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Socioecologically informed use of remote sensing data to predict rural household poverty

Socioecologically informed use of remote sensing data to predict rural household poverty
Socioecologically informed use of remote sensing data to predict rural household poverty

Tracking the progress of the Sustainable Development Goals (SDGs) and targeting interventions requires frequent, up-to-date data on social, economic, and ecosystem conditions. Monitoring socioeconomic targets using household survey data would require census enumeration combined with annual sample surveys on consumption and socioeconomic trends. Such surveys could cost up to $253 billion globally during the lifetime of the SDGs, almost double the global development assistance budget for 2013. We examine the role that satellite data could have in monitoring progress toward reducing poverty in rural areas by asking two questions: (i) Can household wealth be predicted from satellite data? (ii) Can a socioecologically informed multilevel treatment of the satellite data increase the ability to explain variance in household wealth? We found that satellite data explained up to 62% of the variation in household level wealth in a rural area of western Kenya when using a multilevel approach. This was a 10% increase compared with previously used single-level methods, which do not consider details of spatial landscape use. The size of buildings within a family compound (homestead), amount of bare agricultural land surrounding a homestead, amount of bare ground inside the homestead, and the length of growing season were important predictor variables. Our results show that a multilevel approach linking satellite and household data allows improved mapping of homestead characteristics, local land uses, and agricultural productivity, illustrating that satellite data can support the data revolution required for monitoring SDGs, especially those related to poverty and leaving no one behind.

population environment, Poverty, Remote sensing, SDGs, Socioecological systems
0027-8424
1213-1218
Watmough, Gary R.
6ed84a4f-0106-4665-b810-4fa71baefb8c
Marcinko, Charlotte L.J.
b245e735-368f-4b4d-9b3f-48f9f6baaf85
Sullivan, Clare
6bf421c5-743d-4657-8e8f-1a65d854819c
Tschirhart, Kevin
76a57629-8712-4b4b-a06c-76bb468f4d0c
Mutuo, Patrick K.
8552ce4d-387d-4662-a7fb-d797cfa84365
Palm, Cheryl A.
e53524a5-66e3-4fb3-a963-0aa5a129cf64
Svenning, Jens Christian
4f5dba2a-4f89-406a-a11a-416e3aae0d7b
Watmough, Gary R.
6ed84a4f-0106-4665-b810-4fa71baefb8c
Marcinko, Charlotte L.J.
b245e735-368f-4b4d-9b3f-48f9f6baaf85
Sullivan, Clare
6bf421c5-743d-4657-8e8f-1a65d854819c
Tschirhart, Kevin
76a57629-8712-4b4b-a06c-76bb468f4d0c
Mutuo, Patrick K.
8552ce4d-387d-4662-a7fb-d797cfa84365
Palm, Cheryl A.
e53524a5-66e3-4fb3-a963-0aa5a129cf64
Svenning, Jens Christian
4f5dba2a-4f89-406a-a11a-416e3aae0d7b

Watmough, Gary R., Marcinko, Charlotte L.J., Sullivan, Clare, Tschirhart, Kevin, Mutuo, Patrick K., Palm, Cheryl A. and Svenning, Jens Christian (2019) Socioecologically informed use of remote sensing data to predict rural household poverty. Proceedings of the National Academy of Sciences, 116 (4), 1213-1218. (doi:10.1073/pnas.1812969116).

Record type: Article

Abstract

Tracking the progress of the Sustainable Development Goals (SDGs) and targeting interventions requires frequent, up-to-date data on social, economic, and ecosystem conditions. Monitoring socioeconomic targets using household survey data would require census enumeration combined with annual sample surveys on consumption and socioeconomic trends. Such surveys could cost up to $253 billion globally during the lifetime of the SDGs, almost double the global development assistance budget for 2013. We examine the role that satellite data could have in monitoring progress toward reducing poverty in rural areas by asking two questions: (i) Can household wealth be predicted from satellite data? (ii) Can a socioecologically informed multilevel treatment of the satellite data increase the ability to explain variance in household wealth? We found that satellite data explained up to 62% of the variation in household level wealth in a rural area of western Kenya when using a multilevel approach. This was a 10% increase compared with previously used single-level methods, which do not consider details of spatial landscape use. The size of buildings within a family compound (homestead), amount of bare agricultural land surrounding a homestead, amount of bare ground inside the homestead, and the length of growing season were important predictor variables. Our results show that a multilevel approach linking satellite and household data allows improved mapping of homestead characteristics, local land uses, and agricultural productivity, illustrating that satellite data can support the data revolution required for monitoring SDGs, especially those related to poverty and leaving no one behind.

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e-pub ahead of print date: 7 January 2019
Published date: 22 January 2019
Keywords: population environment, Poverty, Remote sensing, SDGs, Socioecological systems

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Local EPrints ID: 427881
URI: https://eprints.soton.ac.uk/id/eprint/427881
ISSN: 0027-8424
PURE UUID: dfdd2df3-6b99-4d09-a673-cb5d5dcf2e43

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Date deposited: 01 Feb 2019 17:30
Last modified: 13 Mar 2019 17:35

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Contributors

Author: Gary R. Watmough
Author: Charlotte L.J. Marcinko
Author: Clare Sullivan
Author: Kevin Tschirhart
Author: Patrick K. Mutuo
Author: Cheryl A. Palm
Author: Jens Christian Svenning

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