ADvISE

Data analysis for understanding the impact of urban design on social performance of a city (ADvISE)

Urban planning decisions greatly affect the life in cities in many perspectives: the transportation network and the appearance are straightforward examples, but implications of the planning go deeper into citizens’ experience. The outcomes of certain design decisions are not well-studied due to the complexity of the problem. Therefore, when working on urban planning projects it is common to decompose the problem into multiple aspects that their influence is known to some extent. Planners typically draw on past experience when subjectively prioritizing which aspects to consider with which degree of importance for their planning concepts. This practice, although understandable, places power and authority in the hands of people who have varying degrees of expertise, which can mean that the best possible solution is not found, because it is either not sought or the problem is regarded as being too complex for human capabilities. To improve this situation, the project presented here intends to aggregate the designers’ past experience using data analysis techniques and optimization algorithms to develop a planning support system that can help to find the best compromises for urban design problems. We aim to produce an open framework with basic functionality to efficiently search for compromise solutions for complex planning problems and an experimental software prototype with an intuitive user interface for representing planning problems and presenting optimal solutions at various stages of the design process. The potential of the framework will be assessed by means of real cases. The approach we propose contributes to making urban planning more evidence‐based.

Software prototype under development: http://www.ia.arch.ethz.ch/modeler.html

Contact: 
Artem Chirkin | chirkin@arch.ethz.ch | +41 (0)44 633 79 62
Dr. Reinhard König | reinhard.koenig@arch.ethz.ch | +41 (0)44 633 72 10

The project is funded for 2016 – 2018

Funded by:

SNF  &  http://www.rfh.ru/

Project partners:

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