WorldPop produces different types of gridded population count datasets, depending on the methods used and end application. Please make sure you have read our Mapping Populations overview page before choosing and downloading a dataset.

A description of the modelling methods used for age and sex structures can be found in "https://pophealthmetrics.biomedcentral.com/articles/10.1186/1478-7954-11-11" target="_blank"> Tatem et al and Pezzulo et al. Details of the input population count datasets used can be found here, and age/sex structure proportion datasets here.
Both top-down 'unconstrained' and 'constrained' versions of the datasets are available, and the differences between the two methods are outlined here. The datasets represent the outputs from a project focused on construction of consistent 100m resolution population count datasets for all countries of the World structured by male/female and 5-year age classes (plus a <1 year class). These efforts necessarily involved some shortcuts for consistency. The unconstrained datasets are available for each year from 2000 to 2020.
The constrained datasets are only available for 2020 at present, given the time periods represented by the building footprint and built settlement datasets used in the mapping.
Data for earlier dates is available directly from WorldPop.

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/WP00646

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 dataset shows census data for Kenya disaggregated as follows, by:

• Urban or rural areas and sex
• Age
• Education levels
• Employment levels

Feed the Future seeks to reduce poverty and undernutrition in 19 developing countries including Kenya by focusing on accelerating growth of the agricultural sector, addressing root causes of undernutrition, and reducing gender inequality. This dataset contains records for all children under 5 years of age in the sampled households (n=1,435, vars=217).

Feed the Future seeks to reduce poverty and undernutrition in 19 developing countries including Kenya by focusing on accelerating growth of the agricultural sector, addressing root causes of undernutrition, and reducing gender inequality. This dataset is an individual -dataset with all women age 15-49 with a completed interview in Module H of the questionnaire (n=1,382, vars=41).

School locations in Kenya. It comprises Primary and Secondary Schools. The dataset was provided by Kenya Ministry of Education.

The Kenya Population by Sex and Age Groups as per the 2009 National census survey

Dataset file can be found in the metadata below under "Attachments". Feed the Future’s Africa Lead II project partnered with The Mediae Company, a Kenya-based media education company, to develop a pilot season of Africa’s first agriculture-focused reality TV program: Don’t Lose the Plot (DLTP). Targeting youth in Kenya and Tanzania, the show aired in Kenya and Tanzania between May and July 2017. The program’s objectives were to encourage youth to consider farming as a lucrative career choice, provide information on how to start agribusinesses, and share useful agronomic information. Africa Lead commissioned Kantar Public East Africa to evaluate the impact of DLTP on knowledge, attitudes, and behavior, or intention to change behavior, related to farming and agribusiness practices. This data asset includes quantitative data collected through a cross-sectional household survey in Kenya and Tanzania. Data collection took place between August and December 2017 and targeted both viewers and non-viewers of DLTP aged 18 to 35 years. A total sample of 3,737 target individuals were interviewed in Kenya, including 406 verified viewers. In Tanzania, 3,383 target individuals were interviewed, including 527 verified viewers.

This data-set contains Virtual Museum records for Kenya, it is part of the project "Prioritizing conservation management in an East African forest landscape" (Project ID BID-AF2017-0274-NAC).

The Virtual Museum (VM) is a database system and corresponding web front-end, it is a research tool with the following main objectives: (1) to provide a platform for citizen scientists to contribute to science-driven biodiversity projects; submitted records are identified and vetted online by a panel of experts; (2) to serve as a repository for the long term curation of distributional data sets; (3) to provide open access to distributional data in the form of maps and lists. The VM has been used as the platform for the Conservation Assessment of reptiles, butterflies, mammals and birds. Currently the VM hosts 17 biodiversity projects: BirdPix (bird pictures archive), BOP (odd plumages of birds), DungBeetleMAP (atlas of dung beetles, Coleoptera: Scarabaeidae), EchinoMAP (atlas of African Echinoderma , sea stars, sea urchins and brittle stars), FishMAP (atlas of freshwater fish in southern and eastern Africa), FrogMAP (atlas of African frogs), LacewingMAP (atlas of African Neuroptera and Megaloptera), MushroomMAP (atlas of South African Mushrooms), OdonataMAP (atlas of African Odonata), OrchidMAP (atlas of African Orchids), PHOWN (photos of weaver nests), LepiMAP (atlas of African Lepidoptera), ReptileMAP (atlas of African Reptiles), ScorpionMAP (atlas of African Scorpions), SpiderMAP (atlas of African Spiders), MammalMAP (atlas of African Mammals), TreeMAP (atlas of South African trees).

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX.

For the 70 percent of the world's poor who live in rural areas, agriculture is the main source of income and employment. But depletion and degradation of land and water pose serious challenges to producing enough food and other agricultural products to sustain livelihoods here and meet the needs of urban populations. Data presented here include measures of agricultural inputs, outputs, and productivity compiled by the UN's Food and Agriculture Organization.

Proportion of Children (0 to 59 Months) who slept under Treated bed nets per county in kenya

This dataset shows the Mombasa population pyramid by Age group as reported by the Kenya National Bureau of statistics during the 2009 National census

Feed the Future Northern Kenya Interim Survey in the Zone of Influence: This dataset contains records for all children under 5 years of age in the sampled households (n=1,435, vars=217).

A species' distribution is the most fundamental information needed in order to conserve it. Almost 30 years ago bird records were collected across Kenya that resulted in the book, A Bird Atlas of Kenya, that mapped and described the status of all the 1,065 species of birds then recorded in the country. Since then much has changed in terms of habitats and climatic conditions in Kenya and as a result the distributions and status of many of our birds have also dramatically changed – but we don’t know how or to what extent! The Kenya Bird Map project aims to map the current distribution of all of Kenya’s bird species and describe their status with the help of valued input from Citizen Scientists – volunteer members of the public who are keen to contribute through going birding and submitting their observations to the project. By pooling the efforts of many Citizen Scientist birders, Kenya Bird Map will tell the story of changing bird distributions and abundance - and in so doing provide a powerful tool for conservation

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 United Nations Children's Fund ( UNICEF ) - Population Modelling for use in Routine Health Planning and Monitoring project (contract no. 43335861). Projects partners included the Kenya Unicef Regional and Country Offices, WorldPop research group at the University of Southampton and the Center for International Earth Science Information Network in the Columbia Climate School at Columbia University. Assane Gadiaga (WorldPop) led the input processing and the modelling work following the Random Forest (RF)-based dasymetric mapping approach developed by Stevens et al. (2015). Thomas Abbott supported the covariates processing work. In-country engagements were done by David Kyalo, Olena Borkovska ( GRID3 , Maria Muniz (Unicef). Using the 2009 and 2019 census data from the Kenya’s National Bureau of Statistics (KNBS), the US Census Bureau released the census-based total population projections, population by age and sex and digital sub-counties boundaries. Duygu Cihan helped in the preparation of these input population data. Attila N Lazar, Edith Darin and Heather Chamberlain advised on the modelling procedure. The work was overseen by Attila N Lazar and Andy J Tatem.

Recommended citations

Gadiaga A. N., Abbott T. J., Chamberlain H., Lazar A. N., Darin E., Tatem A. J. 2023. Census disaggregated gridded population estimates for Kenya (2022), version 2.0. University of Southampton. doi:10.5258/SOTON/WP00762

License

These data may be distributed using a Creative Commons Attribution 4.0 International (CC BY 4.0) License, specified in legal code. Contact release[at]worldpop.org for more information.

The authors followed rigorous procedures designed to ensure that the used data, the applied method and thus the results are appropriate and of reasonable quality. If users encounter apparent errors or misstatements, they should contact WorldPop at release[at]worldpop.org.

WorldPop, University of Southampton, and their sponsors offer these data on a "where is, as is" basis; do not offer an express or implied warranty of any kind; do not guarantee the quality, applicability, accuracy, reliability or completeness of any data provided; and shall not be liable for incidental, consequential, or special damages arising out of the use of any data that they offer.

Kenya KE: Literacy Rate: Adult Female: % of Females Aged 15 and Above data was reported at 74.006 % in 2014. This records an increase from the previous number of 66.863 % for 2007. Kenya KE: Literacy Rate: Adult Female: % of Females Aged 15 and Above data is updated yearly, averaging 74.006 % from Dec 2000 (Median) to 2014, with 3 observations. The data reached an all-time high of 77.893 % in 2000 and a record low of 66.863 % in 2007. Kenya KE: Literacy Rate: Adult Female: % of Females Aged 15 and Above data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Kenya – Table KE.World Bank.WDI: Education Statistics. Adult literacy rate is the percentage of people ages 15 and above who can both read and write with understanding a short simple statement about their everyday life.; ; UNESCO Institute for Statistics; Weighted average; Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018).

In this upload we share processed crop type datasets from both France and Kenya. These datasets can be helpful for testing and comparing various domain adaptation methods. The datasets are processed, used, and described in this paper: https://doi.org/10.1016/j.rse.2021.112488 (arXiv version: https://arxiv.org/pdf/2109.01246.pdf).

In summary, each point in the uploaded datasets corresponds to a particular location. The label is the crop type grown at that location in 2017. The 70 processed features are based on Sentinel-2 satellite measurements at that location in 2017. The points in the France dataset come from 11 different departments (regions) in Occitanie, France, and the points in the Kenya dataset come from 3 different regions in Western Province, Kenya. Within each dataset there are notable shifts in the distribution of the labels and in the distribution of the features between regions. Therefore, these datasets can be helpful for testing for testing and comparing methods that are designed to address such distributional shifts.

More details on the dataset and processing steps can be found in Kluger et. al. (2021). Much of the processing steps were taken to deal with Sentinel-2 measurements that were corrupted by cloud cover. For users interested in the raw multi-spectral time series data and dealing with cloud cover issues on their own (rather than using the 70 processed features provided here), the raw dataset from Kenya can be found in Yeh et. al. (2021), and the raw dataset from France can be made available upon request from the authors of this Zenodo upload.

All of the data uploaded here can be found in "CropTypeDatasetProcessed.RData". We also post the dataframes and tables within that .RData file as separate .csv files for users who do not have R. The contents of each R object (or .csv file) is described in the file "Metadata.rtf".

Preferred Citation:

-Kluger, D.M., Wang, S., Lobell, D.B., 2021. Two shifts for crop mapping: Leveraging aggregate crop statistics to improve satellite-based maps in new regions. Remote Sens. Environ. 262, 112488. https://doi.org/10.1016/j.rse.2021.112488.

-URL to this Zenodo post https://zenodo.org/record/6376160

Currently there are over 50,000 described spider species worldwide according to the world spider catalog (WSC). The study of spiders in Kenya is not well done and as of December 2018, 805 species were described from the region. Some of the species were considered nomen dubium and others described from juvenile species which are at best considered misidentified. The data presented here is a checklist of spider species described from Kenya up-to May of 2022. The species are described from Kenya and other regions of the world with some considered to be cosmopolitan. A total of 801 species from 58 families are represented in the data set. 320 out of the 801 species from this data set are considered endemic to Kenya. 84 species are genus types. The families Salticidae and Linyphiidae had the most species representatives i.e. 163 and 109 species respectively. The bracketed scientific name authorship represent species that have either been transferred to a new/different genus or have been synonymized with other species but maintain the original author. Letters in the scientific name authorship distinguish articles from authors who made multiple publications within the same year.

Feed the Future Northern Kenya Interim Survey in the Zone of Influence: This dataset is an individual -dataset with all women age 15-49 with a completed interview in Module H of the questionnaire (n=1,476, vars=140).

Kenya KE: Prevalence of Underweight: Weight for Age: % of Children Under 5 data was reported at 11.000 % in 2014. This records a decrease from the previous number of 16.400 % for 2009. Kenya KE: Prevalence of Underweight: Weight for Age: % of Children Under 5 data is updated yearly, averaging 17.500 % from Dec 1993 (Median) to 2014, with 8 observations. The data reached an all-time high of 20.100 % in 1993 and a record low of 11.000 % in 2014. Kenya KE: Prevalence of Underweight: Weight for Age: % of Children Under 5 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Kenya – Table KE.World Bank.WDI: Health Statistics. Prevalence of underweight children is the percentage of children under age 5 whose weight for age is more than two standard deviations below the median for the international reference population ages 0-59 months. The data are based on the WHO's child growth standards released in 2006.; ; UNICEF, WHO, World Bank: Joint child malnutrition estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology.; Linear mixed-effect model estimates; Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF, www.childinfo.org). Estimates of child malnutrition, based on prevalence of underweight and stunting, are from national survey data. The proportion of underweight children is the most common malnutrition indicator. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition.