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Introduction to the course 0
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Lecture1.1
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Design and its computational representation 1
In this module we are dealing with the process of designing and how this process can be represented computationally.
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Lecture2.1
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Lecture2.2
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Assignment2.1
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Lecture2.3
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Lecture2.4
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Lecture2.5
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Design Models - From 2D Drawing to Parametric Modeling 0
Here you will learn the principles of parametric modeling.
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Lecture3.1
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Lecture3.2
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Lecture3.3
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Lecture3.4
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Lecture3.5
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Lecture3.6
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Parametric Urban Modeling - Streets 1
Here you apply the principles of parametric design on the generation of urban morphology (streets, plots, buildings).
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Lecture4.1
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Lecture4.2
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Lecture4.3
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Assignment4.1
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Parametric Urban Modeling - Plots Parcellation 1
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Lecture5.1
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Lecture5.2
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Assignment5.1
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Parametric Urban Modeling - Building Typologies 1
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Lecture6.1
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Lecture6.2
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Assignment6.1
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Computational Urban Analysis 0
In this section you will learn at the basics of Computational Design Analysis.
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Lecture7.1
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Computational Urban Analysis - Density 1
In this module you will learn the basics for calculating urban density.
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Lecture8.1
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Lecture8.2
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Lecture8.3
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Lecture8.4
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Lecture8.5
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Assignment8.1
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Lecture8.6
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Computational Urban Analysis - Visibility Analysis 2
In this module you will learn the basics for calculating what one can see of an urban environment.
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Lecture9.1
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Assignment9.1
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Lecture9.2
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Lecture9.3
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Lecture9.4
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Lecture9.5
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Lecture9.6
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Lecture9.7
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Lecture9.8
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Assignment9.2
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Lecture9.9
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Lecture9.10
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Computational Urban Analysis - Spatial Relationships 1
In this module you will learn the basics for calculating how spaces relate to each other.
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Lecture10.1
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Lecture10.2
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Lecture10.3
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Lecture10.4
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Lecture10.5
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Lecture10.6
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Lecture10.7
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Assignment10.1
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Design Space Exploration 3
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Lecture11.1
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Lecture11.2
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Assignment11.1
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Parametric Urban Design and Analysis
Methods for generating, analysing and exploring urban morphology
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18 Comments
In the model, all buildings were taken into calculations, without substracting areas that are used e.g. for retail instead of housing
The considered 45 m2 per person may cause a huge difference considering some places have much bigger (or smaller) typologies and different living arrangements.
All the functions, which are different from the resinential (public buildings) should be excluded. The DDR area lacks the public functions, therefore the calculation was the most precise there.
The closest calculation was in the Weimar West area – meaning either the Area Demand Per Person may be correct was today’s standard of more mass housing complexes then individual houses or smaller housing complexes as in Altstadt, or the different ratio between public functions/space and housing.
The norm of sq.m. per person is changing from one period of time the another one. That means thas parameter was changed several times.
Following are the possible reasons:
1. Considering that all buildings were built with the same parameter of per sqm area required by a person.
2. All floors of a building are used for residential purpose.
3. The value of 0.6 is also debatable since circulation area in newer buildings can be less considering increased efficiency in design and the introduction of elevators.
It might be because in different parts of the city (based on income, preference, buildings and etc.), the average area per person would be different. So there might be 2 store building occupied by a single person or a single flat occupied by 8 people (WG). Moreover, the land use hasn’t been considered.
In the calculation examle we relied to much on the value, we assumed for the parameters. E.g. the average area demand per person in whole Germany might be 45 qm, but Weimar might be an exception.
In the real case, usually the land use is multi functional. Especially in Altstadt, there are a lot of public buildings. In the calculation is all the buildings accounted as houses. Did not consider the other functions e.g. museum or shopping center.
We can develop criteria for each area, this additional coefficient might be introduced in order adjust model closer to reality.
Although the statics of urban settlements shows that residential areas dominate the land use, still there are other categories like industrial, commercial, and service areas. In this case, this issue has not been considered.
Firstly, while calculating the floor area of the building, it is also necessary to take into consideration certain parameters like: 1.The location of the building 2.Form of the building & 3.Function of the building, as in any case the ratio of the inhabitable floor area to the ratio of the circulation area will vary.
Moreover, since certain spaces/buildings could also be spaces of shared use or be multi-functional spaces, certain functions may overlap while also reducing the area demand. Hence it is also important to reconsider the area demand according to the functions of the building.
The calculation of a floor area of buildings has to also consider that in some sections there are also public spaces, comercial areas or government buildings. that are not considered as housing for people.
There are a lot of parameters not considered. What you already mentioned in the video: obviously not all buildings are used for living. Compared to other cities, Weimar has a lot more public buildings. There are so many buildings used for university, library or heritage buildings that are only used for tourism, shopping center, car parks, theatre and so on) Furthermore, the number of qm/person depends on the area. Because in Weimar West the buildings are nearly all used for housing, the difference between reality and your calculation is not so big. Another reason could be that often people living in Weimar West can’t afford as much living space as people in the center of Weimar. I’m not sure about it, but what could have an influence is “changing” the function of a building. If the function of a building is changed, the floorplan is different to a normal floorplan of a flat. So the rooms are maybe bigger or smaller than they used to be and the people can’t use the building as efficient as new buildings. What is definitely wrong, is to not consider that the paramaters change depending on the city AND depending on the city area.
The reason for the inaccuracy of the calculation is the intense simplification without including important parameters. This calculation refers to the assumption, that the proportion of living and used building area is the same in every district in Weimar. But in reality there is a multifunctional using of the buildings, which is not considered. In this context also the space requirements of the buildings is variable.
The calculation considers all houses and buildings in the city are living spaces. In reality, a lot of building are used for different purpose such as : shopping mall, super market, retailed shops, etc.
The final ratio considered all buildings as if they were residential buildings, which in fact is not correct. Specially in the center where, retail, commercial and office spaces forms a very big slice of the pie.
The reason is that not all the parameters were considered; firstly some buildings are not used for residential purpose. Then maybe we need also to define the common spaces, like shared-used, than semi-private spaces.