| id |
caadria2018_245 |
| authors |
Chowdhury, Shuva and Schnabel, Marc Aurel |
| year |
2018 |
| title |
An Algorithmic Methodology to Predict Urban Form - An Instrument for Urban Design |
| source |
T. Fukuda, W. Huang, P. Janssen, K. Crolla, S. Alhadidi (eds.), Learning, Adapting and Prototyping - Proceedings of the 23rd CAADRIA Conference - Volume 2, Tsinghua University, Beijing, China, 17-19 May 2018, pp. 401-410 |
| doi |
https://doi.org/10.52842/conf.caadria.2018.2.401
|
| summary |
We question the recent practices of conventional and participatory urban design approaches and offer a middle approach by exploring computational design tools in the design system. On the one hand, the top-down urban planning approaches investigate urban form as a holistic matter which only can be calibrated by urban professionals. These approaches are not able to offer enough information to the end users to predict the urban form. On the other hand, the bottom-up urban design approaches cannot visualise predicted urban scenarios, and most often the design decisions stay as general assumptions. We developed and tested a parametric design platform combines both approaches where all the stakeholders can participate and visualise multiple urban scenarios in real-time feedback. Parametric design along with CIM modelling system has influenced urban designers for a new endeavour in urban design. This paper presents a methodology to generate and visualise urban form. We present a novel decision-making platform that combines city level and local neighbourhood data to aid participatory urban design decisions. The platform allows for stakeholder collaboration and engagement in complex urban design processes. |
| keywords |
knowledge-based system; algorithmic methodology ; design decision tool; urban form; |
| series |
CAADRIA |
| email |
|
| full text |
 file.pdf (1,686,105 bytes) |
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Content-type: text/plain
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| last changed |
2022/06/07 07:56 |
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