The University of Southampton
University of Southampton Institutional Repository

Towards achieving the UNs data revolution: combining earth observation and socioeconomic data for geographic targeting of resources for the sustainable development goals

Towards achieving the UNs data revolution: combining earth observation and socioeconomic data for geographic targeting of resources for the sustainable development goals
Towards achieving the UNs data revolution: combining earth observation and socioeconomic data for geographic targeting of resources for the sustainable development goals
The UN has called for a ‘data revolution’ to help overcome the low quality and lack of regularly updated statistical data available in developing countries. But how do we achieve this with limited financial resources and insufficient capacity in national statistical offices around the world? Recent studies have demonstrated how information captured by satellite imagery can be combined with social datasets to increase our understanding of socioeconomic systems. Thus, in the future, satellite data may offer a cost-effective way to reliably measure and monitor progress towards development goals. We examine how satellite data can be linked with household and census datasets to provide information on socioeconomic conditions. We suggest that the Sustainable Livelihoods Approach provides an appropriate framework for which to develop remotely sensed earth observation (EO) data proxies for key socioeconomic conditions because it will allow the linking of data in a way that reflects more the way in which populations interact with landscapes. The aim of using EO data for mapping and predicting socioeconomic conditions is not to replace survey data but to provide more frequent information on likely socioeconomic conditions between census and survey enumeration. Timely recalibration of models predicting poverty from EO data would be necessary to reflect often rapid social, economic and political changes. However, if we are to acheive the SDGs more frequent data at finer spatial scales will be required and EO data provides a cos effective solution.
229-254
Elsevier
Watmough, Gary R.
35e3ef1c-950a-4f43-95a1-035ee97ed778
Marcinko, Charlotte
1fbc10e0-5c44-4cac-8a70-862ba0e47a66
Dunn, Jennifer
Balaprakash, Prasanna
Watmough, Gary R.
35e3ef1c-950a-4f43-95a1-035ee97ed778
Marcinko, Charlotte
1fbc10e0-5c44-4cac-8a70-862ba0e47a66
Dunn, Jennifer
Balaprakash, Prasanna

Watmough, Gary R. and Marcinko, Charlotte (2021) Towards achieving the UNs data revolution: combining earth observation and socioeconomic data for geographic targeting of resources for the sustainable development goals. In, Dunn, Jennifer and Balaprakash, Prasanna (eds.) Data Science Applied to Sustainability Analysis. Elsevier, pp. 229-254. (doi:10.1016/B978-0-12-817976-5.00012-7).

Record type: Book Section

Abstract

The UN has called for a ‘data revolution’ to help overcome the low quality and lack of regularly updated statistical data available in developing countries. But how do we achieve this with limited financial resources and insufficient capacity in national statistical offices around the world? Recent studies have demonstrated how information captured by satellite imagery can be combined with social datasets to increase our understanding of socioeconomic systems. Thus, in the future, satellite data may offer a cost-effective way to reliably measure and monitor progress towards development goals. We examine how satellite data can be linked with household and census datasets to provide information on socioeconomic conditions. We suggest that the Sustainable Livelihoods Approach provides an appropriate framework for which to develop remotely sensed earth observation (EO) data proxies for key socioeconomic conditions because it will allow the linking of data in a way that reflects more the way in which populations interact with landscapes. The aim of using EO data for mapping and predicting socioeconomic conditions is not to replace survey data but to provide more frequent information on likely socioeconomic conditions between census and survey enumeration. Timely recalibration of models predicting poverty from EO data would be necessary to reflect often rapid social, economic and political changes. However, if we are to acheive the SDGs more frequent data at finer spatial scales will be required and EO data provides a cos effective solution.

This record has no associated files available for download.

More information

Published date: January 2021

Identifiers

Local EPrints ID: 451006
URI: http://eprints.soton.ac.uk/id/eprint/451006
PURE UUID: 2da05ad4-bb58-417e-b87a-505e60a5c157
ORCID for Charlotte Marcinko: ORCID iD orcid.org/0000-0002-5369-3950

Catalogue record

Date deposited: 01 Sep 2021 16:32
Last modified: 16 Mar 2024 13:43

Export record

Altmetrics

Contributors

Author: Gary R. Watmough
Editor: Jennifer Dunn
Editor: Prasanna Balaprakash

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×