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Using gridded population and quadtree sampling units to support survey sample design in low-income settings

Using gridded population and quadtree sampling units to support survey sample design in low-income settings
Using gridded population and quadtree sampling units to support survey sample design in low-income settings
Background
Household surveys are the main source of demographic, health and socio-economic data in low- and middle-income countries (LMICs). To conduct such a survey, census population information mapped into enumeration areas (EAs) typically serves a sampling frame from which to generate a random sample. However, the use of census information to generate this sample frame can be problematic as in many LMIC contexts, such data are often outdated or incomplete, potentially introducing coverage issues into the sample frame. Increasingly, where census data are outdated or unavailable, modelled population datasets in the gridded form are being used to create household survey sampling frames.

Methods
Previously this process was done by either sampling from a set of the uniform grid cells (UGC) which are then manually subdivided to achieve the desired population size, or by sampling very small grid cells then aggregating cells into larger units to achieve a minimum population per survey cluster. The former approach is time and resource-intensive as well as results in substantial heterogeneity in the output sampling units, while the latter can complicate the calculation of unbiased sampling weights. Using the context of Somalia, which has not had a full census since 1987, we implemented a quadtree algorithm for the first time to create a population sampling frame. The approach uses gridded population estimates and it is based on the idea of a quadtree decomposition in which an area successively subdivided into four equal size quadrants, until the content of each quadrant is homogenous.

Results
The quadtree approach used here produced much more homogeneous sampling units than the UGC (1 × 1 km and 3 × 3 km) approach. At the national and pre-war regional scale, the standard deviation and coefficient of variation, as indications of homogeneity, were calculated for the output sampling units using quadtree and UGC 1 × 1 km and 3 × 3 km approaches to create the sampling frame and the results showed outstanding performance for quadtree approach.

Conclusion
Our approach reduces the manual burden of manually subdividing UGC into highly populated areas, while allowing for correct calculation of sampling weights. The algorithm produces a relatively homogenous population counts within the sampling units, reducing the variation in the weights and improving the precision of the resulting estimates. Furthermore, a protocol of creating approximately equal-sized blocks and using tablets for randomized selection of a household in each block mitigated potential selection bias by enumerators. The approach shows labour, time and cost-saving and points to the potential use in wider contexts.
1476-072X
Qader, Sarchil
b1afb647-aeff-4bb8-84f2-56865c4eb9e4
Lefebvre, Veronique
f7829cbd-38c1-48f8-9034-b914433ce118
Tatem, Andrew
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Pape, Utz J.
40486966-b1eb-41f6-a078-72cc2c05709c
Jochem, Warren
ef65df67-4364-4438-92e9-f93ceedb8da1
Himelein, Kristen
17e55100-eb74-49a8-962c-055471b9238f
Ninneman, Amy
395d7c8a-fbfa-4126-8a7f-1d63d8adf497
Wolburg, Philip
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Nunez-Chaim, Gonzalo
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Bengtsson, Linus
f7585eb4-9e78-422d-8178-4310985aa24e
Bird, Tomas J
b491394a-2b91-42d5-8262-d1c0e9ff17cd
Qader, Sarchil
b1afb647-aeff-4bb8-84f2-56865c4eb9e4
Lefebvre, Veronique
f7829cbd-38c1-48f8-9034-b914433ce118
Tatem, Andrew
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Pape, Utz J.
40486966-b1eb-41f6-a078-72cc2c05709c
Jochem, Warren
ef65df67-4364-4438-92e9-f93ceedb8da1
Himelein, Kristen
17e55100-eb74-49a8-962c-055471b9238f
Ninneman, Amy
395d7c8a-fbfa-4126-8a7f-1d63d8adf497
Wolburg, Philip
16b4b0d9-6b9f-4985-b6c7-b565cb9d18f7
Nunez-Chaim, Gonzalo
d3090770-0d23-47ea-ab69-40019bce310e
Bengtsson, Linus
f7585eb4-9e78-422d-8178-4310985aa24e
Bird, Tomas J
b491394a-2b91-42d5-8262-d1c0e9ff17cd

Qader, Sarchil, Lefebvre, Veronique, Tatem, Andrew, Pape, Utz J., Jochem, Warren, Himelein, Kristen, Ninneman, Amy, Wolburg, Philip, Nunez-Chaim, Gonzalo, Bengtsson, Linus and Bird, Tomas J (2020) Using gridded population and quadtree sampling units to support survey sample design in low-income settings. International Journal of Health Geographics, 19, [10]. (doi:10.1186/s12942-020-00205-5).

Record type: Article

Abstract

Background
Household surveys are the main source of demographic, health and socio-economic data in low- and middle-income countries (LMICs). To conduct such a survey, census population information mapped into enumeration areas (EAs) typically serves a sampling frame from which to generate a random sample. However, the use of census information to generate this sample frame can be problematic as in many LMIC contexts, such data are often outdated or incomplete, potentially introducing coverage issues into the sample frame. Increasingly, where census data are outdated or unavailable, modelled population datasets in the gridded form are being used to create household survey sampling frames.

Methods
Previously this process was done by either sampling from a set of the uniform grid cells (UGC) which are then manually subdivided to achieve the desired population size, or by sampling very small grid cells then aggregating cells into larger units to achieve a minimum population per survey cluster. The former approach is time and resource-intensive as well as results in substantial heterogeneity in the output sampling units, while the latter can complicate the calculation of unbiased sampling weights. Using the context of Somalia, which has not had a full census since 1987, we implemented a quadtree algorithm for the first time to create a population sampling frame. The approach uses gridded population estimates and it is based on the idea of a quadtree decomposition in which an area successively subdivided into four equal size quadrants, until the content of each quadrant is homogenous.

Results
The quadtree approach used here produced much more homogeneous sampling units than the UGC (1 × 1 km and 3 × 3 km) approach. At the national and pre-war regional scale, the standard deviation and coefficient of variation, as indications of homogeneity, were calculated for the output sampling units using quadtree and UGC 1 × 1 km and 3 × 3 km approaches to create the sampling frame and the results showed outstanding performance for quadtree approach.

Conclusion
Our approach reduces the manual burden of manually subdividing UGC into highly populated areas, while allowing for correct calculation of sampling weights. The algorithm produces a relatively homogenous population counts within the sampling units, reducing the variation in the weights and improving the precision of the resulting estimates. Furthermore, a protocol of creating approximately equal-sized blocks and using tablets for randomized selection of a household in each block mitigated potential selection bias by enumerators. The approach shows labour, time and cost-saving and points to the potential use in wider contexts.

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Accepted/In Press date: 16 March 2020
Published date: 26 March 2020

Identifiers

Local EPrints ID: 443118
URI: http://eprints.soton.ac.uk/id/eprint/443118
ISSN: 1476-072X
PURE UUID: 0f2b63d3-dc72-47ef-afb7-1cec340ae71a
ORCID for Andrew Tatem: ORCID iD orcid.org/0000-0002-7270-941X

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Date deposited: 11 Aug 2020 16:31
Last modified: 07 Oct 2020 02:01

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Contributors

Author: Sarchil Qader
Author: Veronique Lefebvre
Author: Andrew Tatem ORCID iD
Author: Utz J. Pape
Author: Warren Jochem
Author: Kristen Himelein
Author: Amy Ninneman
Author: Philip Wolburg
Author: Gonzalo Nunez-Chaim
Author: Linus Bengtsson
Author: Tomas J Bird

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