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.

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

This statistic displays the cities with the best urban planning and design across India in 2017, based on ASICS score. The city of Delhi achieved the highest score that year, with ***, followed by Bhubaneswar with a score of ***.

Population in largest city in India was reported at 33807403 in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. India - Population in largest city - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

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.

In India, the share of the population that earned at least the equivalent of the highest 10 percent of global income earners as of 2022 in purchasing power parity (PPP) terms was *** percent. Hyderabad topped the list with the highest share of the upper or high-class category consumers, at over ** percent. Cities from south India topped the list with the first four ranks, followed by the national capital, Delhi.

The Housing Price Index in India is a statistical measure designed to reflect the changes in housing prices across various regions. It is calculated by the Reserve Bank of India (RBI) using data from housing transactions, which include registration documents and mortgage data from banks and housing finance companies. The HPI is constructed using a base year, and the price levels of that base year are set at 100. Changes in the index from the base year reflect how housing prices have increased or decreased. The Reserve Bank compiles quarterly house price index (HPI) (base: 2010-11=100) for ten major cities, viz., Mumbai, Delhi, Chennai, Kolkata, Bengaluru, Lucknow, Ahmedabad, Jaipur, Kanpur and Kochi. Based on these city indices, the average house price index represents all of India's house price movements. The Housing Price Index (HPI) is a critical economic indicator that measures the changes in residential housing prices over time. In India, the HPI is an essential tool used by policymakers, economists, real estate developers, investors, and homebuyers to gauge the trends in the real estate market. The HPI helps track the inflation or deflation in the housing market, thus providing insights into the economy's overall health.

According to a survey on the best Indian city to work in for IT professionals in 2020, about 45 percent of the respondents chose Bengaluru. It was noted during the survey that Bengaluru offered the best in everything that an IT professional aspired from their career perspective.

This statistic displays the cities with the best governance across India in 2017, based on ASICS scores. South-western Pune had the highest score that year, with ***, followed by the eastern city of Kolkata with a score of ***.

Famous Temples Of India

A list of new and ancient temples in India. This list contains - temple name - temple description - location - location co-ordinates - distance from Mumbai - distance from New Delhi - distance from Chennai - distance from Kolkata

Note: Regarding the distance, I only chose the top 4 metro cities (by population) in India. Please feel free to add any other city of your choice.

About the Dataset: Waste Management and Recycling in India

Overview:

This dataset provides comprehensive information on waste management and recycling practices in various cities across India. It includes key data related to waste generation, recycling rates, population density, municipal efficiency, landfill details, and more. The data spans multiple years (2019–2023) and covers a range of waste types, including plastic, organic waste, electronic waste (e-waste), construction waste, and hazardous waste.

Purpose:

The dataset aims to: - Promote efficient waste management practices across Indian cities. - Analyze trends in recycling and waste disposal methods. - Provide insights for improving municipal management systems. - Support research and development in sustainability, environmental science, and urban planning.

Columns:

1. City/District: The name of the Indian city or district.
2. Waste Type: Type of waste generated, e.g., Plastic, Organic, E-Waste, Construction, Hazardous.
3. Waste Generated (Tons/Day): Amount of waste generated in tons per day.
4. Recycling Rate (%): The percentage of waste that is recycled.
5. Population Density (People/km²): The number of people per square kilometer in the city.
6. Municipal Efficiency Score (1-10): A score indicating how effectively the municipality manages waste (e.g., waste segregation, collection, disposal).
7. Disposal Method: The method used for waste disposal (e.g., Landfill, Recycling, Incineration, Composting).
8. Cost of Waste Management (₹/Ton): The cost of managing one ton of waste in Indian Rupees.
9. Awareness Campaigns Count: The number of awareness campaigns organized by the municipality in that year related to waste management.
10. Landfill Name: The name of the landfill site used by the city.
11. Landfill Location (Lat, Long): The geographical location (latitude and longitude) of the landfill.
12. Landfill Capacity (Tons): The total waste capacity (in tons) that the landfill can hold.
13. Year: The year of the data entry, ranging from 2019 to 2023.

Applications:

• Urban Planning: The dataset can be used to analyze and optimize waste management infrastructure in urban areas.
• Sustainability Research: It can help in studying the progress of recycling and waste reduction strategies.
• Policy Making: Government bodies can use this data to craft policies aimed at improving waste management and recycling rates.
• Machine Learning/AI: The dataset can be used to build models for predicting waste generation trends, recycling outcomes, and municipal efficiency.

Sources:

• The data is simulated for this dataset based on average waste management practices observed in Indian cities.
• Real-world data could come from municipal corporations, environmental agencies, and government reports on waste management.

Overview:

The India AQI dataset provides comprehensive information on air quality across various cities and regions in India. The dataset includes measurements of different air pollutants that contribute to the overall air quality index, enabling researchers, policymakers, and the public to understand and address air quality issues.

Key Components:

Parameters Measured:

PM2.5:

Particulate matter with a diameter of 2.5 micrometers or smaller, which can penetrate the respiratory system.

PM10:

Particulate matter with a diameter of 10 micrometers or smaller.

NO2:

Nitrogen dioxide, primarily produced from vehicle emissions and industrial processes.

SO2:

Sulfur dioxide, which results from burning fossil fuels and industrial processes.

CO:

Carbon monoxide, a colorless, odorless gas produced by burning carbon-based fuels.

O3:

Ozone, which can be beneficial in the upper atmosphere but harmful at ground level.

AQI Calculation:

The dataset may include the calculated AQI values based on the concentrations of the above pollutants, categorized into different levels (e.g., Good, Moderate, Unhealthy, Hazardous). Geographical Coverage:

Information on various states and cities across India, allowing for regional comparisons and analysis. Temporal Coverage:

The dataset may provide historical data over a specific time frame (e.g., daily, weekly, monthly), enabling trend analysis.

Data Sources:

Data collected from government agencies, environmental monitoring stations, and satellite data. Use Cases:

Useful for researchers studying environmental impacts on public health. Helps policymakers in formulating regulations to improve air quality. Provides valuable information for the public to make informed decisions regarding outdoor activities based on air quality levels. Format:

The dataset may be available in formats like CSV, JSON, or Excel, facilitating ease of use in data analysis tools.

Data Accessibility:

Information on how to access the dataset, including links to online repositories or APIs for real-time data retrieval.

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.

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.

Over the past several months, the world has been facing an unprecedented public health crisis in the form of Covid-19. States and union territories in India have been equal partners of the Central Government in managing the Covid-19 outbreak in the country. This document is a compendium of practices from states and union territories that details information about various initiatives implemented by states, districts, and cities in India for containing and managing the Covid-19 outbreak. The practices in the compendium have been dis-aggregated under six sections: (i) public health and clinical response (ii) governance mechanisms (iii) digital health (iv) integrated model (v) welfare of migrants and other vulnerable groups (vi) other practices. A summary of the relevant Government of India guidelines has been included for the aforementioned categories, wherever applicable.

This statistic displays the cities with the best urban capacities and resources across India in 2017, based on ASICS scores. South-western Pune achieved the highest score that year, with ***, followed by the western city of Mumbai with a score of ***.

The actions are categorised as incremental, reformist or transformational but may span these categories depending on depth and scope of implementation. For a detailed list of all solutions mentioned in the urban HAPs, see Table B in S1 Text.

Abstract

The Employment and Unemployment surveys of National sample Survey (NSS) are primary sources of data on various indicators of labour force at National and State levels. These are used for planning, policy formulation, decision support and as input for further statistical exercises by various Government organizations, academicians, researchers and scholars. NSS surveys on employment and un-employment with large sample size of households have been conducted quinquennially from 27th. round(October'1972 - September'1973) onwards. Cotinuing in this series the fourth such all-india survey on the situation of employment and unemployment in India was carried out during the period july 1987 - june 1988 .

The working Group set up for planning of the entire scheme of the survey, among other things, examined also in detail some of the key results generated from the 38th round data and recommended some stream-lining of the 38th round schedule for the use in the 43rd round. Further, it felt no need for changing the engaging the easting conceptual frame work. However, some additional items were recommended to be included in the schedule to obtain the necessary and relevant information for generating results to see the effects on participation rates in view of the ILO suggestions.5.0.1. The NSSO Governing Council approved the recommendations of the working Group and also the schedule of enquiry in its 44th meeting held on 16 January, 1987. In this survey, a nation-wide enquiry was conducted to provide estimates on various characteristics pertaining to employment and unemployment in India and some characteristics associated with them at the national and state levels. Information on various facets of employment and unemployment in India was collected through a schedule of enquiry (schedule 10).

Geographic coverage

The survey covered the whole of Indian Union excepting i) Ladakh and Kargil districts of Jammu & Kashmir ii) Rural areas of Nagaland

Analysis unit

Randomly selected households based on sampling procedure and members of the household

Kind of data

Sample survey data [ssd]

Sampling procedure

It may be mentioned here that in order to net more households of the upper income bracket in the Sample , significant changes have been made in the sample design in this round (compares to the design of the 38th round).

SAMPLE DESIGN AND SAMPLE SIZE The survey had a two-stage stratified design. The first stage units (f.s.u.'s) are villages in the rural sector and urban blocks in the urban sector. The second stage units are households in both the sectors. Sampling frame for f.s.u.'s : The lists of 1981 census villages constituted the sampling frame for rural sector in most districts. But the 1981 census frame could not be used for a few districts because, either the 1981 census was not held there or the list of 1981 census villages could not be obtained or the lists obtained from the census authorities were found to be grossly incomplete. In such cases 1971 census frame were used. In the urban sector , the Urban Frame Survey (U.F.S.) blocks constituted the sampling frame. STRATIFICATION : States were first divided into agro-economic regions which are groups of contiguous districts , similar with respect to population density and crop pattern. In Gujarat, however , some districts have been split for the purpose of region formation In consideration of the location of dry areas and the distribution of the tribal population in the state. The composition of the regions is given in the Appendix. RURAL SECTOR: In the rural sector, within each region, each district with 1981Census rural population less 1.8 million formed a single stratum. Districts with larger population were divided into two or more strata, depending on population, by grouping contiguous tehsils similar, as for as possible, in respect of rural population Density and crop pattern. (In Gujarat, however , in the case of districts extending over more than one region, even if the rural population was less than 1.8 million, the portion of a district falling in each region constituted a separate stratum. Further ,in Assam the old "basic strata" formed on the basis of 1971 census rural population exactly in the above manner, but with cut-off population as 1.5 million have been retained as the strata for rural sampling.) URBAN SECTOR : In the urban sector , strata were formed , again within NSS region , on the basis of the population size class of towns . Each city with population 10 lakhs or more is self-representative , as in the earlier rounds . For the purpose of stratification, in towns with '81 census population 4 lakhs or more , the blocks have been divided into two categories , viz . : One consisting of blocks in areas inhabited by the relatively affluent section of the population and the other consisting of the remaining blocks. The strata within each region were constituted as follows :

Table (1.2) : Composition of urban strata

Stratum population class of town

number

(1) (2)

1 all towns with population less than 50,000 2 -do- 50,000 - 199,999 3 -do- 200,000 - 399,999 4 -do- 400,000 - 999,999 ( affluent area) 5 (other area) 6 a single city with population 1 million and above (affluent area) 7 " (other area) 8 another city with population 1 million and above

9 " (other area)

Note : There is no region with more than one city with population 1 million and above. The stratum number have been retained as above even if in some regions some of the strata are empty. Allocation for first stage units : The total all-India sample size was allocated to the states /U.T.'s proportionate to the strength of central field staff. This was allocated to the rural and urban sectors considering the relative size of the rural and urban population. Now the rural samples were allocated to the rural strata in proportion to rural population. The urban samples were allocated to the urban strata in proportion to urban population with double weight age given to those strata of towns with population 4 lakhs or more which lie in area inhabited by the relatively affluent section. All allocations have been adjusted such that the sample size for stratum was at least a multiple of 4 (preferably multiple of 8) and the total sample size of a region is a multiple of 8 for the rural and urban sectors separately.
Selection of f.s.u.'s : The sample villages have been selected circular systematically with probability proportional to population in the form of two independent interpenetrating sub-samples (IPNS) . The sample blocks have been selected circular systematically with equal probability , also in the form of two IPNS' s. As regards the rural areas of Arunachal Pradesh, the procedure of 'cluster sampling' was:- The field staff will be supplied with a list of the nucleus villages of each cluster and they selected the remaining villages of the cluster according to the procedure described in Section Two. The nucleus villages were selected circular systematically with equal probability, in the form of two IPNS 's. Hamlet-group and sub-blocks : Large villages and blocks were sub- divided into a suitable number of hamlet-groups and sub-blocks respectively having equal population convent and one them was selected at random for surveys. Hamlet-group and sub-blocks : Large villages and blocks were sub- divided into a suitable number of hamlet-groups and sub-blocks respectively having equal population convent and one them was selected at random for surveys. Selection of households : rural : In order to have adequate number of sample households from the affluent section of the society, some new procedures were introduced for selection of sample households, both in the rural and urban sectors. In the rural sector , while listing households, the investigator identified the households in village/ selected hamlet- group which may be considered to be relatively more affluent than the rest. This was done largely on the basis of his own judgment but while exercising his judgment considered factors generally associated with rich people in the localitysuch as : living in large pucca house in well-maintained state, ownership/possession of cultivated/irrigated land in excess of certain norms. ( e.g.20 acres of cultivated land or 10 acres of irrigated land), ownership of motor vehicles and costly consumer durables like T.V. , VCR, VCP AND refrigerator, ownership of large business establishment , etc. Now these "rich" households will form sub-stratum 1. (If the total number of households listed is 80 or more , 10 relatively most affluent households will form sub-stratum 1. If it is below 80, 8 such households will form sub-stratum 1. The remaining households will 'constitute sub-stratum 2. At the time of listing, information relating to each household' s major sources of income will be collected, on the basis of which its means of livelihood will be identified as one of the following : "self-employed in non-agriculture " "rural labour" and "others" (see section Two for definition of these terms) . Also the area of land possessed as on date of survey will be ascertained from all households while listing. Now the households of sub-stratum 2 will be arranged in the order : (1)self-employed in non-agriculture, (2) rural labour, other households, with land possessed (acres) : (3) less than 1.00 (4) 1.00-2.49,(5)2.50-4.99, (6)

As of 2023, Chennai topped the list of Indian cities with the highest social inclusion score (SIS) and industrial inclusion score (IIS) for women standing at ***** and *****, respectively. It was followed by Pune, Bengaluru and Hyderabad. The capital city Delhi did not make it to the top ten list. The SIS reflects social factors that make a city more women-friendly. The IIS, on the other hand, assesses the extent to which organizations in the city cross industries are inclusive for women.