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.

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.

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.

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.

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

Unleash the potential of location-based analysis with this dataset of Indian cities. With over 4000 entries meticulously curated with city names, states, and precise latitude and longitude coordinates, uncover valuable insights into population distribution, urban development, and more.

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.

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 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.

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

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.

Actual value and historical data chart for India Population In The Largest City Percent Of Urban Population

The "Indian Cities Distance Dataset" is a comprehensive collection of distance data between major cities in India, designed to facilitate pathfinding and optimization tasks.

This connected dataset includes information about the distances (in kilometres) between pairs of cities, allowing users to calculate the shortest paths and optimize routes for various purposes.

Key features of this dataset

City Pairings: The dataset provides connectivity information between pairs of prominent Indian cities, enabling users to calculate the shortest paths and travel distances between any two cities included in the dataset. It is an excellent resource for delving into programming route planning, navigation, and logistics optimization programs.

Distance Data: Each entry in the dataset includes the distance in kilometres between two cities. The distances have been curated to reflect the actual road distances between these locations.

A* Search Algorithm: This dataset is ideal for use with the A* (A-star) search algorithm, a widely used optimization and pathfinding algorithm. The A* algorithm can help find the shortest and most efficient routes between cities, making it suitable for transportation, tourism, and urban planning applications.

Beginner friendly: This dataset contains a minimum number of features for better processing and analyzing of data making it suitable for beginners.

There were over *** thousand registered buses across the Indian city of Bengaluru at the end of fiscal year 2020. The transport sector across the south Asian country was ranked third in 2017 in terms of employment opportunities, after textiles and apparel and food processing.

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.

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 the first half of 2024, the existing stock for warehousing in the Mumbai in India accounted for around **** million square meters. It ranks the top among major Indian cities. The Indian warehousing stock was at **** million square meters during the same period.

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

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

This statistic illustrates the consumption expenditure per capita across the largest cities in India in 2015. The nation capital region, Delhi, had a per capita consumer expenditure of approximately ******* Indian rupees. Bangalore had the highest per capita consumption expenditure during the measured time period.

The global per capita expenditure on apparel in 2015 and 2025, broken down by region, can be found here.