This dataset contains information about six census towns, detailing their administrative district, population, area, population density, and geographical coordinates (longitude and latitude). It serves as a valuable resource for demographic analysis, urban planning, and geospatial visualization.

File Type: Tabular dataset (CSV or Excel format suggested) Columns: City: Name of the census town. Status: Type of the administrative region (e.g., Census Town). District: Administrative district of the town. Population: Total population of the town. Area: Area in square kilometers. Density: Population density (persons per square kilometer). Longitude: Geographic longitude of the town. Latitude: Geographic latitude of the town.

In 2022, the union territory of Delhi had the highest urban population density of over ** thousand persons per square kilometer. While the rural population density was highest in union territory of Puducherry, followed by the state of Bihar.

This dataset provides ward-wise population statistics for Delhi, including total population and Scheduled Caste (SC) population for each ward. It is structured as a clean CSV file with numeric IDs and clearly labeled columns.

Each row represents one municipal ward, with the following columns:

• wardno: Numeric identifier of the ward
• ward: Name of the ward
• total_population: Total population of the ward
• sc_population: Scheduled Caste population in the ward

This dataset can be used for urban analysis, social equity research, civic planning, and joining with geospatial data such as KML ward boundaries.

Synthetic populations for regions of the World (SPW) | Delhi

Dataset information

A synthetic population of a region as provided here, captures the people of the region with selected demographic attributes, their organization into households, their assigned activities for a day, the locations where the activities take place and thus where interactions among population members happen (e.g., spread of epidemics).

License

CC-BY-4.0

Acknowledgment

This project was supported by the National Science Foundation under the NSF RAPID: COVID-19 Response Support: Building Synthetic Multi-scale Networks (PI: Madhav Marathe, Co-PIs: Henning Mortveit, Srinivasan Venkatramanan; Fund Number: OAC-2027541).

Contact information

Henning.Mortveit@virginia.edu

Identifiers

Statistics

Sources

Files description

Base data files (ind_140001944_data_v_0_9.zip)

Derived data files

Validation and measures files

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.

BackgroundIn India, acute respiratory infections (ARIs) are a leading cause of mortality in children under 5 years. Mapping the hotspots of ARIs and the associated risk factors can help understand their association at the district level across India.MethodsData on ARIs in children under 5 years and household variables (unclean fuel, improved sanitation, mean maternal BMI, mean household size, mean number of children, median months of breastfeeding the children, percentage of poor households, diarrhea in children, low birth weight, tobacco use, and immunization status of children) were obtained from the National Family Health Survey-4. Surface and ground-monitored PM2.5 and PM10 datasets were collected from the Global Estimates and National Ambient Air Quality Monitoring Programme. Population density and illiteracy data were extracted from the Census of India. The geographic information system was used for mapping, and ARI hotspots were identified using the Getis-Ord Gi* spatial statistic. The quasi-Poisson regression model was used to estimate the association between ARI and household, children, maternal, environmental, and demographic factors.ResultsAcute respiratory infections hotspots were predominantly seen in the north Indian states/UTs of Uttar Pradesh, Bihar, Delhi, Haryana, Punjab, and Chandigarh, and also in the border districts of Uttarakhand, Himachal Pradesh, and Jammu and Kashmir. There is a substantial overlap among PM2.5, PM10, population density, tobacco smoking, and unclean fuel use with hotspots of ARI. The quasi-Poisson regression analysis showed that PM2.5, illiteracy levels, diarrhea in children, and maternal body mass index were associated with ARI.ConclusionTo decrease ARI in children, urgent interventions are required to reduce the levels of PM2.5 and PM10 (major environmental pollutants) in the hotspot districts. Furthermore, improving sanitation, literacy levels, using clean cooking fuel, and curbing indoor smoking may minimize the risk of ARI in children.

As of 2025, Tokyo-Yokohama in Japan was the largest world urban agglomeration, with 37 million people living there. Delhi ranked second with more than 34 million, with Shanghai in third with more than 30 million inhabitants.

As of 2019, the capital Indian territory of Delhi had the highest density of nurses and midwives of about ** per ten thousand people in the country. However, Bihar had the least density of nurses and midwives in the country of about *** per ten thousand people in the state.

In the financial year 2023, the average number of vehicles per one thousand inhabitants in Delhi, India, was ***. Vehicles to population ratio in Delhi experienced steady and subsequent increases from financial year 2006 until 2021, when it reached a peak of *** vehicles per thousand inhabitants.