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.

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.

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: 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;

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).

This dataset is about countries and is filtered where the country includes Kenya, featuring 5 columns: capital city, continent, country, currency, and land area. The preview is ordered by population (descending).

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.

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.

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.

Nairobi is the most populated county in Kenya. The area formed by the country's capital and its surroundings has a population of over 4.3 million inhabitants. Of the 47 counties in Kenya, 18 have a population of more than one million people.

Individual-level household survey dataset for one urban and one rural constituency in Kenya and Zambia, covering questions on media and communications habits, political behaviour and attitudes. The objective of the surveys was to obtain representative samples of two constituencies per country. Constituencies were selected according to their social and economic characteristics, in order to capture a wide variety of contexts. A random procedure was deployed in all stages of sampling, ensuring representativity of households and individuals of voting age in the four constituencies. The results of the survey can be generalised to the particular constituencies with a margin of error of approximately minus or plus 5% for a 95% confidence interval.Politics and Interactive Media in Africa (PIMA) examines whether and how Africans, particularly the poorest and least politically enfranchised, use new communication technologies to voice their opinion and to engage in a public debate on interactive broadcast media, and its effects on modes of political accountability. Through detailed qualitative case-studies in Kenya and Zambia, PIMA critically interrogates the heralded potential for digital communications and liberalised media sectors to promote more responsive and inclusive democratic governance, with a keen eye for turning project insights into relevance for policymakers, media houses, journalists and development organisations. By employing survey-based, qualitiative and ethnographic methods to comparatively analyse interactive radio and TV programmes in the context of electoral and everyday politics, we will probe whose voice counts, why and to what effects in these new digitally-enabled spaces of voice and accountability. The project takes into account local innovation in the use of ICTs and the interactions between different modes, venues and actors of information gathering and dissemination that are particularly prominent in African contexts. PIMA brings together researchers from the Universities of Cambridge, Nairobi and Zambia, working closely with select broadcast stations and other stakeholders. Data collection for the PiMA surveys took place during May 2013 (Kenya) and June-July 2013 (Zambia). In Kenya, surveys were conducted in Ruaraka: a peri-urban constituency in the capital city Nairobi, with mixed demographics including one of the city’s major slums; and Seme: a rural constituency settled around Lake Victoria in a largely fisher-agricultural community in the western Kenyan city of Kisumu. In Zambia, the surveys were conducted in Mandevu: an urban constituency in the capital city Lusaka with a mixed demographic including some of the city’s major slum settlements; and Chipangali: a rural constituency in the country’s largely agricultural Eastern Province. The four samples were designed as representative cross-sections of all households in those constituencies. Although no claim is made that the constituencies themselves were representative of the wider national population, they were selected based on the possibility of capturing variation in terms of socio-economic factors, political context and media landscape. A multi-stage sampling approach was deployed in the four sites, which involved selecting geographically defined units of decreasing size at each stage. The main four stages of the sampling strategy were: (1) cluster sampling for selection of wards; (2) simple random sampling for selection of enumeration areas (EAs) within wards; (3) systematic random sampling for selection of households within EAs (“random walk”); and (4) simple random (Kenya), or stratified by age and gender (Zambia) sampling for selection of individuals within households. Because there were no available lists of voting individuals living in those constituencies based on census data, the population was grouped into units from which reliable data was available, such as EAs. The lists of EAs constituted the sampling frame from which the primary sampling units (PSUs) were randomly selected. In Stages 2 and 3, selection was performed with probabilities proportional to population size. The purpose was to guarantee that more populated areas (wards, EAs) had a proportionally higher probability of being included in the sample. Within each household, individuals were selected using a random procedure. By employing random techniques in all stages of sampling, and using sampling with probability proportional to the population, it may be assumed that all individuals of voting age (18 and over) living in those four constituencies had a known and above zero chance of being included in the sample. The results of the survey allow inferences to the voting population in the four constituencies (macro-units) with some degree of accuracy (but not to the two countries). The sample sizes are 760 for Kenya (383 for Ruaraka and 377 for Seme) and 688 for Zambia (327 for Mandevu and 361 for Chipangali). The margins of error for a 95% confidence level are no more than plus or minus 5% for both Ruaraka and Seme, 5.41% for Mandevu and 5.12% for Chipangali. The response rate for Kenya was 90.4% (84.6% for Ruaraka and 96.3% for Seme). The response rate for Zambia was not available because the team did not record the number and reasons of unsuccessful calls.

Abstract

The Bill & Melinda Gates Foundation’s reproductive health strategy aims to reduce maternal and infant mortality and unintended pregnancy in the developing world by increasing access to high-quality, voluntary FP services. The reproductive health strategy is being implemented at the country level through the Urban Reproductive Health Initiative (URHI) being implemented in Kenya, Nigeria, India and Senegal.

In Kenya, the URHI, hereinafter referred to as Tupange. The main objective of the project is to increase modern contraceptive use in Nairobi, Mombasa and Kisumu by 20 percentage points over the five-year life of the project. The urban centers of Machakos and Kakamega are additional “delayed” interventions sites that are included in the baseline data collection presented here although data in these delayed sites were collected only from women.

Key elements of the Tupange include: • Integrating high-quality FP services with maternal and newborn health services, especially post-abortion, postpartum, antenatal care and HIV/AIDS services; • Improving the overall quality of FP services, particularly in high-volume settings; • Increasing access to FP services for the urban poor through public-private partnerships and other private sector approaches; • Creating sustained demand for FP services among the urban poor; and • Creating a supportive policy environment for ensuring access to FP supplies and services, particularly for the urban poor.

Geographic coverage

Urban areas (five cities in Kenya - Nairobi, Mombasa, Kisumu, Machakos, and Kakamega)

Analysis unit

Household, woman age 15-49 years, man 15-59 years

Universe

All women aged 15-49 years who were either usual residents or visitors present in the sampled households on the night prior to the survey were eligible for a detailed interview. In addition, in half of the sampled households in Nairobi, Mombasa and Kisumu, all men aged 15-59 years were asked to participate in a detailed interview.

Kind of data

Sample survey data [ssd]

Sampling procedure

The household survey sample was drawn from the population residing in the five cities/urban centers. The most recent Population and Housing Census (2009) was used to identify clusters from which a representative sample of households for each city/urban center was drawn. A total of 13,140 households were selected for interviewing, ensuring that the sample was sufficient to allow analysis of the findings by each of the five intervention sites. Nairobi was intentionally oversampled (4,260 vs. 2,220 households) due its significantly larger size. With the exception of Machakos and Kakamega, the sample in each urban area was apportioned equally between formal and informal localities.

A two-stage cluster sampling design was used for each urban area. Stage one involved selecting a random sample of clusters in each urban area. In Nairobi, 71 clusters were randomly selected in each of the formal and informal areas (domains), for a total of 142. In Mombasa and Kisumu, 37 clusters were randomly drawn from each domain, for a total 74 per urban area. In Machakos and Kakamega, 74 clusters were randomly selected per urban area. In the second stage, a random sample of 30 households was selected within each selected cluster. Interviews with women took place in all households selected. In Nairobi, Mombasa and Kisumu, half of the households (15) in each of the selected clusters were also selected to interview men.

Sampling deviation

Nairobi was intentionally oversampled (4,260 vs. 2,220 households) due its significantly larger size. With the exception of Machakos and Kakamega, the sample in each urban area was apportioned equally between formal and informal localities.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three questionnaires were used to collect baseline information-one for each of the households, one for women and one for men. In Machakos and Kakamega, only women were interviewed. Questionnaires were based on the questionnaires used by the Demographic and Health Survey program in Kenya but were modified and expanded by all in-country partners to reflect MLE and Tupange objectives.

Questionnaires were translated from English into Kiswahili, Luhya, Kamba and Dholuo-the four most commonly spoken languages in the five cities. Final revisions were made to the questionnaires following extensive pre-testing and training of field staff. The household questionnaire was administered prior to the women's and men's questionnaires to facilitate the identification of eligible household members. The methodology and questionnaires were tested in Kisumu and Nairobi August 5-8, 2010, in clusters outside the planned intervention areas to minimize chances of contamination. Survey instruments were finalized based on feedback from and lessons learned during the pre-test.

Cleaning operations

A data processing team was selected and trained at the KNBS offices in Nairobi. Most of the data processing staff were selected from the reserve members from the field survey teams. Staff from MLE and APHRC conducted the five-day training between October 26 and November 1, followed by on-the-job training for an additional four days. Fifteen data entry clerks, four office editors, one system administrator, one supervisor and one manager participated in the training. Data processing began in November 2010 and was finalized in March 2011.

To ensure that all questionnaires were processed, a “data audit” was conducted and completed at the end of March 2011. The tabulation of the survey results, particularly the program tables, was done in May 2011. Data analysts from the University of North Carolina and APHRC produced the tables and preliminary results that were shared with program teams on June 2-3, 2011.

To ensure that all questionnaires were processed, a "data audit" was conducted and completed at the end of March 2011. The tabulation of the survey results, particularly the program tables, was done in May 2011. Data analysts from the University of North Carolina and APHRC produced the tables and preliminary results that were shared with program teams on June 2-3, 2011. Further analysis of the data that allowed inclusion of results regarding additional indicators was completed by July 2011 and an initial draft baseline report was prepared by mid-September 2011.

Response rate

Of the 13,140 households selected for inclusion in the sample, 12,565 were occupied and eligible for interviews. Of these, 10,992 households were interviewed successfully (197 declined), a response rate of 84 percent. There were a total of 10,502 eligible women, of whom 8,932 consented and participated in an interview, yielding a response rate of 85.1 percent. There were 3,815 eligible men, of whom 2,503 consented and participated in an interview, a response rate of 65.6 percent.

For the household survey, non -response was primarily due to the absence of a suitable member of the household during each of three visits (37 percent; not displayed). Non-responses during the male and female interviews were due mainly to the subject's absence at the time of the household interview (76 percent and 78 percent respectively) or at any of the three follow-up visits.

Abstract

The Kenya Innovation Survey is a national innovation survey undertaken from March to June 2012. The survey was designed to measure the innovation activity based on a set of core indicators to inform policies that will help the country configure the national system of innovation in order to respond to socio-economic challenges. The survey was based on the Oslo Manual by OECD. The survey covered Nairobi, Mombasa, Kisumu, Nakuru and Eldoret.

This innovation survey, the first in Kenya, was carried out in order to generate crucial learning lessons to inform the planning of the main national innovation survey to be undertaken at a later date. However, the overall objective of the innovation survey, being part of the national ST&I system of indicators that is under development, is to build Kenya’s capacity to develop and use innovation indicators in designing and implementing ST&I policies and strategies for national development.

The survey is therefore an attempt to probe the activity of innovation through the collection of data on various aspects of innovation in order to develop relevant innovation indicators and specific innovation policies for the country. These indicators will then enable key stakeholders to understand the state of the national innovation system and its capacity to deliver the intended results so as to address the components that need attention.

The innovation survey is designed to: • Develop and cause the adoption of internationally comparable innovation indicators; • Build human and institutional capacities to collect innovation indicators; • Inform the country on the state of innovation; and • Provide both qualitative and quantitative data on innovation at firm level.

Geographic coverage

Firms in Major Towns of Kenya (urban)

Mombasa City, Nakuru Town, Eldoret Town and Kisumu City

Analysis unit

• Firms
• Establishment

Universe

Selected towns

Kind of data

Aggregate data [agg]

Sampling procedure

The sample frame consisted of all registered firms, public/private universities and public research institutions, national polytechnics and NGOs. The firms were randomly selected by ISIC sector from the frame. A total of 194 firms were selected in Nairobi and its environs while 102 firms were selected upcountry as follows: Mombasa (25 firms), Kisumu (25 firms), Eldoret (24 firms) and Nakuru (25 firms).

Mode of data collection

Other [oth]

Research instrument

The questionnaire was divided into eleven parts as follows: • Part 1: General information of the firm • Part 2: Product (goods or services) innovation • Part 3: Process innovation • Part 4: Ongoing or abandoned Innovation activities • Part 5: Performed innovation activities and expenditures • Part 6: Sources of information and co-operation for innovation activities • Part 7: Effects / Objectives of innovation • Part 8: Factors hampering innovation activities • Part 9: Intellectual property rights • Part 10: Organizational and marketing innovation • Part 11: Specific innovations

NOTE: The full questionnaire is attached in the external resource

Cleaning operations

In this survey, data processing personnel were drawn from the Kenya National Bureau of Statistics assisted by some officers from the Ministry of Higher Education, Science and Technology. The questionnaires were received from the field, recorded and edited in preparation for data capture.

Data processing and analysis were done at the Kenya National Bureau of Statistics. The Census and Survey Software Programme (CSPro) was used for data capture,editing, validation and tabulation. In developing the data capture system, certain controls were in-built to check the characters entered afterwhich validation was done in preparation for the production of frequency tables and in readiness for data analysis.

Response rate

The Innovation Survey covered business firms in Nairobi, Mombasa, Kisumu, Nakuru and Eldoret. A total of 293 firms were targeted in this innovation survey. Out of these, 160 firms completed and returned the questionnaires, thus representing a 54.6 percent overall response rate. The different regions response rate are listed as follows: Nairobi - 43.3% Mombasa - 68.0% Kisumu - 60.0% Nakuru - 92.0% Eldoret - 87.5% Total - 54.6%

Crop Storage Location Score: Legume (Kenya - ~1km) is a country raster grid with 0.01 decimal degrees resolution, produced under the scope of the sub-Saharan African Corridor project pilot case, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) for the identification of recommended mobile storage locations (movable warehouses). The modeling variables characterize supply, demand and accessibility, main logistical factors for warehousing facilities location. The variables are Legume Crops (supply), human population density (demand) and main transportation network infrastructure (accessibility). The main transportation network infrastructure is the input for the development of raster-based travel time/cost analysis. GIS multicriteria decision analysis GIS-MCDA consists of a method to convert and combine spatial data/geographical information and decision-makers criteria in order to attain evidence for a decision-making process. Considered crops are selected using FAOStat data: Beans, Cow Peas, dry. The location score (0-100) results from an arithmetic weighted sum calculation (cell statistics) of normalized grids. The assumed weight for each of the criteria is as follows. ("Legume Crops Production" * 0.4) + ("Human Population Density" * 0.2) + ("Cities Accessibility" * 0.1) + ("Regional Cities Accessibility" * 0.1) + ("Ports Accessibility" * 0.1)

This qualitative data set comprises transcripts from focus group discussions with informal collectors of plastic and general waste in Kisumu, Kenya. The study aims to determine the extent to which informal waste collectors facilitate waste separation and recycling in off-grid neighborhoods in Kisumu. It also aimed to assess the impact of recycled plastic prices and international policy initiatives on businesses in the water sachet recycling chain in Kisumu as well as other barriers to informal waste collector businesses. A similar set of FGDs with waste collectors in Greater Accra, Ghana, is archived separately. Specialist plastic waste collection businesses are almost non-existent in Kisumu, so the study recruited general (mixed) waste collectors at different points in the supply chain via a grassroots waste collectors’ association. A total sample of 32 collectors were identified via this route within Kisumu City. These were segmented into three broad groups: a) Waste Pickers, b) Intermediate Traders and finally, c) Apex Traders. The Waste Pickers were defined as informal enterprises that mostly pick wastes directly from the waste generation sites such as households, streets, or waste dumps. The intermediate Traders were defined as the relatively more formal enterprises collecting waste from the pickers, carrying out some level of processing and selling the processed waste to apex traders. The Apex traders were then defined as the more formal enterprises purchasing the wastes from the intermediate enterprises and then selling the waste to recycling industries, mostly located in Nairobi. Two focus group discussions were held with two groups of waste pickers and two groups of intermediate traders, with a single small group discussion then held with two apex traders. Focus and small group discussions consisted of open-ended questions on business establishment, business history, waste collection operations, and enablers and barriers to waste collection.According to WHO/UNICEF, whilst 91.8% of urban households in Sub-Saharan Africa (SSA) had access to piped or protected groundwater sources in 2015, only 46.2% had safely managed water available when needed. Vendors provide a key role in supplying urban off-grid populations, with consumption of bottled or bagged water (sachets, water sold in 500ml plastic bags) growing in SSA. Whilst several studies show bottles and bags are usually free from faecal contamination, given that many off-grid urban populations lack solid waste disposal services, when people drink such water, there can be problems disposing of the plastic bags and bottles afterwards. This project aims to deliver evidence on the different ways that people sell water to off-grid populations and what this means for plastic waste management. We plan to do this in Ghana, where most urban household now drink bagged water, and by way of contrast, Kenya, where the government has banned plastic bags. In this way, we want to widen access to safe water and waste management services among urban off-grid populations, by supporting water-sellers and waste collectors to fill the gaps in municipal services. Both countries (and many others elsewhere) already have nationwide household surveys that collect data on the food and goods people consume and the services they have. However, as yet, these surveys have not been connected to the problem of waste management. We plan to visit marketplaces, buying foods and then recording packaging and organic waste. By combining this information with the household survey data, we can work out how much domestic waste like plastics gets collected and how much is discarded or burned, ultimately entering the atmosphere or oceans. In Ghana, we will also survey informal waste collectors in urban Greater Accra. We want to find out how much these small businesses support waste collection and recycling across this urban region (particularly plastic from bagged water), so we can help government identify gaps in waste collection coverage. We also believe highlighting the important role of small waste collectors could lead to greater business support for such collectors. We will also evaluate whether community education campaigns to encourage domestic waste recycling reduce the amount of waste and plastic observed in the local environment. Such campaigns are currently pursued by several local charities with support from the Plastic Waste Management Project. In Kenya, where water is usually sold in jerrycans rather than bagged, the jerrycan water often gets contaminated. We plan to find out whether this jerrycan water is safer under an arrangement known as delegated management. This involves a water utility passing on management of the piped network to a local business in slum areas, so as to reduce vandalism of pipes and bring water closer to slum-dwellers. We will compare water quality in areas with and without this arrangement to see if it makes the water sold safer. We also plan to bring water-sellers and consumers together to find and test ways of reducing contamination of water between a jerry-can being filled and water being drunk at home. Rather than imposing a solution, we want to work together with vendors and consumers on this issue, but there are for example containers designed to keep water cleaner that we could explore. Through these activities, we thus plan to develop evidence on different strategies for water-sellers to deliver safer water to people lacking piped connections, whilst managing plastic waste at the same time. In Ghana, this involves trying to increase recycling and waste collection for bagged water, which is relatively safe. In Kenya, this involves trying to reduce contamination of water sold in reusable jerrycans. Alongside our household survey evidence on how domestic waste is managed in slums, this should help governments plan waste and water services in poorer areas of Africa's expanding cities. Five Focus Group Discussions (FGDs) were organized to contextualize and explore the contributions of informal waste collectors to waste management and waste recycling in Kisumu, Kenya as well as barriers to waste management business among informal waste collectors. Eligible participants (intermediate collectors, Sub-collectors or waste pickers, and apex waste collection traders) were selected within the target area of the Water and Waste project (i.e., 30 Enumeration Areas, all meeting the UN-Habitat definition of a slum).

The average household size in Kenya was 3.9 members according to the last census done in the country in 2019. Nairobi City was the county with the smallest households, formed by an average of 2.9 people. By contrast, Mandera registered the largest household size. In the county located in North Eastern Kenya, households had 6.9 members.

Kenya Power & Lighting Company Ltd (KPLC) is undertaking the installation of high voltage underground cables across the city of Nairobi, Kenya.The project involves the laying of 30.71km of 66kV transmission lines in underground cables, connecting to the new 220kV/66kV city center sub-station. It also includes the construction of two additional sub-stations.The underground cable installation starts at Embakasi sub station to Landmawe, Nairobi west, Muthurwa, Lang’ata sub stations to Kathitu sub station near Laico Regency hotel and then Parklands sub station.The project is being implemented through a twenty-year concessional loan from the Exim Bank of China.TBEA International Company Ltd has been appointed as the main contractor, with AKS East Africa, Voacom Networks, and HK Builders as sub-contractors.Installation activities are underway. Read More