In 2022, the population density in Uganda increased by seven inhabitants per square kilometer (+3.06 percent) compared to 2021. While the growth is slowing down, with 235.95 inhabitants per square kilometer, the population density is at its peak in the observed period. Notably, the population density continuously increased over the last years.Population density refers to the number of people living in a certain country or area, given as an average per square kilometer. It is calculated by dividing the total midyear population by the total land area.Find more key insights for the population density in countries like Ethiopia and Rwanda.

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 integrating census, survey, satellite and GIS data sets 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.

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.

Uganda data available from WorldPop here.

Uganda UG: Population Density: People per Square Km data was reported at 213.759 Person/sq km in 2017. This records an increase from the previous number of 206.902 Person/sq km for 2016. Uganda UG: Population Density: People per Square Km data is updated yearly, averaging 84.311 Person/sq km from Dec 1961 (Median) to 2017, with 57 observations. The data reached an all-time high of 213.759 Person/sq km in 2017 and a record low of 35.066 Person/sq km in 1961. Uganda UG: 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 Uganda – Table UG.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;

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

This statistic shows the total population of Uganda from 2013 to 2023 by gender. In 2023, Uganda's female population amounted to approximately 24.53 million, while the male population amounted to approximately 24.12 million inhabitants.

This statistic shows the median age of the population in Uganda from 1950 to 2100. The median age is the age that divides a population into two numerically equal groups; that is, half the people are younger than this age and half are older. It is a single index that summarizes the age distribution of a population. In 2020, the median age of the population in Uganda was 15.9 years.

Social distancing is a public health measure intended to reduce infectious disease transmission, by maintaining physical distance between individuals or households. In the context of the COVID-19 pandemic, populations in many countries around the world have been advised to maintain social distance (also referred to as physical distance), with distances of 6 feet or 2 metres commonly advised. Feasibility of social distancing is dependent on the availability of space and the number of people, which varies geographically. In locations where social distancing is difficult, a focus on alternative measures to reduce disease transmission may be needed. To help identify locations where social distancing is difficult, we have developed an ease of social distancing index. By index, we mean a composite measure, intended to highlight variations in ease of social distancing in urban settings, calculated based on the space available around buildings and estimated population density. Index values were calculated for small spatial units (vector polygons), typically bounded by roads, rivers or other features. This dataset provides index values for small spatial units within urban areas in Uganda. Measures of population density were calculated from high-resolution gridded population datasets from WorldPop, and the space available around buildings was calculated using building footprint polygons derived from satellite imagery (Ecopia.AI and Maxar Technologies. 2020). These data were produced by the WorldPop Research Group at the University of Southampton. This work was part of the GRID3 project with funding from the Bill and Melinda Gates Foundation and the United Kingdom’s Department for International Development. Project partners included the United Nations Population Fund (UNFPA), Center for International Earth Science Information Network (CIESIN) in the Earth Institute at Columbia University, and the Flowminder Foundation.

The population density in Tanzania increased by 2.1 inhabitants per square kilometer (+2.96 percent) in 2022 in comparison to the previous year. With 73.05 inhabitants per square kilometer, the population density thereby reached its highest value in the observed period. Notably, the population density continuously increased over the last years.Population density refers to the number of people living in a certain country or area, given as an average per square kilometer. It is calculated by dividing the total midyear population by the total land area.Find more key insights for the population density in countries like Somalia and Uganda.

This bar chart displays male population (people) by continent and is filtered where the country includes Uganda. The data is about countries per year.

This bar chart displays male population (people) by date using the aggregation sum and is filtered where the country is Uganda. The data is about countries per year.

The raster dataset consists of a 500m score grid for the plantain storage location, produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location. The location score is achieved by processing sub-model outputs that characterize logistical factors for selected crop warehouse locations: • Supply: Crop. • Demand: Human population density, Major cities population (national and bordering countries). • Infrastructure/accessibility: main transportation infrastructure. It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.4) + ("Human Population Density" * 0.2) + (“Major Cities Accessibility” * 0.1) + (”Regional Cities Accessibility” *0.1 ) + (”Port Accessibility” *0.1 ) + (”Asset Wealth” *0.1 )

UG：人口密度：每平方公里人口在12-01-2017达213.759Person/sq km，相较于12-01-2016的206.902Person/sq km有所增长。UG：人口密度：每平方公里人口数据按年更新，12-01-1961至12-01-2017期间平均值为84.311Person/sq km，共57份观测结果。该数据的历史最高值出现于12-01-2017，达213.759Person/sq km，而历史最低值则出现于12-01-1961，为35.066Person/sq km。CEIC提供的UG：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乌干达 – 表 UG.世行.WDI：人口和城市化进程统计。

Nigeria has the largest population in Africa. As of 2024, the country counted over 232.6 million individuals, whereas Ethiopia, which ranked second, has around 132 million inhabitants. Egypt registered the largest population in North Africa, reaching nearly 116 million people. In terms of inhabitants per square kilometer, Nigeria only ranks seventh, while Mauritius has the highest population density on the whole African continent. The fastest-growing world region Africa is the second most populous continent in the world, after Asia. Nevertheless, Africa records the highest growth rate worldwide, with figures rising by over two percent every year. In some countries, such as Niger, the Democratic Republic of Congo, and Chad, the population increase peaks at over three percent. With so many births, Africa is also the youngest continent in the world. However, this coincides with a low life expectancy. African cities on the rise The last decades have seen high urbanization rates in Asia, mainly in China and India. However, African cities are currently growing at larger rates. Indeed, most of the fastest-growing cities in the world are located in Sub-Saharan Africa. Gwagwalada, in Nigeria, and Kabinda, in the Democratic Republic of the Congo, ranked first worldwide. By 2035, instead, Africa's fastest-growing cities are forecast to be Bujumbura, in Burundi, and Zinder, Nigeria.

236,0 (personas por km2 de superficie de tierra) in 2022. Population density is midyear population divided by land area in square kilometers.

The raster dataset consists of a 500m score grid for the cassava storage location, produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location. The location score is achieved by processing sub-model outputs that characterize logistical factors for selected crop warehouse locations: • Supply: Crop. • Demand: Human population density, Major cities population (national and bordering countries). • Infrastructure/accessibility: main transportation infrastructure. It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.4) + ("Human Population Density" * 0.2) + (“Major Cities Accessibility” * 0.1) + (”Regional Cities Accessibility” *0.1 ) + (”Port Accessibility” *0.1 ) + (”Asset Wealth” *0.1 )

This survey was conducted during the "Policies for Improved Land Management Project in Uganda, 1999-2003." The long term objective of the project was to contribute to improved land management in Uganda, in order to increase agricultural productivity, reduce poverty and ensure sustainable use of natural resources, with an immediate purpose of helping policy makers identify and assess policy, institutional and technological strategies to improve land management. The questionnaires were administered to 107 communities, the lowest administrative units in Uganda called Local Council 1 or LC1. The study region covered most of Uganda, including more densely populated and more secure areas in the southwest, central, eastern and parts of the north, representing seven of the nine major farming systems of the country. Within the study region, communities were selected using a stratified random sample, with the stratification based on population density and development domains defined by the different agro-ecological and market access zones. One hundred villages were selected in this way. Additional communities were purposely selected in southwest Uganda, where the African Highlands Initiative is conducting research, and in Iganga district, where the International Center for Tropical Agriculture (CIAT) is conducting research. Topics covered in the LC1 survey included community concerns and priorities, establishment and change of local council boundaries, population change, use of local council revenue, infrastructure and services, programs and organizations, land rights, and collective resource management. Usually, each LC1 had one village, i.e. a cluster of households living in the neighborhood. In the case where the LC1 had more than one village, a village was randomly selected for the village level survey. Topics in the village survey included livelihood strategies, land use, land tenure and land markets, labor, wage rates and credit, crop production, commercialization and management, livestock management and commercialization, tree product and commercialization. In both the LC1 and village surveys, interviews were conducted with a group of representative people from each selected community.

The raster dataset consists of a 500m score grid for the maize storage location, produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location. The location score is achieved by processing sub-model outputs that characterize logistical factors for selected crop warehouse locations: • Supply: Crop. • Demand: Human population density, Major cities population (national and bordering countries). • Infrastructure/accessibility: main transportation infrastructure. It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.4) + ("Human Population Density" * 0.2) + (“Major Cities Accessibility” * 0.1) + (”Regional Cities Accessibility” *0.1 ) + (”Port Accessibility” *0.1 ) + (”Asset Wealth” *0.1 )

Abstract

The Future of African Remittances (FAR) team conducted research on remittance flows to measure and understand the remittance process in sub-Saharan Africa. This ambitious and important research is initially focused on three countries in East Africa - Ethiopia, Kenya and Uganda.

In order to glean insights into the remittance process in the three designated countries, the World Bank designed a two-phase survey process. Phase 1 involved conducting a national survey in each of the three countries. The purpose of the first phase of research was to collect a large representative sample of the adult population in each country. The national surveys provide important baseline data about international remittance flows including: an estimate of the percent of the total adult population that regularly receives remittances, the average amount of each remittance received, most common methods of receipt and top sending countries. Additionally, through the analysis of the national survey results, World Bank was able to identify areas of each country that have high concentrations of international remittance recipients. This important piece of information guided Phase 2 of the research - surveys of remittance receivers in each country. Whereas the national surveys aimed to collect general data about the remittance process, the surveys of remittance recipients allowed for the collection of more detailed data about the remittance process itself, how remittances are used, the relationship between sender and receiver, and interest in various financial products.

The results of this research will not only provide estimates of total annual amounts of remittances for each country, but also will tell us the percentage of the population in each country that is involved in the international remittance process. Furthermore, it will offer insights as to the degree to which Ethiopians, Kenyans and Ugandans depend on international remittances and how the money is used, saved and/or invested. Results will also measure interest in financial products that, if utilized, can significantly impact the financial well-being of the population and the overall economic stability of each country.

Geographic coverage

National Coverage

Analysis unit

Households Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

General:

The total samples were compiled utilizing multi-stage stratified random sampling through respondent selection. Multi-stage random sampling ensured that a random sample of adults was collected in each country. First, after stratifying the population of each country by region and population density, sampling points (SPs) were determined. SPs were then randomly selected within each stratum. At each SP, respondents were randomly selected to participate in the survey.

Phase 1:

The first phase consisted of national surveys of the adult population of each country. The three survey samples were designed to be representative of the adult populations of these three countries. World Bank coordinated and oversaw all aspects of the sampling and interviewing process. A team of local field experts was hired in each country to conduct the actual interviews. All interviewers were professionally trained and supervised by research personnel. In this phase of the research, a total of 2022 Kenyan adults were interviewed.

Phase 2:

Once the national surveys were completed, the results were analyzed to determine the areas of concentration of the remittance recipient population, after which the second phase of the project was conducted. This phase of the project included a targeted survey of the remittance recipient population of each of the three East African countries. Sampling Points were established based on the analysis of the national survey data and the identification of areas within each country that showed the highest concentrations of remittances received from relatives abroad in proportion to the sample size of all areas surveyed. Once again, local field experts were hired in each country to conduct the interviews, training and supervision of field operations. Languages of interviews were the same as those employed in Phase 1 and, again, all interviews were conducted in person using the PAPI method. A total of 400 interviews with regular international remittance recipients were completed in each country during August and September of 2010. The margin of error for all three surveys is approximately ±5 percentage points and the 95 percent level of confidence.

Detail:

The total sample was compiled utilizing multi-stage stratified random sampling through respondent selection. This sampling method enabled B&A to ensure that a representative random sample of Kenyan adults was collected. There are three stages to this type of sampling methodology. First, after stratifying the Kenyan population by region and population density, sampling points (SPs) were determined. SPs were then randomly selected within each stratum. In the second stage, using the random route method, dwellings were selected within each SP. The random route method involves selecting an address in each SP at random as a starting point. Each interviewer was given instructions to identify additional dwellings by taking alternate left and right turns and stopping at every Nth dwelling. The third and final stage involved selecting actual participants - for each selected dwelling, individual respondents were chosen using a Kish grid. In a Kish grid, prior to beginning the interview, the interviewer first asks for the ages and genders of every household member (only persons aged 18 or older were eligible for selection). The individual to be interviewed was then chosen based on a random number in the grid.

Once the national survey was completed, B&A analyzed the results to determine the areas of concentration of the remittance recipient population, after which the second phase of the project was conducted. This phase included a targeted survey of the remittance recipient population in Kenya. Sampling Points were established based on B&A's analysis of the national survey data and the identification of areas of the country that showed concentrations of international remittance receivers in proportion to the sample size of all areas surveyed. Once again, local field experts were hired to conduct the interviews and B&A conducted all training and supervision of field operations. Interviews were conducted in English or Swahili depending on respondent preference and all interviews were conducted in person using the PAPI method. A total of 401 interviews with regular international remittance recipients were conducted in Kenya during August and September of 2010. The margin of error for the surveys is approximately ±5 percentage points and the 95 percent level of confidence.

Mode of data collection

Face-to-face [f2f]

Research instrument

Phase 1:

This survey consisted of 12 questions that were aimed at helping to identify some of the basic characteristics of the remittance recipient population in each country. Some of the variables included in this survey were - location, age, gender, amount of money received, method of receipt, origin of remittance, etc.

Phase 2:

The survey instrument for Phase 2 consisted of approximately 35 questions and included a number of variables aimed at obtaining greater detail about the remittance receiving process including costs, amounts received, information about the sender and the relationship between sender and receiver. Additionally, the survey measured interest in various financial products.

Response rate

Every effort was made to achieve the maximum possible coverage, taking cost, timing and other factors into account. A coverage rate of 85% was achieved in the national survey and the 15% of the country that was not covered consisted of areas that were either very remote (and difficult to travel to) or that had extremely small populations.

Sampling error estimates

The margin of error is approximately ±5 percentage points and the 95 percent level of confidence.

This bar chart displays male population (people) by ISO 3 country code using the aggregation sum and is filtered where the country is Uganda. The data is about countries per year.

Abstract

The Future of African Remittances (FAR) team conducted research on remittance flows to measure and understand the remittance process in sub-Saharan Africa. This ambitious and important research is initially focused on three countries in East Africa - Ethiopia, Kenya and Uganda.

In order to glean insights into the remittance process in the three designated countries, the World Bank designed a two-phase survey process. Phase 1 involved conducting a national survey in each of the three countries. The purpose of the first phase of research was to collect a large representative sample of the adult population in each country. The national surveys provide important baseline data about international remittance flows including: an estimate of the percent of the total adult population that regularly receives remittances, the average amount of each remittance received, most common methods of receipt and top sending countries. Additionally, through the analysis of the national survey results, World Bank was able to identify areas of each country that have high concentrations of international remittance recipients. This important piece of information guided Phase 2 of the research - surveys of remittance receivers in each country. Whereas the national surveys aimed to collect general data about the remittance process, the surveys of remittance recipients allowed for the collection of more detailed data about the remittance process itself, how remittances are used, the relationship between sender and receiver, and interest in various financial products.

The results of this research will not only provide estimates of total annual amounts of remittances for each country, but also will tell us the percentage of the population in each country that is involved in the international remittance process. Furthermore, it will offer insights as to the degree to which Ethiopians, Kenyans and Ugandans depend on international remittances and how the money is used, saved and/or invested. Results will also measure interest in financial products that, if utilized, can significantly impact the financial well-being of the population and the overall economic stability of each country.

Geographic coverage

National Coverage

Analysis unit

Households Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

General:

The total samples were compiled utilizing multi-stage stratified random sampling through respondent selection. Multi-stage random sampling ensured that a random sample of adults was collected in each country. First, after stratifying the population of each country by region and population density, sampling points (SPs) were determined. SPs were then randomly selected within each stratum. At each SP, respondents were randomly selected to participate in the survey.

Phase 1:

The first phase consisted of national surveys of the adult population of each country. The three survey samples were designed to be representative of the adult populations of these three countries. World Bank coordinated and oversaw all aspects of the sampling and interviewing process. A team of local field experts was hired in each country to conduct the actual interviews. All interviewers were professionally trained and supervised by research personnel. In this phase of the research, a total of 2011 Ugandan adults were interviewed.

Phase 2:

Once the national surveys were completed, the results were analyzed to determine the areas of concentration of the remittance recipient population, after which the second phase of the project was conducted. This phase of the project included a targeted survey of the remittance recipient population of each of the three East African countries. Sampling Points were established based on the analysis of the national survey data and the identification of areas within each country that showed the highest concentrations of remittances received from relatives abroad in proportion to the sample size of all areas surveyed. Once again, local field experts were hired in each country to conduct the interviews, training and supervision of field operations. Languages of interviews were the same as those employed in Phase 1 and, again, all interviews were conducted in person using the PAPI method. A total of 400 interviews with regular international remittance recipients were completed in each country during August and September of 2010. The margin of error for all three surveys is approximately ±5 percentage points and the 95 percent level of confidence.

Mode of data collection

Face-to-face [f2f]

Research instrument

Phase 1:

This survey consisted of 12 questions that were aimed at helping to identify some of the basic characteristics of the remittance recipient population in each country. Some of the variables included in this survey were - location, age, gender, amount of money received, method of receipt, origin of remittance, etc.

Phase 2:

The survey instrument for Phase 2 consisted of approximately 35 questions and included a number of variables aimed at obtaining greater detail about the remittance receiving process including costs, amounts received, information about the sender and the relationship between sender and receiver. Additionally, the survey measured interest in various financial products.

Sampling error estimates

The margin of error is approximately ±5 percentage points and the 95 percent level of confidence.