This dataset provides a detailed breakdown of district-level population, area, and density statistics from the 2011 Census of India. It includes total population, male and female population counts, population density per square kilometer, and the geographical area (in sq. km) for each district.

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.

The southern state of Tamil Nadu in India recorded a population density of 555 people for every square kilometer according to the country's latest census in 2011. This was a significant increase compared to a decade earlier where the figure stood at 480.

According to the 2011 census, the population density in the Indian state of Maharashtra was *** individuals per square kilometer. Located on the Deccan Plateau, it is the second-most populous state in the country. A steady increase in the population of the state can be attributed to growing urban districts such as Mumbai and Pune, with diverse employment opportunities in several sectors.

India's economic powerhouse

With a contribution of over ** trillion Indian rupees in the financial year 2017, the state of Maharashtra had the highest gross state domestic product in the country. A per capita income of over *** thousand Indian rupees was estimated across the state for the preceding year. Based on its economic model, the state was a highly preferred destination for domestic and foreign investments.

The most populous Indian state

Mumbai, the capital city of Maharashtra, was the most populous city after Delhi. As the country's economic core, it serves as the financial and commercial capital while providing numerous job opportunities. Many are attracted to this dream city in search of a lucrative career and to make it big in the world-famous Bollywood film industry.

According to the latest Indian census in 2011, every square kilometer in the southern state of Karnataka was inhabited by 319 people, up from 101 in 1951. The highest population density in the state was in Bangalore.

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.

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 ---

Population density of NCT of Delhi soared by 21.20% from 9,340 people per square kilometer in 2001 to 11,320 people per square kilometer in 2011. Since the 52.47% surge in 1961, population density rocketed by 531.34% in 2011. Notes: a. Includes estimated population of Paomata, Mao Maram and Purul sub-divisions of Senapati District of Manipur for 2001. b. For working out the density of India and Jammu & Kashmir the entire area and population of those portions of Jammu & Kashmir which are under illegal occupation of Pakistan and China have not been taken into account. c. India figures include estimated figures for those of the three sub-divisions viz. Mao Maram, Paomata and Purul of Senapati district of Manipur as population census 2001 in these three subdivisions were cancelled due to technical and administrative reasons although a population census was carried out in this sub-division as per schedule.

2.191 (People per square kilometer) in 2011. Notes: a. Includes estimated population of Paomata, Mao Maram and Purul sub-divisions of Senapati District of Manipur for 2001. b. For working out the density of India and Jammu & Kashmir the entire area and population of those portions of Jammu & Kashmir which are under illegal occupation of Pakistan and China have not been taken into account. c. India figures include estimated figures for those of the three sub-divisions viz. Mao Maram, Paomata and Purul of Senapati district of Manipur as population census 2001 in these three subdivisions were cancelled due to technical and administrative reasons although a population census was carried out in this sub-division as per schedule.

The western state of Gujarat in India recorded a population density of 308 people for every square kilometer according to the country's latest census in 2011. This was a significant increase compared to a decade earlier where the figure stood at 258.

308 (People per square kilometer) in 2011. Notes: a. Includes estimated population of Paomata, Mao Maram and Purul sub-divisions of Senapati District of Manipur for 2001. b. For working out the density of India and Jammu & Kashmir the entire area and population of those portions of Jammu & Kashmir which are under illegal occupation of Pakistan and China have not been taken into account. c. India figures include estimated figures for those of the three sub-divisions viz. Mao Maram, Paomata and Purul of Senapati district of Manipur as population census 2001 in these three subdivisions were cancelled due to technical and administrative reasons although a population census was carried out in this sub-division as per schedule.

The statistic displays the main states and union territories with the highest number of people living in urban areas in India in 2011. In that year, the state of Maharashtra had the highest population with over 50 million people living in urban areas. The population density in India from 2004 to 2014 can be seen here.

319 (People per square kilometer) в 2011. Notes: a. Includes estimated population of Paomata, Mao Maram and Purul sub-divisions of Senapati District of Manipur for 2001. b. For working out the density of India and Jammu & Kashmir the entire area and population of those portions of Jammu & Kashmir which are under illegal occupation of Pakistan and China have not been taken into account. c. India figures include estimated figures for those of the three sub-divisions viz. Mao Maram, Paomata and Purul of Senapati district of Manipur as population census 2001 in these three subdivisions were cancelled due to technical and administrative reasons although a population census was carried out in this sub-division as per schedule.

The north-western state of Rajasthan in India recorded a population density of 200 people for every square kilometer according to the country's latest census in 2011. This was an increase compared to a decade earlier where the figure stood at 165.

394 (People per square kilometer) in 2011. Notes: a. Includes estimated population of Paomata, Mao Maram and Purul sub-divisions of Senapati District of Manipur for 2001. b. For working out the density of India and Jammu & Kashmir the entire area and population of those portions of Jammu & Kashmir which are under illegal occupation of Pakistan and China have not been taken into account. c. India figures include estimated figures for those of the three sub-divisions viz. Mao Maram, Paomata and Purul of Senapati district of Manipur as population census 2001 in these three subdivisions were cancelled due to technical and administrative reasons although a population census was carried out in this sub-division as per schedule.

Abstract

National Statistical Office (NSO) of India will be conducting Annual Survey on Unincorporated Sector Enterprises (ASUSE) 2022-23 during October 2022 to September 2023.

This survey will be devoted exclusively to economic and operational characteristics of unincorporated non-agricultural establishments in manufacturing, trade and other services sector. The unit of enquiry of the ASUSE will be an ‘establishment’.

Unincorporated sector is an integral part of Indian econ my, which not only comprises of large number of establishments but also generates large number ofemployment in this sector. Besides, its contribution to Gross Domestic Product (GDP) of the country is also significant. Unincorporated sector has tremendous potential to grow higher.

The ASUSE 2022-23, which will be launched in October 2022 andcontinue till September 2023, will cover all unincorporated non-agricultural establishments belonging to three sectors viz., Manufacturing, Trade and Other Services.

(i) The survey will cover the following broad categories: (a) Manufacturing establishments excluding those registered under Sections 2m(i) and2m(ii) of the Factories Act, 1948 (b) Manufacturing establishments registered under Section 85 of Factories Act, 1948 (c) Establishments engaged in cotton ginning, cleaning and bailing (code 01632 of NIC-2008) excluding those registered under Sections 2m(i) and 2m(ii) of the Factories Act,1948 (d) Establishments manufacturing Bidi and Cigar excluding those registered under bidi and cigar workers (conditions of employment) Act, 1966 (e) Non-captive electric power generation, transmission and distribution by units not registered with the Central Electricity Authority (CEA) (f) Trading establishments (g) Other Service sector establishments

Geographic coverage

The survey will cover the rural and urban areas of whole of India (except the villages in Andaman and Nicobar Islands which are difficult to access). The definitions of urban and rural areas as per census 2011 are given below:

Urban: Constituents of urban area are Statutory Towns, Census Towns and Outgrowths.

Statutory Town (ST): All places with a municipality, corporation, cantonment board or notified towns area committee, etc.

Census Town (CT): Places that satisfy the following criteria are termed as Census Towns (CTs). a. A minimum population of 5000 b. At least 75% of the male main working population engaged in non-agricultural pursuits c. A density of population of at least 400 per sq.km.

Out Growth (OG): Out Growth should be a viable unit such as a village or part of a village contiguous to a statutory town and possess the urban features in terms of infrastructure and amenities such as pucca roads, electricity, taps, drainage system, education institutions, post offices, medical facilities, banks, etc. Examples of OGs are Railway colonies, University campuses, Port areas, that may come up near a CT or statutory towns outside its statutory limits but within the revenue limit of a village or villages contiguous to the town or city.

Urban Agglomeration (UA): It is a continuous urban spread constituting a town and its adjoining urban outgrowths (OGs) or two or more physically contiguous towns together and any adjoining urban out-growth of such towns. Rural: All area other than urban are rural. The basic unit for rural area is the revenue village.

Sampling procedure

Outline of sample design: A stratified multi-stage sampling design will be adopted for ASUSE.

Rural sector: The first stage units (FSU) will be the census villages in the rural sector. For rural part of Kerala, Panchayat wards (PW) will be taken as FSUs.

Urban sector: The First Stage Units (FSU) will be the latest updated UFS (Urban Frame Survey) blocks.

The Ultimate Stage Units (USU) will be establishments for both the sectors. In the case of large FSUs, one intermediate stage of sampling will be the selection of three hamlet-groups (HGs)/sub-blocks (SBs) from each of the large FSUs.

Sampling frame to be used for selection of FSUs

Census 2011 list of villages will be used as the sampling frame for rural areas. Auxiliary information such as number of workers, etc. available from Sixth Economic Census (EC) frame will be used for stratification, sub-stratification and selection of FSUs, for rural areas (except Kerala). In rural areas of Kerala, list of Panchayat Wards (PW) as per Census 2011 will be used as sampling frame. For all urban areas, the latest updated list of UFS blocks will be the sampling frame.

Stratification of FSUs:

Rural sector: Each NSS State region will constitute a rural stratum.

Urban sector: In urban areas, strata will be formed within each NSS State region on the basis of population of towns as per Census 2011. The tentative stratum numbers and their composition (within each NSS State region) will be as follows:

stratum 1 : all towns with population less than 50,000 stratum 2 : all towns with population 50,000 or more but less than 3 lakhs stratum 3 : all towns with population 3 lakhs or more but less than 10 lakhs stratum 4, 5, 6, ... : each city with population 10 lakhs or more

Mode of data collection

Face-to-face [f2f]

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

The population density of the northern state of Uttar Pradesh in India recorded 829 people for every square kilometer in 2011, the latest available census. This was a doubling compared to the value in 1981.

Replication Data for: "Geographic and socio-economic barriers to rural electrification: New evidence from Indian villages". Citation for the article is the following: Dugoua, Eugenie and Liu, Ruinan and Urpelainen, Johannes, Geographic and Socio-Economic Barriers to Rural Electrification: New Evidence from Indian Villages (March 22, 2017). Energy Policy, Forthcoming. Available at SSRN: https://ssrn.com/abstract=2939880 Abstract: The International Energy Agency estimates that more than a billion people remain without household electricity access. However, countries such as India have recently made major progress in rural electrification. Who has benefited from these achievements? We focus on 714 villages in six energy-poor states of northern and eastern India to investigate trends in electricity access. We use data both from the 2011 Census of India and an original energy access survey conducted in 2014 and 2015. During the three years that separated the surveys, distance to the nearest town and land area lose their power as predictors of the percentage of households in the village that has access to electricity. In this regard, the Indian government's flagship rural electrification program seems to have managed to overcome a major obstacle to grid extension. On the other hand, socio-economic inequalities between villages related to caste status and household expenditure remain strong predictors. These findings highlight the importance of socio-economic barriers to rural electricity access and alleviate concerns about remoteness and population density as obstacles to grid extension. To access the full ACCESS dataset: http://dx.doi.org/10.7910/DVN/0NV9LF. If you want to use the full ACCESS dataset, please, cite both of the following: Aklin, Michaël; Cheng, Chao-yo; Ganesan, Karthik; Jain, Abhishek; Urpelainen, Johannes; Council on Energy, Environment and Water. Access to Clean Cooking Energy and Electricity: Survey of States in India (ACCESS). 2016. Harvard Dataverse, V1. http://dx.doi.org/10.7910/DVN/0NV9LF. Aklin, Michaël, Chao-yo Cheng, Johannes Urpelainen, Karthik Ganesan, and Abhishek Jain. 2016. "Factors Affecting Household Satisfaction with Electricity Supply in Rural India." Nature Energy 1(16170). DOI: 10.1038/nenergy.2016.170. (http://www.nature.com/articles/nenergy2016170)

Abstract

National Statistical Office (NSO) of India will be conducting 2nd round of Annual Survey on Unincorporated Sector Enterprises (ASUSE) during April 2021 – March 2022.

This survey will be devoted exclusively to economic and operational characteristics of unincorporated non-agricultural establishments in manufacturing, trade and other services sector. The unit of enquiry of the ASUSE will be an 'establishment'.

Unincorporated sector is an integral part of Indian econ my, which not only comprises of large number of establishments but also generates large number ofemployment in this sector. Besides, its contribution to Gross Domestic Product (GDP) of the country is also significant. Unincorporated sector has tremendous potential to grow higher.

The coverage of Annual Survey of Unincorporated Sector Enterprises (ASUSE) (April 2021 – March 2022) will be unincorporated non-agricultural establishments belonging to three sectors viz., Manufacturing, Trade and Other Services.

(i) The survey will cover the following broad categories: (a) Manufacturing establishments excluding those registered under Sections 2m(i) and2m(ii) of the Factories Act, 1948 (b) Manufacturing establishments registered under Section 85 of Factories Act, 1948 (c) Establishments engaged in cotton ginning, cleaning and bailing (code 01632 of NIC-2008) excluding those registered under Sections 2m(i) and 2m(ii) of the Factories Act,1948 (d) Establishments manufacturing Bidi and Cigar excluding those registered under bidi and cigar workers (conditions of employment) Act, 1966 (e) Non-captive electric power generation, transmission and distribution by units not registered with the Central Electricity Authority (CEA) (f) Trading establishments (g) Other Service sector establishments

Geographic coverage

The survey will cover the rural and urban areas of whole of India (except the villages in Andaman and Nicobar Islands which are difficult to access). The definitions of urban and rural areas as per census 2011 are given below:

Urban: Constituents of urban area are Statutory Towns, Census Towns and Outgrowths.

Statutory Town (ST): All places with a municipality, corporation, cantonment board or notified towns area committee, etc.

Census Town (CT): Places that satisfy the following criteria are termed as Census Towns (CTs). a. A minimum population of 5000 b. At least 75% of the male main working population engaged in non-agricultural pursuits c. A density of population of at least 400 per sq.km.

Out Growth (OG): Out Growth should be a viable unit such as a village or part of a village contiguous to a statutory town and possess the urban features in terms of infrastructure and amenities such as pucca roads, electricity, taps, drainage system, education institutions, post offices, medical facilities, banks, etc. Examples of OGs are Railway colonies, University campuses, Port areas, that may come up near a CT or statutory towns outside its statutory limits but within the revenue limit of a village or villages contiguous to the town or city.

Urban Agglomeration (UA): It is a continuous urban spread constituting a town and its adjoining urban outgrowths (OGs) or two or more physically contiguous towns together and any adjoining urban out-growth of such towns. Rural: All area other than urban are rural. The basic unit for rural area is the revenue village.

Sampling procedure

Outline of sample design: A stratified multi-stage sampling design will be adopted for ASUSE.

Rural sector: The first stage units (FSU) will be the census villages in the rural sector. For rural part of Kerala, Panchayat wards (PW) will be taken as FSUs.

Urban sector: The First Stage Units (FSU) will be the latest updated UFS (Urban Frame Survey) blocks.

The Ultimate Stage Units (USU) will be establishments for both the sectors. In the case of large FSUs, one intermediate stage of sampling will be the selection of three hamlet-groups (HGs)/sub-blocks (SBs) from each of the large FSUs.

Sampling frame to be used for selection of FSUs

Census 2011 list of villages will be used as the sampling frame for rural areas. Auxiliary information such as number of workers, etc. available from Sixth Economic Census (EC) frame will be used for stratification, sub-stratification and selection of FSUs, for rural areas (except Kerala). In rural areas of Kerala, list of Panchayat Wards (PW) as per Census 2011 will be used as sampling frame. For all urban areas, the latest updated list of UFS blocks will be the sampling frame.

Stratification of FSUs:

Rural sector: Each NSS State region will constitute a rural stratum.

Urban sector: In urban areas, strata will be formed within each NSS State region on the basis of population of towns as per Census 2011. The tentative stratum numbers and their composition (within each NSS State region) will be as follows:

stratum 1 : all towns with population less than 50,000 stratum 2 : all towns with population 50,000 or more but less than 3 lakhs stratum 3 : all towns with population 3 lakhs or more but less than 10 lakhs stratum 4, 5, 6, ... : each city with population 10 lakhs or more

Mode of data collection

Face-to-face [f2f]