Image-Based Surrogates of Socio-Economic Status in Urban Neighborhoods Using Deep Multiple Instance Learning

1) Background: Evidence-based policymaking requires data about the local population's socioeconomic status (SES) at detailed geographical level, however such information is often not available, or is too expensive to acquire. Researchers have proposed solutions to estimate SES indicators by analyzing Google Street View images, however these methods are also resource-intensive, since they require large volumes of manually labeled training data. 2) Methods: We propose a methodology for automatically computing surrogate variables of SES indicators using street images of parked cars and deep multiple-instance learning. Our approach does not require any manually created labels, apart from data already available by statistical authorities, while the entire pipeline for image acquisition, parked car detection, car classification and surrogate variable computation is fully automated. The proposed surrogate variables are then used in linear regression models to estimate the target SES indicators. 3) Results: We implement and evaluate a model based on the proposed surrogate variable at 30 municipalities of varying SES in Greece. Our model has $R^2=0.76$ and correlation coefficient 0.874 with the true unemployment rate, while it achieves mean absolute percentage error 0.089 and mean absolute error 1.87 on a held-out test set. 4) Conclusions: The proposed methodology can be used to estimate socioeconomic status indicators such as unemployment rate at the local level automatically, using images of parked cars detected via Google Street View, without the need for any manual labeling effort.