Increased-specificity famine prediction using satellite observation data
Loading...
Date
2010
Journal Title
Journal ISSN
Volume Title
Publisher
First ACM Symposium on Computing for Development
Abstract
This paper examines the use of remote sensing satellite data
to predict food shortages among di erent categories of house-
holds in famine-prone areas. Normalized Di erence Vegeta-
tion Index (NDVI) and rainfall estimate data, which can
be derived from multi-spectral satellite radiometer images,
has long been used to predict crop yields and hence famine.
This gives an overall prediction of food insecurity in an area,
though in a heterogeneous population it does not directly
predict which sectors of society or households are most at
risk.
In this work we use information on 3094 households across
Uganda collected between 2004-2005. We describe a method
for clustering households in such a way that the cluster de-
cision boundaries are both relevant for improved-speci city
famine prediction and are easily communicated. We then
give classi cation results for predicting food security status
at a household level given di erent combinations of satel-
lite data, demographic data, and household category indices
found by our clustering method. The food security classi -
cation performance of this model demonstrates the potential
of this approach for making predictions of famine for speci c
areas and demographic groups.
Description
Keywords
Citation
Quinn, J. A., Okori, W., & Gidudu, A. (2010, December). Increased-specificity famine prediction using satellite observation data. In Proceedings of the First ACM Symposium on Computing for Development (pp. 1-6).