Abstract
Globally, over 800 million people are food insecure. Current methods for identifying food insecurity crises are not based on statistical models and fail to systematically incorporate readily available data on prices, weather, and demographics. As a result, policymakers cannot rapidly identify food insecure populations. These problems delay responses to mitigate hunger. We develop a replicable, near real-time model incorporating spatially and temporally granular market data, remotely-sensed rainfall and geographic data, and demographic characteristics. We train the model on 2010–2011 data from Malawi and forecast 2013 food security. Our model correctly identifies the food security status of 83 to 99% of the most food insecure village clusters in 2013, depending on the food security measure, while the prevailing approach correctly identifies between 0 and 10%. Our results show the power of modeling food insecurity to provide early warning and suggest model-driven approaches could dramatically improve food insecurity crisis response.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 399-409 |
| Number of pages | 11 |
| Journal | World Development |
| Volume | 122 |
| DOIs | |
| State | Published - Oct 2019 |
Fingerprint
Keywords
- Crisis
- Early warning
- Famine
- Food insecurity
- Prediction
- Sub-Saharan Africa
ASJC Scopus subject areas
- Geography, Planning and Development
- Development
- Sociology and Political Science
- Economics and Econometrics
Cite this
A data-driven approach improves food insecurity crisis prediction. / Lentz, E. C.; Michelson, Hope C; Baylis, Katherine R; Zhou, Y.
In: World Development, Vol. 122, 10.2019, p. 399-409.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - A data-driven approach improves food insecurity crisis prediction
AU - Lentz, E. C.
AU - Michelson, Hope C
AU - Baylis, Katherine R
AU - Zhou, Y.
PY - 2019/10
Y1 - 2019/10
N2 - Globally, over 800 million people are food insecure. Current methods for identifying food insecurity crises are not based on statistical models and fail to systematically incorporate readily available data on prices, weather, and demographics. As a result, policymakers cannot rapidly identify food insecure populations. These problems delay responses to mitigate hunger. We develop a replicable, near real-time model incorporating spatially and temporally granular market data, remotely-sensed rainfall and geographic data, and demographic characteristics. We train the model on 2010–2011 data from Malawi and forecast 2013 food security. Our model correctly identifies the food security status of 83 to 99% of the most food insecure village clusters in 2013, depending on the food security measure, while the prevailing approach correctly identifies between 0 and 10%. Our results show the power of modeling food insecurity to provide early warning and suggest model-driven approaches could dramatically improve food insecurity crisis response.
AB - Globally, over 800 million people are food insecure. Current methods for identifying food insecurity crises are not based on statistical models and fail to systematically incorporate readily available data on prices, weather, and demographics. As a result, policymakers cannot rapidly identify food insecure populations. These problems delay responses to mitigate hunger. We develop a replicable, near real-time model incorporating spatially and temporally granular market data, remotely-sensed rainfall and geographic data, and demographic characteristics. We train the model on 2010–2011 data from Malawi and forecast 2013 food security. Our model correctly identifies the food security status of 83 to 99% of the most food insecure village clusters in 2013, depending on the food security measure, while the prevailing approach correctly identifies between 0 and 10%. Our results show the power of modeling food insecurity to provide early warning and suggest model-driven approaches could dramatically improve food insecurity crisis response.
KW - Crisis
KW - Early warning
KW - Famine
KW - Food insecurity
KW - Prediction
KW - Sub-Saharan Africa
UR - http://www.scopus.com/inward/record.url?scp=85067472949&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85067472949&partnerID=8YFLogxK
U2 - 10.1016/j.worlddev.2019.06.008
DO - 10.1016/j.worlddev.2019.06.008
M3 - Article
AN - SCOPUS:85067472949
VL - 122
SP - 399
EP - 409
JO - World Development
JF - World Development
SN - 1873-5991
ER -