes, and price.

Australia Funds Outflow: Total All Countries data was reported at 292,110.000 AUD mn in 2023. This records an increase from the previous number of 85,415.000 AUD mn for 2022. Australia Funds Outflow: Total All Countries data is updated yearly, averaging -35,971.000 AUD mn from Dec 2001 (Median) to 2023, with 23 observations. The data reached an all-time high of 292,110.000 AUD mn in 2023 and a record low of -115,060.000 AUD mn in 2006. Australia Funds Outflow: Total All Countries data remains active status in CEIC and is reported by Australian Bureau of Statistics. The data is categorized under Global Database’s Australia – Table AU.O015: Australian Investment Abroad: BPM6.

European Arable Land Area Fully Converted to Organic Farming by Country, 2023 Discover more data with ReportLinker!