Historical dataset of population level and growth rate for the Nairobi, Kenya metro area from 1950 to 2025.

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.

While the East African region, including Kenya, is one of first regions believed to have modern humans inhabit it, population growth in the region remained slow to non-existent throughout the 19th century; in the past hundred years, however, Kenya’s population has seen an exponential increase in size, going from 2.65 million in 1920, to an estimated 53.77 million in 2020.

Along with this population growth, Kenya has seen rapid urbanization and industrialization, particularly in recent decades. The metropolitan area of Kenya’s capital, Nairobi, with an estimated population of 9.35 million in 2020, now contains on its own over three and a half times the population of the entire country just a century earlier.

Population density per pixel at 100 metre resolution. WorldPop provides estimates of numbers of people residing in each 100x100m grid cell for every low and middle income country. Through ingegrating cencus, survey, satellite and GIS datasets in a flexible machine-learning framework, high resolution maps of population counts and densities for 2000-2020 are produced, along with accompanying metadata. DATASET: Alpha version 2010 and 2015 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and remaining unadjusted. REGION: Africa SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator) PROJECTION: Geographic, WGS84 UNITS: Estimated persons per grid square MAPPING APPROACH: Land cover based, as described in: Linard, C., Gilbert, M., Snow, R.W., Noor, A.M. and Tatem, A.J., 2012, Population distribution, settlement patterns and accessibility across Africa in 2010, PLoS ONE, 7(2): e31743. FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Example - AGO10adjv4.tif = Angola (AGO) population count map for 2010 (10) adjusted to match UN national estimates (adj), version 4 (v4). Population maps are updated to new versions when improved census or other input data become available. Kenya data available from WorldPop here.

This statistic shows the total population of Kenya from 2013 to 2023 by gender. In 2023, Kenya's female population amounted to approximately 27.82 million, while the male population amounted to approximately 27.52 million inhabitants.

Analysis unit

Persons and households Nairobi oversample. Weighted by district and age.

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

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

Universe

All persons who were in Kenya at midnight on Census Night.

Kind of data

Population and Housing Census [hh/popcen]

Sampling procedure

MICRODATA SOURCE: Statistics Division Ministry of Finance and Planning

SAMPLE SIZE (person records): 659310.

SAMPLE DESIGN: Unknown sample design includes oversample of Nairobi. Data are weighted by age and district of residence.

Mode of data collection

Face-to-face [f2f]

Research instrument

Single enumeration form that requested information on individuals.

Abstract

In 2016, UNHCR became aware of a group of stateless persons living in or near Nairobi, Kenya. Most of them were Shona, descendants of missionaries who arrived from Zimbabwe and Zambia in the 1960s and remained in Kenya. The total number of Shona living in Kenya is estimated to be between 3,000 and 3,500 people. On their first arrival, the Shona were issued certificates of registration, but a change in the Registration of Persons Act of 1978 did not make provision for people of non-Kenyan descent, consequently denying the Shona citizenship. Zimbabwe and Zambia did not consider them nationals either, rendering them stateless. Besides the Shona, there are other groups of stateless persons of different origins and ethnicities, with the total number of stateless persons in Kenya estimated at 18,500. UNHCR and the Government of Kenya are taking steps to address statelessness in the country, among them is the registration of selected groups for nationalization. In April 2019, the Government of Kenya pledged to recognize qualifying members of the Shona community as Kenyan citizens. However, the lack of detailed information on the stateless population in Kenya hinders advocacy for the regularization of their nationality status. Together with the Kenyan Government through the Department of Immigration Services (DIS) and the Kenya National Bureau of Statistics (KNBS), UNHCR Kenya conducted registration and socioeconomic survey for the Shona community from May to July 2019. While the primary objective of the registration was to document migration, residence and family history with the aim of preparing their registration as citizens, this survey was conducted to provide a baseline on the socio-economic situation of the stateless Shona population for comparison with non-stateless populations of Kenya.

Geographic coverage

Githurai, Nairobi, Kiambaa and Kinoo

Analysis unit

Household and individual

Universe

All Shona living in Nairobi and Kiambu counties, Kenya

Kind of data

Census/enumeration data [cen]

Sampling procedure

The objective of the socio-economic survey was to cover the entire Shona population living in areas of the Nairobi and Kiambu counties. This included Shona living in Githurai, Kiambaa, Kinoo and other urban areas in and around Nairobi. Data collection for the socioeconomic survey took place concurrently with a registration verification. The registration verification was to collect information on the Shona's migration history, residence in Kenya and legal documentation to prepare their registration as citizens. The registration activity including questions on basic demographics also covered some enumeration areas outside the ones of the socio-economic survey, such as institutional households in Hurlingham belonging to a religious order who maintain significantly different living conditions than the average population. The total number of households for which socio-economic data was collected for is 350 with 1,692 individuals living in them. A listing of Shona households using key informant lists and respondent-driven referral to identify further households was conducted by KNBS and UNHCR before the start of enumeration for the registration verification and socio-economic survey.

Sampling deviation

None

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The following sections are included: household roster, education, employment, household characteristics, consumption and expenditure.

Cleaning operations

The dataset presented here has undergone light checking, cleaning and restructuring (data may still contain errors) as well as anonymization (includes removal of direct identifiers and sensitive variables, recoding and local suppression).

Response rate

Overall reponse rate was 99 percent, mainly due to refusal to participate.

Abstract

The World Bank in collaboration with the Joint Data Center on Forced Displacement, Kenya National Bureau of Statistics (KNBS) and the United Nations High Commissioner for Refugees (UNHCR) conducted a cross-sectional survey on refugee and host populations living in Nairobi. The survey was based on the Kenya Continuous Household Survey (KCHS) and targets both host populations and refugees living in Nairobi. Through a participatory training format, enumerators learned how to collect quality data specific for refugees as well as nationals. Daily data quality monitoring dashboards were produced during the data collection periods to provide feedback to the field team and correct possible errors. The data was collected with CAPI technique through the World Bank developed Survey Solutions software; this ensured high standards of data storage, protection and pre-processing.

The sample is representative of refugees and other residents living in Nairobi. The refugee sample was drawn from UNHCR’s database of refugees and asylum seekers (proGres) using implicit stratification by sub-county and country of origin. The host community sampling frame was drawn using a two-stage cluster design. In the first stage, eligible enumeration areas (EAs) based on the 2019 Population and Housing Census were selected. In the second stage 12 households were sampled from each EA. The survey differentiates between two types of host communities: ‘core’ host communities were drawn from EAs located within the three areas with the largest number of refugee families: Kasarani, Eastleigh North and Kayole. At least 10 percent of the Nairobi refugee families reside in each of these areas. ‘Wider’ host communities cover the rest of the Nairobi population and were drawn from EAs which do not cover the three areas in which many refugees live.

For a subset of households, a women empowerment module was administered by a trained female enumerator to one randomly selected woman in each household aged 15 to 49.

The data set contains two files. hh.dta contains household level information. The ‘hhid’ variable uniquely identifies all households. hhm.dta contains data at the level of the individual for all household members. Each household member is uniquely identified by the variable ‘hhm_id’.

This cross-sectional survey was conducted between May 22 to July 27, 2021. It comprises a sample of 4,853 households in total, 2,420 of which are refugees and 2,433 are hosts.

Geographic coverage

Nairobi county, Kenya

Analysis unit

Household, Individual

Sampling procedure

The survey has two primary samples contained in the ‘sample’ variable: the refugee sample and the host community sample. The refugee sample used the UNHCR database of refugees and asylum seekers in Kenya (proGres) as the sampling frame. ProGres holds information on all registered refugees and asylum seekers in Kenya including their contact information and data on nationality and approximate location of living. We considered only refugees living in Nairobi and implicitly stratified by nationality and location. In total, the sample comprises 2,420 refugee families.

The host community sample differentiates between two types of communities. We consider ‘core’ host communities as residents who live in Eastleigh North, Kayole or Kasarani – at least 10 percent of the Nairobi refugee families reside in each of these areas. Nationals living outside these areas are considered part of the ‘wider’ host community in Nairobi. The samples for both host communities were drawn using a 2-stage cluster design. In the first stage, eligible enumeration areas (EA) were drawn from the list of EAs covering Nairobi taken from the 2019 Population and Housing Census. In the second stage a listing of all host community households was established through a household census within all selected EAs, ensuring that refugee households were excluded to prevent overlap with the refugee sampling frame. 12 households and 6 replacements were drawn per EA. Our total sample consists of 2,433 host community households, 1,221 core hosts and 1,212 wider hosts.

The three sub-samples – refugees, core hosts, and wider hosts – are reflected in the ‘strata’ variable. The EAs which form the primary sampling units for the two host samples are anonymized and included in the ‘psu’ variable. Please note that the ‘psu’ variable clusters refugees under one numeric code (888).

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The Questionnaire is provided as external resources in pdf format. Questionnaires were produced through the World Bank developed Survey Solutions software. The survey was implemented in English,Swahili and Somali.

Kikuyu was the largest ethnic group in Kenya, accounting for ** percent of the country's population in 2019. Native to Central Kenya, the Kikuyu constitute a Bantu group with more than eight million people. The groups Luhya and Kalenjin followed, with respective shares of **** percent and **** percent of the population. Overall, Kenya has more than 40 ethnic groups.

Abstract

The places we live affect our health status and the choices and opportunities we have (or do not have) to lead fulfilling lives. Over the past ten years, the African Population & Health Research Centre (APHRC) has led pioneering work in highlighting some of the major health and livelihood challenges associated with rapid urbanization in sub-Saharan Africa (SSA). In 2002, the Centre established the first longitudinal platform in urban Africa in the city of Nairobi in Kenya. The platform known as the Nairobi Urban Health and Demographic Surveillance System collects data on two informal settlements - Korogocho and Viwandani - in Nairobi City every four months on issues ranging from household dynamics to fertility and mortality, migration and livelihood as well as on causes of death, using a verbal autopsy technique. The dataset provided here contains key demographic and health indicators extracted from the longitudinal database. Researchers interested in accessing the micro-data can look at our data access policy and contact us.

Geographic coverage

The Demographic Surveillance Area (combining Viwandani and Korogocho slum settlements) covers a land area of about 0.97 km2, with the two informal settlements located about 7 km from each other. Korogocho is located 12 km from the Nairobi city center; in Kasarani division (now Kasarani district), while Viwandani is about 7 km from Nairobi city center in Makadara division (now Madaraka district). The DSA covers about seven villages each in Korogocho and Viwandani.

Analysis unit

Individual

Universe

Between 1st January and 31st December,2015 the Nairobi HDSS covered 86,304 individualis living in 30,219 households distributed across two informal settlements(Korogocho and Viwandani) were observed. All persons who sleep in the household prior to the day of the survey are included in the survey, while non-resident household members are excluded from the survey.

The present universe started out through an initial census carried out on 1st August,2002 of the population living in the two Informal settlements (Korogocho and Viwandani). Regular visits have since then been made (3 times a year) to update information on births, deaths and migration that have occurred in the households observed at the initial census. New members join the population through a birth to a registered member, or an in-migration, while existing members leave through a death or out-migration. The DSS adopts the concept of an open cohort that allows new members to join and regular members to leave and return to the system.

Kind of data

Event history data

Frequency of data collection

Three rounds in a year

Sampling procedure

This dataset is related to the whole demographic surveillance area population. The number of respondents has varied over the last 13 years (2002-2015), with variations being observed at both household level and at Individual level. As at 31st December 2015, 66,848 were being observed under the Nairobi HDSS living in 25,812 households distributed across two informal settlements(Korogocho and Viwandani). The variable IndividualId uniquely identifies every respondent observed while the variable LocationId uniquely identifies the room in which the individual was living at any point in time. To identify individuals who were living together at any one point in time (a household) the data can be split on location and observation dates.

Sampling deviation

None

Mode of data collection

Proxy Respondent [proxy]

Research instrument

Questionnaires are printed and administered in Swahili, the country's national language.

The questionnaires for the Nairobi HDSS were structured questionnaires based on the INDEPTH Model Questionnaire and were translated into Swahili with some modifications and additions.After an initial review the questionnaires were translated back into English by an independent translator with no prior knowledge of the survey. The back translation from the Swahili version was independently reviewed and compared to the English original. Differences in translation were reviewed and resolved in collaboration with the original translators. The English and Swahili questionnaires were both piloted as part of the survey pretest.

At baseline, a household questionnaire was administered in each household, which collected various information on household members including sex, age, relationship, and orphanhood status. In later rounds questionnaires to track the migration of the population observed at baseline, and additonal questionnaires to capture demographic and health events happening to the population have been introduced.

Cleaning operations

Data editing took place at a number of stages throughout the processing, including: a) Office editing and coding b) During data entry c) Structure checking and completeness d) Secondary editing e) Structural checking of STATA data files

Where changes were made by the program, a cold deck imputation is preferred; where incorrect values were imputed using existing data from another dataset. If cold deck imputation was found to be insufficient, hot deck imputation was used, In this case, a missing value was imputed from a randomly selected similar record in the same dataset.

Some corrections are made automatically by the program(80%) and the rest by visual control of the questionnaires (20%).

1. 100% forms filled in by FRAs are rechecked for completeness, ensured that all the necessary event forms are filled in.
2. Spot checks are done on field over data collection by FRAs for reliability of data.
3. FRS instructs revisits wherever required.
4. Forms are checked on sample basis
5. Checks if all the necessary event forms are filled in.
6. Forms with inconsistencies identified at the time of entry are sent back to the field.
7. Creating and managing data entry checks for picking up inconsistencies
8. Monitoring field work: balancing work target and quality.
9. Dealing with data inconsistencies at data level and giving feedbacks to field staff.
10. Conducting training and refresher training wherever required.
11. Data cleaning

Response rate

Over the years the response rate at household level has varied between 95% and 97% with response rate at Individual Level varying between 92% and 95%. Challenges to acheiving a 100% response rate have included: - high population mobility within the study area - high population attrition - respondent fatigue - security in some areas

Sampling error estimates

Not applicable for surveillance data

Data appraisal

CentreId MetricTable QMetric Illegal Legal Total Metric RunDate KE031 MicroDataCleaned Starts 219285 2017-05-16 18:25
KE031 MicroDataCleaned Transitions 825036 825036 0 2017-05-16 18:25
KE031 MicroDataCleaned Ends 219285 2017-05-16 18:25
KE031 MicroDataCleaned SexValues 825036 2017-05-16 18:25
KE031 MicroDataCleaned DoBValues 42 824994 825036 0 2017-05-16 18:25

Abstract

The 2022 Kenya Demographic and Health Survey (2022 KDHS) is the seventh DHS survey implemented in Kenya. The Kenya National Bureau of Statistics (KNBS) in collaboration with the Ministry of Health (MoH) and other stakeholders implemented the survey. Survey planning began in late 2020 with data collection taking place from February 17 to July 19, 2022. ICF provided technical assistance through The DHS Program, which is funded by the United States Agency for International Development (USAID) and offers financial support and technical assistance for population and health surveys in countries worldwide. Other agencies and organizations that facilitated the successful implementation of the survey through technical or financial support were the Bill & Melinda Gates Foundation, the World Bank, the United Nations Children's Fund (UNICEF), the United Nations Population Fund (UNFPA), Nutrition International, the World Food Programme (WFP), the United Nations Entity for Gender Equality and the Empowerment of Women (UN Women), the World Health Organization (WHO), the Clinton Health Access Initiative, and the Joint United Nations Programme on HIV/AIDS (UNAIDS).

SURVEY OBJECTIVES The primary objective of the 2022 KDHS is to provide up-to-date estimates of demographic, health, and nutrition indicators to guide the planning, implementation, monitoring, and evaluation of population and health-related programs at the national and county levels. The specific objectives of the 2022 KDHS are to: Estimate fertility levels and contraceptive prevalence Estimate childhood mortality Provide basic indicators of maternal and child health Estimate the Early Childhood Development Index (ECDI) Collect anthropometric measures for children, women, and men Collect information on children's nutrition Collect information on women's dietary diversity Obtain information on knowledge and behavior related to transmission of HIV and other sexually transmitted infections (STIs) Obtain information on noncommunicable diseases and other health issues Ascertain the extent and patterns of domestic violence and female genital mutilation/cutting

Geographic coverage

National coverage

Analysis unit

Household, individuals, county and national level

Universe

The survey covered sampled households

Sampling procedure

The sample for the 2022 KDHS was drawn from the Kenya Household Master Sample Frame (K-HMSF). This is the frame that KNBS currently operates to conduct household-based sample surveys in Kenya. In 2019, Kenya conducted a Population and Housing Census, and a total of 129,067 enumeration areas (EAs) were developed. Of these EAs, 10,000 were selected with probability proportional to size to create the K-HMSF. The 10,000 EAs were randomized into four equal subsamples. The survey sample was drawn from one of the four subsamples. The EAs were developed into clusters through a process of household listing and geo-referencing. To design the frame, each of the 47 counties in Kenya was stratified into rural and urban strata, resulting in 92 strata since Nairobi City and Mombasa counties are purely urban.

The 2022 KDHS was designed to provide estimates at the national level, for rural and urban areas, and, for some indicators, at the county level. Given this, the sample was designed to have 42,300 households, with 25 households selected per cluster, resulting into 1,692 clusters spread across the country with 1,026 clusters in rural areas and 666 in urban areas.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Eight questionnaires were used for the 2022 KDHS: 1. A full Household Questionnaire 2. A short Household Questionnaire 3. A full Woman's Questionnaire 4. A short Woman's Questionnaire 5. A Man's Questionnaire 6. A full Biomarker Questionnaire 7. A short Biomarker Questionnaire 8. A Fieldworker Questionnaire.

The Household Questionnaire collected information on: o Background characteristics of each person in the household (for example, name, sex, age, education, relationship to the household head, survival of parents among children under age 18) o Disability o Assets, land ownership, and housing characteristics o Sanitation, water, and other environmental health issues o Health expenditures o Accident and injury o COVID-19 (prevalence, vaccination, and related deaths) o Household food consumption

The Woman's Questionnaire was used to collect information from women age 15-49 on the following topics: o Socioeconomic and demographic characteristics o Reproduction o Family planning o Maternal health care and breastfeeding o Vaccination and health of children o Children's nutrition o Woman's dietary diversity o Early childhood development o Marriage and sexual activity o Fertility preferences o Husbands' background characteristics and women's employment activity o HIV/AIDS, other sexually transmitted infections (STIs), and tuberculosis (TB) o Other health issues o Early Childhood Development Index 2030 o Chronic diseases o Female genital mutilation/cutting o Domestic violence

The Man's Questionnaire was administered to men age 15-54 living in the households selected for long Household Questionnaires. The questionnaire collected information on: o Socioeconomic and demographic characteristics o Reproduction o Family planning o Marriage and sexual activity o Fertility preferences o Employment and gender roles o HIV/AIDS, other STIs, and TB o Other health issues o Chronic diseases o Female genital mutilation/cutting o Domestic violence

The Biomarker Questionnaire collected information on anthropometry (weight and height). The long Biomarker Questionnaire collected anthropometry measurements for children age 0-59 months, women age 15-49, and men age 15-54, while the short questionnaire collected weight and height measurements only for children age 0-59 months.

The Fieldworker Questionnaire was used to collect basic background information on the people who collected data in the field. This included team supervisors, interviewers, and biomarker technicians.

All questionnaires except the Fieldworker Questionnaire were translated into the Swahili language to make it easier for interviewers to ask questions in a language that respondents could understand.

Cleaning operations

Data were downloaded from the central servers and checked against the inventory of expected returns to account for all data collected in the field. SyncCloud was also used to generate field check tables to monitor progress and flag any errors, which were communicated back to the field teams for correction.

Secondary editing was done by members of the central office team, who resolved any errors that were not corrected by field teams during data collection. A CSPro batch editing tool was used for cleaning and tabulation during data analysis.

Response rate

A total of 42,022 households were selected for the sample, of which 38,731 (92%) were found to be occupied. Among the occupied households, 37,911 were successfully interviewed, yielding a response rate of 98%. The response rates for urban and rural households were 96% and 99%, respectively. In the interviewed households, 33,879 women age 15-49 were identified as eligible for individual interviews. Interviews were completed with 32,156 women, yielding a response rate of 95%. The response rates among women selected for the full and short questionnaires were the similar (95%). In the households selected for the male survey, 16,552 men age 15-54 were identified as eligible for individual interviews and 14,453 were successfully interviewed, yielding a response rate of 87%.

Abstract

The 1993 Kenya Demographic and Health Survey (KDHS) was a nationally representative survey of 7,540 women age 15-49 and 2,336 men age 20-54. The KDHS was designed to provide information on levels and trends of fertility, infant and child mortality, family planning knowledge and use, maternal and child health, and knowledge of AIDS. In addition, the male survey obtained data on men's knowledge and attitudes towards family planning and awareness of AIDS. The data are intended for use by programme managers and policymakers to evaluate and improve family planning and matemal and child health programmes. Fieldwork for the KDHS took place from mid-February until mid-August 1993. All areas of Kenya were covered by the survey, except for seven northem districts which together contain less than four percent of the country's population.

The KDHS was conducted by the National Council for Population and Development (NCPD) and the Central Bureau of Statistics of the Government of Kenya. Macro International Inc. provided financial and technical assistance to the project through the intemational Demographic and Health Surveys (DHS) contract with the U.S. Agency for International Development.

OBJECTIVES

The KDHS is intended to serve as a source of population and health data for policymakers and the research community. It was designed as a follow-on to the 1989 KDHS, a national-level survey of similar size that was implemented by the same organisations. In general, the objectives of KDHS are to: - assess the overall demographic situation in Kenya, - assist in the evaluation of the population and health programmes in Kenya, - advance survey methodology, and - assist the NCPD to strengthen and improve its technical skills to conduct demographic and health surveys.

The KDHS was specifically designed to: - provide data on the family planning and fertility behaviour of the Kenyan population to enable the NCPD to evaluate and enhance the National Family Planning Programme, - measure changes in fertility and contraceptive prevalence and at the same time study the factors which affect these changes, such as marriage patterns, urban/rural residence, availability of contraception, breastfeeding habits and other socioeconomic factors, and - examine the basic indicators of maternal and child health in Kenya.

KEY FINDINGS

The 1993 KDHS reinforces evidence of a major decline in fertility which was first revealed by the findings of the 1989 KDHS. Fertility continues to decline and family planning use has increased. However, the disparity between knowledge and use of family planning remains quite wide. There are indications that infant and under five child mortality rates are increasing, which in part might be attributed to the increase in AIDS prevalence.

Geographic coverage

The 1993 KDHS sample is national in scope, with the exclusion of all three districts in North Eastern Province and four other northern districts (Samburu and Turkana in Rift Valley Province and Isiolo and 4 Marsabit in Eastern Province). Together the excluded areas account for less than 4 percent of Kenya's population.

Analysis unit

• Household
• Women age 15-49
• Men age 20-54
• Children under five

Universe

The population covered by the 1993 KDHS is defined as the universe of all women age 15-49 in Kenya and all husband age 20-54 living in the household.

Kind of data

Sample survey data

Sampling procedure

The sample for the 1993 KDHS was national in scope, with the exclusion of all three districts in Northeastern Province and four other northern districts (Isiolo and Marsabit from Eastern Province and Samburu and Turkana from Rift Valley Province). Together the excluded areas account for less than four percent of Kenya's population. The KDHS sample points were selected from a national master sample maintained by the Central Bureau of Statistics, the third National Sample Survey and Evaluation Programme (NASSEP-3), which is an improved version of NASSEP2 used in the 1989 survey. This master sample follows a two-stage design, stratified by urban-rural residence, and within the rural stratum, by individual district. In the first stage, 1989 census enumeration areas (EAs) were selected with probability proportional to size. The selected EAs were segmented into the expected number of standard-sized clusters to form NASSEP clusters. The entire master sample consists of 1,048 rural and 325 urban ~ sample points ("clusters"). A total of 536 clusters---92 urban and 444 rural--were selected for coverage in the KDHS. Of these, 520 were successfully covered. Sixteen clusters were inaccessible for various reasons.

As in the 1989 KDHS, selected districts were oversampled in the 1993 survey in order to produce more reliable estimates for certain variables at the district level. Fifteen districts were thus targetted in the 1993 KDHS: Bungoma, Kakamega, Kericho, Kilifi, Kisii, Machakos, Meru, Murang'a, Nakuru, Nandi, Nyeri, Siaya, South Nyanza, Taita-Taveta, and Uasin Gishu; in addition, Nairobi and Mombasa were also targetted. Although six of these districts were subdivided shortly before the sample design was finalised) the previous boundaries of these districts were used for the KDHS in order to maintain comparability with the 1989 survey. About 400 rural households were selected in each of these 15 districts, just over 1000 rural households in other districts, and about 18130 households in urban areas, for a total of almost 9,000 households. Due to this oversampling, the KDHS sample is not self-weighting at the national level.

After the selection of the KDHS sample points, fieldstaff from the Central Bureau of Statistics conducted a household listing operation in January and early February 1993, immediately prior to the launching of the fieldwork. A systematic sample of households was then selected from these lists, with an average "take" of 20 households in the urban clusters and 16 households in rural clusters, for a total of 8,864 households selected. Every other household was identified as selected for the male survey, meaning that, in addition to interviewing all women age 15-49, interviewers were to also interview all men age 20-54. It was expected that the sample would yield interviews with approximately 8,000 women age 15-49 and 2,500 men age 20-54.

Mode of data collection

Face-to-face

Research instrument

Four types of questionnaires were used for the KDHS: a Household Questionnaire, a Woman's Questionnaire, a Man's Questionnaire and a Services Availability Questionnaire. The contents of these questionnaires were based on the DHS Model B Questionnaire, which is designed for use in countries with low levels of contraceptive use. Additions and modifications to the model questionnaires were made during a series of meetings organised around specific topics or sections of the questionnaires (e.g., fertility, family planning). The NCPD invited staff from a variety of organisations to attend these meetings, including the Population Studies Research Institute and other departments of the University of Nairobi, the Woman's Bureau, and various units of the Ministry of Health. The questionnaires were developed in English and then translated into and printed in Kiswahili and eight of the most widely spoken local languages in Kenya (Kalenjin, Kamba, Kikuyu, Kisii, Luhya, Luo, Meru, and Mijikenda).

a) The Household Questionnaire was used to list all the usual members and visitors of selected households. Some basic information was collected on the characteristics of each person listed, including his/her 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 individual interview. In addition, information was collected about the dwelling itself, such as the source of water, type of toilet facilities, materials used to construct the house, and ownership of various consumer goods.

b) The Woman's Questionnaire was used to collect information from women aged 15-49. These women were asked questions on the following topics: Background characteristics (age, education, religion, etc.), Reproductive history, Knowledge and use of family planning methods, Antenatal and delivery care, Breastfeeding and weaning practices, Vaccinations and health of children under age five, Marriage, Fertility preferences, Husband's background and respondent's work, Awareness of AIDS. In addition, interviewing teams measured the height and weight of children under age five (identified through the birth histories) and their mothers.

c) Information from a subsample of men aged 20-54 was collected using a Man's Questionnaire. Men were asked about their background characteristics, knowledge and use of family planning methods, marriage, fertility preferences, and awareness of AIDS.

d) The Services Availability Questionnaire was used to collect information on the health and family planning services obtained within the cluster areas. One service availability questionnaire was to be completed in each cluster.

Cleaning operations

All questionnaires for the KDHS were returned to the NCPD headquarters for data processing. The processing operation consisted of office editing, coding of open-ended questions, data entry, and editing errors found by the computer programs. One NCPD officer, one data processing supervisor, one questionnaire administrator, two office editors, and initially four data entry operators were responsible for the data processing operation. Due to attrition and the need to speed up data processing, another four data entry operators were later hired

Abstract

Background: The burden of cardiovascular disease is rising in sub-Saharan Africa with hypertension being the main

risk factor. However, context-specific evidence on effective interventions for primary prevention of cardiovascular diseases in resource-poor settings is limited. This study aims to evaluate the feasibility and cost-effectiveness of one such intervention-the “Sustainable model for cardiovascular health by adjusting lifestyle and treatment with economic perspective in settings of urban poverty”.

Methods/Design: Design: A prospective quasi-experimental community-based intervention study.

Setting: Two slum settlements (Korogocho and Viwandani) in Nairobi, Kenya.

Study population: Adults aged 35 years and above in the two communities.

Intervention: The intervention community (Korogocho) will be exposed to an intervention package for primary prevention of cardiovascular disease that comprises awareness campaigns, household screening for cardiovascular diseases risk factors, and referral and treatment of people with high cardiovascular diseases risk at a primary health clinic. The control community (Viwandani) will continue accessing the usual standard of care for primary prevention of cardiovascular diseases in Kenya.

Data: Demographic and socioeconomic data; anthropometric and clinical measurements including blood pressure. Population-based data will be collected at the baseline and endline-12 months after implementing the intervention. These data will be collected from a random sample of 1,610 adults aged 35 years and above in the intervention and control sites at both baseline and endline. Additionally, operational (including cost) and clinic-based data will be collected on an ongoing basis.

Main outcomes:

(1) A positive difference in the change in the proportion of the intervention versus control study populations that are at moderate or high risk of cardiovascular disease;

(2) a difference in the change in mean systolic blood pressure in the intervention versus control study populations;

(3) the net cost of the complete intervention package per disability-adjusted life year gained.

Analysis: Primary outcomes comparing pre- and post-, and operational data will be analyzed descriptively and “impact” of the intervention will be calculated using double-difference methods. We will also conduct a cost-effectiveness analysis of the intervention using World Health Organization guidelines

Geographic coverage

Korogocho and Viwandani informal settlements in Nairobi

Analysis unit

Individuals

Universe

Adults 35 years and above in Korogocho and Viwandani who have given informed consent

Sampling procedure

In order to detect a 5% reduction at endline in the proportion of adults aged 35 years and above who are at moderate or high risk of CVD in the intervention population versus no change in the control population (assuming both populations have similar start prevalence at 25%), we need 2,927 respondents in both intervention and control sites, using an alpha of 0.05 and power (1-beta) of 0.90. Taking into account a non-response rate of 10%, we will approach 3,220 individuals per cross-sectional study-that is, 1,610 per site at baseline and endline surveys, respectively. The sampling frame will be based on the most recently updated NUHDSS database. This database contains details of about 72,000 individuals including names, locations, gender, dates of birth and residential status in both slums. In the control site, we will use computer randomization (STATA® statistical software) to select the 1,610 individuals aged 35 years and older per site for each cross-sectional survey. In the intervention site, the same computer randomization process will be followed. However, unlike the control site, the 1,610 individuals to be included in the cross-sectional survey analysis will be collected retrospectively. In other words, the intervention package will be delivered to all adults aged 35 years or older in the intervention site-that is, 6,780 individuals according to the DSS database (as at 15 June 2012). At the clinic level, we calculated that in order to detect a 10 mmHg reduction in blood pressure (at 20 mmHg standard deviation, alpha of 0.05 and 1-ß on 0.9), about 44 participants are needed. However, it is projected that approximately 1,350 participants (out of 6780) will be referred from the door-to-door visit. This number is derived from a 20% prevalence of hypertension among adults aged 35 years and older in the study area.

We estimate that roughly half of these 1,350 participants, being 675, will continue visiting the clinic for treatment. Hence, this number of people is more than

sufficient for the analysis of our main primary outcome at the clinic level.

Sampling deviation

No deviation.

Mode of data collection

Face-to-face [f2f]

Research instrument

POPULATION:

1. Identification Information And Consent

2. Demographics 3 History Of Chronic Conditions

3. Exposure To Health Promotion And Behavior Change

4. Risk Factors And Preventive Behavior 6 Perceived Personal Risk

5. Anthropometrics And Biomarkers 8 Interviewer Assessment

CLINIC:

1. Identification Information And Consent

2. Clinic History

3. Knowledge Of Prevention / Evaluation Intervention

4. Anthropometrics And Biomarkers

Response rate

Population baseline response rate in Korogocho (intervention) was 56.7% and 40.3% in Viwandani (control)

Population endline response rate was 50.2% in Korogocho (intervention) , 77.0% in Viwandani screened at baseline (first control) and 53.6% in Viwandani not screened (second control)

Kenya had over ** million households according to the last census done in 2019. The majority, some *** million, lived in urban areas, while *** million dwelled in rural zones. Nairobi City was the county with more households, approximately *** million.

Abstract

This was a prospective population based study comparing education outcomes and education services among slum and non-slum settlements in Nairobi. The study was being conducted in two slum settlements of Korogocho and Viwandani, and two non-slum settlements of Jericho and Harambee. Korogocho is situated within Korogocho administrative location, Viwandani in Viwandani administrative location, and Jericho and Harambee in Makadara administrative location. The study identified households who had children aged between 5 and 19 years old and living within the boundaries of the study sites. The households were followed untl 2010. New households fitting the inclusion criteria were enrolled each year, while the upper age limit increased by a single year for each additional year. By 2010, the study wa following about 16400 individuals aged bewteen 5 and 24 years. The study targetted also schools where the idenfied pupils attended. Several questionnaires were administered and included schooling history to capture schooling information for the current schooling years and 5 years backwards. Afterwards, an update questionnaire was introduced to capture prospective schooling information. The second questionnaire captured information from the parents on their perceptions towards free primary education and support for their children schooling. In addition, individuals who were 12 years and above responded to a behvaior questionnaires. In the schools, a school characteristics questionniare was administred.

The objectives of the ERP I were:

· What is the impact of free primary education on school enrolment patterns and dropout rates among urban slum and non-slum children?

· What factors are associated with school participation (enrolment, attendance, repetition, performance and dropout) among urban slum and non-slum children?

· What are the (causal) linkages between school participation and the onset and extent of indulgence in risky behaviors in children?

Geographic coverage

Two slums of Nairobi (Korogocho and Viwandani) and two non-slums of Nairobi (Harambee and Jericho)

Analysis unit

1. HOUSEHOLDS

2. INDIVIDUALS WITHIN THE AGE OF STUDY. AVERAGE OF 2.7 INDIVIDIDUALS PER HOUSEHOLD

3. SCHOOLS

Universe

The data covers individuals aged 5 and 19 years in 2005 who were by 2010 aged between 5 and 24 years. It also covered primary schools within Nairobi, where majority of the pupils were reported to be enrolled.

Sampling procedure

Selection of study sites

Using the Kenya 1999 housing and population census, and the 1997 Welfare Monitoring Survey III collected by the Central Bureau of Statistics (Government of Kenya 2000), all the 49 locations of Nairobi were ranked into five groups according to the percentage of the population below the poverty line. NUHDSS slum locations of both Viwandani and Korogocho were in the poorest percentile (ranked 48th and 44th, respectively). Those in the richest quintile were excluded because most children in the wealthy communities go to formal private schools which are scattered all over the city. The majority of the locations in the 4th quintile have a mixture of formal and informal settlement features. In order to have a formal residential area in the middle income category where most children are likely to go to public schools, three locations were explored in the second quintile (i.e. the second richest set of locations). During discussion of the project's design, participants, who were mainly Kenyans with comprehensive knowledge of the areas, recommended carrying out the study in Bahati, as opposed to Umoja or Kariokor, the other locations in the second quintile. APHRC researchers visited the three communities to assess their suitability as a comparison site for the project. Bahati (Harambee and Jericho) was chosen because it is relatively stable, is mostly inhabited by middle-income parents with school-going children who mostly go to public schools in the area. In Bahati, 26% of the population lived below the poverty line while in Korogocho and Viwandani, the corresponding percentages were 60% and 76%, respectively. Having Bahati as the comparison area was therefore to enable the study to assess factors affecting schooling among some relatively poor households that did not reside in slum settlements.

Sampling of households

All households included in the NUHDSS database and with individuals aged between 5 and 19 years in 2005 were included in the study. thereafter they were followed until 2010. In between those who entered into the system or reach the aged of 5 years were also included and followed prospectively.

Sampling of schools

Schools were the pupils under serveillance were reported to be enrolled formed part of the sampling frame for schools. The inclusion criteria for the schools survey was that the school should be located within Nairobi and that it should have a minimum of five pupils in oyr household survey enrolled in it.

Mode of data collection

Face-to-face [f2f]; FGD

Research instrument

The Questionnaires

The questionnaires hereafter are referred to as modules. There are several modules since the beginning of the education project:

1) Household module

2) Primary school module

3) Parent guardian module

4) Education child update (schooling history) module

5) Child school status update questionnaire

6) Education child update module

7) Primary school questionnaire

8) Child Behavior Questionnaire

9) Supplementary primary school module

The household module

The household module served as a starting point of the interview. It identified the respondent's household. The module was administered to the owner of the household or any other adult who was credible and who usually lives in the household. It served to identify individual households and its occupants and thus served as a basis for the other modules to be administered. It contains a complete list of the household members and some basic information on age, sex, parental survivorship, education, and labor force participation.

For each of the household, information on water source and trash disposal methods, type of toilet facility used by the household, materials for the house (roof, floor, and walls), fuel for lighting and cooking, and ownership of assets was collected.

The Primary schools module:

This module serves to generate indicators on schooling participation. The module is meant for headmasters or teachers knowledgeable enough to provide information on the school. It comprises of the following sections:

Background

This serves to identify the name of the school, the date and time of the interview and the location of the school.

Particulars of respondent

This section of the module collected information on the respondent and establishes the respondent's full names, position held by respondent in the school and how long the respondent has been working in the school.

School background

This section sets to establish whether the school is registered, if registered under which ministry the school is registered(ministry of culture or ministry of education), its registration number, the type of curriculum followed by the school and the management of the school. The understanding is that the name of the school being used maybe different from the one under which the school is registered. The information is important especially if we are to link the school to the Ministry of Education or Ministry of Culture records. This information will most probably be obtained from the school records (if they exist).

School facilities

This section sets to collect information on the school facilities, such as textbooks provided by the school to the pupils in each grade they include Mathematics, Science ,Kiswahili and English, a library, science lab for pupils use ,a playground for outdoor sports pupils use and inventory of all school's equipment e.g. desks. For purposes of this project, a library is considered to be a room which has reference books where pupils can go to read.

This section also offers information on the school ownership of a toilet facility for use by the pupils and whether there are separate toilets provided for boys and girls. It also offers information on the school's water source and the availability of electricity in the school.

In addition the module in this section probes the respondent on whether the inspector of schools has visited the school in the current schooling year and requests for the date and year of visit. The inspector of school is from the City Council education department or from the Ministry of Education.

Enrollment for the current school year

Enrollment refers to children who are current registered for specific grades/classes in the school.

The objective of this section is to provide information on the number of boys and girls in each of the streams in the school in the current school year. It also sets to establish whether there were any pupils who were turned away during enrollment in the current year and the approximate number of pupils who were turned away from enrolling in the school.

Expenditure on schooling

The module here asks questions on the school fee structure, it seeks to establish whether the pupils are required to pay fees and how much (Kenya shillings) they pay for the following: tuition, construction fund, Parents Teachers Association (PTA), extra classes, examination fees, school meals and other items.

This was required to be filled for all grades in the school and whether paid annually, termly, monthly and weekly.

It also provides information on whether the pupils are required to wear uniform in order to be allowed in class and the source of purchase of the

Abstract

The 1998 Kenya Demographic and Health Survey (KDHS) is a nationally representative survey of 7,881 wo 881 women age 15-49 and 3,407 men age 15-54. The KDHS was implemented by the National Council for Population and Development (NCPD) and the Central Bureau of Statistics (CBS), with significant technical and logistical support provided by the Ministry of Health and various other governmental and nongovernmental organizations in Kenya. Macro International Inc. of Calverton, Maryland (U.S.A.) provided technical assistance throughout the course of the project in the context of the worldwide Demographic and Health Surveys (DHS) programme, while financial assistance was provided by the U.S. Agency for International Development (USAID/Nairobi) and the Department for International Development (DFID/U.K.). Data collection for the KDHS was conducted from February to July 1998. Like the previous KDHS surveys conducted in 1989 and 1993, the 1998 KDHS was designed to provide information on levels and trends in fertility, family planning knowledge and use, infant and child mortality, and other maternal and child health indicators. However, the 1998 KDHS went further to collect more in-depth data on knowledge and behaviours related to AIDS and other sexually transmitted diseases (STDs), detailed “calendar” data that allows estimation of contraceptive discontinuation rates, and information related to the practice of female circumcision. Further, unlike earlier surveys, the 1998 KDHS provides a national estimate of the level of maternal mortality (i.e. related to pregnancy and childbearing).The KDHS data are intended for use by programme managers and policymakers to evaluate and improve health and family planning programmes in Kenya. Fertility. The survey results demonstrate a continuation of the fertility transition in Kenya. At current fertility levels, a Kenyan women will bear 4.7 children in her life, down 30 percent from the 1989 KDHS when the total fertility rate (TFR) was 6.7 children, and 42 percent since the 1977/78 Kenya Fertility Survey (KFS) when the TFR was 8.1 children per woman. A rural woman can expect to have 5.2 children, around two children more than an urban women (3.1 children). Fertility differentials by women's education level are even more remarkable; women with no education will bear an average of 5.8 children, compared to 3.5 children for women with secondary school education. Marriage. The age at which women and men first marry has risen slowly over the past 20 years. Currently, women marry for the first time at an average age of 20 years, compared with 25 years for men. Women with a secondary education marry five years later (22) than women with no education (17).The KDHS data indicate that the practice of polygyny continues to decline in Kenya. Sixteen percent of currently married women are in a polygynous union (i.e., their husband has at least one other wife), compared with 19 percent of women in the 1993 KDHS, 23 percent in the 1989 KDHS, and 30 percent in the 1977/78 KFS. While men first marry an average of 5 years later than women, men become sexual active about onehalf of a year earlier than women; in the youngest age cohort for which estimates are available (age 20-24), first sex occurs at age 16.8 for women and 16.2 for men. Fertility Preferences. Fifty-three percent of women and 46 percent of men in Kenya do not want to have any more children. Another 25 percent of women and 27 percent of men would like to delay their next child for two years or longer. Thus, about three-quarters of women and men either want to limit or to space their births. The survey results show that, of all births in the last three years, 1 in 10 was unwanted and 1 in 3 was mistimed. If all unwanted births were avoided, the fertility rate in Kenya would fall from 4.7 to 3.5 children per woman. Family Planning. Knowledge and use of family planning in Kenya has continued to rise over the last several years. The 1998 KDHS shows that virtually all married women (98 percent) and men (99 percent) were able to cite at least one modern method of contraception. The pill, condoms, injectables, and female sterlisation are the most widely known methods. Overall, 39 percent of currently married women are using a method of contraception. Use of modern methods has increased from 27 in the 1993 KDHS to 32 percent in the 1998 KDHS. Currently, the most widely used methods are contraceptive injectables (12 percent of married women), the pill (9 percent), female sterilisation (6 percent), and periodic abstinence (6 percent). Three percent of married women are using the IUD, while over 1 percent report using the condom and 1 percent use of contraceptive implants (Norplant). The rapid increase in use of injectables (from 7 to 12 percent between 1993 and 1998) to become the predominant method, plus small rises in the use of implants, condoms and female sterilisation have more than offset small decreases in pill and IUD use. Thus, both new acceptance of contraception and method switching have characterised the 1993-1998 intersurvey period. Contraceptive use varies widely among geographic and socioeconomic subgroups. More than half of currently married women in Central Province (61 percent) and Nairobi Province (56 percent) are currently using a method, compared with 28 percent in Nyanza Province and 22 percent in Coast Province. Just 23 percent of women with no education use contraception versus 57 percent of women with at least some secondary education. Government facilities provide contraceptives to 58 percent of users, while 33 percent are supplied by private medical sources, 5 percent through other private sources, and 3 percent through community-based distribution (CBD) agents. This represents a significant shift in sourcing away from public outlets, a decline from 68 percent estimated in the 1993 KDHS. While the government continues to provide about two-thirds of IUD insertions and female sterilisations, the percentage of pills and injectables supplied out of government facilities has dropped from over 70 percent in 1993 to 53 percent for pills and 64 percent for injectables in 1998. Supply of condoms through public sector facilities has also declined: from 37 to 21 percent between 1993 and 1998. The survey results indicate that 24 percent of married women have an unmet need for family planning (either for spacing or limiting births). This group comprises married women who are not using a method of family planning but either want to wait two year or more for their next birth (14 percent) or do not want any more children (10 percent). While encouraging that unmet need at the national level has declined (from 34 to 24 percent) since 1993, there are parts of the country where the need for contraception remains high. For example, the level of unmet need is higher in Western Province (32 percent) and Coast Province (30 province) than elsewhere in Kenya. Early Childhood Mortality. One of the main objectives of the KDHS was to document current levels and trends in mortality among children under age 5. Results from the 1998 KDHS data make clear that childhood mortality conditions have worsened in the early-mid 1990s; this after a period of steadily improving child survival prospects through the mid-to-late 1980s. Under-five mortality, the probability of dying before the fifth birthday, stands at 112 deaths per 1000 live births which represents a 24 percent increase over the last decade. Survival chances during age 1-4 years suffered disproportionately: rising 38 percent over the same period. Survey results show that childhood mortality is especially high when associated with two factors: a short preceding birth interval and a low level of maternal education. The risk of dying in the first year of life is more than doubled when the child is born after an interval of less than 24 months. Children of women with no education experience an under-five mortality rate that is two times higher than children of women who attended secondary school or higher. Provincial differentials in childhood mortality are striking; under-five mortality ranges from a low of 34 deaths per 1000 live births in Central Province to a high of 199 per 1000 in Nyanza Province. Maternal Health. Utilisation of antenatal services is high in Kenya; in the three years before the survey, mothers received antenatal care for 92 percent of births (Note: These data do not speak to the quality of those antenatal services). The median number of antenatal visits per pregnancy was 3.7. Most antenatal care is provided by nurses and trained midwives (64 percent), but the percentage provided by doctors (28 percent) has risen in recent years. Still, over one-third of women who do receive care, start during the third trimester of pregnancy-too late to receive the optimum benefits of antenatal care. Mothers reported receiving at least one tetanus toxoid injection during pregnancy for 90 percent of births in the three years before the survey. Tetanus toxoid is a powerful weapon in the fight against neonatal tetanus, a deadly disease that attacks young infants. Forty-two percent of births take place in health facilities; however, this figure varies from around three-quarters of births in Nairobi to around one-quarter of births in Western Province. It is important for the health of both the mother and child that trained medical personnel are available in cases of prolonged labour or obstructed delivery, which are major causes of maternal morbidity and mortality. The 1998 KDHS collected information that allows estimation of mortality related to pregnancy and childbearing. For the 10-year period before the survey, the maternal mortality ratio was estimated to be 590 deaths per 100,000 live births. Bearing on average 4.7 children, a Kenyan woman has a 1 in 36 chance of dying from maternal causes during her lifetime. Childhood Immunisation. The KDHS

These are datasets generated from multi-state model (MSM) project on understanding demographic events and migration patterns in two urban slums of Nairobi City in Kenya at the African Population and Health Research Center (APHRC). The project focuses on using MSM techniques to analyze residence demographic events in Nairobi urban slums, with an emphasis on key events such as:

• Births
• Deaths
• Migration (in-migration and out-migration)
• Changes in residence status (exit and entry)

The primary aim of these datasets is to allow those who want to understand and model the demographic transitions in Nairobi's informal settlements, identifying factors that influence residence changes over time.

The African Cities Population Database (ACPD) has been produced by the Birkbeck College of the University of London in 1990 at the request of the United Nations Environment Programme (UNEP) in Nairobi, Kenya. The database contains head counts for 479 cities in Africa which either have a population of over 20,000 or are capitals of their nation state. Listed are the geographical location of the cities and their population sizes. The material is primarily derived from a 1988 report of the Economic Commission for Africa (ECA) and several issues of the United Nations Demographic Yearbook (1973-81). Severe problems were found with several countries such as Togo, Ghana and South Africa. For South Africa, the data were derived from the United Nations Demographic Yearbook 1987.

WCPD is an Arc/Info point coverage. It has no projection, as the cities are located on the basis of their latitude and longitude. Coordinates were assigned on the basis of gazetteers or African maps. Each record in the data base contains details of the city name, country name, latitude and longitude of the city, and its population at a defined time. The Arc/Info attribute table contains the following fields:

AREA Arc/Info item PERIMETER Arc/Info item ACPD# Arc/Info item ACPD-ID Arc/Info item ID-NUM Unique number for each city CITY City name COUNTRY Country name CITY-POP Population of city proper YEAR Latest available year of collection

ACPD comes as an Arc/Info EXPORT file originally called "ACPD.E00" and contains 67 Kb of data. The file has a record length of 80 and a block size of 8000 (blocking factor = 100). The file can be read from tape using Arc/Info's TAPEREAD command or any other generic copy utility. If distributed on a diskette it can be read using the ordinary DOS 'COPY' command. The file has to be converted to Arc/Info internal format using its IMPORT command.

References to the WCPD data set can be found in:

• SERLL News, Issue No. 1, January 1991, Birkbeck College, London, UK.
• D. Rhind. "Cartographically-related research at Birkbeck College 1987-91" in: The Cartographic Journal, Vol. 28, June 1991, pp. 63-66.

The source of the WCPD data set as held by GRID is Birkbeck College, University of London, Department of Geography, London, UK.

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.

A multi-modal population dataset for three cities in Sub-Saharan Africa, namely Dakar (Senegal), Nairobi (Kenya) and Dar es Salaam (Tanzania). The dataset contains patches of rasterized building footprints (0.5 m), Sentinel-2 MSI data (10 m) and population labels at the patch level (100 m).