Monaco led the ranking for countries with the highest population density in 2024, with nearly 26,000 residents per square kilometer. The Special Administrative Region of Macao came in second, followed by Singapore. The world’s second smallest country Monaco is the world’s second-smallest country, with an area of about two square kilometers and a population of only around 40,000. It is a constitutional monarchy located by the Mediterranean Sea, and while Monaco is not part of the European Union, it does participate in some EU policies. The country is perhaps most famous for the Monte Carlo casino and for hosting the Monaco Grand Prix, the world's most prestigious Formula One race. The global population Globally, the population density per square kilometer is about 60 inhabitants, and Asia is the most densely populated region in the world. The global population is increasing rapidly, so population density is only expected to increase. In 1950, for example, the global population stood at about 2.54 billion people, and it reached over eight billion during 2023.

In 2023, Washington, D.C. had the highest population density in the United States, with 11,130.69 people per square mile. As a whole, there were about 94.83 residents per square mile in the U.S., and Alaska was the state with the lowest population density, with 1.29 residents per square mile. The problem of population density Simply put, population density is the population of a country divided by the area of the country. While this can be an interesting measure of how many people live in a country and how large the country is, it does not account for the degree of urbanization, or the share of people who live in urban centers. For example, Russia is the largest country in the world and has a comparatively low population, so its population density is very low. However, much of the country is uninhabited, so cities in Russia are much more densely populated than the rest of the country. Urbanization in the United States While the United States is not very densely populated compared to other countries, its population density has increased significantly over the past few decades. The degree of urbanization has also increased, and well over half of the population lives in urban centers.

The average for 2021 based on 12 countries was 25 people per square km. The highest value was in Ecuador: 72 people per square km and the lowest value was in Guyana: 4 people per square km. The indicator is available from 1961 to 2021. Below is a chart for all countries where data are available.

Mogadishu in Somalia led the ranking of cities with the highest population density in 2023, with ****** residents per square kilometer. When it comes to countries, Monaco is the most densely populated state worldwide.

Mauritius had the highest population density level in Africa as of 2023, with nearly *** inhabitants per square kilometer. The country has also one of the smallest territories on the continent, which contributes to the high density. As a matter of fact, the majority of African countries with the largest concentration of people per square kilometer have the smallest geographical area as well. The exception is Nigeria, which ranks among the largest territorial countries in Africa and is very densely populated at the same time. After all, Nigeria has also the largest population on the continent.

As of 2025, Asia was the most densely populated region of the world, with nearly 156 inhabitants per square kilometer, whereas Oceania's population density was just over five inhabitants per square kilometer.

In 2022, El Salvador had the highest population density in Central America, with over *** people per square kilometer. The second place was Guatemala, slightly over half the density in El Salvador. In 2022, Guatemala ranked as the most populated country in the region, with over ********** inhabitants.

India population density for 2022 was 479.43, a 0.79% increase from 2021.

<ul style='margin-top:20px;'>

<li>India population density for 2021 was <strong>475.65</strong>, a <strong>0.83% increase</strong> from 2020.</li>
<li>India population density for 2020 was <strong>471.76</strong>, a <strong>0.98% increase</strong> from 2019.</li>
<li>India population density for 2019 was <strong>467.19</strong>, a <strong>1.05% increase</strong> from 2018.</li>
</ul>Population density is midyear population divided by land area in square kilometers. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship--except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. Land area is a country's total area, excluding area under inland water bodies, national claims to continental shelf, and exclusive economic zones. In most cases the definition of inland water bodies includes major rivers and lakes.

As of 2023, the population density in London was by far the highest number of people per square km in the UK, at *****. Of the other regions and countries which constitute the United Kingdom, North West England was the next most densely populated area at *** people per square kilometer. Scotland, by contrast, is the most sparsely populated country or region in the United Kingdom, with only ** people per square kilometer. Countries, regions, and cities According to the official mid-year population estimate, the population of the United Kingdom was just almost **** million in 2022. Most of the population lived in England, where an estimated **** million people resided, followed by Scotland at **** million, Wales at **** million and finally Northern Ireland at just over *** million. Within England, the South East was the region with the highest population at almost **** million, followed by the London region at around *** million. In terms of urban areas, Greater London is the largest city in the United Kingdom, followed by Greater Manchester and Birmingham in the North West and West Midlands regions of England. London calling London's huge size in relation to other UK cities is also reflected by its economic performance. In 2021, London's GDP was approximately *** billion British pounds, almost a quarter of UK GDP overall. In terms of GDP per capita, Londoners had a GDP per head of ****** pounds, compared with an average of ****** for the country as a whole. Productivity, expressed as by output per hour worked, was also far higher in London than the rest of the country. In 2021, London was around **** percent more productive than the rest of the country, with South East England the only other region where productivity was higher than the national average.

A dataset listing New York cities by population for 2024.

The population of the world, allocated to 1 arcsecond blocks. This refines CIESIN’s Gridded Population of the World project, using machine learning models on high-resolution worldwide Digital Globe satellite imagery. More information.

There is also a tiled version of this dataset that may be easier to use if you are interested in many countries.

National and subnational mid-year population estimates for England and Wales by administrative area, age and sex (including components of population change, median age and population density).

A dataset listing Florida counties by population for 2024.

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.

Generalised linear mixed-effects models (only 10 top-ranked models according to wBICc shown) of the highest-ranked variable from each theme (1. availability of family planning, 2. quality of family planning, 3. education, 4. religion, 5. socio-economics) in relation to variation in fertility among 46 low- and middle-income countries.

Nigeria has the largest population in Africa. As of 2025, the country counted over 237.5 million individuals, whereas Ethiopia, which ranked second, has around 135.5 million inhabitants. Egypt registered the largest population in North Africa, reaching nearly 118.4 million people. In terms of inhabitants per square kilometer, Nigeria only ranked seventh, while Mauritius had the highest population density on the whole African continent in 2023. The fastest-growing world region Africa is the second most populous continent in the world, after Asia. Nevertheless, Africa records the highest growth rate worldwide, with figures rising by over two percent every year. In some countries, such as Chad, South Sudan, Somalia, and the Central African Republic, the population increase peaks at over 3.4 percent. With so many births, Africa is also the youngest continent in the world. However, this coincides with a low life expectancy. African cities on the rise The last decades have seen high urbanization rates in Asia, mainly in China and India. African cities are also growing at large rates. Indeed, the continent has three megacities and is expected to add four more by 2050. Furthermore, Africa's fastest-growing cities are forecast to be Bujumbura, in Burundi, and Zinder, Nigeria, by 2035.

1. The climate on our planet is changing and the range distributions of organisms are shifting in response. In aquatic environments, species might not be able to redistribute poleward or into deeper water when temperatures rise because of barriers, reduced light availability, altered water chemistry, or any combination of these. How species respond to climate change may depend on physiological adaptability, but also on the population dynamics of the species.

2. Density dependence is a ubiquitous force that governs population dynamics and regulates population growth, yet its connections to the impacts of climate change remain little known, especially in marine studies. Reductions in density below an environmental carrying capacity may cause compensatory increases in demographic parameters and population growth rate, hence masking the impacts of climate change on populations. On the other hand, climate-driven deterioration of conditions may reduce environmental carrying capacities, making compensation less likely and populations more susceptible to the effects of stochastic processes.

3. Here we investigate the effects of climate change on Baltic blue mussels using a 17-year data set on population density. Using a Bayesian modelling framework, we investigate the impacts of climate change, assess the magnitude and effects of density dependence, and project the likelihood of population decline by the year 2030.

4. Our findings show negative impacts of warmer and less saline waters, both outcomes of climate change. We also show that density-dependence increases the likelihood of population decline by subjecting the population to the detrimental effects of stochastic processes (i.e., low densities where random bad years can cause local extinction, negating the possibility for random good years to offset bad years).

5. We highlight the importance of understanding, and accounting for both density dependence and climate variation when predicting the impact of climate change on keystone species, such as the Baltic blue mussel. 08-Oct-2020

Methods Between 1997 and 2013 we monitored blue mussel population densities at six stations within the Tvärminne archipelago, which were on average 4.3 km (± 0.82 SD) apart from each other and ranged from sheltered (zone 1 in column C), intermediate (zone 2 in column C), and exposed to wind and wave action of the open sea (zone 2 in column C) and termed ‘zones’ with increasing distance from the mainland. Each year during May and June, blue mussels were sampled using SCUBA using a quadrat frame (20 x 20 cm). For each quadrat sample the divers scraped the entire frame content into an attached fabric net-bag (0.1 mm mesh). Triplicate samples, distanced by 10-20 m, were randomly taken from each of four depths (3, 5, 8 and 12 m) at each station (1-6). More details in the Journal of Animal Ecology paper related to these data.

1. Models of host-pathogen interactions help to explain infection dynamics in wildlife populations and to predict and mitigate the risk of zoonotic spillover. Insights from models inherently depend on the way contacts between hosts are modelled, and crucially, how transmission scales with animal density.

2. Bats are important reservoirs of zoonotic disease and are among the most gregarious of all mammals. Their population structures can be highly heterogenous, underpinned by ecological processes across different scales, complicating assumptions regarding the nature of contacts and transmission. Although models commonly parameterise transmission using metrics of total abundance, whether this is an ecologically representative approximation of host-pathogen interactions is not routinely evaluated.

3. We collected a 13-month dataset of tree-roosting Pteropus spp. from 2,522 spatially referenced trees across eight roosts to empirically evaluate the relationship between total roost abundance and tree-level measures of abundance and density – the scale most likely to be relevant for virus transmission. We also evaluate whether roost features at different scales (roost-level, subplot-level, tree-level) are predictive of these local density dynamics.

4. Roost-level features were not representative of tree-level abundance (bats per tree) or tree-level density (bats per m2 or m3), with roost-level models explaining minimal variation in tree-level measures. Total roost abundance itself was either not a significant predictor (tree-level 3-D density) or only weakly predictive (tree-level abundance).

5. This indicates that basic measures, such as total abundance of bats in a roost, may not provide adequate approximations for population dynamics at scales relevant for transmission, and that alternative measures are needed to compare transmission potential between roosts. From the best candidate models, the strongest predictor of local population structure was tree density within roosts, where roosts with low tree density had a higher abundance but lower density of bats (more spacing between bats) per tree.

6. Together, these data highlight unpredictable and counterintuitive relationships between total abundance and local density. More nuanced modelling of transmission, spread and spillover from bats likely requires alternative approaches to integrating contact structure in host-pathogen models, rather than simply modifying the transmission function.

Methods This dataset presents data on three roosting structure of three species of flying-fox at eight sites in south-east Queensland and north-east New South Wales, Australia. Species included the grey-headed flying-fox (P. poliocephalus), black flying-fox (Pteropus alecto) and little red flying-fox (Pteropus scapulatus). All sites were previously documented as having a continuous population of grey-headed or black flying-foxes. Little red flying-foxes visited some roost sites intermittently, however no roost sites occurred within the distribution of spectacled flying-foxes.

RAW DATA

We mapped the spatial arrangement of all overstory, canopy and midstory trees in a grid network of 10 stratified random subplots (20 x 20 meters each) per roost site. Subplots were stratified throughout perceived “core” (five subplots) and “peripheral” (five subplots) roosting areas, classed as areas observed to be frequently occupied (core) or infrequently (peripheral) by bats (Welbergen 2005). Core and peripheral areas were evaluated from regular observations made prior to roost tree mapping, though note that these categories were revised subsequently with the quantitative data. Trees were mapped and tagged using tree survey methods described in the “Ausplots Forest Monitoring Network, Large Tree Survey Protocol” (Wood et al. 2015).

To evaluate spatio-temporal patterns in flying-fox roosting, we revisited all tagged trees and scored the extent of species occupancy using the following tree abundance index: 0= zero bats; 1= 1-5 bats; 2=6-10 bats; 3=11-20 bats; 4=21-50 bats; 5=51-100 bats, 6=101-200 bats, 7= >200 bats. For a subset of trees (N=60 per site, consistent through time) absolute counts and minimum/maximum roosting heights of each species were taken. Overall roost perimeter (perimeter of area occupied) was mapped with GPS (accurate to 10 meters) immediately after the tree survey to estimate perimeter length and roost area. Total abundance at each roost was also estimated with a census count of bats where feasible (i.e., where total abundance was predicted to be <5,000 individuals), or by counting bats as they emerged in the evening from their roosts (“fly-out”), as per recommendations in Westcott et al. (2011). If these counts could not be conducted, population counts from local councils (conducted within ~a week of the bat surveys) were used, as the total abundance of roosts is generally stable over short timeframes (Nelson 1965b). Because roost estimates become more unreliable with increasing total abundance, and because our estimation methods were intrinsically linked with total abundance, we converted the total estimated abundance into an index estimate (where bin ranges increase with total abundance) for use in analyses, as per values used by the National Flying-Fox Monitoring Program (2017). Index categories were as follows: 1: 1-499 bats; 2: 500-2,499 bats; 3: 2,500-4,999 bats; 4: 5,000-9,999 bats; 5: 10,000-15,999 bats; 6: 16,000-49,999 bats; and 7: 50,000+ bats.

Roosting surveys were repeated once a month for 13 months (August 2018 - August 2019).

Methodological details are described in detail in the published paper 'Conventional wisdom on roosting behaviour of Australian flying foxes – a critical review, and evaluation using new data' (DOI https://doi.org/10.1002/ece3.8079). Raw data are available from this Dryad Dataset (https://doi.org/10.5061/dryad.g4f4qrfqv)

PROCESSED DATA

Information collected during the bat roosting surveys were used to calculate measures of bat density and abundance at three scales: roost-level, subplot-level and tree-level. For a visual summary of metrics see Figure 2 in the published paper ('Counterintuitive scaling between population abundance and local density: implications for modelling transmission of infectious diseases in bat populations'). Note that where index abundance scores were used in calculations, the middle value of the index range was taken.

Roost-level density was calculated by dividing the total roost index abundance score by the total roost area (Figure 2A). Measures of subplot-level density were estimated with two methods: either as the tally of tree-level index abundance scores per subplot divided by subplot area (“subplot-level density”, Figure 2B), or as the average of fixed-bandwidth weighted kernel estimates, estimated using the spatstat package in R (Diggle 1985) (“subplot-level kernel density”, Figure 2C). Kernel estimates are spatially explicit and give the density of a spatial pattern, estimated per pixel over a smoothed area (Baddeley 2010). Kernels were estimated from the spatial location of trees weighted by tree-level index abundance scores, with Gaussian kernel smoothing and a smoothing bandwidth of 0.6. Bandwidth was selected by comparing projected kernel density values to expected density values based on tree abundance and canopy area. Kernel averages were then calculated per subplot. To prevent dilution of the density estimates with unoccupied space, we included only occupied pixels in the subplot average (pixel size = 0.156 x 0.156 meters). This latter approach has the advantage of explicitly incorporating the spatial distribution of bats into the density estimate, and therefore gives a better representation of aggregations in occupied space. Note that neither roost nor subplot-based density measures consider the vertical distribution of bats.

Measures of tree-level density were estimated in either two-dimension (2-D; for comparison with other two-dimensional estimates) or three-dimension (3-D). Tree-level 2-D density was estimated from tree-level index abundance scores and canopy area (Figure 2D). Tree-level 3-D density was estimated for the tree subset, as the absolute count of bats divided by the volume of tree space occupied (i.e. per cubic metre rather than square metre, Figure 2E). Volume of tree space was calculated from the height range occupied (maximum height minus minimum height) and the approximate crown area of trees. To estimate crown area of tagged trees for both measures, we computed the area of Dirichlet-Voronoi tessellations from tree distribution maps of canopy trees per subplot, with the spatstat package in R (Baddeley 2010). To control for edge effects, and to prevent overestimation of crown area for overstory trees and trees outside of the canopy, we imposed a maximum crown area of 199 m2 (radius ~8 m). This value was selected based on mean values reported across species of eucalypts in New South Wales (Verma et al. 2014), eucalypts being broadly representative of trees in these roost sites (Brooks 2020). In total, 218 of the 2,522 tagged trees (8%) were imposed with the maximum crown area value. Crown area of midstory trees was assigned as the first quartile of canopy tree crown area (5.8 m2), to reflect observations that trees beneath the canopy were typically smaller than trees within the canopy. Mean calculated crown area was 30.4 m2 (crown radius ~ 3.1 m). To investigate whether the choice of maximum crown area impacted results, we also repeated analyses for additional values of maximum crown area (140 m2, 170 m2 and 230 m2) chosen to cover the range in smallest to largest mean values reported for individual eucalypt species in Verma et al. (2014).

Genetic founder effects are often expected when animals colonize restored habitat in fragmented landscapes, but empirical data on genetic responses to restoration are limited. We examined the genetic response of banner-tailed kangaroo rats (Dipodomys spectabilis) to landscape-scale grassland restoration in the Chihuahuan Desert of New Mexico, USA. D. spectabilis is a grassland specialist and keystone species. At sites treated with herbicide to remove shrubs, colonization by D. spectabilis is slow and populations persist at low density for ≥10 yrs (≥6 generations). Persistence at low density and low gene flow may cause strong founder effects. We compared genetic structure of D. spectabilis populations between treated sites and remnant grasslands, and we examined how the genetic response to restoration depended on treatment age, area, and connectivity to source populations. Allelic richness and heterozygosity were similar between treated sites and remnant grasslands. Allelic richness at treated sites was greatest early in the restoration trajectory, and genetic divergence did not differ between recently colonized and established populations. These results indicated that founder effects during colonization of treated sites were weak or absent. Moreover, our results suggested founder effects were not mitigated by treatment area or connectivity. Dispersal is negatively density-dependent in D. spectabilis, and we hypothesize that high gene flow may occur early in the restoration trajectory when density is low. Our study shows genetic diversity can be recovered more rapidly than demographic components of populations after habitat restoration, and that founder effects are not inevitable for animals colonizing restored habitat in fragmented landscapes. Year of sampling, geographic coordinates, and treatment status of sitescosentino_etal_sites.xlsxMicrosatellite genotypes"0" indicates alleles that were not scored due to failed amplification or ambiguous peak calling (0.17% of alleles).cosentino_etal_genotypes.xlsxPairwise geographic and genetic distancesLower triangle indicates genetic distance (Fst), and upper triangle indicates geographic distance (km).cosentino_etal_distance_matrix.xlsxSTRUCTURE input filesInput files for all sites and sites west of the Rio Grande are included as separate sheets in the file.cosentino_etal_structure.xlsx

Abstract

Bhutan Living Standards Survey (BLSS) 2012 gathered data on household consumption expenditure, and thereby provided a means of assessing the level of poverty and well-being in Bhutan. It also collected data on the demographic characteristics of household members, household assets, credit and income, remittances, housing, access to public facilities and services, education, employment, health of household members, and prices of commodities. An additional module pertained to social capital and questions on happiness and selfrated poverty.

The survey was done to collect comprehensive socioeconomic information for use in updating the poverty profile of the country and monitoring poverty–related indicators, assessing the 10th Five-Year Plan and planning socioeconomic policy for the 11th Five-Year Plan, and updating weights required for the estimation of the consumer price index.

Geographic coverage

National

Analysis unit

• Household
• Individual
• Consumption expenditure item/ product

Universe

The survey population coverage included all households in the country except (a) diplomatic and expatriates households; (b) institutional households, i.e., residents of hotels, boarding and lodging houses, monasteries, nunneries, school hostels, orphanages, rescue homes, and under trials in jails and indoor patients of hospitals; and, (c) barracks of military and paramilitary forces, including the police.

A person not of Bhutanese nationality who has been residing in Bhutan for at least 6 months. The household of a non-Bhutanese resident who is an employee of the government or of private enterprises in Bhutan is not considered an expatriate household and is included in BLSS 2012.

Kind of data

Sample survey data [ssd]

Sampling procedure

The selection of sample households for BLSS 2012 was based on two mutually exclusive sampling frames for the rural and urban areas. Household counts for the 2005 Population and Housing Census of Bhutan (PHCB 2005) at the chiwog (village) level, updated after more recent listing activities, e.g., those for the Bhutan Multiple Indicator Survey, were used in constructing the sampling frame of primary sampling units (PSUs) for rural areas. Urban block counts from PHCB 2005, which were greatly supplemented by the NSB's household listing operations in the most densely populated urban areas in December 2011-February 2012, became the basis for the sampling frame of PSUs for the urban areas.

(Refer Section 1.3 (Survey Methodology and Sampling Design) in the final report for detail sampling design information)

Mode of data collection

Face-to-face [f2f]

Research instrument

Three main sets of schedules were used in BLSS 2012. Two sets of schedules were used in listing households for sample selection (one urban and one rural). The third set of schedules comprised the household questionnaire with 12 sections, called “blocks,” for the collection of data on household consumption expenditure, prices, and other socioeconomic variables. Each block of the questionnaire collected detailed information on a specific subject. Some blocks were further divided into subblocks according to the nature of the topic covered.

Cleaning operations

CSPro version 3.2 software was used in designing the data entry application. Twenty temporary data coders entered the data into the computer under the close supervision of NSB programmers. Computer editing, validation, and cleaning went through several stages.

Response rate

There were nonresponses despite the best efforts of the field enumerators and supervisors. After three unproductive revisits, a household was treated as unresponsive. The response rate was 93.1% overall, 91.6% for urban areas, and 94.8% for rural areas. In Reserbu town (Trashigang, urban) and Kanaldang town (Pema Gatshel, urban), both of which were included in the original sampling frame, the response rates were zero.

A major reason for nonresponse, common in both urban and rural areas according to the field staff, was failure to establish contact with any adult member of the household even after at least three attempts. Some living quarters were locked or the survey teams encountered communication problems. In rare cases, households refused to cooperate, particularly in the urban areas. When this happened, the supervisors concerned made serious efforts to obtain participation in the survey by explaining its merits to the heads of households and assuring them that the data collected and their household status would remain confidential.

(Refer Table 1.4 in the final report, response rates by urban/rural)