Daniel Arribas-Bel, University of Liverpool, discusses his work on big data, machine learning and cities.
His recent work integrates novel data sources and machine learning to better quantify different aspects of urban environments. Two main experiments: Peskett et al. (2019), which uses street-level imagery, deep learning and hierarchical modelling to build an indicator of vegetation public exposure at the neighborhood level; and Arribas-Bel, Garcia-Lopez & Viladecans-Marsal (forthcoming), where all building footprints from the Spanish cadastre are used to delineate city boundaries in a meaningful way, accounting only for territory developed at a minimum density threshold. The two examples highlight the opportunities and challenges on the use of novel data and methods in the study of cities and urban activities.
Stubbings, P.; Peskett, J.; Rowe, F.; Arribas-Bel, D. A Hierarchical Urban Forest Index Using Street-Level Imagery and Deep Learning. Remote Sens. 2019, 11, 1395. Open Access and available at: https://www.mdpi.com/2072-4292/11/12/1395
Arribas-Bel, D.; Garcia-Lopez, M. A.; Viladencas-Marsal, E. (forthcoming). Building(s and) cities:Delineating urban areas with a machine learning algorithm. Journal of Urban Economics.