This statistic represents the average height of men in the top 20 countries worldwide as of 2016. On average, men are ***** centimeters tall in Bosnia & Herzegovina.

This statistic represents the average height of women in the top 20 countries worldwide as of 2016. On average, women are ***** centimeters tall in the Netherlands.

In the shown time-period the mean height of men and women has generally increased in England. According to the survey, the average height of males rose slightly during the period in consideration, from 174.4 centimeters in 1998 to 176.2 centimeters (approximately 5'9") in 2022. In comparison, the mean height of women was 162.3 centimeters (5'4") in 2022, up from 161 in 1998. Reasons for height increasing While a large part of an adult’s final height is based on genetics, the environment in which a person grows up is also important. Improvements in nutrition, healthcare, and hygiene have seen the average heights increase over the last century, particularly in developed countries. Average height is usually seen as a barometer for the overall health of the population of a country, as the most developed are usually among the ‘tallest’ countries. Average waist circumference also increasing The prevalence of obesity among adults in England has generally been trending upward since 2000. In that year, 21 percent of men and women in England were classified as obese. By 2021, however, this share was 26 percent among women and 25 percent among men. Every adult age group in England had an average BMI which was classified as overweight, apart from those aged 16 to 24, indicating there is a problem with overweightness in England.

Note: Countries are presented from shortest to tallest differential from the global mean; Adjusted  =  adjusted for year of birth, household wealth, education, and place of residence (urban or rural); SE  =  Standard Error.

In 2023, the average height of South Korean men in their thirties lay at ****** centimeters. Men in older age groups tended to be shorter. On average, South Korean men were ****** centimeters tall that year. Diet and healthcare in South Korea It has been observed that improvements in nutrition and healthcare lead to increased average height over time. With the rapid industrialization in South Korea came improvements in healthcare and nutritional intake. South Korea ranks among the leading countries in the health index, which measures a population’s health and a country’s healthcare system. Even with an excellent healthcare system, South Koreans have increasingly been concerned about their diet and nutrition, exemplified by the share of people trying to consume certain nutrients every day. Height preferences in South Korea  According to a 2019 survey, for most respondents the preferred height for South Korean men was higher than the current average. This discrepancy was similar for the preferred height for women, showing how preferences for taller people stretched across genders. Not only are South Koreans preferring taller partners, but they are also getting taller over time. Another survey found that the ideal height for a spouse in the country came closer to the average height of younger generations.

As per the results of a large scale survey in 2019, the average height of Indian respondents was five feet and *** inches. Among the surveyed Indian cities, Chennai had the tallest people, whereas Hyderabad had the shortest people in the country.

Global and regional Canopy Height Maps (CHM). Created using machine learning models on high-resolution worldwide Maxar satellite imagery.

Kiribati KI: Prevalence of Overweight: Weight for Height: % of Children Under 5, Modeled Estimate data was reported at 2.100 % in 2024. This stayed constant from the previous number of 2.100 % for 2023. Kiribati KI: Prevalence of Overweight: Weight for Height: % of Children Under 5, Modeled Estimate data is updated yearly, averaging 2.000 % from Dec 2000 (Median) to 2024, with 25 observations. The data reached an all-time high of 2.200 % in 2003 and a record low of 1.900 % in 2020. Kiribati KI: Prevalence of Overweight: Weight for Height: % of Children Under 5, Modeled Estimate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Kiribati – Table KI.World Bank.WDI: Social: Health Statistics. Prevalence of overweight children is the percentage of children under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's 2006 Child Growth Standards.;UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME).;Weighted average;Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues. Estimates are modeled estimates produced by the JME. Primary data sources of the anthropometric measurements are national surveys. These surveys are administered sporadically, resulting in sparse data for many countries. Furthermore, the trend of the indicators over time is usually not a straight line and varies by country. Tracking the current level and progress of indicators helps determine if countries are on track to meet certain thresholds, such as those indicated in the SDGs. Thus the JME developed statistical models and produced the modeled estimates.

India IN: Prevalence of Overweight: Weight for Height: % of Children Under 5, Modeled Estimate data was reported at 3.700 % in 2024. This records an increase from the previous number of 3.400 % for 2023. India IN: Prevalence of Overweight: Weight for Height: % of Children Under 5, Modeled Estimate data is updated yearly, averaging 2.300 % from Dec 2000 (Median) to 2024, with 25 observations. The data reached an all-time high of 3.700 % in 2024 and a record low of 2.100 % in 2013. India IN: Prevalence of Overweight: Weight for Height: % of Children Under 5, Modeled Estimate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s India – Table IN.World Bank.WDI: Social: Health Statistics. Prevalence of overweight children is the percentage of children under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's 2006 Child Growth Standards.;UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME).;Weighted average;Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues. Estimates are modeled estimates produced by the JME. Primary data sources of the anthropometric measurements are national surveys. These surveys are administered sporadically, resulting in sparse data for many countries. Furthermore, the trend of the indicators over time is usually not a straight line and varies by country. Tracking the current level and progress of indicators helps determine if countries are on track to meet certain thresholds, such as those indicated in the SDGs. Thus the JME developed statistical models and produced the modeled estimates.

Moldova MD: Prevalence of Stunting: Height for Age: Male: % of Children Under 5 data was reported at 5.800 % in 2012. This records a decrease from the previous number of 11.000 % for 2005. Moldova MD: Prevalence of Stunting: Height for Age: Male: % of Children Under 5 data is updated yearly, averaging 8.400 % from Dec 2005 (Median) to 2012, with 2 observations. The data reached an all-time high of 11.000 % in 2005 and a record low of 5.800 % in 2012. Moldova MD: Prevalence of Stunting: Height for Age: Male: % of Children Under 5 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Moldova – Table MD.World Bank.WDI: Health Statistics. Prevalence of stunting, male, is the percentage of boys under age 5 whose height for age is more than two standard deviations below the median for the international reference population ages 0-59 months. For children up to two years old height is measured by recumbent length. For older children height is measured by stature while standing. The data are based on the WHO's new child growth standards released in 2006.; ; World Health Organization, Global Database on Child Growth and Malnutrition. Country-level data are unadjusted data from national surveys, and thus may not be comparable across countries.; Linear mixed-effect model estimates; Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF, www.childinfo.org). Estimates of child malnutrition, based on prevalence of underweight and stunting, are from national survey data. The proportion of underweight children is the most common malnutrition indicator. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition.

Buoys measure wave height, wave period and (if directional) wave direction. Buoy data from the following organisations contribute to the National Wave Archive: Manly Hydraulics Laboratory (part of the NSW Department of Planning, Industry and Environment (DPIE), which has assumed function of former NSW Office of Environment and Heritage (OEH)) contribute 7 buoys; Bureau of Meteorology contribute 2 buoys; Western Australia Department of Transport (DOT) contribute 29 buoys; the Queensland Department of Environment and Science (DES) contribute 16 buoys; the Defence Technology Agency (DTA), New Zealand Defence Force (NZDF) contribute 2 buoys; and the Integrated Marine Observing System (IMOS) contribute 6 buoys.

The data (aside from IMOS) is gathered by the Waverider system developed by the Dutch company, Datawell. The Waverider system uses an accelerometer mounted in a loose tethered buoy (0.7 or 0.9m in diameter) to measure the vertical accelerations of the buoy as it moves with the water surface. The accelerations are integrated twice within the buoy and the displacement signal so obtained is then transmitted to a shore station where it is processed to provide wave data statistics. The wave data is stored on the receiving station PC before routine transfer to the managing organisation via email. The wave direction capable Waverider buoys utilise a heave-pitch-roll sensor, two fixed X and Y accelerometers and a three axis fluxgate compass to measure both vertical and horizontal motion. An on-board processor converts the buoy motion to three orthogonal (vertical, north-south, east-west) translation signals that are transmitted to the shore station. DOT Buoys: Older wave data was collected using non-directional Waverider buoys. As technology advanced and directional measuring capabilities were developed in wave buoys, the DOT wave buoy network was gradually upgraded to directional Waverider buoys. Therefore, older datasets do not have directional information whereas newer datasets have directional information. The data from IMOS comes from Spotter Wave Buoys, developed by Sofar Ocean Technologies, which collect data similarly to the Waverider system.

The buoy data from the Manly Hydraulics Laboratory replaces data (has been re-formatted) from the following specific collection - https://catalogue-imos.aodn.org.au:443/geonetwork/srv/api/records/bb7e9d82-3b9c-44c6-8e93-1ee9fd30bf21.

This file contains a time series of globally-averaged sea level change, or "global mean sea level" (GMSL) in units of centimeters. The estimate is based on satellite observations of sea surface height anomaly, measured by reference radar altimeter missions such as TOPEX/Poseidon, the Jason series, and Sentinel-6. The indicator values were calculated using NASA-SSH Simple Gridded Sea Surface Height from Standardized Reference Missions Only Version 1 https://podaac.jpl.nasa.gov/dataset/NASA_SSH_REF_SIMPLE_GRID_V1 . GMSL was calculated as the area-weighted average over each map in the time series of Simple Gridded Sea Surface Height. Because maps are computed using 10-days of observations, but are computed once every 7 days, there is a small amount of overlap between data used to compute successive time steps. A version of the estimate smoothed over 60 days is also provided. Expert users, please note that this estimate has NOT been adjusted for Glacial Isostatic Adjustment, to account for the slight long-term depression of the sea floor. Users who study the sea level budget will need to account for this effect in order to properly evaluate closure of the budget. The file with the filename "NASA_SSH_GMSL_INDICATOR.txt" is always the most up-to-date time series containing the most recent data.

Supplementary files for article Maternal height-standardized prevalence of stunting in 67 low- and middle-income countries.Background: Prevalence of stunting is frequently used as a marker of population-level child undernutrition. Parental height varies widely in low- and middle-income countries (LMIC) and is also a major determinant of stunting. While stunting is a useful measure of child health, with multiple causal components, removing the component attributable to parental height may in some cases be helpful to identify shortcoming in current environments.Methods: We estimated maternal height-standardized prevalence of stunting (SPS) in 67 LMICs and parental height-SPS in 20 LMICs and compared with crude prevalence of stunting (CPS) using data on 575,767 children under-five from 67 Demographic and Health Surveys (DHS). We supplemented the DHS with population-level measures of other child health outcomes from the World Health Organization’s (WHO) Global Health Observatory and the United Nations’ Inter-Agency Group for Child Mortality Estimation. Prevalence of stunting was defined as percentage of children with height-for-age falling below −2 z-scores from the median of the 2006 WHO growth standard.Results: The average CPS across countries was 27.8% (95% confidence interval [CI], 27.5–28.1%) and the average SPS was 23.3% (95% CI, 23.0–23.6%). The rank of countries according to SPS differed substantially from the rank according to CPS. Guatemala, Bangladesh, and Nepal had the biggest improvement in ranking according to SPS compared to CPS, while Gambia, Mali, and Senegal had the biggest decline in ranking. Guatemala had the largest difference between CPS and SPS with a CPS of 45.2 (95% CI, 43.7–46.9%) and SPS of 14.1 (95% CI, 12.6–15.8%). Senegal had the largest increase in the prevalence after standardizing maternal height, with a CPS of 28.0% (95% CI, 25.8–30.2%) and SPS of 31.6% (95% CI, 29.5–33.8%). SPS correlated better than CPS with other population-level measures of child health.Conclusions: Our study suggests that CPS is sensitive to adjustment for maternal height. Maternal height, while a strong predictor of child stunting, is not amenable to policy interventions. We showed the plausibility of SPS in capturing current exposures to undernutrition and infections in children.

BackgroundAdult height reflects childhood circumstances and is associated with health, longevity, and maternal–fetal outcomes. Mean height is an important population metric, and declines in height have occurred in several low- and middle-income countries, especially in Africa, over the last several decades. This study examines changes at the population level in the distribution of height over time across a broad range of low- and middle-income countries during the past half century.Methods and findingsThe study population comprised 1,122,845 women aged 25–49 years from 59 countries with women’s height measures available from four 10-year birth cohorts from 1950 to 1989 using data from the Demographic and Health Surveys (DHS) collected between 1993 and 2013. Multilevel regression models were used to examine the association between (1) mean height and standard deviation (SD) of height (a population-level measure of inequality) and (2) median height and the 5th and 95th percentiles of height. Mean-difference plots were used to conduct a graphical analysis of shifts in the distribution within countries over time. Overall, 26 countries experienced a significant increase, 26 experienced no significant change, and 7 experienced a significant decline in mean height between the first and last birth cohorts. Rwanda experienced the greatest loss in height (−1.4 cm, 95% CI: −1.84 cm, −0.96 cm) while Colombia experienced the greatest gain in height (2.6 cm, 95% CI: 2.36 cm, 2.84 cm). Between 1950 and 1989, 24 out of 59 countries experienced a significant change in the SD of women’s height, with increased SD in 7 countries—all of which are located in sub-Saharan Africa. The distribution of women’s height has not stayed constant across successive birth cohorts, and regression models suggest there is no evidence of a significant relationship between mean height and the SD of height (β = 0.015 cm, 95% CI: −0.032 cm, 0.061 cm), while there is evidence for a positive association between median height and the 5th percentile (β = 0.915 cm, 95% CI: 0.820 cm, 1.002 cm) and 95th percentile (β = 0.995 cm, 95% CI: 0.925 cm, 1.066 cm) of height. Benin experienced the largest relative expansion in the distribution of height. In Benin, the ratio of variance between the latest and earliest cohort is estimated as 1.5 (95% CI: 1.4, 1.6), while Lesotho and Uganda experienced the greatest relative contraction of the distribution, with the ratio of variance between the latest and earliest cohort estimated as 0.8 (95% CI: 0.7, 0.9) in both countries. Limitations of the study include the representativeness of DHS surveys over time, age-related height loss, and consistency in the measurement of height between surveys.ConclusionsThe findings of this study indicate that the population-level distribution of women’s height does not stay constant in relation to mean changes. Because using mean height as a summary population measure does not capture broader distributional changes, overreliance on the mean may lead investigators to underestimate disparities in the distribution of environmental and nutritional determinants of health.

The World Settlement Footprint (WSF) 3D provides detailed quantification of the average height, total volume, total area and the fraction of buildings at 90 m resolution at a global scale. It is generated using a modified version of the World Settlement Footprint human settlements mask derived from Sentinel-1 and Sentinel-2 satellite imagery in combination with digital elevation data and radar imagery collected by the TanDEM-X mission. The framework includes three basic workflows: i) the estimation of the mean building height based on an analysis of height differences along potential building edges, ii) the determination of building fraction and total building area within each 90 m cell, and iii) the combination of the height information and building area in order to determine the average height and total built-up volume at 90 m gridding. In addition, global height information on skyscrapers and high-rise buildings provided by the Emporis database is integrated into the processing framework, to improve the WSF 3D Building Height and subsequently the Building Volume Layer. A comprehensive validation campaign has been performed to assess the accuracy of the dataset quantitatively by using VHR 3D building models from 19 globally distributed regions (~86,000 km2) as reference data. The WSF 3D standard layers are provided in the format of Lempel-Ziv-Welch (LZW)-compressed GeoTiff files, with each file - or image tile - covering an area of 1 x 1 ° geographical lat/lon at a geometric resolution of 2.8 arcsec (~ 90 m at the equator). Following the system established by the TDX-DEM mission, the latitude resolution is decreased in multiple steps when moving towards the poles to compensate for the reduced circumference of the Earth.

In 2023, South Korean high school students were among the tallest in Asia, with male students aged 16 years old averaging ***** cm and female students averaging ***** cm. Both South Korean men and women have seen drastic increases in height over the past century, and in the case of women, the change in average height is the largest increase for any population in the world. Height is linked to health The average height of a population can be a good indicator for the overall health of that country; aside from genetic factors, nutrition and childhood infections play an important role in how a child will develop in terms of growth. South Koreans can expect to live longer these days; the average life expectancy at birth has steadily increased and was at **** years as of 2022. Women are expected to live slightly longer than men by around five years. The average weight of South Korean high school students has also generally increased over the years, which may suggest nutrition has improved. However, as with many other OECD countries, the obesity rate has also increased among the population.

A compliant implementation of WMS plus most of the SLD extension (dynamic styling). Can also generate PDF, SVG, KML, GeoRSS

waterfalls type Multi-step/segmented. river, name, image, Average flow, type, Height, elevation, continent, latitude, Country, location, longitude, number of drops, Width, administrative división

Afghanistan Prevalence of Stunting: Height for Age: % of Children Under 5, Modeled Estimate data was reported at 42.000 % in 2024. This records an increase from the previous number of 41.600 % for 2023. Afghanistan Prevalence of Stunting: Height for Age: % of Children Under 5, Modeled Estimate data is updated yearly, averaging 43.500 % from Dec 2000 (Median) to 2024, with 25 observations. The data reached an all-time high of 55.500 % in 2000 and a record low of 40.700 % in 2020. Afghanistan Prevalence of Stunting: Height for Age: % of Children Under 5, Modeled Estimate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Afghanistan – Table AF.World Bank.WDI: Social: Health Statistics. Prevalence of stunting is the percentage of children under age 5 whose height for age is more than two standard deviations below the median for the international reference population ages 0-59 months. For children up to two years old height is measured by recumbent length. For older children height is measured by stature while standing. The data are based on the WHO's 2006 Child Growth Standards.;UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME).;Weighted average;Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition. Estimates are modeled estimates produced by the JME. Primary data sources of the anthropometric measurements are national surveys. These surveys are administered sporadically, resulting in sparse data for many countries. Furthermore, the trend of the indicators over time is usually not a straight line and varies by country. Tracking the current level and progress of indicators helps determine if countries are on track to meet certain thresholds, such as those indicated in the SDGs. Thus the JME developed statistical models and produced the modeled estimates.

Netherlands NL: Prevalence of Overweight: Weight for Height: % of Children Under 5, Modeled Estimate data was reported at 5.400 % in 2024. This records an increase from the previous number of 5.300 % for 2023. Netherlands NL: Prevalence of Overweight: Weight for Height: % of Children Under 5, Modeled Estimate data is updated yearly, averaging 4.000 % from Dec 2000 (Median) to 2024, with 25 observations. The data reached an all-time high of 5.400 % in 2024 and a record low of 3.000 % in 2001. Netherlands NL: Prevalence of Overweight: Weight for Height: % of Children Under 5, Modeled Estimate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Netherlands – Table NL.World Bank.WDI: Social: Health Statistics. Prevalence of overweight children is the percentage of children under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's 2006 Child Growth Standards.;UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME).;Weighted average;Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues. Estimates are modeled estimates produced by the JME. Primary data sources of the anthropometric measurements are national surveys. These surveys are administered sporadically, resulting in sparse data for many countries. Furthermore, the trend of the indicators over time is usually not a straight line and varies by country. Tracking the current level and progress of indicators helps determine if countries are on track to meet certain thresholds, such as those indicated in the SDGs. Thus the JME developed statistical models and produced the modeled estimates.