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.

Comprehensive socio-economic dataset for India including population demographics, economic indicators, geographic data, and social statistics. This dataset covers key metrics such as GDP, population density, area, capital city, and regional classifications.

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.

• The current US Census Bureau world population estimate in June 2019 shows that the current global population is 7,577,130,400 people on Earth, which far exceeds the world population of 7.2 billion in 2015. Our estimate based on UN data shows the world's population surpassing 7.7 billion.
• China is the most populous country in the world with a population exceeding 1.4 billion. It is one of just two countries with a population of more than 1 billion, with India being the second. As of 2018, India has a population of over 1.355 billion people, and its population growth is expected to continue through at least 2050. By the year 2030, India is expected to become the most populous country in the world. This is because India’s population will grow, while China is projected to see a loss in population.
• The following 11 countries that are the most populous in the world each have populations exceeding 100 million. These include the United States, Indonesia, Brazil, Pakistan, Nigeria, Bangladesh, Russia, Mexico, Japan, Ethiopia, and the Philippines. Of these nations, all are expected to continue to grow except Russia and Japan, which will see their populations drop by 2030 before falling again significantly by 2050.
• Many other nations have populations of at least one million, while there are also countries that have just thousands. The smallest population in the world can be found in Vatican City, where only 801 people reside.
• In 2018, the world’s population growth rate was 1.12%. Every five years since the 1970s, the population growth rate has continued to fall. The world’s population is expected to continue to grow larger but at a much slower pace. By 2030, the population will exceed 8 billion. In 2040, this number will grow to more than 9 billion. In 2055, the number will rise to over 10 billion, and another billion people won’t be added until near the end of the century. The current annual population growth estimates from the United Nations are in the millions - estimating that over 80 million new lives are added yearly.
• This population growth will be significantly impacted by nine specific countries which are situated to contribute to the population growth more quickly than other nations. These nations include the Democratic Republic of the Congo, Ethiopia, India, Indonesia, Nigeria, Pakistan, Uganda, the United Republic of Tanzania, and the United States of America. Particularly of interest, India is on track to overtake China's position as the most populous country by 2030. Additionally, multiple nations within Africa are expected to double their populations before fertility rates begin to slow entirely.

Content

• In this Dataset, we have Historical Population data for every Country/Territory in the world by different parameters like Area Size of the Country/Territory, Name of the Continent, Name of the Capital, Density, Population Growth Rate, Ranking based on Population, World Population Percentage, etc. >Dataset Glossary (Column-Wise):
• Rank: Rank by Population.
• CCA3: 3 Digit Country/Territories Code.
• Country/Territories: Name of the Country/Territories.
• Capital: Name of the Capital.
• Continent: Name of the Continent.
• 2022 Population: Population of the Country/Territories in the year 2022.
• 2020 Population: Population of the Country/Territories in the year 2020.
• 2015 Population: Population of the Country/Territories in the year 2015.
• 2010 Population: Population of the Country/Territories in the year 2010.
• 2000 Population: Population of the Country/Territories in the year 2000.
• 1990 Population: Population of the Country/Territories in the year 1990.
• 1980 Population: Population of the Country/Territories in the year 1980.
• 1970 Population: Population of the Country/Territories in the year 1970.
• Area (km²): Area size of the Country/Territories in square kilometers.
• Density (per km²): Population Density per square kilometer.
• Growth Rate: Population Growth Rate by Country/Territories.
• World Population Percentage: The population percentage by each Country/Territories.

Comprehensive socio-economic dataset for British Indian Ocean Territory including population demographics, economic indicators, geographic data, and social statistics. This dataset covers key metrics such as GDP, population density, area, capital city, and regional classifications.

Delhi was the largest city in terms of number of inhabitants in India in 2023.The capital city was estimated to house nearly 33 million people, with Mumbai ranking second that year. India's population estimate was 1.4 billion, ahead of China that same year.

BackgroundThe world is rapidly becoming urban with the global population living in cities projected to double by 2050. This increase in urbanization poses new challenges for the spread and control of communicable diseases such as malaria. In particular, urban environments create highly heterogeneous socio-economic and environmental conditions that can affect the transmission of vector-borne diseases dependent on human water storage and waste water management. Interestingly India, as opposed to Africa, harbors a mosquito vector, Anopheles stephensi, which thrives in the man-made environments of cities and acts as the vector for both Plasmodium vivax and Plasmodium falciparum, making the malaria problem a truly urban phenomenon. Here we address the role and determinants of within-city spatial heterogeneity in the incidence patterns of vivax malaria, and then draw comparisons with results for falciparum malaria.Methodology/principal findingsStatistical analyses and a phenomenological transmission model are applied to an extensive spatio-temporal dataset on cases of Plasmodium vivax in the city of Ahmedabad (Gujarat, India) that spans 12 years monthly at the level of wards. A spatial pattern in malaria incidence is described that is largely stationary in time for this parasite. Malaria risk is then shown to be associated with socioeconomic indicators and environmental parameters, temperature and humidity. In a more dynamical perspective, an Inhomogeneous Markov Chain Model is used to predict vivax malaria risk. Models that account for climate factors, socioeconomic level and population size show the highest predictive skill. A comparison to the transmission dynamics of falciparum malaria reinforces the conclusion that the spatio-temporal patterns of risk are strongly driven by extrinsic factors.Conclusion/significanceClimate forcing and socio-economic heterogeneity act synergistically at local scales on the population dynamics of urban malaria in this city. The stationarity of malaria risk patterns provides a basis for more targeted intervention, such as vector control, based on transmission ‘hotspots’. This is especially relevant for P. vivax, a more resilient parasite than P. falciparum, due to its ability to relapse and the operational shortcomings of delivering a “radical cure”.

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.

Abstract

The main objective of the All India Debt and Investment Survey (AIDIS) is to generate reliable estimates on assets, liabilities and capital expenditure of the household sector. The survey provides the details of household liabilities required for the formulation of credit policy of financial institutions and planning for development.

The required information was collected from the same set of sample households in two visits to each sample. The information to be collected in the second visit to the household was considerably less than that collected in the first visit. In view of this uneven work-load in the two visits, it was decided to extend the survey period of the first visit to eight months - from January to August 1992 - and to reduce the survey period of the second visit to four months - from September to December 1992. As a result, the households which have been visited in the first two months of the first visit were revisited in the first month of the second visit and so on. To reduce the fatigue of the respondent, it was decided that unlike in the 37th round, the sample households in this round would be selected separately for schedules 18.1 and 18.2 in the rural sector. Since the land and livestock holdings were likely to be small in most cases, it was decided to survey the same set of sample households both for schedules 18.1 and 18.2 in the urban sector.

The position in regard to the assets and liabilities of the sample households was required to be collected with reference to a fixed date, namely, as on the 30th June1991. There was a time lag between the reference date and the survey date in the first visit varying from household to household within the range of maximum period of 14 months. To derive the above, therefore, it was decided to collect information on assets and liabilities as on the date of survey and the transactions relating to the said assets and liabilities carried out during the period intervening the date of reference and the date of survey.

Broadly, the following information has been collected in this round of survey:- (i) the asset and the liability position of the households

(ii) the amount of capital expenditure on (a) residential plots, houses or buildings, (b) farm business and (c) non-farm business, incurred by the household during the reference period of agricultural year 1991-22.

(iii) acquisition, disposal and loss of assets during July 1991 to June 1992.The assets owned by the households have been classified into three categories, namely, (a) physical assets contributing to capital formation (b) financial assets and (c) durable household assets. Besides collection of information for deriving the asset and liability position of the households as on 30.6.91, provisions have also been made to collect data on the transactions of physical, financial and household durable assets and also on the cash borrowings and repayments made during the agricultural year 1991-92.

Geographic coverage

The 48th Round was planned to cover the whole of Indian Union except

(i) Ladakh and Kargil districts of Jammu & Kashmir, (ii) 768 interior villages of Nagaland (out of a total of 1119 villages ) located beyond 5 kms. of a bus route, (iii) 172 villages in Andaman & Nicobar Islands (out of a total of 520 villages) which are inaccessible throughout the year. However, the survey could not be conducted in certain districts of Jammu & Kashmir viz. Anantnag, Pulwana, Srinagar, Badgam, Baramula and Kupwara, and the district of Amritsar in Punjab 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

Sample Design A stratified two-stage sampling design was adopted for the survey with the first stage units as census villages for rural areas and the Urban Frame Survey blocks for urban areas. Households formed the second statge units in both rural and urban areas.

Sampling frame for first stage units(FSU's): In the rural sector, the sampling frame in most of the strata was the 1981 census list of villages. However, in Assam, where the 1981 census was not undertaken, and in a few districts of other states, where the available list as per 1981 census was incomplete, the 1971 census list of villages was used. In the urban sector, the sampling frames used in most cases were the lists of NSS Urban Frame Survey (UFS) blocks. However, the 1991 census house listing enumeration blocks were considered as the sampling units for some of the new towns declared as urban areas in the 1991 population census.

Stratification: Each state/union territory (u.t.) was divided into one or more agro-economic regions by grouping contiguous districts which are similar with respect to population density and crop pattern. In Gujarat, however, some districts were subdivided for the purpose of region formation on the basis of location of dry areas and the distribution of tribal population in the state. The total number of regions formed in the India as whole was 78.

In the rural sector, within each region, each district with a rural population of less than 1.8 million according to the 1981 census formed a single basic stratum. Districts with larger population were divided into two or more strata, depending on population, by grouping contiguous tehsils, similar as far as possible in respect of rural population density and crop pattern. In Gujarat, however, in the case of districts extending over more than one region, the portion of a district falling in each region constituted a separate stratum even if the rural population of the district as a whole was less than 1.8 million. Further, in Assam, the strata formed for the earlier NSS rounds on the basis of 1971 census rural population exactly in the above manner, but with a cut-off of 1.5 million population, were retained as the strata for rural sampling.

In the urban sector, strata were formed, again within NSS regions, on the basis of 1981 (1991 in some of the new towns) census population of towns. Each city with a population 10 lakhs or more formed a separate stratum by itself. The remaining towns of each region were grouped to form three different strata on the basis of 1981 (1991 in a few cases) census population.

Allocation of sample: A total all-India sample of 6,812 first stage units (4,328 villages and 2,484 urban blocks) - determined on the basis of investigator strength in different states/u.t.'s and the expected workload per investigator - was initially allocated to the states/u.t's in proportion to central field staff available. The sample thus obtained for each state/u.t. was then allocated to its rural and urban sectors considering the relative sizes of the rural and urban population with almost double weightage being given for the urban sector. Within each sector of state/u.t., the allotted sample size was re-allocated to the different strata in proportion to the stratum population. All allocations were adjusted so that the sample size for a stratum was at least a multiple of 4 for the rural and urban sectors separately. This was done to accomplish equal sized samples in each sub-sample and sub-round. The only exception was Daman & Diu for which the first stage rural sample comprised 2 villages only.

Selection of first stage units: The selection of sample villages was PPS (with replacement) with population as the size variable, in the form of two independent subsamples. The sample blocks were selected by simple random sampling without replacement, also in the form of two independent subsamples.

Selection of hamlet-groups/sub-blocks: Large villages and blocks were divided into a suitable number of hamlet-groups and sub-blocks, respectively, having more or less equal population content. Two hamlet-groups were then selected from large villages, whereas only one sub-block was selected from the large blocks. The hamlet-groups were selected circular systematically and the sub-block with equal probability.

Selection of households: Two different procedures of selection of households were used for the rural and urban sectors. Different procedures for the two sectors were necessary, since in the rural sector schedules of enquiry for LHS survey and Debt & Investment survey were required to be canvassed in two separate sets of sample households, while in the urban sector, both the schedules were to be canvassed in the same set of sample households.

In the rural sector, nine households were selected from each sample village/selected hamlet groups. For selecting a sample of nine households, each sample village/hamlet group was sub-divided into 7 AIDIS sub-strata on the joint consideration of "land possessed" and "indebtedness status" of the households; first, all the households of the sample village/selected hamlet groups were divided into four LHS sub-strata by area of land possessed by them. Households possessing either no land or land less than 0.005 acre were grouped in substratum 1. The rest of the households were then arranged in ascending order by area of land possessed and classified into three substrata, 2, 3 and 4, such that the total area of land possessed by the households in each of the 3 sub-strata was nearly the same. Each of the LHS sub-strata 1 and 2 was further divided into "indebted" and "not indebted" groups to form AIDIS sub-stratum 1 to 4. AIDIS sub-strata 5 to 7 are formed by first merging LHS substrata numbers 3 and 4 and then sub-divided by the merged group into 3 classes, viz., (a) indebted to institutional agencies with or without being

Abstract

The All-India Debt and Investment Survey (AIDIS) was carried out as part of the 48th Round of the National Sample Survey Organisation (NSSO) during January to December 1992. This was the fifth such survey conducted at the all-India level. At present, the decenially conducted AIDIS is the only nation-wide enquiry providing data on household assets, indebtedness and capital expenditure.

Objective: The main objective of the AIDIS is to generate reliable estimates on assets, liabilities and capital expenditure of the household sector. The survey provides the details of household liabilities required for the formulation of credit policy of financial institutions and planning for development.

Geographic coverage

State and all-India level in rural and urban sectors.

The 48th Round was planned to cover the whole of Indian Union except (i) Ladakh and Kargil districts of Jammu & Kashmir, (ii) 768 interior villages of Nagaland (out of a total of 1119 villages ) located beyond 5 kms. of a bus route, (iii) 172 villages in Andaman & Nicobar Islands (out of a total of 520 villages) which are inaccessible throughout the year. However, the survey could not be conducted in certain districts of Jammu & Kashmir viz. Anantnag, Pulwana, Srinagar, Badgam, Baramula and Kupwara, and the district of Amritsar in Punjab due to unfavourable field conditions.

Analysis unit

Household

Universe

Households (self employed and others) in state and all-India level in rural and urban sectors.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Design A stratified two-stage sampling design was adopted for the survey with the first stage units as census villages for rural areas and the Urban Frame Survey blocks for urban areas. Households formed the second statge units in both rural and urban areas.

Sampling frame for first stage units(FSU's): In the rural sector, the sampling frame in most of the strata was the 1981 census list of villages. However, in Assam, where the 1981 census was not undertaken, and in a few districts of other states, where the available list as per 1981 census was incomplete, the 1971 census list of villages was used. In the urban sector, the sampling frames used in most cases were the lists of NSS Urban Frame Survey (UFS) blocks. However, the 1991 census house listing enumeration blocks were considered as the sampling units for some of the new towns declared as urban areas in the 1991 population census.

Stratification: Each state/union territory (u.t.) was divided into one or more agro-economic regions by grouping contiguous districts which are similar with respect to population density and crop pattern. In Gujarat, however, some districts were subdivided for the purpose of region formation on the basis of location of dry areas and the distribution of tribal population in the state. The total number of regions formed in the India as whole was 78.

In the rural sector, within each region, each district with a rural population of less than 1.8 million according to the 1981 census formed a single basic stratum. Districts with larger population were divided into two or more strata, depending on population, by grouping contiguous tehsils, similar as far as possible in respect of rural population density and crop pattern. In Gujarat, however, in the case of districts extending over more than one region, the portion of a district falling in each region constituted a separate stratum even if the rural population of the district as a whole was less than 1.8 million. Further, in Assam, the strata formed for the earlier NSS rounds on the basis of 1971 census rural population exactly in the above manner, but with a cut-off of 1.5 million population, were retained as the strata for rural sampling.

In the urban sector, strata were formed, again within NSS regions, on the basis of 1981 (1991 in some of the new towns) census population of towns. Each city with a population 10 lakhs or more formed a separate stratum by itself. The remaining towns of each region were grouped to form three different strata on the basis of 1981 (1991 in a few cases) census population.

Allocation of sample: A total all-India sample of 6,812 first stage units (4,328 villages and 2,484 urban blocks) - determined on the basis of investigator strength in different states/u.t.'s and the expected workload per investigator - was initially allocated to the states/u.t's in proportion to central field staff available. The sample thus obtained for each state/u.t. was then allocated to its rural and urban sectors considering the relative sizes of the rural and urban population with almost double weightage being given for the urban sector. Within each sector of state/u.t., the allotted sample size was re-allocated to the different strata in proportion to the stratum population. All allocations were adjusted so that the sample size for a stratum was at least a multiple of 4 for the rural and urban sectors separately. This was done to accomplish equal sized samples in each sub-sample and sub-round. The only exception was Daman & Diu for which the first stage rural sample comprised 2 villages only.

Selection of first stage units: The selection of sample villages was PPS (with replacement) with population as the size variable, in the form of two independent subsamples. The sample blocks were selected by simple random sampling without replacement, also in the form of two independent subsamples.

Selection of hamlet-groups/sub-blocks: Large villages and blocks were divided into a suitable number of hamlet-groups and sub-blocks, respectively, having more or less equal population content. Two hamlet-groups were then selected from large villages, whereas only one sub-block was selected from the large blocks. The hamlet-groups were selected circular systematically and the sub-block with equal probability.

Selection of households: Two different procedures of selection of households were used for the rural and urban sectors. Different procedures for the two sectors were necessary, since in the rural sector schedules of enquiry for LHS survey and Debt & Investment survey were required to be canvassed in two separate sets of sample households, while in the urban sector, both the schedules were to be canvassed in the same set of sample households.

In the rural sector, nine households were selected from each sample village/selected hamlet groups. For selecting a sample of nine households, each sample village/hamlet group was sub-divided into 7 AIDIS sub-strata on the joint consideration of “land possessed” and “indebtedness status” of the households; first, all the households of the sample village/selected hamlet groups were divided into four LHS sub-strata by area of land possessed by them. Households possessing either no land or land less than 0.005 acre were grouped in substratum 1. The rest of the households were then arranged in ascending order by area of land possessed and classified into three substrata, 2, 3 and 4, such that the total area of land possessed by the households in each of the 3 sub-strata was nearly the same. Each of the LHS sub-strata 1 and 2 was further divided into “indebted” and “not indebted” groups to form AIDIS sub-stratum 1 to 4. AIDIS sub-strata 5 to 7 are formed by first merging LHS substrata numbers 3 and 4 and then sub-divided by the merged group into 3 classes, viz., (a) indebted to institutional agencies with or without being indebted to non-institutional agencies ( b) indebted to non-institutional agencies alone and (c) not indebted. Independent sample of size 1,1,1,2,1,1&2 were selected circular systematically from the AIDIS sub-strata 1,2,3,4,5,6 and 7 respectively

In the urban sector, a sample of 9 households was selected from each sample urban block/sub-block. The households of a sample block/sub -block were classified into 7 AIDIS sub-strata, considering the monthly per capita consumption expenditure (mpce) and indebtedness status of the households. For this, the households were first grouped in three mpce classes, viz., less than A, A to B and B & above. The cut-off points A and B were determined at the state-level on the basis of mpce obtained from the survey on consumer expenditure, NSS 43rd Round, such that the mpce classes, below A, A to B, and B and above, respectively constituted 30 p.c, 60 p.c, and 10 p.c. of the urban population of the state. These mpce classes were further sub-divided by indebtedness status of the households to form 7 AIDIS sub strata. Independent samples were selected circular systematically from each of the sub-stratum. The number of households was selected from sub-strata 1, 2, 3, 4, 5, 6 and 7 respectively 1, 1, 1, 1, 2, 2 & 1.

Sample size: In all, the survey covered 57,031 households spread over 6,650 sample villages/blocks.

Sampling deviation

There have been no deviations from sample design.

Mode of data collection

Face-to-face [f2f]

Research instrument

The data for this survey is collected in the NSS Schedule 18.2 used for debt & investment. For this round, the schedule had the following blocks:

BLOCK 0 : DESCRIPTIVE IDENTIFICATION OF SAMPLE HOUSEHOLD: This block is meant for recording descriptive identification Particulars of the sample household and the sample items in this block are self-explanatory.

BLOCK 1 : IDENTIFICATION PARTICULARS OF SAMPLE VILLAGE/BLOCK

BLOCK 2 : PARTICULARS OF FIELD OPERATION

BLOCK 3 : REMARKS BY INVETIGATOR

BLOCK 4 : REMARKS BY SUPERVISORY OFFICER

BLOCK 5 : HOUSEHOLD CHARACTERISTICS : Certain household characteristics, such as, household size, social - group, household type, household industry -

Abstract

The Employment and Unemployment surveys of National sample Survey (NSS) are primary sources of data on various indicators of labour force at National and State levels. These are used for planning, policy formulation, decision support and as input for further statistical exercises by various Government organizations, academicians, researchers and scholars. NSS surveys on employment and un-employment with large sample size of households have been conducted quinquennially from 27th. round(October'1972 - September'1973) onwards. Cotinuing in this series the fourth such all-india survey on the situation of employment and unemployment in India was carried out during the period july 1987 - june 1988 . The working Group set up for planning of the entire scheme of the survey, among other things, examined also in detail some of the key results generated from the 38th round data and recommended some stream-lining of the 38th round schedule for the use in the 43rd round. Further, it felt no need for changing the engaging the easting conceptual frame work. However, some additional items were recommended to be included in the schedule to obtain the necessary and relevant information for generating results to see the effects on participation rates in view of the ILO suggestions.5.0.1. The NSSO Governing Council approved the recommendations of the working Group and also the schedule of enquiry in its 44th meeting held on 16 January, 1987. In this survey, a nation-wide enquiry was conducted to provide estimates on various characteristics pertaining to employment and unemployment in India and some characteristics associated with them at the national and state levels. Information on various facets of employment and unemployment in India was collected through a schedule of enquiry (schedule 10).

Geographic coverage

The survey covered the whole of Indian Union excepting i) Ladakh and Kargil districts of Jammu & Kashmir ii) Rural areas of Nagaland

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

It may be mentioned here that in order to net more households of the upper income bracket in the Sample , significant changes have been made in the sample design in this round (compares to the design of the 38th round).

SAMPLE DESIGN AND SAMPLE SIZE The survey had a two-stage stratified design. The first stage units (f.s.u.'s) are villages in the rural sector and urban blocks in the urban sector. The second stage units are households in both the sectors. Sampling frame for f.s.u.'s : The lists of 1981 census villages constituted the sampling frame for rural sector in most districts. But the 1981 census frame could not be used for a few districts because, either the 1981 census was not held there or the list of 1981 census villages could not be obtained or the lists obtained from the census authorities were found to be grossly incomplete. In such cases 1971 census frame were used. In the urban sector , the Urban Frame Survey (U.F.S.) blocks constituted the sampling frame. STRATIFICATION : States were first divided into agro-economic regions which are groups of contiguous districts , similar with respect to population density and crop pattern. In Gujarat, however , some districts have been split for the purpose of region formation In consideration of the location of dry areas and the distribution of the tribal population in the state. The composition of the regions is given in the Appendix. RURAL SECTOR: In the rural sector, within each region, each district with 1981Census rural population less 1.8 million formed a single stratum. Districts with larger population were divided into two or more strata, depending on population, by grouping contiguous tehsils similar, as for as possible, in respect of rural population Density and crop pattern. (In Gujarat, however , in the case of districts extending over more than one region, even if the rural population was less than 1.8 million, the portion of a district falling in each region constituted a separate stratum. Further ,in Assam the old "basic strata" formed on the basis of 1971 census rural population exactly in the above manner, but with cut-off population as 1.5 million have been retained as the strata for rural sampling.)
URBAN SECTOR : In the urban sector , strata were formed , again within NSS region , on the basis of the population size class of towns . Each city with population 10 lakhs or more is self-representative , as in the earlier rounds . For the purpose of stratification, in towns with '81 census population 4 lakhs or more , the blocks have been divided into two categories , viz . : One consisting of blocks in areas inhabited by the relatively affluent section of the population and the other consisting of the remaining blocks. The strata within each region were constituted as follows :

Table (1.2)  : Composition of urban strata

Stratum population class of town

number

(1) (2)

1 all towns with population less than 50,000 2 -do- 50,000 - 199,999 3 -do- 200,000 - 399,999 4 -do- 400,000 - 999,999 ( affluent area) 5 (other area) 6 a single city with population 1 million and above (affluent area)
7 " (other area) 8 another city with population 1 million and above
9 " (other area)

Note : There is no region with more than one city with population 1 million and above. The stratum number have been retained as above even if in some regions some of the strata are empty. Allocation for first stage units : The total all-India sample size was allocated to the states /U.T.'s proportionate to the strength of central field staff. This was allocated to the rural and urban sectors considering the relative size of the rural and urban population. Now the rural samples were allocated to the rural strata in proportion to rural population. The urban samples were allocated to the urban strata in proportion to urban population with double weight age given to those strata of towns with population 4 lakhs or more which lie in area inhabited by the relatively affluent section. All allocations have been adjusted such that the sample size for stratum was at least a multiple of 4 (preferably multiple of 8) and the total sample size of a region is a multiple of 8 for the rural and urban sectors separately.
Selection of f.s.u.'s : The sample villages have been selected circular systematically with probability proportional to population in the form of two independent interpenetrating sub-samples (IPNS) . The sample blocks have been selected circular systematically with equal probability , also in the form of two IPNS' s. As regards the rural areas of Arunachal Pradesh, the procedure of 'cluster sampling' was:- The field staff will be supplied with a list of the nucleus villages of each cluster and they selected the remaining villages of the cluster according to the procedure described in Section Two. The nucleus villages were selected circular systematically with equal probability, in the form of two IPNS 's. Hamlet-group and sub-blocks : Large villages and blocks were sub- divided into a suitable number of hamlet-groups and sub-blocks respectively having equal population convent and one them was selected at random for surveys. Hamlet-group and sub-blocks : Large villages and blocks were sub- divided into a suitable number of hamlet-groups and sub-blocks respectively having equal population convent and one them was selected at random for surveys. Selection of households : rural : In order to have adequate number of sample households from the affluent section of the society, some new procedures were introduced for selection of sample households, both in the rural and urban sectors. In the rural sector , while listing households, the investigator identified the households in village/ selected hamlet- group which may be considered to be relatively more affluent than the rest. This was done largely on the basis of his own judgment but while exercising his judgment considered factors generally associated with rich people in the localitysuch as : living in large pucca house in well-maintained state, ownership/possession of cultivated/irrigated land in excess of certain norms. ( e.g.20 acres of cultivated land or 10 acres of irrigated land), ownership of motor vehicles and costly consumer durables like T.V. , VCR, VCP AND refrigerator, ownership of large business establishment , etc. Now these "rich" households will form sub-stratum 1. (If the total number of households listed is 80 or more , 10 relatively most affluent households will form sub-stratum 1. If it is below 80, 8 such households will form sub-stratum 1. The remaining households will 'constitute sub-stratum 2. At the time of listing, information relating to each household' s major sources of income will be

As of 2019, the capital Indian territory of Delhi had the highest density of nurses and midwives of about ** per ten thousand people in the country. However, Bihar had the least density of nurses and midwives in the country of about *** per ten thousand people in the state.

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 Chad, South Sudan, Somalia, and the Central African Republic, the population increase peaks at over 3.4 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. African cities are also growing at large rates. Indeed, the continent has three megacities and is expected to add four more by 2050. Furthermore, Africa's fastest-growing cities are forecast to be Bujumbura, in Burundi, and Zinder, Nigeria, by 2035.

As of 2025, Tokyo-Yokohama in Japan was the largest world urban agglomeration, with 37 million people living there. Delhi ranked second with more than 34 million, with Shanghai in third with more than 30 million inhabitants.

There were over one million registered Indians in Canada as of December 2020. The region with the largest Indian population was Ontario, with 222 thousand, followed by Manitoba, which counted 164 thousand Indians. The regions with the smallest Indian populations were Yukon, and Northwest Territories.

As of December 2023, the service area with the highest number density of internet subscribers was the nation's capital city, Delhi, which had nearly *** subscribers for every 100 inhabitants. The average internet subscribers per 100 population in India during the same period was around ****.

The number of registered vehicles across the financial capital of India was over ************* at the end of fiscal year 2020. Mumbai was the most car-congested city in the country in 2020. In recent years, the density of privately-owned vehicles increased by ** percent in the city. Despite the private car population being ********* of the nation’s capital Delhi, the lack of infrastructure has proved to be a significant shortcoming.

Automotive in India

India ranked ****** in the passenger car production sector worldwide in 2020, with over ************* units of passenger vehicles produced in fiscal year 2021. Although, in contrast, the domestic market has been dominated by the two-wheeler segment. This was probably due to the latter’s ability to navigate the narrow Indian roads. Sales volume for two-wheelers continued to increase over the past few years.

An e-future

The number of electric vehicle sales in the dominating two-wheeler segment across the country quintupled between 2016 and 2020, with help from government initiatives to enhance e-mobility. The number of electric two-wheelers was estimated to cross the ********** mark by 2030. With strict emission laws and reduced taxes on electric vehicles, the Indian government has been making efforts to make a radical yet streamlined switch to e-mobility.