Using an innovative method, researchers have used satellite imagery to estimate the level of poverty in a particular region. The study using satellite data predicted the poorest households in a Kenyan village with 62% accuracy.
They analysed factors such as the size of houses, extent of agricultural land surrounding them, and the length of growing season in Sauri, a village in rural Kenya that was part of the Millennium Villages Project.
They found that smaller houses were mostly occupied by poor people, who also tended to have more bare farm fields in September when farmers usually prepare their land for a second crop.
The satellite images also found that poorer households grew crops for shorter periods of time.
“Our results show that considering how rural populations derive livelihoods from different spaces from within the landscape and isolating household characteristic in fine-grained RS data increases the accuracy in predicting household poverty using satellite imagery,” the authors of the study wrote.
The technique can cut down the cost of monitoring socioeconomic progress, the researchers say.
Monitoring socioeconomic progress through household surveys could cost up to $253 billion globally during the lifetime of the SDGs, almost double the global development assistance budget for 2013, estimates have found.
Currently conducting a household survey costs $322.99 (USD 2014 prices) per household in Sub-Saharan Africa.
“If the World Bank cost estimates were used to collect the socioeconomic information of the 330 households originally surveyed in our study site in Sauri, the total cost would be in the region of $106,500 per year. In comparison, acquisition of high-resolution satellite imagery for the 100-km2 site ranged from $1,750 to $5,000 per year,” the researchers said.