Estimating the Multidimensional Poverty Index in Bogotá (Colombia) Using Satellite Imagery

This paper proposes an alternative approach to estimating poverty levels using satellite imagery from Bogotá, Colombia, and surrounding municipalities. Two modeling strategies are compared: Convolutional Neural Networks (CNNs) and a newly proposed extension of the Generalized Tensor Regression model based on spatially-weighted aggregations, denoted SWGTR. The models are evaluated using multiple performance metrics, including Average Accuracy (AA), Overall Accuracy (OA), Area Between Curves (ABC), Kappa Index (KI), Mean Absolute Error (MAE), and Cumulative Sums (CS), to determine their effectiveness in capturing the spatial distribution of the Multidimensional Poverty Index (MPI) at the pixel level. The application leverages PlanetScope (PS) satellite images alongside block-level data from the 2018 National Population and Housing Census (CNPV2018). The SWGTR approach demonstrates competitive performance across several metrics, particularly those accounting for the ordinal structure of poverty levels. In addition, we illustrate how the probabilistic outputs of SWGTR can be used to construct pixel-level uncertainty measures, which may inform data collection strategies by identifying regions where predictions are less certain. Overall, the findings suggest that combining spatial modeling with remote sensing data provides a promising avenue to complement traditional survey-based methods, while highlighting important limitations and directions for future research.