Learning Economic Indicators by Aggregating Multi-Level Geospatial Information

TL;DR

This paper introduces a deep learning model that aggregates multi-level geospatial information from satellite imagery to predict economic indicators, outperforming baselines and generalizing across countries.

Contribution

A novel multi-level learning framework that combines hyperlocal and district-level features from satellite imagery for economic prediction.

Findings

Outperforms strong baselines in predicting population, purchasing power, and energy consumption.

Model generalizes well across countries, including Malaysia, Philippines, Thailand, and Vietnam.

Robust to data shortages, maintaining accuracy across different datasets.

Abstract

High-resolution daytime satellite imagery has become a promising source to study economic activities. These images display detailed terrain over large areas and allow zooming into smaller neighborhoods. Existing methods, however, have utilized images only in a single-level geographical unit. This research presents a deep learning model to predict economic indicators via aggregating traits observed from multiple levels of geographical units. The model first measures hyperlocal economy over small communities via ordinal regression. The next step extracts district-level features by summarizing interconnection among hyperlocal economies. In the final step, the model estimates economic indicators of districts via aggregating the hyperlocal and district information. Our new multi-level learning model substantially outperforms strong baselines in predicting key indicators such as population, purchasing power, and energy consumption. The model is also robust against data shortage; the trained features from one country can generalize to other countries when evaluated with data gathered from Malaysia, the Philippines, Thailand, and Vietnam. We discuss the multi-level model's implications for measuring inequality, which is the essential first step in policy and social science research on inequality and poverty.