  Predicting socioeconomic indicators from satellite imagery with deep learning
has become an increasingly popular research direction. Post-hoc concept-based
explanations can be an important step towards broader adoption of these models
in policy-making as they enable the interpretation of socioeconomic outcomes
based on visual concepts that are intuitive to humans. In this paper, we study
the interplay between representation learning using an additional task-specific
contrastive loss and post-hoc concept explainability for socioeconomic studies.
Our results on two different geographical locations and tasks indicate that the
task-specific pretraining imposes a continuous ordering of the latent space
embeddings according to the socioeconomic outcomes. This improves the model's
interpretability as it enables the latent space of the model to associate
concepts encoding typical urban and natural area patterns with continuous
intervals of socioeconomic outcomes. Further, we illustrate how analyzing the
model's conceptual sensitivity for the intervals of socioeconomic outcomes can
shed light on new insights for urban studies.
