Big Data Informed Urban Design

Transcription

1 Big Data Informed Urban Design Keynote at the Global Forum on Urban and Regional Resilience Dr. Reinhard König Chair of Information Architecture, ETH Zurich

2 Motivation Urban planning is traditionally a heuristics problem solving process. We support the planning process with new big data acquisition and analysis techniques with the aim to establish a more evidence informed planning process.

3 Resilience of urban structures Can an evidence informed planning process based on advanced simulation and design synthesis methods lead to more resilient urban structures?

4 ETH Zurich & FCL Information Architecture Research group of 20 people Chen Zong Gideon Aschwanden Eva Friedrich Dr. Bernhard Klein Dr. Matthias Berger We develop computational and visual methods for the analysis, design and simulation of urban systems for sustainable cities.

5 Future Cities Laboratory (FCL) Scales, Stocks and Flows SMALL BUILDING TECHNOLOGY LOW EXERGY DIGITAL FABRICATION MEDIUM URBAN DESIGN A/P ARCHITECTURE & CONSTRUCTION TRANSFORMING & MINING URBAN STOCKS HOUSING URBAN DESIGN STRATEGIES & RESOURCES URBAN SOCIOLOGY A/P ARCHITECTURE & URBAN PLANNING SIMULATION PLATFORM LARGE TERRITORIAL PLANNING TERRITORIAL ORGANISATION LANDSCAPE ECOLOGY MOBILITY & TRANSPORTATION PLANNING A/P ARCHITECTURE & TERRITORIAL PLANNING

6 Future Cities Laboratory FCL 2 We are primarily involved in Cooler Calmer Singapore Responsive Cities

7 From Big Data Analysis to Urban Planning Data-driven decisions are better decisions - it s as simple as that. Using big data enables planners to decide on the basis of evidence rather than intuition. For that reason it has the potential to revolutionize planning and design. But! Big data s power does not erase the need for vision or human insight.

8 From Big Data Analysis to Urban Planning Once there was the idea that we can develop patterns, which represent good solutions for specific aspects of a planning

9 Sustainable Urban Patterns Where we started from

10 Smart Urban Adapt to the spin-off SmarterBetterCities Sika City From SmarterBetterCities: A customized city library for Sika Group.

11 From Big Data Analysis to Urban Planning Once there was the idea that we can develop patterns, which represent good solutions for specific aspects of a planning but we learned that it always depends on the context if a solution is good or not. Now, we have to look for more opportunistic models.

12 From Big Data Analysis to Urban Planning With Big Data methods planners can use cheap and former useless data for decision support. Such a decision process is able to integrate new data much faster and almost without any limitations. Therefore this process becomes much more resilient to changing political, economical or ecological circumstances. The vast amount of integrated data provides a probabilistic holistic model of highly complex systems like social housing and is able to support a broad spectrum of planning decisions in a smart yet simple manner.

13 Understanding Cities Analyzing economic data Top: Modeling money flows to simulate spending behavior and explain spatial economic patterns (Daniel Zünd) Left: Tropical Town: Assessing economic potential of the built form, using agents with vision (Eva Friedrich)

14 Understanding Cities Analyzing transportation data Sensing by mobile application High Efficiency Vehicle Detecting Algorithm Exploring the temporal interchange patterns at the Singapore Metro system Analyzing detailed mobility-related factors Transportation mode classifying Location type defining PhD project of Dongyoun Shin

15 Understanding Cities ESUM research project The secondary emotions are those that have a major cognitive component. They are determined by both their level of arousal (low to high) and their valence (pleasant to unpleasant). Source: Adapted from Russell, J. A. (1980). A circumplex model of affect. Journal of Personality and Social Psychology, 39, Figure from: Hogertz, C. (2009). Emotions of the city walker: sensory mapping, Remember: emotions are not the same as feelings

16 Understanding Cities ESUM research project Top: Isovists along the path. Bottom: sequence of isovist properties (area, perimeter, compactness, occlusivity) along the path.

17 Understanding Cities ESUM research project Basic framework for computationally aided evidence informed design (CEID). Figure from the ESUM research project (ETH Zürich and Bauhaus-Universität Weimar).

18 From Big Data Analysis to Urban Planning From Big Data Analysis we learnt so far: Future planning processes will be based on constantly changing truths.

19 Urban Design Space Exploration Optimization for planning purposes Figures from: Rutton, D. (2010). Evolutionary Principles applied to Problem Solving.

20 Computational Planning Synthesis Optimization for planning purposes ergsteiger_erreicht_gipfel_13946.jpg

21 Computational Planning Synthesis Automated Layout Design Koenig, R., Schneider, S., & Knecht, K. (2012). KREMLAS: Entwicklung einer kreativen evolutionären Entwurfsmethode für Layoutprobleme in Architektur und Städtebau. (R Koenig, D. Donath, & F. Petzold, Eds.) (Koenig, R.). Weimar: Verlag der Bauhaus-Universität Weimar.

22 Computational Planning Synthesis Urban Layouts Optimization based on Isovist field properties Sample-results of the optimzation of the different objective criteria for Area after n=40 generations. Top row: Results achieved through minimizing the objective criteria. Bottom row: Results achieved through maximizing the objective criteria. The configuration is superimposed on the isovsit field. Schneider, S., & Koenig, R. (2012). Exploring the Generative Potential of Isovist Fields - The Evolutionary Generation of Urban Layouts based on Isovist Field Properties. In 30th International Conference on Education and research in Computer Aided Architectural Design in Europe.

23 Computational Planning Synthesis Urban Layouts Top: Inverse Urban Design: Enhancement of urban environment at pedestrian scale (Anastasia Koltsova) Left and top: Applicability of urban synthesis techniques for planning problems (Dr. Reinhard König) Koenig, R.: Software-Prototype, 2013

24 Computational Planning Synthesis Street Networks Koenig, R., Treyer, L., & Schmitt, G. (2013). Graphical smalltalk with my optimization system for urban planning tasks. In ecaade: Computation and Performance. Delft

25 Urban energy analysis Real-Time Solar Analysis Real-Time Solar Analysis: Solar simulation as basis for energy-conscious urban design. Video by Sven Schneider, Bauhaus-University Weimar, 2013

26 Optimization PISA, Aforge.Net & CPlan Multi-criteria optimization framework Existing selector algorithms are connected to the computational planning framework CPLAN CPLAN is coupled with Lucy

27 Explore The Solution Space How can we visualize solutions on a multidimensional pareto-front? How can we use the solutions for further investigations? Volker Mueller. Presentation at eccade 2013: Generation, Exploration and Optimisation. 9/20/2013

28 Urban Design Space Exploration System

29 Managing Urban Big Data Interactive Planning Top: An interactive tool for modeling Ethiopia s energy future (Eva Friedrich / FCL) Left: Interactive Decision Support Tool (Antje Kunze, ia)

30 Collaborative Urban Planning Platform Value Lab I (ETH Zurich) Value Lab II (SEC Singapore)

31 Left: Rule-based city modeling method for the early design stage (Jan Halatsch, ia) Teaching the science of cities Top: New teaching formats (Gerhard Schnitt / FCL)

32 Teaching MOOC Future edx

33 Thank you.