The share of projected population increase in Uttar Pradesh, India from 2011 until 2036 is expected to grow by nearly 20 percent. By contrast, the estimated population increase in Uttarakhand is expected to be less than one percent during the same time period.
Why project population?
Population projections for a country are becoming increasingly important now than ever before. They are used primarily by government policy makers and planners to better understand and gauge future demand for basic services that predominantly include water, food and energy. In addition, they also support in indicating major movements that may affect economic development and in turn, employment and labour productivity. Consequently, this leads to amending policies in order to better adapt to the needs of society and to various circumstances.
Demographic projections and health interventions Demographic figures serve the foremost purpose of improving health and health related services among the population. Some of the government interventions include antenatal and neonatal care with the aim of reducing maternal and neonatal mortality and morbidity rates. In addition, it also focuses on improving immunization coverage across the country. Further, demographic estimates help in better preempting the needs of growing populations, such as the geriatric population within a country.
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Census: Population: Mizoram: Female data was reported at 541,867.000 Person in 03-01-2011. This records an increase from the previous number of 429,464.000 Person for 03-01-2001. Census: Population: Mizoram: Female data is updated decadal, averaging 116,832.000 Person from Mar 1901 (Median) to 03-01-2011, with 12 observations. The data reached an all-time high of 541,867.000 Person in 03-01-2011 and a record low of 43,430.000 Person in 03-01-1901. Census: Population: Mizoram: Female data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAB002: Census: Population: by States.
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Census: Population: Odisha: Female data was reported at 20,762,082.000 Person in 03-01-2011. This records an increase from the previous number of 18,144,090.000 Person for 03-01-2001. Census: Population: Odisha: Female data is updated decadal, averaging 8,090,657.000 Person from Mar 1901 (Median) to 03-01-2011, with 12 observations. The data reached an all-time high of 20,762,082.000 Person in 03-01-2011 and a record low of 5,244,817.000 Person in 03-01-1901. Census: Population: Odisha: Female data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAB002: Census: Population: by States.
In 2022, the union territory of Delhi had the highest urban population density of over 18 thousand persons per square kilometer. While the rural population density was highest in union territory of Puducherry, followed by the state of Bihar.
This feature layers contain demographics about age, gender, education, employment, assets & amenities as reported by Office of the Registrar General & Census Commissioner, India in the Census 2011. These attributes cover topics such as male and female population counts by age, literacy, occupation, and household characteristics.Census of India counts every resident in India at village level. It is mandated by The Census Act 1948 of the Constitution and takes place every 10 years.Other demographics layers are also available:Country DemographicsDistrict DemographicsSub-district DemographicsVillage DemographicsCombined DemographicsEach layer contains the same set of demographic attributes. Each geography level has a viewing range optimal for the geography size, and the map has increasing detail as you zoom in to smaller areas.Data source: Explore Census DataAdmin boundary source (country, states, and districts): Survey of India, 2020For more information: 2011 Census Demographic ProfileFor feedback please contact: content@esri.inData Processing notes:Country, State and District boundaries are simplified representations offered from the Survey of India database.Sub-districts and village boundaries are developed based on the census provided maps.Field names and aliases are processed by Esri India as created for the ArcGIS Platform.For a list of fields and alias names, access the following excel document.Disclaimer:The boundaries may not be perfectly align with AGOL imagery. The Census PDF maps are georeferenced using Survey of India boundaries and notice alignment issues with AGOL Imagery/ Maps. 33k villages are marked as point location on Census PDFs either because of low scale maps where small villages could not have been drawn or digitization has not been completed. These villages are marked as 100m circular polygons in the data.This web layer is offered by Esri India, for ArcGIS Online subscribers. If you have any questions or comments, please let us know via content@esri.in.
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The dataset contains year-, month-, state- and gender-wise compiled data on urban population of India from the year 2011 to 2036. The figures of population given for different years are the projected figures, except for the census year of 2011.
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Context
The dataset tabulates the Indian Shores population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Indian Shores across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.
Key observations
In 2023, the population of Indian Shores was 1,192, a 0.50% decrease year-by-year from 2022. Previously, in 2022, Indian Shores population was 1,198, a decline of 0.17% compared to a population of 1,200 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Indian Shores decreased by 511. In this period, the peak population was 1,777 in the year 2004. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).
When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).
Data Coverage:
Variables / Data Columns
Good to know
Margin of Error
Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.
Custom data
If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.
Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.
This dataset is a part of the main dataset for Indian Shores Population by Year. You can refer the same here
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India's population demographics - total population, growth rate, age-wise and state-wise population, languages spoken, and religion.
POPULATION PROIECTIONS FOR INDIA AND STATES 2011 – 2036 (Downscaled to District, Sub-Districts and Villages/Towns by Esri India)REPORT OF THE TECHNICAL GROUP ON POPULATION PROIECTTONSJuly, 2020The projected population figures provided by the Registrar General of India forms the basis for planning and implementation of various health interventions including RMNCH+A, which are aimed at improving the overall health outcomes by ensuring quality service provision to all the health beneficiaries. These interventions focus on antenatal, intranatal and neonatal care aimed at reducing maternal and neonatal morbidity and mortality; improving coverage and quality of health care interventions and improving coverage for immunization against vaccine preventable diseases. Further, these estimates would also enable us to tackle the special health care needs of various population age groups, thus gearing the system for necessary preventive, promotive, curative, and rehabilitative services for the growing population to this report. PREETI SUDAN, IAS SecretaryThe Cohort Component Method is the universally accepted method of making population projections because of the fact that the growth of population is determined by fertility, mortality, and migration rates. In this exercise, 20 States and two UTs have been applied the Cohort Component method. These are Andhra Pradesh, Assam, Bihar, Gujarat, Haryana, Himachal Pradesh, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Odisha, Punjab, Rajasthan, Tamil Nadu, Telangana, Uttar Pradesh, West Bengal, Jharkhand, Chhattisgarh, Uttarakhand, Jammu & Kashmir (UT) and NCT of Delhi. Based on the residual of the projected population of Jammu & Kashmir (State) and Jammu & Kashmir (UT), for which Cohort Component method has applied, projection of the Ladakh UT have been made. For the projections of Jammu & Kashmir (UT), SRS fertility and mortality estimates of Jammu & Kashmir (State) are used. The projection of the seven northeastern states (excluding Assam) has also been carried out as a whole using the Cohort Component Method. Separate projections for Andhra Pradesh and Telangana were done using the re-casted populations of these states. For the projections, for the years before 2014, combined SRS estimates of Andhra Pradesh and year 2014 onwards, separate SRS estimates of fertility and mortality of Andhra Pradesh and Telangana are used. For the remaining States and Union territories, Mathematical Method has been applied. The sources of data used are 2011 Census and Sample Registration System (SRS). SRS provides time series data of fertility and mortality, which has been used for predicting their future levelsEsri India Efforts:The Population Projections Report published by MoHFW contains output summary tables from series Table 8 to Table 14. Example: TABLE – 8: Projected total population by sex as on 1st March, 2011-2036: India, States and Union territories, TABLE – 9: Projected urban population by sex as on 1st March, 2011-2036: India, States and Union territories, etc. The parameters available with these census data tables are Census Year, Projected Total Persons with Gender categorization and Projected Urban Population from 2011 to 2036.By subtracting “Projected Urban Population” from “Projected Total Population”, a new data column has been added as “Projected Rural Population”. The data is available for all Union Territory and States for 25 years.A factor has been calculated by taking projected population and the base year population (2011). Subsequently, the factor is calculated for each year using the projected values provided by census of India. Projected Population by Sex as on 1st March - 2011 - 2036: India, States and Union Territories* ('000)YearGUJARAT GUJARAT URBANGUJARAT RURALPersonsMaleFemalePersonMaleFemalePersonMaleFemale2011 60,440 (A) 31,49128,94825,74513,69412,05134,69517,79716,8972012 61,383 (B)32,00729,37626,47214,08112,39134,91117,92616,985Factor has been applied below State level- Projected Population by Sex as on 1st March - 2011 - 2036: India, States and Union Territories* ('000)YearGUJARAT GUJARAT URBANGUJARAT RURALPersonsMaleFemalePersonMaleFemalePersonMaleFemale20121.01560225 (B/A)1.0163856341.0147851321.0282384931.0282605521.0282134261.0062256811.0072484131.005208025Esri India has access to SOI admin boundaries up-to district level and developed village, town and sub-district boundaries using census maps. The calculated factors have been applied to smallest geography at villages and towns and upscaled back to sub-district, district, state, and country. The derived values have been compared with the original values provided by census at state level and no deviation is confirmed.Data Variables: Year (2011-2036)Total Population MaleFemaleTotal Population UrbanMale UrbanFemale UrbanTotal Population RuralMale RuralFemale RuralData source: https://main.mohfw.gov.in/sites/default/files/Population Projection Report 2011-2036 - upload_compressed_0.pdfOther related contents are also available:India Population Projections 2011-2036Village Population Projections for India 2011-2036Sub-district Population Projections for India 2011-2036District Population Projections for India 2011-2036State Population Projections for India 2011-2036This web layer is offered by Esri India, for ArcGIS Online subscribers. If you have any questions or comments, please let us know via content@esri.in.
The statistic gives the share of aging population in India across selected states and union territories in India in 2011. The regions with the highest share of elderly people were in the state of Kerala, with some 12.6 percent of the population living there were 60 years or older, followed by the state of Goa with 11.2 percent. The share of aging population in the whole country that year was 8.6 percent.
This dataset was created by Walker_Johnnie
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Census: Population: Uttar Pradesh data was reported at 199,812,341.000 Person in 03-01-2011. This records an increase from the previous number of 166,197,921.000 Person for 03-01-2001. Census: Population: Uttar Pradesh data is updated decadal, averaging 65,208,689.000 Person from Mar 1901 (Median) to 03-01-2011, with 12 observations. The data reached an all-time high of 199,812,341.000 Person in 03-01-2011 and a record low of 44,556,427.000 Person in 03-01-1921. Census: Population: Uttar Pradesh data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAB002: Census: Population: by States.
The projected median age of population in India, at national level, was expected to go up to more than 34 years by 2036 versus almost 25 years in 2011. At state level, Tamil Nadu reflected the highest projected median age with over 40 years in 2036 versus nearly 30 years in 2011.
The projected median age of population of a country is contingent upon several health metrics such as the fertility rate, birth rate, and mortality rate. For instance, if a country or state sees a lower fertility and mortality rate, the geriatric population is expected to increase proportionally.
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The data shows the year-wise and state or union territory-wise literacy and rural and urban literacy, for male, female, and total literacy, in India according to Census.
Note: 1. Literacy rate is defined as the population of literates in the population aged 7 year and above. 2. The 1991 data (Excluding Jammu & Kashmir)and 2001 data (Excludes figures of Paomata, Mao Maran and Pura sub-divisions of Senapati district of Manipur for 2001) refer to Census of India.
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The dataset contains state-wise consolidated primary census abstract of homeless people Note: Primary census abstract It is the information from census that is divided into several categories like, number of males in population, number of females in population etc.
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india - Population Growth for India was 0.98941 % Chg. at Annual Rate in January of 2020, according to the United States Federal Reserve. Historically, india - Population Growth for India reached a record high of 2.33191 in January of 1974 and a record low of 0.98941 in January of 2020. Trading Economics provides the current actual value, an historical data chart and related indicators for india - Population Growth for India - last updated from the United States Federal Reserve on May of 2025.
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Vital Statistics: Natural Growth Rate: per 1000 Population: Punjab: Rural data was reported at 6.600 NA in 2020. This records a decrease from the previous number of 7.000 NA for 2019. Vital Statistics: Natural Growth Rate: per 1000 Population: Punjab: Rural data is updated yearly, averaging 9.900 NA from Dec 1997 (Median) to 2020, with 23 observations. The data reached an all-time high of 15.600 NA in 1997 and a record low of 6.600 NA in 2020. Vital Statistics: Natural Growth Rate: per 1000 Population: Punjab: Rural data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAH004: Vital Statistics: Natural Growth Rate: by States.
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Census: Population: Andhra Pradesh: Male data was reported at 42,442,146.000 Person in 03-01-2011. This records an increase from the previous number of 38,527,413.000 Person for 03-01-2001. Census: Population: Andhra Pradesh: Male data is updated decadal, averaging 16,916,118.000 Person from Mar 1901 (Median) to 03-01-2011, with 12 observations. The data reached an all-time high of 42,442,146.000 Person in 03-01-2011 and a record low of 9,607,091.000 Person in 03-01-1901. Census: Population: Andhra Pradesh: Male data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAB002: Census: Population: by States.
In 2021, Kerala reflected the highest share of its population belonging to the elderly age group with 16.5 percent as opposed to only 10.5 percent in 2001. This was an increase in six percent in two decades.
The National Family Health Survey (NFHS) was carried out as the principal activity of a collaborative project to strengthen the research capabilities of the Population Reasearch Centres (PRCs) in India, initiated by the Ministry of Health and Family Welfare (MOHFW), Government of India, and coordinated by the International Institute for Population Sciences (IIPS), Bombay. Interviews were conducted with a nationally representative sample of 89,777 ever-married women in the age group 13-49, from 24 states and the National Capital Territoty of Delhi. The main objective of the survey was to collect reliable and up-to-date information on fertility, family planning, mortality, and maternal and child health. Data collection was carried out in three phases from April 1992 to September 1993. THe NFHS is one of the most complete surveys of its kind ever conducted in India.
The households covered in the survey included 500,492 residents. The young age structure of the population highlights the momentum of the future population growth of the country; 38 percent of household residents are under age 15, with their reproductive years still in the future. Persons age 60 or older constitute 8 percent of the population. The population sex ratio of the de jure residents is 944 females per 1,000 males, which is slightly higher than sex ratio of 927 observed in the 1991 Census.
The primary objective of the NFHS is to provide national-level and state-level data on fertility, nuptiality, family size preferences, knowledge and practice of family planning, the potentiel demand for contraception, the level of unwanted fertility, utilization of antenatal services, breastfeeding and food supplemation practises, child nutrition and health, immunizations, and infant and child mortality. The NFHS is also designed to explore the demographic and socioeconomic determinants of fertility, family planning, and maternal and child health. This information is intended to assist policymakers, adminitrators and researchers in assessing and evaluating population and family welfare programmes and strategies. The NFHS used uniform questionnaires and uniform methods of sampling, data collection and analysis with the primary objective of providing a source of demographic and health data for interstate comparisons. The data collected in the NFHS are also comparable with those of the Demographic and Health Surveys (DHS) conducted in many other countries.
National
The population covered by the 1992-93 DHS is defined as the universe of all women age 13-49 who were either permanent residents of the households in the NDHS sample or visitors present in the households on the night before the survey were eligible to be interviewed.
Sample survey data
SAMPLE DESIGN
The sample design for the NFHS was discussed during a Sample Design Workshop held in Madurai in Octber, 1991. The workshop was attended by representative from the PRCs; the COs; the Office of the Registrar General, India; IIPS and the East-West Center/Macro International. A uniform sample design was adopted in all the NFHS states. The Sample design adopted in each state is a systematic, stratified sample of households, with two stages in rural areas and three stages in urban areas.
SAMPLE SIZE AND ALLOCATION
The sample size for each state was specified in terms of a target number of completed interviews with eligible women. The target sample size was set considering the size of the state, the time and ressources available for the survey and the need for separate estimates for urban and rural areas of the stat. The initial target sample size was 3,000 completed interviews with eligible women for states having a population of 25 million or less in 1991; 4,000 completed interviews for large states with more than 25 million population; 8,000 for Uttar Pradesh, the largest state; and 1,000 each for the six small northeastern states. In States with a substantial number of backward districts, the initial target samples were increased so as to allow separate estimates to be made for groups of backward districts.
The urban and rural samples within states were drawn separetly and , to the extent possible, sample allocation was proportional to the size of the urban-rural populations (to facilitate the selection of a self-weighting sample for each state). In states where the urban population was not sufficiently large to provide a sample of at least 1,000 completed interviews with eligible women, the urban areas were appropriately oversampled (except in the six small northeastern states).
THE RURAL SAMPLE: THE FRAME, STRATIFICATION AND SELECTION
A two-stage stratified sampling was adopted for the rural areas: selection of villages followed by selection of households. Because the 1991 Census data were not available at the time of sample selection in most states, the 1981 Census list of villages served as the sampling frame in all the states with the exception of Assam, Delhi and Punjab. In these three states the 1991 Census data were used as the sampling frame.
Villages were stratified prior to selection on the basis of a number of variables. The firts level of stratification in all the states was geographic, with districts subdivided into regions according to their geophysical characteristics. Within each of these regions, villages were further stratified using some of the following variables : village size, distance from the nearest town, proportion of nonagricultural workers, proportion of the population belonging to scheduled castes/scheduled tribes, and female literacy. However, not all variables were used in every state. Each state was examined individually and two or three variables were selected for stratification, with the aim of creating not more than 12 strata for small states and not more than 15 strata for large states. Females literacy was often used for implicit stratification (i.e., the villages were ordered prior to selection according to the proportion of females who were literate). Primary sampling Units (PSUs) were selected systematically, with probaility proportional to size (PPS). In some cases, adjacent villages with small population sizes were combined into a single PSU for the purpose of sample selection. On average, 30 households were selected for interviewing in each selected PSU.
In every state, all the households in the selected PSUs were listed about two weeks prior to the survey. This listing provided the necessary frame for selecting households at the second sampling stage. The household listing operation consisted of preparing up-to-date notional and layout sketch maps of each selected PSU, assigning numbers to structures, recording addresses (or locations) of these structures, identifying the residential structures, and listing the names of the heads of all the households in the residentiak structures in the selected PSU. Each household listing team consisted of a lister and a mapper. The listing operation was supervised by the senior field staff of the concerned CO and the PRC in each state. Special efforts were made not to miss any household in the selected PSU during the listing operation. In PSUs with fewer than 500 households, a complete household listing was done. In PSUs with 500 or more households, segmentation of the PSU was done on the basis of existing wards in the PSU, and two segments were selected using either systematic sampling or PPS sampling. The household listing in such PSUs was carried out in the selected segments. The households to be interviewed were selected from provided with the original household listing, layout sketch map and the household sample selected for each PSU. All the selected households were approached during the data collection, and no substitution of a household was allowed under any circumstances.
THE RURAL URBAN SAMPLE: THE FRAME, STRATIFICATION AND SELECTION
A three-stage sample design was adopted for the urban areas in each state: selection of cities/towns, followed by urban blocks, and finally households. Cities and towns were selected using the 1991 population figures while urban blocks were selected using the 1991 list of census enumeration blocks in all the states with the exception of the firts phase states. For the first phase states, the list of urban blocks provided by the National Sample Survey Organization (NSSSO) served as the sampling frame.
All cities and towns were subdivided into three strata: (1) self-selecting cities (i.e., cities with a population large enough to be selected with certainty), (2) towns that are district headquaters, and (3) other towns. Within each stratum, the cities/towns were arranged according to the same kind of geographic stratification used in the rural areas. In self-selecting cities, the sample was selected according to a two-stage sample design: selection of the required number of urban blocks, followed by selection of households in each of selected blocks. For district headquarters and other towns, a three stage sample design was used: selection of towns with PPS, followed by selection of two census blocks per selected town, followed by selection of households from each selected block. As in rural areas, a household listing was carried out in the selected blocks, and an average of 20 households per block was selected systematically.
Face-to-face
Three types of questionnaires were used in the NFHS: the Household Questionnaire, the Women's Questionnaire, and the Village Questionnaire. The overall content
The share of projected population increase in Uttar Pradesh, India from 2011 until 2036 is expected to grow by nearly 20 percent. By contrast, the estimated population increase in Uttarakhand is expected to be less than one percent during the same time period.
Why project population?
Population projections for a country are becoming increasingly important now than ever before. They are used primarily by government policy makers and planners to better understand and gauge future demand for basic services that predominantly include water, food and energy. In addition, they also support in indicating major movements that may affect economic development and in turn, employment and labour productivity. Consequently, this leads to amending policies in order to better adapt to the needs of society and to various circumstances.
Demographic projections and health interventions Demographic figures serve the foremost purpose of improving health and health related services among the population. Some of the government interventions include antenatal and neonatal care with the aim of reducing maternal and neonatal mortality and morbidity rates. In addition, it also focuses on improving immunization coverage across the country. Further, demographic estimates help in better preempting the needs of growing populations, such as the geriatric population within a country.