The north-western state of Rajasthan was the largest in terms of land area in India in 2021 with over 342 thousand square kilometers. Central Madhya Pradesh and south-western Maharashtra followed, while the union territory of Lakshadweep recorded an area of 30 square kilometers. Overall, India's geographical area amounted to about 3.3 million square kilometers.

The statistic shows the 30 largest countries in the world by area. Russia is the largest country by far, with a total area of about 17 million square kilometers.

Population of Russia

Despite its large area, Russia - nowadays the largest country in the world - has a relatively small total population. However, its population is still rather large in numbers in comparison to those of other countries. In mid-2014, it was ranked ninth on a list of countries with the largest population, a ranking led by China with a population of over 1.37 billion people. In 2015, the estimated total population of Russia amounted to around 146 million people. The aforementioned low population density in Russia is a result of its vast landmass; in 2014, there were only around 8.78 inhabitants per square kilometer living in the country. Most of the Russian population lives in the nation’s capital and largest city, Moscow: In 2015, over 12 million people lived in the metropolis.

Agricultural land (sq. km) in India was reported at 1785279 sq. Km in 2021, according to the World Bank collection of development indicators, compiled from officially recognized sources. India - Agricultural land (sq. km) - actual values, historical data, forecasts and projections were sourced from the World Bank on March of 2025.

India Land Cover: Sparse Vegetation: Total data was reported at 5.345 sq km th in 2019. This records an increase from the previous number of 5.314 sq km th for 2018. India Land Cover: Sparse Vegetation: Total data is updated yearly, averaging 5.236 sq km th from Dec 1992 (Median) to 2019, with 5 observations. The data reached an all-time high of 5.345 sq km th in 2019 and a record low of 5.189 sq km th in 1992. India Land Cover: Sparse Vegetation: Total data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s India – Table IN.OECD.ESG: Environmental: Land Cover: Non OECD Member: Annual.

India Land Cover: Bare Area: Total data was reported at 59,105.545 sq km th in 2022. This records an increase from the previous number of 58,557.231 sq km th for 2020. India Land Cover: Bare Area: Total data is updated yearly, averaging 57,656.088 sq km th from Dec 2000 (Median) to 2022, with 6 observations. The data reached an all-time high of 59,105.545 sq km th in 2022 and a record low of 57,244.902 sq km th in 2000. India Land Cover: Bare Area: Total data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s India – Table IN.OECD.ESG: Environmental: Land Cover: Non OECD Member: Annual.

India Land Use: Land Area: Other data was reported at 460,982.500 sq km in 2022. This records a decrease from the previous number of 463,646.500 sq km for 2021. India Land Use: Land Area: Other data is updated yearly, averaging 513,875.500 sq km from Dec 1961 (Median) to 2022, with 62 observations. The data reached an all-time high of 1,223,670.000 sq km in 1961 and a record low of 460,982.500 sq km in 2022. India Land Use: Land Area: Other data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s India – Table IN.OECD.ESG: Environmental: Land Use: Non OECD Member: Annual.

Russia is the largest country in Europe, and also the largest in the world, its total size amounting to 17 million square kilometers (km2). It should be noted, however, that over three quarters of Russia is located in Asia, and the Ural mountains are often viewed as the meeting point of the two continents in Russia; nonetheless, European Russia is still significantly larger than any other European country. Ukraine, the second largest country on the continent, is only 603,000 km2, making it about 28 times smaller than its eastern neighbor, or seven times smaller than the European part of Russia. France is the third largest country in Europe, but the largest in the European Union. The Vatican City, often referred to as the Holy Sea, is both the smallest country in Europe and in the world, at just one km2. Population Russia is also the most populous country in Europe. It has around 144 million inhabitants across the country; in this case, around three quarters of the population live in the European part, which still gives it the largest population in Europe. Despite having the largest population, Russia is a very sparsely populated country due to its size and the harsh winters. Germany is the second most populous country in Europe, with 83 million inhabitants, while the Vatican has the smallest population. Worldwide, India and China are the most populous countries, with approximately 1.4 billion inhabitants each. Cities Moscow in Russia is ranked as the most populous city in Europe with around 13 million inhabitants, although figures vary, due to differences in the methodologies used by countries and sources. Some statistics include Istanbul in Turkey* as the largest city in Europe with its 15 million inhabitants, bit it has been excluded here as most of the country and parts of the city is located in Asia. Worldwide, Tokyo is the most populous city, with Jakarta the second largest and Delhi the third.

The data shows for each state/union territory the area, population by gender and population by urban/rural.

Note: The area figures of States and U.T's do not add up to area of India because : (i) The shortfall of 7 square km. area of Madhya Pradesh and 3 square km. area of Chhattisgarh is yet to be resolved by the Survey of India. (ii) Disputed area of 13 square km. between Pondicherry and Andhra Pradesh is neither included in Pondicherry nor in Andhra Pradesh. For All India: 1) The population figures excludes population of the area under unlawful occupation of Pakistan and China where Census could not be taken. 2) Area figures includes the area under unlawful occupation of Pakistan and China. The area includes 78,114 sq.km. under illegal occupation of Pakistan, 5,180 sq. km.illegally handed over by Pakistan to China and 37,555 sq.km. under illegal occupation of China.

India IN: Population Density: Inhabitants per sq km data was reported at 476.650 Person in 2022. This records an increase from the previous number of 473.420 Person for 2021. India IN: Population Density: Inhabitants per sq km data is updated yearly, averaging 394.320 Person from Dec 1990 (Median) to 2022, with 33 observations. The data reached an all-time high of 476.650 Person in 2022 and a record low of 292.770 Person in 1990. India IN: Population Density: Inhabitants per sq km data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s India – Table IN.OECD.GGI: Social: Demography: Non OECD Member: Annual.

In 2022, the population density in India remained nearly unchanged at around 479.43 inhabitants per square kilometer. Still, the population density reached its highest value in the observed period in 2022. Population density is calculated by dividing the total population by the total land area, to show the average number of people living there per square kilometer of land.Find more key insights for the population density in countries like Sri Lanka and Pakistan.

India Land Use: Land Area: Forest data was reported at 726,928.000 sq km in 2022. This records an increase from the previous number of 724,264.000 sq km for 2021. India Land Use: Land Area: Forest data is updated yearly, averaging 687,340.000 sq km from Dec 1990 (Median) to 2022, with 33 observations. The data reached an all-time high of 726,928.000 sq km in 2022 and a record low of 639,380.000 sq km in 1990. India Land Use: Land Area: Forest data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s India – Table IN.OECD.ESG: Environmental: Land Use: Non OECD Member: Annual.

India Land Cover: Other data was reported at 190.296 sq km th in 2019. This records a decrease from the previous number of 190.405 sq km th for 2018. India Land Cover: Other data is updated yearly, averaging 191.419 sq km th from Dec 1992 (Median) to 2019, with 5 observations. The data reached an all-time high of 205.590 sq km th in 1992 and a record low of 190.296 sq km th in 2019. India Land Cover: Other data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s India – Table IN.OECD.ESG: Environmental: Land Cover: Non OECD Member: Annual.

Census data reveals that population density varies noticeably from area to area. Small area census data do a better job depicting where the crowded neighborhoods are. In this map, the yellow areas of highest density range from 30,000 to 150,000 persons per square kilometer. In those areas, if the people were spread out evenly across the area, there would be just 4 to 9 meters between them. Very high density areas exceed 7,000 persons per square kilometer. High density areas exceed 5,200 persons per square kilometer. The last categories break at 3,330 persons per square kilometer, and 1,500 persons per square kilometer.This dataset is comprised of multiple sources. All of the demographic data are from Michael Bauer Research with the exception of the following countries:Australia: Esri Australia and MapData ServicesCanada: Esri Canada and EnvironicsFrance: Esri FranceGermany: Esri Germany and NexigaIndia: Esri India and IndicusJapan: Esri JapanSouth Korea: Esri Korea and OPENmateSpain: Esri España and AISUnited States: Esri Demographics

The Amazon extends for approximately 8.4 million square kilometers, across nine South American countries. Nearly two thirds of its land area is located in Brazil. Peru and Bolivia follow, with around 11 and eight percent shares of the Amazon land area, respectively.

India Land Cover: Cropland: Rainfed data was reported at 1,119.462 sq km th in 2019. This records a decrease from the previous number of 1,122.010 sq km th for 2018. India Land Cover: Cropland: Rainfed data is updated yearly, averaging 1,126.028 sq km th from Dec 1992 (Median) to 2019, with 5 observations. The data reached an all-time high of 1,132.928 sq km th in 2004 and a record low of 1,119.462 sq km th in 2019. India Land Cover: Cropland: Rainfed data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s India – Table IN.OECD.ESG: Environmental: Land Cover: Non OECD Member: Annual.

PerCapita_CO2_Footprint_InDioceses_FULLBurhans, Molly A., Cheney, David M., Gerlt, R.. . “PerCapita_CO2_Footprint_InDioceses_FULL”. Scale not given. Version 1.0. MO and CT, USA: GoodLands Inc., Environmental Systems Research Institute, Inc., 2019.MethodologyThis is the first global Carbon footprint of the Catholic population. We will continue to improve and develop these data with our research partners over the coming years. While it is helpful, it should also be viewed and used as a "beta" prototype that we and our research partners will build from and improve. The years of carbon data are (2010) and (2015 - SHOWN). The year of Catholic data is 2018. The year of population data is 2016. Care should be taken during future developments to harmonize the years used for catholic, population, and CO2 data.1. Zonal Statistics: Esri Population Data and Dioceses --> Population per dioceses, non Vatican based numbers2. Zonal Statistics: FFDAS and Dioceses and Population dataset --> Mean CO2 per Diocese3. Field Calculation: Population per Diocese and Mean CO2 per diocese --> CO2 per Capita4. Field Calculation: CO2 per Capita * Catholic Population --> Catholic Carbon FootprintAssumption: PerCapita CO2Deriving per-capita CO2 from mean CO2 in a geography assumes that people's footprint accounts for their personal lifestyle and involvement in local business and industries that are contribute CO2. Catholic CO2Assumes that Catholics and non-Catholic have similar CO2 footprints from their lifestyles.Derived from:A multiyear, global gridded fossil fuel CO2 emission data product: Evaluation and analysis of resultshttp://ffdas.rc.nau.edu/About.htmlRayner et al., JGR, 2010 - The is the first FFDAS paper describing the version 1.0 methods and results published in the Journal of Geophysical Research.Asefi et al., 2014 - This is the paper describing the methods and results of the FFDAS version 2.0 published in the Journal of Geophysical Research.Readme version 2.2 - A simple readme file to assist in using the 10 km x 10 km, hourly gridded Vulcan version 2.2 results.Liu et al., 2017 - A paper exploring the carbon cycle response to the 2015-2016 El Nino through the use of carbon cycle data assimilation with FFDAS as the boundary condition for FFCO2."S. Asefi‐Najafabady P. J. Rayner K. R. Gurney A. McRobert Y. Song K. Coltin J. Huang C. Elvidge K. BaughFirst published: 10 September 2014 https://doi.org/10.1002/2013JD021296 Cited by: 30Link to FFDAS data retrieval and visualization: http://hpcg.purdue.edu/FFDAS/index.phpAbstractHigh‐resolution, global quantification of fossil fuel CO2 emissions is emerging as a critical need in carbon cycle science and climate policy. We build upon a previously developed fossil fuel data assimilation system (FFDAS) for estimating global high‐resolution fossil fuel CO2 emissions. We have improved the underlying observationally based data sources, expanded the approach through treatment of separate emitting sectors including a new pointwise database of global power plants, and extended the results to cover a 1997 to 2010 time series at a spatial resolution of 0.1°. Long‐term trend analysis of the resulting global emissions shows subnational spatial structure in large active economies such as the United States, China, and India. These three countries, in particular, show different long‐term trends and exploration of the trends in nighttime lights, and population reveal a decoupling of population and emissions at the subnational level. Analysis of shorter‐term variations reveals the impact of the 2008–2009 global financial crisis with widespread negative emission anomalies across the U.S. and Europe. We have used a center of mass (CM) calculation as a compact metric to express the time evolution of spatial patterns in fossil fuel CO2 emissions. The global emission CM has moved toward the east and somewhat south between 1997 and 2010, driven by the increase in emissions in China and South Asia over this time period. Analysis at the level of individual countries reveals per capita CO2 emission migration in both Russia and India. The per capita emission CM holds potential as a way to succinctly analyze subnational shifts in carbon intensity over time. Uncertainties are generally lower than the previous version of FFDAS due mainly to an improved nightlight data set."Global Diocesan Boundaries:Burhans, M., Bell, J., Burhans, D., Carmichael, R., Cheney, D., Deaton, M., Emge, T. Gerlt, B., Grayson, J., Herries, J., Keegan, H., Skinner, A., Smith, M., Sousa, C., Trubetskoy, S. “Diocesean Boundaries of the Catholic Church” [Feature Layer]. Scale not given. Version 1.2. Redlands, CA, USA: GoodLands Inc., Environmental Systems Research Institute, Inc., 2016.Using: ArcGIS. 10.4. Version 10.0. Redlands, CA: Environmental Systems Research Institute, Inc., 2016.Boundary ProvenanceStatistics and Leadership DataCheney, D.M. “Catholic Hierarchy of the World” [Database]. Date Updated: August 2019. Catholic Hierarchy. Using: Paradox. Retrieved from Original Source.Catholic HierarchyAnnuario Pontificio per l’Anno .. Città del Vaticano :Tipografia Poliglotta Vaticana, Multiple Years.The data for these maps was extracted from the gold standard of Church data, the Annuario Pontificio, published yearly by the Vatican. The collection and data development of the Vatican Statistics Office are unknown. GoodLands is not responsible for errors within this data. We encourage people to document and report errant information to us at data@good-lands.org or directly to the Vatican.Additional information about regular changes in bishops and sees comes from a variety of public diocesan and news announcements.GoodLands’ polygon data layers, version 2.0 for global ecclesiastical boundaries of the Roman Catholic Church:Although care has been taken to ensure the accuracy, completeness and reliability of the information provided, due to this being the first developed dataset of global ecclesiastical boundaries curated from many sources it may have a higher margin of error than established geopolitical administrative boundary maps. Boundaries need to be verified with appropriate Ecclesiastical Leadership. The current information is subject to change without notice. No parties involved with the creation of this data are liable for indirect, special or incidental damage resulting from, arising out of or in connection with the use of the information. We referenced 1960 sources to build our global datasets of ecclesiastical jurisdictions. Often, they were isolated images of dioceses, historical documents and information about parishes that were cross checked. These sources can be viewed here:https://docs.google.com/spreadsheets/d/11ANlH1S_aYJOyz4TtG0HHgz0OLxnOvXLHMt4FVOS85Q/edit#gid=0To learn more or contact us please visit: https://good-lands.org/Esri Gridded Population Data 2016DescriptionThis layer is a global estimate of human population for 2016. Esri created this estimate by modeling a footprint of where people live as a dasymetric settlement likelihood surface, and then assigned 2016 population estimates stored on polygons of the finest level of geography available onto the settlement surface. Where people live means where their homes are, as in where people sleep most of the time, and this is opposed to where they work. Another way to think of this estimate is a night-time estimate, as opposed to a day-time estimate.Knowledge of population distribution helps us understand how humans affect the natural world and how natural events such as storms and earthquakes, and other phenomena affect humans. This layer represents the footprint of where people live, and how many people live there.Dataset SummaryEach cell in this layer has an integer value with the estimated number of people likely to live in the geographic region represented by that cell. Esri additionally produced several additional layers World Population Estimate Confidence 2016: the confidence level (1-5) per cell for the probability of people being located and estimated correctly. World Population Density Estimate 2016: this layer is represented as population density in units of persons per square kilometer.World Settlement Score 2016: the dasymetric likelihood surface used to create this layer by apportioning population from census polygons to the settlement score raster.To use this layer in analysis, there are several properties or geoprocessing environment settings that should be used:Coordinate system: WGS_1984. This service and its underlying data are WGS_1984. We do this because projecting population count data actually will change the populations due to resampling and either collapsing or splitting cells to fit into another coordinate system. Cell Size: 0.0013474728 degrees (approximately 150-meters) at the equator. No Data: -1Bit Depth: 32-bit signedThis layer has query, identify, pixel, and export image functions enabled, and is restricted to a maximum analysis size of 30,000 x 30,000 pixels - an area about the size of Africa.Frye, C. et al., (2018). Using Classified and Unclassified Land Cover Data to Estimate the Footprint of Human Settlement. Data Science Journal. 17, p.20. DOI: http://doi.org/10.5334/dsj-2018-020.What can you do with this layer?This layer is unsuitable for mapping or cartographic use, and thus it does not include a convenient legend. Instead, this layer is useful for analysis, particularly for estimating counts of people living within watersheds, coastal areas, and other areas that do not have standard boundaries. Esri recommends using the Zonal Statistics tool or the Zonal Statistics to Table tool where you provide input zones as either polygons, or raster data, and the tool will summarize the count of population within those zones. https://www.esri.com/arcgis-blog/products/arcgis-living-atlas/data-management/2016-world-population-estimate-services-are-now-available/

The statistic displays the Indian states with the largest area of national parks in 2016. During the measured time period, the largest area of national parks belonged to the state of Uttarakhand with close to 5,000 square kilometers, followed by Rajasthan with approximately 3,950 square kilometers.

India Land Cover: Cropland: Irrigated or Post-Flooding data was reported at 678.654 sq km th in 2019. This records a decrease from the previous number of 679.362 sq km th for 2018. India Land Cover: Cropland: Irrigated or Post-Flooding data is updated yearly, averaging 680.970 sq km th from Dec 1992 (Median) to 2019, with 5 observations. The data reached an all-time high of 684.302 sq km th in 2004 and a record low of 678.654 sq km th in 2019. India Land Cover: Cropland: Irrigated or Post-Flooding data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s India – Table IN.OECD.ESG: Environmental: Land Cover: Non OECD Member: Annual.

The statistic displays the protected forest area across India from 2001 to 2015. In 2015, the protected forest area in the country amounted to approximately 209,000 square kilometers. This was an decrease from the previous year's 215,000 square kilometers.

The average for 2021 based on 11 countries was 671160 sq. km. The highest value was in India: 2973190 sq. km and the lowest value was in Singapore: 718 sq. km. The indicator is available from 1961 to 2022. Below is a chart for all countries where data are available.