A parametric model of residential built form for forecasting the viability of sustainable technologies

A machine learning methodology to quantify the potential of urban densification in the Oxford-Cambridge Arc, United Kingdom

“…In a large-scale bottom-up approach, Eggimann et al (2021) developed a geospatial simulation framework to assess densification potentials at a neighbourhood scale and then applied this to simulate densification potentials at a national scale for Switzerland using supervised archetype classifications. By contrast, Hargreaves (2015) provided a top-down regional modelling framework to forecast the number of dwellings and their average residential density based on a spatial interaction model, which was later calibrated and validated at a district scale using detailed GIS data (Hargreaves, 2021).…”