1,695 research outputs found
How Bayesian modelling could use the Big Tech competition in producing built-up maps: predicting population in data-scarce contexts
No full textSatellite-imagery derived products represent an exciting opportunity to map and estimate current population with high spatial precision in context where traditional demographic data is not available. Bayesian methods have been developped to harness the potential of built-up maps as a proxy for human settlements, thereby predicting population sizes of hard-to-reach areas across sub-saharan Africa. The increased availibility of high-resolution satellite imagery has fostered the competition between high-profile institutions to produce global-scale built-up maps. Given the key role of built-up maps to estimate population sizes we need to understand (1) how do the different sources impact population predictions, (2) how do they compare with human-made maps and finally (3) how can they be articulated together
Modelling urban deprivation in Kinshasa, DRC
No full textWe modelled and predicted an urban deprivation score for the 23 communes of Kinshasa province and stored in urban_deprivation.tif. It is the outcome of a confirmatory factor analysis stored in code_simplified.R that combined four geospatial covariates stored in the covariates folder: distance to river and residential road density in a 1km window derived from OpenStreetMap and building landscape shape and building area coefficient of variation derived from a building footprint layer. The model structure was validated through several metrics that indicate a good reproduction of the correlation matrix between the covariates at study sites. The predicted gridded score was then compared with qualitative information collected from the litterature. We provide for comparison this mapped information in qualitative_info.shp where the source is stored in the attribute table and matches the bibliography stored in qualitative_info_source.txt.</span
Gridded disaggregated population estimates for Kenya, version 2.0
No full textThese 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 Inc), 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.</span
Gridded disaggregated population estimates for Kenya (2021), version 1.0.
No full textThese 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, as well as Christopher Lloyd, particularly for the processing of residential/non-residential building footprints. In-country engagements were done by Benard Mitto, Justine Dowden (CIESIN) and 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.</span
[News Clip: Edith Deen]
No full textVideo footage from the WBAP-TV television station in Fort Worth, Texas, to accompany a news story about author, columnist, and lecturer Edith Alderman Deen receiving an honorary Doctor of Letters degree from Texas Women's University
Statistical population modelling for census support
No full textThis github repo contains the raw teaching materials for the Statistical Population Modelling for Census Support workshop, funded by the United Nations Population Fund. It has been developed by the WorldPop Research Group, University of Southampton.The repo consists in a series of tutorials in Bayesian statistics for population modelling with hands-on experience. It includes example code and other resources designed to expedite the learning curve.The key concepts that are covered in the tutorial series include: Introduction to software for Bayesian statistical modelling: R and Stan, Simple linear regression in a Bayesian context, Random effects to account for settlement type (e.g. urban/rural) and other types of stratification in survey data, Quantifying and mapping uncertainties in population estimates and Diagnostics to evaluate model performance (e.g. cross-validation).It has been first taught to the Brazilian Stats Office, Instituto Brasileiro de Geografia e Estatística (IBGE), in October 2021
A bottom-up population modelling approach to complement the population and housing census
No full textPopulation and housing censuses provide essential demographic information for local, national and international decision-making and response. However, census data in the most vulnerable countries are often outdated or partial because political instability, conflict and natural disasters prevent a nationwide enumeration. The bottom-up modelling approach complements outdated or incomplete census data by estimating population counts and age/sex structures in grid cells of about 100 m using required population data on a set of fully enumerated locations and auxiliary geospatial covariates. We present the modelling effort in the Democratic Republic of Congo - the last census was conducted in 1984 - and in Burkina Faso - the last census was conducted in 2020 but covered only 70% of the country. Both models showed good predictive performance, denoted by R2 values of 0.73 and 0.63 for the respective out-of-sample predictions of population counts. The resulting bottom-up and gridded population estimates are currently used for census support and humanitarian response in both countries. This work has highlighted the flexibility of the bottom-up modelling approach, in terms of input population data, model specification and aggregation of population estimates to support specific use cases
Conversations with authors: Edith Pearlman
No full textA 2011 conversation with the author Edith Pearlman about her life and the inspiration for her work
Interview with Major Edith Vowell Part 2
No full textAnna Maria Island author included Major Edith Vowell in his book, Combat Nurses of World War II. Here she tells her story, with adventures in Brisbane, Australia, on ships and a GI troop train. She also lists her postwar nursing postings
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