GVC/City REDI Seminar Series: Dani Arribas-Bel

Title: Remote Sensing-Based Measurement of Living Environment Deprivation - Improving Classical Approaches with Machine Learning

Speaker: Dani Arribas-Bel (University of Liverpool)

Abstract

This paper provides evidence on the usefulness of very high spatial resolution (VHR) imagery in gathering socioeconomic information in urban settlements. We use land cover, spectral, structure and texture features extracted from a Google Earth image of Liverpool (UK) to evaluate their potential to predict Living Environment Deprivation at a small statistical area level. We also contribute to the methodological literature on the estimation of socioeconomic indices with remote-sensing data by introducing elements from modern machine learning. In addition to classical approaches such as Ordinary Least Squares (OLS) regression and a spatial lag model, we explore the potential of the Gradient Boost Regressor and Random Forests to improve predictive performance and accuracy. In addition to novel predicting methods, we also introduce tools for model interpretation and evaluation such as feature importance and partial dependence plots, or cross-validation. Our results show that Random Forest proved to be the best model with an R2 of around 0.54, followed by Gradient Boost Regressor with 0.5. Both the spatial lag model and the OLS fall behind with significantly lower performances of 0.43 and 0.3, respectively.

About the speaker

Dani Arribas-Bel isa Lecturer in Geographic Data Science at the Department of Geography and Planning , and member of the Geographic Data Science Lab, at the University of Liverpool (UK), where he directs the MSc in Geographic Data Science. He is also part of the development team of the open source library PySAL for spatial analysis in Python. Dani’s main research interests are Urban Economics and Regional Science, Spatial Analysis and Spatial Econometrics, and Open source scientific computing.