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 August of 2025.

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

Content

The data set contains geolocations of all the cities in India with a population of more than 1000.

There are total 10 columns in the dataset. Geoname - Unique Geo-ID for the city Name - Name of the city ACSII Name - ASCII name of the city for interpretability Alternate Names - Alternate names for the city Latitude - Latitude of the city Longitude - Longitude of the city Population - Population of the city Digital Elevation Model - Digital elevation of the city Country - Country of the city Coordinates - Coordinates of the city

Acknowledgements

The data set is contributed by opendatasoft Data Network

All Urban Areas' Energy Use Data Across 640 Indian Districts (AllUrE-India) provides electricity and fossil fuel use in households, industrial manufacturing, transportation, commercial buildings, and agricultural activities across 640 Indian urban districts for the year 2011. A novel top-down and bottom-up method was developed to estimate energy use, in a manner that aligns city-level data with national-level data.Please cite the data as: Tong, K., Nagpure, A. S. & Ramaswami, A. All urban areas’ energy use data across 640 districts in India for the year 2011. Sci Data, http://doi.org/10.1038/s41597-021-00853-7 (2021).

Trade, hotel and restaurant, and other services accounted for almost **** of the employed urban workers in India in 2024. This was followed by the manufacturing sector, with over ** percent of the urban population. The trend was similar in the past two years.

Census: Number of Households: All India: Urban data was reported at 78,865,937.000 Unit in 2011. This records an increase from the previous number of 55,832,570.000 Unit for 2001. Census: Number of Households: All India: Urban data is updated yearly, averaging 67,349,253.500 Unit from Mar 2001 (Median) to 2011, with 2 observations. The data reached an all-time high of 78,865,937.000 Unit in 2011 and a record low of 55,832,570.000 Unit in 2001. Census: Number of Households: All India: Urban data remains active status in CEIC and is reported by Census of India. The data is categorized under India Premium Database’s Demographic – Table IN.GAF002: Census: Number of Households.

Urban India was made up mainly of males and females who were between *************** old in 2020. Children up to 14 years old ranked second in terms of population distribution in the same time period.

Urban population growth (annual %) in India was reported at 2.2645 % in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. India - Urban population growth (annual %) - actual values, historical data, forecasts and projections were sourced from the World Bank on August of 2025.

OVERVIEW This dataset contains data from a survey of low income households in four cities across south India. This fileset includes a guidance document on how the data was collected and how to interpret and use the data. The survey data was collected between April-June 2019. A team of 11 survey enumerators and researchers were involved in the data collection which was collected through a collaboration between the University of Cambridge and the Indian Institute for Human Settlements. Data collection for this project received ethical approval from both the Department of Engineering, University of Cambridge and Indian Institute for Human Settlements. This anonymised dataset is being released to allow full use by others.

DATASET CONTENTS This dataset contains the following files: - Indian_Low_Income_Household_Energy_Survey_Codebook.pdf - south_indian_household_energy_survey_19.csv - south_indian_household_energy_survey_19.Rda - README.txt Data contained in the csv files is the same as data contained in the Rda file.

HOW TO USE All csv files can be opened using any appropriate software. Rdata script files must be opened and run using R. We recommend using RStudio and R version 3.5.1 (“Feather Spray”) or later.

This survey followed the same methodology and as an earlier survey of low-income households in Bangalore, India. The dataset from this earlier survey can be found at: https://doi.org/10.17863/CAM.59870

This dataset was used as external validation dataset for a microsimulation of cooking fuel use in India cities. Code for the microsimulation model can be found in the following GitHub repository: github.com/anetobradley/urban_energy_microsimulation_india

India Census: Population: Urban: Female data was reported at 181,616,925.000 Person in 2011. This records an increase from the previous number of 135,565,591.000 Person for 2001. India Census: Population: Urban: Female data is updated yearly, averaging 158,591,258.000 Person from Mar 2001 (Median) to 2011, with 2 observations. The data reached an all-time high of 181,616,925.000 Person in 2011 and a record low of 135,565,591.000 Person in 2001. India Census: Population: Urban: Female data remains active status in CEIC and is reported by Census of India. The data is categorized under India Premium Database’s Demographic – Table IN.GAB001: Census: Population.

Total Fertility Rate: India: Urban data was reported at 1.600 NA in 2020. This records a decrease from the previous number of 1.700 NA for 2019. Total Fertility Rate: India: Urban data is updated yearly, averaging 1.800 NA from Dec 2005 (Median) to 2020, with 16 observations. The data reached an all-time high of 2.100 NA in 2005 and a record low of 1.600 NA in 2020. Total Fertility Rate: India: Urban data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAH006: Vital Statistics: Total Fertility Rate.

As of 2020, according to a survey, a majority of India's internet users were male and in rural areas. Around ** percent of rural internet users were male while only ** percent of rural users were female that year. In comparison, there were more female internet users in urban areas with a share of ** percent and ** percent were urban male users.

In 2020, the northern state of Uttar Pradesh had the highest urban birth rate of 22.1 births per 1,000 inhabitants. It was followed by states of Bihar and Rajasthan. Among other states, Himachal Pradesh had the lowest birth rate in the urban areas that year.

Data file will be uploaded soon.

The dataset contains year-, month-, state- and gender-wise compiled data on ratio of urban to total population of India from the year 2011 to 2036. The figures of population given for different years are the projected figures, except for the census year of 2011.

People practicing open defecation, urban (% of urban population) in India was reported at 0.55469 % in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. India - People practicing open defecation, urban - actual values, historical data, forecasts and projections were sourced from the World Bank on August of 2025.

In this paper, we are presenting databases and results for 20 Indian cities. Due to space constraints and to avoid repetition, several figures and tables are not included in the main text. The Supplementary contains the following (a) for 20 cities - selected urban airsheds as google KML files showing 0.01º grids (b) for 20 cities - urban-rural built area maps, emission projections for PM2.5 between 2015 and 2030, gridded PM2.5 emission maps, modeled PM2.5 concentration maps, and modeled monthly variation of PM2.5 concentrations for the selected airshed (c) for 20 cities – the WRF modeled wind speed, wind direction, temperature, precipitation and mixing height for the airshed as annual summary figures, summary table showing monthly variation, and database of hourly values (d) for all India – list of number of operational ambient monitoring stations and recommendation number of monitoring stations by state (37) and by district (640) (e) for all India – daily average ambient monitoring data for the period of 2011-2015 from all the stations operational under the national ambient monitoring programme (f) for all India – summary of satellite data + global model based surface PM2.5 estimates for the period of 1998 and 2016 by state (37) and by district (640) and (g) for 20 cities – summary of WRF-CAMx modeled PM2.5 source apportionment.

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 dataset is about cities in India. It has 2,995 rows. It features 7 columns including country, population, latitude, and longitude.

AllUrE-India provides data on urban areas' electricity and fossil fuel use in households, industrial sectors, transportation, and commercial buildings and agricultural activities across 640 Indian districts for the year 2011, in a manner that aligns city-level data with national-level data. A novel top-down and bottom-up method was developed to estimate energy use.