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

Context

The dataset was created by keeping in mind the necessity of such historical weather data in the community. The datasets for top 8 Indian cities as per the population.

Content

The dataset was used with the help of the worldweatheronline.com API and the wwo_hist package. The datasets contain hourly weather data from 01-01-2009 to 01-01-2020. The data of each city is for more than 10 years. This data can be used to visualize the change in data due to global warming or can be used to predict the weather for upcoming days, weeks, months, seasons, etc. Note : The data was extracted with the help of worldweatheronline.com API and I can't guarantee about the accuracy of the data.

Acknowledgements

The data is owned by worldweatheronline.com and is extracted with the help of their API.

Inspiration

The main target of this dataset can be used to predict weather for the next day or week with huge amounts of data provided in the dataset. Furthermore, this data can also be used to make visualization which would help to understand the impact of global warming over the various aspects of the weather like precipitation, humidity, temperature, etc.

The dataset contains air quality information for various cities across India. It includes parameters such as Air Quality Index (AQI), concentrations of particulate matter (PM2.5 and PM10), ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), as well as geographical coordinates and time stamps. This dataset enables analysis and comparison of air quality levels among different cities, aiding in understanding environmental health impacts and informing policy decisions.

About this dataset

Context

This list ranks the 333 cities in the Massachusetts by Indian population, as estimated by the United States Census Bureau. It also highlights population changes in each city over the past five years.

Content

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

• 2019-2023 American Community Survey 5-Year Estimates
• 2014-2018 American Community Survey 5-Year Estimates
• 2009-2013 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Indian Population: This column displays the rank of city in the Massachusetts by their Indian population, using the most recent ACS data available.
• City: The City for which the rank is shown in the previous column.
• Indian Population: The Indian population of the city is shown in this column.
• % of Total City Population: This shows what percentage of the total city population identifies as Indian. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Massachusetts Indian Population: This tells us how much of the entire Massachusetts Indian population lives in that city. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: This column displays the rank trend across the last 5 years.

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/.

As of September 2025, Mumbai had the highest cost of living among other cities in the country, with an index value of ****. Gurgaon, a satellite city of Delhi and part of the National Capital Region (NCR) followed it with an index value of ****.  What is cost of living? The cost of living varies depending on geographical regions and factors that affect the cost of living in an area include housing, food, utilities, clothing, childcare, and fuel among others. The cost of living is calculated based on different measures such as the consumer price index (CPI), living cost indexes, and wage price index. CPI refers to the change in the value of consumer goods and services. The wage price index, on the other hand, measures the change in labor services prices due to market pressures. Lastly, the living cost indexes calculate the impact of changing costs on different households. The relationship between wages and costs determines affordability and shifts in the cost of living. Mumbai tops the list Mumbai usually tops the list of most expensive cities in India. As the financial and entertainment hub of the country, Mumbai offers wide opportunities and attracts talent from all over the country. It is the second-largest city in India and has one of the most expensive real estates in the world.

As per the recently released survey list, India is currently at the second position but may soon move to the first position. The survey found that - "24 April 2023 - China will soon lose its long-standing position as the world's most populous country. By the end of this month, India's population is set to reach 1,425,775,850 people." In such a situation, this dataset can be useful in population calculation of different cities.

This dataset contains the details of various major cities of Indian states, such as-

      1. Delhi.
      2. Mumbai.
      3. Bengaluru.
      4. Ahmedabad.
      5. Pune.
      6. Kolkata.
      7. Hyderabad etc.

And also information about the population of its corporation has been given.

📋 Dataset Overview

A comprehensive hourly dataset tracking atmospheric conditions and air quality across 29 major Indian cities (28 states + Delhi) from 2022 to 2025. This dataset provides detailed insights into India's environmental patterns, pollution trends, and meteorological conditions.

🗺️ Geographic Coverage

• Cities Covered: 29 major cities representing all 28 Indian states + Delhi
• Time Period: 2022 to 2025
• Records: 842,160 hourly observations
• Features: 63 parameters covering weather, pollution, and temporal data

📊 Dataset Specifications

• Total Records: 842,160
• Features: 63 columns
• Temporal Resolution: Hourly data
• Period: 2022-2025 (4 years)
• Memory Usage: ~405 MB

🏙️ Indian Cities Included

(Representing all 28 states + Union Territory of Delhi) - Delhi - Mumbai (Maharashtra) - Chennai (Tamil Nadu) - Kolkata (West Bengal) - Bengaluru (Karnataka) - Hyderabad (Telangana) - Ahmedabad (Gujarat) - Pune (Maharashtra) - Jaipur (Rajasthan) - Lucknow (Uttar Pradesh) - And other major state capitals...

🗂️ Data Columns

📍 Geographic & Temporal Context

🌡️ Meteorological Parameters

Temperature & Humidity - Temp_2m_C - Ambient temperature (°C) - Humidity_Percent - Relative humidity - Dew_Point_C - Dew point temperature - Humidity_Category - Comfort levels

Wind Patterns - Wind_Speed_10m_kmh - Surface wind speed - Wind_Dir_10m - Wind direction (critical for pollution dispersion) - Wind_Gusts_kmh - Wind gusts - Wind_Stagnation - Air stagnation events

Precipitation & Pressure - Precipitation_mm, Rain_mm - Monsoon rainfall tracking - Is_Raining, Heavy_Rain - Rain events - Pressure_MSL_hPa - Monsoon pressure systems

☀️ Solar & Cloud Data

• Solar_Radiation_Wm2 - Total solar radiation
• Direct/Diffuse_Radiation_Wm2 - Radiation components
• Cloud_Cover_Percent - Total cloud cover
• Cloud_Low/Mid/High_Percent - Cloud altitude distribution
• Sunshine_Seconds - Bright sunshine duration
• Is_Daytime - Day/night indicator

🏭 Air Quality Metrics (Critical for India)

Particulate Matter - PM2_5_ugm3 - Fine particulate matter (primary concern) - PM10_ugm3 - Coarse particulate matter - PM_Ratio - PM2.5/PM10 ratio (source identification) - Dust_ugm3 - Dust concentrations - AOD - Aerosol Optical Depth

Gaseous Pollutants - CO_ugm3 - Carbon monoxide (vehicular/industrial) - NO2_ugm3 - Nitrogen dioxide (traffic, industries) - SO2_ugm3 - Sulfur dioxide (industrial, power plants) - O3_ugm3 - Ozone (secondary pollutant)

📊 Air Quality Indices

US AQI System - US_AQI - Overall US AQI - US_AQI_PM25, US_AQI_PM10 - PM-specific indices - US_AQI_NO2, US_AQI_O3, US_AQI_CO - Gas-specific indices

EU AQI System - EU_AQI - European Air Quality Index - EU_AQI_PM25, EU_AQI_PM10 - European standards

India-Specific Categories - AQI_Category - Overall air quality category - PM25_Category_India - India-specific PM2.5 categorization

🌪️ Special Features

• Temp_Inversion - Temperature inversion events (critical for winter pollution in North India)

⚠️ Data Quality Notes

• High Completeness: Most columns have 842,160 non-null values
• Minor Missing Values:
  • US/EU AQI columns: ~145 missing records
  • AQI_Category: 2,516 missing
  • US_AQI_NO2: 2 missing
  • US_AQI_O3: 73 missing

🎯 India-Specific Analysis Opportunities

Seasonal Patterns

• Winter (Dec-Feb): Severe PM2.5 pollution in Indo-Gangetic plain
• Summer (Mar-May): Dust storms, high O3 levels
• Monsoon (Jun-Sep): Natural cleansing, improved AQI
• Post-Monsoon (Oct-Nov): Crop burning impacts in North India

Geographic Hotspots

• Northern Plains: Delhi, Lucknow - Winter smog, crop burning
• Coastal Cities: Mumbai, Chennai - Marine influence, humidity
• Southern Plateau: Bengaluru, Hyderabad - Moderate pollution
• Eastern India: Kolkata - Industrial and vehicular pollution

Event-Based Analysis

• Festival Impacts: Diwali fireworks, Holi bonfires
• Agricultural Cycles: Stubble burning seasons
• Meteorological Events: Western disturbances, monsoon progress

🛠️ Suggested Research Topics

Public Health

• C...

As of 2024, Mumbai had a gross domestic product of *** billion U.S. dollars, the highest among other major cities in India. It was followed by Delhi with a GDP of around *** billion U.S. dollars. India’s megacities also boast the highest GDP among other cities in the country. What drives the GDP of India’s megacities? Mumbai is the financial capital of the country, and its GDP growth is primarily fueled by the financial services sector, port-based trade, and the Hindi film industry or Bollywood. Delhi in addition to being the political hub hosts a significant services sector. The satellite cities of Noida and Gurugram amplify the city's economic status. The southern cities of Bengaluru and Chennai have emerged as IT and manufacturing hubs respectively. Hyderabad is a significant player in the pharma and IT industries. Lastly, the western city of Ahmedabad, in addition to its strategic location and ports, is powered by the textile, chemicals, and machinery sectors. Does GDP equal to quality of life? Cities propelling economic growth and generating a major share of GDP is a global phenomenon, as in the case of Tokyo, Shanghai, New York, and others. However, the GDP, which measures the market value of all final goods and services produced in a region, does not always translate to a rise in quality of life. Five of India’s megacities featured in the Global Livability Index, with low ranks among global peers. The Index was based on indicators such as healthcare, political stability, environment and culture, infrastructure, and others.

About this dataset

Context

This list ranks the 473 cities in the California by Indian population, as estimated by the United States Census Bureau. It also highlights population changes in each city over the past five years.

Content

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

• 2019-2023 American Community Survey 5-Year Estimates
• 2014-2018 American Community Survey 5-Year Estimates
• 2009-2013 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Indian Population: This column displays the rank of city in the California by their Indian population, using the most recent ACS data available.
• City: The City for which the rank is shown in the previous column.
• Indian Population: The Indian population of the city is shown in this column.
• % of Total City Population: This shows what percentage of the total city population identifies as Indian. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total California Indian Population: This tells us how much of the entire California Indian population lives in that city. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: This column displays the rank trend across the last 5 years.

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/.

As per the Global Liveability Index of 2024, five Indian cities figured on the list comprising 173 across the world. Indian megacities Delhi and Mumbai tied for 141st place with a score of **** out of 100. They were followed by Chennai (****), Ahmedabad (****), and Bengaluru (****). What are indicators for livability The list was topped by Vienna for yet another year. The index measures cities on five broad indicators such as stability, healthcare, culture and environment, education, and infrastructure. As per the Economic Intelligence Unit’s suggestions, if a city’s livability score is between ** to ** then “livability is substantially constrained”. Less than ** means most aspects of living are severely restricted. Least Liveable cities on the index The least liveable cities were in Sub-Saharan Africa and the Middle East and North Africa regions. Damascus and Tripoli ranked the lowest. Tel Aviv also witnessed significant drop due to war with Hamas.

The data has been collected from the Central Pollution Control Board (CPCB)'s repository - https://cpcb.nic.in/

The dataset contains the following attributes:

Timestamp: Date when data was recorded (DD-MM-YYYY). Ranges from 01-01-2020 to 31-12-2024.

Location: Locations of the collected data. Its values are 'Bengaluru - Silk Board', 'Chennai - Alandur Bus Depot', 'Delhi - Punjabi Bagh', 'Hyderabad - Central University', 'Kolkata - Rabindra Bharati University', and 'Mumbai - Chhatrapati Shivaji Intl. Airport (T2)', 'Gwalior - City Center', 'Jaipur - Police Commissionerate', 'Meghalaya - Shillong - Lumpyngngad', and 'Visakhapatnam - GVM Corporation'.

PM2.5: PM2.5 are ultra-fine particles having a size of less than or equal to 2.5 microns. Caused by Combustion of fossil fuels (vehicles, industrial processes, power plants), Residential burning (wood stoves, biomass), Wildfires, dust storms, Secondary formation from gaseous pollutants like SO2 and NOx. PM10 is caused by Road dust, construction activities, mining, industrial emissions, and agricultural activities like ploughing, burning residues, sea salt, etc.

PM10: PM10 are particles with a size of less than or equal to 10 microns. Caused by Combustion of fossil fuels (vehicles, industrial processes, power plants), Residential burning (wood stoves, biomass), Wildfires, dust storms, Secondary formation from gaseous pollutants like SO2 and NOx. PM10 is caused by Road dust, construction activities, mining, industrial emissions, and agricultural activities like ploughing, burning residues, sea salt, etc.

Nitrogen Dioxide (NO2): Nitrogen dioxide is a highly reactive gas present in the atmosphere. Its poisoning is as hazardous as carbon monoxide poisoning. When inhaled, it can cause serious damage to the heart, absorption by the lungs, inflammation, and irritation of the airways. Smog formation and foliage damage are some environmental impacts of nitrogen dioxide. It is caused by vehicle emissions, industrial combustion, and the burning of biomass and fossil fuels.

Ammonia (NH3): Ammonia is a colourless, reactive, and soluble alkaline gas with a strong pungent odour and is only used by the Indian government as a parameter of AQI. Ammonia is a major reason for eutrophication in water bodies. It contributes to climate change, the formation of particulate matter, a reduction in visibility, and the atmospheric deposition of nitrogen atoms. Human beings experience immediate eyes, nose, throat, and respiratory tract burning, blindness, and lung damage upon exposure to high levels. It may cause coughing and irritation in the eyes, nose, and throat with low-concentration exposure. Caused by fertilizer applications, livestock waste, wastewater treatment plants, biomass burning and industrial emissions.

Sulphur Dioxide (SO2): Sulfur dioxide is a colourless gas with a burnt odour and the chemical formula SO2. The gas is acidic & corrosive and can react in the atmosphere with other compounds to form sulfuric acid and other oxides of sulfur. Sulfur dioxide is a major cause of haze production, acid rain, and damage to foliage, monuments & buildings, reacts and forms particulate matter. In humans, it causes breathing discomfort, asthma, eyes, nose, and throat irritation, inflammation of airways, and heart diseases. This is caused by the burning of coal and oil, smelting, chemical manufacturing, and using high-sulphur fuels (in ships).

Carbon Monoxide (CO): It is a colourless gas, released from automobile emissions, fires, industrial processes, gas stoves, kitchen chimneys, generators, wood-burning smoking, etc. into the atmosphere. Exposure to carbon monoxide causes carbon monoxide poisoning (interference with oxygen-hemoglobin binding) in human beings, chest pain, heart diseases, reduced mental capabilities, vision problems, and contributes to smog formation. Caused by incomplete combustion of fossil fuels, wildfires and biomass burning, cigarettes, etc.

Ozone (O3): Ozone is composed of three oxygen atoms. It forms a protective layer that prevents harmful ultraviolet radiation from entering the earth. Ground ozone is very harmful to humans and the environment. It interferes with plants' respiration processes and enhances their susceptibility to environmental stressors. When humans inhale ozone, they experience reduced lung function, inflammation of the airways, and irritation in the eyes, nose, and throat. This is caused by indirect emissions from vehicles and industrial sources.

All measurements are in micrograms/cubic meter (µg/m³), except for CO which is in milligrams/cubic meter (mg/m³).

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.

Sources of PM2.5 levels vary widely across National Clean Air Programme (NCAP) cities in India. In 2024, transportation was the biggest contributor to PM2.5 levels in Bangalore. Meanwhile, around ** percent of PM2.5 levels in Allahabad and Varanasi were linked to MSW/biomass burning.

In 2023, approximately a third of the total population in India lived in cities. The trend shows an increase of urbanization by more than 4 percent in the last decade, meaning people have moved away from rural areas to find work and make a living in the cities. Leaving the fieldOver the last decade, urbanization in India has increased by almost 4 percent, as more and more people leave the agricultural sector to find work in services. Agriculture plays a significant role in the Indian economy and it employs almost half of India’s workforce today, however, its contribution to India’s GDP has been decreasing while the services sector gained in importance. No rural exodus in sightWhile urbanization is increasing as more jobs in telecommunications and IT are created and the private sector gains in importance, India is not facing a shortage of agricultural workers or a mass exodus to the cities yet. India is a very densely populated country with vast areas of arable land – over 155 million hectares of land was cultivated land in India as of 2015, for example, and textiles, especially cotton, are still one of the major exports. So while a shift of the workforce focus is obviously taking place, India is not struggling to fulfill trade demands yet.

Descriptive analysis of under-5 children in total slum population of eight Indian cities, NFHS-4, 2015–16.

In 2024, Delhi recorded ** percent of monitored days when PM2.5 levels exceeded India's daily National Air Quality Standards (NAAQS) 24-hour standard of ** micrograms per cubic meter of air (μg/m3). This was the highest proportion of PM2.5 daily exceedances among India's most populated cities that year. In comparison, just *** percent of monitored days exceeded PM2.5 NAAQS in Chennai. However, India's daily NAAQS for PM2.5 is far less strict than the World Health Organization air quality guideline of ** μg/m3.

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

Urbanisation is a form of social transformation from traditional rural societies to modern, industrial and urban communities. It is long term continuous process. It is progressive concentration of population in urban unit. Kingsley Davies has explained urbanisation as process of switch from spread out pattern of human settlements to one of concentration in urban centers. Migration is the key process underlying growth of urbanization.

Challenges in urban development--->;

Institutional challenges

Urban Governance 74th amendment act has been implemented half-heartedly by the states, which has not fully empowered the Urban local bodies (ULBs). ULBs comprise of municipal corporations, municipalities and nagar panchayats, which are to be supported by state governments to manage the urban development. For this , ULBs need clear delegation of functions, financial resources and autonomy. At present urban governance needs improvement for urban development, which can be done by enhancing technology, administrative and managerial capacity of ULBs.

Planning Planning is mainly centralized and till now the state planning boards and commissions have not come out with any specific planning strategies an depend on Planning commission for it. This is expected to change in present government, as planning commission has been abolished and now focus is on empowering the states and strengthening the federal structure.

In fact for big cities the plans have become outdated and do not reflect the concern of urban local dwellers, this needs to be take care by Metropolitan planning committee as per provisions of 74th amendment act. Now the planning needs to be decentralized and participatory to accommodate the needs of the urban dwellers.

Also there is lack of human resource for undertaking planning on full scale. State planning departments and national planning institutions lack qualified planning professional. Need is to expand the scope of planners from physical to integrated planning- Land use, infrastructure, environmental sustainability, social inclusion, risk reduction, economic productivity and financial diversity.

Finances Major challenge is of revenue generation with the ULBs. This problem can be analyzed form two perspectives. First, the states have not given enough autonomy to ULBs to generate revenues and Second in some case the ULBs have failed to utilize even those tax and fee powers that they have been vested with.

There are two sources of municipal revenue i.e. municipal own revenue and assigned revenue. Municipal own revenue are generated by municipal own revenue through taxes and fee levied by them. Assigned revenues are those which are assigned to local governments by higher tier of government.

There is growing trend of declining ratio of own revenue. There is poor collection property taxes. Use of geographical information system to map all the properties in a city can have a huge impact on the assessment rate of properties that are not in tax net.

There is need to broaden the user charge fee for water supply, sewerage and garbage disposal. Since these are the goods which have a private characteristics and no public spill over, so charging user fee will be feasible and will improve the revenue of ULBs , along with periodic revision. Once the own revenue generating capacity of the cities will improve, they can easily get loans from the banks. At present due to lack of revenue generation capabilities, banks don’t give loan to ULBs for further development. For financing urban projects, Municipal bonds are also famous, which work on the concept of pooled financing.

Regulator

There is exponential increase in the real estate, encroaching the agricultural lands. Also the rates are very high, which are not affordable and other irregularities are also in practice. For this, we need regulator, which can make level playing field and will be instrumental for affordable housing and checking corrupt practices in Real estate sector.

Infrastructural challenges

Housing Housing provision for the growing urban population will be the biggest challenge before the government. The growing cost of houses comparison to the income of the urban middle class, has made it impossible for majority of lower income groups and are residing in congested accommodation and many of those are devoid of proper ventilation, lighting, water supply, sewage system, etc. For instance in Delhi, the current estimate is of a shortage of 5,00,000 dwelling units the coming decades. The United Nations Centre for Human Settlements (UNCHS) introduced the concept of “Housing Poverty” which includes “Individuals and households who lack safe, secure and healthy shelter, with basic infrastructure such as piped water and adequate provision for sanitation, drainage and the removal of hou...

Nigeria has the largest population in Africa. As of 2025, the country counted over 237.5 million individuals, whereas Ethiopia, which ranked second, has around 135.5 million inhabitants. Egypt registered the largest population in North Africa, reaching nearly 118.4 million people. In terms of inhabitants per square kilometer, Nigeria only ranked seventh, while Mauritius had the highest population density on the whole African continent in 2023. The fastest-growing world region Africa is the second most populous continent in the world, after Asia. Nevertheless, Africa records the highest growth rate worldwide, with figures rising by over two percent every year. In some countries, such as Chad, South Sudan, Somalia, and the Central African Republic, the population increase peaks at over 3.4 percent. With so many births, Africa is also the youngest continent in the world. However, this coincides with a low life expectancy. African cities on the rise The last decades have seen high urbanization rates in Asia, mainly in China and India. African cities are also growing at large rates. Indeed, the continent has three megacities and is expected to add four more by 2050. Furthermore, Africa's fastest-growing cities are forecast to be Bujumbura, in Burundi, and Zinder, Nigeria, by 2035.

Background: Particulate matter (PM) is one among the crucial air pollutants and has the potential to cause a wide range of health effects. Indian cities ranked top places in the World Health Organization list of most polluted cities by PM. Objectives: Present study aims to assess the trends, short- and long-term health effects of PM in major Indian cities. Methods: PM-induced hospital admissions and mortality are quantified using AirQ+ software. Results: Annual PM concentration in most of the cities is higher than the National Ambient Air Quality Standards of India. Trend analysis showed peak PM concentration during post-monsoon and winter seasons. The respiratory and cardiovascular hospital admissions in the male (female) population are estimated to be 31,307 (28,009) and 5460 (4882) cases, respectively. PM2.5 has accounted for a total of 1,27,014 deaths in 2017. Conclusion: Cities with high PM concentration and exposed population are more susceptible to mortality and hospital admissions.