Developing New Computational Methodologies for Data Integrated Design for Landscape Architecture

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Researchers

Research units

  • Swiss Federal Institute of Technology Zurich

Abstract

The form language of the currently ongoing trend of parametric design remains often symbolic and arbitrarily interchangeable. In order to counteract this general trend within landscape architecture, the increasing digitalization in design should not contribute to generating even greater meaningless complexity. The main goal within the postgraduate study program Master of Advanced Studies in Landscape Architecture (MAS LA) at the Chair of Prof. Chrsitophe Girot at ETH Zurich, is to examine which workflows are suitable for understanding a place with its given potentials as local data sets to generate a responsive and sustainable landscape design. Often data is integrated at the outset of the design process – in contrast, we would like to propose the thesis that an understanding of a site and the conceptual stance drawn from it influences the choice of data and not the contrary. It is therefore necessary to search for new methodic approaches and workflows in order to understand a place with its different contextual layers and integrate the right data as parameters in the process.

Details

Original languageEnglish
Title of host publicationCAADence in Architecture
Subtitle of host publicationBack to Command
EditorsMihály Szoboszlai
Publication statusPublished - 2016
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Computer Aided Architectural Design - Budapest University of Technology and Economics, Budapest, Hungary
Duration: 16 Jun 201617 Jun 2016
http://www.caadence.bme.hu/

Conference

ConferenceInternational Conference on Computer Aided Architectural Design
CountryHungary
CityBudapest
Period16/06/201617/06/2016
Internet address

    Research areas

  • algorithmic Design, BIM, parametric modeling

ID: 10407344