The largest city of Tanzania, Dar es Salam, had a population of around 2.7 million people in 2022. On the shore of Lake Victoria, Mwanza ranked as the second most populated city in the country, with some 437 thousand inhabitants. Zanzibar City, the capital of Zanzibar's archipelago, had around 404 thousand dwellers.

This statistic shows the biggest cities in Tanzania in 2022. In 2022, approximately **** million people lived in Dar es Salaam, making it the biggest city in Tanzania.

Population in largest city in Tanzania was reported at 8161231 in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. Tanzania - Population in largest city - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Population in the largest city (% of urban population) in Tanzania was reported at 31.21 % in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. Tanzania - Population in the largest city - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Tanzania TZ: Population in Largest City: as % of Urban Population data was reported at 30.194 % in 2017. This records an increase from the previous number of 30.104 % for 2016. Tanzania TZ: Population in Largest City: as % of Urban Population data is updated yearly, averaging 30.753 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 34.123 % in 1967 and a record low of 29.695 % in 1996. Tanzania TZ: Population in Largest City: as % of Urban Population 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: Population and Urbanization Statistics. Population in largest city is the percentage of a country's urban population living in that country's largest metropolitan area.; ; United Nations, World Urbanization Prospects.; Weighted average;

Major cities demand dataset is modelled as raster-based travel time/cost analysis and weighted using the population/market size dimension as a measure of demand. Individual cumulative travel time/cost maps were produced for the country’s 10 largest cities (>200k habitants). The final market/demand layer consists of an arithmetic weighted sum of normalized (0-100) city accessibility grids. The following values were assumed for major cities population of Tanzania: City - Population - Weight % Dar es Salaam - 4,364,541 - 0.579 Mwanza - 706,453 - 0.094 Zanzibar - 501,459 - 0.067 Arusha - 416,442 - 0.055 Mbeya - 385,279 - 0.051 Morogoro - 305,840 - 0.041 Tanga - 221,127 - 0.029 Kigoma - 215,458 - 0.029 Dodoma - 213,636 - 0.028 Songea - 203,309 - 0.027 This 1km resolution raster dataset is part of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (optimal location).

Accessibility to major cities dataset is modelled as raster-based travel time/cost analysis, computed for the 10 largest cities (>200k habitants) in the country, normalized from 0 to 100, where 0 corresponds to the lowest city accessibility. The following cities are included: City - Population Dar es Salaam - 4,364,541 Mwanza - 706,453 Zanzibar - 501,459 Arusha - 416,442 Mbeya - 385,279 Morogoro - 305,840 Tanga - 221,127 Kigoma - 215,458 Dodoma - 213,636 Songea - 203,309 This 1km resolution raster dataset is part of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (or optimal location)

Tanzania TZ: Population in Largest City data was reported at 5,719,486.000 Person in 2017. This records an increase from the previous number of 5,409,174.000 Person for 2016. Tanzania TZ: Population in Largest City data is updated yearly, averaging 1,380,124.000 Person from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 5,719,486.000 Person in 2017 and a record low of 162,126.000 Person in 1960. Tanzania TZ: Population in Largest City 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 in largest city is the urban population living in the country's largest metropolitan area.; ; United Nations, World Urbanization Prospects.; ;

Reliable market accessibility data is critical to developing agricultural policies and investment plans for ensuring smallholder farmers’ market participation and their profitable farming, yet this data is less frequently updated. Most of the publicly available data are outdated and hard to reflect the rapid development of transportation infrastructure in African countries. For this, using a newly available accessibility model input dataset, such as new land cover data from satellites, crowdsourced road network data, and the updated population of major human settlements in Tanzania are used to update the existing market accessibility data and provides new market accessibility data layers benchmarking around the year 2015. The dataset includes three data layers representing travel time to the nearest market of five sizes (population of 20K, 50K, 100K), respectively, on 1 arc-minute (~1km) grids in Tanzania.

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.

Abstract

The Measuring Living Standards in Cities (MLSC) survey is a new instrument designed to enhance understanding of cities in Africa and support evidence based policy design. The instrument was developed under the World Bank’s Spatial Development of African Cities Program, and was piloted in Dar es Salaam (Tanzania) and Durban (South Africa) over the course of 2014/15. These geo-referenced surveys provide information on urban living standards at an unprecedented level of granularity: they can be compared across different geographic levels within the cities, and between areas of ‘regular’ and ‘irregular’ settlement patterns. They also respond to the need to increased understanding of specifically ‘urban’ dimensions of quality of living: housing attributes, access to basic services, and commuting patterns, among others.

Geographic coverage

The survey covered households in Dar es Salaam, Tanzania.

Analysis unit

• Household

• Individual

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLE FRAME

16,000 EAs generated by the Tanzania National Bureau of Statistics (NBS) for the 2012 Census.

STAGE ONE

200 EAs sorted into four strata. The central strata was divided into ‘central core, shanty’ and ‘central core, non-shanty’. Two EAs were replaced with reserve EAs as the original EAs were found to be inaccessible.

STAGE TWO

12 households randomly selected by systematic equal-probability from updated listing of each EA.

LISTING METHODOLOGY

The listing exercise took place between the first and the second stage of sampling. The household listing operations were implemented with computer assisted paperless interviewing (CAPI) techniques, which generates electronic files directly. Enumerators collected basic information about household: the name of the household head name, phone number and total number of household members living in the dwelling. Enumerators also recorded the GPS location of all structures,18 defined the type of structure, and aimed to provide measurement of structure size.

Listing was preceded by community sensitisation in both cities. In Dar es Salaam, enumerators visited the local chief (Mjumbe) of their assigned EA two days in advance of listing and on the day of listing.

Enumerators were equipped with maps created on Google My Maps to display shapefiles for the listing exercise. Hardcopies of their respective EA maps were also provided to be use in case of network failure. In Dar es Salaam, enumerators conducted a listing of all households in each of the selected EAs.

The listing exercise was conducted by 30 enumerators, each of which was assigned between 3 and 9 EAs for listing (enumerators were selected on the basis of performance from a group of 35 that were trained for listing). Enumerators were allocated EAs based on: (i) distance from enumerators’ homes in order to minimize transport time and cost; (ii) distance between the EAs; and (iii) safety and response rate considerations.

SURVEY IMPLEMENTATION

The surveys were fielded over the course of several months. The Dar es Salaam survey was implemented between November 2014 and January 2015.

Cases were assigned to interviewers using Survey Solutions. Interviewers were provided with both an electronic and hardcopy map, as well as a printed completion form, and could contact the listing manager through email, WhatsApp, or google hangouts if they were unable to find the assigned house.

Completing the survey often required repeat visits. This is because the survey required input from up to three separate respondents: the main respondent, who could be any present household member, and answered questions on household composition, basic information on members, assets, remittances, grants, housing, properties and consumption; the household head, who answered questions on residential history, satisfaction, employment, time use and commuting; and a random respondent, who was randomly selected from household members over the age of 12 (not including the head), who responded questions on satisfaction, employment, time use and commuting. Enumerators visited each house at least twice before a component could be marked as unavailable - in many cases, however, more than two visits were conducted.

Quality assurance procedures included: (i) In-interview feedback from CAPI, which provided a check that modules or questions were not missing, and alerted interviewers to mistakes and inconsistencies in given answers, so that these could be addressed while the interviewer was still with the respondent; (ii) Aggregate checks conducted using the Survey Solutions Supervisor application, which allows supervisors to identify common mistakes (applied to all initial interviews, and then through spot checks); interviewer performance and completion monitoring conducted by the implementing firm, through interviewer and EA level summaries of response rates, interview completion, and progress; (iii) weekly summaries of key indictors provided by the World Bank team (following each data delivery); (iv) direct observation of fieldwork; and (v) back check interviews. A key lesson learned is that the portion of back check interviews should be agreed in advance with the implementing firm: in Dar es Salaam back checks were conducted on 5% of the sample.

Mode of data collection

Computer Assisted Personal Interview [capi]

Response rate

Non-response rate: 13%

regional cities demand dataset is modelled as raster-based travel time/cost analysis and weighted using the population/market size dimension as a measure of demand. Individual cumulative travel time/cost maps were produced for major regional cities (>250k habitants) less than 500 km from the border. The final market/demand layer consists of an arithmetic weighted sum of the normalized (0-100) city accessibility grids. The following values were assumed: City - Country - Population - Weight % Nairobi - KEN - 4,397,073 - 0.277 Kampala - UGA - 2,094,000 - 0.132 Mombasa - KEN - 1,208,333 - 0.076 Lilongwe - MWI - 1,055,700 - 0.067 Kigali - RWA - 859,332 - 0.054 Blantyre - MWI - 830,100 - 0.052 Nampula - MOZ - 663,212 - 0.042 This 1km resolution raster dataset is part of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (optimal location).

Abstract

The RFS In Tanzania was conducted in three major cities, Dar es Salaam, Tanga and Dodoma, between September and November 2018. The survey focused on firms in manufacturing and service industries and the data was collected via in-person interviews. The survey focused on how firms are affected by floods and which strategies they use to cope and adapt, Impact of flooding considers both direct damages and indirect effects through infrastructure systems, supply chains and workers.

This project was a collaborative effort between Global Facility for Disaster Reduction and Recovery (GFDRR) and Urban, Disaster Risk Management, Resilience and Land Global Practice (GPURL).

Geographic coverage

Dar es Salaam, Dodoma, and Tanga

Analysis unit

• Firm level

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame consisted of the official business registry obtained from the National Bureau of Statistics' (NBS) in Tanzania. It contained a list of 58,959 firms in Dar es Salaam, Tanga, and Dodoma. Firms listed without a functioning telephone number were excluded from the sampling frame.

All registered firms were divided into five distinct strata, depending on their reliance on transport systems. Ordered from low to high transport reliance, these strata contain firms from the following sectors: 1 Accommodation and food service activities, Construction 2 Communication and other services 3 Manufacturing, Mining, Water, Energy, Agriculture 4 Transportation and storage 5 Wholesale and retail trade, Repair of motor vehicles and motorcycles

The sample selection was completed in one stage, with firms selected through a systematic random sampling method from each stratum. In terms of regional distribution, the sample contains 623 firms from Dar es Salaam, 101 from Dodoma, and 113 from Tanga. About 10 percent of listed firms were interviewed, with the main reasons for the low response rate being incorrect addresses, expectation of payment for survey participation, and concerns about disclosure of tax-relevant information.

All survey results, using the survey weights computed, should be interpreted as representative of private businesses located in the 3 cities Dar es Salaam, Dodoma, and Tanga and registered in the NBS business registry with phone access.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

• Firm characteristics o Sector o Number of workers • Infrastructure dependence and disruptions o Water o Electricity o Communication (phone and internet) • Suppliers • Workers and transport disruptions • Clients • Most serious natural shock that affected the firm • Operational costs • Investments

Cleaning operations

The following data editing was done for anonymization purposes:
- Precise location data, such as GPS coordinates, and;
- Identifying information, such as firm names, respondent names, phone numbers and emails contacts, were dropped

Response rate

About 10 percent of listed firms were interviewed, with the main reasons for the low response rate being incorrect addresses, expectation of payment for survey participation, and concerns about disclosure of tax-relevant information.

In order to bring a thorough and comprehensive understanding of social, economic and environmental sustainability challenges faced by cities and local communities in the developing countries, the SHLC team conducted a major household survey followed by a neighbourhood focus group interview in seven Asian and African countries from late 2021 to early 2022. In each country the study includes two case study cities: one large city and one smaller regional cities. Within each case study cities, neighbourhoods were identified and categorised into five income and wealth bands: the rich, upper middle income, middle income, lower middle and low income neighbourhoods.

A household survey was carried out face to face by trained interviewers with a random adult member of the household. The 20 page common questionnaire was designed and adopted by all teams, which cover topics of housing, residence, living conditions, migration, education, health, neighbourhood infrastructure, facilities, governance and relations, income and employments, gender equality and impacts from Covid-19. The sample was distributed in the city to representative the five neighbourhood types. The survey was completed in 13 of the 14 case study cities (fieldwork in Chongqing in China was delayed by the Covid-19 lockdowns and implemented in August 2023). The target sample for each city was 1000; the total sample in the database (SPSS and STATA) include 14245 households.

The survey was followed by focus group interviews. A carefully designed and agreed common interview guide was used by all team. The target was to have one focus group for one neighbourhood in each income band in each city. A total of 74 focus group interviews were conducted (Fieldwork in Datong and Chongqing in China was delayed). The transcripts are the qualitative data shared here.

The Centre for Sustainable, Healthy and Learning Cities and Neighbourhoods (SHLC) was funded by UKRI Global Challenge Research Fund (GCRF) from 2017 to 2023. Its main aim was to grow research capability to meet the challenges faced by developing countries (Grow). SHLC, led by University of Glasgow, was set up as an international collaborative research centre to address urban challenges across communities in Africa and Asia. Its work contributed to three UN 2030 Sustainable Development Goals: 11 - Make cities and human settlements sustainable; 3 - Ensure healthy lives for all; 4 - Ensure inclusive and equitable quality education for all. SHLC brought together the expertise of urban studies, education, health, geography, planning and data science from nine institutions in eight countries. Its international partners included: Ifakara Health Institute (Tanzania), Khulna University (Bangladesh), Nankai University (China), National Institute of Urban Affairs (India), The Human Sciences Research Council and University of Witwatersrand (South Africa), The University of the Philippines and The University of Rwanda. SHLC working programme had two streams of work and eight specific task packages. Stream one included four Capacity Strengthening Packages which involved the training of over 100 researchers and enhancing the associated academic networks. Steam two work consisted of four Research Task Packages. The co-designed research programme adopted a common research framework in all seven countries (14 case study cities), aiming to bring a thorough and comprehensive understanding of social, economic and environmental sustainability challenges faced by these cities and local communities. Apart from policy reviews, secondary data analysis, the project employed two major primary data collection methods – household questionnaire survey and neighbourhood focus groups. The team have overcome many challenges brought by the Covid-19 pandemics and completed the household survey in 13 cities with a total sample size of 14245, which covered five different types of neighbourhoods ranging from the rich to the poor. The team also completed 74 neighbourhood focus group interviews. Data collection was carried out from late 2021 to early 2022. Huge resources and researchers’ time were dedicated to coordinate, collect, translate, clean and merge these quantitative and qualitative data.

Religion adoption varied across residential areas in Tanzania as of 2021. In urban regions, over 40 percent of respondents declared being Muslims, while this share stood at nearly 25 percent in rural areas. In urban and rural regions, around 15 percent and 14 percent of the surveyed population, respectively, identified as Christians only. Among Christian religions, Roman Catholic was the most followed - by 27 percent of respondents in rural areas and 20.8 percent in urban areas.

As of 2019, most bank branches in Tanzania were located in the region of Dar es Salaam, the country's largest city. A total of 290 bank branches were situated in the formal capital. Together, the regions of Arusha, Mwanza, Moshi, and Dodoma housed a share of nearly 25 percent of the bank branches. Other 445 bank branches were distributed in other regions of the country that year.

The dataset represents an estimated cumulative travel time/cost (raster grid) accessibility map, for Kenya's regional major cities . The map is an output of the sub-Saharan African Corridor, Mobile Warehouse Location pilot project version 2. Modeled cities are: Tanzania: Dar es Salam (5,572,776); Mwanza (830,342); Zanzibar (796,903); Dodoma (571,629); Arusha (466,892); Somalia: Mogadiscio (2,275,976); Merka (480,543); Kismaayo (402,691); Ethiopia: Addis-Abeba (4,567,857); Diré Dawa (453,000); South Sudan: Djouba (917,910); Wau (328,651); Uganda: Kampala (4,101,302); Jinja (589,661); The calculation of cost/time distance surfaces is based on some assumptions: A. Road travel time/cost is computed for large trucks, it is assumed accessibility for large cargo freight vehicles, tertiary and local traffic roads are not included; B. Lake and river navigation are treated as a surface (polygons) not taking into consideration navigation infrastructure (points). Regional travel time surfaces production steps are: rasterization of transportation network and surfaces and definition of cell travel time; creation of countries time/cost layers; combining countries into a regional cost layer; computation of a cumulative cost/time accessibility layer from cities (Regional Major Cities Accessibility Map).

In 2023, Gabon had the highest urbanization rate in Africa, with over 90 percent of the population living in urban areas. Libya and Djibouti followed at around 82 percent and 79 percent, respectively. On the other hand, many countries on the continent had the majority of the population residing in rural areas. As of 2023, urbanization in Malawi, Rwanda, Niger, and Burundi was below 20 percent. A growing urban population On average, the African urbanization rate stood at approximately 45 percent in 2023. The number of people living in urban areas has been growing steadily since 2000 and is forecast to increase further in the coming years. The urbanization process is being particularly rapid in Burundi, Uganda, Niger, and Tanzania. In these countries, the urban population grew by over 4.2 percent in 2020 compared to the previous year. The most populous cities in Africa Africa’s largest city is Lagos in Nigeria, counting around nine million people. It is followed by Kinshasa in the Democratic Republic of the Congo and Cairo in Egypt, each with over seven million inhabitants. Moreover, other cities on the continent are growing rapidly. The population of Bujumbura in Burundi will increase by 123 percent between 2020 and 2035, registering the highest growth rate on the continent. Other fast-growing cities are Zinder in Niger, Kampala in Uganda, and Kabinda in the Democratic Republic of the Congo.

Abstract

The main objective of the NPS is to provide high-quality household-level data to the Tanzanian government and other stakeholders for monitoring poverty dynamics, tracking the progress of the Five Year Development Plan (FYDP) II poverty reduction strategy and its predecessor plans, and evaluating the impact of other major, national-level government policy initiatives. As an integrated survey covering a number of different socioeconomic factors, it compliments other more narrowly focused survey efforts, such as the Demographic and Health Survey (DHS) on health, the Integrated Labour Force Survey (ILFS) on labour markets, the Household Budget Survey (HBS) on expenditure, and the National Sample Census of Agriculture (NSCA). Secondly, as a panel household survey in which the same households are revisited over time, the NPS allows for the study of poverty and welfare transitions and the determinants of living standard changes.

Geographic coverage

Designed for analysis of key indicators at four primary domains of inference, namely: Dar es Salaam, Other Urban, Rural, Zanzibar,

Analysis unit

Households; Individuals

Sampling procedure

The NPS is based on a stratified, multi-stage cluster sample design which recognizes four analytical strata: Dar es Salaam, Other Urban areas in Mainland, Rural areas in Mainland, and Zanzibar. The sample design for the NPS 2020/21 targeted the sub-sample of households from the initial NPS 2014/15 cohort considered the “Refresh Panel”. These specific households had never previously been a part of the NPS sample design. This sample consisted of 3,352 households from 419 clusters in the NPS 2014/15 that were tracked and interviewed in the NPS 2020/21. An additional “Booster Sample” of 545 households from major cities and urban areas (specifically, Mbeya, Arusha, Mwanza, Tanga, and Dodoma) was also interviewed to allow for improved estimates in urban centres.

In previous NPS rounds, the sample design included complete households that could not be interviewed in a particular year but were found in later rounds, excluding those households that had refused to be interviewed (i.e. a household that was interviewed in Round 1, lost in Round 2, and found again in Round 3). This situation does not exist in the NPS 2020/21 as they have only been included in, at most, two rounds.

The eligibility requirement for inclusion of a household in this round of the NPS and all others is defined as any household having at least one member aged 15 years and above, excluding live-in servants. Households with at least one eligible member were completely interviewed, including any non-eligible members present in the household.

Additionally, the final sample for NPS 2020/21 included any split-off household or eligible members identified during data collection (i.e. a previous NPS member who had moved or started another household in between rounds). Marriage and migration are the most common reasons for households splitting over time. Ultimately, the final sample size for NPS 2020/21 was 23,592 individuals in 4,709 households. Of these, 4,164 households allow for panel analysis as they have been found and interviewed in both NPS 2014/15 and NPS 2020/21, while the remaining 545 (in the “Booster Sample”) will only have data available in the NPS 2020/21. The complete cohort interviewed in NPS 2020/21 will be maintained and tracked in all future waves of the NPS.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The NPS 2020/21 consists of four survey instruments: a Household Questionnaire, Agriculture Questionnaire, Livestock Questionnaire, and a Community Questionnaire. A detailed description of the questionnaires is provided in the Survey Instruments section of the Basic Information Document (available under Downloads). All questionnaires are in English and available for download.

In 2020, Dar es Salaam was the region in Tanzania with the highest Gross Domestic Product (GDP), which amounted to roughly 25.3 trillion Tanzanian shillings (TZS), approximately 10.9 billion U.S. dollars. To follow, the Mwanza region recorded a GDP at about 11 trillion TZS (4.7 billion U.S. dollars). By the same year, Tanzania's GDP reached around 64.4 billion U.S. dollars.