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The City Biosphere

A novel theoretical and experimental methodology for the identification

of catalysing mutations in city generation, assembly and development

Sofia Georgakopoulou1, Daniel Zünd2, Gerhard Schmitt3 ETH Zurich, Switzerland.

http://www.ia.arch.ethz.ch/

1georgakopoulou@arch.ethz.ch, 2zuend@arch.ethz.ch, 3gerhard.schmitt@sl.ethz.ch Abstract. This paper introduces a new experimental city generation, assembly and development platform, the urban mutations platform. We describe in detail a methodology for modeling urban systems and their dynamics, based on self-organization principles. The urban area is seen as an organism comprised of different “body parts”, the urban subunits. Upon creation of an initial 3D urban environment, it is possible to add to the subunits the so-called mutations, i.e. structural and functional components that can have beneficial or detrimental effects to the future city development. After addition of the mutations we allow the city to reorganize itself and observe possible changes in the urban configuration. These changes can be directly correlated to the added mutations and their urban qualities and allow us to probe the effect that different structural and functional elements have on the dynamic behaviour of the city, when placed at specific locations. Keywords. Self-organization; mutation; urban qualities; urban grid; urban mutations platform, UMP.

INTRODUCTION

Organisms are complex systems, comprised of many different subunits, each serving a specific function. They are capable of, among others, response to stimuli, growth and development, and regulation of their internal environment. Proteins (from the Greek “πρώτος”, which means “primary”) are a fundamen-tal part of all living organisms. They function as ma-jor structural components of body tissues (muscle, hair, collagen, etc.), and as enzymes and antibodies (Stryer, 1988). Proteins are assembled through the step-by-step addition of an array of 20 essential compounds called amino acids. The order in which the amino acids are added onto the growing pro-tein chain is determined by the organism’s genetic

code: each amino acid is the combination of 3 bases of the DNA. In order to mutate a specific amino acid in a protein, a scientist needs to follow a detail de-constructive and rede-constructive process: a subunit of the amino acid sequence containing the targeted amino acid is constructed and at least one of the DNA bases that make up the specific amino acid is exchanged; finally, the original subunit is replaced by the newly constructed one in the protein (Geor-gakopoulou et al., 2009). The amino acid sequence of a protein defines its three-dimensional structure and consequently its function.

Cities have often been compared to organisms in literature. In her book “The Death and Life of Great

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American Cities” (1961), Jane Jacobs writes: “Cities happen to be problems in organized complexity, like life sciences. They present situations in which half a dozen or several dozen quantities are all vary-ing simultaneously and in subtly interconnected ways”. Urban scientists have since long understood that the problems leading to a degradation of a city are too complex and have roots in too many differ-ent aspects of the city’s structure and function to be promptly identified and successfully treated simply through observation. They have therefore often turned to using techniques taken from biology and other life sciences. Very common is the analogy to living organisms and their cardiovascular networks, when studying urban networks such as traffic, en-ergy and other resources (Odum, 1971; 1973; Sam-aniego and Moses, 2008). The analogy is also made when studying the ecology of a city system, where scientists often refer to the city’s metabolism and footprint (Decker et al., 2000; Luck et al., 2001; Deck-er et al., 2007).

Similarly to life sciences, where large amounts of complex data need to be analysed and understood, various computational methods have been em-ployed by scientists in order to simulate the dynam-ics and describe the complexities within a city. Land use patterns as well as traffic organization are com-monly studied using CA (Simon and Nagel, 1998; Batty et al., 1999; van Vliet et al., 2012; Vasic and Rus-kin, 2012), while more computational methods and models, such as self-organizing maps (SOMs), fluid and system dynamics, agent simulations and com-binations thereof are emerging in order to tackle a city’s social, environmental and structural problems (Castilla and Blas, 2008; Tuia et al., 2008; Wang and Feng, 2011; Lauf et al., 2012).

This paper aims to bridge the gap between life sciences and urban sciences and introduce an inter-disciplinary approach towards a comprehensive the-ory, which can be used to study cities with diverse structural and cultural characteristics and at differ-ent stages of evolution. A typical way of studying complex systems is by reducing the problem into smaller sub-problems and examining one specific

area of the system at a time. Thus it is possible to gain a thorough understanding of the functioning of each part (area / factor) before gradually putting the pieces back together and studying the interac-tions within the whole. The main goal in this study of the urban environment is to develop a theory according to which it is possible to identify “muta-tions”, i.e. single factors that can affect the well being of a city.

METHODOLOGY

The inspiration for this project is derived from high-er organisms, these vhigh-ery complex biological systems that function with great efficiency and in which every part has a specific role and performs a special-ized task. Higher organisms are comprised of amino

acids, which come together to construct proteins,

essential elements of the organism’s body parts and functions. In our analogy body parts are compared to different urban subunits, proteins to specific structural and functional urban elements (hereby called “mutations”) and amino acids to urban quali-ties (Figure 1).

In particular, our framework works as follows: at the first step we consider four different subunits: residential, industrial, old city and commercial. The generated subunits actually assemble into a city only when their placement in space is optimal; fail-ure to assemble may signify i.e. that the subunits are too far apart and don’t “see” each other, or that con-flicting subunits are placed too close together (i.e. according to the areas mentioned above, an indus-trial subunit right next to a historic centre).

In an analogous way that proteins are made up of amino acids whose properties give the protein certain attributes, city areas are seen here as com-prised of structural and functional elements (the mutations), each encompassing several urban

quali-ties that make up the area’s character and function.

As soon as the city has formed out of its assigned urban subunits, the mutations are added. Essen-tially this means that certain areas within an urban subunit will be altered. Mutations are structural or functional city elements, such as roundabouts,

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play-grounds, landmarks, pedestrian areas, (air)ports, changes in land use or zoning laws, but also aban-doned industries, inefficient buildings, dark alleys, run-down squares, criminal activity or demonstra-tions (Figure 2). Eventually, geographical elements can be included.

As mentioned above, every mutation encom-passes a series of urban qualities, each bearing a grade from 0 to 1. In our framework, these grades are translated into characteristic colour compo-nents. The urban qualities will be eventually select-ed and gradselect-ed after thorough investigation of the related literature (see an example of urban qualities in literature in Table 1). Currently, in order to test our framework and experimental platform, we have completed a first selection of possible urban quali-ties, which is shown in Figure 3 (Koltsova et al., 2012).

Effectively, mutations are characterized by a dis-tinct multi-dimensional colour code (Figure 4), each component reflecting the grade of an urban quality. As mutations are added to the city grid, their colour codes interact and reorganize themselves according to self-organization rules (Kohonen, 1982a; b; 1983; 1985; 1990).

Self-organization is commonly seen in litera-ture as a way to organize complex data by cluster-ing observations with similar attribute patterns in space (Spielman and Thill, 2008). In the field of ar-chitecture and urban planning it is commonly used as a method to manage and visualize data such as demographics or urban sprawl (Spielman and Thill, 2008; Arribas-Bel et al., 2011), to create and distort meshes (Castilla and Blas, 2008), or for identification of patterns of urban functions (Diappi et al., 2004);

Figure 1

Analogy between and organ-ism and a city, as visualized in the context of the presented project.

Urban

qualities grading0 0.2 0.4 0.6 0.8 1 literature ref.

sociability limited enriched (Hunter, 1979)

accessibility long distances short distances (Lynch, 1960)

green space damaged enhanced (Tibbalds et al.,

1993)

openness obstructed open (Stiles et al., 2009)

imageability generic distinct (Ewing and

Handy, 2009)

complexity no variety visual richness

Table 1

Examples of urban qualities derived from literature.

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the latter study bears the closest resemblance in the use of self-organization as the one described here, however it is not used as a direct tool for configuring city organization.

In this study self-organization acts as the un-derlying mechanism according to which the city areas and functions are (re)distributed every time a new mutation is inserted into the urban plan. The mutation-specific self-organizing codes distort the city grid and quantify (on the city level) parameters such as:

the type of disturbances caused (negative, such as pollution, criminality, traffic, abandonment, but also positive, like job creation, commerce, cultural life),

• the gravity of each disturbance (how strong is the mutations’ influence, from severe to

be-nign),

• the size of the affected area (may vary depend-ing on the mutations’ position in the city).

PRELIMINARY RESULTS AND

DISCUS-SION

The developed experimental platform – the urban

mutations platform, UMP – is based on the attrac-tive city generator (Augustynowicz et al., 2010): an interactive tool for the creation of virtual cities using

Figure 2

Schematic representations of exemplary urban structural and functional mutations.

Figure 3

Example of possible urban qualities and a first estimation of their values, which could characterize the different mutations.

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physical objects (Figure 5). Each object represents a different city area, which is characterized by a specif-ic colour. The recognition of a certain colour and its assigned area prompts the creation of a specific pat-tern of the urban grid. Upon creation of the city grid, characteristic buildings rise on each block. The tool is created on Java-based Processing (Fry and Reas, 2011) and uses L-systems (Lindenmayer, 1968a; b) to distort an urban grid based on the movements of the coloured objects on a given surface. The output is a growing urban environment, which, though not thorough in its urban rules, gives the user insight into the complexity and dynamics of urban evolu-tion.

The attractive city generator has several distinct advantages that make it a very good basis for the newly developed platform. Primarily, it has a robust user interface which is able to respond to changes made by the user with minimal delay: when a user

changes the configuration of the coloured objects, the platform reads the new input and almost simul-taneously translates it into a new three-dimensional urban environment, thus providing direct feedback to the wishes and visions of its users. For this, it uses an efficient colour-recognition code, which also makes it very versatile in terms of having different types of colour-based input sources. Additionally, it features minimal 3D design that allows for a certain level of abstraction in the final result, since the goal is not to recreate exact cities in 3D, but to reproduce the general characteristics and ambience of a cer-tain city.

On the other hand, the attractive city generator did not feature any educated interaction among the urban subunits, other than a very simple shrinking or growing of certain areas depending on the posi-tion of the rest. Moreover, there was no possibility to intervene within a subunit, so the city was

eventu-Figure 4

Example of an input (middle) and output (right) self-organization code including mutations with characteristic qualities (left).

Figure 5

Prototype for the UMP platform: The attractive city generator.

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ally made up of a clear segregation of four distinct areas.

In the UMP we are exploiting all the advantages of the attractive city generator, while addressing its disadvantages. The platform is able to translate the colour and position information of input markers – each signifying a different urban subunit and, later, mutation – into a dynamic city pattern. However, its main strength and contribution is in the analysis of the interaction between the different subunits, ing the rules of self-organization. In addition, the us-ers are able to intervene within the structure of the subunits by adding different mutations. The design of the buildings remains minimal, to ensure at all times an immediate and dynamic response of the city to user input.

The self-organizing code is colour-based and tries to balance the clustering of similar colours with retaining the overall topology of the grid. The first version of the code is kept very simple. Each cell in the grid is characterized by four colour dimensions: red, green, blue and yellow. The radius R of the in-teraction is limited to one fifth of the grid’s diagonal and it drops by 1/3 with the addition of a mutation, in order to keep the mutation effect more localized. The time constant depends on the number of itera-tions, while the radius decay and learn decay are typ-ical exponential decays. Finally, the influence among the cells is also exponential and depends on the distance between two nodes. The parameters that define the interaction between the cells are summa-rized in Table 2. In the following examples we have started with a very basic stetting. We have initialized

the self-organized grids based on different urban morphologies, i.e. city centre (depicted in red), com-mercial centre (in blue), industrial area (green) and residential area (yellow). Next we allowed the areas to interact, by running the first round of iterations. Once the grid has stabilized, various mutations are added in parts of the new grid – seen as small pools of colour. The mutations are also very basic, which means each one represents an area fully, and no mixed-qualities mutations are allowed. On the sec-ond round of iterations, the mutations are now in-teracting with the areas. The result can lead to very different outcomes in terms of the areas’ character and reorganization upon the addition of mutations, depending on the position of the mutation and the size of the various areas.

In the first case, addition of the mutations leads to a complete reversal of the affected areas and the creation of a new area (Figure 6a). A city-centre-type mutation (i.e. cultural centre or theatre depicted in red) in an industrial area (green), in combination with an industrial-type mutation (i.e. a new industry, in green) within a similar-sized city-centre area (red), leads to the reversal of the two. At the same time a new residential area is created upon addition of a residential-type mutation (i.e. favourable landuse and zoning laws) in previously empty plots.

In the second case (Figure 6b) we have partial change of a large industrial area into a commercial one by addition of a commercial-type mutation (i.e. a commercial skyscraper or office building). Moreo-ver, addition of a residential-type mutation in a small area of empty plots, leads to the creation of a new Input parameters

four dimensions R, G, B, Y

number of iterations n = c constant number of iterations

learn rate L = c constant learn rate

radius R = (h + w) / Rf h and w are the grid’s height and width

Rf = 5 and triples with mutation addition

time constant t = n / logR

radius decay Rdec = R x e-n/t

learn decay Ldec = L x e-n/t

influence e-d2/2Rdecn

Table 2

Self-organization parameters, as set for the preliminary results of the UMP.

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pool of residential plots, party on the expense of the adjacent commercial area.

Figure 6c presents an example where an indus-trial area is added in empty plots, in the same way as the residential areas in the previous examples, while the addition of a city-centre-type mutation within a well-developed commercial area leads to no changes, as there is already a large city-centre nearby. Finally, Figure 6d shows an example where the mutations added have no effect in the already well-established and balanced areas.

Above we have illustrated the proof of concept of how to use self-organization in order to probe city development and dynamics. The results so far show that a certain level of self-organization can explain changes that happen in an urban expanse on a larg-er scale and long timeframe. In ordlarg-er to identify to which extend these results are able to describe real city dynamics, further analysis must be undertaken. This will be discussed further in the outlook section.

OUTLOOK

Several important steps are still necessary in order to verify and complete the UMP. On a first level, it is central to introduce the possibility to add “mixed-qualities” mutations, such as the ones described in figure 3, rather than the basic one-colour mutations used in the proof of concept. This will allow for more diversity in the types of interventions into the vari-ous city areas, which corresponds better to reality. Moreover, it will also add diversity within the four, currently very strictly assigned initial areas.

The final urban grid patterns can be analysed by calculating their first and second derivatives. Measurements and calculations of difference spec-tra (patterns) is a common practice while studying the functional characteristics of complex systems (Georgakopoulou et al., 2002; 2003; 2006a; b). By subtracting the patterns created by a mutation event and the initial grid, or by two different muta-tion events, we derive the difference pattern, which contains characteristic information on the effect of each event on the city. Comparison of difference patterns allows for grouping seemingly irrelevant

mutation events and can lead to a deeper under-standing of the underlying reasons causing specific city disturbances. Eventually, a list of mutations will be compiled, indicating which are the structural ele-ments of great importance that are unique in their properties and functions.

In order to verify the ground-truth of the results of self-organization in a city, a historical analysis on specific urban areas will be undertaken, using his-toric maps and information on urban development plans throughout the years. The long-term and large-scale effects of introducing new structures within these areas will be probed. These will be com-pared to the results derived from the self-organiza-tion results of a similarly arranged city. Thus, we will be able to fine-tune the input parameters that de-scribe the self-organization code and estimate the exact extend to which this technique can simulate the future of a city.

The UMP can then be extended as a city-plan-ning tool, with an improved user interface, which will take advantage of the latest technologies in touch screens and digital communication. The users will be able to test their planned development and add urban as well as geographical elements. The Value Lab of the chair of Information Architecture will be used for the development of a program us-ing the touch table interface. In the future, the ICG should be possible to use on any tablet or computer, for easy access to all professionals and it will become an invaluable tool for planners and stakeholders.

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Examples of different urban configurations before and after the addition of urban mutations. Top left: initial user-fed organization of four areas: city-centre (red), commercial centre (blue), industrial (green), residential (yellow). Top middle: first round of iterations leading to a self-organized urban expansion. Top right: the 3D representation of the urban area after the first round of iterations. Bottom left: muta-tions added by users. Bottom middle: resulting urban expansion after the second round of iterations. Bottom right. 3D representation of the final urban area.

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