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.

Delhi was the largest city in terms of number of inhabitants in India in 2025. The capital city was estimated to house nearly 35 million people, with Mumbai ranking second that year. India's population estimate was 1.4 billion, ahead of China that same year.

Actual value and historical data chart for India Population In Largest City

Graph and download economic data for Geographical Outreach: Number of Automated Teller Machines (ATMs) in 3 Largest Cities for India (INDFCACLNUM) from 2007 to 2015 about ATM, India, banks, and depository institutions.

Content

It contains latitudes and longitudes ,population of major cities of India.

Acknowledgements

https://simplemaps.com/data/in-cities

Inspiration

I wanted to create interactive maps for one of my project so i created this dataset.

Image Cover

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1842206%2Fcc0eb2fff946bf6e89ce8a5a1fcf2b57%2Fnewdelhi2.png?generation=1730130146299892&alt=media" alt="">

Context

Hourly and Daily Weather Dataset of Top 50 Most populous Indian cities. Weather data from https://open-meteo.com/ from January 01, 2020 to October 27, 2024.

Field documentation go here

• https://open-meteo.com/en/docs/historical-weather-api#start_date=2020-01-01&end_date=2024-10-27
• Datetime is in GMT

Visually verify coordinates

• Replace the placeholders with the latitude and longitude values of the cities https://www.latlong.net/c/?lat=(latitude)&long=(longitude)
• Another way to visualize the coordinates https://www.ncei.noaa.gov/maps/radar/

Citations and Acknowledgements

• Zippenfenig, P. (2023). Open-Meteo.com Weather API [Computer software]. Zenodo. https://doi.org/10.5281/ZENODO.7970649
• Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N. (2023). ERA5 hourly data on single levels from 1940 to present [Data set]. ECMWF. https://doi.org/10.24381/cds.adbb2d47
• Muñoz Sabater, J. (2019). ERA5-Land hourly data from 2001 to present [Data set]. ECMWF. https://doi.org/10.24381/CDS.E2161BAC
• Schimanke S., Ridal M., Le Moigne P., Berggren L., Undén P., Randriamampianina R., Andrea U., Bazile E., Bertelsen A., Brousseau P., Dahlgren P., Edvinsson L., El Said A., Glinton M., Hopsch S., Isaksson L., Mladek R., Olsson E., Verrelle A., Wang Z.Q. (2021). CERRA sub-daily regional reanalysis data for Europe on single levels from 1984 to present [Data set]. ECMWF. https://doi.org/10.24381/CDS.622A565A

India's Largest Cities Weather Data Weather Datasets

• 🇮🇳 India's Largest Cities Weather Data 1990-1999
• 🇮🇳 India's Largest Cities Weather Data 2000-2009
• 🇮🇳 India's Largest Cities Weather Data 2010-2019
• 🇮🇳 India's Largest Cities Weather Data 2020-YTD

Note

Image generated with Bing Image Generator

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.

Dataset for Indian cities, including images, unsplash, Airbnb, and Wikipedia content about each location, along with geospatial data such as latitude, longitude and Google Maps URL

The purpose of the dataset is to enable devs accross the world and mainly in india to build apps and websites The data is scraped locally using honojs and cleaned using LLM

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

The demand for commercial real estate space in top seven cities in India stood at ** million square feet as of 2023. It was the same as previous year.

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/.

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/.

The below dataset shows the top 800 biggest cities in the world and their populations in the year 2024. It also tells us which country and continent each city is in, and their rank based on population size. Here are the top ten cities:

• Tokyo, Japan - in Asia, with 37,115,035 people.
• Delhi, India - in Asia, with 33,807,403 people.
• Shanghai, China - in Asia, with 29,867,918 people.
• Dhaka, Bangladesh - in Asia, with 23,935,652 people.
• Sao Paulo, Brazil - in South America, with 22,806,704 people.
• Cairo, Egypt - in Africa, with 22,623,874 people.
• Mexico City, Mexico - in North America, with 22,505,315 people.
• Beijing, China - in Asia, with 22,189,082 people.
• Mumbai, India - in Asia, with 21,673,149 people.
• Osaka, Japan - in Asia, with 18,967,459 people.

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.

Passenger Trips to Major Cities (India Air Travel)

Investigating Passenger Numbers to and from Indian Cities

By Telangana Open Data [source]

About this dataset

This dataset provides comprehensive insights into the air traveling activity in the year 2017 for Hyderabad, India. It displays a list of domestic air travelers to and from this city to all other cities in India. You can access valuable specifics like the number of passengers recorded on each journey until October 2017. This useful collection of data from data.telangana.gov.in provides an essential glimpse into trends and patterns amongst Hyderabad's domestic air traffic, helping city planners and business make more informed decisions!

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How to use the dataset

How to Use 2017 Hyderabad Domestic Air Traffic Data

This dataset provides information about the number of air travelers that arrived in or left from Hyderabad, India in 2017. The data covers all major cities in India until October, giving users a chance to analyze and compare domestic air traffic between cities. This guide will provide an overview on how to use this data set effectively.

Exploring the Dataset

The dataset contains two columns: ‘level_0’ which is the index of the dataframe and ‘M passengers’ which is the number of passengers listed for each airport. It is important to remember that the numbers correspond to they year 2017 only and not current passenger rates. Exploring this data will allow users understand trends in travel patterns across different cities throughout India over a period of time.

Analyzing Trends with Maps

Using mapping technologies such as CartoDB will allow users build dynamic visualizations and gain a better understanding on temporal changes that occur within Indian domestic air travel since start of 2017 up until October 2017. Comparing these maps with socio-economic metrics will also allow deeper analysis on population demographics across India’s top flight routes; useful information when creating marketing plans or proposals related aviation expansion projects etc...

### Additional Analysis Tools Besides mapping tools such as CartoDB; other tools like R can be used to run various statistical models related estimating future traffic volumes based on present passenger patterns, creating correlation networks between selected cities compared side by side against socio-economic trends etc.. Finally SPSS can be used run qualitative analysis those interested in analyzing more subjective avaiation industry related studies such as airliners customer services ratings by destinations city or feedback surveys pre post domestic flights taken throughout certain regions within India etc.

Research Ideas

• Constructing a detailed visualization of the air transportation patterns from Hyderabad to all other cities in India, offering an increased understanding of both high traffic and low traffic destinations.
• Understanding passenger demand for different travel providers such as AirAsia, Indigo etc in the city and predicting possible growth trends for them.
• Refining marketing strategies for flight-based travel services by establishing their target market within the Hyerabad area and subsequently utilizing data-driven tactics to increase sales

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

Unknown License - Please check the dataset description for more information.

Columns

File: 2017 Hyderabad Domestic Air Traffic.csv | Column name | Description | |:--------------|:------------------------------------------| | level_0 | Unique identifier for each row. (Integer) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Telangana Open Data.

In 2021, Delhi had the highest metro coverage among major metropolitan cities in India with ** operational kilometers per *********** people. At the same time, Mumbai had the lowest coverage with only *** operational kilometers per *********** people. Poor public transportation in many Indian cities was responsible for traffic congestion and air pollution.

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.

The COVID-19 pandemic has affected severely the economic structure and health care system, among others, of India and the rest of the world. The magnitude of its aftermath is exceptionally devastating in India, with the first case reported in January 2020, and the number has risen to ~31.3 million as of July 23, 2021. India imposed a complete lockdown on March 25, which severely impacted migrant population, industrial sector, tourism industry, and overall economic growth. Herein, the impacts of lockdown and unlock phases on ambient atmospheric air quality variables have been assessed across 16 major cities of India covering the north-to-south stretch of the country. In general, all assessed air pollutants showed a substantial decrease in AQI values during the lockdown compared with the reference period (2017–2019) for almost all the reported cities across India. On an average, about 30–50% reduction in AQI has been observed for PM2.5, PM10, and CO, and maximum reduction of 40–60% of NO2 has been observed herein, while the data was average for northern, western, and southern India. SO2 and O3 showed an increase over a few cities as well as a decrease over the other cities. Maximum reduction (49%) in PM2.5 was observed over north India during the lockdown period. Furthermore, the changes in pollution levels showed a significant reduction in the first three phases of lockdown and a steady increase during subsequent phase of lockdown and unlock period. Our results show the substantial effect of lockdown on reduction in atmospheric loading of key anthropogenic pollutants due to less-to-no impact from industrial activities and vehicular emissions, and relatively clean transport of air masses from the upwind region. These results indicate that by adopting cleaner fuel technology and avoiding poor combustion activities across the urban agglomerations in India could bring down ambient levels of air pollution at least by 30%.

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.

India has extensive production and usage of organochlorine pesticides (OCPs) for agriculture and vector control. Despite this, few data are available on the levels and distribution of OCPs in the urban atmosphere of India. Passive and active air sampling was therefore conducted between Dec 2006 and March 2007 in 7 metropolitan cities: New Delhi, Kolkata, Mumbai, Chennai, Bangalore, Goa, and Agra. Concentrations (pg·m−3) were as follows: HCHs 890−17000 (mean: 5400 ± 4110); DDTs 250−6110 (1470 ± 1010); chlordanes 290−5260 (1530 ± 790); endosulfans 240−4650 (1040 ± 610); and hexachlorobenzene 120−2890 (790 ± 510). HCHs observed in India appear to be the highest reported across the globe. Chlordanes and endosulfans are lower than levels reported from southern China. Passive sampling enabled within- and between-city variations to be assessed. As expected, paired-sample t-test analysis revealed higher regional than local variation. Comparisons with the limited data available from studies conducted in 1989 suggest general declines of HCHs and DDTs for most regions. γ-HCH dominated the HCH signal, reflecting widespread use of Lindane in India, although the isomeric composition in Kolkata suggests potential technical HCH use. High o,p′-/p,p′-DDT ratios in northern India indicate recent DDT usage. High HCB levels in the industrial areas of New Delhi and Kolkata indicate ongoing sources. Correlation between trans- and cis-chlordane implies ongoing usage. Endosulfan sulfate generally dominated the endosulfan signal, but high values of α/β-endosulfan at Chennai, Mumbai and Goa suggest ongoing usage. Backward trajectories were computed using the NOAA HYSPLIT model to trace the air mass history. Result shows local/regional sources of OCPs within India.