Geospatial simulation of urban neighbourhood densification potentials

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

“…We integrate the natural capital value of the land in the density-scenario development, so that land that has a high value for food production, ecosystem services, or biodiversity is protected from development. The methods of analysis developed here provide a better understanding of spatial planning than traditional methods of urban densifications at regional scale (Casali et al, 2022;Eggimann et al, 2021;Tekouabou et al, 2022) and can be applied to other regions in the UK (and for other countries). Here these methods have been used to provide accurate estimates of the housing capacity for the potential land identified in Local Plans and as brownfield lands in the Arc under different density scenarios.…”

“…Bolton (2021) developed a new tool, the Space Ratio, defined as the ratio of the existing density to the permissible density (the developable density according to a specified policy or scenario), for measuring and mapping density potentials in London. 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).…”

Suburban Development: Spatial and Physical Transformation of Residence as a Determinant of Settlement Densification in Makassar City, Indonesia

“…The process of transformation and densification has the potential to cause impacts, so it is necessary to consider and balance the spatial and physical transformation of residence. Eggimann's research [21] large-scale analysis of densification potentials and their evaluation are lacking.…”

“…Additionally, change in building characteristics contributed to the spatial transformation. In the case of the extensive settlement of Bumi Tamalanrea Permai, modifications were made to all residential types, including 18,21,30,32,36,45, 54, and 70 with various land parcels, either by building owners or tenants. Type 70/187 has the propensity to alter by binary fission or house changes from one unit to two house units on the same plot.…”

A new approach for national-scale Building Energy Models based on Energy Performance Certificates in European countries: The case of Spain

“…The AutoBEM model is automatically updated as new data is available, requiring for this the use of a supercomputer to update the model. The CESAR-P model uses geospatial data, allowing to generate multiple analyses that are not possible on tabular data [ 15 ], however it also requires a supercomputer for the simulations. Additionally, since these national models require the generation of archetypes, only the most common cases of every cluster are considered and for the rest of cases the same behaviour is assumed.…”