Population density (people per sq. km of land area) in Bahrain was reported at 1930 sq. Km in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. Bahrain - Population density (people per sq. km) - actual values, historical data, forecasts and projections were sourced from the World Bank on June of 2025.

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

Bahrain BH: Population Density: People per Square Km data was reported at 1,852.234 Person/sq km in 2021. This records a decrease from the previous number of 1,882.126 Person/sq km for 2020. Bahrain BH: Population Density: People per Square Km data is updated yearly, averaging 775.964 Person/sq km from Dec 1961 (Median) to 2021, with 61 observations. The data reached an all-time high of 1,915.626 Person/sq km in 2019 and a record low of 241.986 Person/sq km in 1961. Bahrain BH: 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 Bahrain – Table BH.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;

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

Explore population data by governorate, citizenship, and gender in Bahrain. Find information on population, capital, and population density in this comprehensive dataset.

Population, Capital, Population Density, Bahrain population data

BahrainFollow data.kapsarc.org for timely data to advance energy economics research.

Explore population data at mid-year by age groups, nationality, and gender in Bahrain. Find valuable insights on population density and trends.

Population, Population Density, Bahrain population data

BahrainFollow data.kapsarc.org for timely data to advance energy economics research..Statistical Abstract Chapters -> Population

Bahrain BH: Population Density: Inhabitants per sq km data was reported at 1,941.090 Person in 2022. This records an increase from the previous number of 1,900.970 Person for 2021. Bahrain BH: Population Density: Inhabitants per sq km data is updated yearly, averaging 1,344.080 Person from Dec 1990 (Median) to 2022, with 33 observations. The data reached an all-time high of 1,941.090 Person in 2022 and a record low of 746.080 Person in 1990. Bahrain BH: 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 Bahrain – Table BH.OECD.GGI: Social: Demography: Non OECD Member: Annual.

Distribution Population by Governorate & Nationality of Households

The share of urban population in Bahrain saw no significant changes in 2023 in comparison to the previous year 2022 and remained at around 89.87 percent. Still, the share reached its highest value in the observed period in 2023. 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 United Arab Emirates and Saudi Arabia.

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 male population (people) by demonym using the aggregation sum in Bahrain. The data is about countries per year.

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

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

人口密度：每平方公里人口在12-01-2021达1,852.234Person/sq km，相较于12-01-2020的1,882.126Person/sq km有所下降。人口密度：每平方公里人口数据按年更新，12-01-1961至12-01-2021期间平均值为775.964Person/sq km，共61份观测结果。该数据的历史最高值出现于12-01-2019，达1,915.626Person/sq km，而历史最低值则出现于12-01-1961，为241.986Person/sq km。CEIC提供的人口密度：每平方公里人口数据处于定期更新的状态，数据来源于World Bank，数据归类于全球数据库的巴林 – Table BH.World Bank.WDI: Population and Urbanization Statistics。

Abstract

In order to develop various methods of comparable data collection on health and health system responsiveness WHO started a scientific survey study in 2000-2001. This study has used a common survey instrument in nationally representative populations with modular structure for assessing health of indviduals in various domains, health system responsiveness, household health care expenditures, and additional modules in other areas such as adult mortality and health state valuations.

The health module of the survey instrument was based on selected domains of the International Classification of Functioning, Disability and Health (ICF) and was developed after a rigorous scientific review of various existing assessment instruments. The responsiveness module has been the result of ongoing work over the last 2 years that has involved international consultations with experts and key informants and has been informed by the scientific literature and pilot studies.

Questions on household expenditure and proportionate expenditure on health have been borrowed from existing surveys. The survey instrument has been developed in multiple languages using cognitive interviews and cultural applicability tests, stringent psychometric tests for reliability (i.e. test-retest reliability to demonstrate the stability of application) and most importantly, utilizing novel psychometric techniques for cross-population comparability.

The study was carried out in 61 countries completing 71 surveys because two different modes were intentionally used for comparison purposes in 10 countries. Surveys were conducted in different modes of in- person household 90 minute interviews in 14 countries; brief face-to-face interviews in 27 countries and computerized telephone interviews in 2 countries; and postal surveys in 28 countries. All samples were selected from nationally representative sampling frames with a known probability so as to make estimates based on general population parameters.

The survey study tested novel techniques to control the reporting bias between different groups of people in different cultures or demographic groups ( i.e. differential item functioning) so as to produce comparable estimates across cultures and groups. To achieve comparability, the selfreports of individuals of their own health were calibrated against well-known performance tests (i.e. self-report vision was measured against standard Snellen's visual acuity test) or against short descriptions in vignettes that marked known anchor points of difficulty (e.g. people with different levels of mobility such as a paraplegic person or an athlete who runs 4 km each day) so as to adjust the responses for comparability . The same method was also used for self-reports of individuals assessing responsiveness of their health systems where vignettes on different responsiveness domains describing different levels of responsiveness were used to calibrate the individual responses.

This data are useful in their own right to standardize indicators for different domains of health (such as cognition, mobility, self care, affect, usual activities, pain, social participation, etc.) but also provide a better measurement basis for assessing health of the populations in a comparable manner. The data from the surveys can be fed into composite measures such as "Healthy Life Expectancy" and improve the empirical data input for health information systems in different regions of the world. Data from the surveys were also useful to improve the measurement of the responsiveness of different health systems to the legitimate expectations of the population.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample was a multi-stage random probability sample representative of the population residing in urban and rural areas of Bahrain. A sample design method in 2 stages was used: 1. The survey was conducted in the three main municipalities of Bahrain. 2. The second stage consisted in the selection of the households within the primary sampling areas.

The sampling design employed for the main urban centres was as follows: - The three main urban centers were divided into Administrative Units which themselves were subdivided into clusters (consisting of an agglomeration within a determined polygon of roads and streets). - Each cluster has a certain number of blocks. The latter are defined as the smallest tract of land outlined by streets or roads that contain houses and buildings. - In each block, buildings and houses are identified and counted. The selection of the respondent was done using the Kish Method.

Statistical data acquired from the Block census was used in the sample design of this study. The density of the population varies from one Administrative Unit to another. They were classified into 3 categories: • high density • medium density • low density areas.

The number of sampling units assigned for interviewing per Administrative Unit varied so that the population density was adequately represented.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

Data Coding At each site the data was coded by investigators to indicate the respondent status and the selection of the modules for each respondent within the survey design. After the interview was edited by the supervisor and considered adequate it was entered locally.

Data Entry Program A data entry program was developed in WHO specifically for the survey study and provided to the sites. It was developed using a database program called the I-Shell (short for Interview Shell), a tool designed for easy development of computerized questionnaires and data entry (34). This program allows for easy data cleaning and processing.

The data entry program checked for inconsistencies and validated the entries in each field by checking for valid response categories and range checks. For example, the program didn’t accept an age greater than 120. For almost all of the variables there existed a range or a list of possible values that the program checked for.

In addition, the data was entered twice to capture other data entry errors. The data entry program was able to warn the user whenever a value that did not match the first entry was entered at the second data entry. In this case the program asked the user to resolve the conflict by choosing either the 1st or the 2nd data entry value to be able to continue. After the second data entry was completed successfully, the data entry program placed a mark in the database in order to enable the checking of whether this process had been completed for each and every case.

Data Transfer The data entry program was capable of exporting the data that was entered into one compressed database file which could be easily sent to WHO using email attachments or a file transfer program onto a secure server no matter how many cases were in the file. The sites were allowed the use of as many computers and as many data entry personnel as they wanted. Each computer used for this purpose produced one file and they were merged once they were delivered to WHO with the help of other programs that were built for automating the process. The sites sent the data periodically as they collected it enabling the checking procedures and preliminary analyses in the early stages of the data collection.

Data quality checks Once the data was received it was analyzed for missing information, invalid responses and representativeness. Inconsistencies were also noted and reported back to sites.

Data Cleaning and Feedback After receipt of cleaned data from sites, another program was run to check for missing information, incorrect information (e.g. wrong use of center codes), duplicated data, etc. The output of this program was fed back to sites regularly. Mainly, this consisted of cases with duplicate IDs, duplicate cases (where the data for two respondents with different IDs were identical), wrong country codes, missing age, sex, education and some other important variables.

As of 2020, the number of new cancer cases amongst women in Bahrain was 643. In that year, the total population of Bahrain was just over 1.7 million and over five hundred deaths in the nation were caused by cancer.

As of 2020, the number of deaths caused by cancer amongst women in Bahrain was 281. In that year, breast cancer and Colorectum cancer were the leading causes of death amongst all types of cancers in Bahrain.

人口密度：每平方公里的居民在12-01-2022达1,941.090人，相较于12-01-2021的1,900.970人有所增长。人口密度：每平方公里的居民数据按年更新，12-01-1990至12-01-2022期间平均值为1,344.080人，共33份观测结果。该数据的历史最高值出现于12-01-2022，达1,941.090人，而历史最低值则出现于12-01-1990，为746.080人。CEIC提供的人口密度：每平方公里的居民数据处于定期更新的状态，数据来源于Organisation for Economic Co-operation and Development，数据归类于全球数据库的巴林 – Table BH.OECD.GGI: Social: Demography: Non OECD Member: Annual。

This bar chart displays birth rate (per 1,000 people) by ISO 3 country code using the aggregation average, weighted by population in Bahrain. The data is about countries per year.

This article explores the ongoing COVID-19 pandemic, asking how long it might last. Focusing on Bahrain, which has a finite population of 1.7M, it aimed to predict the size and duration of the pandemic, which is key information for administering public health policy. We compare the predictions made by numerical solutions of variations of the Kermack-McKendrick SIR epidemic model and Tsallis-Tirnakli model with the curve-fitting solution of the Bass model of product adoption. The results reiterate the complex and difficult nature of estimating parameters, and how this can lead to initial predictions that are far from reality. The Tsallis-Tirnakli and Bass models yield more realistic results using data-driven approaches but greatly differ in their predictions. The study discusses possible sources for predictive inaccuracies, as identified during our predictions for Bahrain, the United States, and the world. We conclude that additional factors such as variations in social network structure, public health policy, and differences in population and population density are major sources of inaccuracies in estimating size and duration.