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.

A look-up table with formatting for population-density categoriesUsed here:https://github.com/michaeldgarber/global-ndvi-pop/blob/main/scripts/figures-main-text.Rhttps://github.com/michaeldgarber/global-ndvi-pop/blob/main/scripts/figures-main-text.R

This layer was created by Duncan Smith and based on work by the European Commission JRC and CIESIN. A description from his website follows:--------------------A brilliant new dataset produced by the European Commission JRC and CIESIN Columbia University was recently released- the Global Human Settlement Layer (GHSL). This is the first time that detailed and comprehensive population density and built-up area for the world has been available as open data. As usual, my first thought was to make an interactive map, now online at- http://luminocity3d.org/WorldPopDen/The World Population Density map is exploratory, as the dataset is very rich and new, and I am also testing out new methods for navigating statistics at both national and city scales on this site. There are clearly many applications of this data in understanding urban geographies at different scales, urban development, sustainability and change over time.

Urban and regional planners rely on Average Household Size as a foundational indicator for many of their models, calculations, and plans. Average household size (also known as "people per household") is a reflection of many dynamics at play, for example:Age of the population, as many older people tend to live in smaller households (one-person or two-person households)Housing prices in the area, proximity to colleges and universities, and how likely people are to live with roommatesFamily norms and traditions (e.g., multigenerational families are more common in some areas and with some population groups)This feature layer contains the Average Household Size and Population Density for states, counties, and tracts. Data from U.S. Census Bureau's 2014-2018 American Community Survey's 5-year estimates, Tables B25010 and B01001. Population Density was calculated based on the total population and area of land fields, which both came from the U.S. Census Bureau. See the field description for the formula used.This layer is symbolized to show the average household size. Population density, as well as average household size breakdown by housing tenure is presented in the pop-up. Click the Data tab -> Fields list to see all available attributes and their definitions.

According to the latest Indian census in 2011, every square kilometer in the southern state of Karnataka was inhabited by 319 people, up from 101 in 1951. The highest population density in the state was in Bangalore.

Population density of Peru went up by 0.96% from 25.9 people per sq. km in 2021 to 26.2 people per sq. km in 2022. Since the 0.84% improve in 2012, population density jumped by 13.28% in 2022. Population density is midyear population divided by land area in square kilometers.

Population density of Kazakhstan went up by 1.47% from 7.3 people per sq. km in 2021 to 7.4 people per sq. km in 2022. Since the 1.41% improve in 2012, population density rocketed by 15.77% in 2022. Population density is midyear population divided by land area in square kilometers.

This dataset features three gridded population dadasets of Germany on a 10m grid. The units are people per grid cell.

Datasets

DE_POP_VOLADJ16: This dataset was produced by disaggregating national census counts to 10m grid cells based on a weighted dasymetric mapping approach. A building density, building height and building type dataset were used as underlying covariates, with an adjusted volume for multi-family residential buildings.

DE_POP_TDBP: This dataset is considered a best product, based on a dasymetric mapping approach that disaggregated municipal census counts to 10m grid cells using the same three underyling covariate layers.

DE_POP_BU: This dataset is based on a bottom-up gridded population estimate. A building density, building height and building type layer were used to compute a living floor area dataset in a 10m grid. Using federal statistics on the average living floor are per capita, this bottom-up estimate was created.

Please refer to the related publication for details.

Temporal extent

The building density layer is based on Sentinel-2 time series data from 2018 and Sentinel-1 time series data from 2017 (doi: http://doi.org/10.1594/PANGAEA.920894)

The building height layer is representative for ca. 2015 (doi: 10.5281/zenodo.4066295)

The building types layer is based on Sentinel-2 time series data from 2018 and Sentinel-1 time series data from 2017 (doi: 10.5281/zenodo.4601219)

The underlying census data is from 2018.

Data format

The data come in tiles of 30x30km (see shapefile). The projection is EPSG:3035. The images are compressed GeoTiff files (*.tif). There is a mosaic in GDAL Virtual format (*.vrt), which can readily be opened in most Geographic Information Systems.

Further information

For further information, please see the publication or contact Franz Schug (franz.schug@geo.hu-berlin.de).
A web-visualization of this dataset is available here.

Publication

Schug, F., Frantz, D., van der Linden, S., & Hostert, P. (2021). Gridded population mapping for Germany based on building density, height and type from Earth Observation data using census disaggregation and bottom-up estimates. PLOS ONE. DOI: 10.1371/journal.pone.0249044

Acknowledgements

Census data were provided by the German Federal Statistical Offices.

Funding
This dataset was produced with funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950).

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.

In 2023, the population density in South Korea stood at around *** inhabitants per square kilometer, slightly up from *** in the previous year. The nationwide population density has been increasing steadily over the past decades. The highest density was in Seoul, the capital of South Korea, with ****** people per square kilometer. UrbanizationSouth Korea was primarily an agricultural nation. In the decades following its independence from Japanese rule in 1945, both the dictatorships and democratic governments that governed South Korea focused on industrialization and modernization of the country. The urban population has grown by about **** million over the past 20 years, while the rural population has fallen by around *** million. In 2023, around ** percent of the population lived in an urban area. The most populous city SeoulSeoul’s high population density is not surprising. The capital city is typically grouped with the province of Gyeonggi, which resembles a donut with Seoul at its center, and the metropolitan port city of Incheon, collectively known as the Seoul Capital Area. This is one of the largest metropolitan areas in the world and serves as the political, economic, and cultural center of South Korea. With more than **** millio* residents, half of South Korea’s population lives in this area.

Theory predicts the bottom–up coupling of resource and consumer densities, and epidemiological models make the same prediction for host–parasite interactions. Empirical evidence that spatial variation in local host density drives parasite population density remains scarce, however. We test the coupling of consumer (parasite) and resource (host) populations using data from 310 populations of metazoan parasites infecting invertebrates and fish in New Zealand lakes, spanning a range of transmission modes. Both parasite density (no. parasites per m2) and intensity of infection (no. parasites per infected hosts) were quantified for each parasite population, and related to host density, spatial variability in host density and transmission mode (egg ingestion, contact transmission or trophic transmission). The results show that dense and temporally stable host populations are exploited by denser and more stable parasite populations. For parasites with multi-host cycles, density of the ‘source’ host did not matter: only density of the current host affected parasite density at a given life stage. For contact-transmitted parasites, intensity of infection decreased with increasing host density. Our results support the strong bottom–up coupling of consumer and resource densities, but also suggest that intraspecific competition among parasites may be weaker when hosts are abundant: high host density promotes greater parasite population density, but also reduces the number of conspecific parasites per individual host.

Population density of India went up by 0.79% from 475.7 people per sq. km in 2021 to 479.4 people per sq. km in 2022. Since the 1.38% improve in 2012, population density jumped by 11.48% in 2022. Population density is midyear population divided by land area in square kilometers.

Data Input: Settlement footprint from Facebook's High-Resolution Population Density Maps https://data.humdata.org/dataset/marshall-islands-high-resolution-population-density-maps-demographic-estimates Population allocated proportionally using 2011 census population counts at enumeration area level. Year Population Growth Rate of 0.3% has been applied to update population up to 2020 The human settlement footprint with census population allocated has been converted into a 100 m resolution raster.

Population Raster New Caledonia 2020 Data Input: Settlement footprint from Facebook's High-Resolution Population Density Maps https://data.humdata.org/dataset/new-caledonia-high-resolution-population-density-maps-demographic-estimates Population allocated proportionally using 2011 census population counts at enumeration area (districts de recensement) level. Year Population Growth Rate of 0.2% (0.001957) has been applied to update population up to 2020 The human settlement footprint with census population allocated has been converted into a 100 m resolution raster.

China is a vast and diverse country and population density in different regions varies greatly. In 2023, the estimated population density of the administrative area of Shanghai municipality reached about 3,922 inhabitants per square kilometer, whereas statistically only around three people were living on one square kilometer in Tibet. Population distribution in China China's population is unevenly distributed across the country: while most people are living in the southeastern half of the country, the northwestern half – which includes the provinces and autonomous regions of Tibet, Xinjiang, Qinghai, Gansu, and Inner Mongolia – is only sparsely populated. Even the inhabitants of a single province might be unequally distributed within its borders. This is significantly influenced by the geography of each region, and is especially the case in the Guangdong, Fujian, or Sichuan provinces due to their mountain ranges. The Chinese provinces with the largest absolute population size are Guangdong in the south, Shandong in the east and Henan in Central China. Urbanization and city population Urbanization is one of the main factors which have been reshaping China over the last four decades. However, when comparing the size of cities and urban population density, one has to bear in mind that data often refers to the administrative area of cities or urban units, which might be much larger than the contiguous built-up area of that city. The administrative area of Beijing municipality, for example, includes large rural districts, where only around 200 inhabitants are living per square kilometer on average, while roughly 20,000 residents per square kilometer are living in the two central city districts. This is the main reason for the huge difference in population density between the four Chinese municipalities Beijing, Tianjin, Shanghai, and Chongqing shown in many population statistics.

If you would like to view a straightforward comparison between the Population density (by State) of Nigeria as at 2006 and 2016, this is just for you.

This web app showcases a simple and at-a-glance comparison between the Population density of Nigeria in 2006 and 2016. It features side-by-side, two individual web apps that display the population density, by state, for each corresponding year (2006, 2016). The population density was calculated by dividing the states total population by the area of its landmass in m². Within the app, there are easy-to-use navigation tools that have been configured to help users better access its features. Examples of these include the zoom tool, Expand tool, synced pop-ups, legend and many more. Clicking on any state on either map enables its pop-up from which you can access that particular states population details. One wonderful feature of this app is that popups for the 2 maps are synced! This means that clicking on a state in one map to get its pop-up details, will effect the same in the second map. (How cool is that!) Don't hesitate to leave comment about your experience with this web app, as well as suggestions on what can be done to make it even better.Thank you!

Saint Lucia population density for 400m H3 hexagons.

Built from Kontur Population: Global Population Density for 400m H3 Hexagons Vector H3 hexagons with population counts at 400m resolution.

Fixed up fusion of GHSL, Facebook, Microsoft Buildings, Copernicus Global Land Service Land Cover, Land Information New Zealand, and OpenStreetMap data.

Population Raster American Samoa 2020 Data Input: Settlement footprint from Facebook's High-Resolution Population Density Maps (https://data.humdata.org/dataset/american-samoa-high-resolution-population-density-maps-demographic-estimates) Population allocated proportionally using 2011 census population counts at enumeration area level. Year Population Growth Rate of 0.23% has been applied to update population up to 2020

Population Raster Guam 2020 Data Input: Settlement footprint from Facebook's High-Resolution Population Density Maps https://data.humdata.org/dataset/guam-high-resolution-population-density-maps-demographic-estimates Population allocated proportionally using 2011 census population counts at district level. Year Population Growth Rate of 1.64% has been applied to update population up to 2020 The human settlement footprint with census population allocated has been converted into a 100 m resolution raster.

The Global Human Footprint Dataset of the Last of the Wild Project, Version 2, 2005 (LWP-2) is the Human Influence Index (HII) normalized by biome. The HII is a global dataset of 1-kilometer grid cells, created from nine global data layers covering human population pressure (population density), human land use and infrastructure (built-up areas, nighttime lights, land use/land cover), and human access (coastlines, roads, railroads, navigable rivers). The dataset in Interrupted Goode Homolosine Projection (IGHP) is produced by the Wildlife Conservation Society (WCS) and Columbia University Center for International Earth Science Information Network (CIESIN).