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; Svenning, Jens Christian

doi:10.1073/pnas.1812969116

Cited by 121 publications

(75 citation statements)

References 33 publications

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“…This is changing, albeit slowly. For example, a recent paper [47], using the example of a set of 330 rural households in western Kenya, suggests that savings in the order of $US 100,000 over a 15-year monitoring period can be achieved by using EO data for some socioecological conditions for the SDGs compared with conventional household survey costs (based on World Bank estimates). Nonetheless, this lack of economic assessment on the use of EO data to populate indicators is clearly revealed by the MMF 2.0 framework and remains a major gap in knowledge that should be addressed with urgency.…”

Section: Discussionmentioning

confidence: 99%

Seeing Sustainability from Space: Using Earth Observation Data to Populate the UN Sustainable Development Goal Indicators

et al. 2019

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In 2015, member countries of the United Nations adopted the 17 Sustainable Development Goals (SDGs) at the Sustainable Development Summit in New York. These global goals have 169 targets and 232 indicators based on the three pillars of sustainable development: economic, social, and environmental. However, substantial challenges remain in obtaining data of the required quality and quantity to populate these indicators efficiently. One promising and innovative way of addressing this issue is to use Earth observation (EO). The research reported here updates our original work to develop a Maturity Matrix Framework (MMF) for assessing the suitability of EO-derived data for populating the SDG indicators, with a special focus on those indicators covering the more social and economic dimensions of sustainable development, as these have been under-explored in terms of the contribution that can be made by EO. The advanced MMF 2.0 framework set out in this paper is based on a wide consultation with EO and indicator experts (semi-structured interviews with 38 respondents). This paper provides detail of the evolved structure of MMF 2.0 and illustrates its use for one of the SDG indicators (Indicator 11.1.1). The revised MMF is then applied to published work covering the full suite of SDG indicators and demonstrates that EO can make an important contribution to providing data relevant to a substantial number of the SDG indicators.

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Section: Discussionmentioning

confidence: 99%

Seeing Sustainability from Space: Using Earth Observation Data to Populate the UN Sustainable Development Goal Indicators

et al. 2019

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“…Monitoring annual progress toward the United Nations' sustainable development goals (SDGs) using household surveys is prohibitively expensive. Watmough et al [80] explored whether remotely sensed (RS) satellite data could be used to monitor rural poverty in low-income and middle-income countries. The authors analyzed RS land use and land cover data for a cluster of rural villages in Kenya at multiple spatial levels, from individual homesteads to the wider village periphery.…”

Section: Use Of Satellite Imaging Data To Predict Energy Povertymentioning

confidence: 99%

Big Data and Energy Poverty Alleviation

Hassani

Yeganegi

Beneki

et al. 2019

BDCC

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The focus of this paper is to bring to light the vital issue of energy poverty alleviation and how big data could improve the data collection quality and mechanism. It also explains the vicious circle of low productivity, health risk, environmental pollution and energy poverty and presents currently used energy poverty measures and alleviation policies and stresses the associated problems in application due to the underlying dynamics.

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“…and make comparisons between these groups. Asset indices are also used to check the accuracy of new proxies of poverty, such as mobile phone records (Blumenstock, Cadamuro, and On 2015) or remotely sensed images (Jean et al 2016;Watmough et al 2019). Thus one proxy of wealth (social media data, built infrastructure visible from space) are used to verify another (asset indices).…”

Section: Defining Wealth and Povertymentioning

confidence: 99%

The multiple meanings of prosperity and poverty: a cross-site comparison from Tanzania

Howland

Noe

Brockington

2019

The Journal of Peasant Studies

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Assets are important to local definitions of poverty and wealth in rural Africa. Yet their use in asset indices can miss locally valued change. We present data from 17 villages across Tanzania to explore differences in the meaning of wealth and poverty across the country. Despite limitations in our site selection we found considerable diversity that makes a single asset index difficult to compile. Current abbreviated asset indices risk counting assets that do not matter locally.

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

Cited by 121 publications

References 33 publications

Seeing Sustainability from Space: Using Earth Observation Data to Populate the UN Sustainable Development Goal Indicators

Seeing Sustainability from Space: Using Earth Observation Data to Populate the UN Sustainable Development Goal Indicators

Big Data and Energy Poverty Alleviation

The multiple meanings of prosperity and poverty: a cross-site comparison from Tanzania

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