Iran: Population density, people per square km: The latest value from 2021 is 54 people per square km, unchanged from 54 people per square km in 2020. In comparison, the world average is 456 people per square km, based on data from 196 countries. Historically, the average for Iran from 1961 to 2021 is 33 people per square km. The minimum value, 13 people per square km, was reached in 1961 while the maximum of 54 people per square km was recorded in 2020.

Iran IR: Population Density: People per Square Km data was reported at 49.831 Person/sq km in 2017. This records an increase from the previous number of 49.287 Person/sq km for 2016. Iran IR: Population Density: People per Square Km data is updated yearly, averaging 33.631 Person/sq km from Dec 1961 (Median) to 2017, with 57 observations. The data reached an all-time high of 49.831 Person/sq km in 2017 and a record low of 13.802 Person/sq km in 1961. Iran IR: Population Density: People per Square Km data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Iran – Table IR.World Bank.WDI: Population and Urbanization Statistics. 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.; ; Food and Agriculture Organization and World Bank population estimates.; Weighted average;

Population density (people per sq. km of land area) in Iran was reported at 55.18 sq. Km in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. Iran - Population density (people per sq. km) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Iran: Population density, in people per sq. mile: The latest value from is people per sq. mile, unavailable from people per sq. mile in . In comparison, the world average is 0 people per sq. mile, based on data from countries. Historically, the average for Iran from to is people per sq. mile. The minimum value, people per sq. mile, was reached in while the maximum of people per sq. mile was recorded in .

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

Comprehensive socio-economic dataset for Iran 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.

Density of physicians of Iran shot up by 40.45% from 1.1 number per thousand population in 2017 to 1.5 number per thousand population in 2018. Since the 24.09% slump in 2015, density of physicians soared by 36.89% in 2018.

Density of nursing and midwifery personnel of Iran plummeted by 20.96% from 2.5 number per thousand population in 2017 to 2.0 number per thousand population in 2018. Since the 19.74% surge in 2015, density of nursing and midwifery personnel surged by 9.37% in 2018.

This statistic illustrates the distribution of Sunni and Shia amongst Muslim population in Iran and Saudi Arabia as of 2009. As of 2009, around ** million Iranian Muslims belonged to the Shia faith, whereas only *** million people in Saudi Arabia followed Shia Islam.

Chart and table of population level and growth rate for the Tehran, Iran metro area from 1950 to 2025.

Constrained estimates, total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel. The mapping approach is Random Forest-based dasymetric redistribution.

More information can be found in the Release Statement

The difference between constrained and unconstrained is explained on this page: https://www.worldpop.org/methods/top_down_constrained_vs_unconstrained

This bar chart displays urban population (people) by region using the aggregation sum in Iran. The data is filtered where the date is 2021. The data is about countries per year.

This statistic shows the age structure of Iran inhabitants from 2013 to 2023. In 2023, about 22.84 percent of inhabitants were aged 0 to 14 years, while approximately 69.25 percent were aged 15 to 64, and 7.92 percent of Iran inhabitants were aged 65 or older.

This bar chart displays male population (people) by continent using the aggregation sum in Iran. The data is about countries per year.

In 2023, the share of urban population in Iran remained nearly unchanged at around 77.26 percent. Nevertheless, 2023 still represents a peak in the share in Iran. A population may be defined as urban depending on the size (population or area) or population density of the village, town, or city. The urbanization rate then refers to the share of the total population who live in an urban setting. International comparisons may be inconsistent due to differing parameters for what constitutes an urban center.Find more key insights for the share of urban population in countries like Iraq and Saudi Arabia.

BackgroundSocial factors play the main role in the vulnerability of exposed countries to disasters. The COVID-19 pandemic as a disaster is not an exception to this fact. This study aimed to determine the main social vulnerability indicators in the COVID-19 pandemic in Iran.MethodsThis study was conducted during the period of 2021–2022 in three phases, including a systematic review, a virtual panel expert, and the Analytical Hierarchy Process. First, the draft of social vulnerability indicators in COVID-19 was extracted through a systematic review. Then, the extracted indicators were finalized and prioritized by the expert panel and the AHP, respectively.ResultsInitially, the literature review found five domains and 38 indicators of social vulnerability in COVID-19. The outcome of the expert panel increased the related domains to six but decreased the indicators to 31. The three prioritized social vulnerability indicators that were determined by the AHP were population density, accessibility to healthcare facilities, and relevant services and vulnerable groups.ConclusionMeasuring social vulnerability with the identified indicators is valuable for addressing high COVID-19 incidence among socially vulnerable hotspot areas. Regarding the result of this study, further research should be conducted to validate the identified indicators.

Background: Obesity has become a common health problem all over the world. Benefiting from a national representative sample, the present study aimed to estimate the prevalence of overweight/obesity and the distribution of Body Mass Index (BMI) levels in the Iranian adult population, by sex, age, and geographical distribution.Methods: This was a large-scale national cross-sectional study of Non-communicable Diseases risk factor surveillance in Iran. Through a systematic random sampling cluster, 31,050 Iranian adult participants aged 18 years and over were enrolled in the study. The main research tools were used to assess three different levels of data, namely: (1) demographic, epidemiologic, and risk-related behavioral data, (2) physical measurements, and (3) lab measurements. Anthropometric measurements were taken using standard protocols and calibrated instruments.Results: In 2016, the national prevalence rates of normal weight, obesity, and overweight/obesity among Iranian adults were, 36.7% (95% CI: 36.1–37.3), 22.7% (22.2–23.2), and 59.3% (58.7–59.9), respectively. There was a significant difference between the prevalence of obesity among males [15.3% (14.7–15.9)] and females 29.8% (29.0–30.5). The 55–64 [31.5% (30.1–33.0)] and the 18–24 [8.3% (7.3–9.4)] year-old age groups had the highest and lowest prevalence of obesity, respectively. The results show a geographical pattern at provincial level, where the level of BMI increases among populations ranging from the southeastern to the northwestern regions of the country. The highest provincial prevalence of obesity was almost 2.5-fold higher than the lowest provincial prevalence.Conclusion: We found a significant difference between the prevalence of obesity in males and females. Moreover, there was a considerable difference in the geographical pattern of the prevalence of obesity and overweight. Further evidence is warranted to promote strategies and interventions related to prevention and control of factors that are associated with weight gain.

This bar chart displays population (people) by ISO 2 country code using the aggregation sum in Iran. The data is filtered where the date is 2021. The data is about countries per year.

Linkage disequilibrium (LD) across the genome provides information to identify the genes and variations related to quantitative traits in genome-wide association studies (GWAS) and for the implementation of genomic selection (GS). LD can also be used to evaluate genetic diversity and population structure and reveal genomic regions affected by selection. LD structure and Ne were assessed in a set of 83 water buffaloes, comprising Azeri (AZI), Khuzestani (KHU), and Mazandarani (MAZ) breeds from Iran, Kundi (KUN) and Nili-Ravi (NIL) from Pakistan, Anatolian (ANA) buffalo from Turkey, and buffalo from Egypt (EGY). The values of corrected r2 (defined as the correlation between two loci) of adjacent SNPs for three pooled Iranian breeds (IRI), ANA, EGY, and two pooled Pakistani breeds (PAK) populations were 0.24, 0.28, 0.27, and 0.22, respectively. The corrected r2 between SNPs decreased with increasing physical distance from 100 Kb to 1 Mb. The LD values for IRI, ANA, EGY, and PAK populations were 0.16, 0.23, 0.24, and 0.21 for less than 100Kb, respectively, which reduced rapidly to 0.018, 0.042, 0.059, and 0.024, for a distance of 1 Mb. In all the populations, the decay rate was low for distances greater than 2Mb, up to the longest studied distance (15 Mb). The r2 values for adjacent SNPs in unrelated samples indicated that the Affymetrix Axiom 90 K SNP genomic array was suitable for GWAS and GS in these populations. The persistency of LD phase (PLDP) between populations was assessed, and results showed that PLPD values between the populations were more than 0.9 for distances of less than 100 Kb. The Ne in the recent generations has declined to the extent that breeding plans are urgently required to ensure that these buffalo populations are not at risk of being lost. We found that results are affected by sample size, which could be partially corrected for; however, additional data should be obtained to be confident of the results.

Urban sprawl and urbanization as driving forces of land degradation have direct and indirect impacts on local climate dynamic. In this paper, the hypothesis that urban sprawl and unsustainable land use change cause local climate changes has been studied. Tehran as a megacity has been considered to show the urban sprawl and urbanization impacts on local climate. The methodology is divided into two main parts based on the primary datasets (satellite imagery and local climate data). The Landsat images and digital elevation model maps extracted from Shuttle Radar Topography Mission 1 Arc-Second Global data of Tehran acquired in every 5 years during June and July from 1975 to 2015 have been used for this study. The second dataset that has been used in this study contains daily mean temperature and precipitation (from 1990 to 2010) of eight meteorological synoptic stations in the study area. The results show that the rapid and unsustainable urban growth have significant effects on local climate. Moreover, it has been found that the urbanization and urban sprawl as well as unsustainable land use change caused significant change (P = 0.005) in evaporation rate in the study area (especially in east and center regions of the city with high population density).