India population density for 2022 was 479.43, a 0.79% increase from 2021.

<ul style='margin-top:20px;'>

<li>India population density for 2021 was <strong>475.65</strong>, a <strong>0.83% increase</strong> from 2020.</li>
<li>India population density for 2020 was <strong>471.76</strong>, a <strong>0.98% increase</strong> from 2019.</li>
<li>India population density for 2019 was <strong>467.19</strong>, a <strong>1.05% increase</strong> from 2018.</li>
</ul>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.

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 refers to the number of people living in a certain country or area, given as an average per square kilometer. It is calculated by dividing the total midyear population by the total land area.Find more key insights for the population density in countries like Sri Lanka and Pakistan.

India: Population density, people per square km: The latest value from 2021 is 473 people per square km, an increase from 470 people per square km in 2020. In comparison, the world average is 456 people per square km, based on data from 196 countries. Historically, the average for India from 1961 to 2021 is 305 people per square km. The minimum value, 153 people per square km, was reached in 1961 while the maximum of 473 people per square km was recorded in 2021.

In 2011, there were over 382 million individuals on average per square kilometer across the south east Asian country of India. Furthermore, there were over 3.6 thousand individuals per square kilometer in the urban regions and over 279 individuals per square kilometer in the rural regions of the country.

In 2022, the union territory of Delhi had the highest urban population density of over 18 thousand persons per square kilometer. While the rural population density was highest in union territory of Puducherry, followed by the state of Bihar.

Population density (people per sq. km of land area) in India was reported at 479 sq. Km in 2022, 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 July of 2025.

The population density in Pakistan increased by *** inhabitants per square kilometer (+**** percent) in 2022. Therefore, the population density in Pakistan reached a peak in 2022 with ****** inhabitants per square kilometer. Notably, the population density continuously increased over the last years.Population density refers to the number of people living in a certain country or area, given as an average per square kilometer. It is calculated by dividing the total midyear population by the total land area.Find more key insights for the population density in countries like India and Sri Lanka.

Monaco led the ranking for countries with the highest population density in 2024, with nearly 26,000 residents per square kilometer. The Special Administrative Region of Macao came in second, followed by Singapore. The world’s second smallest country Monaco is the world’s second-smallest country, with an area of about two square kilometers and a population of only around 40,000. It is a constitutional monarchy located by the Mediterranean Sea, and while Monaco is not part of the European Union, it does participate in some EU policies. The country is perhaps most famous for the Monte Carlo casino and for hosting the Monaco Grand Prix, the world's most prestigious Formula One race. The global population Globally, the population density per square kilometer is about 60 inhabitants, and Asia is the most densely populated region in the world. The global population is increasing rapidly, so population density is only expected to increase. In 1950, for example, the global population stood at about 2.54 billion people, and it reached over eight billion during 2023.

Rural population (% of total population) in India was reported at 63.13 % in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. India - Rural population - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Analysis of ‘Indian Census Data with Geospatial indexing’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/sirpunch/indian-census-data-with-geospatial-indexing on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Dataset Description:

• This dataset has population data of each Indian district from 2001 and 2011 censuses.
• The special thing about this data is that it has centroids for each district and state.
• Centroids for a district are calculated by mapping border of each district as a polygon of latitude/longitude points in a 2D plane and then calculating their mean center.
• Centroids for a state are calculated by calculating the weighted mean center of all districts that constitutes a state. The population count is the weight assigned to each district.

Example Analysis:

• The complete code for calculating the centroids and web scraping for the data is shared on GitHub.

• The purpose of this project was to map population density center for each state.

• You can also read about the complete project here: https://medium.com/@sumit.arora/plotting-weighted-mean-population-centroids-on-a-country-map-22da408c1397

Output Screenshots: Indian districts mapped as polygons https://i.imgur.com/UK1DCGW.png" alt="Indian districts mapped as polygons">

Mapping centroids for each district https://i.imgur.com/KCAh7Jj.png" alt="Mapping centroids for each district">

Mean centers of population by state, 2001 vs. 2011 https://i.imgur.com/TLHPHjB.png" alt="Mean centers of population by state, 2001 vs. 2011">

National center of population https://i.imgur.com/yYxE4Hc.png" alt="National center of population">

--- Original source retains full ownership of the source dataset ---

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 growth in India's overall population is driven by its young population. Nearly ** percent of the country's population was between the ages of 15 and 64 years old in 2020. With over *** million people between 18 and 35 years old, India had the largest number of millennials and Gen Zs globally.

The population density in Sri Lanka saw no significant changes in 2022 in comparison to the previous year 2021 and remained at around 358.57 inhabitants per square kilometer. Still, the population density reached its highest value in the observed period in 2022. Population density refers to the number of people living in a certain country or area, given as an average per square kilometer. It is calculated by dividing the total midyear population by the total land area.Find more key insights for the population density in countries like India and Bhutan.

This data package includes spatial environmental and social layers for Shivamogga District, Karnataka, India that were considered as potential predictors of patterns in human cases of Kyasanur Forest Disease (KFD). KFD is a fatal tick-borne viral haemorrhagic disease of humans, that is spreading across degraded forest ecosystems in India. The layers encompass a range of fifteen metrics of topography, land use and land use change, livestock and human population density and public health resources for Shivamogga District across 1km and 2km study grids. These spatial proxies for risk factors for KFD that had been jointly identified between cross-sectoral stakeholders and researchers through a co-production approach. Shivamogga District is the District longest affected by KFD in south India. The layers are distributed as 1km and 2km GeoTiffs in Albers equal area conic projection. For KFD, spatial models incorporating these layers identified characteristics of forest-plantation landscapes at higher risk for human KFD. These layers will be useful for modelling spatial patterns in other environmentally sensitive infectious diseases and biodiversity within the district.

Methods Processing of environmental predictors of Kyasanur Forest Disease distribution

This file details the sources and processing of environmental predictors offered to the statistical analysis in the paper. All processing was performed in the raster package [1] of the R program [2] unless otherwise specified, with function names as specified below.

Topography predictors

Elevation data was extracted in tiles from Shuttle Radar Topography Mission data version 4 [3] an original resolution of 0.000833 degrees Latitude and Longitude resolution (approximately 90m by 90m grid cells). Tiles were mosaicked across the study region using the merge function. A slope value for each pixel was calculated (in degrees) using the terrain function of the raster package, and a focal window of 3 by 3 cells. Both the resulting elevation and slope rasters were cropped to the administrative boundaries of the Shivamogga District (raster package: crop function) and re-projected to an equal area projection (Albers equal area conic projection) using the projectRaster function (method=”bilinear”). Mean elevation and slope values were then calculated across the study 1km and 2km grid cells, using the aggregate function to average values across the appropriate number of ~90m grid cells and then the resample function to align the resulting grid to the study grids.

Landscape predictors

Metrics of the current availability (and fragmentation) of forest, agricultural and built-up land use types as well as that of water-bodies were extracted from the MonkeyFeverRisk Land Use Land Cover map of Shimoga. The latter was produced from classification of earth observation data from 2016 to 2018 using the methods described in the Supplementary information S3 file of the paper linked to this dataset. The LULC map had an original grid square resolution of 0.000269 degrees Latitude and Longitude resolution (or 30m x 28m grid cells) and nine different LULC classes. It was cropped to the administrative boundaries of the Shimoga District (raster package: crop function) and re-projected to the equal area projection (Albers equal area conic projection) using the projectRaster function (method=”ngb” for categorical data). The agriculture and fallow land classes were combined before landscape analysis (due to the difficulty of separating them accurately in the classification process).

An algorithm was developed in R to identify which of the pixels in the LULC map coincided with each 1km and 2km grid cell of the study area. The ClassStat function of the SDM Tools package [4] was used to calculate the proportional area of each 1km or 2km grid cell landscape that was made up of a particular land class, as well patch density and edge density metrics for the forest classes as indicators of fragmentation and forest-agriculture interface habitat respectively (Fig. S2B). The proportional area values (pi) of the n different forest classes (wet evergreen forest, moist deciduous forest, dry deciduous forest and plantation) were used to calculate an index of forest type diversity per grid cell as follows, after Shannon & Weaver (1949) [5]:

H'= -1npi(lognpi)

Metrics of longer term forest changes in Shimoga since 2000 were derived from a global product by Hansen et al. (2013) [6] available at a spatial resolution of 1 arc-second per pixel, (~ 30 meters per pixel at equator). Forest loss during the period 2000–2014, is defined as a stand-replacement disturbance, or a change from a forest to non-forest state, encoded as either 1 (loss) or 0 (no loss). Forest gain during the period 2000–2012, is defined as a non-forest to forest change entirely within the study period, encoded as either 1 (gain) or 0 (no gain).These layers were again cropped to the administrative boundaries of the Shimoga District (raster package: crop function) and re-projected to an equal area projection (Albers equal area conic projection) using the projectRaster function (method=”ngb”) in R. An algorithm was developed in R to identify which of the pixels in the loss and gain rasters coincided with each 1km and 2km grid cell of the study area. The ClassStat function of the SDM Tools package [4] was used to calculate the proportional area of each 1km or 2km grid cell that was made up of loss pixels or gain pixels. Forest gain and loss are very highly correlated (r=0.986) and occur in similar places in the landscape (Fig. S2C). Forest loss was a much more common transition than a forest gain affecting 1.2% of land pixels rather than 0.16% of land pixels for forest gain.

To assess how forest loss or gain from a global product like Hansen et al. (2013) should be interpreted locally in south India, we analysed how the loss and gain pixels from Hansen et al. 2013 coincided with classes in the MonkeyFeverRisk LULC map (by extracting the value of the LULC map for the centroids of loss or gain pixels).

The distribution of loss and gain pixels across forest classes from the MonkeyFeverRisk LULC map is shown in Table 1. Locations categorised as a loss by Hansen et al. were most commonly classified currently as plantation, followed by moist evergreen forest, followed by

moist or dry deciduous forest by the MonkeyFeverRisk LULC map. The pattern was similar for the gain pixels. Since not all forest loss pixels were non-forest in the current day and not all forest gain pixels were forest in the current day, the precise meaning of the Hansen et al. (2013) forest loss layer was unclear for south India, though we expect that it is at least indicative of areas where the forest has undergone a larger degree of change since 2000.

Table 1: Percentage of loss (n= 108398) and gain (n= 14646) land pixels from the global Hansen et al. (2013) product that fall into different forest classes according to the MonkeyFeverRisk LULC map

    Land use class


    Gain


    Loss




    moist evergreen


    30.4


    26.1




    moist deciduous


    6.5


    16.2




    dry deciduous


    3.0


    9.7




    plantation


    46.2


    37.2




    Non-forest classes


    14.0


    10.9

Host and public health predictors

Livestock host density data, namely buffalo and indigenous cattle densities in units of total head per village were obtained from Department of Animal Husbandry, Dairying and Fisheries, Government of India Census from 2011 at village level. These were linked to village boundaries from the Survey of India using the village census codes in R. The village areas were calculated from the spatial polygons dataframe of villages using the rgeos package in R, so that the total head per village metrics could be convert into an areal density of buffalo and indigenous cattle per km and then rasterized at 1km and 2km using the rasterize function of the raster package.

The human population size and public health metrics were obtained from the Government of India Population Census 2011. The human population size (census field TOT_P) was again linked to the spatial polygon village boundaries using the census village code (census field VCT_2011) and converted to an areal metric of population density per km and rasterized at 1km and 2km as above. The number of medics per head of population was derived by summing all doctors and para-medicals “in position” across all types of health centres, clinics and dispensaries per village and dividing by the total population of the village (TOT_P) and then linked to village boundaries and rasterized as above. The proximity to health centres was a categorical variable derived from the “Primary.Health.Centre..Numbers” field, where 1 = Primary Health Centre (PHC) within village boundary, 2 = PHC within 5km of village, 3=PHC within 5-10km of village, 4= PHC further than 10km from village. It was linked to village boundaries and rasterized as above.

The resulting raster layers for all predictors were saved in GeoTiff format.

References

Robert J. Hijmans (2017). raster: Geographic Data Analysis and Modeling. R package version 2.6-7. https://CRAN.R-project.org/package=raster
R Core Team (2018). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.URL https://www.R-project.org/ 
Jarvis, A., Reuter, I., Nelson, A., Guevara, E. Hole-filled SRTM for the globe Version 4. 2008.
VanDerWal, J., Falconi, L., Januchowski, S., Shoo, L., & Storlie, C. (2014). SDMTools: Species Distribution Modelling Tools: Tools for processing data associated with species distribution modelling exercises. R

Abstract

The National Sample Survey Organisation (NSSO) has been carrying out All-India surveys quinquennially on consumer expenditure and employment - unemployment. The 50th round (July 1993 - June 1994) was the Fifth quinquennial survey on Consumer Expenditure and Employment - Unemployment. The previous four quinquennial surveys were the 27th (Oct. 1972 - Sept. 1973), the 32nd (Jul.1977 - Jun. 1978), the 38th ( Jan. - Dec. 1983) and 43rd (Jul. 1987 - Jun. 1988) rounds. In other rounds of NSS, also, a consumer expenditure inquiry on a limited scale was being carried out from the 42nd round (1986-87) onwards. From the 45th round onwards the subject coverage of this schedule has been expanded to include some important questions on employment so that an annual series of consumer expenditure and employment data is now available. While some of these smaller-scale surveys are spread over a full year and others over six months only, the quinquennial (full-scale) surveys have all been of a full year's duration. Household consumer expenditure is measured as the expenditure incurred by a household on domestic account during a specified period, called reference period. It includes the imputed values of goods and services, which are not purchased but procured otherwise for consumption. In other words, it is the sum total of monetary values of all the items (i.e. goods and services) consumed by the household on domestic account during the reference period. The imputed rent of owner-occupied houses is excluded from consumption expenditure. Any expenditure incurred towards the productive enterprises of the households is also excluded from household consumer expenditure. The household consumer expenditure schedule used for the survey collected information on quantity and value of household consumption with a reference period of "last 30 days" for some items of consumption and "last 365 days" for some less frequently purchased items. To minimise recall errors, a very detailed item classification was, as usual, adopted to collect information, including 148 items of food, 13 items of fuel, 28 items of clothing, bedding and footwear, 18 items of educational and medical expenses, 52 items of durable goods, and about 85 other items. The schedule also collected some other household particulars including age, sex and educational level etc. of each household member.

The schedule design for the survey was more or less similar to that adopted in the previous quinquennial round. The field work for the survey was conducted, as usual, by the Field Operations Division of the Organisation. The collected data were processed by the Data Processing Division of NSSO and tabulated by the Computer Centre of Department of Statistics. The reports have been prepared by Survey Design & Research Division (SDRD) of NSSO under the guidance of the Governing Council, NSSO.

Geographic coverage

The survey period of the 50th round was from July 1993 to June 1994. The geographical coverage of the survey was to be the whole of the Indian Union except Ladakh and Kargil districts of Jammu & Kashmir, 768 interior villages of Nagaland and 172 villages in Andaman & Nicobar Islands which remain inaccessible throughout the year. However, certain districts of Jammu & Kashmir viz., Doda, Anantnag, Pulwama, Srinagar, Badgam, Baramula and Kupwara, and Punjab's Amritsar district, had to be excluded from the survey due to unfavourable field conditions.

Analysis unit

Randomly selected households based on sampling procedure and members of the household

Universe

The survey used the interview method of data collection from a sample of randomly selected households and members of the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample design adopted for this round of survey was similar to that followed in the past surveys in its general aspects. The general scheme was a two stage stratified design with the first stage units being villages in the rural areas and urban frame survey blocks (UFS) in the urban areas. The second stage units were the households.

Sampling frame for first stage units: The latest available lists of census villages (which are mostly the 1981 census lists) constitute the sampling frame for the rural sector. For Nagaland, the villages located within 5kms of a bus route constitute the sampling frame whereas, for Andaman & Nicobar Islands, the list of accessible villages constituted the sampling frame. For the urban sector, the lists of NSSO Urban Frame Survey (UFS) blocks have been considered as the sampling frame. However, for some of the newly declared towns of 1991 census for which UFS frame has not been received, the lists of 1991 census EBs have been considered as the sampling frame.

Region formation and stratification: States were divided into regions by grouping contiguous districts similar in respect of population density and cropping pattern. In rural sector each district was treated a separate stratum if the population was below 2 million and where it exceeded 2 million, it was split into two or more strata. This cut off point of population was taken as 1.8 million ( in place of 2 million ) for the purpose of stratification for districts for which the 1981 census frame was used. In the urban sector, strata were formed, within each NSS region on the basis of population size class of towns. However, for towns with population of 4 lakhs or more the urban blocks were divided into two classes viz. one consisting of blocks inhabited by affluent section of the population and the other consisting of the remaining blocks.

Selection of first stage units : Selection of sample villages was done circular systematically with probability proportional to population and sample blocks circular systematically with equal probability. Both the sample villages and the sample blocks were selected in the form of two or more independent sub-samples. In Arunachal Pradesh the procedure of cluster sampling has been followed. Further large villages/blocks having present population of 1200 or more were divided into a suitable number of hamlet- groups/ sub-blocks having equal population content. Two hamlet- groups were selected from the larger villages while one sub-block was selected in urban sector for larger blocks.

Selection of households : While listing the households in the selected villages, certain relatively affluent households were identified and considered as second stage stratum 1 and the rest as second stage stratum 2.

A total of 10 households were surveyed from the selected village/hamlet-groups, 2 from the first category and remaining from the second.Further in the second stage stratum-2, the households were arranged according to the means of livelihood. The means of livelihood were identified on the basis of the major source of income as i) self-employed in non-agriculture, ii) rural labour and iii) others. The land possessed by the households was also ascertained and the frame for selection was arranged on the basis of this information. The households were selected circular systematically from both the second stage strata.

In the urban blocks a different method was used for arranging the households for selection. This involved the identification means of livelihood of households as any one of a) self-employed, b) regular salaried/wage earnings, c) casual labour, d) others. Further the average household monthly per capita consumer expenditure (mpce) was also ascertained. All households with MPCE of (i) Rs. 1200/- or more (in towns with population less than 10 lakhs or (ii) Rs. 1500/- or more (in towns with population 10 lakh or more) formed second-stage stratum 1 and the rest, second-stage stratum 2.The households of second-stage stratum 2 were arranged according to means of livelihood class and MPCE ranges before selection of sample households. A total of 10 households were selected from each sample block as follows (i) For affluent strata/classes : 4 households from second- stage stratum 1 and 6 households from second-stage stratum 2 (ii) For other strata/classes : 2 households from second-stage stratum 1 and 8 from second-stage stratum 2. Households were then selected circular systematically with a random start.

Shortfall in the required number of household in any second-stage stratum was made up by increasing the quota for the other second stage stratum.

A total of 7284 sample villages (Rural) and 4792 sample blocks (Urban) were allotted in central sample. 6983 sample villages and 470 sample blocks were successfully surveyed covering 356351 persons in sample villages and 208389 persons in sample blocks.

Sampling deviation

There was no deviation from the original sampling design.

Mode of data collection

Face-to-face [f2f]

This statistic depicts the age distribution of India from 2013 to 2023. In 2023, about 25.06 percent of the Indian population fell into the 0-14 year category, 68.02 percent into the 15-64 age group and 6.92 percent were over 65 years of age. Age distribution in India India is one of the largest countries in the world and its population is constantly increasing. India’s society is categorized into a hierarchically organized caste system, encompassing certain rights and values for each caste. Indians are born into a caste, and those belonging to a lower echelon often face discrimination and hardship. The median age (which means that one half of the population is younger and the other one is older) of India’s population has been increasing constantly after a slump in the 1970s, and is expected to increase further over the next few years. However, in international comparison, it is fairly low; in other countries the average inhabitant is about 20 years older. But India seems to be on the rise, not only is it a member of the BRIC states – an association of emerging economies, the other members being Brazil, Russia and China –, life expectancy of Indians has also increased significantly over the past decade, which is an indicator of access to better health care and nutrition. Gender equality is still non-existant in India, even though most Indians believe that the quality of life is about equal for men and women in their country. India is patriarchal and women still often face forced marriages, domestic violence, dowry killings or rape. As of late, India has come to be considered one of the least safe places for women worldwide. Additionally, infanticide and selective abortion of female fetuses attribute to the inequality of women in India. It is believed that this has led to the fact that the vast majority of Indian children aged 0 to 6 years are male.

In 1800, the population of the region of present-day India was approximately 169 million. The population would grow gradually throughout the 19th century, rising to over 240 million by 1900. Population growth would begin to increase in the 1920s, as a result of falling mortality rates, due to improvements in health, sanitation and infrastructure. However, the population of India would see it’s largest rate of growth in the years following the country’s independence from the British Empire in 1948, where the population would rise from 358 million to over one billion by the turn of the century, making India the second country to pass the billion person milestone. While the rate of growth has slowed somewhat as India begins a demographics shift, the country’s population has continued to grow dramatically throughout the 21st century, and in 2020, India is estimated to have a population of just under 1.4 billion, well over a billion more people than one century previously. Today, approximately 18% of the Earth’s population lives in India, and it is estimated that India will overtake China to become the most populous country in the world within the next five years.

Quantitative Structure and Composition of Tropical Forests of Mudumalai Wildlife Sanctuary, Western Ghats, India The present study deals with the assessment of quantitative structure and floristic composition of tropical forests of Mudumalai Wildlife Sanctuary, Western Ghats, India. Forest structure was analyzed across girth classes and height intervals. Altogether 156 tree species were analyzed. Vegetation type-wise Importance Value Index, Shannon-Weiner index, Simpson index, Margalef’s index and Pielou Index were calculated. The tree stand density varies from 112-406.8 ha-1 with the average basal area of 26.25m2/ha-1. Shannon-Weiner Index (H) ranges from 3.94-4.90. The Simpson Index of dominance varies from 0.86-0.94. The Margalef Species Richness Index varies from 4.61-8.31.The population density of tree species across girth class intervals shows that 65.4% and 36.4% of individuals belong to 30-60 cm gbh. Tree distribution by height class intervals shows that around 28.7% of individuals are in the height class of 20-25m, followed by 24.4% in the height of 15-20m, whereas 3.37% of individuals are in the height class of >30m.

The internet penetration rate in India rose over 55 percent in 2025, from about 14 percent in 2014. Although these figures seem relatively low, it meant that more than half of the population of 1.4 billion people had internet access that year. This also ranked the country second in the world in terms of active internet users. Internet availability and accessibility By 2021 the number of internet connections across the country tripled with urban areas accounting for a higher density of connections than rural regions. Despite incredibly low internet prices, internet usage in India has yet to reach its full potential. Lack of awareness and a tangible gender gap lie at the heart of the matter, with affordable mobile handsets and mobile internet connections presenting only a partial solution. Reliance Jio was the popular choice among Indian internet subscribers, offering them wider coverage at cheap rates. Digital living Home to one of the largest bases of netizens in the world, India is abuzz with internet activities being carried out every moment of every day. From information and research to shopping and entertainment to living in smart homes, Indians have welcomed digital living with open arms. Among these, social media usage was one of the most common reasons for accessing the internet.

Nigeria has the largest population in Africa. As of 2025, the country counted over 237.5 million individuals, whereas Ethiopia, which ranked second, has around 135.5 million inhabitants. Egypt registered the largest population in North Africa, reaching nearly 118.4 million people. In terms of inhabitants per square kilometer, Nigeria only ranked seventh, while Mauritius had the highest population density on the whole African continent in 2023. The fastest-growing world region Africa is the second most populous continent in the world, after Asia. Nevertheless, Africa records the highest growth rate worldwide, with figures rising by over two percent every year. In some countries, such as Niger, the Democratic Republic of Congo, and Chad, the population increase peaks at over three percent. With so many births, Africa is also the youngest continent in the world. However, this coincides with a low life expectancy. African cities on the rise The last decades have seen high urbanization rates in Asia, mainly in China and India. However, African cities are currently growing at larger rates. Indeed, most of the fastest-growing cities in the world are located in Sub-Saharan Africa. Gwagwalada, in Nigeria, and Kabinda, in the Democratic Republic of the Congo, ranked first worldwide. By 2035, instead, Africa's fastest-growing cities are forecast to be Bujumbura, in Burundi, and Zinder, Nigeria.