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]

India Census: Number of Households: Delhi: by Size: 6 to 8 Members data was reported at 853,773.000 Unit in 2011. India Census: Number of Households: Delhi: by Size: 6 to 8 Members data is updated yearly, averaging 853,773.000 Unit from Mar 2011 (Median) to 2011, with 1 observations. India Census: Number of Households: Delhi: by Size: 6 to 8 Members data remains active status in CEIC and is reported by Census of India. The data is categorized under India Premium Database’s Demographic – Table IN.GAF013: Census: Number of Households: by Size: Delhi.

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.

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

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.

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.

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.

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 contains year, state and district wise number of Asthma Cases in children of age group 0-5 years

Note: Asthma is a condition in which your airways narrow and swell and may produce extra mucus. This can make breathing difficult and trigger coughing, a whistling sound (wheezing) when you breathe out and shortness of breath. For some people, asthma is a minor nuisance.

The datasets contains date- and state-wise historically compiled data on air quality (by pollution level) in rural and urban areas of India from the year 2015 , as measured by Central Pollution Board (CPCB) through its daily (24 hourly measurements, taken at 4 PM everyday) Air Quality Index (AQI) reports.

The CPCB measures air quality by continuous online monitoring of various pollutants such as Particulate Matter10 (PM10), Particulate Matter2.5 (PM2.5), Sulphur Dioxide (SO2), Nitrogen Oxide or Oxides of Nitrogen (NO2), Ozone (O3), Carbon Monoxide (CO), Ammonic (NH3) and Lead (Pb) and calculating their level of pollution in the ambient air. Based on the each pollutant load in the air and their associated health impacts, the CPCB calculates the overall Air Pollution in Air Quality Index (AQI) value and publishes the data. This AQI data is then used by CPCB to report the air quality status i.e good, satisfactory, moderate, poor, very poor and severe, etc. of a particular location and their related health impacts because of air pollution.

This database is a unique achievement to which we are happy to give you a free access. It covers the members of the lower house of the Indian parliament (the House of the People or Lok Sabha) who have been elected between the first general elections of 1951-52 and the fourteenth ones in 2004 in the Hindi-speaking states (Bihar, Chandigarh, Chhattisgarh, Delhi, Haryana, Himachal Pradesh, Jharkhand, Madhya Pradesh, Rajasthan, Uttar Pradesh, Uttarakhand). Many of these states did not exist in 1951-52. We have done as if they did by pooling together the constituencies which were to form them in order to make inter-temporal comparisons possible. For each Lok Sabha MP, this database provides their surname, first name, constituency, state of birth, party, gender, date of birth, religion, caste, level of education, and occupation. These data draw from the Election Commission publications, the website Who's Who in Lok Sabha [the only website I found today is the Parliament of India, Lok Sabha, https://loksabha.nic.in/]. and individual interviews with the MPs themselves or party old timers. Such a database can only result from a collective endeavour. It was initiated by Christophe Jaffrelot who collected most of the data year after year from the mid-1990s onwards. Elisabeth Theunissen, Cyril Robin, Virginie Dutoya, and Zuheir Desai played a major role successively over the last fifteen years. These data have been analysed in two books dealing with the growing presence of the low caste groups on the Indian political scene: Christophe Jaffrelot, India's silent revolution - The rise of the lower castes in North Indian politics (New York, Columbia University Press, 2003), and Christophe Jaffrelot, "Introduction", in Christophe Jaffrelot and Sanjay Kumar (eds), Rise of the plebeians ? The changing face of Indian legislative assemblies, New Delhi, Routledge, 2009.

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 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

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.

Context

A Google Forms Survey Was Conducted In May 2021 Among College Students Of Delhi. The Data Was Aimed At Opinions Regarding Employment Among The Indian Urban Youth.

Image Credits-Unsplash

Inspiration

Analyse The Data And Find Some Insights Regarding The Opinion Of Youngsters. Try to find patterns among the choices.

As per the Census data dated 2011, the slum dwellers population in Mumbai was the highest among all other major metropolitan cities of India, at around ************. Hyderabad and Delhi followed it. A total of about ** million people were estimated to be living in slums across the country.

Per capita carbon dioxide (CO₂) emissions in India have soared in recent decades, climbing from 0.4 metric tons per person in 1970 to a high of 2.07 metric tons per person in 2023. Total CO₂ emissions in India also reached a record high in 2023. Greenhouse gas emissions in India India is the third-largest CO₂ emitter globally, behind only China and the United States. Among the various economic sectors of the country, the power sector accounts for the largest share of greenhouse gas emissions in India, followed by agriculture. Together, these two sectors were responsible for more than half of India's total emissions in 2023. Coal emissions One of the main reasons for India's high emissions is the country's reliance on coal, the most polluting of fossil fuels. India's CO₂ emissions from coal totaled roughly two billion metric tons in 2023, a near sixfold increase from 1990 levels.

In 2022, more than 21 million Indian nationals departed on outbound travels from India, marking a significant increase from the previous year. The coronavirus pandemic in 2020 restricted traveling around the world. Travel bug and the economy
Indian nationals are traveling more than ever before. However, far fewer Indians travel internationally compared to domestic travels. Since 2012, over one billion Indian nationals have traveled within the country. The various tax exemptions announced by the government in recent years was one of the reasons for an increase in disposable incomes among people. This seems to have been a welcome move, since a large section of the society in India travel on a need basis. The newly growing economy seems to have triggered an increase in travel and tourism expenditures especially by the middle and lower class of people who have built more capacity for savings. India’s busiest airport    The aviation industry has also grown drastically over the last decade, with over 125 operational airports in the country as of today. The Indira Gandhi International Airport in Delhi was the busiest airport in terms of passenger traffic in 2019, while the United Arab Emirates was the leading destination for passengers traveling from India. The UAE was both, a leisure and business destination for many Indians.

Number of Students: Delhi: Colleges data was reported at 299,597.000 Person in 2021. This records an increase from the previous number of 281,983.000 Person for 2020. Number of Students: Delhi: Colleges data is updated yearly, averaging 261,089.000 Person from Sep 2010 (Median) to 2021, with 12 observations. The data reached an all-time high of 299,597.000 Person in 2021 and a record low of 150,323.000 Person in 2010. Number of Students: Delhi: Colleges data remains active status in CEIC and is reported by Ministry of Education. The data is categorized under India Premium Database’s Education Sector – Table IN.EDD005: Number of Students: Colleges.