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.

This dataset is about cities in India, featuring 7 columns including city, continent, country, latitude, and longitude. The preview is ordered by population (descending).

In 2021, Delhi had the highest metro coverage among major metropolitan cities in India with 12 operational kilometers per one million people. At the same time, Mumbai had the lowest coverage with only 0.5 operational kilometers per one million people. Poor public transportation in many Indian cities was responsible for traffic congestion and air pollution.

As of 2024, Mumbai had a gross domestic product of 368 billion U.S. dollars, the highest among other major cities in India. It was followed by Delhi with a GDP of around 167 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 population in New Delhi was approximately 28.5 million, the most among the leading Indian cities in 2019. Mumbai and Kolkata rounded up the three most populated cities across the country that year.

The dataset comprises eleven series of land use/land cover (LULC) maps, each corresponding to one city in India—Ahmedabad, Belgaum, Hindupur, Hyderabad, Jalna, Kanpur, Parbhani, Pune, Singrauli, Sitapur, or Vijayawada—and containing one map per year for 2015–2018. Every map contains several layers: areal LULC (open space, nonresidential, or residential), roadways, a water mask, and contemporaneous satellite imagery.

The maps were generated by bespoke models created with machine learning. A distinct convolutional neural network (CNN) was trained for each city and LULC type (areal, roadways). Training data were constituted from Sentinel-2 imagery and LULC ground-truth from the Atlas of Urban Expansion project.

LULC information has emerged as a key input to decision-making for a host of actors, from national policymakers to urban planners to disaster relief organizations. Strikingly simple at its core—showing what lies where and when—LULC information has a wide and ever-expanding range of applications.

The demand for commercial real estate space in top seven cities in India stood at 38 million square feet as of 2023. It was the same as previous year.

This horizontal bar chart displays male population (people) by capital city using the aggregation sum and is filtered where the country is India. The data is about countries per year.

In 2022, the Indian capital city of Delhi had the highest length of roads amongst metropolitan cities, at over 33 thousand kilometers. It was followed distantly by Kolkata with just over four thousand kilometers. The total number of vehicles registered in Delhi at the end of that year was over eight million.

All cities with a population > 1000

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In the first half of 2024, the existing stock for warehousing in the Mumbai in India accounted for around 13.3 million square meters. It ranks the top among major Indian cities. The Indian warehousing stock was at 42.9 million square meters during the same period.

As of September 2024, Mumbai had the highest cost of living among other cities in the country, with an index value of 26.5. Gurgaon, a satellite city of Delhi and part of the National Capital Region (NCR) followed it with an index value of 25.1.  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.

The data contains the survey data of 226 low-income migrant wokers in Guwahati and Jalandhar cities in India on more than 60 variables that include: socio-economic background, migratory experience, access to services, ill-treatment and access to justice.

Indian cities attract a considerable number of low-income migrants from marginal rural households experiencing difficult economic, political and social conditions at home who migrate in search of livelihoods and security. These migrants come from around the country as well as across the border from Nepal, Bangladesh and Myanmar to work in low-income manual occupations in a range of small-scale petty trade, service sector work, transport and construction work. Low-income migrants live and work in precarious conditions and are often denied basic amenities and fundamental rights. Poorly-paid intermittent and insecure jobs make them vulnerable to abuse, extortion or bribery. Many such migrants, both internal and international, lack documentation and proof of identity, whether for basic services such as health care and schooling or electoral voting. Their marginal position entails poorer access to health care provisions and other determinants of health than general (non-migrant) populations, thereby enhancing their vulnerability to ill-health, abuse and ill treatment whilst simultaneously compromising their ability to access protection, legal support or redress, and forms of accountability. Language, appearance and cultural differences exposes many low-income migrants from interior parts of the country or across the border to harassment and political exclusion. Moreover, despite their ubiquitous presence, their precarious livelihoods, informality and invisibility keep them unnoticed in urban planning, in the work of civil society organisations and in social science research. In this context, this collaborative project was designed to generate evidence to advance the rights and protection mechanisms that must be planned and provided for low-income urban migrants. We examined what India's urban transformation means for low-income migrants, their inclusion and social justice by exploring: 1. Low-income migrants' views on transformations in Indian cities, and the opportunities and challenges that confront them; 2. Low-income migrants perceptions of their entitlements, claim-making processes and attempts to protect their own health in a context of poor living and working conditions; 3. The prevalence of violence and extent of exclusion experienced by low-income migrants and how they protect themselves from various forms of violence; 4. The legal, developmental, humanitarian and human rights responses to low-income migrants in Indian cities. Fieldwork based in Guwahati (Assam) and Jalandhar (Punjab), two of India's fastest growing cities, aimed to enrich our understanding of access to health care, the social determinants of health, and experiences of violence, inclusion/exclusion and accessing justice, from the vantage point of diverse low-income migrant workers, from within India as well as cross-border. The project focussed on migrants' perceptions and lived experiences and generated evidence to advance the rights and protection mechanisms that must be planned and provided for low-income urban migrants. Low-income migrants are mobile, dispersed and invisible, so they present methodological challenges, especially for creating a sampling frame or mapping in a particular locality. A distinctive strength of the project is its innovative methods for accessing these 'hard-to-reach' groups. The proposed research adopted a mixed methods approach. In order to unravel the nuances and complexities of low-income migrants' experiences and situate these within the broader processes of urban transformation in Jalandhar and Guwahati, we combined ethnographic fieldwork with in-depth interviews, a brief survey, and participatory methods such as photovoice.

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 spatial layout of cities is an important feature of urban form, highlighted by urban planners but overlooked by economists. This paper investigates the causal economic implications of city shape in India. I measure cities’ geometric properties over time using satellite imagery and historical maps. I develop an instrument for urban shape based on geographic obstacles encountered by expanding cities. Compact city shape is associated with faster population growth and households display positive willingness to pay for more compact layouts. Transit accessibility is an important channel. Land use regulations can contribute to deteriorating city shape.

This horizontal bar chart displays urban population (people) by capital city using the aggregation sum and is filtered where the country is India. The data is about countries per year.

High Frequency Indicator: The dataset contains year-, quarter- and city-wise data on the Housing Price Index in Indian and among its various cities such as Ahmedabad, Bangalore, Chennai, Delhi, Jaipur, Kanpur, Kochi, Kolkata, Lucknow, Mumbai, etc.

This dataset is about countries and is filtered where the country includes India, featuring 5 columns: capital city, continent, country, currency, and region. The preview is ordered by population (descending).

This data collection is comprised of interviews with Smart City stakeholders and actors across four Smart Cities in India as well as a set of interviews with national-level actors in Delhi. These interviews took place between September 2018 and October 2019 and are a reflection of the nationally-led Smart City Mission from 2015-2020. The cities represented include Jaipur, Bengaluru, Kochi, Indore, and Delhi.

This research has two primary aims. The first is to develop cutting edge, theoretically informed, insights into the nature of mobility governance reform and the potential to generate more sustainable urban mobility in India. The combined pressures of a growing urban population, increasing urban sprawl, and rapidly rising income, coupled with inadequate public transport, lack of coordinated infrastructure, and increased motorisation have placed huge and unequal burdens on India's urban areas. This has resulted in highly congested roads, poor air quality, high pedestrian casualty rates and poor accessibility and quality of life particularly for the urban poor. In this context, redesigning urban mobility governance has been identified as a critical element of progress in delivering more inclusive and economically, environmentally and socially sustainable cities in India (MoUD, 2006, MoUD, 2015 and NITI Aayog, 2017). Efforts to reform urban transport governance, primarily through the bolstering of local-level capacity, have been underway in India since 2006 but with limited affect due to lack of meaningful delegation of authority and financial power. However, in 2015 the Indian national government launched the Smart Cities Mission, aimed at going beyond what has been achieved before at the local level. The focus of the initiative is to promote 'cities that provide core infrastructure and give a decent quality of life to its citizens' through the application of 'Smart' Solutions (MoUD, 2015, p5). Within this context then, this research uses the Smart Cities Mission as a major opportunity to understand the aims and processes of transport governance reform and the extent to which these reforms are capable of achieving a significant improvement in the mobility system. To this end, the research will undertake a qualitative comparative analysis of previous and planned reforms in four of India's designated smart cities; Jaipur, Kochi, Indore and Bangalore. The research will characterise governance arrangements and governance reforms across each of the four cities, and in using the multi-level governance framework to guide empirical analysis, will be innovative in developing this framework within a non-Western context. The research will also trace the impacts of governance reforms through to impacts on the economic prosperity and quality of life of citizens through analysing changing processes and outcomes. This is essential if we are to move beyond identifying problems to understanding how to overcome them. The second aim of the research is to bring together, develop and inspire a community of researchers and practitioners to advance the study and understanding of mobility governance across India and between the UK and India. The research will be bottom-up in its approach; working with WRI India, the project will engage practitioners in the four cities from the outset to ensure the findings are as meaningful as possible. The interview protocol will be co-created with stakeholders and the data collection informed by the key challenges of urban mobility governance identified by stakeholders through exploratory workshops at the start of the project. A study visit to three UK cities that have experienced different levels of transport governance reform will be held for stakeholders from each of the four 'smart cities' to learn lessons from the UK experience and draw on practitioner expertise. A special session of the World Conference on Transport Research in Mumbai will also be convened to bring practitioners into dialogue with scholars at the forefront of research on transport governance in India and beyond. The project will also convene a 'summer school' in India for researchers to develop their research methods, theoretical perspectives and networks in relation to transport governance and reform. These activities will build both professional and research capacity to address future transport governance challenges.

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.