Population density per pixel at 100 metre resolution. WorldPop provides estimates of numbers of people residing in each 100x100m grid cell for every low and middle income country. Through ingegrating cencus, survey, satellite and GIS datasets in a flexible machine-learning framework, high resolution maps of population counts and densities for 2000-2020 are produced, along with accompanying metadata.

DATASET: Alpha version 2010 and 2015 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and remaining unadjusted.

REGION: Africa

SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator)

PROJECTION: Geographic, WGS84

UNITS: Estimated persons per grid square

MAPPING APPROACH: Land cover based, as described in: Linard, C., Gilbert, M., Snow, R.W., Noor, A.M. and Tatem, A.J., 2012, Population distribution, settlement patterns and accessibility across Africa in 2010, PLoS ONE, 7(2): e31743.

FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org)

FILENAMES: Example - AGO10adjv4.tif = Angola (AGO) population count map for 2010 (10) adjusted to match UN national estimates (adj), version 4 (v4). Population maps are updated to new versions when improved census or other input data become available.

India data available from WorldPop here.

Historical dataset showing India population density by year from 1961 to 2022.

India population density for 1975 - 2020 for 400m H3 hexagons.

Fixed up fusion of GHSL and OpenStreetMap data.

Visit India: Population Density for 400m H3 Hexagons for up-to-date data.

WorldPop produces different types of gridded population count datasets, depending on the methods used and end application. Please make sure you have read our Mapping Populations overview page before choosing and downloading a dataset.

Datasets are available to download in Geotiff and ASCII XYZ format at a resolution of 30 arc-seconds (approximately 1km at the equator)

-Unconstrained individual countries 2000-2020: Population density datasets for all countries of the World for each year 2000-2020 – derived from the corresponding Unconstrained individual countries 2000-2020 population count datasets by dividing the number of people in each pixel by the pixel surface area. These are produced using the unconstrained top-down modelling method.
-Unconstrained individual countries 2000-2020 UN adjusted: Population density datasets for all countries of the World for each year 2000-2020 – derived from the corresponding Unconstrained individual countries 2000-2020 population UN adjusted count datasets by dividing the number of people in each pixel, adjusted to match the country total from the official United Nations population estimates (UN 2019), by the pixel surface area. These are produced using the unconstrained top-down modelling method.

Data for earlier dates is available directly from WorldPop.

WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00674

Population density (people per sq. km of land area) in India was reported at 484 sq. Km in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. India - Population density (people per sq. km) - actual values, historical data, forecasts and projections were sourced from the World Bank on October of 2025.

India IN: Population Density: Inhabitants per sq km data was reported at 476.650 Person in 2022. This records an increase from the previous number of 473.420 Person for 2021. India IN: Population Density: Inhabitants per sq km data is updated yearly, averaging 394.320 Person from Dec 1990 (Median) to 2022, with 33 observations. The data reached an all-time high of 476.650 Person in 2022 and a record low of 292.770 Person in 1990. India IN: Population Density: Inhabitants per sq km data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s India – Table IN.OECD.GGI: Social: Demography: Non OECD Member: Annual.

Census data reveals that population density varies noticeably from area to area. Small area census data do a better job depicting where the crowded neighborhoods are. In this map, the yellow areas of highest density range from 30,000 to 150,000 persons per square kilometer. In those areas, if the people were spread out evenly across the area, there would be just 4 to 9 meters between them. Very high density areas exceed 7,000 persons per square kilometer. High density areas exceed 5,200 persons per square kilometer. The last categories break at 3,330 persons per square kilometer, and 1,500 persons per square kilometer.This dataset is comprised of multiple sources. All of the demographic data are from Michael Bauer Research with the exception of the following countries:Australia: Esri Australia and MapData ServicesCanada: Esri Canada and EnvironicsFrance: Esri FranceGermany: Esri Germany and NexigaIndia: Esri India and IndicusJapan: Esri JapanSouth Korea: Esri Korea and OPENmateSpain: Esri España and AISUnited States: Esri Demographics

Rural population (% of total population) in India was reported at 63.13 % in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. India - Rural population - actual values, historical data, forecasts and projections were sourced from the World Bank on October of 2025.

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 contains estimates of the number of persons per square kilometer consistent with national censuses and population registers. There is one image for each modeled year. General Documentation The Gridded Population of World Version 4 (GPWv4), Revision 11 models the distribution of global human population for the years 2000, 2005, 2010, 2015, and 2020 on 30 arc-second (approximately 1 km) grid cells. Population is distributed to cells using proportional allocation of population from census and administrative units. Population input data are collected at the most detailed spatial resolution available from the results of the 2010 round of censuses, which occurred between 2005 and 2014. The input data are extrapolated to produce population estimates for each modeled year.

Comprehensive socio-economic dataset for India including population demographics, economic indicators, geographic data, and social statistics. This dataset covers key metrics such as GDP, population density, area, capital city, and regional classifications.

The spreadsheet consists of multiple attributes of case study cities.

Population density in the Malaprabha (K4) sub-basin area. The dataset, calculated as number of people per hectare, is derived from the WorldPop datasets (https://www.worldpop.org/)

The dataset contains Year and State wise Density of Population

Note: 1. The 1981 Census could not be held in Assam. Total Population for 1981 has been worked out by Interpolation. 2. Includes estimated population of Paomata, Mao Maram and Purul sub-divisions of Senapati District of Manipur for 2001. 3. For working out the density of India and Jammu & Kashmir for 1991,2001, the entire area and population of those portions of Jammu & Kashmir which are under illegal occupation of Pakistan and China have not been taken into account.

Data on the population of India from 1955 to 2020.

COLUMN DESCRIPTION

• 'year': year
• 'population': population of India
• 'yearly_perc _change': yearly percentage change of the population
• 'yearly_change': yearly change in the population
• 'migrants': number of migrants in the country (net)
• 'fertility_rate': fertility rate of the country
• 'density': density of the population
• 'urban_pop _perc': urban population percentage of the population
• 'urban_pop': urban population in India
• 'share_of_ world _pop': share of the world's population
• 'world_pop': world's population
• 'rank': rank of India's population in the world.

Photo by Naveed Ahmed on Unsplash

Population density in the Ghataprabha (K3) sub-basin area. The dataset, calculated as number of people per hectare, is derived from the WorldPop datasets (https://www.worldpop.org/)

The population density of the northern state of Uttar Pradesh in India recorded 829 people for every square kilometer in 2011, the latest available census. This was a doubling compared to the value in 1981.

Population density in the Middle Krishna (K2) sub-basin area. The dataset, calculated as number of people per hectare, is derived from the WorldPop datasets (https://www.worldpop.org/)

Density of nursing and midwifery personnel of India increased by 1.23% from 1.7 number per thousand population in 2018 to 1.7 number per thousand population in 2020. Since the 209.04% surge in 2000, density of nursing and midwifery personnel shot up by 48.71% in 2020.

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.