Urban Planning Table – Integrated Infrastructure

Urban Planning Table – Integrated Infrastructure

Video with impressions of the urban planning table, a walkthrough and created plans of all participants.


Many different urban stakeholders such as authorities, planners, investors or local residents are involved in urban planning processes. Coordination and communication between the parties involved is often difficult, especially since not all parties have the same level of knowledge. This is particularly problematic when plans are not understood by those affected by the planning decisions.

The Ethiopian Ambassador in Germany (right) listens to our explanation of the project.

 As part of the research project “Integrated Infrastructure – IN3”, we want to support planning participants in developing Ethiopian villages into towns in tow ways. First, we create and discuss digital tools with the participants to enable fast urban planning. Second, we experiment with explanatory communication tools to provide village residents and local authorities with easy-to-use and game-like access to planning to enable conscious, participatory decision-making.

In a discussion with village representatives, we gained valuable insights using a prototypical communication tool. We drew attention to drastic changes in the village environment during a growth process from previous population of approx. 1,000 to the expected 10,000 inhabitants. This highlighted, for example, how land use and water infrastructure would be affected.

For the exhibition “ሠላም Bauhaus” (Selam Bauhaus), we designed a simplified version that should give the users access to planning aspects in a fictious Ethiopian context without further help. The generated planning scenarios surely do not represent the final plans. We hope, however, that this type of interaction will stimulate discussion and reflection on new possibilities for planning and participation.

“I believe that we need more knowledge and experience on town planning”

– Villager

“The supportive maps and their relation to the existing context that you presented today are very interesting. We feel happy about the maps of our village that you […] showed us. You showed us, how the growth and plans can affect our village.”

– Local representative

“This kind of planning is an improvement for our generation. We are in the 21st century and our aim is to give farmers and the rural community this opportunity of empowerment.”

– Local representative

User Experience

Being confronted with the complexities of planning and the multitude of challenges that arise, a user would be overwhelmed by the amount of information. Although the exhibited urban planning table simplifies planning relevant issues to estimatable and visualisable results, a direct confrontation with all parameters would lead to a visual clutter. Therefore, planning issues and population growth are introduced step by step with assisting information and contextutual visualisations:

1. Placement of city and setting its density – The city is small with a population of 1.000 inhabitants. The user has time to get used to the navigation and can move around the city through interaction with its icon.

2. Prioritising areas for agriculture or forest – The city grows to 3.000 inhabitants. A slider is introduced to weight the importance of nature against jobs in food production. The user can explore the difference not only on the map but also through performance evaluation.

3. Loss of agriculture demands new employment – The city has a population of 6.000 inhabitants. A new icon for industry is introduced. Moving it around affects air quality. Through two new sliders jobs in commercial or industrial sector can be created. It affects directly the land use plan of the city.

Exhibited Planning Table with an interactive map, the four planning steps on the bottom and performance evaluation on the upper right.

4. Satisfying water demand – The city reaches a population of 10.000 inhabitants. A water drop icons is now available and sets the location of a water tank. To reach water supply for every household the water pressure needs to be high enough and therefore the water tank should be placed on high altitude.

Finish. Based on the four steps, the user can simulate the population growth through usage of a slider. Within the exhibition, the final plan has been print out for the user by the tool. 

The interaction with the planning table happens through intuitive touch commands and the user is continously informed on the feasability of the chosen city layout through real-time performance evaluation.

Rotate Camera
Camera Zoom
Move Camera
Move Objects
Performance Evaluation

Performance Evaluation

The urban planning table evaluates certain factors such as walkability, water demand, infrastructure affordability, air quality, employment and connection to nature to give continous feedback on planning relevant issues. The planning table users will realise that the factors are interrelated and a benefit for one criteria will challenge another. Therefore, the users will have to weight and compromise on their own demands. The criteria are explained in more detailled:



In a city of short distances, the most important facilities can be reached quickly. The closer people live to each other, the easier it is to meet on foot. A round city would be ideal, but there are other factors that belong to a good city. So is it worth losing a bit of walkability for other qualities?



Sufficient green spaces are important in many ways. Forests in particular can improve air quality, protect soil from erosion and prevent rain from seeping into the ground too quickly. In addition, green spaces close to cities can also be used for recreation. However, they are also an important building material that requires space and time. So to what extend should forest be given space?



Agriculture cannot provide enough jobs for a growing population. New commercial and industrial land use can help here. If too few jobs are available, parts of the population will have to work in neighbouring cities or will not get a job. If too many are available, there is a danger that no one will be able to occupy them or that commuter traffic will be too high. With which mix of functions can a balanced employment be achieved?

Water & Affordability

In order to supply a city with water, physical rules need to be considered. If there is not enough water pressure at a tap, no water flows. The pressure results from the difference in height between tank and tap, accordingly a water tank should be located high and close enough to the city. Furthermore, the larger a city is, the more expensive the infrastructure becomes. A dense city is therefore often cheaper to maintain. Where is an optimal location for a water tank for an affordable and complete water supply? 

Air Quality

A city with fresh air has good conditions to be a healthy city. However, industry produces dirty exhaust air, which is blown into the residential areas by the wind. The local weather has east and west winds, depending on the day. The wind is visualised by the smoke clouds. So how should industry be placed to avoid pollution of the city?


By combining hard factors calculated by the computer with soft factors evaluated in discussion rounds. People who know little about urban planning can be involved in the early stages of the planning process and gain important decision-making competencies. According to the feedback given to us, we will continue improving and extending our tools and hope to share the results in the near future.


Now I am wondering how it looks on the ground?


I like that this city has a lot of green! City, people and nature are in balance and the air quality is good! Like to LIVE HERE!


A very cool Project. It will be the future of design so that human have more time to think about details.


Richtig cool, aber Navigation mit zwei Fingern funktioniert nicht so richtig gut. Ansonstend spannend!


good air is the most important!


Being an Ethiopian myself i am sure that this would play a handy role through the process of achieving a responsible and responsive design our country needs.


Great stuff!


Gutes Tool, um strategisch denken zu lernen! Education?


Maybe we can extend the design table to mobile device.


very interesting simulation


Wonderful simulation ♥ interactive+++ – More forest, less industry! Maybe some other employment options (services instead of industrial production? 🙂 )


This table should have as many variables as it can (sanitary lines, electical, public areas etc.). Maybe make two versions easy (now) & hard for academics.


I like this one ♥


sehr anschaulich!




shows consequences of planning directly


a very nice Simulation – also from an IT Perspective!


Ich finde das Programm fragwürdig. Auf jeden Fall eine gute Lösung um schnell etwas geplant zu haben. Trotzdem finde ich, dass so eine Art zu planen gegen die Realität stößt. Bspw. wo bekomme ich das Wasser her? Wie kann ich das Grüne unterhalten? Sind die Gegebenheiten dort für Industrie?… etc.


Integrated Infrastructure (IN³ – uni-weimar.de/integrated-infrastructure) is an interdisciplinary international research project at the Bauhaus-Universität Weimar (BUW) and the Ethiopia Institute for Architecture, Building Construction and City Development (EiABC) working within the Emerging Cities Lab – Addis Ababa (ECL-AA).

The urban planning table is conceptualised and developed within the Integrated Infrastructure Research Team.
EiABC: Zegeye Cherenet, Tesfaye Hailu, Ephrem Gebremariam, Kirubel Nigussie, Metadel Selashi, Bilisaf Teferri, Israel Tesfu
BUW: Andreas Aicher, Nicole Baron, Martin Dennemark, Philippe Bernd Schmidt, Sven Schneider

Interface design and programming of the planning table by Martin Dennemark

The project is funded by the Federal Ministry of Education and Research in Germany (BMBF), German Academic Exchange Service (DAAD) and German Aerospace Center (DLR).

Satellite imagery aquired through educational license from © Planet Labs Inc. www.planet.com.


Evolving Configurational Properties

Evolving Configurational Properties

article published at 12th Space Syntax Symposium at Jiaotong University, Beijing, China



Martin Bielik 1, Reinhard Koenig2,3 , Ekaterina Fuchkina1, Sven Schneider1, Abdulmalik Abdulmawla1

1 Bauhaus University, Weimar, Computer Science in Architecture
2 Bauhaus University, Weimar,  Computational Architecture
3 Digital Resilient Cities, Austrian Institute of Technology


In this paper, we introduce simulation framework for examining the effect of street network configuration on the evolution of the relationship between movement and land use allocation over time. The causal chain introduced in Space Syntax literature suggests that the potential generated by spatial configuration of a street network influences how people move and that these movement flows attract specific types of land uses. These land uses generate in turn additional movement creating an endless cycle of mutual interactions. In Space Syntax, this interaction between movement flow and land use is assumed to work in a positive feedback loop, multiplying the initial potentials given by a street network configuration. The practical consequence of this hypothetical assumption for the Space Syntax method is that the outcome of the feedback loop can be predicted as multiplication of the initial state and therefore doesn’t have to be simulated.
In this paper, we introduce a computational method for testing the multiplier effect hypothesis and identify the cases in which it holds true and those ones in which more detailed investigation considering feedback loops might be necessary. We demonstrate how such investigation based on the simulation of the interactions between movement and land use in time can be operationalized and conduct series of studies exploring the spatio-temporal effects of street network configuration.
We conclude that these exemplary studies show how the presented simulation model can be used to test the core assumption behind the Space Syntax method. We also offer preliminary insides about when and under which conditions it can be reliably applied and when system dynamic simulation might be necessary to predict not only the immediate, but also the long-term effects of street network configurations on centrality, movement and land use distribution.


Street network configuration, System dynamics, Centrality, Movement, Land use, Multiplier effects, Natural movement


One sentence summary:

We explore the evolution of the configurational effects of urban form on the movement and land use allocation over time.

Paper overview

In this section, we compiled a short review of the main ideas discussed in the paper. The purpose is to get a general idea that can be deepened by reading the full paper attached below.

1. Background | What do we already know?


“Street network configuration affects how people move”

(Golledge 1995)

“Street network configuration affects where human activities allocate”

(Hansen 1959)

“Movement influences allocation of land uses and allocation of land uses influence movement”

(Hillier 1996a)


“Space Syntax is about bringing the configuration, movement and allocation of activities together”

(Hillier 1996b)

2. Research gap | What do we don’t know?

“Does the effect of street network configuration on movement and land use allocation evolve over time?”


“The effect of configuration on movement and land use is stable over time, it DOES NOT EVOLVE


“The effect of configuration on movement and land use does DOES EVOLVE over time”





Source: http://labs.minutelabs.io/Chaotic-Planets

3. Research question | What do we want to know?

Question A

Under which conditions is the result of the feedback loop between movement and land use distribution predictable as a function of the multiplier effect?


Question B

Under which conditions does the natural movement potential of the network configuration correct the effect of unequal distribution of land uses (disruptions) throughout the feedback cycle?


“Space Syntax method is based on the assumption that option A holds true. In other words, it is assumed that the outcome of the interaction between land use and movement can be predicted without simulation.”

This assumption is based on:

1. Multiplier effect hypothesis

The result of the feedback loop between movement and land use distribution is predictable as a function of the multiplier effect

(Hillier 1996b)
2. Natural movement hypothesis

The natural movement potential of the network configuration corrects the effect of unequal distribution of land uses (disruptions) throughout the feedback cycle

(Hillier 1999)

4. Research method | Spatio-temporal simulation engine

Left ) UML diagram of the Space Syntax underlying urban dynamics model. a) current model of the feedback loop between movement and land use. b) displays the “multiplier effect” hypothesis.
Right) Exemplary illustration of two iterations of our dynamic simulation. The initial state is an equally loaded network. The centrality values show the angular betweenness centrality with radius Rn (global radius).Value range: high movement potential = red, low movement potential  = blue

5. Results | Simulation experiments

We exemplary explore the impact of different conditions on the multiplier and natural movement effect. The conditions explored in this study relates to how we model the effect of land use on movement and what is the initial distribution of land use in the simulation.

Explored conditions & model parameters:

  • Definitions of movement potential centrality MPC.
    Closeness vs. Betweenness centrality
  • Analysis radius
    Local radius of 600m vs. global radius
  • Initial land use weightings w
    Equally loaded vs. Disrupted network*

For the following experiments, we used the street network of the inner city of Weimar as an exemplary case study area. Weimar is a medieval, mid-size German city with approximately 60.000 inhabitants. The size, historical development and the overall variety of street network patterns makes it a good candidate for testing the proposed simulation model. On the one hand, the size of the city makes it possible to quickly calculate many iterations of the simulation, but on the other hand it is large and diverse enough to let non-trivial configurational patterns emerge.

*Disrupted network in context of this paper means that in the initial stage of the simulation, not all street are loaded with equal amount of land use. In other words some streets produce more movement than others, which is represented by higher weighting of selected graph vertices.

Experiment 1 | Measuring the multiplier effects  on equally loaded network

Equally loaded network in context of this paper means that in the initial stage of the simulation, all street are loaded with equal amount of land use. In other words all streets produce same amount of movement, which is represented by equal weighting of all graph vertices.



  • Equally loaded network
  • Betweenness centrality
  • Global radius


  • Equally loaded network
  • Betweenness centrality
  • Local radius


  • Equally loaded network
  • Closeness centrality
  • Global radius


  • Equally loaded network
  • Closeness centrality
  • Local radius

Experiment 1 | Summary

Quantifying the multiplier effect by measuring linear relationship between the movement pattern at the first and the last iteration of the simulation


  • Multiplier effect is reliable predictor for:
    i) global closeness centrality
    ii) global betweenness centrality
  • Strong effect of scale of the centrality model
    Multiplier effect Global > Multiplier effect Local

Experiment 2 | Measuring the multiplier effects on disrupted network

Disrupted network in context of this paper means that in the initial stage of the simulation, not all street are loaded with equal amount of land use. In other words some streets produce more movement than others, which is represented by higher weighting of selected graph vertices.



  • Disrupted network
  • Betweenness centrality
  • Global radius


  • Disrupted network
  • Betweenness centrality
  • Local radius


  • Disrupted network
  • Closeness centrality
  • Global radius


  • Disrupted network
  • Closeness centrality
  • Local radius

Experiment 2 | Summary

Quantifying the multiplier effect by measuring linear relationship between the movement pattern at the first and the last iteration of the simulation


  • Multiplier effect is NOT reliable predictor
  • Doesn’t pick up most central streets*.* Non normality & heterocsedascity

Experiment 1&2 | Measuring the natural movement effect

Quantifying the natural movement effect by measuring linear relationship between the movement pattern at the first iteration of equally loaded network and last iteration of the disrupted network.


  • Natural movement is reliable predictor for:
    i) global closeness centrality

6. Conclusions | Joined multiplier and natural movement effect

Evaluating the impact of scale, initial network loading, and type of centrality measure on natural movement and multiplier effect. In other words, under which conditions is the influence of configuration on movement predictable without simulations and when the simulation is needed.



  • For global closeness and betweenness centrality in equally loaded networks
  • For global closeness and in disrupted networks


  • When running local models
  • When running models on disrupted – non equally loaded networks

7. Outlook | Next steps

A) Testing the simulation results against empirical longitudinal data

B) Testing the sensitivity of multiplier and natural movement effect on conditions related to street network configuration and type of land use (i.e. does multiplier effect depends on the type of the street network layout and how we model the impact of movement on land use).


Download the article

Bielik et al. – 2019 – Evolving configurational properties.pdf

Cite the article

Bielik, M., Koenig, R., Fuchkina, E., Schneider, S., & Abdulmalik, A. (2019). EVOLVING CONFIGURATIONAL PROPERTIES – Simulating multiplier effects between land use and movement patterns. Presented at the 12th Space Syntax Symposium, Beijing, China.

Supplementary materials


Grasshopper scripts

Following grasshopper script  simulates the interaction between movement flows and land use distribution through street network configuration. The street network used in the example is simplified example of city of Weimar, Germany which was used as an example in the paper. The data for the street network is directly embedded in this script.

Versions of the script:

1. Simplified script, easy to run. This script requires only DeCodingSpaces toolbox and Anemone plugins being installed (see instructions bellow)
evolving configurational properties_simple.gh

2. Full script. This script requires also R language additionally to DeCodingSpaces toolbox and Anemone plugins being installed (see instructions bellow). This version of the script offers some additional visualization and possibility to save the simulation results
evolving configurational properties_full.gh

NOTE: Get sure the GPU acceleration is enabled.
(otherwise is the simulation too slow to execute in real time)
NOTE: Check the requirements section bellow and get sure you get all the plugins installed using the links on this website.(otherwise the version might not fit the version of the script)




Install instructions

Unblocking plugins

After downloading the RequiredGHPlugins_SimAUD19.zip file, check if its unblocked before extracting the zip archive. Right click on the file > Properties > select unblock > select ok

Install components

After unlocking and extracting the RequiredGHPlugins_SimAUD19.zip archive, copy the “SimAUD19 components” folder into the grasshopper component folder. The grasshopper component folder can be found at:


or via grasshopper file menu:

Install user objects

After unlocking and extracting the RequiredGHPlugins_SimAUD19.zip archive, copy the “SimAUD19 user objects” folder into the grasshopper UserObjects folder. The grasshopper component folder can be found at:


or via grasshopper file menu:

Enable GPU acceleration

The requirement to run the GPU accelerated street network analysis is the CUDA platform enabled NVIDIA GPU.
If this requirement is fulfilled, you have to copy the folders “Alea.CUDA.CT.LibDevice” and “Alea.CUDA.CT.Native.X86.B64.Windows” from the GPU acceleration folder to your Rhino install folder (i.e. “Program Files\Rhinoceros 6 (64-bit)\System\”).

Spatial Resilience Towards Flooding Hazards

Spatial Resilience Towards Flooding Hazards

Urban development projects in flood-prone areas are usually complex tasks where failures can cause disastrous outcomes. To tackle this problem, we introduce a toolbox (Spatial Resilience Toolbox – Flooding, short: SRTF) to integrate flooding related aspects into the planning process. This so-called toolbox enables stakeholders to assess risks, evaluate designs and identify possible mitigations of flood-related causes within the planning software environment Rhinoceros 3D and Grasshopper. This states a convenient approach to integrating flooding simulation and analysis at various scales and abstractions into the planning process. The toolbox conducts physically based simulations to give the user feedback about the current state of flooding resilience within an urban fabric. It is possible to evaluate existing structures, ongoing developments as well as future plans. The toolbox is designed to handle structures on a building scale as well as entire neighborhood developments or cities. Urban designers can optimize the spatial layout according to flood resilience in an early phase of the planning process. In this way, the toolbox can help to minimize the risk of flooding and simultaneously reduces the cost arising from the implementation and maintenance of drainage infrastructure.

Research Team: Julius Morschek (contact author)Reinhard Koenig (contact author) and Sven Schneider 



This page contains accompanying material for the SimAUD 2019 conference paper “An integrated urban planning and simulation method to enforce spatial resilience towards flooding hazards“.

Presentation at SimAUD conference, Georgia Tech, Atlanta GA, April 2019


The rain runoff simulation is conducted with the help of the interactive physics/constraint solver Kangaroo for Grasshopper by Daniel Piker. The toolbox can represent a rainfall event by equipping particles with a certain mass and gravitation force. During the simulation, the particles are attracted by the external gravitational force, which results in runoff. Thereby, the particles search for ways downwards comparable to rain runoff. They behave as spherules running off the 3D geometry.


The second simulation the toolbox is capable of is the tidal and river flooding simulation. It illustrates and evaluates the impact of different water levels in the area. To measure the inundation, a plane is moved from a given altitude up to a predetermined value. The plane is considered as the surface area of a river, lake or the sea. To get precise information about which part of the geometry is flooded, the toolbox calculates the intersection between the plane and the surroundings.


The SRTF provides information about the status of flooding-resilience for urban inundation, tidal and river flooding. The rain-runoff simulation provides information about the status of inundation and the level of erosion in the area.

The risk assessment of the rain runoff inundation is conducted based on the location of each particle after the simulation. The toolbox counts the number of particles that are in a specific range within every building. The range is set to two meters by default. This allows compromising the rating of a building in a negative way when it is surrounded by water under pressure. The value of the range distinguishes water that is running along the housing units from water that accumulates and pushes against buildings. Then the number is divided by the footprint area of each building. The higher the value the greater the risk of damages through flooding. This means that the density of particles near or at the buildings is responsible for the outcome of the evaluation. Buildings with a high risk of inundation are always characterized by an accumulation of particles nearby. The street network is treated similarly. Each street is further divided into segments at junctions or bends. Then the number of particles measured that are within a specific range near each street segment. The value is the same that is used for the housing units. The number is then divided by the area of the range. Now each building and each street segment is assigned with specific risk value. The information is visualized with color gradation in the viewport.


With the outcome of the rain runoff simulation, one can also conduct the rain-runoff erosion risk evaluation. Hereby the path of each particle is used to evaluate the runoff erosion risk. This helps to mitigate fast runoff and therefore the risk of damages caused by erosion, debris, and landslides. Urban planners can take this information into account when developing buildings, neighborhoods or cities. The toolbox visualizes the evaluation by means of the flow paths. Combined with the description of the velocity the user gets profound data for the area. The first concept seems to perform better because the affected area is smaller. But as it is visible in, the runoff in the east gets slowed down by the green space in front of the houses. That means that the buildings are not harmed by the debris. By contrast, in the first proposal, the overall area at risk is less but the buildings that are affected are hit directly by the fast runoff. 


The figures above depict the outcome of the tidal & river flooding simulation. The legend in the left bottom corner states, that the first concept hosts its housing units in a way that during a high tide of 8 meters, there are 32 buildings flooded. At the same water level, there are only 25 buildings at stake in the second concept. This means that 7 homes can be saved from severe damages due to flooding by changing the spatial layout. 

During the simulation, the risk assessment for the houses and the street network is presented. When the water level reaches the top of a platform of a building, it is marked with a red color. The toolbox applies a darker tone of red according to the depth of water. The depth is computed by iteration so each frame represents a depth of eight centimeters. It counts the number of iterations after a building is considered as flooded. In this case, the water levels that are deeper than 24 centimeters are considered equal. The values can be adjusted as needed. For this case study, the value is set to balance imprecisions and to match the threshold of lasting damages. The same methodology for assessing the risk applies to the city network. Hereby the lowest point of the street segment is evaluated. When the water reaches it, it is marked with a red color in the same manner as the risk assessment for the buildings.


The last part of the evaluation phase is called the mean risk assessment. It is related to the tidal and river flooding simulation and gives an understanding of the risk distribution in the area. Whereas the prior evaluation is useful for evaluating the site for specific water levels, the mean risk assessment shows the risk of all scenarios combined. In this case, every single state of the tidal and river flooding simulation is recorded to compute the mean value. The toolbox then colors all affected buildings and street segments according to its mean associated risk from low to high. 

The mean risk assessment provides useful information about locations that are not endangered by flooding and therefore suitable for e.g. housing units. Alongside comes the ability to divide a plot into parcels with different functions. For example, locations with a high risk of inundation are not suitable for housing or commercial estates but rather can be used for green spaces or public spaces with mobile structures such as markets.


Based on the evaluation of the two proposals, we prepared a third concept with minimal interventions. The result is proposal 3 and can be seen in the figure above. Now there are three ditches aligned downwards to control the runoff. Two are located in those areas where there were strong accumulations visible in the rain runoff inundation evaluation of proposal 2. One is in the north and one is in the eastern part of the region. Both ditches were trenched into the ground and both reach down to the shoreline. The third ditch is situated at the western border of the third district. Besides that, there is now a bridge over the ditch, connecting the second district with the surroundings. The third measurement is forestation in those areas where the risk of erosion is particularly high. The trees are illustrated as green dots in the viewport. Lastly, we elevated the living quarters of the Rubahs that are affected by tidal and river flooding. 

The significant change of the rain runoff inundation evaluation is the reduction of affected Rubahs by more than 50 percent. The rain runoff erosion evaluation shows significant changes as well. Due to forestation, the endangered area is decreased to 0.38 hectares. Regarding the tidal and river evaluation, the third proposal performs better as well. The number of homes vulnerable to flooding decreased by 20. The legend of figure 21 states, that there are still two Rubahs in the north with a mean risk value of over 50 percent.

Download the Rhino3D/Grasshopper Source Files

Please enter a password to see the content:

Wrong password, please try again
Rural Urban Metabolism

Rural Urban Metabolism

The videos on this site document the results of the integrated computational design summer semester course 2018 “Rural-Urban Metabolism – Metabolism-based Planning Strategies for Rural-Urban Transformation in Ethiopia” at the Bauhaus-University Weimar as part of the Advanced Urbanism MSc and European Studies MSc. Both study programs are continued in the future as Integrated Urban Development and Design master program.

The urban design course is related to the research project Integrated Infrastructure (IN³), which is an interdisciplinary international research project at the Bauhaus-Universität Weimar and the Ethiopia Institute for Architecture, Building Construction and City Development (EiABC). The project is funded by the Federal Ministry of Education and Research in Germany (BMBF), German Academic Exchange Service (DAAD) and German Aerospace Center (DLR).

Supervisors team: Sven Schneider, Philippe Schmidt, Reinhard Koenig, Abdulmalik Abdulmawla, Martin Dennemark

Project context

The transformation from a mainly agricultural society to industrialisation that is faced these days in Ethiopia is linked to substantial changes of the country’s rural and urban areas. With these shifts, the processes of urbanisation and expectations towards modernisation is seen as a chance to create new and adaptive urban planning proposals that meet specific needs and conditions of the Ethiopian development context in Sub-Saharan Africa. While the World Bank is promoting rapid economic growth for Ethiopia, still the country is one of the poorest countries in the world, and the question arises in how far urban design and planning can create concepts and flexible urban models that are reactive enough to stimulate different scenarios responding for  balanced development.

One of the main frameworks to create such balance for emerging cities are the United Nations Sustainable Development Goals. Different key factors like food security, energy, water and sanitation are linked to resource questions of material and land and how those can be influential on the development of prospective cities. Thus, for the development of new towns in rapidly urbanizing regions the understanding of material flows and circulation within the urban system is crucial when it comes about any building activity that determines the urban form and what we finally experience as urban, including open and public space and healthy living conditions. 

To better understand how such flows of material resources and energy are linked to building activities in rural  urbanisation processes and their impact on the existing environment, in our study project, we are referring to urban metabolism as a framework for urban design and planning of small cities.

Participants will be analysing urban patterns and flows of small cities, learn about the context between urban metabolism and its spatial implications and apply tools and methods for a spatial analysis and finally implement that knowledge in spatial models and concepts to simulate possible development scenarios. The findings should also make visible the opportunities and limitations of such concepts for disciplines concerned with urban development, taking into account environmental, social and economic factors.

Introduction Lecture

to the project “Rural Urban Metabolism” in Ethiopia by Vertr. Prof. Dr. Sven Schneider


by Constantin Friedrich Kozák, Jonas Wiel, Shunsuke Yoshida & Silke Weise

Bio Communal City

by Harneet Kaur, Truc Anh Nguyen & Yun Shu

SAN City

by Aurelija Matuleviciute, Marina Evstifeeva & Philip Schäffler

Seriti 2.0

by Andrej Sluka, Lina Ayser Jamil Halaseh & Siim Kuusik

Walkable City

by Furui Yang, Maria Dorothea Mönig & Ting-Yu Hsu

Frontiers of Water

by Alejandra Urrutia Pinto, Jakob Moritz Becker & Nils Fabian Voerste

Radius City

by Ayah Al-Sabbagh, Bastiaan Woudenberg & Xuanyu Li

Recreation City

by Mengxi Kou, Yuanji Shi & Yulin Wang

Balancing Wurer

by Michaela Mösing, Benjamin Rothmeier, Anthea Swart & Yunhang Wang

Cognitive Urban Design Computing @ FCL

Cognitive Urban Design Computing @ FCL

This post documents our research in the Cognitive Design Computing (CoDeC) workstream of the Big Data Informed Urban Design and Governance project at the Future Cities Lab in Singapore with status July 2018. It is structured in our research on generation, analysis, and exploration methods. We demonstrate our methods in the context of the synergy project Waterfront Tanjong Pagar in Singapore. In this context we used partially inputs from other FCL research groups. The idea of Cognitive Urban Design Computing is to combine unique human design competences with computational methods for the generation, analysis, and exploration of urban designs. The loop of analysis and generation methods is the basis for automated spatial synthesis. Design space exploration methods are used for the presentation and selection of synthesized design variants.

Research Team: Katja Knecht, Yufan Miao, Kateryna Konieva, Pol Foreman, Reinhard Koenig (contact author), Gerhard Schmitt, in collaboration with Dietmar Leyk.


The image illustrates the framework for Cognitive Urban Design Computing.

Overview of the spatial synthesis process for Waterfront Tanjong Pagar

Generation and Analysis

Kateryna Konieva

We use the Waterfront Tanjong Pagar area for demonstrating the automated loop of generation and analysis of urban design variants. The video below shows the parametric design workflow with various analysis methods integrated in the spatial synthesis process. Currently we use network analysis, economic potentials, view analysis, as well as solar and shadow analysis.


Yufan Miao

For the automatization of the spatial synthesis process, we use evolutionary multi-criteria optimization algorithms. Therefore we needed to develop an appropriate data structure to represent spatial configurations (streets, parcels, and buildings), which allows the application of evolutionary operators (crossover, mutation, and adaption).

Design Space Exploration

Katja Knecht

The spatial synthesis process we presented allows to generate a large number of urban design variants. To present these variants in a meaningful way to urban planners or stakeholders, we explore the usage of design space exploration tools and new interface concepts. The aim is to provide a platform to discuss, compare and evaluate variants based on analysis results in order to allow stakeholders to take informed design decisions.

Comparison of design alternatives using the beta.speckle online interface.

The following video shows how design variants can be filtered according to specified design and performance characteristics in the Design Explorer application:


How design alternatives can be assessed and evaluated in the Speckle online viewer, e.g. by comparing analysis results such as shading and sunlight hours analysis, can be seen in the following clip:

Building Typologies

Pol Foreman

In collaboration with the FCL synergy project Waterfront Tanjong Pagar led by Dietmar Leyk, we developed a parametric urban masterplan, which allows the generation of adequately detailed urban block typologies and buildings.


Big Data Informed Urban Design and Governance

The Cognitive Design Computing (CoDeC) workstream is part of the Big Data Informed Urban Design and Governance project at the Future Cities Lab in Singapore. The video below gives an overview of all workstreams of the project and how they are related to each other.

Acknowledgement This research was conducted at the Future Cities Laboratory at the Singapore- ETH Centre, which was established cooperatively between ETH Zurich and Singapore’s National Research Foundation (FI 370074016) under its Campus for Research Excellence and Technological Enterprise programme (CREATE).

Related Publications

Sorry, but the selected Zotpress account can't be found.

Please enter a password to see the content.

Wrong password, please try again
Workshop | Network analysis based dynamic urban simulation in Grasshopper

Workshop | Network analysis based dynamic urban simulation in Grasshopper

DeCodingSpaces workshop on network analysis based dynamic urban simulation in Grasshopper hosted by: SimAUD 2018, Netherlands, TU Delft, 04.Jun 2018

After a general introduction to spatial analysis methods, you learn in this workshop how to use components from the DeCodingSpaces-Toolkit in Rhino/GH for the quantitative analysis of urban space. Based on these analyses we implement a basic urban development simulation for a case study city. The presented DeCodingSpaces-Toolbox for Grasshopper is a collection of analytical and generative components for algorithmic architectural and urban planning. The toolbox is free software released by the Computational Planning Group (CPlan). It integrates established urban analysis methods, extend them with new features and introduces new methods for the analysis and synthesis of urban morphology.

Workshop files
Part 1


Analysis 2d, 3d Isovist

Download Grasshopper file


Analysis 2d, 3d Isovist

Download Grasshopper file


Analysis 2d, 3d Isovist

Download Grasshopper file


Analysis 2d, 3d Isovist

Download Grasshopper file


Analysis 2d, 3d Isovist

Download Grasshopper file

Part 2


Analysis Street Network

Download Grasshopper file



Analysis Street Network

Download Grasshopper file


Analysis Street Network

Download Grasshopper file


Analysis Street Network

Download Grasshopper file

Part 3




Part 4


02| More Information

Please find more detailed information in the related paper on Urban Simulation with Grasshopper for Rhino3D.


Pin It on Pinterest