CAAD MAS >> Computer Aided Architectural Design, Institute for Technology in Architecture (ITA), ETH Zürich » M2 Scenic Drive http://www.mas.caad.arch.ethz.ch/blog Wed, 25 Jun 2014 13:26:14 +0000 en-US hourly 1 http://wordpress.org/?v=4.1 M2 Final Presentations http://www.mas.caad.arch.ethz.ch/blog/m2-final-presentations/ http://www.mas.caad.arch.ethz.ch/blog/m2-final-presentations/#comments Mon, 11 Feb 2013 21:41:03 +0000 http://www.mas.caad.arch.ethz.ch/blog/?p=716 Panoramic alpine urbanism

Nicolas Miranda, Jiang Nan, Akihiko Tanigaito : MorphoCity
MorphoCity

M2_final2

M2_final3


Joel Letkemann, Jessica In, Maria Smigielska : Phantom City

 

Achilleas Xydis, David Schildberger, Demetris Shammas, Irene Prieler : Alpine Nomadology

A little nomadic folk decides to settle down at a high alpine region close to a beautiful lake somewhere in the Swiss Alps. They spend their summers beside the lake – fish, farm and gather herbs to prepare for the harsh winter. During the cold season they move to their winter camp on the highest mountain peak, under meters-thick snow.
The land’s conditions such as altitude, steepness, permafrost, glacier, crops, are the starting points for the development of their settlement. The summer camp consists of temporary huts on a scaffold of poles, connected horizontally and vertically through bridges and stairs. Steep paths connect the vertical summer camp to the horizontal megastructure under the eternal ice.

 

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M2 Scenic Drive http://www.mas.caad.arch.ethz.ch/blog/panoramic-alpine-urbanism/ http://www.mas.caad.arch.ethz.ch/blog/panoramic-alpine-urbanism/#comments Fri, 30 Nov 2012 10:49:43 +0000 http://www.mas.caad.arch.ethz.ch/blog/?p=539

Panoramic alpine urbanism

What would a truly 3-dimensional city look like? How can one design a city that engages with an extremely dynamic topography? We will populate an unsettled valley in the heart of the Swiss Alps with an agglomeration of 100.000 people. How can we devise design strategies for a vertical master-plan. How will the architecture of such a city look? How can we conquer the timber line?

This city cannot be organized exclusively through a bird’s-eye view, but through the perspective of the inhabitants. Our focus will be the infrastructure (tunnels, lifts, escalators, serpentines, bridges), the scenic drives, the vistas and panoramas.

The computer will help us explore unconventional design methods and to face the challenges of climate and topography of the complex environment. We will code our concepts, design rules, define relations, compose networks, and generate, simulate, and visualize our visions for an alpine urban structure in processing.

Task

Focus on the view, population, overall density, distribution of density, function.Create the street-network, the infrastructure and adapt the geography.Create the network of parcels and streets.Distribute the program.We will work in groups of 3 people in order to develop 5 distinct scenarios of an urban live in the mountains, all of them labeled with a specific slogan. We will have two intermediate critics and in addition individual discussions.

Keywords

Valley, 100.000 people, Panorama, Bellevue, Alpine, Urban, Alm,Topography, Landscape, Neighborhood, Networks, Lifts, Tunnels, Path, Bridges, Waterfalls, Snow, Rocks, Shadow, Avalanche, High altitude, Density, View,  Population, Function, Density, Composition

Output

Drive-through animation in high resolutionPanoramic views in high resolutionModel of the citymax. 2 minute movie toexplain your city with diagrams and renderings

Schedule

Friday, 30.11.12, 11am

Introduction to the assignment

Thursday, 6.12.12 11am

Intermediate review

Wednesday, 12.12.12 12.12pm

Intermediate review

Thursday, 20.12.12. 11am

Final presentation

Module 2: Alpine Urbanism

Joel Letkemann, Jessica In, Maria Smigielska:
Our city’s organization relies on the dispersed intelligence of an object oriented approach to planning. Each node in our city know’s it’s own character and suitability. The subjective, scenic view of our city is illuminated by a car’s-eye perspective.

Nicolás Miranda, Nan Jiang, Akihiko Tanigaito:
MorphoCity – Description coming soon….

Yuko Ishizu, Mark Baldwin: Parametric City

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M2 Scenic Drive http://www.mas.caad.arch.ethz.ch/blog/m2-neighbourhood/ http://www.mas.caad.arch.ethz.ch/blog/m2-neighbourhood/#comments Tue, 27 Nov 2012 08:14:33 +0000 http://www.mas.caad.arch.ethz.ch/blog/?p=417

From micro decisions to macro constellations

In this week we will explore the relations between local dependencies (for example our neighbor) and global structures. We will test our hypotheses in simulation-models in a bottom up strategy.  Does our micro- specifications lead to emergent patterns?

We will use agent based models and heuristics, all programmed in processing.

Task:

Define a model of a set of micro-specifications of neighbourhood and simulate its consequences for the urban scale.

Monday 03.12.12 3pm Presentation of the results

References:
Thomas C. Schelling (1971). “Dynamic Models of Segregation,” Journal of Mathematical Sociology, 1(2), pp. 143–186
Induction cities: http://www.makoto-architect.com/idc2000/index2.htm

Module 2: Neighbourhoods

This week the class explored visualising, generating and simulating different kind of neighbourhoods, working in Processing. See below for a preview of the presentations.

Joel Letkemann: Voxel Neighbourhood
This program is a 3D voxel-based heuristic algorithm that adapts to both the local neighborhood, grouping like with like, and also the larger area, testing the distance to larger ‘avenues of traffic’. As the program runs, it continually adapts to its surroundings, and eventually settles into a stable configuration.

Mark Baldwin: Topographic Neighbourhood
This exercise sought to express relationships and intensities between various cells within a grid. The starting condition was a random arrangement of cells of Type 1, Type 2 and empty cells. The cell grid underwent successive interations (numbering in the thousands) based on the following rules: Cells with less than 5 like (same type) neighbours will move to a new random location; neighbours surrounded on all sides by like neighbours will increase in intensity by one level – depicted as a darker colour and a raised or lowered (depending on the type) elevation. The image below collages various states from the starting (front) to ending (rear) conditions of the sequence.

Yuko Ishizu:  Neighbourhood

Tihomir Janjusevic: Death Game
Cells multiply and eventually produce monsters who destroy everything.


Maria Smigielska: Line Neighbourhood
Two types of cell populate this neighbourhood in both vertical and horizontal directions within a grid. The more popular cells are, the wider the line that represents them.

David Schildberger: Terrain Neighbourhood
Population of a terrain with different typologies influenced by variations of the topography and relations within the neighbourhood.

Nicolas Miranda: Regular / Organic
Two different neighbourhoods: one based on a regular street structure and the other on an organic structure. In both cases buildings are located on the first row along the streets while the rest of them are located on the green spaces (empty cells).

Demetris Shammas: Nicosia 1 2 X O █
Nicosia 1 2 X O █ is an urban experiment that explores the relations between local dependencies and global structure. Each cell’s starting condition is specified by the city’s present datascape, while a series of successive iterations determined by the combination of simple rules, results in an unplanned and complex urban arrangement.

Jessica In: Chess Neighbourhood
The rules of a game of Chess become the starting point for this neighbourhood. Each chessman has two neighbourhoods – the first a set of neighbours it can see defined at ‘n’ dimensions of a Moore neighbourhood, the second a set of neighbours it could move to based on each chess piece’s movement rules. Pawns have a preference to not attack or move if their King is within the 1st neighbourhood dimension, and Kings have a preference to not attack or move if they are in a ‘safe’ place (e.g. protected by more than 3 immediate team pieces).

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M2 Scenic Drive http://www.mas.caad.arch.ethz.ch/blog/m2-first-assignment-atlas-of-networks/ http://www.mas.caad.arch.ethz.ch/blog/m2-first-assignment-atlas-of-networks/#comments Thu, 15 Nov 2012 10:20:13 +0000 http://www.mas.caad.arch.ethz.ch/blog/?p=134

Thursday, 15.11.12, 10am Individual Assignment 

Data: Find, analyze and compare the characteristics of at least two different urban networks. Examine their elements and their relations. The networks can be trees, cycles and nets, public, neuronal, private, physical, virtual, social, spatial, hidden…

Visualize the network so as to convey a maximum amount of information. Develop an algorithm to generate a family of related networks and simulate its growth and adaption.

Work in Processing.

Monday 25.11.12 3pm Presentation of the results

 

References

http://www.visualcomplexity.com/vc/

http://dataviz.tumblr.com/

http://visualization.geblogs.com/

http://www.freebase.com/ : database of freely available sets of data to be visualized

http://infosthetics.com/

http://www.handsomeatlas.com/

 

Alexander, C., a city is not a tree, in Architectural Forum1965. p. 58-62.
Batty, M., Urban Modelling, in International Encyclopedia of Human Geography, R.K.a.N. Thrift, Editor 2008.
Forrester, J.W., Urban dynamics1969, Cambridge, Massachusetts: MIT Press. 285.
Lynch, K., The image of the city. Publications of the Joint Center for Urban Studies1960, Cambridge Mass.: The Technology Press & Harvard University Press. 194 p.
Martin, L. and L. March, Urban space and structures1972, London,: Cambridge University Press. vii, 272 p.
Pascal, M., Procedural modeling of cities, in ACM SIGGRAPH 2006 Courses2006, ACM: Boston, Massachusetts.
Watanabe, M.S. the induction cities. 1990; Available from: http://makoto-architect.com/idc2000/.
Wright, W., Dr. Wright’s Urban Planning Guide 1989: Nintendo.

Module 2:  Atlas of Networks

This week the class explored visualising, generating and simulating different kind of networks, working in Processing. See below for a preview of the presentations.

Achilleas Xydis: Twitter Network
A “tool” to search, compare, follow, track, spy, interact with people and their tweets from all over the word.

Maria Smigielska: Movie User Network
This map aims to represent relationships between movies and users who rated them, according to their age, occupation and gender, as well as genre, rating and relase date of the movie. Data set consisting of 100,000 ratings from 1000 users on 1700 movies is available here: http://www.grouplens.org/node/73

Evi Xexaki: Flight Network
Flight network visualizes the air connections between cities around the globe. Departures and arrivals are displayed with arcs of a different color, with the thickness of each arc dependent on the number of the weekly routes between two cities.

Yuko Ishizu: Swissgrid
This visualisation of the Swissgrid assumes ownership of the 6,700 kilometre-long Swiss transmission system no later than 1 January 2013, as envisaged by the Electricity Supply Act (StromVG), which entered into force on 1 January 2008. The diagram shows the location of transmission station and ownership of the rote of this system.

Mark Baldin: Australian inter-state migration
This network tracks inter-state migration in Australian between 2001 and 2006. Each state population is represented as a sphere located at the state capital city. Migration paths between all states are indicated as grey dotted arcs, with actual migration simulated by smaller spheres (representing the movement of 100 inhabitants) following the same flight trajectory. State populations are updated progressively as migrants leave and enter the states. The data is derived from Australian census statistics.

Joel Letkemann: Toronto Bicycle Network
This program visualizes the bicycle traffic into and out of Toronto’s downtown core, using data from Toronto’s Open Data platform. The program then uses this data to project the traffic through the downtown network, using such algorithms as ‘degree’ and ‘betweenness’ centrality.

David Schildberger: Spatial organization
This tool simulates a series of spatial arrangements due to different graph relationships.

Tihomir Janjusevic: Connectivity Map
The Connectivity Map calculates the connectivity of terms inside the given textual input. The program attempts to establish an equilibrium between the elements of the map.

Akihiko Tanigaito: Stik Graffiti
Maps the location of graffiti artist Stik in London.

Nicolas Miranda: Fly Away
Every year thousands of migratory birds make their journey through the world. Multiple migration patterns are drawn based on the seasonal movement of a group of 193 birds. Six different bird species are represented and visualised according to a time variable. Brighter areas on the patterns describe flight routes, while darker ones show places of longer stay.


Jessica In: A Flickr Atlas
25 categories; 25,000 images; 92,902 image-category links; 16,777,216 user generated meta tags; 20,540,808 tag matches. A Flickr Atlas visualises data collected by LIACS Medialab and graphs the images according to the dataset’s explicit categories. This Atlas also graphs the images according to user generated meta tags, providing a comparison to the dataset’s (generally) more defined categories. Data set: http://press.liacs.nl/mirflickr/

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M2 Scenic Drive http://www.mas.caad.arch.ethz.ch/blog/grammar-machines/ http://www.mas.caad.arch.ethz.ch/blog/grammar-machines/#comments Wed, 14 Nov 2012 12:25:56 +0000 http://www.mas.caad.arch.ethz.ch/blog/?p=54

monstroCITY is the result of a 3-day group programming exercise, exploring Shape Grammar concepts in Processing.  Setup like a game of The Exquisite Corpse,  each student was assigned a part of the city to code – from terrain to window mullion – while developing rules to generate the city using a prescribed set of geometric functions.  Just as in the Surrealist’s game, each collaborator adds to the composition without seeing what came before and without knowing what will come after – the only connection between each part is a set of agreed tags that will allow the final sequence to be connected.  The resulting monstroCITY is a quirky, sometimes-ugly-but-often-charming place, autonomously conceived by the class’s first introductory steps into programming.

orthoAerial elevView01 01orthographic 6 aa3a view2 02elevation view1 aerial02_2 aa4a 'MonstroCITY' Aerial view 04
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