The population density in Tanzania stood at 73.05 people in 2022. In a steady upward trend, the population density rose by 61.55 people from 1961.

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.

Tanzania data available from WorldPop here.

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

Tanzania TZ: Population Density: People per Square Km data was reported at 64.699 Person/sq km in 2017. This records an increase from the previous number of 62.737 Person/sq km for 2016. Tanzania TZ: Population Density: People per Square Km data is updated yearly, averaging 27.840 Person/sq km from Dec 1961 (Median) to 2017, with 57 observations. The data reached an all-time high of 64.699 Person/sq km in 2017 and a record low of 11.711 Person/sq km in 1961. Tanzania TZ: 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 Tanzania – Table TZ.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;

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

Tanzania TZ: Population Density: Inhabitants per sq km data was reported at 73.050 Person in 2022. This records an increase from the previous number of 70.930 Person for 2021. Tanzania TZ: Population Density: Inhabitants per sq km data is updated yearly, averaging 45.490 Person from Dec 1990 (Median) to 2022, with 33 observations. The data reached an all-time high of 73.050 Person in 2022 and a record low of 29.480 Person in 1990. Tanzania TZ: 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 Tanzania – Table TZ.OECD.GGI: Social: Demography: Non OECD Member: Annual.

The Population Distribution and Density product contains spatial explicit information about population distribution at a certain degree, within the Core City Districts of Tanga. The Population Distribution and Density product is estimated for all objects which belong to the residential class (LULC class 11), using 5 different data sources: LULC Product, WorldPop data (2005 / 2018) with a spatial resolution of 100m, official population census data from 2002 and 2012, Soil Sealing layer and Administrative Boundaries

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 Tanzania. 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.

Official dataset to paper "Fine-grained Population Mapping from Coarse Census Counts and Open Geodata".

Comprehensive socio-economic dataset for Tanzania 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.

Dar es Salaam was the most populated region in Tanzania as of 2021. Around 5.5 million people lived in the area. The namesake Dar es Salaam city is the capital of the region and Tanzania's largest city. Mwanza followed as second-leading region by number of inhabitants, roughly four million people.

The Population Distribution and Density 2005 and 2015 products contains spatial explicit information about population distribution at a certain degree, within the Core Urban Districts of Arusha. The following eight classes are identified, based on a frequency distribution analysis of the changes that occur: Unchanged Population Distribution; Up to –100% decrease; Up to 200% increase; 201% - 400% increase; 401% - 600% increase; 601% - 800% increase; 801% - 1000% increase; More than 1000% increase. The Population Distribution and Density product is estimated for all objects which belong to the residential class (LULC class 11), using 5 different data sources: LULC Product, WorldPop data (2005 and 2015 respectively) with a spatial resolution of 100m, official population census data from 2002 and 2012 (for 2005 and 2015 products respectively), Soil Sealing layer and Administrative Boundaries.

The Population Distribution and Density product contains spatial explicit information about population distribution changes at a certain degree, within the Core Urban Districts of Dodoma. The following eight change classes are identified, based on a frequency distribution analysis of the changes that occur: Unchanged Population Distribution; Up to –100% decrease; Up to 200% increase; 201% - 400% increase; 401% - 600% increase; 601% - 800% increase; 801% - 1000% increase; More than 1000% increase. The Population Distribution and Density (2005 & 2015) products are estimated for all objects which belong to the residential class (LULC class 11), using 5 different data sources: LULC Product, WorldPop data (2005/2015) with a spatial resolution of 100m, official population census data from 2002/2012, Soil Sealing layer and Administrative Boundaries.

The Population Distribution and Density product contains spatial explicit information about population distribution changes at a certain degree, within the Core Urban Districts of Kigoma. The following eight change classes are identified, based on a frequency distribution analysis of the changes that occur: Unchanged Population Distribution; Up to –100% decrease; Up to 200% increase; 201% - 400% increase; 401% - 600% increase; 601% - 800% increase; 801% - 1000% increase; More than 1000% increase. The Population Distribution and Density product is estimated for all objects which belong to the residential class (LULC class 11), using 5 different data sources: LULC Product, WorldPop data (2005) with a spatial resolution of 100m, official population census data from 2002 and 2012, Soil Sealing layer and Administrative Boundaries

Multivariable association of socio-demographic and environmental factors with S. haematobium infection.

Analysis unit

Persons and households

UNITS IDENTIFIED: - Dwellings: no - Vacant Units: No - Households: yes - Individuals: yes - Group quarters: no

UNIT DESCRIPTIONS: - Dwellings: no - Households: Yes - Group quarters: no

Universe

All persons in the country at the census date, except for diplomats and their families

Kind of data

Population and Housing Census [hh/popcen]

Sampling procedure

MICRODATA SOURCE: National Bureau of Statistics

SAMPLE SIZE (person records): 2310424.

SAMPLE DESIGN: Sample drawn by NBS from long form questionnaire. Approximately 15% of rural enumeration areas within each district received the long form questionnaire; urban areas were sampled at a higher density. The NBS calculated expansion factors to account for sampling. IPUMS drew a systematic 1-in-2 sample from the original 20% sample.

Mode of data collection

Face-to-face [f2f]

Research instrument

A short questionnaire for all dwellings and a long questionnaire for a sample of the population. There was a separate "collective questionnaire" for group living arrangements.

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 Tanzania. 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.

Pre-treatment S. haematobium infection prevalences in the nine EMINI study sites in Mbeya Region, Tanzania.

The raster dataset consists of a 1km score grid for dairy processing industry (UHT and milk powder) facilities siting, produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location. The analysis is based on dairy production intensification potential, defined using crop production, livestock production systems and dairy herd density. The score is achieved by processing sub-model outputs that characterize logistical factors: 1. Supply - Feed, livestock production systems, dairy herd distribution. 2. Demand - Human population density, large cities, urban areas. 3. Infrastructure - Transportation network (accessibility). It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.3) + ("Human Population Density" * 0.1) + (“Major Cities Accessibility” * 0.1) + (”dairyIntensification” * 0.5)

Nigeria has the largest population in Africa. As of 2025, the country counted over 237.5 million individuals, whereas Ethiopia, which ranked second, has around 135.5 million inhabitants. Egypt registered the largest population in North Africa, reaching nearly 118.4 million people. In terms of inhabitants per square kilometer, Nigeria only ranked seventh, while Mauritius had the highest population density on the whole African continent in 2023. 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 Chad, South Sudan, Somalia, and the Central African Republic, the population increase peaks at over 3.4 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. African cities are also growing at large rates. Indeed, the continent has three megacities and is expected to add four more by 2050. Furthermore, Africa's fastest-growing cities are forecast to be Bujumbura, in Burundi, and Zinder, Nigeria, by 2035.