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.

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

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 ---

A database (NDP-068) was generated from estimates of geographically referenced carbon densities of forest vegetation in tropical Southeast Asia for 1980. A geographic information system (GIS) was used to incorporate spatial databases of climatic, edaphic, and geomorphological indices and vegetation to estimate potential (i.e., in the absence of human intervention and natural disturbance) carbon densities of forests. The resulting map was then modified to estimate actual 1980 carbon density as a function of population density and climatic zone. The database covers the following 13 countries: Bangladesh, Brunei, Cambodia (Campuchea), India, Indonesia, Laos, Malaysia, Myanmar (Burma), Nepal, the Philippines, Sri Lanka, Thailand, and Vietnam.

The data sets within this database are provided in three file formats: ARC/INFOTM exported integer grids; ASCII (American Standard Code for Information Interchange) files formatted for raster-based GIS software packages; and generic ASCII files with x, y coordinates for use with non-GIS software packages.

The database includes ten ARC/INFO exported integer grid files (five with the pixel size 3.75 km x 3.75 km and five with the pixel size 0.25 degree longitude x 0.25 degree latitude) and 27 ASCII files. The first ASCII file contains the documentation associated with this database. Twenty-four of the ASCII files were generated by means of the ARC/INFO GRIDASCII command and can be used by most raster-based GIS software packages. The 24 files can be subdivided into two groups of 12 files each.

The files contain real data values representing actual carbon and potential carbon density in Mg C/ha (1 megagram = 10^6 grams) and integer-coded values for country name, Weck's Climatic Index, ecofloristic zone, elevation, forest or non- forest designation, population density, mean annual precipitation, slope, soil texture, and vegetation classification. One set of 12 files contains these data at a spatial resolution of 3.75 km, whereas the other set of 12 files has a spatial resolution of 0.25 degree. The remaining two ASCII data files combine all of the data from the 24 ASCII data files into 2 single generic data files. The first file has a spatial resolution of 3.75 km, and the second has a resolution of 0.25 degree. Both files also provide a grid-cell identification number and the longitude and latitude of the centerpoint of each grid cell.

The 3.75-km data in this numeric data package yield an actual total carbon estimate of 42.1 Pg (1 petagram = 10^15 grams) and a potential carbon estimate of 73.6 Pg; whereas the 0.25-degree data produced an actual total carbon estimate of 41.8 Pg and a total potential carbon estimate of 73.9 Pg.

Fortran and SASTM access codes are provided to read the ASCII data files, and ARC/INFO and ARCVIEW command syntax are provided to import the ARC/INFO exported integer grid files. The data files and this documentation are available without charge on a variety of media and via the Internet from the Carbon Dioxide Information Analysis Center (CDIAC).

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.

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

Multivariate model prediction accuracy on the test dataset (RMSE mean and standard deviation for 30 experimental runs across 4 prediction horizons).

In 2023, the annual population growth in India was 0.88 percent. Between 1961 and 2023, the figure dropped by 1.52 percentage points, though the decline followed an uneven course rather than a steady trajectory.

This contains gridded non-methane volatile organic compound (NMVOC) emission inventories for India derived as part of burning studies performed during the APHH-INDIA campaign. For data files with more than 1 million rows, NASA AMES metadata headers have been provided as a separate document, which has the identical name of the data it applies to but also includes _metadata. For years 1993, 1994, 1999, 2002, 2005, 2006, 2007, 2010, 2011 and 2016 inventories have been produced in terms of total NMVOC emission from each source sector (kg/km2). There are also two upper limit scenarios of emissions from cow dung cake combustion based on data from PPAC and PPAC supplemented with additional cow dung cake consumption for states now covered by this survey. The speciation factors of NMVOCs released from particular sources are also provided so that these years can be speciated by source simply by multiplying the total emission from each source by the ratio of species released from the source. This allows future users to produce speciated emission inventories for years other than 2011 if they need. Gridded inventories are also provided for emissions of 21 polycyclic aromatic hydrocarbons for the year 2011 from fuelwood, cow dung cake, charcoal, liquefied petroleum gas and municipal solid waste. These are provided as total PAH emissions from a source with speciation factors also provided to allow speciation should it be required by multiplying the total NMVOC emission from a source by the speciation factors from that source. Gridded inventories are provided for crop residue burning at 1km2 and 10km2. These were calculated with total agricultural area identified in a state from either NASA MODIS (1 km2) or Ramankutty et al. (2008) (10 km2). A second inventory was produced at 10km2 as it was felt that the NASA data offered little variation within respective states. These have been split into total emissions from each of the 5 emission factors applied, RiceEFyearlyVOCKG (for rice), WheatEFyearlyVOCKG (for wheat, coarse cereal and maize), JowarEFyearlyVOCKG (for Jowar and Bajra), MeanEFyearlyVOCKG (for 9 oilseeds, groundnut, rapeseed, mustard, sunflower, cotton, jute and mesta) and SugarcaneEFyearlyVOCKG (for sugarcane). The inventories were produced using emission factors developed as part of the APHH-INDIA project as well as from a different publication focussed on the burning of crops. The inventories have been developed in the following manner. The emission factors used in this study come from a variety of recently published sources. All emission factors applied in this study included measurement by PTR-ToF-MS, a technique well suited to species released in significant quantities from solid fuel combustion such as small oxygenated species, phenolics and furanics. These species are often missed by GC measurement alone. Preference has been given to emission factors from studies which: (1) have many measurements (n), (2) use samples collected from India or (3) use samples collected from similar countries. Fully speciated emission factors are available from the references given. For residential fuel combustion, the emission factors measured by Stewart et al. (2021a) were used and were developed from 76 combustion experiments of fuel wood, cow dung cake, LPG and MSW samples collected from around Delhi. This study was extremely detailed and measured online, gas-phase, speciated NMVOC emission factors for up to 192 chemical species using dual-channel gas chromatography with flame ionisation detection (DC-GC-FID, n = 51), two-dimensional gas chromatography (GC×GC-FID, n = 74), proton-transfer-reaction time-of-flight mass spectrometry (PTR-ToF-MS, n = 75) and solid-phase extraction two-dimensional gas chromatography with time-of-flight mass spectrometry (SPE-GC×GC-ToF-MS, n = 28). Comparison of these emission factors to those obtained in similar studies is provided in Stewart et al. (2021a). The emission factors used as part of this study are larger than those measured by Stockwell et al. (2016), Fleming et al. (2018) and several other studies which were based on gas chromatography techniques alone. The emission factors here measure many more NMVOC species, use techniques which target a range of species which more traditional GC analyses do not detect and make online measurements which minimise loss of intermediate-volatility and semi-volatile organic species, which may be lost through the collection of whole air samples, but have been shown to represent a large proportion of total emissions from biomass burning (Stockwell et al., 2015). Emission factors for combustion of crop residues on fields were taken from measurements by Stockwell et al. (2015) made using PTR-ToF-MS of 115 NMVOCs (Stockwell et al., 2015) for wheat straw (n = 6), sugarcane (n=2), rice straw (n=7) and millet (n=2). This study also included the mean crop residue emission factor for 19 food crops, for use when no current emission factor had been comprehensively measured using PTR-ToF-MS. The emission factor applied (38.8 g kg-1) was evaluated against that for crop residues used for domestic combustion in Delhi (37.9 g kg-1). Whilst the values measured by Stockwell et al. (2015) and Stewart et al. (2021a) were comparable, the value from Stockwell et al. (2015) was used as the crop types were more reflective of the crop residues burnt on fields after harvest, compared to those burnt to meet residential energy requirements. The mean emission factor for crop residue combustion on fields was used for specific crop types with smaller levels of cultivation. Emissions from coal burning were estimated using a mean emission factor from the combustion of bituminous coal from China (n = 14), a neighbouring Asian country, made using PTR-ToF-MS. Whilst the chemical composition of the coal may be more important than the development status of the country, there was overall a low level of reported residential coal use and this estimate was included for completeness. A total of 89 NMVOCs were identified, which represented 90-96% of the total mass spectra (Cai et al., 2019). Indian specific PAH emission factors were recently measured in gas- and particle-phases using PTR-ToF-MS and GC×GC-ToF-MS (Stewart et al., 2021). This dataset provided PAH emission factors collected from combustion of fuel wood (n = 16), cow dung cake (n = 3), crop residue from domestic combustion (n = 3), MSW (n = 3), LPG (n = 1) and charcoal (n = 1) samples. High resolution, gridded population data for India (WorldPop, 2017) was used at a resolution of 1 km2. Officially, urban populations in India are defined as having a population density > 400 people km-2, 75% of men employed in non-agricultural industries and a population of town > 5000 people. Rural populations in India cannot be identified simply by having a population density of < 400 people km-2, as some states such as Uttar Pradesh have an average population density of around 800 people km-2. Rural grid squares were therefore identified by calculating the population density threshold in each state in which the sum of the 1km2 grid squares below this threshold correctly reproduced the rural populations in these states from the 2001 and 2011 censuses (Government of India, 2014). A small uncertainty existed over the exact population of India and we used population statics indicated by the 2011 census. NMVOC and PAH emissions from domestic solid fuel combustion were plotted against this high-resolution population data in the R statistical programming language at 1 km2 for 2001 and 2011, with the population datasets scaled to the percentage changes in Indian population indicated by the World Bank for additional years of interest. Preference was given to large fuel usage surveys which included tens to hundreds of thousands of respondents. The Household Consumption of Goods and Services in India survey by the National Sample Survey Office (NSSO, 2007a, 2012a, 2014) gave state-wise kg capita-1 fuel wood, LPG, charcoal and coal burning statistics for rural and urban environments and was used for the years 2004-2005, 2009-2010 and 2011-2012. NMVOC emissions for these years were calculated by multiplying the NMVOC emission factor for the fuel, by the yearly fuel consumption per capita by the population of the grid cell. Data were collected from additional large surveys previously conducted. These surveys collected data in terms of the number of households using specific fuels per 1000 households in different Indian states in rural and urban environments. The Fifth Quinquennial Survey on Consumer Expenditure provided data for 1993-1994 (NSSO, 1997), the Energy Sources of Indian Households for Cooking and Lighting provided data for years 2004-2005, 2009-2010 and 2010-2011 (NSSO, 2007b, 2012b, 2015) and the Household Consumer Expenditure and Employment-Unemployment Situation in India for 2002 and 2006-2007 (NSSO, 2003, 2008). The National Family Health Survey presented India-wide fuel use as a percentage of the population. To reflect spatial variation in fuel use, the raw data from these surveys were accessed (from the DHS Programme, U.S. Agency for International Development), extracted through the SPSS statistics software package and processed in the R programming language. This increased fuel usage data availability as the number of households per 1000 households using specific fuels in Indian states and covered the years 1992-1993, 1998-1999, 2005-2006 and 2015-2016 (International Institute for Population Sciences, 1995, 2000, 2007, 2017). These were extensive datasets with 1992-1993, 1998-1999 and 2005-2006 surveying just under 100,000 households and 2015-2016 around 600,000 households. To allow the incorporation of data from years which were based on the number of households using a particular fuel per 1000 households (1993, 1994, 1999, 2002, 2006, 2007 and 2016), a scaling factor was developed. The scaling factor was based on the ratio of fuel use in the

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.

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]

The average for 2020 based on 27 countries was 3.56 doctors per 1,000 people. The highest value was in Austria: 5.35 doctors per 1,000 people and the lowest value was in Brazil: 2.05 doctors per 1,000 people. The indicator is available from 1960 to 2021. Below is a chart for all countries where data are available.

Over 909 million people in India lived in rural areas in 2023, a decrease from 2022. Urban India, although far behind with over 508 million people, had a higher year-on-year growth rate during the measured period.

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.

In 2023, approximately a third of the total population in India lived in cities. The trend shows an increase of urbanization by more than 4 percent in the last decade, meaning people have moved away from rural areas to find work and make a living in the cities. Leaving the fieldOver the last decade, urbanization in India has increased by almost 4 percent, as more and more people leave the agricultural sector to find work in services. Agriculture plays a significant role in the Indian economy and it employs almost half of India’s workforce today, however, its contribution to India’s GDP has been decreasing while the services sector gained in importance. No rural exodus in sightWhile urbanization is increasing as more jobs in telecommunications and IT are created and the private sector gains in importance, India is not facing a shortage of agricultural workers or a mass exodus to the cities yet. India is a very densely populated country with vast areas of arable land – over 155 million hectares of land was cultivated land in India as of 2015, for example, and textiles, especially cotton, are still one of the major exports. So while a shift of the workforce focus is obviously taking place, India is not struggling to fulfill trade demands yet.