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.

Dataset Card for Dataset Name

Includes Images for different Indian Cities.

  Dataset Details

Each city has 2500 images

  Dataset Description

This dataset contains 2500 images per Cities of popular indian Cities, City included are Ahmendabad, Mumbai, Delhi, Koklakta and A state Kerala.

Curated by: Divax Shah and Team

  Dataset Sources

Google

Demo: here

arXiv : https://arxiv.org/abs/2403.10912

As of September 2024, Pune was the leading Indian city in local purchasing power among other Indian cities, with an index score of over ***. It was followed by Gurgaon and Hyderabad. The local purchasing power index depicts the relative purchasing power of goods and services in a city for the average net salary in that city.

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 June of 2025.

As of September 2024, 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.

In India, the share of the population that earned at least the equivalent of the highest 10 percent of global income earners as of 2022 in purchasing power parity (PPP) terms was *** percent. Hyderabad topped the list with the highest share of the upper or high-class category consumers, at over ** percent. Cities from south India topped the list with the first four ranks, followed by the national capital, Delhi.

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.

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.

Chart and table of population level and growth rate for the Mumbai, India metro area from 1950 to 2025.

Urban population (% of total population) in India was reported at 36.87 % in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. India - Urban population (% of total) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

The metropolitan city of Mumbai had the highest number of internet users across India in 2019 with over 11.7 million users. This was followed by the capital city of Delhi with 11.2 million internet users. However, the national capital territory of Delhi had the highest internet penetration rate that year at 69 percent.

Chart and table of population level and growth rate for the Bangalore, India metro area from 1950 to 2025.

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

Intracity Fare Estimation Chennai ( formely known as Madras ) , located on the Coromandel Coast off the Bay of Bengal . People from all over the world come to the marvelous here to spend their holidays, enjoy the natural splendor and to collect unforgettable memories.

Recently, Devesh Sethia went to Chennai to met his friend Kartikay Singh , who is a reputed minister of Ministry of Transport and Highways , India . While he was there , many times he was being cheated by drivers . He asked his friend Kartikay to look into the matter and suggested him to declare a fare rate for different modes of transports in a city. He liked his idea . But analyzing these fares was difficult task for him. For this , he called one of his college friend , Mohammad Ausaf Jafri who has deep knowledge in Machine Learning and provided him dataset containing 20k samples of data for intracity fares in different cities of India .

He got a month period to come up with the best possible solution . But these days , he is so busy in his office works . He is not getting much time to spend on analyzing the data-set .So , he need help from others . Help him to solve the problem .

We are providing a training dataset( of 20k samples ) describing trips/samples for past 2 years ( from 01/01/2015 to 31/12/2016 ) for different modes of transport in different cities of India . Each data sample corresponds to one complete trip . it contains 11 features as follows 👎

Data Dictionary Here's a brief version of what you'll find in the data description file.

Variable Description ID It is a unique identifier for different samples TIMESTAMP( Datetime ) It is trip start time . It’s format is like (Year) - (Month) - (Day) (Hours) : ( Minutes ) : (Seconds ). STARTING_LATITUDE( Float ) It is trip start time position’s latitude in degree North STARTING_LONGITUDE( Float ) It is trip start time position’s longitude in degree East DESTINATION_LATITUDE( Float ) It is trip stop time position’s latitude in degree North. DESTINATION_LONGITUDE( Float ) It is trip stop time position’s longitude in degree East. VEHICLE_TYPE( String ) It tells different transport vehicle type used for the trip TOTAL_LUGGAGE_WEIGHT( Float ) It is total luggage carried by the passenger in kilograms WAIT_TIME( Float ) It is the time for which driver waited for the passenger before start of the trip in minutes TRAFFIC_STUCK_TIME( Integer ) It is the time for which vehicle waited in traffic in minutes DISTANCE( Integer ) It is total distance covered in a trip in kilometres FARE Trip Cost

The Indian Out-of-Home (OOH) and Digital Out-of-Home (DOOH) advertising market presents a compelling growth opportunity. With a 2025 market size of ₹471.5 million and a projected Compound Annual Growth Rate (CAGR) of 6.21% from 2025 to 2033, the market is poised for significant expansion. Several factors drive this growth, including increasing urbanization, rising disposable incomes leading to higher consumer spending, and the burgeoning adoption of digital technologies. The increasing popularity of programmatic OOH advertising, enabling targeted and data-driven campaigns, further fuels this expansion. Moreover, the diversification of OOH formats beyond traditional billboards, encompassing LED screens, transit advertising (airports, buses, etc.), street furniture, and place-based media in malls and other venues, caters to a wider range of advertising needs and expands the market's reach. While challenges like regulatory hurdles and competition from digital channels exist, the strategic integration of DOOH with data analytics and the increasing focus on creative and impactful ad campaigns are expected to mitigate these challenges. The Automotive, Retail & Consumer Goods, and BFSI sectors are key end-user industries driving demand. The segment-wise breakdown reveals a dynamic market landscape. While traditional static OOH holds a substantial share, the rapid growth of DOOH, particularly programmatic OOH and LED screen advertising, is reshaping the industry. Transportation advertising, including airports and public transit, is a significant segment, offering high visibility and captive audiences. The success of leading players like JCDecaux SE, Laqshya Media Group, and Times OOH indicates a competitive but thriving market. Future growth will likely be fueled by innovations in ad technology, enhanced data-driven targeting capabilities, and a broader acceptance of OOH as a measurable and effective advertising medium. The market's expansion will also be influenced by government infrastructure development initiatives and the rising adoption of smart city projects. The forecast period of 2025-2033 offers substantial growth prospects for businesses operating in this sector. Recent developments include: February 2024: The Dadasaheb Phalke International Film Festival Awards 2024, India's premier film accolade, was scheduled for February 20th, 2024, in Mumbai, Maharashtra. Organizers revealed Adonmo as the sole DOOH partner, marking a collaboration between cinematic excellence and innovative outdoor advertising., February 2024: Aditya Birla Finance enhanced its brand recognition and attracted potential customers through a comprehensive out-of-home campaign in partnership with Platinum Outdoor, a division of Madison World. This well-coordinated media mix allowed the campaign to engage audiences in 12 different cities.. Key drivers for this market are: Ongoing Shift Toward Digital Advertising, Increasing Use of Recommendation Engines. Potential restraints include: Ongoing Shift Toward Digital Advertising, Increasing Use of Recommendation Engines. Notable trends are: Ongoing Shift Toward Digital Advertising Expected to Boost Market Growth.

In 2024, the average monthly salary was **** thousand Indian rupees in Mumbai city of India. The average monthly salary in the capital city of Delhi was around **** thousand Indian rupees. In comparison, the average monthly salary was over ** thousand Indian rupees in Madurai during the same year.

India Real Estate Industry Report is Segmented by Property Type (Residential, Office, Retail, Hospitality, and Industrial) and Key Cities (Mumbai Metropolitan Region (MMR), Delhi NCR, Pune, Chennai, Hyderabad, Bengaluru and Rest of India). The Report Offers the Market Size and Forecasts in Value (USD) for all the Above Segments.

This statistic depicts the age distribution of India from 2013 to 2023. In 2023, about 25.06 percent of the Indian population fell into the 0-14 year category, 68.02 percent into the 15-64 age group and 6.92 percent were over 65 years of age. Age distribution in India India is one of the largest countries in the world and its population is constantly increasing. India’s society is categorized into a hierarchically organized caste system, encompassing certain rights and values for each caste. Indians are born into a caste, and those belonging to a lower echelon often face discrimination and hardship. The median age (which means that one half of the population is younger and the other one is older) of India’s population has been increasing constantly after a slump in the 1970s, and is expected to increase further over the next few years. However, in international comparison, it is fairly low; in other countries the average inhabitant is about 20 years older. But India seems to be on the rise, not only is it a member of the BRIC states – an association of emerging economies, the other members being Brazil, Russia and China –, life expectancy of Indians has also increased significantly over the past decade, which is an indicator of access to better health care and nutrition. Gender equality is still non-existant in India, even though most Indians believe that the quality of life is about equal for men and women in their country. India is patriarchal and women still often face forced marriages, domestic violence, dowry killings or rape. As of late, India has come to be considered one of the least safe places for women worldwide. Additionally, infanticide and selective abortion of female fetuses attribute to the inequality of women in India. It is believed that this has led to the fact that the vast majority of Indian children aged 0 to 6 years are male.

Byrnihat was the most polluted city in India in 2024, with an average PM2.5 concentration of nearly 130 micrograms per cubic meter of air (μg/m³). This high level of pollution made the small industrial town on the Assam Meghalaya border the most polluted cities worldwide in 2024. Poor air quality across India India was the fifth-most polluted country in the world in 2024, with an average PM2.5 concentration of 50.6 μg/m³. That same year, the country's capital New Delhi was also the most polluted capital city in the world. Vehicle exhaust and wood burning are some of the main sources of particulate air pollution in India, together with soil, road and construction dust . Impacts of air pollution in India The severe air pollution in India can have detrimental health impacts on the country's population. Fine particle pollutants penetrate deeply in the lungs, causing respiratory problems and can even result in premature death. More than two million deaths are attibuted to air pollution in India every year.

As of 2025, Tokyo-Yokohama in Japan was the largest world urban agglomeration, with 37 million people living there. Delhi ranked second with more than 34 million, with Shanghai in third with more than 30 million inhabitants.