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Exploring nationally and regionally defined models for large area population mapping

Exploring nationally and regionally defined models for large area population mapping
Exploring nationally and regionally defined models for large area population mapping
Interactions between humans, diseases and the environment take place across a range of temporal and spatial scales, making accurate, contemporary data on human population distributions critical for a variety of disciplines. Methods for disaggregating census data to finer-scale, gridded population density estimates continue to be refined as computational power increases and more detailed census, input, and validation data sets become available. However, the availability of spatially detailed census data still varies widely by country. In this study, we develop quantitative guidelines for choosing regionally parameterized census count disaggregation models over country-specific models. We examine underlying methodological considerations for improving gridded population data sets for countries with coarser scale census data by investigating regional versus country-specific models used to estimate density surfaces for redistributing census counts. Consideration is given to the spatial resolution of input census data using examples from East Africa and Southeast Asia. Results suggest that for many countries more accurate population maps can be produced by using regionally parameterized models where more spatially refined data exists than that which is available for the focal country. This study highlights the advancement of statistical toolsets and considerations for underlying data used in generating widely used gridded population data.
1753-8947
1-25
Gaughan, A.E
2d662a72-b844-4875-9c52-e687b609537a
Stevens, F.R
1fe9084b-17b3-4cc3-af43-d7081c65843f
Linard, C
1f95d23c-893a-462c-9175-659aacf105c8
Patel, N.
1cd9d48d-dc57-445a-bb7d-e70edd515932
Tatem, A.J
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Gaughan, A.E
2d662a72-b844-4875-9c52-e687b609537a
Stevens, F.R
1fe9084b-17b3-4cc3-af43-d7081c65843f
Linard, C
1f95d23c-893a-462c-9175-659aacf105c8
Patel, N.
1cd9d48d-dc57-445a-bb7d-e70edd515932
Tatem, A.J
6c6de104-a5f9-46e0-bb93-a1a7c980513e

Gaughan, A.E, Stevens, F.R, Linard, C, Patel, N. and Tatem, A.J (2014) Exploring nationally and regionally defined models for large area population mapping. International Journal of Digital Earth, 1-25. (doi:10.1080/17538947.2014.965761).

Record type: Article

Abstract

Interactions between humans, diseases and the environment take place across a range of temporal and spatial scales, making accurate, contemporary data on human population distributions critical for a variety of disciplines. Methods for disaggregating census data to finer-scale, gridded population density estimates continue to be refined as computational power increases and more detailed census, input, and validation data sets become available. However, the availability of spatially detailed census data still varies widely by country. In this study, we develop quantitative guidelines for choosing regionally parameterized census count disaggregation models over country-specific models. We examine underlying methodological considerations for improving gridded population data sets for countries with coarser scale census data by investigating regional versus country-specific models used to estimate density surfaces for redistributing census counts. Consideration is given to the spatial resolution of input census data using examples from East Africa and Southeast Asia. Results suggest that for many countries more accurate population maps can be produced by using regionally parameterized models where more spatially refined data exists than that which is available for the focal country. This study highlights the advancement of statistical toolsets and considerations for underlying data used in generating widely used gridded population data.

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More information

Published date: 16 September 2014
Organisations: Global Env Change & Earth Observation, WorldPop, Geography & Environment, Population, Health & Wellbeing (PHeW)

Identifiers

Local EPrints ID: 369221
URI: http://eprints.soton.ac.uk/id/eprint/369221
ISSN: 1753-8947
PURE UUID: 90490edf-d3bd-4e97-a612-35cfaa082e9a
ORCID for A.J Tatem: ORCID iD orcid.org/0000-0002-7270-941X

Catalogue record

Date deposited: 30 Sep 2014 11:06
Last modified: 15 Mar 2024 03:43

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Contributors

Author: A.E Gaughan
Author: F.R Stevens
Author: C Linard
Author: N. Patel
Author: A.J Tatem ORCID iD

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