About this dataset

Context

The dataset tabulates the Non-Hispanic population of Delhi by race. It includes the distribution of the Non-Hispanic population of Delhi across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of Delhi across relevant racial categories.

Key observations

With a zero Hispanic population, Delhi is 100% Non-Hispanic. Among the Non-Hispanic population, the largest racial group is White alone with a population of 45 (100% of the total Non-Hispanic population).

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (for Non-Hispanic) for the Delhi
• Population: The population of the racial category (for Non-Hispanic) in the Delhi is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Delhi total Non-Hispanic population. Please note that the sum of all percentages may not equal one due to rounding of values.

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.

Inspiration

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/.

Recommended for further research

This dataset is a part of the main dataset for Delhi Population by Race & Ethnicity. You can refer the same here

Population: Delhi data was reported at 21.588 Person mn in 2024. This records an increase from the previous number of 21.195 Person mn for 2023. Population: Delhi data is updated yearly, averaging 16.001 Person mn from Mar 1994 (Median) to 2024, with 31 observations. The data reached an all-time high of 21.588 Person mn in 2024 and a record low of 10.446 Person mn in 1994. Population: Delhi data remains active status in CEIC and is reported by Ministry of Statistics and Programme Implementation. The data is categorized under Global Database’s India – Table IN.GBG001: Population. [COVID-19-IMPACT]

As of the year 2024, the population of the capital city of India, Delhi was over ** million people. This was a 2.63 percent growth from last year. The historical trends show that the population doubled between 1990 and 2010. The UN estimated that the population was expected to reach around ** million by 2030. Reasons for population growth   As per the Delhi Economic Survey, migration added over *** thousand people to Delhi’s population in 2022. The estimates showed relative stability in natural population growth for a long time before the pandemic. The numbers suggest a sharp decrease in birth rates from 2020 onwards and a corresponding increase in death rates in 2021 due to the Covid-19 pandemic. The net natural addition or the remaining growth is attributed to migration. These estimates are based on trends published by the Civil Registration System. National Capital Region (NCR) Usually, population estimates for Delhi represent the urban agglomeration of Delhi, which includes Delhi and some of its adjacent suburban areas. The National Capital Region or NCR is one of the largest urban agglomerations in the world. It is an example of inter-state regional planning and development, centred around the National Capital Territory of Delhi, and covering certain districts of neighbouring states Haryana, Uttar Pradesh, and Rajasthan. Noida, Gurugram, and Ghaziabad are some of the key cities of NCR. Over the past decade, NCR has emerged as a key economic centre in India.

Vital Statistics: Natural Growth Rate: per 1000 Population: Delhi: Urban data was reported at 10.600 NA in 2020. This records a decrease from the previous number of 11.200 NA for 2019. Vital Statistics: Natural Growth Rate: per 1000 Population: Delhi: Urban data is updated yearly, averaging 13.100 NA from Dec 1997 (Median) to 2020, with 23 observations. The data reached an all-time high of 15.400 NA in 1999 and a record low of 10.600 NA in 2020. Vital Statistics: Natural Growth Rate: per 1000 Population: Delhi: Urban 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.

Content

The Dataset is fully dedicated for the developers who want to train the model on Weather Forecasting for Indian climate. This dataset provides data from 1st January 2013 to 24th April 2017 in the city of Delhi, India. The 4 parameters here are meantemp, humidity, wind_speed, meanpressure.

Acknowledgements

This dataset has been collected from Weather Undergroud API. Dataset ownership and credit goes to them.

Submission Deadline

Assignment 4 must be submitted by October 19, 2019 (10:00 PM). Any kernel published after this deadline will be evaluated for only 50% of the total marks.

Inspiration

This dataset was developed as a part Assignment 4 of Data Analytics Course, 2019 at PES University, Bangalore.

In India's capital territory of Delhi, the share of males with multiple disability was at 1.4 percent and with locomotor disabilities at 0.9 percent in 2018. The same among females was less prevalent. According to the 76th round of the NSO survey conducted between July and December 2018, a higher percentage of disabled men than disabled women were present in India. The National Statistical Office (NSO) is the statistical wing of the Ministry of Statistics and Programme Implementation (MOSPI), mainly responsible for laying down standards for statistical analysis, data collection, and implementation.

Census: Population: by Religion: Sikh: Delhi data was reported at 570,581.000 Person in 03-01-2011. This records an increase from the previous number of 555,602.000 Person for 03-01-2001. Census: Population: by Religion: Sikh: Delhi data is updated decadal, averaging 563,091.500 Person from Mar 2001 (Median) to 03-01-2011, with 2 observations. The data reached an all-time high of 570,581.000 Person in 03-01-2011 and a record low of 555,602.000 Person in 03-01-2001. Census: Population: by Religion: Sikh: Delhi 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.GAE005: Census: Population: by Religion: Sikh.

Census: Number of Migrants: Delhi data was reported at 7,224,514.000 Person in 03-01-2011. This records an increase from the previous number of 6,014,458.000 Person for 03-01-2001. Census: Number of Migrants: Delhi data is updated decadal, averaging 6,014,458.000 Person from Mar 1991 (Median) to 03-01-2011, with 3 observations. The data reached an all-time high of 7,224,514.000 Person in 03-01-2011 and a record low of 3,723,462.000 Person in 03-01-1991. Census: Number of Migrants: Delhi 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.GAG001: Census of India: Migration: Number of Migrants: by States.

In the year 2022, *** thousand people were estimated to have migrated to Delhi. This was a decrease from 2021. Migration contributed more to Delhi's population growth than the number of births, standing at *** thousand.

The table presents number of researchers per million people and population figures for select countries. The data are taken from the UNESCO, World Bank and DST, New Delhi.

IBTrACS - International Best Track Archive for Climate Stewardship - version v04r00, Position, intensity and other information for known tropical cyclones. The intent of the IBTrACS project is to overcome best track data availability issues that arise from multiple agencies producing data for different storms in different formats. This was achieved by working directly with all the Regional Specialized Meteorological Centers and other international centers and individuals to create a global best track dataset, merging storm information from multiple agencies into one product and archiving the data for public use. acknowledgement=IBTrACS was produced by a team of scientists from NOAA in collaboration with scientists worldwide. cdm_data_type=Trajectory cdm_trajectory_variables=sid comment=The tracks of TCs generally look like a trajectory except that it wasn't expedient to use the CF trajectory type. The team stored data in a way that approximates the trajectory profile, where each new track (each new storm) is a new trajectory. contributor_name="National Hurricane Center, National Weather Service, NOAA, U.S. Department of Commerce","Central Pacific Hurricane Center, National Weather Service, NOAA, U.S. Department of Commerce","Japan Meteorological Agency, RSMC Tokyo, Japan","India Meteorological Department, RSMC New Delhi, India","Bureau of Meteorology, Australia","MetService, TCWC Wellington, New Zealand","Fiji Meteorological Service, RSMC Fiji, Fiji","MeteoFrance, La Reunion, RSMC La Reunion","Shanghai Typhoon Institute, Chinese Meteorological Administration, China","Hong Kong Observatory, Hong Kong""Korea Meteorological Administration, South Korea""Joint Typhoon Warning Center, U.S. Department of Defense","National Center for Atmospheric Research, University Corporation for Atmospheric Research","Charlie Neumann Southern Hemisphere Dataset","Mike Chenoweth North Atlantic Dataset" contributor_role=These agencies and people provide track data and best track data used to produce IBTrACS. Conventions=ACDD-1.3, COARDS, CF-1.10 Conventions_note=Data are nearly CF-1.7 compliant. The sole issue is the storage of missing data in the latitude/longitude/time variables. Otherwise, data are CF compliant. Easternmost_Easting=253.6 featureType=Trajectory geospatial_lat_max=63.3 geospatial_lat_min=-36.4 geospatial_lat_resolution=0.10 geospatial_lat_units=degrees_north geospatial_lon_max=253.6 geospatial_lon_min=-179.8 geospatial_lon_resolution=0.10 geospatial_lon_units=degrees_east history=Tue Jan 14 05:41:37 2025: ncks --no_abc --cnk_byt 5000000 -4 -L 5 temp.nc -O netcdf/IBTrACS.last3years.v04r01.nc Tue Jan 14 05:41:25 2025: ncrcat -6 -H -O netcdf/ibtracs.last3years.v04r01.nc Produced by IBTrACS for individual tracks and merged into basin and temporal collections using netCDF operators (ncrcat) id=2022008S13148.ibtracs_int.v04r01.nc infoUrl=https://www.ncdc.noaa.gov/ibtracs/ institution=National Centers for Environmental Information, NESDIS, NOAA keywords_vocabulary=GCMD Science Keywords metadata_link=doi:10.25921/82ty-9e16 naming_authority=gov.noaa.ncei NCO=netCDF Operators version 4.8.1 (Homepage = http://nco.sf.net, Code = https://github.com/nco/nco) Northernmost_Northing=63.3 processing_level=NOAA Processing Level 2, Data products are derived geophysical variables at the same resolution and locations as the level 1 source data project=International Best Track Archive for Climate Stewardship (IBTrACS) references=https://www.ncei.noaa.gov/products/international-best-track-archive, doi:10.1175/2009BAMS2755.1 source=The original data are tropical cyclone position, intensity and otherinformation provided by various agencies and people. This is a collection of all data on each tropical cyclone recorded. sourceUrl=(local files) Southernmost_Northing=-36.4 standard_name_vocabulary=CF Standard Name Table v52 subsetVariables=source_td5, source_td6, source_ds8, source_neu, source_mlc, newdelhi_poci, reunion_r64, ds824_lat, ds824_lon, ds824_stage, ds824_wind, ds824_pres, td9636_lat, td9636_lon, td9636_stage, td9636_wind, td9636_pres, td9635_lat, td9635_lon, td9635_wind, td9635_pres, td9635_roci, neumann_lat, neumann_lon, neumann_class, neumann_wind, neumann_pres, mlc_lat, mlc_lon, mlc_class, mlc_wind, mlc_pres, reunion_gust testOutOfDate=now-10days time_coverage_end=2025-01-14T00:00:00Z time_coverage_start=2022-01-09T00:00:00Z Westernmost_Easting=-179.8

It is estimated that more than 8 billion people live on Earth and the population is likely to hit more than 9 billion by 2050. Approximately 55 percent of Earth’s human population currently live in areas classified as urban. That number is expected to grow by 2050 to 68 percent, according to the United Nations (UN).The largest cities in the world include Tōkyō, Japan; New Delhi, India; Shanghai, China; México City, Mexico; and São Paulo, Brazil. Each of these cities classifies as a megacity, a city with more than 10 million people. The UN estimates the world will have 43 megacities by 2030.Most cities' populations are growing as people move in for greater economic, educational, and healthcare opportunities. But not all cities are expanding. Those cities whose populations are declining may be experiencing declining fertility rates (the number of births is lower than the number of deaths), shrinking economies, emigration, or have experienced a natural disaster that resulted in fatalities or forced people to leave the region.This Global Cities map layer contains data published in 2018 by the Population Division of the United Nations Department of Economic and Social Affairs (UN DESA). It shows urban agglomerations. The UN DESA defines an urban agglomeration as a continuous area where population is classified at urban levels (by the country in which the city resides) regardless of what local government systems manage the area. Since not all places record data the same way, some populations may be calculated using the city population as defined by its boundary and the metropolitan area. If a reliable estimate for the urban agglomeration was unable to be determined, the population of the city or metropolitan area is used.Data Citation: United Nations Department of Economic and Social Affairs. World Urbanization Prospects: The 2018 Revision. Statistical Papers - United Nations (ser. A), Population and Vital Statistics Report, 2019, https://doi.org/10.18356/b9e995fe-en.

Cities ranking and mega citiesTokyo is the world’s largest city with an agglomeration of 37 million inhabitants, followed by New Delhi with 29 million, Shanghai with 26 million, and Mexico City and São Paulo, each with around 22 million inhabitants. Today, Cairo, Mumbai, Beijing and Dhaka all have close to 20 million inhabitants. By 2020, Tokyo’s population is projected to begin to decline, while Delhi is projected to continue growing and to become the most populous city in the world around 2028.By 2030, the world is projected to have 43 megacities with more than 10 million inhabitants, most of them in developing regions. However, some of the fastest-growing urban agglomerations are cities with fewer than 1 million inhabitants, many of them located in Asia and Africa. While one in eight people live in 33 megacities worldwide, close to half of the world’s urban dwellers reside in much smaller settlements with fewer than 500,000 inhabitants.About the dataThe 2018 Revision of the World Urbanization Prospects is published by the Population Division of the United Nations Department of Economic and Social Affairs (UN DESA). It has been issued regularly since 1988 with revised estimates and projections of the urban and rural populations for all countries of the world, and of their major urban agglomerations. The data set and related materials are available at: https://esa.un.org/unpd/wup/

In Indian cities many people live in marginal areas, with insecure housing, and inadequate provision of most public services, such as water and sanitation, electricity, garbage collection and policing. The research project will explore how "failures" in service delivery relate to interactions between individuals, their networks and state actors. Slumdwellers develop strategies to improve their lot, developed from learning from daily struggles, within a local social and political system shaped by unequal relations of power and status. The project will involve surveys of households in a few low income communities in greater Delhi, extensive interviews of the range of other actors involved (community leaders, politicians, fixers, local "big men", managers and frontline workers in state agencies) and archival work. It will initially involve in-depth work in four communities formed largely from past migrations from rural India. This is expected to be complemented by smaller surveys in several other communities in greater Delhi to place the in-depth work in broader context. The research will provide a deep analysis of the nature and formation of citizenship in marginal areas, and develop practical policy proposals for both state actors and civil society activists. Household surveys

This dataset provides information about the number of properties, residents, and average property values for 2nd Street cross streets in Delhi, NY.

Abstract

The District Level Household and facility Survey (DLHS) is a household survey at the district level and in DLHS-3, the survey covered 611 districts in India. The total number of households representing a district varies from 1000 to 1500 households. The DLHS-3 is designed to provide information on family planning, maternal and child health, reproductive health of ever married women and adolescent girls, utilization of maternal and child healthcare services at the district level for India. In addition, DLHS-3 also provides information on new-born care, post-natal care within 48 hours, role of ASHA in enhancing the reproductive and child health care and coverage of Janani Suraksha Yojana (JSY). An important component of DLHS-3 is the integration of Facility Survey of health institution (Sub centre, Primary Health Centre, Community Health Centre and District Hospital) accessible to the sampled villages. The focus of DLHS-3 is to provide health care and utilization indicators at the district level for the enhancement of the activities under National Rural Health Mission (NRHM).

Documentation

You can access the data at the International Institute for Population Sciences.\

Methodology

Survey design and sample size

The survey as well as the preparation of reports was carried out in two separate phases. Approximately 50 percent of the districts from each state and union territory were covered in each phase. The survey for phase I was carried out from May to November, 1998 and for phase II it was carried out from to October, 1999. In the first phase of the RHS, 50 percent of the total districts in India as existing in 1995 were selected for the survey. Systematic random sampling was adopted for the selection of the districts for phase1. For selection purposes, districts within the state were arranged alphabetically, and starting at random from either first or second district, alternative districts were selected. The second phase covered all the remaining districts of the country.

In each of the selected districts, 50 Primary Sampling Units (PSUs), i.e. either villages or urban wards were selected adopting probability proportional to size (PPS) sampling. The village/ ward level population as per the 1991 census was used for this purpose. The sample size for DLHS-DLHS was fixed at 1000 households with 20 households from each PSU. In order to take care of non-response due to various reasons, 10 percent over sampling was done. In other words, 22 households from each PSU were selected. The selection of the households in a PSU was done after listing of all the households in the PSUs. For the selection of households circular systematic random sampling was adopted. In the first phase the work of drawing sample of PSUs was entrusted to the Institute of Research in Medical Statistics (IRMS), New Delhi and in the second phase IIPS did the sampling of PSUs in all the districts.

House listing

House listing involved the preparation of a location map of each PSU and layout sketch of the structures and recording details of the households in the village/census enumeration block. An independent team comprising of one lister and one mapper carried out the houselisting exercise.

Complete listing was carried out in villages with population up to 1500. In the case of larger villages, with more than 1500 population, the village was divided into two or more segments of equal size, one segment was selected at random for listing and in the selected segment complete listing was carried out. In the urban wards with population exceeding 1500 one census enumeration block was selected at random.

** ****Questionnaires**

Two types of questionnaires were used in the survey: the household questionnaire and the woman’s questionnaire. IIPS in consultation with MoHFW and World Bank decided the overall contents of the questionnaires. These questionnaires were discussed and finalized in training-cum-workshop organized at IIPS during the third week of May 1998. Representatives of Regional Agencies, MoHFW, IIPS and World Bank participated in this workshop. IIPS carried out pre-testing of these questionnaires in Maharashtra. Questionnaires were also pre-tested in different languages by regional agencies. Though the overall contents of questionnaire for both the phases were the same, there were some changes in the second phase. The changes were mainly regarding ordering and phrasing of the questions. The household questionnaire was used to list all the eligible women in the selected households (de jure) and to collect information on marriages and births among the usual residents. In the first phase the reference period for the recording of marriages and births was from 1st January 1995 to survey date and in the second phase it was from 1st January 1996 to survey date. For all the marriages reported in the survey, age at marriage of boy/ girl of that household who got marri

In 2022, the majority of Indian adults had a wealth of 10,000 U.S. dollars or less. On the other hand, about *** percent were worth more than *********** dollars that year. India The Republic of India is one of the world’s largest and most economically powerful states. India gained independence from Great Britain on August 15, 1947, after having been under their power for 200 years. With a population of about *** billion people, it was the second most populous country in the world. Of that *** billion, about **** million lived in New Delhi, the capital. Wealth inequality India suffers from extreme income inequality. It is estimated that the top 10 percent of the population holds ** percent of the national wealth. Billionaire fortune has increase sporadically in the last years whereas minimum wages have remain stunted.

Abstract

The National Family Health Surveys (NFHS) programme, initiated in the early 1990s, has emerged as a nationally important source of data on population, health, and nutrition for India and its states. The 2005-06 National Family Health Survey (NFHS-3), the third in the series of these national surveys, was preceded by NFHS-1 in 1992-93 and NFHS-2 in 1998-99. Like NFHS-1 and NFHS-2, NFHS-3 was designed to provide estimates of important indicators on family welfare, maternal and child health, and nutrition. In addition, NFHS-3 provides information on several new and emerging issues, including family life education, safe injections, perinatal mortality, adolescent reproductive health, high-risk sexual behaviour, tuberculosis, and malaria. Further, unlike the earlier surveys in which only ever-married women age 15-49 were eligible for individual interviews, NFHS-3 interviewed all women age 15-49 and all men age 15-54. Information on nutritional status, including the prevalence of anaemia, is provided in NFHS3 for women age 15-49, men age 15-54, and young children.

A special feature of NFHS-3 is the inclusion of testing of the adult population for HIV. NFHS-3 is the first nationwide community-based survey in India to provide an estimate of HIV prevalence in the general population. Specifically, NFHS-3 provides estimates of HIV prevalence among women age 15-49 and men age 15-54 for all of India, and separately for Uttar Pradesh and for Andhra Pradesh, Karnataka, Maharashtra, Manipur, and Tamil Nadu, five out of the six states classified by the National AIDS Control Organization (NACO) as high HIV prevalence states. No estimate of HIV prevalence is being provided for Nagaland, the sixth high HIV prevalence state, due to strong local opposition to the collection of blood samples.

NFHS-3 covered all 29 states in India, which comprise more than 99 percent of India's population. NFHS-3 is designed to provide estimates of key indicators for India as a whole and, with the exception of HIV prevalence, for all 29 states by urban-rural residence. Additionally, NFHS-3 provides estimates for the slum and non-slum populations of eight cities, namely Chennai, Delhi, Hyderabad, Indore, Kolkata, Meerut, Mumbai, and Nagpur. NFHS-3 was conducted under the stewardship of the Ministry of Health and Family Welfare (MOHFW), Government of India, and is the result of the collaborative efforts of a large number of organizations. The International Institute for Population Sciences (IIPS), Mumbai, was designated by MOHFW as the nodal agency for the project. Funding for NFHS-3 was provided by the United States Agency for International Development (USAID), DFID, the Bill and Melinda Gates Foundation, UNICEF, UNFPA, and MOHFW. Macro International, USA, provided technical assistance at all stages of the NFHS-3 project. NACO and the National AIDS Research Institute (NARI) provided technical assistance for the HIV component of NFHS-3. Eighteen Research Organizations, including six Population Research Centres, shouldered the responsibility of conducting the survey in the different states of India and producing electronic data files.

The survey used a uniform sample design, questionnaires (translated into 18 Indian languages), field procedures, and procedures for biomarker measurements throughout the country to facilitate comparability across the states and to ensure the highest possible data quality. The contents of the questionnaires were decided through an extensive collaborative process in early 2005. Based on provisional data, two national-level fact sheets and 29 state fact sheets that provide estimates of more than 50 key indicators of population, health, family welfare, and nutrition have already been released. The basic objective of releasing fact sheets within a very short period after the completion of data collection was to provide immediate feedback to planners and programme managers on key process indicators.

Geographic coverage

• National (29 states )
• Regional (for HIV Prevalence : Andhra Pradesh, Karnataka, Maharashtra, Manipur, and Tamil Nadu)
• Local (population and health indicators for slum and non-slum populations for eight cities, namely Chennai, Delhi, Hyderabad, Indore, Kolkata, Meerut, Mumbai, and Nagpur)

Analysis unit

• Household
• Women age 15-49
• Men age 15-59

Universe

The population covered by the 2005 DHS is defined as the universe of all ever-married women age 15-49, NFHS-3 included never married women age 15-49 and both ever-married and never married men age 15-54 as eligible respondents.

Kind of data

Sample survey data

Sampling procedure

SAMPLE SIZE

Since a large number of the key indicators to be estimated from NFHS-3 refer to ever-married women in the reproductive ages of 15-49, the target sample size for each state in NFHS-3 was estimated in terms of the number of ever-married women in the reproductive ages to be interviewed.

The initial target sample size was 4,000 completed interviews with ever-married women in states with a 2001 population of more than 30 million, 3,000 completed interviews with ever-married women in states with a 2001 population between 5 and 30 million, and 1,500 completed interviews with ever-married women in states with a population of less than 5 million. In addition, because of sample-size adjustments required to meet the need for HIV prevalence estimates for the high HIV prevalence states and Uttar Pradesh and for slum and non-slum estimates in eight selected cities, the sample size in some states was higher than that fixed by the above criteria. The target sample was increased for Andhra Pradesh, Karnataka, Maharashtra, Manipur, Nagaland, Tamil Nadu, and Uttar Pradesh to permit the calculation of reliable HIV prevalence estimates for each of these states. The sample size in Andhra Pradesh, Delhi, Maharashtra, Tamil Nadu, Madhya Pradesh, and West Bengal was increased to allow separate estimates for slum and non-slum populations in the cities of Chennai, Delhi, Hyderabad, Indore, Kolkata, Mumbai, Meerut, and Nagpur.

The target sample size for HIV tests was estimated on the basis of the assumed HIV prevalence rate, the design effect of the sample, and the acceptable level of precision. With an assumed level of HIV prevalence of 1.25 percent and a 15 percent relative standard error, the estimated sample size was 6,400 HIV tests each for men and women in each of the high HIV prevalence states. At the national level, the assumed level of HIV prevalence of less than 1 percent (0.92 percent) and less than a 5 percent relative standard error yielded a target of 125,000 HIV tests at the national level.

Blood was collected for HIV testing from all consenting ever-married and never married women age 15-49 and men age 15-54 in all sample households in Andhra Pradesh, Karnataka, Maharashtra, Manipur, Tamil Nadu, and Uttar Pradesh. All women age 15-49 and men age 15-54 in the sample households were eligible for interviewing in all of these states plus Nagaland. In the remaining 22 states, all ever-married and never married women age 15-49 in sample households were eligible to be interviewed. In those 22 states, men age 15-54 were eligible to be interviewed in only a subsample of households. HIV tests for women and men were carried out in only a subsample of the households that were selected for men's interviews in those 22 states. The reason for this sample design is that the required number of HIV tests is determined by the need to calculate HIV prevalence at the national level and for some states, whereas the number of individual interviews is determined by the need to provide state level estimates for attitudinal and behavioural indicators in every state. For statistical reasons, it is not possible to estimate HIV prevalence in every state from NFHS-3 as the number of tests required for estimating HIV prevalence reliably in low HIV prevalence states would have been very large.

SAMPLE DESIGN

The urban and rural samples within each state were drawn separately and, to the extent possible, unless oversampling was required to permit separate estimates for urban slum and non-slum areas, the sample within each state was allocated proportionally to the size of the state's urban and rural populations. A uniform sample design was adopted in all states. In each state, the rural sample was selected in two stages, with the selection of Primary Sampling Units (PSUs), which are villages, with probability proportional to population size (PPS) at the first stage, followed by the random selection of households within each PSU in the second stage. In urban areas, a three-stage procedure was followed. In the first stage, wards were selected with PPS sampling. In the next stage, one census enumeration block (CEB) was randomly selected from each sample ward. In the final stage, households were randomly selected within each selected CEB.

SAMPLE SELECTION IN RURAL AREAS

In rural areas, the 2001 Census list of villages served as the sampling frame. The list was stratified by a number of variables. The first level of stratification was geographic, with districts being subdivided into contiguous regions. Within each of these regions, villages were further stratified using selected variables from the following list: village size, percentage of males working in the nonagricultural sector, percentage of the population belonging to scheduled castes or scheduled tribes, and female literacy. In addition to these variables, an external estimate of HIV prevalence, i.e., 'High', 'Medium' or 'Low', as estimated for all the districts in high HIV prevalence states, was used for stratification in high HIV prevalence states. Female literacy was used for implicit stratification (i.e., villages were

Visitor Arrivals: Local: Delhi data was reported at 39,415,000.000 Person in 2023. This records an increase from the previous number of 27,186,200.000 Person for 2022. Visitor Arrivals: Local: Delhi data is updated yearly, averaging 9,583,671.000 Person from Dec 1997 (Median) to 2023, with 27 observations. The data reached an all-time high of 39,415,000.000 Person in 2023 and a record low of 1,228,059.000 Person in 2002. Visitor Arrivals: Local: Delhi data remains active status in CEIC and is reported by Ministry of Tourism. The data is categorized under Global Database’s India – Table IN.QD001: Resident Visits: by States.

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.