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. Thailand data available from WorldPop here.

Thailand TH: Population Density: People per Square Km data was reported at 135.132 Person/sq km in 2017. This records an increase from the previous number of 134.791 Person/sq km for 2016. Thailand TH: Population Density: People per Square Km data is updated yearly, averaging 109.212 Person/sq km from Dec 1961 (Median) to 2017, with 57 observations. The data reached an all-time high of 135.132 Person/sq km in 2017 and a record low of 55.245 Person/sq km in 1961. Thailand TH: 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 Thailand – Table TH.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;

The population density in Thailand stood at 140.41 people in 2022. In a steady upward trend, the population density rose by 86.29 people from 1961.

In 2023, the population density of Bangkok was around ***** people per square kilometer, lower than the previous year, which was over ***** people per square kilometer. This value has slightly decreased over the past few years.

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

Actual value and historical data chart for Thailand Population Density People Per Sq Km

In 2023, Bangkok had the highest population density of all provinces in Thailand, with almost ***** people per square kilometer. Nonthaburi followed second, with a population density of around ***** people per square kilometer.

View yearly updates and historical trends for Thailand Population Density. Source: World Bank. Track economic data with YCharts analytics.

Thailand Population: By Province: Bangkok data was reported at 5,682.415 Person th in 2017. This records a decrease from the previous number of 5,686.646 Person th for 2016. Thailand Population: By Province: Bangkok data is updated yearly, averaging 5,682.415 Person th from Dec 1993 (Median) to 2017, with 25 observations. The data reached an all-time high of 5,844.607 Person th in 2003 and a record low of 5,570.743 Person th in 1995. Thailand Population: By Province: Bangkok data remains active status in CEIC and is reported by Department of Provincial Administration. The data is categorized under Global Database’s Thailand – Table TH.G001: Population: By Region and Registration.

Comprehensive socio-economic dataset for Thailand 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.

In 2023, there were approximately **** million males in Thailand who were 25 to 54 years old, which was the largest population group compared to other age groups. In comparison, there were around *** million males who were over 65 years old in that same year.

Population density of Thailand, Myanmar, Laos, Cambodia, and Vietnam based on data from the World Data Bank. This dataset also includes a visualization of the data using CartoDB. The time period is from 1995 to 2000.

Density of nursing and midwifery personnel of Thailand jumped by 14.30% from 2.7 number per thousand population in 2018 to 3.1 number per thousand population in 2019. Since the 1.24% fall in 2007, density of nursing and midwifery personnel shot up by 103.30% in 2019.

Around ***** people on the island of Koh Samui were between the ages of * and * in 2023. Due to the island's primarily tourism and hospitality-focused work economy, the notable decline in the teenager and young adult age groups on Koh Samui is likely the result of numerous young people leaving the island for higher education or broader career options in larger cities like Bangkok or Surat Thani.

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

Please note that these data represent 2025 Alpha release versions, constructed in September 2025

This bar chart displays population (people) by ISO 3 country code using the aggregation sum in Thailand. The data is filtered where the date is 2021. The data is about countries per year.

In 2023, there were around ***** million females in Thailand who were 25 to 54 years old, which was the largest population group compared to other female age groups. In comparison, there were almost **** million females who were 15 to 24 years old in that same year.

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

This study was aimed to explore the association between potential factors including public health and social measures and the number of influenza patients in Thailand between 2014–2021. Secondary data from relevant agencies were collected. Generalized Estimating Equation (GEE) and regression coefficient (β) were performed at a significance level of 0.05. We found factors associated with number of influenza patients during the time prior to COVID-19 pandemic were monthly income per household (Adjusted β = -0.02; 95% CI: -0.03, -0.01), population density (Adjusted β = 1.00; 95% CI: 0.82, 1.18), rainy season (Adjusted β = 137.15; 95% CI: 86.17, 188.13) and winter time (Adjusted β = 56.46; 95% CI: 3.21, 109.71). During the time of COVID-19 pandemic, population density (Adjusted β = 0.20; 95% CI: 0.15, 0.26), rainy season (Adjusted β = -164.23; 95% CI: -229.93, -98.52), winter time (Adjusted β = 61.06; 95% CI: 0.71, 121.41), public health control measures (prohibition of entering to into an area with high number of COVID-19 infections (Adjusted β = -169.34; 95% CI: -233.52, -105.16), and restriction of travelling also reduced the number of influenza patients (Adjusted β = -66.88; 95% CI: -125.15, -8.62) were associated with number of influenza patients. This study commends strategies in monitoring influenza patients to focus on the areas with low income, high population density, and in specific seasons. Public health and social measures which can be implemented are prohibition of entering to risk-areas (lock down), and restriction of travelling across provinces which their effectiveness in reducing influenza infections.

In 2020, there were approximately 54.1 million adults in Thailand. This was a slight increase from the previous year, which there were around 53.1 adults in the country. Adults are classified as individuals above the age of 18 years.