An evaluation framework for predictive models of neighbourhood change with applications to predicting residential sales in Buffalo, NY

Abstract

New data and technologies, in particular machine learning, may make it possible to forecast neighbourhood change. Doing so may help, for example, to prevent the negative impacts of gentrification on marginalised communities. However, predictive models of neighbourhood change face four challenges: accuracy (are they right?), granularity (are they right at spatial or temporal scales that actually matter for a policy response?), bias (are they equitable?) and expert validity (do models and their predictions make sense to domain experts?). The present work provides a framework to evaluate the performance of predictive models of neighbourhood change along these four dimensions. We illustrate the application of our evaluation framework via a case study of Buffalo, NY, where we consider the following prediction task: given historical data, can we predict the percentage of residential buildings that will be sold or foreclosed on in a given area over a fixed amount of time into the future?

Método de Identificación Mixta de Vulnerabilidad Urbana (IMVU). Fusión de enfoques a escala local y regional

“…De otra parte, debe tenerse en cuenta el carácter dinámico de la vulnerabilidad, que hace que los afectados puedan oscilar entre diferentes situaciones. Esto obliga a que frente al hecho de que muchos de los estudios sobre un tema dinámico sean estáticos, al considerar únicamente datos de un momento acotado, algunos autores estén desarrollando modelos predictivos que consideran dicha movilidad (Barton et al, 2017;Vergara et al, 2023).…”