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The validity of an area-based method to estimate the size of hard-to-reach populations using satellite images: the example of fishing populations of Lake Victoria

BACKGROUND: Information on the size of populations is crucial for planning of service and resource allocation to communities in need of health interventions. In resource limited settings, reliable census data are often not available. Using publicly available Google Earth Pro and available local hous...

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Autores principales: Nash, Stephen, Tittle, Victoria, Abaasa, Andrew, Sanya, Richard E., Asiki, Gershim, Hansen, Christian Holm, Grosskurth, Heiner, Kapiga, Saidi, Grundy, Chris
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6090724/
https://www.ncbi.nlm.nih.gov/pubmed/30123310
http://dx.doi.org/10.1186/s12982-018-0079-5
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author Nash, Stephen
Tittle, Victoria
Abaasa, Andrew
Sanya, Richard E.
Asiki, Gershim
Hansen, Christian Holm
Grosskurth, Heiner
Kapiga, Saidi
Grundy, Chris
author_facet Nash, Stephen
Tittle, Victoria
Abaasa, Andrew
Sanya, Richard E.
Asiki, Gershim
Hansen, Christian Holm
Grosskurth, Heiner
Kapiga, Saidi
Grundy, Chris
author_sort Nash, Stephen
collection PubMed
description BACKGROUND: Information on the size of populations is crucial for planning of service and resource allocation to communities in need of health interventions. In resource limited settings, reliable census data are often not available. Using publicly available Google Earth Pro and available local household survey data from fishing communities (FC) on Lake Victoria in Uganda, we compared two simple methods (using average population density) and one simple linear regression model to estimate populations of small rural FC in Uganda. We split the dataset into two sections; one to obtain parameters and one to test the validity of the models. RESULTS: Out of 66 FC, we were able to estimate populations for 47. There were 16 FC in the test set. The estimates for total population from all three methods were similar, with errors less than 2.2%. Estimates of individual FC populations were more widely discrepant. CONCLUSIONS: In our rural Ugandan setting, it was possible to use a simple area based model to get reasonable estimates of total population. However, there were often large errors in estimates for individual villages.
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spelling pubmed-60907242018-08-17 The validity of an area-based method to estimate the size of hard-to-reach populations using satellite images: the example of fishing populations of Lake Victoria Nash, Stephen Tittle, Victoria Abaasa, Andrew Sanya, Richard E. Asiki, Gershim Hansen, Christian Holm Grosskurth, Heiner Kapiga, Saidi Grundy, Chris Emerg Themes Epidemiol Research Article BACKGROUND: Information on the size of populations is crucial for planning of service and resource allocation to communities in need of health interventions. In resource limited settings, reliable census data are often not available. Using publicly available Google Earth Pro and available local household survey data from fishing communities (FC) on Lake Victoria in Uganda, we compared two simple methods (using average population density) and one simple linear regression model to estimate populations of small rural FC in Uganda. We split the dataset into two sections; one to obtain parameters and one to test the validity of the models. RESULTS: Out of 66 FC, we were able to estimate populations for 47. There were 16 FC in the test set. The estimates for total population from all three methods were similar, with errors less than 2.2%. Estimates of individual FC populations were more widely discrepant. CONCLUSIONS: In our rural Ugandan setting, it was possible to use a simple area based model to get reasonable estimates of total population. However, there were often large errors in estimates for individual villages. BioMed Central 2018-08-13 /pmc/articles/PMC6090724/ /pubmed/30123310 http://dx.doi.org/10.1186/s12982-018-0079-5 Text en © The Author(s) 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Nash, Stephen
Tittle, Victoria
Abaasa, Andrew
Sanya, Richard E.
Asiki, Gershim
Hansen, Christian Holm
Grosskurth, Heiner
Kapiga, Saidi
Grundy, Chris
The validity of an area-based method to estimate the size of hard-to-reach populations using satellite images: the example of fishing populations of Lake Victoria
title The validity of an area-based method to estimate the size of hard-to-reach populations using satellite images: the example of fishing populations of Lake Victoria
title_full The validity of an area-based method to estimate the size of hard-to-reach populations using satellite images: the example of fishing populations of Lake Victoria
title_fullStr The validity of an area-based method to estimate the size of hard-to-reach populations using satellite images: the example of fishing populations of Lake Victoria
title_full_unstemmed The validity of an area-based method to estimate the size of hard-to-reach populations using satellite images: the example of fishing populations of Lake Victoria
title_short The validity of an area-based method to estimate the size of hard-to-reach populations using satellite images: the example of fishing populations of Lake Victoria
title_sort validity of an area-based method to estimate the size of hard-to-reach populations using satellite images: the example of fishing populations of lake victoria
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6090724/
https://www.ncbi.nlm.nih.gov/pubmed/30123310
http://dx.doi.org/10.1186/s12982-018-0079-5
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