Wrapping Urbanism

Wrapping Urbanism — Collective Face of the City: Application of Information Theory to Urban Behavior of Tokyo

Jan Vranovský

T—ADS

Š Jan Vranovský, 2016 Version for digital distribution

Wrapping Urbanism — Collective Face of the City: Application of Information Theory to Urban Behavior of Tokyo

Obuchi Laboratory Advanced Design Studies Graduate School of Engineering Department of Architecture Jan Vranovský Primary Advisor: Professor Yusuke Obuchi Collaborative Advisors: Professor Manabu Chiba Professor Takeshi Ito Professor Jun Sato Course Assistant: Toshikatsu Kiuchi Technical Advisors: Kosuke Nagata Kensuke Hotta Yosuke Komiyama

2016

T—ADS

2016 Obuchi Laboratory Advanced Design Studies The University of Tokyo Jan Vranovský Printed in Tokyo, Japan, 2016 Obuchi Lab www.t–ads.org Obuchi Laboratory University of Tokyo Graduate School of Engineering Department of Architecture 7-3-1 Hongō, Bunkyō–ku Tokyo, 113–8656 Japan

I would like take this opportunity to personally thank the following people for their contribution and inspiration to this thesis book: Professor Juval Portugali (Tel Aviv University), Professor Hermann Haken (University of Stuttgart), Professor Joy Hendry (University of Oxford), my project and technical advisors; namely Professor Yusuke Obuchi, Toshikatsu Kiuchi and Kosuke Nagata (all of The University of Tokyo), Alisha Ivelich, my family, especially my brother Karel Vranovský Jr. and father Karel Vranovský, and finally my beloved Chiawen Lin. Without these people, this book could not have been written.

Jan Vranovský

1 Introduction 18

1.1 The Amoeba City 20

1.1.1 Change 20

1.1.2 Construction Site 22

1.1.3 Collective Face 25

1.2 Morphology of Change 30

1.2.1 Forming the Face 30

1.2.2 Action and Reaction 31

1.2.3 Homogenization 32

1.2.4 Urban Device 33

1.2.5 Project Objectives 36 2 Urban Device 38

2.1 Preceding Research 40

2.1.1 Spatial Explorations 40

2.1.2 Distributed Living Space 42

2.1.3 Memory Explorations 44

2.2 Methodology 46

2.2.1 Introduction 46

2.2.2 Information Theory 47

2.2.3 Tendency of Change 48

2.2.4 Alternative Approaches 49

2.3 Device 52

2.3.1 Device Structure 52

2.3.2 Data Collection 54

2.3.2.1 GIS Data 54

2.3.2.2 Street View 54

2.3.2.3 Self–Driving Cars 54

2.3.2.4 Category Labels 55

2.3.3 Data Analysis 56

2.3.3.1 Image Processing 56

2.3.3.2 Machine Learning 56

2.3.3.3 Facade Evaluation 58

2.3.4 Data Categorization 65

2.3.4.1 Hierarchical Cluster Analysis 65

2.3.5 Information Theory-based Evaluation 68

2.3.5.1 Information Entropy Calculation 68

2.3.5.2 Spatial Distribution Sensitive Method 69

2.3.5.3 Shannon's Information Gradient 72

2.3.5.4 Other Variables 74

2.3.5.5 Summary to Shannon's Information Calculation 76

2.3.6 Content Visualization 78

2.3.6.1 Self-sufficient content 78

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2.3.6.2 Curated Content 78

2.3.6.3 Hybridized Content 80

2.3.6.4 Spatial Logic of Content Selection

2.4 City–Device System 86 3 Caste Study 90

3.1 Target Site Analysis 92

3.1.1 Case Study Objectives 92

3.1.2 Mapping the Site 94

3.2 Facade Analysis and Categorization 98

3.2.1 Target Site Facades 98

3.2.2 Model Data Pool Facades 106

3.2.3 Concluding Remarks to Facade Categorization

3.3 Information Calculations, Visualizations and Suggestions 4 Conclusion 122 5 References 126 6 Appendix 132

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1

Introduction Introdu

Introduction

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nuction

Wrapping Urbanism – Jan Vranovský

The Amoeba City

1.1

Change

1.1.1

1

One thing that clearly distinguishes Tokyo from majority of Prolific Japanese architect and theorist other cities in Japan is the pace of inner metamorphosis. Yoshinobu Ashihara describes Tokyo as an “amoeba city�, constantly changing While the country as a whole is quickly aging and many and renewing, likening it to a living urban and rural areas are facing growing issue of depopula- organism rather than to the typical tion and the related problem of derelict, abandoned houses Western city, or the idea of what city is (Ashihara and Riggs 1989, 58).

(Otake 2014), Tokyo metropolitan area maintains vigorous metabolism with high demand for properties and accordingly high prices. Similar description would fit many other developed countries and their respective capital cities, but the situation comes with a twist in case of Japan: lifespan of its buildings is unusually short here.

Yukio Komatsu of Waseda university explains that estimated building life span in Japan is bellow 40 years (as in 1987) (Komatsu 2010), while Yoshiharu Tsukamoto argues that average life expectancy of a Japanese house is as low as 26 years, value strikingly different to the one in UK for instance (100 years) (Kitayama, Tsukamoto and Fig. 1 → Construction site Nishizawa 2010, 29). This phenomenon cannot be attributed to mere in Otsuka, Tokyo 1

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physical durability of the structures. Instead, a number of different explanations can be speculated, such as Japan traditionally being a timber–based culture facing fast physical deterioration as natural part of life–cycle of material culture in general. This is connected with concepts of mono no aware (specific Japanese notion of impermanence) and wabi-sabi, celebrating, among other things, transience (Juniper 2011, 27). Traditional inclination to purity and cleanliness could be another possible factor (this phenomenon can be well illustrated on the famous Sengu ceremony: traditional renewal of the Ise inner shrine taking place every 20 years). More temporary and pragmatic reasonings can be also suggested, such as frequent revisions of the construction seismic codes ever since Urban Building The Market of “Lemons”: Quality Law (1919) and later Building Standard Law (1950) were Uncertainty and the Market Mechanism

by George Akerlof is theory describing introduced, change of life style connected to industrial rev- issue of asymmetric information in marolution and growth of Japanese economy after World War ket with used goods, where customer II or, according to some, more specific economic concept doesn’t have access to information about true value of the good, while the described in Akerlof’s The Market for “Lemons” 2, offering seller does. The customer therefore possible explanation to extreme economic deterioration of always assumes low value, making properties in Tokyo (Yamazaki 2014, 138). Koh Kitayama high-value second hand goods difficult to sell with profit for the seller, thus offers another perspective when he mentions that 'the undesirable, driving them away from the short life-cycle of Japanese architecture is said to stem market. (Akerlof 1970, 490–492). from a social system that guarantees change.' (Kitayama, Tsukamoto and Nishizawa 2010, 21)

It is also worth to take into account that present time Tokyo is facing ending life-cycle of buildings constructed at the end of economic bubble period in late 1980s and early 1990s, making the phenomenon even more visibly present in the streets of the city.

1.1.2 Construction Sites 2

This very situation, however, results in a unique set of behaviors and side–products to be explored and exploited in order to find tools to measure, understand and ultimately control vast range of problems, such as the specific infrastructure and construction industry needed to maintain this state of permanent transformation. Perhaps the most visible and architecturally pronounced are the ubiquitous construction sites, present in Tokyo metropolitan area in such numbers they became almost quintessential part of its urban landscape.

Construction sites are topic of growing popularity not only amongst architects, but also artists and photographers.

Perhaps parallel to tendencies in contemporary art and design where emphasis on process is increasing and often taking central role in the

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project, construction sites are stepping out from being viewed as pure technical necessities and their complexity is becoming recognized source of inspiration. As Glaser points out, 'in recent years the building site has become the symbol of a society aspiring to progress and growth.' (Glaser 2008, 12).

In the specific cultural context of Japan, construction sites and their enclosed forms could be analyzed from more unusual, non-technical point of view. In her book titled Wrapping Culture, social anthropologist Joy Hendry examines the subject of both practical and symbolic culture of wrapping (in the sense of spacial enclosure as well in Fig. 2 ↑ Construction site in Higashi the sense protection or padding) as uniquely pronounced Shinjuku (2015) and symbolically significant within Japanese culture context (Hendry 1993, 98–122). Similarly, Roland Barthes argues that in Japan, it is the packaging that often gives actual meaning to otherwise insignificant content (Barthes 1982, 43–47). In perspective of these theoretical positions, we could potentially look at construction sites in Tokyo through the lenses of a specific Japanese cultural tendency: a semantically void space that, once wrapped meticulously in protective drapes, posses capacity to offer surprisingly rich canvas for meanings and function.

While the scaffoldings and construction areas are typically display of maximal utility in minimal space and for safety and legal reasons need to stay inaccessible to public (thus being difficult to exploit in any way), the external surfaces are highly visible, even accessible, yet having no other public function than to protect from the ongoing construction. It is these protective wrappings, or drapes, that form

Fig. 3 → Construction sites populating central Tokyo

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hugely underutilized resource in the city. According to our calculations (see chapter 5), about 3,3 percent of visible building surfaces is under construction at any given time in central Tokyo. But even more interesting than the sheer amount is spatial distribution of the sites.

Once we plot positions of sites recorded in past four years on map of Tokyo (using publicly available data for Minato, Shinjuku and Shibuya districts), we see largely randomized, "democratic" spatial distribution within the urban fabric. There are very few repeating typologies in Tokyo with similar behavior and in comparable amount, only vending machine distributions seem to create similar network. Compared to convenience stores locations (Fig. 4), whose distribution seems to correlate strongly with high concentration of traffic, construction site distributions reveals a unique behavior within the city and could be potentially described as a permanent network with dynamic position of its nodes.

Fig. 4 �

Simplified diagram of construction site (blue) distribution within the urban fabric of Tokyo, compared to convenience stores (red)

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1.1.3

Collective Face

In his short text Investigation in Collective Form (1964) Fumihiko Maki addresses the issue of the so called “collective form”: the often unintended interaction between separate structures forming the urban fabric (more on that in 2.2.4). The work refocuses the emphasis of urban space from individual facades to their role and connection to deeper urban systems. The shift from interest in facade understood as a singular, separate architectural issue unique to individual building, to facade of a street, neighborhood or city, (that is to say, a collective visual system), has to do with question of function of the facade in context of human cognition (reading) of the city space.

In order to understand and expand on this concept, let us now consider the intersection between the perspective of an architect / builder and his pragmatic options within the process of achieving the set of goals he is asked to fulfill within his task (as well as within the economic and legal boundaries of the project), and the perspective of the inhabitant / visitor, walking through finished or partially finished urban space. The result is a sum of visually perceivable space, modeled on one hand by functionality of the buildings, on the other hand by the necessity to orient oneself within the spacial fabric of the Fig. 5 ↑ Facade with unusual semantic city. Let us now omit everything that is decided and formed meaning: Kazumasa Yamashita, Face outside of the intersection of these two perspectives House, 1974 (Jencks 1977, 116) such as associations, personal memories or taste (both of the architect and the observer), customs, immediate economic needs, contemporary fashion etc. The result is a semiotically nearly void space, an endless parade of facades, masks, shapes and other visual patterns. Within such theoretical position on the city, a singular facade is just as important as the sum of all of them and one cannot be considered without considering the other. It’s the context of an individual facade, rather than its individual and unique features, that defines its relevance to the observer. This emergent quality of urban landscape, the sum of all facades irreducible to the qualities displayed by an individual one, but impossible to describe without consideration of the individual ones, is what we will consider as "collective face" of the urban unit (be it a single street, its segment, a city district, or even the entire town). The properties of this collective face are non–random and statistically significant, their role in human life is

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non–trivial due to their relevance to cognitive processes related to our understanding and orientation in space.

Without a doubt, reduction of an entire city to the emergent properties exhibited by the visual network and pattern of its facades is a conscious simplification and reduction. The full sum of systems that allow us to understand and inhabit physical space is much broader. Meanings, human interactions, customs, personal and collective memory constituting an undeniably large role in perspective of urban space.

However, the "collective face" may have a considerable role, and perhaps more importantly, it is an aspect of urbanism that architects and engineers can actively participate and improve upon.

It appears to us that a considerable suite of technical and mathematical tools can be employed in order to understand and influence it.

We will discuss the relevance of collective form to cognitive processing in chapter 1.2. In section 2.2, we will discuss how its properties can be described, measured and analyzed in quantifiable fashion.

The following chapters will discuss a proposition of practical application of the theoretical assumptions. We will propose an "urban device", a set of self-regulated and self-governed tools and steps allowing architects to directly influence the collective space from in a bottom– up fashion, without having to rely on broad overarching legal guidelines or restrictions. Utilization of the "void space" of construction sites and their wrappings will be part of this process.

Fig. 6 → Various facades articulating street in Hamamatsuchō, Tokyo Fig. 7 ↓ (Next page) Diagram of collective face based on various facade classes defined by complexities, hues, brightnesses and other features and categorized by machine learningbased algorithm

Chapter name / Subchapter name

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Morphology of change

1.2

Forming the Face

1.2.1

One could say that the issue of governing mutual visual relationship between houses, that is to say, building collective form of the city, can be easily done through classic regulation and city planning.

Governments of many cities in Europe use such tools in order to protect their historical centers: a precious commodity in the age of free movement and unlimited tourism. But how did these collective forms, so adorned in these days, emerged in the first place?

Protecting them might make in many cases sense, but it is also a form of admitting that something changed, that we are not part of the system that allowed them to appear anymore, or perhaps that our current system is too brutal in its lack of technical and economical limitations and can change too much in too short time, not allowing for the slow, emergent processes to take place the way it used to.

In case of Tokyo, as well as many other Japanese cities, we're facing somewhat unique situation. The amount of buildings

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traditionally understood as heritage is low due to World War II, large part of the city turned into tabula rasa after the bombing, and so forming face of the city purely through protection is out of question.

At the same time, Tokyo's rapid metabolism and short life span of buildings provides us with a petri dish of hyper–metamorphosis waiting to be grafted with a system somehow governing its tendencies.

1.2.2

Action and reaction

All topics mentioned in previous sub–chapter are samples of a single fact: city is a dynamic system, one similar to organism: growing, shrinking, morphing, but never reaching any finite form.

Yet it is a system officially governed and to large degree formed by top–down processes: big decisions rather than small day–to–day reactions. Although generally there's nothing wrong about that, these traditionally top–down–governed fields (in case of the city we typically talk about urban, architectural and infrastructure planning systems) are, while often being powerful and efficient tools, often unable to fully communicate with each other, as well as to cover all the issues and their nuances. This results in emergence of various non–intentional "border conditions": states, forms, phenomenons and behaviors existing either as side–effects of often surprising Overlay interaction between large systems, or as islands of behavConditions iors for some reason untouched by these systems. Although often traditionally considered as of secondary importance, System 1 these conditions form huge part of what cities really are and how do we, inhabitants, experience them. One of the System 2 reasons why are these conditions typically being ignored is seeming lack of feasible tools through which planner and authorities can deal with them: more top–down intervenSystem 3 Island tions only lead to more and more side–effects. With every Conditions next iteration of traditional attempts to study them we finds our selves facing increasing amount of increasingly Fig. 8 ↑ Border conditions between complex issues. governed systems

It is here where complexity theories of cities, emergence and cybernetics open different perspective from which we can attempt to approach difficult questions. This paradigm shift is not without parallel to other fields of science: in biology, we can observe leap from understanding organism based on their observable morphological and behavioral features to studying their DNA, that is to say, core rule–sets defining what they are. In evolution, we study rules that govern the ongoing change. Also parallel to science, this

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shift comes hand in hand not only with change of perspective, but also evolution of technology: as DNA could have been never discovered without advanced microscopes and screening techniques, cybernetic simulations are nearly impossible to run without recent advances in computation. And so while basic theoretical ground for such approaches exists for at least a hundred years now 1, First modern seminal paper on the wider spread of practical applications was limited with what topic emergent urbanism was written by Patrick Geddes (looking at cities from technology can do. This gap has been bridged during the Darwinian perspective) in 1915 (Dovey 2014), basic rules of cybernetics were last years however.

Current state of technology gives us a unique laid down by Norbert Wiener in Cybernetics, or Control and Communication in opportunity and perhaps even responsibility to start asking the Animal and the Machine (1948). questions and theorize about systems that wouldn't be imaginable just a decade or two ago for simple lack of computation power allowing us to run simulations proving or revealing those.

In context of urban design, focal point of interest can shift to the embedded urban evolutionary tendencies and envisioning possible techniques to govern them rather than with traditional top–down urban planning and zoning codes as they appear to be inefficient when dealing with complex evolving and morphing systems, by default being unable to automatically respond. That doesn't mean traditional urban plan isn't important or needed anymore, far from it. However, set of cybernetic bottom up "subsystems" complementing classic planning could strongly improve its feasibility and ability to organically react to equally organic behavior of the target.

1

Homogenization

1.2.3

The rapid process of metamorphosis Tokyo is often understood as negative due to its environmental and economic implications, as well as question of sustainability. But it also comes with issues more directly related to acute quality of the urban environment: the constant erasure of its own memory; never–ending plastic surgery of its own face. In case of this project, the more usual interest in change of the city shifts from questioning the speed or magnitude to slightly more abstract concept of tendencies: namely the tendency of development of the face of the city, that is to say, interior of streets defined by sets of exteriors of the buildings.

Motivation behind such scope of interest lays in concerns regarding extensive homogenization of architectural and construction production. To be more specific, concern about what kind of impact has this standardization on urban environment. Essence of these concerns is captured in text by American urban theorist and activist

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Jane Jacobs: 'If the sameness of use is shown for what it is – sameness – it looks monotonous. Superficially, this monotony might be thought of as a sort of order, however dull. But aesthetically, it unfortunately also carries with it a deep disorder: the disorder of conveying no direction.'

(Jacobs 1961, 223). Stan Allen mentions in his Points + Lines: Diagrams and Projects for the City that 'As we move from economy dominated by technologies of production to economy dominated by technology of reproduction, the differences between things seem less significant than the potential sameness of images.' (Allen 1999, 14). Fumihiko Maki's position on the issue is slightly more technology– optimistic: 'Homogenization of environment is not, as many people feel, the inevitable result of mass technology and communication. These same forces can produce entirely new products. With modern communication systems one element (cultural product) will soon be transmitted to other regions, and vice versa' (Maki 1964, 22). It is thus possible Fig. 9 ↑ Variation in tile color (as an to conclude that industrialization and standardization of attempt to differentiate corners inside architectural production is a relatively new issue as it's underpass in Kanazawa) illustrates issue homogeneous environment pose for hu- related to industrial revolution and global market, and that man perception and navigation in space the consequent homogenization of urban environment, on similar level historically unprecedented, leads to new type of challenges which need to be addressed.

1.2.4

Urban Device With all previously mentioned topics, points and concerns in mind, a new type of urban device can be speculated. This device would be a cybernetic (that is adaptive, self-regulating) mechanism capable of reading change of face of city: tracking what kind of impact new houses and their facades have on it as they appear and disappear, and ultimately stepping in the process by choosing and publicly suggesting alternative architectural options which would lead to better whole.

Generally, this would be a method that understands relationship between particle (house, building) and system (street, neighborhood, city) on level of appearance, that is to say, on level of visual features which make both the particle and the system legible for human.

Through repetition of measurements and calculations, it would also understand tendencies in development of these relationships.

Another speculation is that construction sites, the side–products of the change within the city, could be used for communicating

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Fig. 10 ↑ ↗ Example of moderate homogenity as a result of mass housing construction in Adachi-ku (2016)

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Chapter 1.2.3

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results and suggestions of those sets of analysis. They would thus become "front–ends" of the device, essentially huge urban displays, visualizing in real environment and real scale alternative architectural designs chosen by the system or, alternatively, by curator, based on criteria defined by the system. This is expected to have two types of results: from short term point of view (aside from the very fact it communicates specific changing condition of the environment) it transforms construction site with very little semantic meaning to distinguishable interactive architectural installation: every time displaying different content, thus being unique. Using logic explained

by Hendry and Barthes, we could say the device will wrap the semantically void construction site into meaning. This by itself should have positive effect on overly homogeneous environment. From long term point of view, the system is understood as a device for implanting architectural stimuli, real–life experiences with alternative building designs, which are expected to get reproduced to some degree in future development of the city. From this point of view the device is targeting primarily at inhabitants of the city, potential future homeowners, builders, or simply "users" of the city. The logic expects that iteration and imitation are strong mechanisms behind development of architectural tendencies, both locally and globally, and it's core idea could be linked to transgenic engineering were bits of DNA from different (yet typically not completely alien) systems are implanted in the target species in order to modify its further development (Fig. 12).

Furthermore, aside from already mentioned underutilization of construction site protective surfaces and their vast network in urban fabric, using them would come with another advantage: analysis and communication of change would be only happening when the change is happening, and always in relevant quantity. If the amount of change drops, amount of devices also drop, if it intensifies, amount of devices grow, intensifying the suggestion. If change stops, hypothetically, completely, the device naturally disappears, having nothing to analyze and govern.

Project Objectives

1.2.5

Primary objective of the research is to design a device capable of governing development of the face of Tokyo through bottom up interventions in order to avoid undesirable tendency toward excessive homogenization and lost of identity. In order to do that, methodology through which seemingly personal and abstract visual qualities of

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the cities such as "memorable", "distinct", "boring" etc. could be quantified and transformed into framework for building the device rule–sets needs to be established. Furthermore, the device needs to be designed in a way it could form cybernetic, self–regulating system once applied to the city, requiring minimum human intervention.

Fig. 11 → Augmented construction site breaking homogeneity of its surroundings

Fig. 12 → Diagram of the "transgenic" urbanism: current local architectural "gene pool" is being manipulated by infusion of foreign stimuli, which then spreads as it's replicated, gradually becoming natural part of the target environment

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2

Introduction Urban D

Urban Device

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nDevice

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Preceding Research

2.1

Spatial Explorations

2.1.1

Before focusing on the protective surfaces of a construction site, number of studies were done on possibility of utilization of the scaffolding interior area. At the earlies stages, possibility of "shared" usage of specific parts of the scaffolding by both construction workers and public (alternating based on time of a day or phase of construction, Fig. 13) were explored, but deemed unrealistic and dangerous, shifting focus towards adding extra layers of area designed exclusively for public. This way, a multi–layered spaces Fig. 13 ↑ Modular, shared scaffolding divided by membranes would be created, allowing public to use parts of the construction site for various activities (Fig. 14).

One proposal envisioned a pneumatic airlock–like device capable of wrapping space into inflatable pressurized cocoon, keeping it protected from the ongoing construction (Fig. 15). Air would be continuously pumped in order to keep the bubble inflated.

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Fig. 14 → Multilayered scaffolding with publicly accessible space

Fig. 15 → Pneumatic interior space embedded into scaffolding

Chapter 2.1.1

Wrapping Urbanism – Jan Vranovský

Convenience Store (Fridge, Kitchen, Living Room, WC)

Laundry Store (Washing Machine)

Vending Machine (Refrigerator)

Smoking Spot (Balcony)

Vendi Flat (Bedroom)

Parking Lot (Garage)

Vending Machine (Refrigerator)

Distributed Living Space

2.1.2

Question of program for the augmented construction sites was crucial.

Taking in account results of the initial research on construction sites in Tokyo (highlighting their potential if understood as a network), concept of distributed living in city which basic facilities spread around the neighborhoods and are shared was proposed. This is already happening to some degree: vending machines, convenience stores, public toilets, storage rooms and other facilities, combined with often extremely small flats, do have direct impact on lifestyle of inhabitants of Tokyo; Ashihara described this situation already in late 1980s.1

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Soba Restaurant (Kitchen, WC)

Ramen Restaurant (Kitchen, WC)

ing Machine (Refrigerator)

Playground (Gym)

Storage Lockers (Storage Room)

Fig. 16 ← Proposal of distributed living space: Tokyo as a network of semi-public spaces and services.

Main objective of the proposal wasn't to create utopian–like community with almost no private space however. The goal becomes a mammoth cluster of "bedwas to allow more flexibility and shorter reaction times to rooms" interspersed with "family rooms" changing needs of inhabitants of local neighbourhoods (parks), "parlors" (office buildings), "entryways" (airports, harbors) and the as scaffolding–embed facilities would change very often. like.' (Ashihara, 1989, 45) This was understood as an opportunity for offering higher quality, rather than replacement of existing minimum inside actual flats. Unfortunately, due to spatial, technical and safety issues, the overall scenario was ultimately deemed unrealistic.

'Imagine, thus, that each house in

1 →

Japan is a private bedroom. The city

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Memory Explorations

2.1.3

As scaffolding interior space based programs proved to be generally infeasible due to safety and spatial issues and with continuous research of urban phenomenons connected with permanent change and metamorphosis, notion of environmental cognition begin to emerge.

First phases of this new direction generally concentrated on capturing, saving and visualising past iterations of the city through construction sites, which would then form a networked museum of history and future of Tokyo. Visualisation was speculated to be realised through augmented reality techniques, such as mapping images displayed through mobile phone or tablet application on real surfaces with QR codes in real environment (Fig. 17). This approach generally lacked strong motivation for limiting such system only to construction site surfaces however. Overall objective was to make inhabitants of Tokyo more aware and critical about outcome of rapid change happening around them, giving opportunity to compare now and before in real scale.

Fig. 17 �

Augmented realityinfused drapes.

Tablet image: Archipad (Archipad 2016).

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Second phase focused on ability of human to remember his closest surroundings. A simple game where public user / inhabitant could test his memory was designed: construction site protective sheets doubled as a display, mobile phone as a controller. Player would be asked to trace (draw) previous state of the building that is now being replaced, demolished or repaired by memory (Fig. 19). The closer he gets, the higher the score; the more advanced level, the more back in time version of house he has to trace. Objective was similar as with the previous concepts: to motivate people to be more aware of their architectural environment. Additional result of this device would be series of memory traces, images of city as we remember it (Fig. 18). Such traces could be useful research tool for architects and urban planners1.

Negative aspect of the game was uncertainty that it could ever get popular enough to start being effective. Also, cheating, using historical photographs or Google Street View application would be far too easy. The game approach generally didn't generate a convincing enough long–term urban scenario.

Fig. 18 → Overlaid memory traces as side-effect of the game

Fig. 19 → Game scenario utilized construction site drapes as canvases for architecture-related game

Chapter 2.1.3

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Methodology

2.2

Introduction

2.2.1

When dealing with question of changing face (facade) of Tokyo, the positions typically divide into either supporting its protection (heritage, culture, memory) or supporting growth (economy, seismic security, prestige etc.), rendering the situation in somewhat black and white colors without really articulating the key issue: what defines the quality of city we are looking for? Both growth and memory are important values so taking sides in such argument can hardly lead to balanced, fully sustainable system. However, even realization that ideal approach lays somewhere in between (to some degree already practiced by Japanese government through strict building codes combined with exceptions for cultural heritage sites) cannot guarantee interesting, memorable, distinguishable cities (one might call it cities with genius loci). Especially when speaking of what was traditionally a timber structure culture which went through war that erased large part of it's physical cultural heritage, leaving many cities and districts with nothing physical to protect at all.

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2.2.2

These realizations combined with numerous personal observations of both places with and without genius loci (or, to be less abstract, places that are interesting or not, memorable or not) lead to question of cognitive and mathematical properties of such hard to define state.

Although memory and specific semantic meanings might form part of what this quality is made of, according to research of Prof. Juval Portugali (Tel Aviv University) and Prof. Hermann Haken (Universtity of Stuttgart) there might be rather straight–forward mathematical pattern behind it.

Information Theory In simple words, Information Theory, originally grounded by E. C. Shannon in his publication A Mathematical Theory of Communication (1948) is a method developed for quantifying information entropy within closed systems. Value of its basic formula can be understood as amount of uncertainty of outcome: zero value indicates a certain outcome, low value indicates low level of uncertainty, high value indicates high level of uncertainty. The more variation in parts forming the system there is, the higher is uncertainty, if there is no variations, result is certain. This simple logic shows us different understanding of what information is: moving from singularities to description of a system based on variation of its content rather than on how we understand meaning of the differences. While Shannon's Information Theory is known for many decades, it wasn't until 2003, when Portugali and Haken first published a research outlining basic steps of its utilization in context of cities, that it was recognized as a theoretical tool in urban planning. In their research, Portugali and Haken lay down basic rules of how the original equation and logic could be used in new context, updating it with findings of cognitive psychology as well as complexity theories of cities.

Their basic position is that the higher Shannon's information within the system (city) is, the better, because it makes it more interesting, thus legible and memorable. If we imagine city formed by repetition of a single identical house, it is easy enough to agree that such place lacks information (that is, variation), rendering it boring, hard to navigate through and hard to remember. The other extreme opposite is less clear however: city with maximum variation, houses that each look completely different from each other, isn't necessary perceived as the most interesting one. Portugali and Haken explain this phenomenon through perspective of findings of another research, Miller's (1956) The magic number seven, plus or

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minus two: some limits to our capacity for processing information, defining constrains of human brain's 'capability to process one-dimensional information to "plus minus seven", that is, to about 2,5 bits of Shannon's information' (ibid., 392). Based on this, authors of the research implement limitation of amount of different categories of houses human can differentiate to "plus minus seven", explaining why too heterogeneous city actually ends up being almost as boring as the completely homogeneous one: too many categories, once perceived, merge into a single, unified parent category, "houses", and results in zero Shannon's information. (Ibid., 386–408)

This limitation leads the authors to introduction of another logic: distinguishing between "normal" houses and "landmarks" limiting the amount of members we need to compare, but also leading to need of calculating spatial correlation of the landmarks. As Portugali and Haken point out, once landmarks get spatially too correlated, they cease being perceived as landmarks, thus again leading to low Shannon's information values. (Ibid., 394–395). Shannon's information (I) is expressed as: I = log2 Z where Z stands for number of possibilities. Units of the result are Bits or Shannons; if we'd use different base of logarithm function, it would be measured in Nats or Hartleys instead (Weik 1983, 447).

Next iteration of the formula is: N

i = -�pj log2 pj j = 1

where

pj =

Nj N'

In this case "i" stands for amount of information per house, "pj " for relative occurrence of related object category, "Nj " for total number of houses of the same category and "N'" for total of houses in the city (Haken and Portugali 2003, 394–395).

It’s clear at this point that defining correct types, amounts and "thresholds" of categories (index j) is the core issue here as it very much states the question, to which "i" would then be an answer.

Tendency of Change

2.2.3

Even though some points in Portugali's and Haken's adaptation of Shannon's Information formula may appear questionable once applied to the specific urban situation of Tokyo and Japanese city planning tradition, it proves to be excellent tool for measuring level of homo-

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geneity or heterogeneity, which by itself to large extent answers one of the main goals of the project: to find a tool allowing to quantify and thus clarify the issue. In context of the overall agenda of the project based on specific behavior of Tokyo, the core value we are interested in is, however, tendency of change, rather than simply calculating amount of information within single frozen frame of the permanently changing system. In order to express such condition, formula similar to average acceleration can be proposed: m =

Δi Δt

where m (tendency of change) stands for change of average Shannon's information per house in time (Δi) divided by this time (Δt). We can measure such value in Shannons per day, Shannons per month and so on. Negative results can be then read as ongoing loss of information, positive ones as accumulation of amount of information.

Although tightly connected to, these readings go beyond simple homogeneous / heterogeneous classification as, for example, loss of information can be caused by overly extensive heterogenization as well as extensive homogenization.

2.2.4

Alternative Approaches

Thom Mayne understands a slightly different approach to using information as a design methodology. Concept known as "Information Landscapes" 'rethink(s) architecture, urbanism, or landscape in terms of information exchange' (Mayne and Allen 2001, 60). In this rather complex paradigm, various information describing an environment (such as engineering, history, geography, geology, ecology, architecture, regulatory codes, managements etc.) are understood as embedded information, and interaction of their variables as information exchange. The outcome is then understood as organizational diagrams 'that makes possible an open but not infinite series of movements and connection' (Ibid., 60–61).

While concept of dealing with information and their mutual relationship and treating these as a type of Fig. 20 ↑ Pudong Cultural Park Competi- form–generating system is similar to this project's urban tion Model (Morphosis 2003) device based on information theory, the big difference lays in the idea of centralized intelligence gained trough linking all the variables together, leading to equally centralized and formalized outcome. Similar to many other architects and urban

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designers theorizing about radically new approaches connected to complexity theories, computational design and bottom-up systems at the end of 20th and beginning of 21st century, Mayne seems to allow traditional understanding of control and boundaries of architecture to override otherwise eye-opening new paradigms, subordinating them into mere formal rules behind often massive, monumental and, ironically, variation–lacking architecture, in many cases designed on scale of urban planning (stepping into the realm and corresponding set of issues of Kenzo Tange's Megastructure).

This reveals issue of scale and intensity by which certain theoretical approaches are applied. Complex organizational systems derived from nature and mathematics typically deal with existence of fourth dimension: time, related to growth. Their biggest virtues, sustainability and adaptability, are tightly connected to time, and to variables subsequent to time and growth in real conditions, such as chaos. If used purely as form-generating processes dependent on virtual simulations and applied to finite, one-off architectural projects, the results will have very little to do with selling points of the original mathematical models and will transform into potentially dangerous formalism. However, in case of urban planning, these models seem to make more sense. Cities, same as nature, are complex dynamic systems changing in time and seeking both sustainability and adaptability. With that in mind, objective of this project is to introduce heavily decentralized and soft system, dealing with large–scale issues through small–scale interventions, effective through cumulation and repetition in time.

Due to Allen, Mayne's Information Landscapes approach overlaps with some positions formulated by Fumihiko Maki, namely with his seminal work mentioned earlier in this work (1.1.3): Investigation in Collective Form, a methodological study on the subject of collective form and linkage. Maki speaks here about importance of 'not a "master plan," but a "master program," since the latter term includes a time dimension' (Maki 1964, 4), and importance of study of a collective form, turning away from traditional understanding of building as separate entities (Maki 1964, 5). Furthermore, he highlights significance of so called "Group form" Fig. 21 ↑ Diagrams of three types of – sequentially generated structure with repeated elements collective forms by Maki: Compositional form, The Megastructure and Group and organizational behavior, a 'unifying force', typical for form (Maki 1964, 6). vernacular architecture. He notes that 'a further inquiry of the basic elements and particularly of the relationship between the elements reveals interesting principles involved in making collective form' (Maki 1964, 14–18).

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Maki's methodology inspired by his study of Group forms focuses on linkage, that is to say, he strives 'to make unity from diversity' (Maki 1964, 32). For that he developed five operational categories (based on research of historic case studies) intended to be used when designing or altering an urban system: to mediate (connect), to define (enclose), to repeat (give system a common feature), to make a sequential path (group activities into sequences) and finally to select (Maki 1964, 36–42).

In many ways Maki's concerns about collective form of the city are very close to the one discussed and researched in this project. The difference lays in practical approach. Although clearly fascinated and inspired by vernacular, historical cities and emergent structures, his main concern lays in techniques of unification, largely omitting the significance (and practical difficulty) of differentiation within members of a fabricated system. This might be tightly connected with the time the paper was written Fig. 22 ↑ Image of Japanese village and in. When we are presented image of traditional Japanese image of Children's home by Aldo van village next to project of Children's Home by Aldo van Eyck (Maki 1964, 46–47).

Eyck, the intended connection is clearly the repetition within distributed structure, uniting the form. Once understood from perspective of information theory however, we see a striking difference: where the Japanese village displays similarity, van Eyck's design shows sameness. With building categories correctly adjusted to the environment, the village's Shannon's information value is clearly relatively high, while the Children's Home one low, no matter how sensitive differentiation of the categories we set.

Chapter 2.2.4

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Device

2.3

Device Structure

2.3.1

With Shannon’s, or Haken’s and Portugali’s discoveries in mind, a new type of urban device can be theorized. General goal of this device would be to read the facade of the city, calculate amount and distribution of information it contains and suggest shifts in local development tendencies in order to improve each neighborhood through targeted visual performance as well as constantly indicate the changing information value.

Basic structure of the device comprises of four basic steps (units): data collection, data analysis and categorization, calculation of Shannon's information value (information entropy) with two possible outcomes, and finally generating appropriate suggestion which is then visualized in the environment via physical device.

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Data Collection

Building facade images are collected in the target city by either scanning them or utilizing data from available sources (street view services, GIS data, data collected by self-driving cars etc.)

Data Analysis & Categorisation

Collected facade images are analyzed, quantified and machinecategorized based on their observable features, such as 2D and 3D complexity or color hue and saturation

Calculating information entropy of the face of a city

With facade categories assigned to their respective houses, information entropy (Shannon's information value) can be calculated, allowing us to quantify tendencies in distributions of different types of facades

Overly heterogeneous

Strongest already existing pattern is identified and its further expansion suggested

Chapter 2.3.1

Two possible ranges of outcomes

Overly homogeneous

Implementation of our urban device

Addition of houses with different information categories is suggested

Wrapping Urbanism – Jan Vranovský

Data Collection

2.3.2

Data collection forms first logical step in creating information based image of the city. Logistics and funds wise, it is also the most complex and potentially limiting one. There are however several options of obtaining data describing building facades other than organizing dedicated scanning.

GIS Data

2.3.2.1

Full 3D maps of the city, including photo–scans of the building facades are available through Google Map and Google Earth applications. Aside from relatively low resolution, main issue of these are that in face of pace of change taking place in Tokyo, they outdate relatively fast.

Fig. 23 ↑ Google Earth 3D (Google, 2016)

Street View Services

2.3.2.2

After Japanese street view service Location View developed by Location View Co. (Egan 2008) became defunct, Google Street View remains the only functioning street view service in Japan. It provides near–to–complete coverage with high resolution panoramatic images of Tokyo streets, taken from perspective similar to the one of human. This Fig. 24 ↑ Google street view makes it currently the most preferable readily available (Google, 2016) source of visual data. Its disadvantage is the same as in case of GIS data: it is currently impossible to keep all images up to date on daily, weekly or even monthly basis. The service could be useful as a starting point for fast mapping of Tokyo facades, but less for continuously updating it.

Self–Driving Cars

2.3.2.3

Interesting alternative to GIS or standard street view service in relatively near future could be data collected by navigation systems of self–driving cars. Usenko et al. propose method of point cloud '3D reconstruction using a stereo extension of large-scale direct simultaneous localization and mapping', allowing for 'generating visually pleasing and globally consistent semi–dense reconstructions of the environment in real–time' (Usenko et al. 2016). Point cloud images of houses could provide good alternative to street view images.

If networked, the system could overcome issue of current street view

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services (not being always up to date), and it is possible to speculate it might eventually even replace current method of obtaining street view data.

2.3.2.4

Category Labels Since every new building needs to go through official approval procedure (change of the face of a city is controlled process), it is also possible to speculate that instead of randomly scanning the city, every new house could automatically receive label indicating its facade features (similar to ecology labels indicating carbon fiber footprint or energy consumptions). In such scenario, large city scan would be required only as an initial step, mapping all the facades built prior to existence of the labeling system.

Fig. 25 ↗ → 3D space reconstruction (Usenko et al. 2016)

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Data Analysis

2.3.3

Once visual data of facades in the city are obtained, they need to be analyzed and quantified before being categorized in the next step (see 2.3.4). There are generally two different groups of methods applicable in this task: those that belong to "fixed point of view" approach, and those that belong to "abstract point of view" approach.

Image Processing

2.3.3.1

"Fixed" approach methods of image processing (that is methods fully dependent on predefined evaluation methodology), such as edge detection or contour detection, are powerful and potentially very accurate systems for image analysis but require well prepared, "clean" visual data as an input, that is data that contain none or minimum amount of noise features. For needs of the proposed device, a simple Fig. 26 ↑ Output of an edge detection algorithm, revealing issues with noise edge detection algorithm has been created in Processing information using OpenCV libraries and tested for purpose of counting amount of rectangles in the image (Fig. 27). Although threshold of the detail sensitivity could be adjusted, the system worked well only in case of clear, high contrast images with only low amount of relatively uniform noise which could be then canceled through careful sensitivity threshold settings. The experiment proved initial expectation that images taken in real environment, which are typically full of noise in form of non-related features (surroundings, cars, people, electric lines, shadows, trees etc.), are not suitable for standard image processing methods as this noise cannot be identified and canceled effectively.

Machine Learning 2.3.3.2

"Abstract" approach methods, generally machine learning and methods derived from machine learning, are not always as accurate as image processing, but offer fundamental advantage of adaptability.

Due to Mitchell, in machine learning 'a computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured

by P, improves with experience E' (Mitchell 1997, 2). Machine learning program typically improves through supervised learning where a teacher presents the algorithm with set of outputs mapped to inputs, letting the program to "understand" patterns between them.

There is, however, also category of machine learning algorithms that

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import gab.opencv.*; import org.opencv.imgproc.Imgproc; import org.opencv.core.Core; import org.opencv.core.Mat; import org.opencv.core.MatOfPoint; import org.opencv.core.MatOfPoint2f; import org.opencv.core.MatOfPoint2f; import org.opencv.core.CvType; import org.opencv.core.Point; import org.opencv.core.Rect; import org.opencv.core.Size; OpenCV opencv; PImage src, dst; ArrayList<MatOfPoint> cnts; ArrayList<MatOfPoint2f> approximations; ArrayList<Rect> rects; void setup() { src = loadImage("imagename.jpg"); size(src.width, src.height/2); opencv = new OpenCV(this, src); //dst = opencv.getOutput(); Mat gray = OpenCV.imitate(opencv.getGray()); opencv.getGray().copyTo(gray); Mat thresholdMat = OpenCV.imitate(opencv.getGray()); opencv.blur(20); Imgproc.adaptiveThreshold(opencv.getGray(), thresholdMat, 255, Imgproc.ADAPTIVE_THRESH_GAUSSIAN_C, Imgproc.THRESH_BINARY_INV, 451, -5); dst = createImage(src.width, src.height, RGB); opencv.toPImage(thresholdMat, dst); cnts = new ArrayList<MatOfPoint>(); Imgproc.findContours(thresholdMat, cnts, new Mat(), Imgproc.RETR_LIST, Imgproc.CHAIN_APPROX_ NONE); //approximations = createPolygonApproximations(cnts); rects = boundingRects(cnts); } void draw() { scale(0.5); image(src, 0, 0); noFill(); stroke(0, 255, 0); strokeWeight(4); //drawContours(approximations); drawRects(rects); pushMatrix(); translate(src.width, 0); image(dst, 0, 0); popMatrix(); } ArrayList<MatOfPoint2f> createPolygonApproximations(ArrayList<MatOfPoint> cntrs) { ArrayList<MatOfPoint2f> result = new ArrayList<MatOfPoint2f>(); for (MatOfPoint contour : cntrs) { MatOfPoint2f approx = new MatOfPoint2f(); Imgproc.approxPolyDP(new MatOfPoint2f(contour.toArray()), approx, 50, true); result.add(approx); } }

return result;

ArrayList<Rect> boundingRects(ArrayList<MatOfPoint> cntrs) { ArrayList<Rect> result = new ArrayList<Rect>(); for (MatOfPoint contour : cntrs) { MatOfPoint2f approx = new MatOfPoint2f(); Imgproc.approxPolyDP(new MatOfPoint2f(contour.toArray()), approx, 50, true); result.add(Imgproc.boundingRect(new MatOfPoint(approx.toArray()))); } }

return result;

void drawRects(ArrayList<Rect> rs) { for (Rect r : rs) { if(r.width>80 || r.height>80){ rect(r.x,r.y,r.width,r.height); } } }

Fig. 27 → Edge detection system utilizing OpenCV

void drawContours(ArrayList<MatOfPoint2f> cntrs) { for (MatOfPoint2f contour : cntrs) { beginShape(); Point[] points = contour.toArray(); for (int i = 0; i < points.length; i++) { vertex((float)points[i].x, (float)points[i].y); } endShape(); } }

libraries, coded in Processing 2.2.1

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doesn't require teaching, evaluating correctness of results utilizing predefined evaluation method. Such method will be used in 2.3.4 for data categorization.

Within the field of machine learning, neural networks (Fig. 28) represent an advanced, more complex object detection system. A well known type of neural network algorithm is Deep learning, which is still essentially analogical to machine learning, but typically deals with big data, that is very large amount of inputs. Due to Berenzeweig, deep learning is 'an opportunity to bridge the gap between physical world and the world of computing' (Berenzweig 2014). For the specific case of image recognition, convolutional neural networks (CNN) are typically utilized as they are specifically designed to work with pixels. Convolution is it this case a 'sliding, flipping filter searching for particular thing it was trained for' (Berenzweig 2014).

Neural networks and Deep learning are already being practically utilized on many levels including current Google search engine (Metz 2016) and is accessible through various readyâ&#x20AC;&#x201C;made API cloud services such as Google Cloud. Unfortunately, for the time being, it is impossible to "feed" these by one's own data, that is to say, perform teaching seasons without advanced enough programing skills. Readily available examples thus show powerful potential of neural networks in pattern recognition within image content, but practical sample of system adjusted specifically to our cases, that is understanding facades of Tokyo, will need to be coded or derived and adjusted. It is also important to note that without large amount of images to analyze, the algorithm itself cannot learn and improve, so for practical realization of this step data collection (2.3.2) as well as the issue of manual interpretation of the data needs to be fully resolved.

Facade Evaluation

2.3.3.3

Since image processing methods proved to be inapplicable in case of real environment evaluation and machine learning algorithms, such as CNN, are (although promising) currently unavailable for testing due to their coding complexity and data and time related requirements for the teaching process, facade evaluation was performed manually, without aid of any algorithm.

Generally speaking, we experience buildings and their facades as complex sets of visual features influenced by one's position, field of view, time in the day and weather conditions (as well as specific personal cognition skills, such as language abilities and

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Input layer

Hidden layer

Hidden layer

Hidden layer

Fig. 28 → Neural network

Output layer

diagram

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Wrapping Urbanism – Jan Vranovský

obtained knowledge, allowing us to read more specific semantic meanings (Haken and Portugali 2003, 402-403), which will be omitted for sake of simplicity in this proposal). However, human brain reads and interprets these complex changing visual information into a stable image of observed object: blue tint of white facades caused by atmospheric light conditions is automatically canceled, vertical perspective corrected, geometrical patterns read as sets rather than separate objects. Objective of the facade evaluation is to quantify its visual features in a way that corresponds to human perception, although majority of information embed in the facades has to be ignored in order to make such analysis feasible in huge scale.

Three basic features of the building shell are being measured: color, 3D complexity and 2D complexity.

In ideal scenario, all visible facades are evaluated, possibly as separate or semi-separate entities (facades of a single building creating parent categories of facades), and 3D complexity is measured as analysis of amount of faces of 3D model. For sake of simplification allowing higher amount of buildings being analyzed in case studies developed as part of this project, this complex measurement logic is simplified Fig. 29 ↑ Issue of perspective distortion to analyzing only most prominent (longest, parallel to main overly affecting outline analysis road) facade of every house in case there are more than one visible. Moreover, 3D complexity is measured only as an outline of the object, while 2D complexity is measured as amount of unique features of the facade. The basic analysis routine is following:

– Measurement of facade color saturation (SAT) – Measurement of facade color brightness (BRI) – Amount of lines forming building elevation outline (OUT) – Amount of facade elevation unique features (FAC)

In order increase sensitivity of the analysis, four additional simple checks are performed:

– Facade contains non-orthogonal geometry yes/no (FOR) – Outline contains non-orthogonal geometry yes/no (OOR) – Outline contains cantilever yes/no (OCA) – Outline proportions are 2.5:1 or taller yes/no (OTO)

Measurements of saturation and brightness are performed in HSB color space. Target section of facade is first averaged to single homogeneous

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color, and then analyzed. In case of facades containing two or more colors, both or all of them are averaged together in the same ratio as the facade displays.

Counting amount of lines forming building outline is straight forward, but has to be done on idealized elevation rather than tracing photography. Although this step contains controversy as it isn't without loss of information important in real cognition, it's a way of avoiding inconsistency in measurements caused by varying position of camera and focal length of used lens (Fig. 29).

Counting amount of unique facade features is much more delicate process than counting lines forming outline as issues of thresholds (is that edge distinct enough to be considered a feature?), semantic meanings (is that grid part of a window, or are they two separate objects?), sameness (are those two windows so similar they are considered same?) and patterns (do those objects form a pattern which then becomes a new object by itself?). In hypothetical scenario where all these features are detected by advanced adaptive algorithms, performance and methodology of evaluation would be to large extent defined by their technical

Building

Facade saturation (13)

Facade brightness (92)

Elevation outline (11)

Elevation facade unique

Fig. 30 â&#x2020;&#x2018; Facade evalua-

features

tion method as a diagram

(6)

Chapter 2.3.3.3

Wrapping Urbanism â&#x20AC;&#x201C; Jan VranovskĂ˝

limitations. In case of this project's case studies in which all features were evaluated by human, emphasis is placed on consistency – that is treating similar configurations on different buildings in similar manner. However, until an algorithm is utilized instead of human, absolute consistency of measurements cannot be guaranteed in case of facade features.

The four additional yes/no checks are designed to detect features which typically make a building more memorable given typical, average context of Tokyo and at the same time cannot be addressed by the basic set of measurements: non-orthogonal geometry (in case of building outline actually often down amount of lines), cantilevers as case of mass defying basic natural forces and towers, for similar reasons.

In order to further improve precision and range of sensitivity of the measurements, additional data related to appearance of facade, yet not directly quantifiable from images or scans may be introduced.

Most significantly, years of construction completion contain potentially substantial mixture of information about style, age and potential level of degradation. At this point years of construction completion of houses in Tokyo are not yet publicly accessible, but this is most likely to change in near future. As an temporary alternative, time-related category based on morphological and functional features can be used instead, introducing another evaluation category:

– Generation (0 to 3) (GEN)

which is a feature loosely based on Yoshiharu Tsukamoto's research on changing typologies of Tokyo residential houses. Tsukamoto divides them into four categories: 1st, 2nd, 3rd and 4th generation houses, following the historical process of partially forced land subdivision causing gradual change in morphology and typology since 1923 – year when first suburb

BLD P42

GEN 3 SAT BRI FAC FOR OUT OOR OCA OTO

15 80 6 0 8 0 0 0

Fig. 31 ← Sample of a single facade data evaluation label Fig. 32 → House in Adachi with OUT count 6 (highlighted in magenta) and FAC 7 (cyan).

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2

7

1

5

6

3

6

3

2 1

5

4

4

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was developed (Kitayama, Tsukamoto and Nishizawa 2010, 41). In our case, the values 1 to 3 don't overlay with year of house completion, but rather with morphological features described by Tsukamoto as typical for each generation. Value 0 is that applied to non-residential houses and those to which the morphological categorization isn't applicable.

Another type of potentially relevant data is function (for example: residential house will typically have different morphological features than a wood shop or fashion store). It is important to note that all these additional categorization systems are non-hierarchical (as opposed to the direct measurements of facade features) and need to be treated as such: separately from the hierarchical, linear values obtained by quantification of facade features which all relate to complexity or "amount of unusualness" of each grain of city interior. For this reason, any additional data groups form separate categories which can be directly analyzed through the information entropy evaluation method (2.3.5), skipping the categorization intermediate step (2.3.4). The same applies to any semantic, non-hierarchical category types in general.

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2.3.4

Data Categorization Once each house facade is evaluated and translated into pure data (as described in 2.3.3.3), these data can be used for generating categories to which each associated house belongs to.

Most simple method of doing this is measuring distance from point zero. To do that, all values need to be converted to mutually comparable scales first. Easy way of doing this is dividing all values in single facade feature category (such as saturation, brightness, outline etc.) by highest value in the list. This way we get values ranging between 0 and 1 for each category, avoiding "gaps" in form of theoretical but never achieved (achievable) values. It is also important to note here that because of this operation, all ranges are contextual and potentially floating, dependent on content of database. Furthermore, all yes-no categories need to be merged with the scale categories in order to avoid confusing results. Values of scale categories, such as facade complexity and outline complexity, thus get increased by predefined values corresponding to specific yes-no categories such as orthogonal geometry or cantilever, leaving us with only scale categories at the end.

Once all data are on the same scales, they can be used as coordinates in n-dimensional graph (n stands for amount of data categories). In resulting point cloud, absolute distance from each point to point zero can be easily measured. Once distances from point zero of all points (houses) are compared, categories can be easily create in form of scale (amount of segments needs to be decided). Resulting house categories are, however, strongly hierarchical: going from simple to complex, from desaturated to saturated, from dark to white (based on how facade features categories are chosen and scaled). Moreover, as the values get higher, actual spatial dispersion of houses with same absolute distance to point zero grow. Thus, categories closer to point zero wrap around more homogeneous content, while categories in more distant parts of the graph get gradually very heterogeneous.

2.3.4.1

Hierarchical Cluster Analysis In order to address issues connected with simple distance from point zero method, hierarchical clustering method is used instead. Hierarchical clustering is a specific machine learning algorithm which doesn't require teaching process. In case of this proposal, group average method has been tested with satisfactory results, but alternative methods, such as nearest neighbor method (MIN), furthest neighbor method (MAX) or ward method can be utilized instead.

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Preparation preceding the clustering is the same as with the distance from point zero: all data need to be converted to mutually comparable scales and then used as coordinates for points representing associated houses. There is no limitation to amount of dimensions the graph (point cloud) must have, and thus no limitation to amount of monitored facade feature categories utilized to generate the cloud, as long as they are mutually comparable.

Hierarchical clustering is a recursive iterative process and thus requires environment supporting recursive iteration. Test code was written in Python, allowing for simple implementation into wider Grasshopper definition. Utilized method (group average) represents a "middle ground" between previously mentioned MIN and MAX methods, defining proximity between two clusters as average distances between points in the first cluster and points in the second cluster ("Agglomerative Hierarchical Clustering" 2016).

As the clustering is not scale-sensitive, final amount of clusters needs to be defined top-down. This pose a delicate issue in the process.

Overall amount of clusters has critical effect on sensitivity threshold of the system: too many clusters will provide us with overly segmented reading of facade categories while too few will sort majority of houses into only one or two categories. Generally speaking, it is important to consider the far-extreme points, typically those most distant from zero point of the graph. As these are typically far away from each other, they tend to "use up" the amount of categories if the amount is set too low, rendering the largest inner point cloud being read as one large category.

Based on demands on the sensitivity, the amount of clusters has to be set accordingly.

It is possible to speculate existence of certain ideal distribution of categories (such as Pareto's 80â&#x20AC;&#x201C;20 distribution or even Zipf's distribution known to quantitative linguists) and according system sensitivity, revealing most "true" distinction between facade categories in majority of cases. However, for verification of such hypothesis or conduction of research leading to alternative hypothesis, large amount of rigid, real environment-based data is needed. For testing the device proposal, only limited amount of house facades were evaluated. Moreover, in order to incorporate various high and low extremes to otherwise average houses, number of buildings from multiple areas in Tokyo and Japan were selected and added to the list, forming "artificial" facade pool constituting theoretical Tokyo. This pool logically doesn't represent Tokyo's actual quantitative distribution of various potential categories, but provides database of diverse-enough houses.

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Fig. 33 ↗ → Group average method based clustering of 273 facade samples divided into 26 categories distributed in 4D space. Visualizations are only approximate as they omit one dimension.

Perspective and front view.

Chapter 2.3.4.1

Wrapping Urbanism – Jan Vranovský

Information Theory-based Evaluation

2.3.5

Once all houses in selected area are sorted into categories, final step of the evaluation process can be conducted. This step comprise of two parts: general Shannon's information calculation (see 2.2.2 for more details) and subsequent calculation of Shannon's information gradient based on actual spatial distribution of analyzed houses.

Information Entropy Calculation

2.3.5.1

Calculation of Shannon's information value follows the basic logic outlined in 2.2.2. In this step, we generally operate with two scale and data pool units: global (city or wider area) and local (specifically selected area within the global unit). Let's recapitulate the basic information entropy equation mentioned in 2.2.2: N

i = -�pj log2 pj j = 1

where

pj =

Nj N'

From this we can easily conclude that overall Shannon's information of a system is a sum of Shannon's informations of each category (index j). Thus: ij = - ( pj log2 pj ) On global scale, we can calculate Shannon's information of every single house based on relative occurrence ( pj ) of its facade category within the system. Simple sum of Shannon's information per house situated in analyzed area forms Shannon's information of that area.

This calculation completely ignores specific spatial distribution of facades: perfectly randomized distributions of houses will result in the same value as uniform clusters of repeated houses distributed within the same area. It will give us, however, some understanding of general character of the analyzed area compared to the rest of the city, such as "area with high occurrence of rare facade categories" in case of above-average Shannon's information value compare to the surrounding area. But based on Haken's and Portugali's research on Shannon's information and human cognition, we understand limitations of value of such calculation when it comes to understanding actual human perception of specific space. For that reason, second step of analysis needs to be performed.

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2.3.5.2

Spatial Distribution Sensitive Method In order to visualize and calculate value more relevant to actual specific spatial distribution of facades in selected area, method sensitive to spatial distribution needed to be developed. Initial version of this device worked with separate calculation of deviation from average (ideal) distribution. Shannon's information of the area would then get penalized by other–than–ideal distributions as it was multiplied by the value of deviation from average spatial distribution (Fig. 34).

This method was tested in various theoretical and real conditions.

Definition running simulations of the system with time-based iterations was designed to see theoretical tendency of a city-device system where device fully governs what type of facades will be built.

Only random element was selection of next item to be changed: system was operating with ages of each house and selecting random one from pool of 10% of the oldest ones – those around 30 years of age. System managed to rise the Shannon's information

Fig. 34 → Grasshopper definition of initial type of Shannon information calculation sensitive to spatial distribution based on deviation from average, capable of running itterative simulations.

Chapter 2.3.5 / 2.3.5.1 / 2.3.5.2

Wrapping Urbanism – Jan Vranovský

Fig. 35 â&#x2020;&#x2018; Selected frames from 101â&#x20AC;&#x201C;step simulation of a city information entropy value transformation. Starting with a grid formed by houses belonging to a single category, the algorithm selects category of each new house facade iteration in order to increase the entropy value of whole as much as possible. The algorithm operates with seven categories: A to F each represented by two similar facade icons, G being unique facades only (never repeated). In situations where multiple categories would lead to same information entropy value, the system always choose the first one

Chapter name / Subchapter name

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in alphabetic order. Finally, the algorithm employs spatial distribution detection system measuring deviation from "ideal" (most spread) distribution of the houses in small range, so it detects microclusters of already present facade categories. Randomly formed clusters of higher facade variety thus induce even more variety (literally forcing the system to outdo the preceding situation with every new iteration).

Chapter 2.3.5.2

Wrapping Urbanism â&#x20AC;&#x201C; Jan VranovskĂ˝

significantly by introducing different facade categories (Fig. 35, Fig. 36 and Fig. 37). However, the system also revealed numerous limitations and quirks of the deviation from average spatial distribution method, such as tendency of creating clusters of same categories if houses were closer to each other than the sampling level could detect.

Also, the method only understood theoretical distances between each facade, rather than actual visible and perceivable conditions (such as streets). Moreover, relationship between the deviation value and Shannon's information value isn't self-explanatory and easy to visualize, rendering the results somewhat mysterious and hard to clearly interpret. Finally and most importantly, the system didn't provide tools which could be utilized to detect overly heterogeneous environment with too many facade categories, issue described by Haken and Portugali 2003, 394–395.

Shannon's Information Gradient

2.3.5.3

For these reasons we propose alternative method of evaluation sensitive to spatial distribution. This time, the values of Shannon's information per category ( ij ) are graphed to planar positions of each house in the city / analyzed area. Once these variable values are connected to each other in direction of streets, they form sinusoid–like graphs copying the street skyline. Distributions of facades belonging to the same categories will thus appear as straight lines, distributions of houses of variable categories will appear as variously undulating lines. Total of the absolute values of overall gradient of A A A A A A A A A A A A A A A A A A A A A A A AA A A A AA A AA A A A A A A A A A AA A A A A A A A A A AA A A A A A A A A A A A A A AA A A A AA A A A A A A A A A A A A A A A A A A AA A A A A A A A A AA A A A A AA A AA A AA A A A A A A AA A A A A A AAA A A AA A A A A A A A A A A A A A A A A AA A A A AA A A A A A A A AA A A A A AA A A A A A A A A A A A A AAA A A A A A A A A A A A A A A A A A A A

C G E C B F A B D F C D F G D A G A G D G D G GG A A A GG G GE A G G A D A D D G DE A A D E B C G G B GA A C A F A A F B A G A A G DG G G A GA C GA A A G DA A D A A G A D A E G AA A D G G D A A G G AA E A G G A A DF G GG A A A A A A AG G C A B A AGG G D BF A B D A G D D D A A G A G A G C GA A G A AG G A G A G A D CA G E A A AA A A G D G G D G D G D A ABG A F A G E D D G G A F F G F C A B G D B

Fig. 36 ↑ Initial state of a simulation:

Fig. 37 ↑ Final step of a simulation:

iteration = 0, i = 0.

iteration: 396, i = 1,342203.

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B

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Shannon information, that is to say, total of all Δi, reflects relationship between global relative occurrence of certain facade type and its specific local distribution, that is, relationship to neighboring facades.

When utilizing such method to graph information gradient following streets (or any publicly accessible areas), issue of multiple facades influencing amount of information legible at every point emerges: in case of streets, there are typically two rows of houses forming the path. For this reason, method to combine multiple values associated with multiple facades in close range had to be developed. Utilization of softmax function combining values of four different houses every 30 cm based on their distance from origin point proved to generate satisfactory results and legible graphs (Fig. 38).

The resulting graph contains both global context hidden in its y axis (Shannon's information of each house category based on the appearance in global context) and purely local spatial context embedded in the x axis. We are not exactly interested in absolute values the graph reveals however, but rather in undulation of the line. Looking at Fig. 39, we can see results showing logic similar to Haken's and Portugali's postulations regarding landmarks (Haken and Portugali 2003, 394-395). However, instead of going through separating houses into "landmarks" and "non-landmarks" and potentially cumbersome quantification of their spatial distributions, we are given more fluid array of various categories ranging from common to unique (with Shannon's information values indicating this). Since we are looking at the graph undulation, we are naturally interested in variation in category-related Shannon's information values as all

Fig. 38 ↑ Shannon's information gradient rendered as a graph following street line. Hight of each segment of the line is calcuated separately based on value and distance from four closest buildings using softmax function to combine the values. Finally, gradient is calculated as sum of absolute amounts of differences between each segment.

Chapter 2.3.5.2 / 2.3.5.3

Wrapping Urbanism – Jan Vranovský

"unique" houses will give as exactly as straight line as all "common" would (Fig. 39).

Such method provides us with basic tool to visualize and calculate architectural context in which every building in the city exists. It is easy to see that various averaging filters would need to be utilized in order to avoid suggestions of simple repeating patterns (such as A-Z-A-Z-A-Z or A-X-A-Y-A-Z). Perhaps even more importantly, the general goal needs to be set carefully and with clear intention since simple "highest information gradient" will likely not lead to desired urban environment (although it is a clear starting point). For that reason, to study existing neighborhoods using the described method and obtaining various graphs describing their Shannon's information distributions could be an effective way to understand actual meaning behind each specific undulation, and thus specifying goals for various types of urban environment.

To conclude the information gradient issue, we believe that utilization of similar method could prove to be more relevant to Tokyo urban structure and generally Asian cities than the basic landmark logic proposed by Haken and Portugali. Unlike historical cities in the West, those in Japan typically didn't operate with landmark-based urban structure. Historically, the streets were memorable through complex distribution of relatively mild variation of repeating materials, patterns, sizes and structures and, to some degree, this is still the case nowadays, although the range of materials and themes increased significantly. The method thus needs to recognize these often mild differences and meanings behind facade distributions rather than simply suggest placement of instantly memorable landmarks generating memorable contrast.

Other Variables

2.3.5.4

Although we now have a device that "understands" specific spatial distributions, large amount of variables affecting actual perception of spatial diversity is being omitted. Haken and Portugali are mentioning significant impact of street layout on human cognition of space (Haken and Portugali 2003, 395â&#x20AC;&#x201C;397). Among other similarly significant factors, landscape topography and street narrowness should be mentioned. Landscape topography has strong impact on what can be seen: planes tend to compress amount of visual information we receive at a single moment, while hilly areas typically reveal more features (rooftops, inner facades) and more houses and other reference points we can perceive. Narrow streets typically lower down

Chapter name / Subchapter name

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All common facades

Common facades mixed with a rare one

All rare facades

Fig. 39 → Basic facade variation distribution scenarios and their indication

Chapter 2.3.5.3 / 2.3.5.4

Wrapping Urbanism – Jan Vranovský

importance of upper parts of building facades, making finer details at street level more significant compare to wider streets, where overall shapes of the houses are usually perceived instead (Fig. 40).

In ideal scenario, all these additional variables should be measured, quantified and affecting the Shannon's information calculation. We can envision that flatlands and regular grid streets would thus have higher requirements on facade diversity than hilly areas and complex street layouts, narrow streets would affect facade evaluation system, focusing sensitivity more towards street level features and partially or fully omitting upper floors. However, as practical implementation of these points to the device represent considerable technical complications, in context of this proposal they are mentioned rather as side-thoughts for future possible explorations.

Summary to Shannon's Information Calculation

Fig. 40 â&#x2020;&#x2018; Additional variables speculated to influence amount of information in the city: street narrowness, landscape topography and street layout

2.3.5.5

To conclude this section, let's remind that final result of both pure Shannon's information calculation (2.3.5.1) and calculation based on Shannon's category gradient (2.3.5.2) is a single numeric value, allowing to easily speculate which other facade category of specific house facade would lead to higher overall information value. This allows for speculating an automated system of analysis and suggestions, always reacting to the current situation.

Fig. 41 â&#x2020;&#x2019; Full grasshopper definition categorizing facades based on evaluation data, calculationg Shannon's information, graphing Shannon's information gradients and simulating results with alternatives facade categories applied to selected lot.

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Chapter 2.3.5.4 / 2.3.5.5

Wrapping Urbanism – Jan Vranovský

Content Visualization

2.3.6

Final step is to select concrete images to represent visual complexity or semantic category chosen by the system. These images will then be visualized in real scale and real site as a reaction to the context.

General approach behind image selection can be divided into three categories: self–sufficient, curated and hybridized.

Self-sufficient Content

2.3.6.1

Self–sufficient content selection logic is understood as a default setting of the urban device, ensuring it can run with minimal requirements on human intervention and function as fully cybernetic, self–feeding system. It is operating with growing database of both historical (as time progresses and houses change) and contemporary facades limited to certain geographical or cultural area (such as "Tokyo" or "Japan"), but practically limited only to content of the database.

As the "pool" with facade files contain both images and categories, including data describing color, 2D and 3D complexity of each of them, the system is able to automatically select facade which will, based on the logic of Information theory, fit to the particular lot the best. That is to say, the system has capacity to go beyond selection based purely on categories as it also takes in account scale, location and specific facade attributes while avoiding repetition of selected images within predefined range. Basic logic of successive steps behind image selections is explained in Fig. 42.

The system can be thus described as an area / city / nation / system–scale "facade shuffle", repositioning images of buildings and placing them in order to obtain maximum amount of Shannon's information for analyzed area (Fig. 43). Addition of new facades, that is to say, new inputs which can be used in this shuffle scenario is dependent purely on architectural development of the area in question.

Curated Content

2.3.6.2

Once the device is self–sufficiently functioning in the city, it is possible to speculate option of human–initiated inputs, "hacks", which would override the basic database shuffling logic for defined range of sites and defined duration of time. Most basic logic behind selection of images (outcome of the information entropy calculation) remains valid, but the pool out of which facades are typically drawn is replaced by

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E E E E E

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Fig. 43 →

Chapter 2.3.6 / 2.3.6.1 / 2.3.6.2

Wrapping Urbanism – Jan Vranovský

alternative, curated ones. The device would thus become a platform for exhibitions taking place in the streets of Tokyo, perhaps even a "distributed gallery" or "museum", which can popâ&#x20AC;&#x201C;up on demand.

This shift from understanding the device as a one-off shuffle machine to seeing it as a platform for new types of content and even additional devices is potentially important one as it has capacity to vastly enlarge usability and thus sustainability of the whole system, to some degree parallel to the way television or smart phone did.

As mentioned before, it is important to still understand this optional, curated content in context of the overall goal of the device: improving legibility of the city and inspiring inhabitants and investors to build more interesting-looking houses through logic of Information theory. Depending on amount of deflection from the basic idea of facade images, more flexible and abstract concept related to the categories needs to be established. In case of semantic categorization, a system of analogies might be developed. For example: a "traditional house" category can be linked to images of traditional paintings, pottery or carpentry. In case of categorization based on complexity-based facade labels, data analysis system similar to the one used for buildings can be applied to the curated content, distinguishing between visual complex or simple one. Categories assigned to structurally complicated facades could then render as Jackson Pollock's paintings, colorful ones as works by Barnett Newman for instance. The content could also stay in realm of architecture, displaying buildings from different places, times, unbuilt architecture, alternative competition designs etc.

Alternatively, content could be generated algorithmically (various visualizations of analyzed parameters defining complexity, similar to music visualizers), participatively (allowing public to draw, build or alter the visualized content) or through direct cooperation with interested third parties such as architecture students, architects and designers, creating interesting opportunity to showcase designs in real environment and real scale.

Hybridized Content

2.3.6.3

In the hybridized content scenario, basic selfâ&#x20AC;&#x201C;sufficient, automatically collected content pool is being permanently updated with curated, out-of-the-system content, allowing for more heterogeneity within the selection.

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Fig. 44 ↑ Content generated from various

Fig. 45 ↑ Curated content: unbuilt, theoretical,

facade features in the pool

conceptual architecture. Image: Study for Maximum Mass Permitted by the 1916 New York Zoning Law, Stage 3 (Ferriss 1922)

Fig. 46 ↑ Content generated based on facade

Fig. 47 ↑ Participatory facades: content gener-

complexity data (abstract)

ated by public participation

Fig. 48 ↑ Curated content: alternative, curated

Fig. 49 ↑ Curated content: fine art. Image:

architecture. Image: Chemist Plant, Luban

Yume (Inoue 1966)

(Poelzig 1909–1911)

Chapter 2.3.6.2 / 2.3.6.3

Wrapping Urbanism – Jan Vranovský

Spatial Logic of Content Selection

2.3.6.4

As the augmented construction site protective drape is envisioned as multi-faced, lenticular-like screen capable of displaying multiple images visible from different angles, a correlation between type of facade feature peaking value (such as 3D complexity, facade or color) and distance from which passerby views the site can be established.

Therefore each particular augmented construction site visualizes several different suggestions, several different alternative facades, each visible from specific, defined distance and position and each correlated with specific type of facade feature.

This logic answers to a question of what are the most relevant perceivable features of a on object and how these change as we move from larger to closer distance from the object. Due to our observations, content visible from large distances, often under acute angles, is typically distinguishable mainly based on color as other basic features are either too small, or too distorted, sometimes even hidden behind other houses (Fig. 51). Viewing facade from shorter distance under angle around 35â&#x20AC;&#x201C;60 degrees typically reveals its three-dimensional qualities, if there are any (Fig. 52). And finally viewing a facade from short distance and full frontal view typically highlights two-dimensional details and surfaces, while overall shape becomes less legible (Fig. 53). The selection filter thus functions analogically to these observations with intention to favor facades of qualities most relevant to viewing position. Since the three basic measured features of each facade are color, 2D complexity and 3D complexity, the system is provided with data suitable for such operation.

Fig. 50 â&#x2020;?

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Fig. 51 → Large distance visualization preferring color-related features

Fig. 52 → Medium distance visualization preferring 3D complexity related features

Fig. 53 → Short distance visualization preferring 2D facade complexity features

Chapter 2.3.6.4

Wrapping Urbanism – Jan Vranovský

Another spatial issue is question of scale. The system needs to be generally capable of determining scale of analyzed facades. Aside from direct methods as laser distance measuring or 3D scanning, basic dimensions of the facade can be read from maps. Finally GIS data provide with full, scaled 3D models.

Preference of choosing facades with scale and ratio corresponding with construction site is mentioned in Fig. 42, but various niche conditions must be anticipated, as well as the basic assumptions behind the suggestion system needs to be addressed. This leads to establishment of specific rule for aboveâ&#x20AC;&#x201C;average sized construction sites: that is to say, for construction sites significantly larger than average of surrounding buildings. Since the suggestion is supposed to be applicable mainly for future construction in the neighborhood, suggesting completely out of scale facade would be contradictory to the basic logic of the system. In such cases, fragmentation of the construction site surface into multiple smaller facades corresponding to average size of surrounding buildings would be thus automatically preferred (Fig. 50). Furthermore, when it comes to matching scale and ratio (size) of chosen facade to scale and ratio of construction site, width is treated as more important than hight since eventual cropping of topper part of the facade is potentially less harmful than cropping its side part (especially from closer distances, human tends to not perceive topper floors of buildings at all).

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Chapter 2.3.6.4

Wrapping Urbanism – Jan Vranovský

City–Device System

2.4

Although last few paragraphs touched the topic of city–device relationship from perspective of practical requirements of the device, general implications and properties of the of the combined system understood from perspective of the city needs to be clarified.

Primarily, the hypothetical city–device system is cybernetic, that is, self-regulating. This is due to constant repetition of scans and suggestions reacting to constant, largely unpredictable metamorphosis happening in the city: every change within the urban network of facades will trigger change in readings of Shannon's information on multiple scales, thus updating the visualized content of each construction device. Once the information entropy equation is adjusted to properly react to both overly homogeneous and heterogeneous environments, the suggestion will always aim for most balanced type of collective face.

With that, we can imagine the system as a new type of integrated urban measurement device, similar to the ubiquitous city clock, thermometer or decibel meter construction sites are often equipped with. All these devices measure and indicate values

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describing phenomenons that have direct impact on our lives, yet are difficult to accurately quantify and, thus, react to, without aid of technology. In case of the proposed urban device, aim and consequences are unusually long-termed and ultimately transform it into soft, bottom-up subsystem of urban planning and regulation, yet the notion of using device to make one notice, realize and possibly react to the dynamic phenomenon of collective face of the city remains one of the core properties of the system.

Fig. 54 → Centrifugal governor invented by James Watt in the 18th century as a sample of early, fully mechanical cybernetic system (Routledge 1900)

Chapter 2.4

Wrapping Urbanism – Jan Vranovský

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3

Introduction Case S

Case Study

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nStudy

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Target Site Analysis

3.1

Case Study Objectives

3.1.1

In order to test the information entropy analysis methodology explained in chapter 2.3, sample target site in Okino, Adachi-ku (Tokyo) was selected. The area can be characterized as typical Tokyo residential suburbia and is populated with two types of urban layouts: relatively chaotic "organic" layout with houses of varying ages and facade styles and a homogeneous cluster of planned housing complex revealing more organized layout, practically no variation in house ages and limited variation in house facades. Only two large building are located in the area: elementary school and a library.

Our main objective is simple: to prove feasibility of our methodology by successfully distinguishing between metabolizing neighborhood and planned housing complex, identifying the first as relatively heterogeneous environment and the latter as homogeneous one. Although houses in the planned complex are subjectively very similar to each other (due to unified layout, age and design style), objectively speaking each of them is different. This pose potentially

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Fig. 56 â&#x2020;&#x2019; Okino, Adachi-ku

interesting challenge for the analysis and categorizing method (exsplained in 2.3.3 and 2.3.4). Second objective is to test the suggestion system, selecting alternative facade for target site (as explained in chapters 2.3.5 and 2.3.6).

Mapping the site

3.1.2

First step of the analysis is complete mapping of target site. A map of visible vertical surfaces, that is, house surfaces has been created with every house in target area being numbered, documented and cataloged (Fig. 57). In ideal scenario, facades as entities independent on houses would substitute current logic of one house – one facade.

The simplified house–based catalogue approach has been chosen for feasibility reasons in case of this case study.

The map reveals clear difference between the matabolizing neighborhoods on South and East and planned housing cluster in North-West. A37 is a local library, while A73 and A72 are to volumes belonging to area of a large elementary school.

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Fig. 57 ↑ Map visualizing the city as a system of visible surfaces.

Chapter 3.1.1 / 3.1.2

Wrapping Urbanism – Jan Vranovský

Fig. 58 â&#x2020;&#x2018; Target street: East view

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Chapter 3.1.2

Wrapping Urbanism – Jan Vranovský

Facade Analysis and Categorization

3.2

Target Site Facades

3.2.1

All facades in target area are presented in numeric order under categories they were assigned to through the categorization algorithm after they were analyzed, using additional facade pool as context.

We are omitting the Generation (GEN) attribute since one of the main objectives of this study is to test feasibility of analysis based purely on quantifiable facade features.

Following pages present complete list of target site facades, analysis results and categories facades were assigned to through the machine-learning clustering algorithm.

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SAT BRI FAC FOR OUT OOR OCA OTO

BLD A45

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Chapter 3.2.1

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11 80 4 0 5 0 0 0

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BLD A63

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Wrapping Urbanism – Jan Vranovský

BLD A88 SAT BRI FAC FOR OUT OOR OCA OTO

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15 87 3 0 4 0 0 0

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5 67 6 0 5 0 0 0

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Case Study / Facade Analysis and Categorization

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Page 102 / 103

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↓

C

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↓

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Chapter 3.2.1

SAT BRI FAC FOR OUT OOR OCA OTO

1 85 7 0 10 0 0 0

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↓ Google, 2014

B

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Wrapping Urbanism – Jan Vranovský

E

BLD A119

BLD A70

↓

↓

↓ Google, 2015

D

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H

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F

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I

↓

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Case Study / Facade Analysis and Categorization

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↓

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↓

BLD A117

BLD A76 SAT BRI FAC FOR OUT OOR OCA OTO

BLD A73

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BLD A52

BLD A79

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BLD A40 SAT BRI FAC FOR OUT OOR OCA OTO

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↓ Google, 2014

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SAT BRI FAC FOR OUT OOR OCA OTO

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BLD GEN SAT BRI FAC FOR OUT OOR OCA OTO

A86 5 59 4 0 6 0 0 0

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J

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↓

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K

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↓

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Chapter 3.2.1

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Wrapping Urbanism – Jan Vranovský

Model Data Pool Facades

3.2.2

Facades comprising model data pool, that is, wider urban context substituting global scan of face Tokyo, have been collected within years 2014, 2015 and 2016 in number of Japanese cities, most notably Tokyo, Osaka, Kanazawa, Kyoto and Kawagoe. The catalogue contains both samples of “random” houses (every house in a single street located in Hongō, Bunkyō-ku, Tokyo was documented for this purpose and added to the selection) and “specific” houses, documented for their above-average features. Presence of both groups is fundamental for the categorization as it formulates distinction between core of the cluster and its outskirts.

Pool facades are presented as categorized by the algorithm and in order defined by the algorithm in order to fully reveal gradient of complexities and color features as organized in the cluster.

Concluding Remarks to Facade Categorization

3.2.3

In order to perform categorization, amount of categories had to defined top–down. Determining ideal amount of categories comprise of trial–and–error process: testing amounts of categories and cross checking them with results, that is to say, with how legible and clearly pronounced the resulting groups of facades are. Challenging part of this process is that it is initially unclear what are we looking for.

However, after several rounds certain categories become clear, and it is possible to then “focus” the sensitivity of clustering on these.

Based on this logic, 26 was chosen as amount of categories since it turned to be the lowest amount that kept resolution between some less distant categories (such as “minimal” – J and more average categories like B and C) clear. Fig. 59 reveals the final result as indicated by grasshopper definition governing the calculations and simulations.

Fig. 59 ← Results of clustering algorithm as displayed in the grasshopper definition

Case Study / Facade Analysis and Categorization

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A

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Chapter 3.2.2 / 3.2.3

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Wrapping Urbanism – Jan Vranovský

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Case Study / Facade Analysis and Categorization

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Page 108 / 109

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Chapter 3.2.3

BLD P168

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Wrapping Urbanism – Jan Vranovský

BLD P270 SAT BRI FAC FOR OUT OOR OCA OTO

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B

BLD P269 SAT BRI FAC FOR OUT OOR OCA OTO

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↓

SAT BRI FAC FOR OUT OOR OCA OTO BLD P208 SAT BRI FAC FOR OUT OOR OCA OTO

BLD P194

SAT BRI FAC FOR OUT OOR OCA OTO

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BLD P204

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BLD P232

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Case Study / Facade Analysis and Categorization

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E

↓

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↓

BLD P192 SAT BRI FAC FOR OUT OOR OCA OTO

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28 68 4 0 4 0 0 0

BLD P231

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Chapter 3.2.3

D

0 68 8 0 10 0 0 0

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Wrapping Urbanism – Jan Vranovský

F

BLD P267

↓

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15 69 4 0 4 0 0 0

H

↓

BLD P171

BLD P258

BLD P172

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17 86 5 0 4 0 0 0

BLD P148

BLD P252 SAT BRI FAC FOR OUT OOR OCA OTO

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BLD P165 SAT BRI FAC FOR OUT OOR OCA OTO

15 80 6 0 8 0 0 0

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G

BLD P175 SAT BRI FAC FOR OUT OOR OCA OTO

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↓

SAT BRI FAC FOR OUT OOR OCA OTO

BLD P181

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9 41 8 1 5 0 0 0

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12 34 14 0 4 0 0 0

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Case Study / Facade Analysis and Categorization

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J

SAT BRI FAC FOR OUT OOR OCA OTO BLD P140

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635 51 6 0 4 0 0 0

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K

↓

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L

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Chapter 3.2.3

13 45 3 0 5 0 0 0

3 72 1 0 4 0 0 0

M

24 56 9 0 4 0 0 0

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SAT BRI FAC FOR OUT OOR OCA OTO BLD P264 SAT BRI FAC FOR OUT OOR OCA OTO

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Wrapping Urbanism – Jan Vranovský

BLD P214 SAT BRI FAC FOR OUT OOR OCA OTO

N

13 64 11 1 4 0 0 0

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R

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O

P

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SAT BRI FAC FOR OUT OOR OCA OTO

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57 96 3 0 4 0 0 0

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Case Study / Facade Analysis and Categorization

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T

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U

SAT BRI FAC FOR OUT OOR OCA OTO

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V

SAT BRI FAC FOR OUT OOR OCA OTO

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↓

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X

42 67 2 0 4 0 0 0

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39 80 12 1 4 0 0 1

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3.3

Information Calculations, Visualizations and Suggestions

Once all facades located on target site are categorized (Fig. 60), it is possible to perform information entropy calculations. We will be testing two different methods: basic Shannon's information calculation without utilizing detection of ideal spatial distribution deviation (2.3.5.1) and Shannon's information gradient (2.3.5.3). In both cases, categories assigned by clustering mixture of facades located both at target site and model facades located in wider context (data pool) are being used, so comparison between target site and city average can be made.

First type of calculation reveals that our target street in Okino contains 0.439497 Bits of information, target neighborhood 1.763839 Bits of information per house, while city (in our case target site + pool) contains 2.73959 Bits of information per house. The value comprise general notion of amount of “valuable” (sparse) and “non-valuable” (common) houses within target area. We can thus conclude that our target site has bellow–average occurrence of sparse types of facades, or, in other words, it’s unusually generic.

Second type of calculation and visualization combines

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local and global understanding of the information values, allowing us to understand more complex local distributions, localize overly homogeneous or heterogeneous clusters and react accordingly.

In case of this study, we used value indicating “value”, or sparsity of information within the system -(log2 pj) rather than its Shannon’s information -(pj log2 pj). The resulting graph is more pronounced and has highs in rare categories and lows in common categories, but absolute relationship between values is parallel to that of Shannon’s information, providing us in the end with the same suggestion as Shannon’s information based graph would (Fig. 61).

Looking at the graph, it is possible to confirm that the method was capable of detecting extreme homogeneity of our target street as its information entropy gradient is calculated to be only 0.021681 points, while overall area average is 0.220657 points.

In the next and final step, we will calculate which category of alternative facade would reach highest information gradient if placed instead of the current one, generating suggestions for the architectural device, and then choose actual facade based on ideal volume correlation with the target site.

First graph (Fig. 62) shows initial situation with gradient of 0.021681 as already mentioned. Next, all other possible facade categories were tested to see which will create the highest information gradient. Categories K and L reached the highest values, but didn’t contain facades of size and scale comparable to target house. Thus,

Fig. 60 ↑ Target site with facade categories assigned

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Fig. 61 → Shannon's information gradient method site analysis

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category reaching second highest value (G) was selected instead, and facade with the most fitting dimensions within the category was finally chosen (Fig. 63). This alternative house reaches above average values in facade complexity, thus it is selected as close distance suggestion for the architectural device. With this facade, target street reaches information gradient of 0.120926 points, and target area average is 0.224919 points with 0.699858 Bits of information per house street wise and 1.827745 Bits of information area wise.

Finally, second, alternative house to be visualized from medium and long distance was selected from the pool (Fig. 64).

It belongs to category P which reaches third highest values in information gradient graph and performs high in 3D complexity values compared to previously selected facade. With this face, the target street would reach information gradient of 0.113086 points, target area average reaching 0.224583 points. Amount of Bits of information per house rises the same as in previous case (as both are categories foreign to the whole area) to 0.699858 Bits of information per house for target street and 1.2827745 Bits of information 1.827745 Bits of information.

Fig. 62 â&#x2020;&#x2018; Target site with original A class facade

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Fig. 63 ↑ Target site with alternative G class facade

Fig. 64 ↑ Target site with alternative P class facade

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Conclusion

It is possible to conclude that primary objectives of the research, that is: identification of methodology through which collective face of the city could be understood, designing a device that would allow the collective face to be governed through bottomâ&#x20AC;&#x201C;up, distributed interventions and doing so in such a way that the device can ultimately become part of permanent cybernetic system with the city, have all been achieved to a point where every basic step can be simulated and tested in small scale. Through research of the all involved subjects, two general fields of discussion have been identified.

One would be the general issue of specific qualities of urban and architectural design in Tokyo, especially in consideration of the topic of "metabolizing urbanism" (discussed by Ashihara, Tsukamoto, Kitayama among others). We discussed the outline of a system primarily targeted at otherwise disorganized, disconnected sum of architects, builders and inhabitants rather than urban planners and city authorities. Here, the emphasis must be placed on refocusing attention to the relationships between individual facades and the urban system, which promises to become a potential catalyst sparking a whole new set of generative

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processes forming urban environment as it becomes infused with new type of intelligence and new relevant factors of analysis.

Second field of discussion addresses the utilization of an analytical tool, the Information theory, and its adaptation to urban planning process, as originally outlined by Haken and Portugali in their various papers and books. The proposal offers methods designed to work specifically in Tokyo environment, relying on the utilization of construction sites and facade categorization as tools to theoretically bridge the gap between theory and praxis. The proposal also suggests and tests several methodical and mathematical tools, most importantly the facade analysis logic, the cluster-based categorization device (providing us with specific understanding of a house beyond typical classification based on function, size, age, specific style etc.). We further look at Shannon's information gradient calculation and visualization method, and offer an alternative approach to the issue identified by Haken and Portugali regarding limitations of utilization of pure Information theory logic when it comes to cognition of real environment with specific, non-idealized spatial distributions of houses within it. All these methods are aimed to contribute to wider discussion within fields of complexity theories of cities, information theory applied to cities and cybernetic and emergent urbanism in general.

The proposal also identifies number of challenges. Majority of these are connected with automation of processes connected with reading and analyzing the face of the city (data collection, data analysis).

Without this automation, the proposal can be understood only as a smallscale testing and evaluation oriented methodology. Second category of challenging and not fully resolved questions is connected to the issue of categorization of facades, related to problematics of human cognition of environment. The system currently works only with highly idealized notion of facades, omitting substantial part of what forms an actual image of a city (such as topography, regularity or irregularity of streets, narrowness of streets, semantic categories). Furthermore, clear methodology of defining ideal number of categories and values needed to perform clustering categorization of evaluated facades is incomplete. For these problems, we have only speculative directions towards available solutions.

The difficulty of our search is increased by the lack of existing "big data" collection methods and databases that would allow us to perform a deeper and wider analysis of patterns in facade types occurring throughout the city. However, we believe these problems present potentially very interesting and fruitful opportunities for further research with capacity to considerably improve feasibility and accuracy of the device as they get gradually expanded upon.

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References

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Ashihara, Yoshinobu and Lynne E Riggs. 1989. The Hidden Order.

Tokyo: Kodansha International.

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Berenzweig, Adam. 2014. "Deep Learning: Intelligence From Big Data". Presentation, Stanford Graduate School of Business, Knight Management Center – Cemex Auditorium.

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nendix

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Calculations & Estimations

For creating animated visualization of how construction sites populate urban fabric of Tokyo, data obtained from Kensetsu Databank website (http://www.kensetsu-databank.co.jp) were utilized. The website contains day-by-day information listing finished constructions of houses in Shinjuku, Minato and Shibuya districts, including floor areas and addresses, and covers years 2013 to 2016. Although incomplete (doesn't include reconstructions, list of demolitions is very limited and generally contains only sites potentially interesting for real estate agencies) and without dates of beginning of constructions, it allowed to visualize general behavior of construction sites network in time based on real data rather than sheer estimations (Fig. 65).

In order to calculate rough estimation of amount of protective construction drape surfaces in the city (mentioned in 1.1.3), following logic was used:

In a sample area of 1 km2 of central Tokyo (located between Chiyoda and Chūō districts with Kodemmachō metro station roughly in the center), there is 3093 buildings with average ground area of 149 m2 (Fig. 34). Average amount of floors in the area is estimated

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Fig. 65 → Construction sites populating Tokyo over years 2013 and 2014.

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to be 6 (calculated as average of typical block in the area, namely in Iwamotochō 1–chōme, omitting less accessible buildings inside the block (Fig. 68)). If construction height is 3 m, than an average house in the area is 18 m tall, 12,2 m wide and 12,2 deep. A single side (1/4 of the vertical surface) is then 219,6 m2 (Fig. 34).

Let's assume that visible surface area of construction site is in average only this 1/4 of its surface (typical situation in case of row buildings, corner and solitary buildings are being omitted).

Furthermore, lets assume that average life span of the buildings is 30 years, and given relatively larger size and predominantly concrete construction of houses in the area, average time a construction takes to complete is assumed to be one year. For sake of simplicity, lets omit demolition sites and reconstruction sites (which typically take place every 10 to 15 years but take shorter time, usually several weeks). Due to this relatively humble estimation, there is 679.222,8 m2 of visible facade surfaces in the area, from which every 30th is being under construction (22.640 m2). This doesn't automatically mean there is as much as 22.640 m2 of construction drape surfaces for every square km at any given moment since scaffoldings typically grow gradually as the building is being constructed, but it gives some idea of how much of the potential facade surface of the city always exist in form of a construction site.

18 m

219,6 m2

Fig. 66 ← Average building based on the 12,2 m

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3093 buildings

Fig. 67 → Sample central Tokyo area with Kodemmachō station in the middle 1000 m

Fig. 68 → Iwamotochō (Google, 2016)

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Chapter name / Subchapter name Tâ&#x20AC;&#x201D;ADS

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