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.

Dataset

This dataset was created by Rushikesh

Contents

This data contains all the essential data in the form of % with respect to rural and urban Indian states . This dataset is highly accurate as this is taken from the Indian govt. it is updated till 2021 for all states and union territories. source of data is data.gov.in titled - ******All India and State/UT-wise Factsheets of National Family Health Survey******

it is advised to you pls search the data keywords you need by using (Ctrl+f) , as it will help to avoid time wastage. States/UTs

Different columns it contains are Area

Number of Households surveyed Number of Women age 15-49 years interviewed Number of Men age 15-54 years interviewed

Female population age 6 years and above who ever attended school (%)

Population below age 15 years (%)

Sex ratio of the total population (females per 1,000 males)

Sex ratio at birth for children born in the last five years (females per 1,000 males)

Children under age 5 years whose birth was registered with the civil authority (%)

Deaths in the last 3 years registered with the civil authority (%)

Population living in households with electricity (%)

Population living in households with an improved drinking-water source1 (%)

Population living in households that use an improved sanitation facility2 (%)

Households using clean fuel for cooking3 (%) Households using iodized salt (%)

Households with any usual member covered under a health insurance/financing scheme (%)

Children age 5 years who attended pre-primary school during the school year 2019-20 (%)

Women (age 15-49) who are literate4 (%)

Men (age 15-49) who are literate4 (%)

Women (age 15-49) with 10 or more years of schooling (%)

Men (age 15-49) with 10 or more years of schooling (%)

Women (age 15-49) who have ever used the internet (%)

Men (age 15-49) who have ever used the internet (%)

Women age 20-24 years married before age 18 years (%)

Men age 25-29 years married before age 21 years (%)

Total Fertility Rate (number of children per woman) Women age 15-19 years who were already mothers or pregnant at the time of the survey (%)

Adolescent fertility rate for women age 15-19 years5 Neonatal mortality rate (per 1000 live births)

Infant mortality rate (per 1000 live births) Under-five mortality rate (per 1000 live births)

Current Use of Family Planning Methods (Currently Married Women Age 15-49 years) - Any method6 (%)

Current Use of Family Planning Methods (Currently Married Women Age 15-49 years) - Any modern method6 (%)

Current Use of Family Planning Methods (Currently Married Women Age 15-49 years) - Female sterilization (%)

Current Use of Family Planning Methods (Currently Married Women Age 15-49 years) - Male sterilization (%)

Current Use of Family Planning Methods (Currently Married Women Age 15-49 years) - IUD/PPIUD (%)

Current Use of Family Planning Methods (Currently Married Women Age 15-49 years) - Pill (%)  

Current Use of Family Planning Methods (Currently Married Women Age 15-49 years) - Condom (%)

Current Use of Family Planning Methods (Currently Married Women Age 15-49 years) - Injectables (%)

Total Unmet need for Family Planning (Currently Married Women Age 15-49 years)7 (%)

Unmet need for spacing (Currently Married Women Age 15-49 years)7 (%)

Health worker ever talked to female non-users about family planning (%)

Current users ever told about side effects of current method of family planning8 (%)

Mothers who had an antenatal check-up in the first trimester (for last birth in the 5 years before the survey) (%)

Mothers who had at least 4 antenatal care visits (for last birth in the 5 years before the survey) (%)

Mothers whose last birth was protected against neonatal tetanus (for last birth in the 5 years before the survey)9 (%)

Mothers who consumed iron folic acid for 100 days or more when they were pregnant (for last birth in the 5 years before the survey) (%)

Mothers who consumed iron folic acid for 180 days or more when they were pregnant (for last birth in the 5 years before the survey} (%)

Registered pregnancies for which the mother received a Mother and Child Protection (MCP) card (for last birth in the 5 years before the survey) (%)

Mothers who received postnatal care from a doctor/nurse/LHV/ANM/midwife/other health personnel within 2 days of delivery (for last birth in the 5 years before the survey) (%)

Average out-of-pocket expenditure per delivery in a public health facility (for last birth in the 5 years before the survey) (Rs.)

Children born at home who were taken to a health facility for a check-up within 24 hours of birth (for last birth in the 5 years before the survey} (%)

Children who received postnatal care from a doctor/nurse/LHV/ANM/midwife/ other health personnel within 2 days of delivery (for last birth in the 5 years before the survey) (%)

Institutional births (in the 5...

The dataset contains year- and state-wise compiled data on the total area of land under mangroves in India.

In financial year 2022, Uttar Pradesh had the largest area under irrigation, which was about ** million hectares. This was followed by Madhya Pradesh, which had about ** million hectares of irrigated area.

The dataset contains Year and State wise Pattern of Land Use - Gross Sown Area, Net Sown Area, Gross Irrigated Area, Net Irrigated Area and Cropping Intensity

Note: 1. All India data are inclusive of all States and Union Territories.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The American Indian/Alaska Native/Native Hawaiian (AIANNH) Areas Shapefile includes the following legal entities: federally recognized American Indian reservations and off-reservation trust land areas, state-recognized American Indian reservations, and Hawaiian home lands (HHLs). The statistical entities included are Alaska Native village statistical areas (ANVSAs), Oklahoma tribal statistical areas (OTSAs), tribal designated statistical areas (TDSAs), and state designated tribal statistical areas (SDTSAs). Joint use areas are also included in this shapefile refer to areas that are administered jointly and/or claimed by two or more American Indian tribes. The Census Bureau designates both legal and statistical joint use areas as unique geographic entities for the purpose of presenting statistical data. Note that tribal subdivisions and Alaska Native Regional Corporations (ANRCs) are additional types of American Indian/Alaska Native areas stored by the Census Bureau, but are displayed in separate shapefiles because of how they fall within the Census Bureau's geographic hierarchy. The State of Hawaii's Office of Hawaiian Home Lands provides the legal boundaries for the HHLs. The boundaries for ANVSAs, OTSAs, and TDSAs were delineated for the 2020 Census through the Participant Statistical Areas Program (PSAP) by participants from the federally recognized tribal governments. The Bureau of Indian Affairs (BIA) within the U.S. Department of the Interior (DOI) provides the list of federally recognized tribes and only provides legal boundary information when the tribes need supporting records, if a boundary is based on treaty or another document that is historical or open to legal interpretation, or when another tribal, state, or local government challenges the depiction of a reservation or off-reservation trust land. The boundaries for federally recognized American Indian reservations and off-reservation trust lands are as of January 1, 2022, as reported by the federally recognized tribal governments through the Census Bureau's Boundary and Annexation Survey (BAS). The boundaries for state-recognized American Indian reservations and for SDTSAs were delineated by a state governor-appointed liaisons for the 2020 Census through the State American Indian Reservation Program and PSAP respectively.

Rajasthan had the largest combined area of reservoirs in India, with around ******* hectares of reservoirs in 2021. The state of Madhya Pradesh ranked second with a reservoir area of almost ******* hectares.

📝 Dataset Description

This dataset provides a clean and structured mapping of 1,209 Indian cities to their respective 33 states and union territories (as of February 2015). It is an essential resource for data analysts, geospatial researchers, and anyone working with location-based data in India.

🔍 Key Features:

• 📍 Cities: 1,188 unique entries
• 🗺️ States/UTs: 33 unique states and union territories
• 📊 Columns: 2 (city, state)
• 📅 Date Reference: February 2015
• ✅ Ready for use in data cleaning, enrichment, geolocation mapping, and visualization tasks.

💡 Use Cases:

• Mapping cities to states in Indian datasets
• Preparing geospatial plots or choropleths
• Cleaning inconsistent location fields
• Enriching datasets lacking state information

Overview

This dataset provides comprehensive data from the Youth Tobacco Survey conducted across various states and union territories in India. It captures tobacco use behaviors, exposure to secondhand smoke, awareness of tobacco-related policies, and cessation attempts among youth. The data is disaggregated by area (Total, Urban, Rural) for India as a whole and for individual states/territories, offering insights into regional and demographic variations in tobacco use and related attitudes.

The dataset is valuable for researchers, policymakers, and public health professionals studying tobacco consumption patterns, the effectiveness of anti-tobacco campaigns, and the impact of regulations like the Cigarettes and Other Tobacco Products Act (COTPA) in India. It can be used to analyze trends, identify high-risk areas, and inform targeted interventions to reduce tobacco use among youth.

Source

The data appears to be sourced from a national or regional youth tobacco survey, likely conducted by a public health authority or research organization in India. It includes detailed metrics on tobacco use, exposure, and awareness, reflecting a systematic effort to monitor tobacco-related behaviors among young populations.

Column Descriptions

Below is a detailed description of each column in the dataset:

State: The name of the Indian state or union territory (e.g., India, Andhra Pradesh, Bihar). "India" represents aggregated national data.

Area: The geographic area within the state (Total, Urban, Rural).

Ever_Tob_Use: Percentage of youth who have ever used any tobacco product.

Curr_Tob_Use: Percentage of youth currently using any tobacco product.

Ever_Smoke: Percentage of youth who have ever smoked any tobacco product.

Curr_Smoke: Percentage of youth currently smoking any tobacco product.

Ever_Cig: Percentage of youth who have ever smoked cigarettes.

Curr_Cig: Percentage of youth currently smoking cigarettes.

Ever_Bidi: Percentage of youth who have ever smoked bidis (a type of hand-rolled tobacco product).

Curr_Bidi: Percentage of youth currently smoking bidis.

Ever_SLT: Percentage of youth who have ever used smokeless tobacco (SLT) products (e.g., chewing tobacco, gutkha).

Curr_SLT: Percentage of youth currently using smokeless tobacco products.

Ever_PM_Tob: Percentage of youth who have ever used paan masala with tobacco.

Suscept_Cig: Percentage of youth susceptible to starting cigarette smoking.

Age_Init_Cig: Average age of initiation for cigarette smoking.

Age_Init_Bidi: Average age of initiation for bidi smoking.

Age_Init_SLT: Average age of initiation for smokeless tobacco use.

E_Cig_Aware: Percentage of youth aware of electronic cigarettes.

E_Cig_Ever: Percentage of youth who have ever used electronic cigarettes.

Quit_Smoke_12mo: Percentage of youth who attempted to quit smoking in the past 12 months.

TryQuit_Smoke_12mo: Percentage of youth who tried to quit smoking in the past 12 months.

WantQuit_Smoke: Percentage of youth who want to quit smoking.

Quit_SLT_12mo: Percentage of youth who attempted to quit smokeless tobacco in the past 12 months.

TryQuit_SLT_12mo: Percentage of youth who tried to quit smokeless tobacco in the past 12 months.

WantQuit_SLT: Percentage of youth who want to quit smokeless tobacco.

Smoke_Exposure: Percentage of youth exposed to secondhand smoke (general).

Smoke_Home: Percentage of youth exposed to secondhand smoke at home.

Smoke_Enclosed: Percentage of youth exposed to secondhand smoke in enclosed public places.

Smoke_Outdoor: Percentage of youth exposed to secondhand smoke in outdoor public places.

Seen_Smoke_School: Percentage of youth who observed smoking on school premises.

Source_Cig_Store: Percentage of youth who obtained cigarettes from a store.

Source_Cig_Paan: Percentage of youth who obtained cigarettes from a paan shop.

Source_Bidi_Store: Percentage of youth who obtained bidis from a store.

Source_Bidi_Paan: Percentage of youth who obtained bidis from a paan shop.

Source_SLT_Store: Percentage of youth who obtained smokeless tobacco from a store.

Source_SLT_Paan: Percentage of youth who obtained smokeless tobacco from a paan shop.

Bought_Cig_Loc: Percentage of youth who purchased cigarettes from a local source.

Bought_Bidi_Loc: Percentage of youth who purchased bidis from a local source.

Refused_Cig_Sale: Percentage of youth refused cigarette sales due to age restrictions.

Refused_Bidi_Sale: Percentage of youth refused bidi sales due to age restrictions.

Refused_SLT_Sale: Percentage of youth refused smokeless tobacco sales due to age restrictions.

Cig_Stick: Percentage of youth purchasing cigarettes as single sticks.

Bidi_Stick: Percentage of youth purchasing bidis as single sticks.

Seen_AT_Message: Percentage of youth who have seen anti-tobacc...

The dataset contains Year and State wise Area, Production and Yield of Foodgrains - Rice

Note: 1. All India data are inclusive of all States and Union Territories.

The statistic displays the Indian states with the smallest protected forest area in 2015. During the measured time period, the protected forest area in the union territory of Puducherry along with the state of Tripura had the smallest protected forest areas with about *** square kilometers each.

The dataset contains Year and State wise Area, Production of Total Vegetables

Note: 1. All India data are inclusive of all States and Union Territories. 2. The data for 2023–24 is based on the 3rd Advance Estimates

India Area: Horticulture Crops: Plantations: Coconut: Others data was reported at 63.070 ha th in 2017. This records an increase from the previous number of 62.180 ha th for 2016. India Area: Horticulture Crops: Plantations: Coconut: Others data is updated yearly, averaging 50.000 ha th from Mar 2001 (Median) to 2017, with 17 observations. The data reached an all-time high of 84.820 ha th in 2014 and a record low of 20.000 ha th in 2002. India Area: Horticulture Crops: Plantations: Coconut: Others data remains active status in CEIC and is reported by Department of Agriculture and Cooperation. The data is categorized under India Premium Database’s Agriculture Sector – Table IN.RIN082: Area of Horticulture Crops in Major States: Plantations: Coconut.

The Dataset consist of distribution of population across different states. The dataset also gives information regarding the area of the state, urban-rural distribution of population, population density, sex ratio and literacy rates in different states with reference from 2011 census. The dataset helps in analysis of population distribution of India.

Note: *Disputed area of 13 km^2 between Puducherry and Andhra Pradesh is included in neither. *The shortfall of 7 km^2 area of Madhya Pradesh and 3 km^2 area of Chhattisgarh is yet to be resolved by the Survey of India. *Area figures do not include the areas claimed by India that are in Pakistani or Chinese administrative control. This includes 78,114 km^2 of area in Azad Kashmir and Gilgit-Baltistan under Pakistani administration, 5,180 km^2 of area in Shaksgam Valley ceded to China by Pakistan and 37,555 km^2 of area in Aksai Chin under Chinese administration totaling to 120,849 km^2.

India Agricultural Area: Jute & Mesta: Others data was reported at 0.030 ha mn in 2017. This stayed constant from the previous number of 0.030 ha mn for 2016. India Agricultural Area: Jute & Mesta: Others data is updated yearly, averaging 0.010 ha mn from Mar 2001 (Median) to 2017, with 17 observations. The data reached an all-time high of 0.897 ha mn in 2010 and a record low of -0.050 ha mn in 2005. India Agricultural Area: Jute & Mesta: Others data remains active status in CEIC and is reported by Department of Agriculture and Cooperation. The data is categorized under India Premium Database’s Agriculture Sector – Table IN.RIA024: Area of Non Foodgrains in Major States: Jute & Mesta.

Shapefile Description:

The shapefile of India with district-level details is a geospatial dataset that provides detailed geographic boundaries and attributes of districts across the country.

Key Features:

• Geographic Boundaries: Contains precise polygon boundaries outlining each district within India's states, allowing for detailed mapping and spatial analysis.
• District-Level Details: Includes attributes such as district names, state affiliations, and potentially other demographic or administrative information.
• Projection and Coordinate System: Typically includes information on the geographic projection and coordinate system used, ensuring accurate mapping and integration with other spatial data.
• Usage: Useful for spatial analysis, visualization, and geographic information system (GIS) applications related to administrative divisions, resource management, and planning.

Applications:

• Mapping and Visualization: Enables the creation of detailed maps showing district boundaries and distribution across India.
• Spatial Analysis: Facilitates analysis of data at the district level, including demographic studies, resource allocation, and policy planning.
• Integration with Other Data: Can be combined with various datasets (e.g., crop production, economic indicators) for comprehensive analysis and reporting.

Data about Forest Cover in States/UTs in India in 2019, includes state-wise data which contains the geographical area(area in sq. km), various types of forest, percentage of geographical area.

Thanks to ENVIS RP on Forestry and Forest Related Livelihoods which is Sponsored by Ministry of Environment, Forests & Climate Change, Govt of India, for making this data publically available

In fiscal year 2023, the area under cultivation for cotton was **** million hectares in the Indian state of Maharashtra. In comparison, Odisha had a cultivation area of *** thousand hectares for cotton during the same year.

India Area: Horticulture Crops: Plantations: Others data was reported at 127.710 ha th in 2019. This records a decrease from the previous number of 127.830 ha th for 2018. India Area: Horticulture Crops: Plantations: Others data is updated yearly, averaging 125.183 ha th from Mar 2016 (Median) to 2019, with 4 observations. The data reached an all-time high of 127.830 ha th in 2018 and a record low of 59.280 ha th in 2017. India Area: Horticulture Crops: Plantations: Others data remains active status in CEIC and is reported by Department of Agriculture and Cooperation. The data is categorized under India Premium Database’s Agriculture Sector – Table IN.RIN078: Area of Horticulture Crops in Major States: Plantations.