The population density in Bangladesh reached its highest in 2020, amounting to approximately 1.27 thousand people per square kilometer. The South Asian country was the tenth most densely populated country in the world in 2019. Within the Asia Pacific region, Bangladesh’s population density was only exceeded by Macao, Singapore, Hong Kong, and the Maldives. Overall, Asia had the highest population density in the world in 2018.

Population growth in Bangladesh

In 1971, Bangladesh gained its independence from Pakistan. Bangladesh’s birth rate and mortality rate had declined significantly in the past years with a life expectancy of 72.59 years in 2019. In general, the population in Bangladesh had been growing at a slow pace, slightly fluctuating around an annual rate of one percent. This growth was forecasted to continue, although it was estimated to halve by 2040. As of today, Dhaka is the largest city in Bangladesh.

Population density explained

According to the source, “population density is the mid-year population divided by land area in square kilometers.” Further, “population is based on the de facto definition of population, which counts all residents.” Bangladesh’s population reached an estimated number of 164.69 million inhabitants in 2020. In 2018, the country’s land area amounted 130.2 thousand square kilometers.

Population density (people per sq. km of land area) in Bangladesh was reported at 1301 sq. Km in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. Bangladesh - Population density (people per sq. km) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

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.

Bangladesh BD: Population Density: Inhabitants per sq km data was reported at 1,301.260 Person in 2022. This records an increase from the previous number of 1,288.000 Person for 2021. Bangladesh BD: Population Density: Inhabitants per sq km data is updated yearly, averaging 1,124.730 Person from Dec 1990 (Median) to 2022, with 33 observations. The data reached an all-time high of 1,301.260 Person in 2022 and a record low of 857.600 Person in 1990. Bangladesh BD: Population Density: Inhabitants per sq km data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Bangladesh – Table BD.OECD.GGI: Social: Demography: Non OECD Member: Annual.

Historical chart and dataset showing Bangladesh population density by year from 1961 to 2022.

Bangladesh BD: Population Density: People per Square Km data was reported at 1,301.259 Person/sq km in 2022. This records an increase from the previous number of 1,287.999 Person/sq km for 2021. Bangladesh BD: Population Density: People per Square Km data is updated yearly, averaging 882.459 Person/sq km from Dec 1961 (Median) to 2022, with 62 observations. The data reached an all-time high of 1,301.259 Person/sq km in 2022 and a record low of 409.544 Person/sq km in 1961. Bangladesh BD: 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 Bangladesh – Table BD.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 share of urban population in Bangladesh increased by 0.8 percentage points (+2.01 percent) in 2023 in comparison to the previous year. With 40.47 percent, the share thereby reached its highest value in the observed period. Notably, the share continuously increased over the last years.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.

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. Bangladesh data available from WorldPop here.

Comprehensive socio-economic dataset for Bangladesh 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.

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.

The annual population growth in Bangladesh increased by 0.2 percentage points (+19.61 percent) compared to the previous year. With 1.22 percent, the population growth thereby reached its highest value in the observed period. Annual population growth refers to the change in the population over time, and is affected by factors such as fertility, mortality, and migration.Find more key insights for the annual population growth in countries like Bhutan and India.

Urban population (% of total population) in Bangladesh was reported at 41.23 % in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. Bangladesh - Urban population (% of total) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Tentative IAPP Locations: Source: Project Appraisal Document (PAD). Population: (Total population) (2011): Total 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. Source: Government of the People’s Republic of Bangladesh -Ministry of Planning - Bangladesh Bureau of Statistics. “Population and Housing Census 2011.Preliminary Results.” Population Density (Persons per 1 square kilometer) (2011): Population divided by land area in square kilometers. Source: Government of the People’s Republic of Bangladesh -Ministry of Planning - Bangladesh Bureau of Statistics. “Population and Housing Census 2011.Preliminary Results.” Poverty (Proportion of population below the poverty line) (2010): Proportion of the population living on less than US$1.25 a day, measured at 2005 international prices, adjusted forpurchasing power parity (PPP). Source: Government of the People’s Republic of Bangladesh -Ministry of Planning - Bangladesh Bureau of Statistics. “HIES Survey 2010, Chapter 6.” Malnutrition (Proportion of underweight children under 5 years) (2011): Prevalence of severely underweight children is the percentage of children aged 0-59 months whose weight for age is less than minus 3 standard deviations below the median weight for age of the international reference population. Source: “Bangladesh Demographic and Health Survey 2011. Preliminary Report.”Measure DHS. MEASURE DHS (Demographic and Health Surveys) Project is responsible for collecting and disseminating accurate, nationally representative data on health and population in developing countries. The project is implemented by Macro International, Inc. and is funded by the United States Agency for International Development (USAID) with contributions from other donors such as UNICEF, UNFPA, WHO, UNAIDS. Irrigation (2009/10): Total Irrigated Area in Acres (Thousands). Source: Government of the People’s Republic of Bangladesh -Ministry of Planning - Bangladesh Bureau of Statistics.2010 Yearbook of Agricultural Statistics of Bangladesh.

This interactive map of Bangladesh highlights the project locations of the Integrated Agricultural Productivity Project (IAPP) and PRAN. Bangladesh is divided into seven administrative divisions, which are broken down into 64 districts, and further divided into 485 upazilas. This map overlays sub-national poverty data, demographic indicators, and other information relevant to the program. IAPP will target the districts of Rangpur, Kurigram, Lalmonirhat, and Nilfamari in the north and the districts of Barisal, Patuakhali, Barguna and Jhalokathi in the south. The project is expected to increase the productivity of major crops like cereals and pulses, increase the productivity of fish and livestock, increase the availability of certified seed, increase the irrigated area, and the income of farmers in all 54 upazilas in these eight districts. The project areas were selected for their high rates of poverty, food insecurity, and their vulnerability to natural shocks such as tidal surge in the south, and flash flood and drought in the north. GAFSP is financing the expansion of food processing and manufacturing capacity of Natore Agro Limited from PRAN group. PRAN group is the largest food and nutrition company in Bangladesh, with more than 40,000 employees and over 200 different products. The enhancement of operations is creating new jobs (over 1,200 expected), in a region severely affected by unemployment and is increasing the opportunities for local producers as raw material suppliers for the company. Data Sources: PRAN Project LocationSource: GAFSP Documents. IAPP Project Areas

Source: Project Appraisal Document (PAD). Poverty Incidence (Proportion of population below the poverty line) (2010): Proportion of the population living on less than US$1.25 a day, measured at 2005 international prices, adjusted for purchasing power parity (PPP).Source: Bangladesh Bureau of Statistics. “HIES Survey 2010 Chapter 6.” Malnutrition (Proportion of underweight children under 5 years) (2011): Prevalence of severely underweight children is the percentage of children under age 5 whose weight-for-age is more than 3 standard deviations below the median for the international reference population ages 0-59 months.Source: Measure DHS. “Bangladesh Demographic and Health Survey 2011. Preliminary Report.” Total Population (2011): Total 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.Source: Bangladesh Bureau of Statistics. “Population and Housing Census 2011. Preliminary Results.” Population Density (2011): Population divided by land area in square kilometers.Source: Bangladesh Bureau of Statistics. “Population and Housing Census 2011. Preliminary Results.” Irrigated Area (2009/10): Total irrigated area in hectares.Source: Bangladesh Bureau of Statistics. 2010 Yearbook of Agricultural Statistics of Bangladesh. Potato Production (2009-10 and 2010-11): Total production in tons by variety and total production in tons per hectare by variety.Source: Bangladesh Bureau of Statistics. “2012 Yearbook of Agricultural Statistics of Bangladesh.” Boro Rice (2009-10 and 2010-11): Total production in tons by variety and total production in tons per hectare by variety.Source: Bangladesh Bureau of Statistics. “2012 Yearbook of Agricultural Statistics of Bangladesh.” Bangladesh Soil Salinity (2009): Saline soils, salinity boundary, and coastlines.

Source: Soil Resource Development Institute SRMAF Project – Bangladesh Ministry of Agriculture. “Saline Soils in Bangladesh 2010.”The maps displayed on this website are for reference only. The boundaries, colors, denominations and any other information shown on these maps do not imply, on the part of GAFSP (and the World Bank Group), any judgment on the legal status of any territory, or any endorsement or acceptance of such boundaries.

In this analysis we have combined several data sources around the floods in Bangladesh in August 2017.

Visualization

• See attached map for a map visualization of this analysis.
• See http://bit.ly/2uFezkY for a more interactive visualization in Carto.

Situation

Currently, in Bangladesh many water level measuring stations measure water levels that are above danger levels. This sets in triggers in motion for the partnership of the 510 Data Intitiative and the Red Cross Climate Centre to get into action.

Indicators and sources

In the attached map, we combined several sources:

• Locations of waterlevel stations and their respective excess water levels (cms above danger level) at 14/08/2017 (Source: http://www.ffwc.gov.bd/index.php/googlemap?id=20)
• Population density in Bangladesh to quickly see where many people live in comaprison to these higher water-level stations. (Source: http://www.worldpop.org.uk/data/summary/?doi=10.5258/SOTON/WP00018 >> the People per hectare 2015 UN-adjusted totals file is used.)
• Vulnerability Index: we constructed a Vulnerability Index (0-10) based on two sources. First poverty incidence was collected from Worldpop (Source: http://www.worldpop.org.uk/data/summary/?doi=10.5258/SOTON/WP00020 >> The estimated likelihood of living below $2.50/day). Second, we used a Deprivation Index which is estimated in the report Lagging District Reports 2015 (Source: http://www.plancomm.gov.bd/wp-content/uploads/2015/02/15_Lagging-Regions-Study.pdf > Appendices > Table 20), which combines many socio-economic variables into one Deprivation Index through PCA analysis.

Detailed methodology Vulnerability

• The above-mentioned poverty source file is on a raster level. This raster level poverty was transformed to admin-4 level geographic areas (source: https://data.humdata.org/dataset/bangladesh-admin-level-4-boundaries), by taking a population-weighted average. (Source population also Worldpop).
• The district-level PCA components from abovementioned reports were matched to the geodata based on district names, and thus joined to the admin-4 level areas, which now contain a poverty value as well as Deprivation Index value. Note that all admin-4 areas within one district (admin-2) obviously all have the same value. The poverty rates do differ between all admin-4 areas.
• Lastly, both variables were transformed to a 0-10 score (linearly), and a geomean was taken to calculate the final index of the two. A geomean (as opposed to an arithmetic mean) is often used in calculating composite risk indices, for example in the widely used INFORM-framework (www.inform-index.org).

The data set is a 2015 heat wave hazard, exposure and vulnerability data set in Dhaka, Bangladesh, with a spatial resolution of 30m and a temporal resolution of yearly. Heat wave hazard is an index to measure the severity of heat wave event, which is expressed by surface temperature; heat wave exposure refers to the degree that human, livelihood and economy may be adversely affected, which is expressed by nighttime lighting data, and population density. The population older than 65 and younger than 5 years old constitute vulnerable groups; heat wave vulnerability is a measure of increased / reduced risk in the environment. The distance from road / hospital and ambulance station / water body, NDVI, impervious layer and slum area are used to represent the vulnerability of high temperature heat wave. The data set has been proved by experts, which can provide support for regional high temperature heat wave risk assessment.

A database (NDP-068) was generated from estimates of geographically referenced carbon densities of forest vegetation in tropical Southeast Asia for 1980. A geographic information system (GIS) was used to incorporate spatial databases of climatic, edaphic, and geomorphological indices and vegetation to estimate potential (i.e., in the absence of human intervention and natural disturbance) carbon densities of forests. The resulting map was then modified to estimate actual 1980 carbon density as a function of population density and climatic zone. The database covers the following 13 countries: Bangladesh, Brunei, Cambodia (Campuchea), India, Indonesia, Laos, Malaysia, Myanmar (Burma), Nepal, the Philippines, Sri Lanka, Thailand, and Vietnam.

The data sets within this database are provided in three file formats: ARC/INFOTM exported integer grids; ASCII (American Standard Code for Information Interchange) files formatted for raster-based GIS software packages; and generic ASCII files with x, y coordinates for use with non-GIS software packages.

The database includes ten ARC/INFO exported integer grid files (five with the pixel size 3.75 km x 3.75 km and five with the pixel size 0.25 degree longitude x 0.25 degree latitude) and 27 ASCII files. The first ASCII file contains the documentation associated with this database. Twenty-four of the ASCII files were generated by means of the ARC/INFO GRIDASCII command and can be used by most raster-based GIS software packages. The 24 files can be subdivided into two groups of 12 files each.

The files contain real data values representing actual carbon and potential carbon density in Mg C/ha (1 megagram = 10^6 grams) and integer-coded values for country name, Weck's Climatic Index, ecofloristic zone, elevation, forest or non- forest designation, population density, mean annual precipitation, slope, soil texture, and vegetation classification. One set of 12 files contains these data at a spatial resolution of 3.75 km, whereas the other set of 12 files has a spatial resolution of 0.25 degree. The remaining two ASCII data files combine all of the data from the 24 ASCII data files into 2 single generic data files. The first file has a spatial resolution of 3.75 km, and the second has a resolution of 0.25 degree. Both files also provide a grid-cell identification number and the longitude and latitude of the centerpoint of each grid cell.

The 3.75-km data in this numeric data package yield an actual total carbon estimate of 42.1 Pg (1 petagram = 10^15 grams) and a potential carbon estimate of 73.6 Pg; whereas the 0.25-degree data produced an actual total carbon estimate of 41.8 Pg and a total potential carbon estimate of 73.9 Pg.

Fortran and SASTM access codes are provided to read the ASCII data files, and ARC/INFO and ARCVIEW command syntax are provided to import the ARC/INFO exported integer grid files. The data files and this documentation are available without charge on a variety of media and via the Internet from the Carbon Dioxide Information Analysis Center (CDIAC).

Bangladesh is a South Asian country located at the crossroads of the Indochina and Indo-Himalayan subregions, making it a country of rich faunal diversity. Bangladesh's high population density paired with rapid habitat alteration leaving only 6% of its natural habitats threatens its faunal diversity. Over 1,455 bat species live on earth, providing immense ecological services to maintain biodiversity. The paucity of bat research in Bangladesh and the lack of comprehensive work has led us to set the goal of checking how many species are present in Bangladesh, and the possibility of bat species yet to have occurred. Here we compiled species occurrence data on the bats of Bangladesh and states in neighboring countries (India – states are West Bengal, Sikkim, Meghalaya, Assam, Tripura, Mizoram; Myanmar – states are Chin, Rakhine) from the museums (American Museum of Natural History, Smithsonian National Museum of Natural History, Natural History Museum at United Kingdom, Field Museum of Natural History, Hungarian Natural History Museum, and Royal Ontario Museum), Global Biodiversity Information Facility, and literature, and constructed distribution maps for each species. The maps depicted both the fine-scale and coarse-scale distribution of the species. We confirmed 31 species are occurring in Bangladesh – among them, 22 species are confirmed with the voucher specimen, 15 species are associated with the preserved tissues, and one is confirmed with the morphometric data and key characteristics. Based on the species occurrence in the states of India and Myanmar, along with the habitat preference, an additional 83 species are yet to have occurred in Bangladesh. Among them, 38 species are categorized as Highly Probable, 33 species are Probable, and 10 species are Possible. We recommend bat surveys are urgent in Bangladesh using all complementary capture techniques that will contribute to voucher specimen collections and confirm the presence of bats. In addition, echolocation calls of bats can help establish call libraries.

On the basis of the global tropical cyclone track dataset, the global disaster events and losses dataset, the global tide level observation dataset and DEM data, coastline distribution data, land cover information, population and other related data of the Belt and Road, indicators related to the vulnerability of storm surge in each unit are extracted and calculated using 100 meter grid as evaluation unit, such as population density, land cover type, etc. The comprehensive index of storm surge vulnerability is constructed, and the vulnerability index of storm surge is obtained by using the weighted method. Finally, the storm surge vulnerability index is normalized to 0-1, which can be used to evaluate the vulnerability level of storm surge in each assessment unit. The key nodes data set only contains 11 nodes which have risks (Chittagong port, Bangladesh; Kyaukpyu Port, Myanmar; Kolkata, India; Yangon Port, Myanmar; Karachi, Pakistan; Dhaka, Bangladesh; Mumbai, India; Hambantota Port, Sri Lanka; Bangkok, Thailand; China-Myanmar Oil and Gas Pipeline; Jakarta-Bandung High-speed Railway).

BackgroundThe Educational Development Index (EDI) is a critical tool for assessing and tracking the progress of education systems from local to national, and even global scales and needs to be chosen for every layer of the subnational boundaries to secure the basic human rights of the people. In reality, there are significant variations within the consecutive time breaks and the geographical boundaries that need to be examined. The authors aim to examine how the EDI relates to various spatiotemporal variables.Methods and MaterialsThis research is based on secondary data on literacy rates (EDI) from 64 districts of Bangladesh and 6 relevant variables over the period 2001 to 2021. The optimal model for the data was identified from Bayesian spatial-temporal modeling (Linear, Analysis of Variance (ANOVA), Autoregressive (AR1), and AR2) and the Markov Chain Monte Carlo (MCMC) method used to generate data about the prior and posterior realizations. To select the best model different model selection and validation criteria such as the Deviance Information Criterion (DIC), Watanabe-Akaike information criterion (WAIC), and Root Mean Square Error (RMSE) were employed in this study.ResultsThe ‘AR1’ model is a ‘temporal model’ performed better than others. Significant spatial (=0.994) and temporal (=0.347) variations were identified for the suited model. Of the factors considered for model fitting, the health index, income index, expected years of schooling, population density, and dependency ratio are found to be important components of educational development in Bangladesh.ConclusionThe variation in the spatial domain can be used to identify the districts to improve the educational index controlling responsible factors by the policymakers.