Land area (sq. km) in India was reported at 2973190 sq. Km in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. India - Land area (sq. km) - actual values, historical data, forecasts and projections were sourced from the World Bank on March of 2026.

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.

India: Land area in sq. km: The latest value from 2023 is 2973190 sq. km, unchanged from 2973190 sq. km in 2022. In comparison, the world average is 656095 sq. km, based on data from 196 countries. Historically, the average for India from 1961 to 2023 is 2973190 sq. km. The minimum value, 2973190 sq. km, was reached in 1961 while the maximum of 2973190 sq. km was recorded in 1961.

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.

Surface area (sq. km) in India was reported at 3287260 sq. Km in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. India - Surface area (sq. km) - actual values, historical data, forecasts and projections were sourced from the World Bank on February of 2026.

India: Forest area, sq. km: The latest value from 2023 is 729592 sq. km, an increase from 726930 sq. km in 2022. In comparison, the world average is 207390.9 sq. km, based on data from 194 countries. Historically, the average for India from 1990 to 2023 is 687496.2 sq. km. The minimum value, 639380 sq. km, was reached in 1990 while the maximum of 729592 sq. km was recorded in 2023.

Agricultural land (sq. km) in India was reported at 1785710 sq. Km in 2023, 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 2026.

Population density (people per sq. km of land area) in India was reported at 484 sq. Km in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. India - Population density (people per sq. km) - actual values, historical data, forecasts and projections were sourced from the World Bank on March of 2026.

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.

India: Population density, people per square km: The latest value from 2023 is 484 people per square km, an increase from 479 people per square km in 2022. In comparison, the world average is 471 people per square km, based on data from 196 countries. Historically, the average for India from 1961 to 2023 is 309 people per square km. The minimum value, 150 people per square km, was reached in 1961 while the maximum of 484 people per square km was recorded in 2023.

📊 India States & Union Territories Dataset 🇮🇳

This dataset provides a comprehensive overview of India's States and Union Territories, offering detailed information about their administrative structure, demographics, and linguistic diversity. It serves as a valuable resource for understanding the vastness and complexity of India's regional distribution.

📑 Dataset Contents:

• Name: Name of the State or Union Territory 🏙️
• Type: Administrative type (State or Union Territory) 🏛️
• ISO: Official ISO Code 🌍
• Vehiclecode: Unique Vehicle Registration Code 🚗
• Zone: Geographical Zone 🌏
• Capital: Administrative Capital 🏛️
• Largest City: The most populous city within the state 🏙️
• Statehood: Year the state gained full statehood 🎉
• Population: Total population of the state or UT 👥
• Area: Geographic area in square kilometers 🌍
• Official Languages: Primary official languages of the state 🗣️
• Additional Official Languages: Secondary official languages 📚

🔍 Purpose:

This dataset is designed to provide an in-depth look at the various States and Union Territories across India. It combines vital information on governance, geography, population, and language diversity, making it an essential tool for researchers, analysts, and anyone interested in India's regional landscape.

🚀 Applications:

• Data Analysis: Understand regional distribution and demographics 📊
• Geography Research: Explore geographical zones and area details 🌍
• Linguistic Studies: Study the diversity of languages spoken across India 🗣️
• Governance Comparison: Compare States and Union Territories based on statehood and type ⚖️

📝 Contributing:

Feel free to fork this repository and contribute! 💡 Pull requests are welcome for improvements or additional information.

📬 Contact:

For any questions or suggestions, raise suggestions! 📧

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 Cover: Tree Cover: Total data was reported at 568,382.200 sq km th in 2022. This records an increase from the previous number of 557,621.559 sq km th for 2020. India Land Cover: Tree Cover: Total data is updated yearly, averaging 536,267.471 sq km th from Dec 2000 (Median) to 2022, with 6 observations. The data reached an all-time high of 568,382.200 sq km th in 2022 and a record low of 533,827.182 sq km th in 2000. India Land Cover: Tree Cover: 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.

Forest area (sq. km) in India was reported at 729592 sq. Km in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. India - Forest area (sq. km) - actual values, historical data, forecasts and projections were sourced from the World Bank on March of 2026.

India IN: Population Density: Inhabitants per sq km data was reported at 483.680 Person in 2023. This records an increase from the previous number of 479.430 Person for 2022. India IN: Population Density: Inhabitants per sq km data is updated yearly, averaging 394.490 Person from Dec 1990 (Median) to 2023, with 34 observations. The data reached an all-time high of 483.680 Person in 2023 and a record low of 290.920 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.

As of financial year 2019, the rural road density in India was close to 1.5 thousand kilometers per one thousand square kilometers. It was a significant increase in comparison with financial year 2012, when the road denisty was equal to 887 km per one thousand sq. km. During the presented period, a gradual increase trend can be observed.

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 Land Cover: Bare Area: Unconsolidated data was reported at 0.111 sq km th in 2019. This stayed constant from the previous number of 0.111 sq km th for 2018. India Land Cover: Bare Area: Unconsolidated data is updated yearly, averaging 0.111 sq km th from Dec 1992 (Median) to 2019, with 5 observations. The data reached an all-time high of 0.111 sq km th in 2019 and a record low of 0.103 sq km th in 1992. India Land Cover: Bare Area: Unconsolidated 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.

Context

India has a diverse range of agro-climatic zones, each with its unique geographical and environmental characteristics. Understanding these factors is crucial for sustainable agriculture, water resource management, and policy-making. This dataset provides a comprehensive compilation of key agricultural, climatic, and geographical indicators for each state and union territory of India.

This data can be used by researchers, data scientists, and policy analysts to explore correlations between geographical features and agricultural outcomes, model the impact of climate on cropping patterns, and identify regions facing environmental stress.

Content

The dataset contains one CSV file (India_States.csv) with 13 columns, providing a detailed profile for each Indian state and union territory.

Column Descriptions:

• States: The name of the Indian State or Union Territory.
• Soil_Type: The predominant soil type found in the region (e.g., Alluvial Soil, Red & Black Soil).
• Geographical_Region: The primary geographical or topographical region (e.g., Indo-Gangetic Plains, Eastern Himalayas).
• Climate_Zone: The main climate classification for the state (e.g., Tropical Wet-Dry, Humid Subtropical).
• Latitude: The geographical latitude of the state's approximate center.
• Longitude: The geographical longitude of the state's approximate center.
• Precipitation_mm: The average annual rainfall in millimeters.
• Avg_Summer_Temp_C: The average temperature during the summer season in Celsius.
• Avg_Winter_Temp_C: The average temperature during the winter season in Celsius.
• Irrigation_Coverage_%: The percentage of agricultural land covered by irrigation.
• Groundwater_Availability: A categorical measure of groundwater availability (e.g., Low, Medium, High).
• Cropping_Intensity_%: The ratio of the gross cropped area to the net sown area, expressed as a percentage. It indicates how many times a single field is used for cultivation in a year.
• Water_Stress_Index: A categorical index representing the level of water stress in the region (e.g., Low, Medium, High).

Potential Use Cases & Inspiration

This dataset is ideal for a variety of data analysis and machine learning projects:

• Predictive Modeling: Can you build a model to predict the Water_Stress_Index based on climatic and geographical factors?
• Clustering Analysis: Can you group states with similar agro-climatic profiles together? What insights can be drawn from these clusters?
• Geospatial Analysis: Create insightful maps of India to visualize precipitation, temperature, soil types, or cropping intensity across the country.
• Exploratory Data Analysis (EDA): What is the relationship between irrigation coverage and cropping intensity? How does precipitation affect groundwater availability?

The Socioeconomic High-resolution Rural-Urban Geographic Dataset on India (SHRUG) is a new administrative data source describing socioeconomic development in India. The first version of the SHRUG describes demographic, socioeconomic, firm and political outcomes at a high geographic resolution for the universe of Indian households and non-farm productive establishments, in both rural and urban India, from 1990-2013. The SHRUG is a platform for future collaboration and data sharing between researchers working with administrative data in India. We have created a set of consistent location identifiers for all geographic locations in India from 1990-2013, and establish a methodology to extend this classification to units from future data sources. Researchers working with geographic variation in India can thus benefit from linking to the SHRUG, and can benefit other researchers by making their data available to others through the SHRUG platform. In the accompanying paper, we describe the construction of the data and the strengths and weaknesses of administrative data like these for research on economic development. We then perform several validation exercises to show that the SHRUG is consistent with other data sources. Finally, we present an illustrative data exercise on recent trends in rural and urban development. This is an archive of a prior version of SHRUG (v1.3, or Harvard Dataverse version number 3.x). Harvard Dataverse v2.0 corresponds to SHRUG v1.2. For the most recent version, please go to devdatalab.org/shrug