As of 2043, Nairobi was the most populated city in Kenya, with more than 2.7 million people living in the capital. The city is also the only one in the country with a population exceeding one million. For instance, Mombasa, the second most populated, has nearly 800 thousand inhabitants. As of 2020, Kenya's population was estimated at over 53.7 million people.

Actual value and historical data chart for Kenya Population In Largest City

This statistic shows the biggest cities in Kenya as of 2019. In 2019, approximately *** million people lived in Nairobi, making it the biggest city in Kenya.

Kenya KE: Population in Largest City: as % of Urban Population data was reported at 31.985 % in 2017. This records a decrease from the previous number of 32.132 % for 2016. Kenya KE: Population in Largest City: as % of Urban Population data is updated yearly, averaging 35.120 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 50.731 % in 1962 and a record low of 31.985 % in 2017. Kenya KE: 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 Kenya – Table KE.World Bank.WDI: 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 Towns by PopulationTowns in Kenya: Kenya’s capital city is Nairobi. It is the largest city in East Africa and the region’s Financial, Communication and Diplomatic Capital. In Kenya there are only three incorporated cities but there are numerous municipalities and towns with significant urban populations. Two of the cities, Nairobi and Mombasa are cities whose county borders run the same as their city limits, so in a way they could be thought of as City-CountiesNairobi is the only city in the world with a game park. Nairobi National Park is a preserved ecosystem where you can view wildlife in its natural habitat. Hotels, airlines and numerous tour firms and agencies offer tour packages for both domestic and foreign tourists visiting Nairobi and the park. The tourism industry provides direct employment to thousands of Nairobi residents.

Kenya KE: Population in Largest City data was reported at 4,222,389.000 Person in 2017. This records an increase from the previous number of 4,065,018.000 Person for 2016. Kenya KE: Population in Largest City data is updated yearly, averaging 1,285,227.500 Person from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 4,222,389.000 Person in 2017 and a record low of 292,622.000 Person in 1960. Kenya KE: Population in Largest City data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Kenya – Table KE.World Bank: 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.; ;

This dataset provides 100-meter gridded estimates of slum prevalence for four major cities in Kenya (Nairobi, Mombasa, Nakuru, and Kisumu), derived from a Random Forest classifier trained on the building level. Each grid cell contains information on the number and area of predicted slum buildings, the total building stock, and corresponding ratios.

The dataset is intended to support urban studies and slum mapping research, offering open-access spatial data that can be compared across cities and integrated with other socioeconomic or environmental indicators.

Building footprints are sourced from the Google–Microsoft Open Buildings dataset (harmonized by VIDA), and the urban extents follow the Worldwide Delineation of Multi-Tier City-Regions。

Abstract

The objective of the survey was to produce baselines for 15 large urban centers in Kenya. The urban centers covered Nairobi, Mombasa, Naivasha, Nakuru, Malindi, Eldoret, Garissa, Embu, Kitui, Kericho, Thika, Kakamega, Kisumu, Machakos, and Nyeri. The survey covered the following issues: (a) household characteristics; (b) household economic profile; (c) housing, tenure, and rents; and (d) infrastructure services. The survey was undertaken to deepen understanding of the cities’ growth dynamics, and to identify specific challenges to quality of life for residents. The survey pays special attention to living conditions for residents of formal versus informal settlements, poor versus non-poor, and male and female headed households.

Analysis unit

Household Urban center

Kind of data

Sample survey data [ssd]

Sampling procedure

The Kenya State of the Cities Baseline Survey is aimed to produce reliable estimates of key indicators related to demographic profile, infrastructure access and economic profile for each of the 15 towns and cities based on representative samples, including representative samples of households (HHs) residing in slum and non-slum areas. For this baseline household survey, NORC used a two- or three-stage stratified cluster sampling design within each of the 15 urban centers. Our first-stage sampling frame was based on the 2009 census frame of enumeration areas. For each of the 15 towns and cities, NORC received the sampling frame of EAs from the Kenya National Bureau of Statistics (KNBS). In the first stage, NORC selected a sample of enumeration areas (PSUs). The second stage involved a random selection of households (SSUs) from each selected EA. In order to manage the field interviewing efficiently, we drew a fixed number of HHs from each selected EA, irrespective of EA size. The third stage arose in instances of very large EAs (EAs containing more than 200 households) in which EAs were divided into 2, 3 or 4 segments, from which one segment was selected randomly for household selection.

Stratification of Enumeration Areas: A few stratification factors were available for stratifying the EAs to help to achieve the survey objectives. As mentioned earlier, for this baseline survey we wanted to draw representative samples from slum and non-slum areas and also to include poor/non-poor households (HHs). For the 2009 census, depending on the location, KNBS divided the EAs into three categories: rural, urban, and peri-urban.

Although there is a clear distinction of EAs into slum and non-slum areas, it is hard to classify EAs into poor and non-poor categories. To guarantee enough representation of HHs living in slum and non-slum areas (also referred to as formal and informal areas) as well as HHs living below and above the poverty line, NORC stratified the first-stage sampling units (EAs) into strata, based on EA type (3 types) and settlement type (2 types). Given the resources available, we believe this stratification would serve our purpose as HHs living in slum and in rural areas tend to be poor. Table 1 in Appendix C of final Overview Report (provided under the Related Materials tab) presents the allocation of sampled EAs across the strata for each of the 15 cities in the baseline survey.

Sampling households is not as straightforward as the first-stage sampling of EAs, since the 2009 census frame of HHs does not exist. In the absence of a household sampling frame, NORC carried out a listing of HHs within each EA selected in the first stage. Trained listers, accompanied by local cluster guides (local residents with some form of authority in the EA), systematically listed all households in each selected EA, gathering the address, names of head of household and spouse, household description, latitude and longitude. To ensure completeness of listing data, avoid duplication and improve ease of locating households that were eventually selected for interview, listers enumerated households by chalking household identification number above the household doorway (an accepted practice for national surveys). The sampling frame of HHs produced from the listing activity was, therefore, up-to-date and included new formal and informal settlements that appeared after the 2009 census.

For adequate representativeness and to manage the interviewing task efficiently, NORC planned seven completed household interviews per EA. The final recommended sample size for the Kenya State of the Cities baseline survey is found in Table 2 in Appendix C of the final Overview Report.

Because the expected response rate was unknown prior to the start of the field period, the sampling team randomly selected ten households per enumeration area and distributed them to the interviewers working within the EA. Interviewing teams were instructed to complete at least seven interviews per EA from among the ten selected households. Interviewers were instructed to attempt at least three contacts with each selected household, approaching potential respondents on different days of the week and different times of day. Table 2 presents the final number of EAs listed per city and the final number of completed interviews per city. The table also presents the percent of planned EAs and interviews that were completed vs. planned. Please note that in several cities more interviews were completed than planned. As part of NORC's data quality plan, data collection teams were instructed to overshoot slightly the target of seven interviews per EA, if feasible, to mitigate any potential loss of cases due to poor quality or uncooperative respondents. Few cases were lost due to poor quality, therefore the target number of interviews remains over 100 percent in ten of the fifteen cities.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire was developed by World Bank staff with input from stakeholders in the Kenya Municipal Program and NORC researchers and survey methodologists. The base questionnaire for the project was a 2004 World Bank survey of Nairobi slums. However, an extended iterative review process led to many changes in the questionnaire. The final version that was used for programming provided under the Related Materials tab, and in Volume II of the Overview.

The questionnaire’s topical coverage is indicated by the titles of its nine modules: 1. Demographics and household composition 2. Security of housing, land and tenure 3. Housing and settlement profile 4. Economic profile 5. Infrastructure services 6. Health 7. Household enterprises7 8. Civil participation and respondent tracking

Response rate

The completion rate is reported as the number of households that successfully completed an interview over the total number of households selected for the EA. These are shown by city in Table 5 in Appendix C of the final Overview Report, and have an average rate of 68.66 percent, with variation from 66 to 74 percent (aside from Nairobi at 61.47 percent and Machakos at 56 percent). As described earlier, ten households were selected per EA if the EA contained more than 10 households. For EAs where fewer than ten households were selected for interviews, all households were selected. In some EAs, more than ten households were selected due to a central office error.

Nakuru is a city in the Rift Valley region of Kenya. It is the capital of Nakuru County, and is the third largest city in Kenya.

Major urban centers like Nairobi, Mombasa, and Kisumu, plus regional trade hubs near the port of Mombasa, dominate the Kenya B2B marketplace landscape. These cities host dense clusters of informal retail (dukas), wholesalers, and distribution nodes. Nairobi’s superior logistics connectivity, concentration of manufacturers and supplier headquarters, and higher digital penetration make it a natural epicenter. The coastal corridor’s proximity to the Port of Mombasa enables efficient import flows for cross-border B2B supply, giving coastal towns an edge in supplier sourcing and inventory turnover. Secondary cities along major trade corridors (e.g. Nakuru, Eldoret) benefit from spill-over distribution demand. The Kenya B2B marketplace market size is estimated to be USD 1.2 billion in 2023, based on aggregated platform and marketplace revenue disclosures from leading firms coupled with public data on Kenya’s e-commerce growth. This figure is underpinned by a broader e-commerce environment in Kenya where the entire e-commerce GMV for 2023 was ~ USD 2.3 billion (for B2C + others). The B2B subset captures orders placed by informal retailers, small enterprises, clinics, and HORECA via digital platforms. Growth is driven by accelerated mobile penetration, spread of FinTech and digital credit, MSME digitization mandates, and logistics investments. In 2024, the broader e-commerce market is projected to reach USD 2.6 billion, which implies further tailwinds for the B2B segment as digital procurement gains deeper adoption. Kenya B2B Marketplaces Market Overview and Size

Nairobi has been the Kenyan county most affected by the coronavirus (COVID-19) pandemic. As of March 31, 2022, the capital registered most of the confirmed COVID-19 cases in the country, around 129 thousand. The amount corresponded to nearly 40 percent of the total cases in Kenya. In Kiambu, within the Nairobi Metropolitan Region, 19,778 infected people were registered, whereas Mombasa, Kenya's oldest and second largest city, had 17,794 cases. As of March 2021, Kenya started the vaccination campaign against the coronavirus with doses received through the COVAX initiative.

Kenya's economy rebounds amid vaccination campaign

The coronavirus outbreak had a significant negative impact on Kenya's economy. In the second quarter of 2020, the quarterly country’s GDP decreased by 5.5 percent, the first contraction in recent years. Around one year later, in the third quarter of 2021, Kenya already registered an improved economic performance, with the quarterly GDP growth rate measured at 9.9 percent. The educational sector pushed the result, with an expansion of 65 percent. Mining and quarrying, and accommodation and food services followed, each with a 25 percent growth rate.

Signs of recovery in the tourism sector

Extensively known for its rich nature and wildlife, Kenya felt dramatically the impacts of the COVID-19 pandemic in the tourism industry. The sector's contribution to the country’s GDP roughly halved in 2020, compared to 2019. By the end of 2021, however, signals of recovery were already spotted. The monthly number of arrivals in both Jomo Kenyatta and Moi international airports in December that year corresponded to roughly 70 percent of that registered in December 2019. Additionally, as of March 2022, the bed occupancy rate in Kenyan hotels amounted to 57 percent, against 23 percent in March 2021.

Mean body and leg titers for Aedes aegypti from three major cities in Kenya exposed to chikungunya virus.

Abstract

This detailed report presents the major findings of the 2003 Kenya Demographic and Health Survey (2003 KDHS). The 2003 KDHS is the fourth survey of its kind to be undertaken in Kenya, others being in 1989, 1993, and 1998. The 2003 KDHS differed in two aspects from the previous KDHS surveys: it included a module on HIV prevalence from blood samples, and it covered all parts of the country, including the arid and semi-arid districts that had previously been omitted from the KDHS. The 2003 KDHS was implemented by the Central Bureau of Statistics. Fieldwork was carried out between April and September 2003. The primary objective of the 2003 KDHS was to provide up-to-date information for policymakers, planners, researchers, and programme managers, which would allow guidance in the planning, implementation, monitoring and evaluation of population and health programmes in Kenya. Specifically, the 2003 KDHS collected information on fertility levels, marriage, sexual activity, fertility preferences, awareness and use of family planning methods, breastfeeding practices, nutritional status of women and young children, childhood and maternal mortality, maternal and child health, and awareness and behavior regarding HIV/AIDS and other sexually transmitted infections (STIs). In addition, it collected information on malaria and use of mosquito nets, domestic violence among women, and HIV prevalence of adults. The 2003 KDHS results present evidence of lower than expected HIV prevalence in the country, stagnation in fertility levels, only a very modest increase in use of family planning methods since 1998, continued increase in infant and under-five mortality rates, and overall decline in indicators of maternal and child health in the country. There is a disparity between knowledge and use of family planning methods. There is also a large disparity between knowledge and behaviour regarding HIV/AIDS and other STIs. Some of the critical findings from this survey, like the stagnation in fertility rates and the declining trend in maternal and child health, need to be addressed without delay. I would like to acknowledge the efforts of a number of organisations that contributed immensely to the success of the survey. First, I would like to acknowledge financial assistance from the Government of Kenya, the United States Agency for International Development (USAID), the United Kingdom Department for International Development (DFID), the United Nations Population Fund (UNFPA), the Japan International Co-operation Agency (JICA), the United Nations Development Programme (UNDP), the United Nations Children's Fund (UNICEF), and the Centers for Disease Control and Prevention (CDC). Second, in the area of technical backstopping, I would like to acknowledge ORC Macro, CDC, the National AIDS and STIs Control programme (NASCOP), the Kenya Medical Research Institute (KEMRI), and the National Council of Population and Development (NCPD). Special thanks go to the staff of the Central Bureau of Statistics and the Ministry of Health who coordinated all aspects of the survey. Finally, I am grateful to the survey data collection personnel and, more importantly, to the survey respondents, who generously gave their time to provide the information and blood spots that form the basis of this report.

Analysis unit

Clusters, Districts, National, Male and Female, Urban, Rural

Sampling procedure

The sample for the 2003 KDHS covered the population residing in households in the country. A representative probability sample of almost 10,000 households was selected for the KDHS sample. This sample was constructed to allow for separate estimates for key indicators for each of the eight provinces in Kenya, as well as for urban and rural areas separately. Given the difficulties in traveling and interviewing in the sparsely populated and largely nomadic areas in the North Eastern Province, a smaller number of households was selected in this province. Urban areas were oversampled. As a result of these differing sample proportions, the KDHS sample is not self-weighting at the national level; consequently, all tables except those concerning response rates are based on weighted data. The survey utilised a two-stage sample design. The first stage involved selecting sample points (“clusters”) from a national master sample maintained by CBS (the fourth National Sample Survey and Evaluation Programme [NASSEP IV]). The list of enumeration areas covered in the 1999 population census constituted the frame for the NASSEP IV sample selection and thus for the KDHS sample as well. A total of 400 clusters, 129 urban and 271 rural, were selected from the master frame. The second stage of selection involved the systematic sampling of households from a list of all households that had been prepared for NASSEP IV in 2002. The household listing was updated in May and June 2003 in 50 selected clusters in the largest cities because of the high rate of change in structures and household occupancy in the urban areas. All women age 15-49 years who were either usual residents of the households in the sample or visitors present in the household on the night before the survey were eligible to be interviewed in the survey. In addition, in every second household selected for the survey, all men age 15-54 years were eligible to be interviewed if they were either permanent residents or visitors present in the household on the night before the survey. All women and men living in the households selected for the Men's Questionnaire and eligible for the individual interview were asked to voluntarily give a few drops of blood for HIV testing.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three questionnaires were used in the survey: the Household Questionnaire, the Women's Questionnaire and the Men's Questionnaire. The contents of these questionnaires were based on model questionnaires developed by the MEASURE DHS+ programme. In consultation with a broad spectrum of technical institutions, government agencies, and local and international organisations, CBS modified the DHS model questionnaires to reflect relevant issues in population, family planning, HIV/AIDS, and other health issues in Kenya. A number of thematic questionnaire design committees were organised by CBS. Periodic meetings of each of the thematic committees, as well as the final meeting, were also arranged by CBS. The inputs generated in these meetings were used to finalise survey questionnaires. These questionnaires were then translated from English into Kiswahili and 11 other local languages (Embu, Kalenjin, Kamba, Kikuyu, Kisii, Luhya, Luo, Maasai, Meru, Mijikenda, and Somali). The questionnaires were further refined after the pretest and training of the field staff. The Household Questionnaire was used to list all of the usual members and visitors in the selected households. Some basic information was collected on the characteristics of each person listed, including age, sex, education, and relationship to the head of the household. The main purpose of the Household Questionnaire was to identify women and men who were eligible for the individual interview. The Household Questionnaire also collected information on characteristics of the household's dwelling unit, such as the source of water, type of toilet facilities, materials used for the floor and roof of the house, ownership of various durable goods, and ownership and use of mosquito nets. In addition, this questionnaire was used to record height and weight measurements of women age 15-49 years and children under the age of 5 years, households eligible for collection of blood samples, and the respondents' consent to voluntarily give blood samples. The HIV testing procedures are described in detail in the next section. The Women's Questionnaire was used to collect information from all women age 15-49 years and covered the following topics: • Background characteristics (e.g., education, residential history, media exposure) • Reproductive history • Knowledge and use of family planning methods • Fertility preferences • Antenatal and delivery care • Breastfeeding • Vaccinations and childhood illnesses • Marriage and sexual activity • Woman's work and husband's background characteristics • Infant and child feeding practices • Childhood mortality • Awareness and behaviour about AIDS and other sexually transmitted diseases • Adult mortality including maternal mortality. The Women's Questionnaire also included a series of questions to obtain information on women's experience of domestic violence. These questions were administered to one woman per household. In households with two or more eligible women, special procedures were followed, which ensured that there was random selection of the woman to be interviewed. The Men's Questionnaire was administered to all men age 15-54 years living in every second household in the sample. The Men's Questionnaire collected similar information contained in the Women's Questionnaire, but was shorter because it did not contain questions on reproductive history, maternal and child health, nutrition, maternal mortality, and domestic violence. All aspects of the KDHS data collection were pretested in November and December 2002. Thirteen teams (one for each language) were formed, each with one female interviewer, one male interviewer, and one health worker. The 39 team members were trained for two week s in the various districts in which their language was spoken. In total, 260 households were covered in the pretest. The lessons learnt from the pretest were used to finalise the survey instruments and logistical arrangements for the survey. The pretest underscored the desirability of inluding voluntary counselling and testing (VCT) for

Abstract

The Mombasa Informal Settlement Survey 2009 is a representative sample survey drawn using the informal settlement classification of 1999 Census Enumeration Areas (EAs) as the sample frame. The classification of 1999 Census EAs was carried out in major cities of Kenya by the Kenya National Bureau of Statistics (KNBS) under a project funded by United Nations Environment Program (UNEP) in 2003. The 45 EAs were sampled using the probability proportional to size sampling methodology, and information from a total of 1,080 households were collected using structured questionnaires. The Mombasa informal settlement survey is one of the largest household sample surveys ever conducted exclusively for the informal settlements in Mombasa district.

The survey used a two-stage design. In the first stage, EAs were selected and in the second stage households were selected circular systematically using a random start from the list of households. The data was collected by three teams comprising of six members each (one supervisor, one editor, one measurer and three investigators).

The objective of the Mombasa Informal Settlement Survey 2009 is to provide estimates relating to the wellbeing of children and women living in the informal settlements of Mombasa, to create baseline information and to enable policymakers, planners, researchers, and program managers to take actions based on credible evidence. In Mombasa Informal Settlement Survey 2009, information on specific areas such as reproductive health, child mortality, child health, nutrition, child protection, childhood development, water and sanitation, hand washing practices, education, and HIV/AIDS and orphans were collected. The results indicate that the conditions of people living in the informal settlements are very poor and need immediate attention.

Geographic coverage

Mombasa district

Analysis unit

• individuals,
• households.

Universe

The survey covered all de jure household members (usual residents), all women aged between 15-49 years, all children under 5 living in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The primary objective of the sample design for the Mombasa Informal Settlement Survey, Kenya (MICS4) was to produce statistically reliable estimates of development indicators related to children and women living in the informal settlements of Mombasa. A two-stage cluster sampling approach was used for the selection of the survey sample.

The target sample size for the Mombasa Informal Settlement Survey was calculated as 1,080 households. For the calculation of the sample size, the key indicator used was proportion of institutional deliveries.

The resulting number of households from this exercise was 1,074 households which is the sample size needed, however, it was decided to cover 1,080 households. The average cluster size was determined as 24 households, based on a number of considerations, including the budget available, and the time that would be needed per team to complete one cluster. This implies a total of 45 clusters for the Mombasa informal settlement survey.

The sampling procedures are more fully described in "Kenya Mombasa Informal Settlements Multiple Indicator Cluster Survey 2009 - Report" pp.95-96.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaires for the Generic MICS were structured questionnaires based on the MICS4 model questionnaire with some modifications and additions. Household questionnaires were administered to a knowledgeable adult living in the household. The household questionnaire includes Household Listing, Education, Water and Sanitation, Indoor Residual Spraying, Insecticide Treated Mosquito Nets (ITN), Children Orphaned & Made Vulnerable By HIV/AIDS, Child Labour, Child Discipline, Disability, Handwashing Facility, and Salt Iodization.

In addition to a household questionnaire, the Questionnaire for Individual Women was administered to all women aged 15-49 years living in the households. The women's questionnaire includes Child Mortality, Birth history, Tetanus Toxoid, Maternal and Newborn Health, Marriage/Union, Contraception, Attitude towards Domestic Violence, Female Genital Mutilation/Cutting, Sexual Behaviour and HIV/AIDS.

The Questionnaire for Children Under-Five was administered to mothers or caretakers of children under 5 years of age living in the households. The children's questionnaire includes Birth Registration and Early Learning, Childhood Development, Vitamin A, Breastfeeding, Care of Illness, Malaria, Immunization, and Anthropometry.

Cleaning operations

Data were entered using the CSPro software. In order to ensure quality control, all questionnaires were double entered and internal consistency checks were performed, and the whole process was monitored initially by the MICS Global data processing specialist, followed by KNBS data processing expert. Procedures and standard programs developed under the global MICS project and adapted to the modified questionnaire were used throughout. Data entry began simultaneously with data collection in February 2009 and was completed at the end of March 2009. Data were analysed using the Statistical Package for Social Sciences (SPSS) software program, and the model syntax and tabulation plans developed by UNICEF were customized for this purpose.

Response rate

Of the 1,080 households selected for the sample, 1,076 were found occupied. Of these, 1,016 were successfully interviewed yielding a household response rate of 94.4 percent. In the interviewed households, 878 women (age 15-49) were identified and information collected from 821 women in these households, yielding a response rate of 93.5 percent. In addition, 464 children under age five were listed in the household questionnaire, and information on 454 children were obtained, which corresponds to a response rate of 97.8 percent. Overall response rates of 88.3 and 92.4 are calculated for the women's and under-5's interviews respectively.

Sampling error estimates

Sampling errors are a measure of the variability between all possible samples. The extent of variability is not known exactly, but can be estimated statistically from the survey results.

The following sampling error measures are presented in this appendix for each of the selected indicators: - Standard error (se): Sampling errors are usually measured in terms of standard errors for particular indicators (means, proportions etc). Standard error is the square root of the variance. The Taylor linearization method is used for the estimation of standard errors. - Coefficient of variation (se/r) is the ratio of the standard error to the value of the indicator. - Design effect (deff) is the ratio of the actual variance of an indicator, under the sampling method used in the survey, to the variance calculated under the assumption of simple random sampling. The square root of the design effect (deft) is used to show the efficiency of the sample design. A deft value of 1.0 indicates that the sample design is as efficient as a simple random sample, while a deft value above 1.0 indicates the increase in the standard error due to the use of a more complex sample design. - Confidence limits are calculated to show the interval within which the true value for the population can be reasonably assumed to fall. For any given statistic calculated from the survey, the value of that statistics will fall within a range of plus or minus two times the standard error (p + 2.se or p - 2.se) of the statistic in 95 percent of all possible samples of identical size and design.

For the calculation of sampling errors from the survey data, SPSS Version 17 Complex Samples module has been used. The results are shown in the tables that follow. In addition to the sampling error measures described above, the tables also include weighted and un-weighted counts of denominators for each indicator.

Sampling errors are calculated for indicators of primary interest. Three of the selected indicators are based on households, 10 are based on household members, 14 are based on women, and 14 are based on children under 5. All indicators presented here are in the form of proportions.

Data appraisal

A series of data quality tables are available to review the quality of the data and include the following:

• Age distribution of household population
• Age distribution of eligible and interviewed women
• Age distribution of eligible and interviewed under-5s
• Age distribution of under-five children
• Heaping on ages and periods
• Completeness of reporting
• Presence of mother in the household and the person interviewed for the under-5 questionnaire
• School attendance by single age
• Sex ratio at birth among children ever born and living
• Distribution of women by time since last birth

The results of each of these data quality tables are shown in appendix D in document "Kenya Mombasa Informal Settlements Multiple Indicator Cluster Survey 2009 - Report" pp.102-109.

Kenyan Restaurant Market Outlook

According to our latest research, the Kenyan Restaurant market size was valued at $1.2 billion in 2024 and is projected to reach $2.3 billion by 2033, expanding at a CAGR of 7.5% during 2024–2033. This impressive growth trajectory is primarily driven by the rapid urbanization and rising disposable incomes across Kenya, which have significantly altered consumer eating habits and spurred demand for diverse dining experiences. The burgeoning middle class, coupled with an influx of international tourists and expatriates, is reshaping the restaurant landscape, creating opportunities for both local and international investors. The proliferation of digital platforms and food delivery services is further accelerating market expansion, making dining out and food ordering more accessible than ever before.

Regional Outlook

Nairobi dominates the Kenyan restaurant market, accounting for the largest share of both revenue and outlet concentration. As the nation’s capital and economic hub, Nairobi benefits from a cosmopolitan population, a vibrant business environment, and a robust tourism sector. The city’s dynamic culinary scene features a blend of traditional Kenyan, international, and fusion cuisines, catering to diverse consumer preferences. High footfall in commercial districts, shopping malls, and entertainment hubs fuels demand for quick service restaurants, fine dining establishments, and casual cafes. Nairobi’s mature infrastructure, favorable regulatory environment, and access to skilled hospitality talent have attracted significant investment from both domestic and global restaurant chains, further consolidating its leadership position in the market.

In contrast, Mombasa is emerging as the fastest-growing region in the Kenyan restaurant market, posting a projected CAGR of 8.4% through 2033. The city’s growth is underpinned by its status as a leading tourist destination, drawing both local and international visitors to its coastal attractions. Mombasa’s restaurant scene is evolving rapidly, with increased investments in beachfront dining, seafood specialty outlets, and experiential culinary concepts. The expansion of hospitality infrastructure, coupled with government initiatives to promote tourism, is driving the proliferation of new restaurant formats, including quick service and casual dining. The region’s growing expatriate community and rising disposable incomes are further fueling demand for diverse cuisines and premium dining experiences, making Mombasa a hotspot for restaurant market growth.

Other key cities, such as Kisumu, Eldoret, and Nakuru, are witnessing steady growth, albeit at a slower pace compared to Nairobi and Mombasa. These emerging urban centers face unique challenges, including limited supply chain networks, lower urbanization rates, and evolving consumer preferences. However, increasing investments in infrastructure, rising urban migration, and local government support are gradually improving market conditions. The adoption of modern restaurant concepts and digital platforms is still nascent in these regions, but there is significant potential for growth as awareness and disposable incomes rise. The rest of Kenya, encompassing smaller towns and rural areas, presents untapped opportunities but faces barriers such as inconsistent power supply, limited access to quality ingredients, and lower purchasing power.

Report Scope

Abstract

The 2003 Kenya Demographic and Health Survey (2003 KDHS) is a nationally representative sample survey of 8,195 women age 15 to 49 and 3,578 men age 15 to 54 selected from 400 sample points (clusters) throughout Kenya. It is designed to provide data to monitor the population and health situation in Kenya as a follow-up of the 1989, 1993 and 1998 KDHS surveys. The survey utilised a two-stage sample based on the 1999 Population and Housing Census and was designed to produce separate estimates for key indicators for each of the eight provinces in Kenya. Unlike prior KDHS surveys, the 2003 KDHS covered the northern half of Kenya. Data collection took place over a five-month period, from 18 April to 15 September 2003.

OBJECTIVES

The 2003 Kenya Demographic and Health Survey (KDHS) is the latest in a series of national level population and health surveys to be carried out in Kenya in the last three decades. The 2003 KDHS is designed to provide data to monitor the population and health situation in Kenya and to be a follow-up to the 1989, 1993, and 1998 KDHS surveys.

The survey obtained detailed information on fertility levels; marriage; sexual activity; fertility preferences; awareness and use of family planning methods; breastfeeding practices; nutritional status of women and young children; childhood and maternal mortality; maternal and child health; and awareness and behaviour regarding HIV/AIDS and other sexually transmitted infections. New features of the 2003 KDHS include the collection of information on malaria and the use of mosquito nets, domestic violence, and HIV testing of adults.

More specifically, the objectives of the 2003 KDHS were to: - At the national and provincial level, provide data that allow the derivation of demographic rates, particularly fertility and childhood mortality rates, which can be used to evaluate the achievements of the current national population policy for sustainable development; - Measure changes in fertility and contraceptive prevalence use and at the same time study the factors that affect these changes, such as marriage patterns, desire for children, availability of contraception, breastfeeding habits, and important social and economic factors; - Examine the basic indicators of maternal and child health in Kenya, including nutritional status, use of antenatal and maternity services, treatment of recent episodes of childhood illness, use of immunisation services, use of mosquito nets, and treatment of children and pregnant women for malaria; - Describe the patterns of knowledge and behaviour related to the transmission of HIV/AIDS and other sexually transmitted infections; - Estimate adult and maternal mortality ratios at the national level; - Ascertain the extent and pattern of domestic violence and female genital cutting in the country; - Estimate the prevalence of HIV in the country at the national and provincial level and use the data to corroborate the rates from the sentinel surveillance system.

Geographic coverage

The 2003 KDHS was the first survey in the Demographic and Health Surveys (DHS) programme to cover the entire country, including North Eastern Province and other northern districts that had been excluded from the prior surveys (Turkana and Samburu in Rift Valley Province and Isiolo, Marsabit, and Moyale in Eastern Province).

Analysis unit

• Household
• Women age 15-49
• Men age 15-54
• Children under five

Universe

All women age 15-49 years who were either usual residents of the households in the sample or visitors present in the household on the night before the survey were eligible to be interviewed in the survey. The survey collected information on demographic and health issues from a sample of women in the reproductive ages (15-49) and from men age 15-54 years in the one-in-two sub-sample of households selected for the male survey.

Kind of data

Sample survey data

Sampling procedure

The sample for the 2003 KDHS covered the population residing in households in the country. A representative probability sample of almost 10,000 households was selected for the KDHS sample. This sample was constructed to allow for separate estimates for key indicators for each of the eight provinces in Kenya, as well as for urban and rural areas separately. Given the difficulties in traveling and interviewing in the sparsely populated and largely nomadic areas in the North Eastern Province, a smaller number of households was selected in this province. Urban areas were oversampled. As a result of these differing sample proportions, the KDHS sample is not self-weighting at the national level; consequently, all tables except those concerning response rates are based on weighted data.

The survey utilised a two-stage sample design. The first stage involved selecting sample points (“clusters”) from a national master sample maintained by CBS (the fourth National Sample Survey and Evaluation Programme [NASSEP IV]). The list of enumeration areas covered in the 1999 population census constituted the frame for the NASSEP IV sample selection and thus for the KDHS sample as well. A total of 400 clusters, 129 urban and 271 rural, were selected from the master frame. The second stage of selection involved the systematic sampling of households from a list of all households that had been prepared for NASSEP IV in 2002. The household listing was updated in May and June 2003 in 50 selected clusters in the largest cities because of the high rate of change in structures and household occupancy in the urban areas.

All women age 15-49 years who were either usual residents of the households in the sample or visitors present in the household on the night before the survey were eligible to be interviewed in the survey. In addition, in every second household selected for the survey, all men age 15-54 years were eligible to be interviewed if they were either permanent residents or visitors present in the household on the night before the survey. All women and men living in the households selected for the Men's Questionnaire and eligible for the individual interview were asked to voluntarily give a few drops of blood for HIV testing.

Mode of data collection

Face-to-face

Research instrument

Three questionnaires were used in the survey:a) the Household Questionnaire, b) the Women's Questionnaire and c) the Men's Questionnaire. The contents of these questionnaires were based on model questionnaires developed by the MEASURE DHS+ programme.

In consultation with a broad spectrum of technical institutions, government agencies, and local and international organisations, CBS modified the DHS model questionnaires to reflect relevant issues in population, family planning, HIV/AIDS, and other health issues in Kenya. A number of thematic questionnaire design committees were organised by CBS. Periodic meetings of each of the thematic committees, as well as the final meeting, were also arranged by CBS. The inputs generated in these meetings were used to finalise survey questionnaires. These questionnaires were then translated from English into Kiswahili and 11 other local languages (Embu, Kalenjin, Kamba, Kikuyu, Kisii, Luhya, Luo, Maasai, Meru, Mijikenda, and Somali). The questionnaires were further refined after the pretest and training of the field staff.

a) The Household Questionnaire was used to list all of the usual members and visitors in the selected households. Some basic information was collected on the characteristics of each person listed, including age, sex, education, and relationship to the head of the household. The main purpose of the Household Questionnaire was to identify women and men who were eligible for the individual interview. The Household Questionnaire also collected information on characteristics of the household's dwelling unit, such as the source of water, type of toilet facilities, materials used for the floor and roof of the house, ownership of various durable goods, and ownership and use of mosquito nets. In addition, this questionnaire was used to record height and weight measurements of women age 15-49 years and children under the age of 5 years, households eligible for collection of blood samples, and the respondents' consent to voluntarily give blood samples. The HIV testing procedures are described in detail in the next section.

b) The Women's Questionnaire was used to collect information from all women age 15-49 years and covered the following topics: - Background characteristics (e.g., education, residential history, media exposure) - Reproductive history - Knowledge and use of family planning methods - Fertility preferences - Antenatal and delivery care - Breastfeeding
Vaccinations and childhood illnesses
- Marriage and sexual activity - Woman's work and husband's background characteristics - Infant and child feeding practices - Childhood mortality - Awareness and behaviour about AIDS and other sexually transmitted diseases
- Adult mortality including maternal mortality.

The Women's Questionnaire also included a series of questions to obtain information on women's experience of domestic violence. These questions were administered to one woman per household. In households with two or more eligible women, special procedures were followed, which ensured that there was random selection of the woman to be interviewed.

c) The Men's Questionnaire was administered to all men age 15-54 years living in every second household in the sample. The Men's Questionnaire collected similar information contained in the Women's Questionnaire, but was shorter because it did not contain questions on reproductive history, maternal and child

KE：最大城市人口在12-01-2017达4,222,389.000人，相较于12-01-2016的4,065,018.000人有所增长。KE：最大城市人口数据按年更新，12-01-1960至12-01-2017期间平均值为1,285,227.500人，共58份观测结果。该数据的历史最高值出现于12-01-2017，达4,222,389.000人，而历史最低值则出现于12-01-1960，为292,622.000人。CEIC提供的KE：最大城市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Global Database的肯尼亚 – 表 KE.世界银行：人口和城市化进程统计。

KE：最大城市人口占城市总人口的百分比在12-01-2017达31.985%，相较于12-01-2016的32.132%有所下降。KE：最大城市人口占城市总人口的百分比数据按年更新，12-01-1960至12-01-2017期间平均值为35.120%，共58份观测结果。该数据的历史最高值出现于12-01-1962，达50.731%，而历史最低值则出现于12-01-2017，为31.985%。CEIC提供的KE：最大城市人口占城市总人口的百分比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肯尼亚 – 表 KE.世行.WDI：人口和城市化进程统计。

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.