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

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Standard

Socioecologically informed use of remote sensing data to predict rural household poverty. / Watmough, Gary R.; Marcinko, Charlotte L. J.; Sullivan, Clare; Tschirhart, Kevin; Mutuo, Patrick K.; Palm, Cheryl A.; Svenning, Jens-Christian.

I: Proceedings of the National Academy of Sciences of the United States of America, Bind 116, Nr. 4, 22.01.2019, s. 1213-1218.

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisTidsskriftartikelForskningpeer review

Harvard

Watmough, GR, Marcinko, CLJ, Sullivan, C, Tschirhart, K, Mutuo, PK, Palm, CA & Svenning, J-C 2019, 'Socioecologically informed use of remote sensing data to predict rural household poverty', Proceedings of the National Academy of Sciences of the United States of America, bind 116, nr. 4, s. 1213-1218. https://doi.org/10.1073/pnas.1812969116

APA

Watmough, G. R., Marcinko, C. L. J., Sullivan, C., Tschirhart, K., Mutuo, P. K., Palm, C. A., & Svenning, J-C. (2019). Socioecologically informed use of remote sensing data to predict rural household poverty. Proceedings of the National Academy of Sciences of the United States of America, 116(4), 1213-1218. https://doi.org/10.1073/pnas.1812969116

CBE

Watmough GR, Marcinko CLJ, Sullivan C, Tschirhart K, Mutuo PK, Palm CA, Svenning J-C. 2019. Socioecologically informed use of remote sensing data to predict rural household poverty. Proceedings of the National Academy of Sciences of the United States of America. 116(4):1213-1218. https://doi.org/10.1073/pnas.1812969116

MLA

Watmough, Gary R. o.a.. "Socioecologically informed use of remote sensing data to predict rural household poverty". Proceedings of the National Academy of Sciences of the United States of America. 2019, 116(4). 1213-1218. https://doi.org/10.1073/pnas.1812969116

Vancouver

Watmough GR, Marcinko CLJ, Sullivan C, Tschirhart K, Mutuo PK, Palm CA o.a. Socioecologically informed use of remote sensing data to predict rural household poverty. Proceedings of the National Academy of Sciences of the United States of America. 2019 jan 22;116(4):1213-1218. https://doi.org/10.1073/pnas.1812969116

Author

Watmough, Gary R. ; Marcinko, Charlotte L. J. ; Sullivan, Clare ; Tschirhart, Kevin ; Mutuo, Patrick K. ; Palm, Cheryl A. ; Svenning, Jens-Christian. / Socioecologically informed use of remote sensing data to predict rural household poverty. I: Proceedings of the National Academy of Sciences of the United States of America. 2019 ; Bind 116, Nr. 4. s. 1213-1218.

Bibtex

@article{f0a970c2460943189870867cc0d9e04f,
title = "Socioecologically informed use of remote sensing data to predict rural household poverty",
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.",
keywords = "SDGs, remote sensing, poverty, socioecological systems, population environment, SATELLITE, DETERMINANTS, LIVELIHOODS, FRAMEWORK, DYNAMICS, ASSAM, GAP",
author = "Watmough, {Gary R.} and Marcinko, {Charlotte L. J.} and Clare Sullivan and Kevin Tschirhart and Mutuo, {Patrick K.} and Palm, {Cheryl A.} and Jens-Christian Svenning",
year = "2019",
month = "1",
day = "22",
doi = "10.1073/pnas.1812969116",
language = "English",
volume = "116",
pages = "1213--1218",
journal = "Proceedings of the National Academy of Sciences of the United States of America",
issn = "0027-8424",
publisher = "The National Academy of Sciences of the United States of America",
number = "4",

}

RIS

TY - JOUR

T1 - Socioecologically informed use of remote sensing data to predict rural household poverty

AU - Watmough, Gary R.

AU - Marcinko, Charlotte L. J.

AU - Sullivan, Clare

AU - Tschirhart, Kevin

AU - Mutuo, Patrick K.

AU - Palm, Cheryl A.

AU - Svenning, Jens-Christian

PY - 2019/1/22

Y1 - 2019/1/22

N2 - 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.

AB - 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.

KW - SDGs

KW - remote sensing

KW - poverty

KW - socioecological systems

KW - population environment

KW - SATELLITE

KW - DETERMINANTS

KW - LIVELIHOODS

KW - FRAMEWORK

KW - DYNAMICS

KW - ASSAM

KW - GAP

U2 - 10.1073/pnas.1812969116

DO - 10.1073/pnas.1812969116

M3 - Journal article

C2 - 30617073

VL - 116

SP - 1213

EP - 1218

JO - Proceedings of the National Academy of Sciences of the United States of America

JF - Proceedings of the National Academy of Sciences of the United States of America

SN - 0027-8424

IS - 4

ER -