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.

Title: Population Data of Indian Cities (2011 and 2001)

Description: This dataset contains population information for various cities in India, categorized by rank, city name, and population figures for the years 2011 and 2001. Additionally, it includes the corresponding state or union territory to which each city belongs. The dataset provides insights into population changes over a decade in different cities across India.

Columns:

Rank: This column represents the rank of each city based on its population in the year 2011. Cities are typically ranked in descending order of population, with the most populous city having the rank 1.

City: This column contains the names of the cities for which population data is recorded.

Population (2011): This column displays the population count of each city as of the year 2011. The population figures are likely to be recorded in thousands or millions

Population (2001): This column provides the population count of each city as of the year 2001. Comparing this data with the 2011 population figures allows for an analysis of population growth or decline over the decade.

State or Union Territory: This column indicates the administrative division to which each city belongs. In India, cities are typically grouped into states or union territories, and this column helps identify the geographical context of each city.

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.

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.

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.

I have compiled this dataset from different sources, listing the sources below -

1) For States and UT's - https://igod.gov.in/sg/states 2) For Cities - https://simplemaps.com/data/in-cities, https://github.com/thatisuday/indian-cities-database/blob/master/cities.json 3) For Villages - https://villageinfo.in/, https://github.com/pranshumaheshwari/indian-cities-and-villages/tree/master, https://data.gov.in/catalog/complete-villages-directory-indiastatedistrictsub-district-level-census-2011

All the credit goes to the government census and various people who have complied this data before, I have just brought these 3 things in a place.

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

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

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.

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.

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.

In 2022, the Indian capital city of Delhi had the highest length of roads amongst metropolitan cities, at over ** thousand kilometers. It was followed distantly by Kolkata with just over **** thousand kilometers. The total number of vehicles registered in Delhi at the end of that year was over ***** million.

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

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.

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 "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.

City Profile Census Data
This dataset falls under the category Planning & Policy Planning.
It contains the following data: Information on the population
This dataset was scouted on 2022-02-24 as part of a data sourcing project conducted by TUMI. License information might be outdated: Check original source for current licensing. The data can be accessed using the following URL / API Endpoint: https://smartcities.data.gov.in/resources/city-profile-census-data

The India Smart Cities Market size is valued at USD 8.13 billion in 2023, driven by market opportunities, strategic insights, and top players. Explore market segmentation, growth projection, and key trends.

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/.

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.