Population in largest city in India was reported at 33807403 in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. India - Population in largest city - actual values, historical data, forecasts and projections were sourced from the World Bank on September of 2025.

Population in the largest city (% of urban population) in India was reported at 6.3201 % in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. India - Population in the largest city - actual values, historical data, forecasts and projections were sourced from the World Bank on September of 2025.

The "Indian IT Cities Used Car Dataset 2023" is a comprehensive collection of data that offers valuable insights into the used car market across major Information Technology (IT) cities in India. This dataset provides a wealth of information on a wide range of used car listings, encompassing details such as car models, variants, pricing, fuel types, dealer locations, warranty information, colors, kilometers driven, body styles, transmission types, ownership history, manufacture dates, model years, dealer names, CNG kit availability, and quality scores.

Researchers, data enthusiasts, and industry professionals can leverage this dataset for in-depth analysis, market research, and predictive modeling within the Indian used car sector, focusing on the unique dynamics of IT-driven cities. With data sourced from the year 2023, this dataset is a valuable resource for anyone seeking to explore the nuances of the used car market within the thriving IT hubs of India.

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.

About this dataset

Context

The dataset tabulates the median household income in Indian Village. It can be utilized to understand the trend in median household income and to analyze the income distribution in Indian Village by household type, size, and across various income brackets.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Indian Village, IN Median Household Income Trends (2010-2021, in 2022 inflation-adjusted dollars)
• Median Household Income Variation by Family Size in Indian Village, IN: Comparative analysis across 7 household sizes
• Income Distribution by Quintile: Mean Household Income in Indian Village, IN
• Indian Village, IN households by income brackets: family, non-family, and total, in 2022 inflation-adjusted dollars

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Indian Village median household income. You can refer the same here

This dataset presents a multi-dimensional view of urban ride safety across Mumbai and Delhi, capturing diverse parameters from crime reports to ride dynamics, traffic congestion, and environmental conditions. It is designed to power intelligent mobility systems, safety-aware navigation tools, and urban research.

Key data sources include:

Crime incidents with timestamps, severity, and resolution status. Ride-hailing metrics such as duration, fare, surge pricing, and driver ratings. Traffic data including congestion levels, reported accidents, and average speed. Environmental conditions: temperature, rainfall, visibility, wind speed, and weather type. Urban safety infrastructure like CCTV camera density and police station proximity.

Applications:

Ride safety scoring models Route deviation detection systems Smart urban planning Socio-spatial crime analysis Public safety dashboards for transport authorities All data is anonymized and includes geographic coordinates (latitude/longitude), enabling integration with GIS systems or map APIs.

Graph and download economic data for Geographical Outreach: Number of Branches in 3 Largest Cities, Excluding Headquarters, for Commercial Banks for India (INDFCBODCLNUM) from 2004 to 2015 about branches, India, banks, and depository institutions.

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.

In 2021, the south Indian city Chennai with average internet speed of ***** Mbps ranked the first among cities in India. It was followed by Bengaluru and Hyderabad, both with internet speed around ** Mbps. Internet access speed has a crucial influence on the colocation of data center in the country.

This statistic displays the share of data analytics firms across India in 2016, by city. In that year, the southern city of Bangalore housed the largest share of analytics firms in India, with a share of around ** percent.

The dataset we have reports survey responses. The survey was collected from 400 households in a large city in the western part of India. A method of stratified sampling was used where the city were divided into various areas and then households were selected on a random basis. Responses were collected from the head of the household. The survey asks questions when jobs moved Work-from-Home during the period of strictest lockdowns in India.The survey consists of questions asking about energy use at a household level during the pandemic, individual energy choices by the head of the household and transport choices when needed to go to office, among other questions. We think the dataset is rich and can be used for further research. As data were collected from human subjects, we undertook the approval process from the Ethics Committee of our insititution.

Context

Zomato restaurants and their counts, ratings, votes along with other importants attributes to do some research work for understanding the indian online food systems.

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

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.

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.

Abstract

The National Family Health Survey (NFHS) was carried out as the principal activity of a collaborative project to strengthen the research capabilities of the Population Reasearch Centres (PRCs) in India, initiated by the Ministry of Health and Family Welfare (MOHFW), Government of India, and coordinated by the International Institute for Population Sciences (IIPS), Bombay. Interviews were conducted with a nationally representative sample of 89,777 ever-married women in the age group 13-49, from 24 states and the National Capital Territoty of Delhi. The main objective of the survey was to collect reliable and up-to-date information on fertility, family planning, mortality, and maternal and child health. Data collection was carried out in three phases from April 1992 to September 1993. THe NFHS is one of the most complete surveys of its kind ever conducted in India.

The households covered in the survey included 500,492 residents. The young age structure of the population highlights the momentum of the future population growth of the country; 38 percent of household residents are under age 15, with their reproductive years still in the future. Persons age 60 or older constitute 8 percent of the population. The population sex ratio of the de jure residents is 944 females per 1,000 males, which is slightly higher than sex ratio of 927 observed in the 1991 Census.

The primary objective of the NFHS is to provide national-level and state-level data on fertility, nuptiality, family size preferences, knowledge and practice of family planning, the potentiel demand for contraception, the level of unwanted fertility, utilization of antenatal services, breastfeeding and food supplemation practises, child nutrition and health, immunizations, and infant and child mortality. The NFHS is also designed to explore the demographic and socioeconomic determinants of fertility, family planning, and maternal and child health. This information is intended to assist policymakers, adminitrators and researchers in assessing and evaluating population and family welfare programmes and strategies. The NFHS used uniform questionnaires and uniform methods of sampling, data collection and analysis with the primary objective of providing a source of demographic and health data for interstate comparisons. The data collected in the NFHS are also comparable with those of the Demographic and Health Surveys (DHS) conducted in many other countries.

Geographic coverage

National

Analysis unit

• Household
• Data collected for women 13-49, indicators calculated for women 15-49

Universe

The population covered by the 1992-93 DHS is defined as the universe of all women age 13-49 who were either permanent residents of the households in the NDHS sample or visitors present in the households on the night before the survey were eligible to be interviewed.

Kind of data

Sample survey data

Sampling procedure

SAMPLE DESIGN

The sample design for the NFHS was discussed during a Sample Design Workshop held in Madurai in Octber, 1991. The workshop was attended by representative from the PRCs; the COs; the Office of the Registrar General, India; IIPS and the East-West Center/Macro International. A uniform sample design was adopted in all the NFHS states. The Sample design adopted in each state is a systematic, stratified sample of households, with two stages in rural areas and three stages in urban areas.

SAMPLE SIZE AND ALLOCATION

The sample size for each state was specified in terms of a target number of completed interviews with eligible women. The target sample size was set considering the size of the state, the time and ressources available for the survey and the need for separate estimates for urban and rural areas of the stat. The initial target sample size was 3,000 completed interviews with eligible women for states having a population of 25 million or less in 1991; 4,000 completed interviews for large states with more than 25 million population; 8,000 for Uttar Pradesh, the largest state; and 1,000 each for the six small northeastern states. In States with a substantial number of backward districts, the initial target samples were increased so as to allow separate estimates to be made for groups of backward districts.

The urban and rural samples within states were drawn separetly and , to the extent possible, sample allocation was proportional to the size of the urban-rural populations (to facilitate the selection of a self-weighting sample for each state). In states where the urban population was not sufficiently large to provide a sample of at least 1,000 completed interviews with eligible women, the urban areas were appropriately oversampled (except in the six small northeastern states).

THE RURAL SAMPLE: THE FRAME, STRATIFICATION AND SELECTION

A two-stage stratified sampling was adopted for the rural areas: selection of villages followed by selection of households. Because the 1991 Census data were not available at the time of sample selection in most states, the 1981 Census list of villages served as the sampling frame in all the states with the exception of Assam, Delhi and Punjab. In these three states the 1991 Census data were used as the sampling frame.

Villages were stratified prior to selection on the basis of a number of variables. The firts level of stratification in all the states was geographic, with districts subdivided into regions according to their geophysical characteristics. Within each of these regions, villages were further stratified using some of the following variables : village size, distance from the nearest town, proportion of nonagricultural workers, proportion of the population belonging to scheduled castes/scheduled tribes, and female literacy. However, not all variables were used in every state. Each state was examined individually and two or three variables were selected for stratification, with the aim of creating not more than 12 strata for small states and not more than 15 strata for large states. Females literacy was often used for implicit stratification (i.e., the villages were ordered prior to selection according to the proportion of females who were literate). Primary sampling Units (PSUs) were selected systematically, with probaility proportional to size (PPS). In some cases, adjacent villages with small population sizes were combined into a single PSU for the purpose of sample selection. On average, 30 households were selected for interviewing in each selected PSU.

In every state, all the households in the selected PSUs were listed about two weeks prior to the survey. This listing provided the necessary frame for selecting households at the second sampling stage. The household listing operation consisted of preparing up-to-date notional and layout sketch maps of each selected PSU, assigning numbers to structures, recording addresses (or locations) of these structures, identifying the residential structures, and listing the names of the heads of all the households in the residentiak structures in the selected PSU. Each household listing team consisted of a lister and a mapper. The listing operation was supervised by the senior field staff of the concerned CO and the PRC in each state. Special efforts were made not to miss any household in the selected PSU during the listing operation. In PSUs with fewer than 500 households, a complete household listing was done. In PSUs with 500 or more households, segmentation of the PSU was done on the basis of existing wards in the PSU, and two segments were selected using either systematic sampling or PPS sampling. The household listing in such PSUs was carried out in the selected segments. The households to be interviewed were selected from provided with the original household listing, layout sketch map and the household sample selected for each PSU. All the selected households were approached during the data collection, and no substitution of a household was allowed under any circumstances.

THE RURAL URBAN SAMPLE: THE FRAME, STRATIFICATION AND SELECTION

A three-stage sample design was adopted for the urban areas in each state: selection of cities/towns, followed by urban blocks, and finally households. Cities and towns were selected using the 1991 population figures while urban blocks were selected using the 1991 list of census enumeration blocks in all the states with the exception of the firts phase states. For the first phase states, the list of urban blocks provided by the National Sample Survey Organization (NSSSO) served as the sampling frame.

All cities and towns were subdivided into three strata: (1) self-selecting cities (i.e., cities with a population large enough to be selected with certainty), (2) towns that are district headquaters, and (3) other towns. Within each stratum, the cities/towns were arranged according to the same kind of geographic stratification used in the rural areas. In self-selecting cities, the sample was selected according to a two-stage sample design: selection of the required number of urban blocks, followed by selection of households in each of selected blocks. For district headquarters and other towns, a three stage sample design was used: selection of towns with PPS, followed by selection of two census blocks per selected town, followed by selection of households from each selected block. As in rural areas, a household listing was carried out in the selected blocks, and an average of 20 households per block was selected systematically.

Mode of data collection

Face-to-face

Research instrument

Three types of questionnaires were used in the NFHS: the Household Questionnaire, the Women's Questionnaire, and the Village Questionnaire. The overall content

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.

Kolkata had the highest average water consumption per person across major Indian cities in 2021, at **** liters per day. Bhubaneshwar followed, with an average consumption of *** liters per day. The recommended amount of water intake to stay hydrated is at least two liters every day.

The Occurrence dataset for plant species in Banabitan Biodiversity Park(Central Park), North 24 Parganas, West Bengal, India is published by Nature Mates Nature Club. Banabitan is an urbanized park located in the centre of Salt Lake City, is the second largest park in Kolkata. It is maintained under the Urban Recreation Forestry Division, Directorate of Forest, Government of West Bengal. Presently it encompasses a total area of around 65 acres , of which 46acers are land mass and the remaining 19 acres are a water body located in the centre of the park. Urban ecosystems depend on land usage and other multiple factors. With the increase in urbanization parks, gardens inside the city play a very important role in maintain natural vegetation and sustaining its biodiversity. Large areas covered with trees are a good habitat for avian diversity. For this reason Banabitan (Central Park) which is surrounded by large trees and diverse species of herbs and shrubs along with a water body situated at the centre of the park which is partially covered with Water Hyacinth (Eichhornia crassipes).

This dataset enlists all the plant species in Banabitan. There are 390 plant species that were recorded belonging to 42 orders and 95 families in the span of one year.

We are thankful to the forest department for giving us the opportunity to work at Banabitan since long. The respected CF, and DFO sirs of Urban Recreation Forestry Division are specially mentioned here for their unconditional support and guidance.

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.

Location Intelligence Analytics Market Outlook

The global location intelligence analytics market size is projected to grow from USD 14.2 billion in 2023 to USD 31.7 billion by 2032, exhibiting a CAGR of approximately 9.4% during the forecast period. This robust growth is primarily driven by the increasing demand for spatial data and analytical tools across various industries to enhance decision-making processes and optimize business operations. As organizations increasingly recognize the value of location-based insights, they are investing in sophisticated analytics solutions that leverage geographic data to drive business outcomes and gain competitive advantages.

One of the primary growth factors for the location intelligence analytics market is the proliferation of IoT devices and the consequent surge in location-based data generation. With billions of connected devices expected to be operational in the coming years, the volume of location-specific data is set to explode. Businesses across industries are eager to harness this data to gain insights into consumer behavior, improve operational efficiency, and develop targeted marketing strategies. Moreover, advancements in AI and machine learning are enabling more sophisticated analysis of location data, providing deeper insights and predictive capabilities that are invaluable to enterprises.

Another significant driver for market growth is the growing adoption of smart city initiatives across the globe. Governments and municipalities are increasingly implementing location intelligence solutions to enhance urban planning, traffic management, and public safety. By leveraging location-based analytics, cities can optimize resource allocation, improve citizen services, and drive sustainable development. Furthermore, the integration of real-time data from various sources, such as sensors and social media, with geographic information systems (GIS) is facilitating more dynamic and responsive urban management systems, thus propelling the demand for location intelligence analytics.

The increasing emphasis on business intelligence and data-driven decision-making is also fueling the demand for location intelligence analytics. In today's competitive landscape, organizations are seeking to leverage every bit of data to gain actionable insights and stay ahead. Location intelligence provides a unique perspective by overlaying geographic data on traditional business data, offering a holistic view of trends and patterns. This capability is particularly valuable in sectors such as retail, transportation, and logistics, where location-based insights can directly impact revenue generation, cost savings, and customer satisfaction.

Regionally, North America is expected to hold the largest share of the location intelligence analytics market, driven by the presence of major technology companies and the rapid adoption of advanced analytics solutions across industries. The region's commitment to innovation and technological advancement is further supported by substantial investments in R&D activities. Additionally, Europe is anticipated to witness significant growth, influenced by stringent regulatory frameworks and a heightened focus on data privacy and security. In contrast, the Asia Pacific region is projected to demonstrate the highest growth rate, attributed to the rapid digital transformation and increasing investments in smart city projects across emerging economies like India and China.

Component Analysis

The location intelligence analytics market is broadly segmented into software and services. Software solutions are a critical component of this market, offering the necessary tools and platforms for collecting, analyzing, and visualizing geographic data. These software solutions are designed to process large volumes of spatial data, integrate various data sources, and provide users with intuitive and interactive interfaces for data exploration. The advancements in cloud computing and the increasing adoption of Software as a Service (SaaS) models are further driving the demand for location intelligence software, as they offer greater scalability, flexibility, and cost-effectiveness to organizations of all sizes.

Within the software segment, Geographic Information System (GIS) solutions are particularly prominent. GIS technology enables the mapping and analysis of spatial data, allowing users to visualize relationships, patterns, and trends in complex datasets. The ability to integrate GIS with other enterprise systems, such as Customer Relationship Management (CRM) and Enterprise Resource Planning (ERP), enhances its ut