Measuring Consumption Changes in Rural Villages based on Satellite Image Data — A Case Study for Thailand and Vietnam

Abstract

Obtaining accurate and timely estimates of socioeconomic status at fine geographical resolutions is essential for global sustainable development and the fight against poverty. However, data related to local socio-economic dynamics in rural villages is often either unavailable or outdated. To fill this gap, predicting local economic well-being with satellite imagery and machine learning has shown promising results. While state-of-the-art analyses currently mostly focus on predicting the levels of socio-economic status, finding temporal changes in rural villages’ economic well-being is essential for tracking the impacts of public policies (targeting e.g., poverty alleviation or access to various public services). In this paper, we propose an approach that utilizes pixel-wise differences in satellite images to classify temporal changes in average and median consumption expenditures (and income) in rural villages in Thailand and Vietnam between 2007 and 2017. We can distinguish between “Decline”, “Stagnation” and “Growth” in these outcomes with an F1 score of 58.8% using a Logistic Regression model. Regression-based approaches achieve an R 2 of up to 32.5% when predicting actual changes in these outcomes. Our approach demonstrates the feasibility of satellite-based estimates for measuring changes in local socio-economic dynamics