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Zeitschriftenartikel:

S. Eggimann, M. Wagner, N. Ho, M. Züger, U. Schneider, K. Orehounig:
"Geospatial simulation of urban neighbourhood densification potentials";
Sustainable Cities and Society, 72 (2021), 14 S.



Kurzfassung englisch:
Although densification of urban areas is being proposed as a sustainable urbanisation strategy, frameworks for detailed large-scale analysis of densification potentials and their evaluation are lacking. A geospatial simulation framework is presented to assess and evaluate densification potentials at the neighbourhood level of already built-up residential areas. The focus is on post-war neighbourhoods, which are particularly promising for sus-tainable densification. Neighbourhoods are localised using geospatial analysis and based on literature and architectural designs, potentials are estimated for different neighbourhood archetypes and densification strate-gies. Potentials are simulated at a national scale using supervised archetype classification. The embeddedness into current mobility infrastructure is used as a proxy for evaluating the sustainability of neighbourhood densification. The developed framework is tested for Switzerland. Depending on the densification strategy, the simulated additional inhabitants for populating post-war urban neighbourhoods range between 4-15 % of the current population. More than half of this potential is located in central areas and is well connected by public transportation. The presented approach is suitable for assessing spatially explicit densification potential and for prioritising densification locations. We show that in countries with a high number of post-war neighbourhoods in well-connected locations, considerable densification opportunities could be realised in already built-up resi-dential areas.

Schlagworte:
Urban structure unit, Redensification, Urban design, Archetype, Machine learning, Compact city


"Offizielle" elektronische Version der Publikation (entsprechend ihrem Digital Object Identifier - DOI)
http://dx.doi.org/10.1016/j.scs.2021.103068

Elektronische Version der Publikation:
https://www.sciencedirect.com/science/article/pii/S2210670721003528?via%3Dihub


Erstellt aus der Publikationsdatenbank der Technischen Universität Wien.