Details of the Kenya population maps produced, as well as existing products, and accuracy statistics calculated using the 43,733 EA census counts.

These 28 tiff files represent 2015 population estimates. However, please note that many of the country-level files include 2020 population estimates including: Angola, Benin, Botswana, Burundi, Cameroon, Cabo Verde, Cote d'Ivoire, Djibouti, Eritrea, Eswatini, The Gambia, Ghana, Lesotho, Liberia, Mozambique, Namibia, Sao Tome & Principe, Sierra Leone, South Africa, Togo, Zambia, and Zimbabwe. South Sudan, Sudan, Somalia and Ethiopia are intentionally omitted from this dataset. However, a country-level dataset for Ethiopia can be found at https://data.humdata.org/dataset/ethiopia-high-resolution-population-density-maps-demographic-estimates.

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.

The population desnity per Km2 in each County in Kenya

This interactive map of Kenya highlights the following counties: Kitui, Makueni, Machakos, Tana River, Bomet, Meru, Tharaka Nithi, Nyandarua, Murang'a, Kajiado and Nyeri, which were selected for the implementation of the Small Scale Irrigation and Value Addition Project (SIVAP). These eleven counties were selected based on high levels of poverty, high food insecurity, potential for agriculture and low or moderate rainfall. The project builds on the success of the Small-Scale Horticulture Development Project (SHDP-1) and it will focus on improving high-value crop production through construction and rehabilitation of twelve (12) irrigation schemes (3,205 ha) in eleven counties. Additionally, the project aims to improve access to markets, enhance agro-processing, storage and post-harvest handling technologies and strengthen community-based institutions (Farmer Associations, Irrigation Water Users Associations and Women Groups). The project is expected to improve the livelihoods of more than 100,000 households.

Data Sources:

SIVAP Selected Counties

Source: African Development Bank and GAFSP Documents.

Poverty Incidence (Proportion of population below the poverty line) (2009): Proportion of the population below the national poverty line.

Source: Kenya National Bureau of Statistics KNBS. "Economic Survey 2014."

Malnutrition (Proportion of underweight children under 5 years) (2014): Prevalence of severely underweight children is the percentage of children under age 5 whose weight-for-age is more than 3 three standard deviations below the median for the international reference population ages 0-59 months.

Source: Kenya National Bureau of Statistics, Kenya Ministry of Health, Kenya National AIDS Control Council, Kenya Medical Research Institute, Kenya National Council for Population and Development. Measure DHS. “Kenya Demographic and Health Survey 2014.”

Total Population (2009): Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin.

Source: Kenya National Burea of Statistics KNBS. "Population and Housing Census 2009 - County Statistics."

Population Density (2009): Population divided by land area in square kilometers.

Source: Kenya National Burea of Statistics KNBS. "Population and Housing Census 2009 - County Statistics."

Livelihood Zones (2011): FEWS NET uses the Household Economy Approach (HEA) as the framework for its livelihoods work. For early warning of food insecurity, livelihoods analysis provides invaluable insight into the ability of households such as these to contend with shocks. The analysis also provides detailed information for humanitarian assistance planning and ongoing monitoring.

Source: FEWS NET - USAID. “Livelihood zoning plus activity in Kenya 2010.”

The maps displayed on the GAFSP website are for reference only. The boundaries, colors, denominations and any other information shown on these maps do not imply, on the part of GAFSP (and the World Bank Group), any judgment on the legal status of any territory, or any endorsement or acceptance of such boundaries.

The population desnity per Km2 in each County in Kenya

WorldPop produces different types of gridded population count datasets, depending on the methods used and end application. An overview of the data can be found in Tatem et al, and a description of the modelling methods used found in Stevens et al. The 'Global per country 2000-2020' datasets represent the outputs from a project focused on construction of consistent 100m resolution population count datasets for all countries of the World for each year 2000-2020. These efforts necessarily involved some shortcuts for consistency. The 'individual countries' datasets represent older efforts to map populations for each country separately, using a set of tailored geospatial inputs and differing methods and time periods. The 'whole continent' datasets are mosaics of the individual countries datasets

WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00645

This map shows the total population in Cameroon in 2019, in a multiscale map (Country, Region, and Department). Nationally, there are 25,876,380 people in Cameroon.The pop-up is configured to show the following information at each geography level:Total PopulationThe source of this data is Michael Bauer Research. The vintage of the data is 2019.Additional Esri Resources:Esri DemographicsPermitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

V1.5 The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Kenya: (1) Overall population density (2) Women (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of reproductive age (ages 15-49).

This layer shows the average household size in Kenya in 2023, in a multiscale map (Country and County). Nationally, the average household size is 3.8 people per household. It is calculated by dividing the household population by total households.The pop-up is configured to show the following information at each geography level:Average household size (people per household)Total populationTotal householdsCounts of population by 15-year age increments The source of this data is Michael Bauer Research. The vintage of the data is 2023. This item was last updated in October, 2023 and is updated every 12-18 months as new annual figures are offered.Additional Esri Resources:Esri DemographicsThis item is for visualization purposes only and cannot be exported or used in analysis.We would love to hear from you. If you have any feedback regarding this item or Esri Demographics, please let us know.Permitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

Kenya Country Boundary provides a 2023 boundary with a total population count. The layer is designed to be used for mapping and analysis. It can be enriched with additional attributes using data enrichment tools in ArcGIS Online.The 2023 boundaries are provided by Michael Bauer Research GmbH. These were published in October 2023. A new layer will be published in 12-18 months. Other administrative boundaries for this country are also available: Wilaya

This map shows the average household size in Kenya in 2023, in a multiscale map (Country and County). Nationally, the average household size is 3.8 people per household. It is calculated by dividing the household population by total households.The pop-up is configured to show the following information at each geography level:Average household size (people per household)Total populationTotal householdsCounts of population by 15-year age increments The source of this data is Michael Bauer Research. The vintage of the data is 2023. This item was last updated in October, 2023 and is updated every 12-18 months as new annual figures are offered.Additional Esri Resources:Esri DemographicsThis item is for visualization purposes only and cannot be exported or used in analysis.We would love to hear from you. If you have any feedback regarding this item or Esri Demographics, please let us know.Permitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

Social distancing is a public health measure intended to reduce infectious disease transmission, by maintaining physical distance between individuals or households. In the context of the COVID-19 pandemic, populations in many countries around the world have been advised to maintain social distance (also referred to as physical distance), with distances of 6 feet or 2 metres commonly advised. Feasibility of social distancing is dependent on the availability of space and the number of people, which varies geographically. In locations where social distancing is difficult, a focus on alternative measures to reduce disease transmission may be needed. To help identify locations where social distancing is difficult, we have developed an ease of social distancing index. By index, we mean a composite measure, intended to highlight variations in ease of social distancing in urban settings, calculated based on the space available around buildings and estimated population density. Index values were calculated for small spatial units (vector polygons), typically bounded by roads, rivers or other features. This dataset provides index values for small spatial units within urban areas in Kenya. Measures of population density were calculated from high-resolution gridded population datasets from WorldPop, and the space available around buildings was calculated using building footprint polygons derived from satellite imagery (Ecopia.AI and Maxar Technologies. 2020). These data were produced by the WorldPop Research Group at the University of Southampton. This work was part of the GRID3 project with funding from the Bill and Melinda Gates Foundation and the United Kingdom’s Department for International Development. Project partners included the United Nations Population Fund (UNFPA), Center for International Earth Science Information Network (CIESIN) in the Earth Institute at Columbia University, and the Flowminder Foundation.

Social distancing is a public health measure intended to reduce infectious disease transmission, by maintaining physical distance between individuals or households. In the context of the COVID-19 pandemic, populations in many countries around the world have been advised to maintain social distance (also referred to as physical distance), with distances of 6 feet or 2 metres commonly advised. Feasibility of social distancing is dependent on the availability of space and the number of people, which varies geographically. In locations where social distancing is difficult, a focus on alternative measures to reduce disease transmission may be needed. To help identify locations where social distancing is difficult, we have developed an ease of social distancing index. By index, we mean a composite measure, intended to highlight variations in ease of social distancing in urban settings, calculated based on the space available around buildings and estimated population density. Index values were calculated for small spatial units (vector polygons), typically bounded by roads, rivers or other features. This dataset provides index values for small spatial units within urban areas in Kenya. Measures of population density were calculated from high-resolution gridded population datasets from WorldPop, and the space available around buildings was calculated using building footprint polygons derived from satellite imagery (Ecopia.AI and Maxar Technologies. 2020). These data were produced by the WorldPop Research Group at the University of Southampton. This work was part of the GRID3 project with funding from the Bill and Melinda Gates Foundation and the United Kingdom’s Department for International Development. Project partners included the United Nations Population Fund (UNFPA), Center for International Earth Science Information Network (CIESIN) in the Earth Institute at Columbia University, and the Flowminder Foundation.

Abstract

The survey of the Pemba was an attempt to reach all households in Kenya with links to Pemba in Tanzania. It was conducted in the two counties of Kilifi and Kwale on the coast, north and south of Mombasa, respectively. According to information from village elders familiar with the Pemba community in Kenya, most of the Pemba population resides in these two counties. While there are some Pemba residents in Lamu, the security situation prevented data collection there. Further, a few Pemba are believed to live in the city of Mombasa and elsewhere in the country. But due to lack of further information, no data were collected in Mombasa or elsewhere.

The objectives of the full survey, conducted in August 2016, were: 1. To establish the number and characteristics of the Pemba living in Kenya, including their arrival period in Kenya, nationality and their problems; 2. To make recommendations for the issuance of the documentation that is required for those who apply for citizenshiop by registration

Geographic coverage

Kwale and Kilifi counties, Kenya.

Analysis unit

Households, individuals

Universe

The total number of households with links to Pemba in Tanzania, in Kilifi and Kwale counties.

Kind of data

Census/enumeration data [cen]

Sampling procedure

A household mapping exercise was conducted in Kilifi and Kwale to identify Pemba households and to make it easier to locate them on the ground. The mapping was done from 4 to 12 August 2016 by a team from UNHCR Kenya office and KNBS.

The mapping in each village commenced with a visit to the chief's office, who put the team in touch with the village chair. The team explained the purpose of its visit to the village chair and began the mapping exercise. The importance of involving the chiefs and village chairpersons is that they are well connected, recognised and trusted by residents in their communities. The same procedure is followed by KNBS when they are mapping for sample surveys and censuses.

The team established physical boundaries of the area to be mapped, located the boundaries on the map and then identified and listed the Pemba households within the enumeration boundary. A Pemba household, in this context, is one identified by the informants as having at least one person with origins or links to Pemba. The links may include a person's spouse, parents or grandparents, who migrated to Kenya from Pemba or where a person has migrated from Pemba to Kenya.

The mapping team was followed by the village chair to the Pemba households, where the UNHCR and Haki Centre staff listed number of persons in each, while the KNBS staff marked the location of the household on the map. The entrances of identified Pemba households were marked in chalk with the letters HCR and a number starting at 001 to make it easier to find the houses during the enumeration. Since it seems to be generally well known where the Pemba live it was not considered stigmatising to mark their doors. During the feedback forums with the Pemba after the survey, there was no mention of stigmatization due to marking the door with chalk.

The maps were from the 2009 national housing and population census, purchased from KNBS. The team made lists with information about the location, number and size of each household. The mapping team visited 17 villages in Kilifi and Kwale (see Table 1 in Section 2.7). All villages visited were identified before the mapping exercise by key informants as locations being home to the Pemba of Kenya. The key informants were Pemba elders in different sub-counties previously identified for providing background information on the Pemba arrival and history in Kenya. In each sub-country, the chief, the assistant chief or the village chair also accompanied the team. In Kwale, 358 households were identified with 2,220 persons, and in Kilifi, 86 households with 558 persons.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire was developed before the pilot survey and revised during and after the pilot survey, based on the experience gained. The pilot survey was used to test the questions and to check for inconsistences and misinterpretations due to unclear concepts and definitions. The testing process also revealed some important themes that had been left out.

The structure of the questionnaire was altered, including the order of the questions and the introductory pages, to facilitate administration of the questionnaire.

Finally, the questionnaire was translated into Swahili. Both the English and Swahili versions were used in the survey, even though the English version was preferred by almost all interviewers. The two versions of the questionnaire are attached in Annex 4 and 5. Enumerators used the English questionnaire to frame the questions in the local and less academic version of Swahili.

Cleaning operations

The data were imported into a Statistics Analysis Software (SAS) file and validated. Several errors were identified during the validation process, both on how the data had been recorded by the interviewers in the field and how the data had been entered by the clerks. There were particularly many errors in the entry of the variable “Relation to the household head” (Q.2). There were also many errors in the entry of the age of the household head, which was mostly due to errors in recording the right codes. A substantial amount of time was spent cleaning the data after the data had been entered, which included consulting many paper questionnaires. The quality of the survey data was significantly improved after the data entry revision. The data were analysed using both SAS software and Excel spreadsheets.

Response rate

The rate of non-response was low. Of the 452 households visited, visits to only 23 households can be categorised as non-response. A lot of effort was made to revisit non-responding households, using interviewers living nearby. Out of the 23 non-responsive households, 12 were not at home or there was no adult at home. There were 2 interrupted interviews, 7 refusals and 2 with no links to Pemba. In one household the respondent was not mentally stable enough to be interviewed, according to the enumerator.

Fibian Lukalo, Catherine Boone, and Sandra Joireman. Mapping Settlement Schemes in Kenya. Nairobi: National Commission, 2019. ISBN 978-9966-1928-5-1. This is an approximately 70 page booklet that presents maps and some descriptive statistics pertaining to all Kenyan settlement schemes since 1962 for which we were able to obtain Survey of Kenya maps. This document is to be used for scholarly purposes only (ie., not for commercial or legal purposes). The citation to the underlying dataset is: This research project is conducted in partnership between Dr. Fibian Lukalo of Kenya’s National Land Commission, Prof. Catherine Boone of the London School of Economics (funded by UK Economic and Social Research Council Grant # ES/R005753/1 'Spatial Dynamics in African Political Economy' and Kenya NACOSTI Research Permits # NACOSTI/P/16/48539/13282 and /24668), and Professors Kimberley Browne and Sandra Joireman at the University of Richmond. The data and maps have been prepared for the purposes of academic and policy research. All boundaries are approximate; they are indicative only and are not intended to have legal standing or be used for official purposes. Users outside the NLC should cite this work as Lukalo, Boone, Browne, and Joireman, "Kenya Settlement Schemes Data Project,” London, Nairobi, and Richmond: NCL, LSE, and UoR, 2019.

This map shows the average household size in Sudan in 2019, in a multiscale map (Country and State). Nationally, the average household size is 5.9 people per household. It is calculated by dividing the household population by total households.The pop-up is configured to show the following information at each geography level:Average household size (people per household)Total populationTotal householdsCount of population by 15-year age increments The source of this data is Michael Bauer Research. The vintage of the data is 2019.Additional Esri Resources:Esri DemographicsPermitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

This layer shows the purchasing power per capita in Kenya in 2023, in a multiscale map (Country and County). Nationally, the purchasing power per capita is 170,615 Kenyan shilling. Purchasing Power describes the disposable income (income without taxes and social security contributions, including received transfer payments) of a certain area's population. The figures are in Kenyan shilling (KES) per capita.The pop-up is configured to show the following information at each geography level:Purchasing power per capitaThe source of this data is Michael Bauer Research. The vintage of the data is 2023. This item was last updated in October, 2023 and is updated every 12-18 months as new annual figures are offered.Additional Esri Resources:Esri DemographicsThis item is for visualization purposes only and cannot be exported or used in analysis.We would love to hear from you. If you have any feedback regarding this item or Esri Demographics, please let us know.Permitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

This map illustrates potentially affected population by flooding in the eastern sub counties of Kenya. The analysis was conducted by analyzing Sentinel-1 imagery acquired on the 4 May 2018. Within the analysis extent, more than 200,000 people are potentially affected by the floods. Magarini sub county in Kilifi County, is the one with more than 40,000 people living inside flood affected areas, followed by Dadaab, Wajir South, Garsen and Malindi sub counties. Note that some sub counties have been partially analyzed depending on the area covered by satellite imagery. It is likely that flood waters have been systematically underestimated along highly vegetated areas, along main river banks and within built-up urban areas because of the special characteristics of the satellite data used. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR UNOSAT.

Abstract

The Kilifi Health and Demographic Surveillance System (KHDSS), located on the Indian Ocean coast of Kenya, was established in 2000 as a record of births, pregnancies, migration events, deaths and cause of deaths and is maintained by 4-monthly household visits. The study area was selected to capture the majority of patients admitted to Kilifi District Hospital. The KHDSS has 284 000 residents and covers 891 km2 and the hospital admits 4400 paediatric patients and 3400 adult patients per year. At the hospital, morbidity events are linked in real time by a computer search of the population register. Linked surveillance was extended to KHDSS vaccine clinics in 2008.

KHDSS data have been used to define the incidence of hospital presentation with childhood infectious diseases (e.g. rotavirus diarrhoea, pneumococcal disease), to test the association between genetic risk factors (e.g. thalassaemia and sickle cell disease) and infectious diseases, to define the community prevalence of chronic diseases (e.g. epilepsy), to evaluate access to health care and to calculate the operational effectiveness of major public health interventions (e.g. conjugate Haemophilus influenzae type b vaccine). Rapport with residents is maintained through an active programme of community engagement. A system of collaborative engagement exists for sharing data on survival, morbidity, socio-economic status and vaccine coverage.

Geographic coverage

Kilifi District is situated 60km to the north of Mombasa on the Kenyan coast. It has an area of approximately 2,500 square kilometres and a population of 650,000. A flat coastal strip extends approximately 10km inland to low hills rising to an altitude of 250 metres

An area of 891 km2 was selected as the smallest number of administrative sublocations that collectively included the stated sublocation of residence of at least 80% of paediatric inpatients in the preceding 3 years (1998-2000). KDH is located in Kilifi town, 3° south of the equator and KHDSS extends up and down the coastal strip for 35 km from Kilifi. KDH is the only inpatient facility offering paediatric services in the KHDSS area. The local economy is based on subsistence farming of maize, cassava, cashew nuts and coconuts as well as goats and dairy cows. Two large agricultural estates, two research institutes and several tourist hotels contribute to local employment.

Analysis unit

Individual

Universe

All individuals in the HDSS area

Frequency of data collection

Three rounds in a year

Sampling procedure

No sampling, complete population surveyed

Sampling deviation

Not Applicable

Mode of data collection

Proxy Respondent [proxy]

Research instrument

1. Enumeration of persons

The Enumeration of People Data Entry Form has all names of residents within an homestead (Hm). This form bears the Enumeration Zone ( EZ) and Hm numbers, Hm name and name of homesteadhead. Also, it has details of each individual such as name, sex, ethinicity, pregnancy status, Kenya national identification number, Mother's national identification card number as well as the BU where an individual sleeps. A Fw uses this form to up-date the residence status of people.

1. Enumeration of buildings

This form has a list of all homesteads and existing buildings in each homestead (Hm). The form indicates: Hm name, Hm number and building units(BUs) in alphabet numbers. The geographical co-ordinates and materials used to make each building are also included. The census FWs update this form to show if the building unit still exists or if the BU has been demolished.

1. Listing of all registered Homesteads The Listing of All Registered Homesteads form has all active Hms in a sub-enumeration zone (sub- EZ) according to the previous census round. It is used to confirm number and specific HMs in a sub-EZ with the records of Building Structure (BS) Data Entry Form

2. In migrants

This form is used to record new people who have moved into an existing or a new homestead, or people who have been present but missed in the previous census rounds and intend to stay for the next three or more months.

5 .Births

This form is used to record all new born babies by resident mothers. In this form, all personal details of the baby are recorded and linked to those of the mother if she is a resident.

1. Pregnancy All resident women within the reproductive age bracket i.e., between 15 and 49 years, are usually flagged in the Enumeration Data Entry form to be asked about their pregnancy status.

2. Change person details Change Personal Details Data Entry form is designed to record changes of personal details.The Change Personal Details Data Entry form provides fields and codes used to effect such changes or corrections. Accuracy of the new value must be supported by evidence, preferrably documented evidence for example, a national identification card for date of birth.

8 .Change buildings details The change buildings details data entry form is designed to record changes relating to building materials, category and coordinates of a building unit as well as change of homestead names. Potential areas for changes and corrections include the Hm name, roof, wall, storey, longitudes, latitudes and elevation. Specific codes are used to describe the type of a building characteristic to be changed.

1. Maps KHDSS enumerators use EZ maps with a list of Hms that bears coresponding Hm numbers. Vital landmarks, roads and other features are displayed on a map to assist locate and identify Hms. These maps are up-dated every census round by the mapping team and enumerators. Global Positioning System (GPS) and Geographic Information System (GIS) technologies are used to develop and maintain a mapping database. ETrex garmin GPS receivers are used to collect spatial data and ArcGIS 10 is used to manipulate, edit, store and generate maps.

10.Verbal autopsy

11.Extra Questions

Cleaning operations

Manual editing A manual editor on daily basis checks completed tools for completeness and consistency. Those that have issues are returned to the responsible fieldworkers for correction and/or follow ups. Manual editor’s reports are instrumental in evaluating fieldworkers after every two weeks.

Complementary nature of KEMRI studies Kemri-Wellcome Trust Programme has a number of research studies being conducted in the same KHDSS census area. Some of these studies are nested within the KHDSS and have proved useful in improving data quality. For example, issues have been raised concerning some details such as date of birth and sex, which prompted verifications in the field and corrections.

The following processing checks are done during the ETL process.

1. If the first event is legal. Like the first event must beenumeration, birth or inmigration.
2. If the last event is legal. Like the last event must be end of observtion, death or outmigration.

3. If the transition events are legal. The list of legal transitions:

   Birth followed by death Birth followed by exit Birth followed by end of observation Birth followed by outmigration

   Death followed by none

   Entry followed by death Entry followed by exit Entry followed by end of observation Entry followed by outmigration Enumeration followed by death Enumeration followed by exit Enumeration followed by outmigration

   Exit followed by entry

   Inmigration followed by Death Inmigration followed by exit Inmigration followed by end of observation Inmigration followed by outmigration

   End of observation followed by none

   Outmigration followed by none Outmigration followed by enumeration Outmigration followed by inmigration

   The list of illegal transitions:

   Birth followed by none Birth followed by birth Birth followed by entry Birth followed by enumeration Birth followed by inmigration

   Death followed by birth Death followed by death Death followed by entry Death followed by enumeration Death followed by exit Death followed by inmigration Death followed by outmigration Death followed by end of observation

   Entry followed by none Entry followed by birth Entry followed by entry Entry followed by enumeration Entry followed by inmigration

   Enumeration followed by none Enumeration followed by birth Enumeration followed by entry Enumeration followed by enumeration Enumeration followed by inmigration

   Exit followed by birth Exit followed by death Exit followed by exit Exit followed by end of observation Exit followed by outmigration

   Inmigration followed by none Inmigration followed by birth Inmigration followed by entry Inmigration followed by enumeration Inmigration followed by inmigration

   End of observation followed by birth End of observation followed by death End of observation followed by entry End of observation followed by enumeration End of observation followed by exit End of observation followed by inmigration End of observation followed by end of observation End of observation followed by outmigration

   Outmigration followed by birth Outmigration followed by death Outmigration followed by exit Outmigration followed by end of observation Outmigration followed by outmigration

   List of edited events:

   Exit followed by none Exit followed by enumeration Exit followed by inmigration Outmigration followed by entry

Response