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Exploring the high-resolution mapping of gender-disaggregated development indicators

Exploring the high-resolution mapping of gender-disaggregated development indicators
Exploring the high-resolution mapping of gender-disaggregated development indicators

Improved understanding of geographical variation and inequity in health status, wealth and access to resources within countries is increasingly being recognized as central to meeting development goals. Development and health indicators assessed at national or subnational scale can often conceal important inequities, with the rural poor often least well represented. The ability to target limited resources is fundamental, especially in an international context where funding for health and development comes under pressure. This has recently prompted the exploration of the potential of spatial interpolation methods based on geolocated clusters from national household survey data for the high-resolution mapping of features such as population age structures, vaccination coverage and access to sanitation. It remains unclear, however, how predictable these different factors are across different settings, variables and between demographic groups. Here we test the accuracy of spatial interpolation methods in producing gender-disaggregated high-resolution maps of the rates of literacy, stunting and the use of modern contraceptive methods from a combination of geolocated demographic and health surveys cluster data and geospatial covariates. Bayesian geostatistical and machine learning modelling methods were tested across four low-income countries and varying gridded environmental and socio-economic covariate datasets to build 1×1 km spatial resolution maps with uncertainty estimates. Results show the potential of the approach in producing high-resolution maps of key gender-disaggregated socio-economic indicators, with explained variance through cross-validation being as high as 74-75% for female literacy in Nigeria and Kenya, and in the 50-70% range for many other variables. However, substantial variations by both country and variable were seen, with many variables showing poor mapping accuracies in the range of 2-30% explained variance using both geostatistical and machine learning approaches. The analyses offer a robust basis for the construction of timely maps with levels of detail that support geographically stratified decision-making and the monitoring of progress towards development goals. However, the great variability in results between countries and variables highlights the challenges in applying these interpolation methods universally across multiple countries, and the importance of validation and quantifying uncertainty if this is undertaken.

Journal Article
Bosco, C.
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Alegana, Victor
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Bird, T.
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Pezzulo, C.
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Bengtsson, L
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Sorichetta, A.
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Steele, J.
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Hornby, G.
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Ruktanonchai, C.
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Ruktanonchai, N
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Wetter, E.
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Tatem, A.J.
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Bosco, C.
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Alegana, Victor
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Bird, T.
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Pezzulo, C.
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Bengtsson, L
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Sorichetta, A.
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Steele, J.
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Hornby, G.
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Ruktanonchai, C.
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Ruktanonchai, N
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Wetter, E.
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Tatem, A.J.
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Bosco, C., Alegana, Victor, Bird, T., Pezzulo, C., Bengtsson, L, Sorichetta, A., Steele, J., Hornby, G., Ruktanonchai, C., Ruktanonchai, N, Wetter, E. and Tatem, A.J. (2017) Exploring the high-resolution mapping of gender-disaggregated development indicators. Journal of the Royal Society Interface, 14 (129). (doi:10.1098/rsif.2016.0825).

Record type: Article

Abstract

Improved understanding of geographical variation and inequity in health status, wealth and access to resources within countries is increasingly being recognized as central to meeting development goals. Development and health indicators assessed at national or subnational scale can often conceal important inequities, with the rural poor often least well represented. The ability to target limited resources is fundamental, especially in an international context where funding for health and development comes under pressure. This has recently prompted the exploration of the potential of spatial interpolation methods based on geolocated clusters from national household survey data for the high-resolution mapping of features such as population age structures, vaccination coverage and access to sanitation. It remains unclear, however, how predictable these different factors are across different settings, variables and between demographic groups. Here we test the accuracy of spatial interpolation methods in producing gender-disaggregated high-resolution maps of the rates of literacy, stunting and the use of modern contraceptive methods from a combination of geolocated demographic and health surveys cluster data and geospatial covariates. Bayesian geostatistical and machine learning modelling methods were tested across four low-income countries and varying gridded environmental and socio-economic covariate datasets to build 1×1 km spatial resolution maps with uncertainty estimates. Results show the potential of the approach in producing high-resolution maps of key gender-disaggregated socio-economic indicators, with explained variance through cross-validation being as high as 74-75% for female literacy in Nigeria and Kenya, and in the 50-70% range for many other variables. However, substantial variations by both country and variable were seen, with many variables showing poor mapping accuracies in the range of 2-30% explained variance using both geostatistical and machine learning approaches. The analyses offer a robust basis for the construction of timely maps with levels of detail that support geographically stratified decision-making and the monitoring of progress towards development goals. However, the great variability in results between countries and variables highlights the challenges in applying these interpolation methods universally across multiple countries, and the importance of validation and quantifying uncertainty if this is undertaken.

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Accepted/In Press date: 13 March 2017
e-pub ahead of print date: 5 April 2017
Additional Information: © 2017 The Authors.
Keywords: Journal Article

Identifiers

Local EPrints ID: 412111
URI: https://eprints.soton.ac.uk/id/eprint/412111
PURE UUID: eb3d245a-a1df-4065-bfe1-e08d9235b668
ORCID for Victor Alegana: ORCID iD orcid.org/0000-0001-5177-9227
ORCID for A. Sorichetta: ORCID iD orcid.org/0000-0002-3576-5826
ORCID for G. Hornby: ORCID iD orcid.org/0000-0002-2833-8711
ORCID for A.J. Tatem: ORCID iD orcid.org/0000-0002-7270-941X

Catalogue record

Date deposited: 11 Jul 2017 09:44
Last modified: 15 Aug 2019 04:56

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Contributors

Author: C. Bosco
Author: Victor Alegana ORCID iD
Author: T. Bird
Author: C. Pezzulo
Author: L Bengtsson
Author: A. Sorichetta ORCID iD
Author: J. Steele
Author: G. Hornby ORCID iD
Author: C. Ruktanonchai
Author: N Ruktanonchai
Author: E. Wetter
Author: A.J. Tatem ORCID iD

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