Semi-automatic mapping of pre-census enumeration areas and population sampling frames
Semi-automatic mapping of pre-census enumeration areas and population sampling frames
Enumeration Areas (EAs) are the operational geographic units for the collection and dissemination of census data and are often used as a national sampling frame for various types of surveys. In many poor or conflict-affected countries, EA demarcations are incomplete, outdated, or missing. Even for countries that are stable and prosperous, creating and updating EAs is one of the most challenging yet essential tasks in the preparation for a national census. Commonly, EAs are created by manually digitising small geographic units on high-resolution satellite imagery or physically walking the boundaries of units, both of which are highly time, cost, and labour intensive. In addition, creating EAs requires considering population and area size within each unit. This is an optimisation problem that can best be solved by a computer. Here, for the first time, we produce a semi-automatic mapping of pre-defined census EAs based on high-resolution gridded population and settlement datasets and using publicly available natural and administrative boundaries. We demonstrate the approach in generating rural EAs for Somalia where such mapping is not existent. In addition, we compare our automated approach against manually digitised EAs created in urban areas of Mogadishu and Hargeysa. Our semi-automatically generated EAs are consistent with standard EAs, including having identifiable boundaries for field teams to follow on the ground, and appropriate sizing and population for coverage by an enumerator. Furthermore, our semi-automated urban EAs have no gaps, in contrast, to manually drawn urban EAs. Our work shows the time, labour and cost-saving value of automated EA delineation and points to the potential for broadly available tools suitable for low-income and data-poor settings but applicable to potentially wider contexts.
Qader, Sarchil
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Lefebvre, Veronique
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Tatem, Andrew
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Pape, Utz J.
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Himelein, Kristen
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Ninneman, Amy
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Bengtsson, Linus
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Bird, Tomas J.
763cc96b-c03e-422f-95cd-dc65c7491448
4 January 2021
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
Himelein, Kristen
17e55100-eb74-49a8-962c-055471b9238f
Ninneman, Amy
395d7c8a-fbfa-4126-8a7f-1d63d8adf497
Bengtsson, Linus
f7585eb4-9e78-422d-8178-4310985aa24e
Bird, Tomas J.
763cc96b-c03e-422f-95cd-dc65c7491448
Qader, Sarchil, Lefebvre, Veronique, Tatem, Andrew, Pape, Utz J., Himelein, Kristen, Ninneman, Amy, Bengtsson, Linus and Bird, Tomas J.
(2021)
Semi-automatic mapping of pre-census enumeration areas and population sampling frames.
Humanities & Social Sciences Communications, 8 (1), [3 (2021)].
(doi:10.1057/s41599-020-00670-0).
Abstract
Enumeration Areas (EAs) are the operational geographic units for the collection and dissemination of census data and are often used as a national sampling frame for various types of surveys. In many poor or conflict-affected countries, EA demarcations are incomplete, outdated, or missing. Even for countries that are stable and prosperous, creating and updating EAs is one of the most challenging yet essential tasks in the preparation for a national census. Commonly, EAs are created by manually digitising small geographic units on high-resolution satellite imagery or physically walking the boundaries of units, both of which are highly time, cost, and labour intensive. In addition, creating EAs requires considering population and area size within each unit. This is an optimisation problem that can best be solved by a computer. Here, for the first time, we produce a semi-automatic mapping of pre-defined census EAs based on high-resolution gridded population and settlement datasets and using publicly available natural and administrative boundaries. We demonstrate the approach in generating rural EAs for Somalia where such mapping is not existent. In addition, we compare our automated approach against manually digitised EAs created in urban areas of Mogadishu and Hargeysa. Our semi-automatically generated EAs are consistent with standard EAs, including having identifiable boundaries for field teams to follow on the ground, and appropriate sizing and population for coverage by an enumerator. Furthermore, our semi-automated urban EAs have no gaps, in contrast, to manually drawn urban EAs. Our work shows the time, labour and cost-saving value of automated EA delineation and points to the potential for broadly available tools suitable for low-income and data-poor settings but applicable to potentially wider contexts.
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Semi-automatic mapping
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Accepted/In Press date: 4 November 2020
Published date: 4 January 2021
Identifiers
Local EPrints ID: 446180
URI: http://eprints.soton.ac.uk/id/eprint/446180
ISSN: 2662-9992
PURE UUID: 798c8e3c-e1a8-4234-9930-0f2bf43607f2
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Date deposited: 26 Jan 2021 17:32
Last modified: 06 Jun 2024 01:50
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Contributors
Author:
Veronique Lefebvre
Author:
Utz J. Pape
Author:
Kristen Himelein
Author:
Amy Ninneman
Author:
Linus Bengtsson
Author:
Tomas J. Bird
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