Estimating intraurban socioeconomic status using users’interactions registered on digital data

Abstract

The thesis addresses the challenge of estimating socioeconomic status (SES) at an intraurban level using digital data sources. Traditional methods for measuring SES, such as censuses and surveys, are often limited by their infrequency and coarse spatial granularity, which hinders timely and accurate assessments, especially at the neighborhood level. The study proposes leveraging alternative digital data sources, including mobile phone top-up transactions and supermarket purchase data, to model and predict SES, providing the potential for more frequent, cost-effective, and spatially granular analysis. The research focuses on urban neighborhoods in Ecuador, aiming to develop machine learning models that can accurately predict Neighborhood SES (NSES). The research employs two machine learning models: a Regression Model using mobile phone top-up transactions and a Graph Neural Network (GNN) Model using supermarket transaction data. The first model focuses on linear relationships between variables derived from top-up transaction data and NSES. The model is designed to estimate the NSES by aggregating the average denomination and the denomination diversity at the neighborhood level. The second model leverages the complex, non-linear relationships inherent in supermarket transactions. The GNN model transforms these transactions into a graph representation, where items purchased together are linked, and the frequency and diversity of these links are analyzed to infer SES. The model is particularly suited for capturing the socioeconomic patterns that emerge from the co-purchase behaviors of individuals within a neighborhood. Both models demonstrate significant predictive power in estimating SES at the intraurban level. The Regression Model achieves a prediction accuracy of up to 74%. This model is particularly effective in identifying the relationship between average top-up denomination and neighborhood SES, with higher denominations indicating wealthier neighborhoods. The GNN Model outperforms the Regression Model, achieving a prediction accuracy of up to 91%. The GNN model is able to model the intricate patterns of co-purchases within neighborhoods, allowing for a more detailed and accurate representation of NSES. The results highlight the potential of digital data sources as viable alternatives to complement traditional SES measurement methods.