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.

Population in the largest city (% of urban population) in Kenya was reported at 32.68 % in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. Kenya - Population in the largest city - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

This statistic shows the biggest cities in Kenya as of 2019. In 2019, approximately 4.4 million people lived in Nairobi, making it the biggest city in Kenya.

Population in largest city in Kenya was reported at 5541172 in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. Kenya - Population in largest city - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

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;

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.; ;

The dataset represents an estimated cumulative travel time/cost (raster grid) accessibility map, for Kenya's major cities . The map is an output of the sub-Saharan African Corridor, Mobile Warehouse Location pilot project. Modeled cities are: Nairobi (7,626,752); Mombasa (1,535,899); Nakuru (610,637); Kisumu (567,963) 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). The production of the travel time surfaces followed the steps: rasterization of transportation network vector layers and surfaces; production of cost/time layer; computation of a cumulative cost/time layer from cities (Major Cities Accessibility Map).

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.

Cairo, in Egypt, ranked as the most populated city in Africa as of 2025, with an estimated population of over 23 million inhabitants living in Greater Cairo. Kinshasa, in Congo, and Lagos, in Nigeria, followed with some 17.8 million and 17.2 million, respectively. Among the 15 largest cities in the continent, another one, Kano, was located in Nigeria, the most populous country in Africa. Population density trends in Africa As of 2023, Africa exhibited a population density of 50.1 individuals per square kilometer. Since 2000, the population density across the continent has been experiencing a consistent annual increment. Projections indicated that the average population residing within each square kilometer would rise to approximately 58.5 by the year 2030. Moreover, Mauritius stood out as the African nation with the most elevated population density, exceeding 627 individuals per square kilometre. Mauritius possesses one of the most compact territories on the continent, a factor that significantly influences its high population density. Urbanization dynamics in Africa The urbanization rate in Africa was anticipated to reach close to 45.5 percent in 2024. Urbanization across the continent has consistently risen since 2000, with urban areas accommodating only around a third of the total population then. This trajectory is projected to continue its rise in the years ahead. Nevertheless, the distribution between rural and urban populations shows remarkable diversity throughout the continent. In 2024, Gabon and Libya stood out as Africa’s most urbanized nations, each surpassing 80 percent urbanization. As of the same year, Africa's population was estimated to expand by 2.27 percent compared to the preceding year. Since 2000, the population growth rate across the continent has consistently exceeded 2.3 percent, reaching its pinnacle at 2.63 percent in 2013. Although the growth rate has experienced a deceleration, Africa's population will persistently grow significantly in the forthcoming years.

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).

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.

Kenya had a Muslim population of roughly 5.6 million people, according to the last country census conducted in 2019. Nearly 50 percent of individuals adhering to Islam lived in the Northern-East counties of Mandera (856.5 thousand people), Garissa (815.8 thousand people), and Wajir (767.3 thousand people). Overall, around 10 percent of Kenya's population identified as Muslim.

Abstract

Handwashing with soap is necessary for promoting the public health of communities as it contributes to the prevention of adverse health outcomes such as diarrhoeal disease and acute respiratory infections. The availability of handwashing facilities, which are necessary for handwashing to happen, is notably low in sub-Saharan Africa. In Kenya, available literature shows disparities in access to handwashing facilities in urban and rural areas, but scanty and inconsistent data is available on access to handwashing facilities within low income urban settlements in Kenyan cities. In addition to handwashing facilities that are nonexistent, residents of low-income urban settlements also face challenges of access to water and sanitation facilities within their compounds. This study aims to evaluate the status of handwashing practices in Kenya, and design handwashing facilities that will be managed and maintained by community members in low-income urban settlements within Nairobi, Kisumu, and Nakuru. This will be a multi-stage study where a mixed-methods approach will be applied. The first phase of the study will entail a cross-sectional survey, in-depth interviews, and focus group discussions to assess and explore handwashing practices and their determinants in the lowincome urban settlements. A second co-design phase will build from the first phase and will entail designing and testing compound-led initiatives for improving hand hygiene within the settlements. This second phase will be a participatory phase that will entail co-designing appropriate handwashing facilities and the accompanying messaging to encourage handwashing with soap with selected residents in the low-income urban settlements. Results from these two phases will inform a subsequent trial to evaluate the effectiveness of these interventions. The study will be conducted in three cities in Kenya; i.e. Nairobi, Nakuru and Kisumu. The three cities have been selected because they reflect urban cities in Africa; Nairobi represents a capital city, Kisumu a mid-size secondary city, and Nakuru represents a rapidly urbanizing and expanding secondary city. The three cities will provide a reflection of different urban environments in Africa, and a comparison of hand hygiene practices in three different contexts. Results from this study will provide evidence on hygiene facilities and their determinants in poor settings in urban Kenya, evidence that is useful for decision making, planning, and practice. At the global level, the evidence will provide data on global monitoring and reporting of hygiene, in urban areas of Sub-Saharan Africa.

Geographic coverage

Low and Middle income settlements in Kenyan cities

Analysis unit

families/households

Universe

Household members residing in Low and Middle income settlements in Kenyan cities

Sampling procedure

Household Survey Prevalence of handwashing facilities in low income urban settlements in the four cities was used to estimate the sample size, i.e 66% in Kisumu (ResilienceThink, 2021), 18.6% in Nakuru (County Government of Nakuru, 2018), 27% in Mombasa (Jason Cardosi & Rufus, 2007), and 21.1% in Nairobi (Kamau & Njiru, 2018). The sample size determination formula for finite population was used ?? = ( (??) 2 × ??(??) (??) 2 ) Where, n = Desired sample size Z = Critical value and standard value for the corresponding level of confidence (At 95% CI of 1.96 P = Expected prevalence based on previous research q = 1-p d = Margin of error or precision (at 5%) The estimated sample sizes was adjusted upwards by 10% to cater for refusals and/or drop outs. Based on the formula, the final estimated sample size in each of the three cities was be 379 respondents in Kisumu, 260 respondents in Nakuru, 333 respondents in Mombasa, and 280 respondents in Nairobi.

In-Depth Interviews (IDI) Households were selected randomly if they live within the selected settlements in Nairobi, Mombasa, Nakuru and Kisumu cities. Participants comprised adult male and female household heads, including landlords, who consented to participate in the study. Field staff randomly selected respondents who were residents within the study sites, and purposively select landlords who provided insights on barriers and opportunities for handwashing interventions.

Key Informant Interviews Eligible stakeholders werel identified and purposively sampled from already existing listing of Key stakeholders (at national and county levels) involved in handwashing interventions, and they comprised individuals from the Ministry of Health (MoH), Ministry of Water, Sanitation and Irrigation (MoWSI), and development and implementing organizations such as the United Nations Children’s Fund (UNICEF)

Sampling deviation

N/A

Mode of data collection

Face-to-face [f2f]

Research instrument

Household questionnaire A cross sectional household survey was conducted to assess the availability of handwashing facilities at the household level in the low income urban settlements. The surveyl also provide information on the availability of hygiene and handwashing facilities, hygiene commodities such as soap, availability of water, and challenges in practicing hand hygiene or handwashing with soap.

KII tool guide Key Informant interviews (KIIs) wiere conducted with stakeholders at the policy level at national and county levels and stakeholders from organizations involved in handwashing interventions. The aim of the KIIs was to understand barriers and opportunities related to handwashing in low income urban settlements, including handwashing interventions that have been implemented within low income urban settlements, policies on handwashing with soap, coordination mechanisms, sources of funding, and monitoring and evaluation approaches.

IDI tool guide IDIs were conducted with households from each of the four study sites to get deeper understanding of existing handwashing infrastructure and hygiene practices; including where handwashing facilities are located, how and when handwashing is done, why handwashing is done the way it is done, barriers for handwashing, and opportunities for improvement.

Cleaning operations

Data was collected using the Ipsos iField application on android tablets

Response rate

N/A

Sampling error estimates

N/A

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.

Abstract

The STEP (Skills Toward Employment and Productivity) Measurement program is the first ever initiative to generate internationally comparable data on skills available in developing countries. The program implements standardized surveys to gather information on the supply and distribution of skills and the demand for skills in labor market of low-income countries.

The uniquely-designed Household Survey includes modules that measure the cognitive skills (reading, writing and numeracy), socio-emotional skills (personality, behavior and preferences) and job-specific skills (subset of transversal skills with direct job relevance) of a representative sample of adults aged 15 to 64 living in urban areas, whether they work or not. The cognitive skills module also incorporates a direct assessment of reading literacy based on the Survey of Adults Skills instruments. Modules also gather information about family, health and language.

Geographic coverage

• The STEP target population is the urban population aged 15 to 64 (inclusive).

Analysis unit

The units of analysis are the individual respondents and households. A household roster is undertaken at the start of the survey and the individual respondent is randomly selected among all household members aged 15 to 64 included. The random selection process was designed by the STEP team and compliance with the procedure is carefully monitored during fieldwork.

Universe

The target population is defined as all non-institutionalized persons aged 15 to 64 (inclusive) living in private dwellings in the urban areas of the country at the time of the data collection. This includes all residents, except foreign diplomats and non-nationals working for international organizations
The following are considered "institutionalized" and excluded from the STEP survey:
- Residents of institutions (prisons, hospitals, etc)
- Residents of senior homes and hospices
- Residents of other group dwellings such as college dormitories, halfway homes, workers' quarters, etc

Other acceptable exclusions are:
- Persons living outside the country at the time of data collection, e.g., students at foreign universities
Deviation Requested from the Standard: The statistical population is composed of core urban households and excludes the categories identified here, as well as itinerants (as classified in the Population Census 2009 in Kenya).

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample size was 3894 households. The Kenya sample design is a stratified 3 stage sample design. The sample was stratified by 4 geographic areas: 1-Nairobi, 2-Other Large Cities (over 100,000 households), 3- Medium cities (60,000 to 100,000 HHs), and 4-Other Urban Areas. For detailed description of the sample design and sampling methodologies, refer to Part 3 of the National Survey Design Planning Report (NSDPR) as well as the STEP Survey Weighting Procedures Summary. Both documents are provided as external resources.

Sampling deviation

War marred and unstable regions of Kenya were excluded from the survey. Itinerants (as classified in the Population Census 2009 in Kenya) were also excluded.

Mode of data collection

Face-to-face [f2f]

Research instrument

The STEP survey instruments include: (i) A Background Questionnaire developed by the WB STEP team. (ii) A Reading Literacy Assessment developed by Educational Testing Services (ETS).

All countries adapted and translated both instruments following the STEP Technical Standards: 2 independent translators adapted and translated the Background Questionnaire and Reading Literacy Assessment, while reconciliation was carried out by a third translator. In Kenya the section of the questionnaire assessing behavior and personality traits (Module 6) was translated into Swahili to adapt to respondents' language preferences, so that the respondent could choose to answer in either English or Swahili.
- The survey instruments were both piloted as part of the survey pretest. - The adapted Background Questionnaires are provided in English as external resources. The Reading Literacy Assessment is protected by copyright and will not be published.

Cleaning operations

EEC Canada Inc. was responsible for data entry and processing.

The STEP Data management process is as follows:

1. Raw data is sent by the survey firm
2. The WB STEP team runs data checks on the Background Questionnaire data.
   • ETS runs data checks on the Reading Literacy Assessment data.
   • Comments and questions are sent back to the survey firm.
3. The survey firm reviews comments and questions. When a data entry error is identified, the survey firm corrects the data.
4. The WB STEP team and ETS check the data files are clean. This might require additional iterations with the survey firm.
5. Once the data has been checked and cleaned, the WB STEP team computes the weights. Weights are computed by the STEP team to ensure consistency across sampling methodologies.
6. ETS scales the Reading Literacy Assessment data.
7. The WB STEP team merges the Background Questionnaire data with the Reading Literacy Assessment data and computes derived variables.

Response rate

An overall response rate of 91.8% was achieved in the Kenya STEP Survey. Table 21 of the STEP Survey Weighting Procedures Summary provides the detailed percentage distribution by final status code.

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.

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。

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.

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

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。