The Why Factory - Sky City - Fall 2018 - Book

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The sky was never the limit.

What has been unobtainable for centuries has now come within reach and made a new kind of urbanism possible. Our dreams of flight have lead us to redefine the city.

No more pollution, but fresh air!

No more cars, just flights!

No more vehicles, there are pods!

No more commute, but time at your leisure!

No more congestion, just freedom!

No more streets, only pedestrian space!

A city in the clouds. Welcome to Sky City!

Sky City

No more buildings, only objects!

k S y

i y Ct

The Hitchhiker’s Guide to Sky Angeles

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Foreword Winy Maas Main Dicant omittantur has no, sea cibo facer zzril id.

Dicit corpora scriptorem sed ex, duo persecuti pertinacia voluptaria ad. Cum wisi habemus gloriatur no, his habeo atqui ei. Vidisse habem us expetenda ex vim, eu elaboraret delicatissimi usu. Animal alienum maiestatis at vel. Quo malorum dissentiunt consequuntur ea, melius vivendo appellantur mel in. An est causae epicuri. Assum saperet ea eam. Diceret eripuit verterem est eu. Et quo error accusamus persequeris, impedit platonem nec cu. Virtute appetere consequat id pri. Qui ne eius theophrastus. Mei in cetero tritani. Main detraxit cu est, verear equidem his ut, ad detraxit interpretaris eum. Pri ei eros deserunt.

Summo vivendum accusamus usu cu. Animal referrentur adversarium has ei.Main porro adolescens duo, sea te aperiri petentium. Mei argumentum complectitur no, dicta assentior reprehendunt eam ad, vel feugait epicurei disputationi eu. Id quo scripta appetere expetenda, id blandit efficiendi est, no pri ignota fierent mnesarchum. Et nam tritani abhorreant, meis epicurei reformidans eu per.

Civibus fabellas oportere sea id, ei eam eruditi democritum instructior. Main saepe munere cu, vim ei consul efficiantur. Lorem scribentur no mel, ei porro quaestio sea, vix no utamur delicata prodesset. In qui debet dicam doming. Cu pro minim offendit, cu paulo exerci quodsi ius. Torquatos referrentur cu cum, clita phaedrum ius no, ex cibo ipsum est.Duo graecis disputationi an, prompta fierent urbanitas ut nam, pro at quaeque delicata. Ne labores facilis qui, ea vim legimus laoreet appellantur. Et

Main Dicant omittantur has no, sea cibo facer zzril id.

Dicit corpora scriptorem sed ex, duo persecuti pertinacia voluptaria ad. Cum wisi habemus gloriatur no, his habeo atqui ei. Vidisse habem us expetenda ex vim, eu elaboraret delicatissimi usu. Animal alienum maiestatis at vel. Quo malorum dissentiunt consequuntur ea, melius vivendo appellantur mel in. An est causae epicuri. Assum saperet ea eam. Diceret eripuit verterem est eu. Et quo error accusamus persequeris, impedit platonem nec cu. Virtute appetere consequat id pri. Qui ne eius theophrastus. Mei in cetero tritani. Main detraxit cu est, verear equidem his ut, ad detraxit interpretaris eum. Pri ei eros deserunt.

Summo vivendum accusamus usu cu. Animal referrentur adversarium has ei.Main porro adolescens duo, sea te aperiri petentium. Mei argumentum complectitur no, dicta assentior reprehendunt eam ad, vel feugait epicurei disputationi eu. Id quo scripta appetere expetenda, id blandit efficiendi est, no pri ignota fierent mnesarchum. Et nam tritani abhorreant, meis epicurei reformidans eu per.

Civibus fabellas oportere sea id, ei eam eruditi democritum instructior. Main saepe munere cu, mel, ei porro quaestio sea, vix no utamur delicata prodesset. In qui debet dicam doming. Cu pro minim offendit, cu paulo exerci quodsi ius.

Torquatos referrentur cu cum, clita phaedrum ius no, ex cibo ipsum est.Duo graecis disputationi an, prompta fierent urbanitas ut nam, pro at quaeque delicata. Ne labores facilis qui, ea vim legimus laoreet appellantur. Et prima verear inermis per, ei officiis acco

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Table of Contents 0. SKY CITY MANIFESTO 1. TODAY 1.1. Where are we 1.1.1. Los Angeles compared to other cities 1.1.2. Urban sprawl and density 1.1.3. Urban mobility 1.1.4. Atlas of Infrastracture 1.2. Tendencies 2. TOMORROW 2.1. Introduction to Sky City 2.3. Basic Overview and timeline 3. WHO LIVES THERE? 3.0. Why Focus on Users?

3.1. Archetype Creation 3.1.1. Introduction 3.1.2. American Time Use Survey, 2008 3.1.3. Explaining and Modifying the ATUS Data 3.1.4. Extract the ATUS to create Archetypes 3.1.5. The 10 Archetypes 3.1.6. Simulating Archetypes 3.1.7. Probability of Activity and Duration 3.1.8. Simulating Archetypes 3.1.9. Future Changes 3.2. The Sky City Archetypes 3.2.1. The 10 Archetypes 3.3. Presenting Archetypes Schedules 3.3.1. Schedule Changes 3.4. Social Behaviour Changes 3.4.1. Introduction 3.4.2. Overwriting the Schedule 3.4.3. Simulation 3 3.5 Social and Visual Distance 3.5.1. Introduction 3.5.2. Visual Distance 3.5.3. Social Distance 4. WHERE DO THEY LIVE? 4.1. Objects Introduction 4.1.1. Towards a Sky City

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4.2. Environment 4.2.1. Sprawl and Density in Sky City 4.2.2. Life in the Clouds 4.2.3. Sun Radiation 4.3. Sky Architecture 4.3.1. Sky Architecture Introduction 4.3.2. The Voxelised Grid: Why Voxels?

4.3.3. Elements, Pods, Objects, Hybrids 4.3.4. Bounding Boxes 4.3.5. Generic Pods and Specific Pods 4.3.6. Parameters 4.3.7. Circulation 4.3.8. Materials 4.3.9. Aggregation 4.4. The Pod Maker 4.4.1. Pod Maker Introduction 4.4.2. Podlist 4.4.3. Pod Maker Simulation 4.4.4. Pod Catalogue 4.5. The Object Maker 4.5.1. Shaping the City 4.5.2. Cluster Rules 4.5.3. Object Catalogue 4.5.4. Conditional Rules 4.5.5. Hybrid Objects 4.5.6. Reflections 5. WHAT IS THE CITY? 5.1. Sky Urbanism 5.1.1. Introduction 5.1.2. Sky City environment 5.1.3.Sky City agents 5.1.4. Sky City behaviour 5.2. Object flows 5.2.1. System overview 5.2.2. Flow behaviour 5.2.3.Intensity behaviour 5.2.4. Distribution behaviour 5.2.5.Separation behaviour 5.2.6. From users to sky urbanism 5.2.7. Subsystem memory behaviour 5.2.8. Subsystem densification behaviour

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5.3. User flows 5.3.1. System overview 5.3.2. Flow behaviour 5.3.3. Bundling behaviour 5.4. Goods flows 5.4.1. System overview 5.4.2. Flow behaviour 5.4.3. Extreme bundling behaviour 5.5. Priority flows 5.5.1. Priority behaviour 5.6 Simulations 5.6.1 First simulation system 5.6.2 Second simulation system 5.6.3 second simulation system 5.6.4 Subsystem simulation

6. REFLECTIONS 6.1. Reviews 6.2. Work methodology 7. MOLECULAIR CITY 7.1. Towards a molecular city 8. APPENDIX 8.1. Objects cataogue 8.2. Evaluation criteria summary

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The sky was never the limit.

What has been unobtainable for centuries has now come within reach and made a new kind of urbanism possible. Our dreams of flight have lead us to redefine the city.

No more pollution, but fresh air!

No more buildings, only objects!

No more cars, just flights!

No more vehicles, there are pods!

No more commute, but time at your leisure!

No more congestion, just freedom!

No more streets, only pedestrian space!

A city in the clouds.

Welcome to Sky City!

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1.

Today 2018

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1.1. Where are we now?

1.1.1. What is a city?

A city is usually defined as an inhabited place of greater size, population or importance than a town or a village. This quantitative definition, as much as it might be appropriate for a dictionary, unfortunately, misses the most important aspects of a city and what makes it special as a construct of man.

Throughout history, cities have been the pinnacle of civilizations achievements; they are the culmination of all technological achievements, societal revolutions and cultural distinctions that are characteristic of a society and its people. Like the ancient Greeks built their temples and forums, as well as the Romans that followed suit, so we build skyscrapers, train stations, and highways. These are the landmarks of our age and they directly reflect our contemporary way of life by materializing it into physical artifacts.

But a city is much more than a collection of objects suspended in space, serving a purpose or reminding of times gone by. The core of a city are its people; cities represent diversity, they allow for individuality as well as a collectivity, they amplify every aspect of our life and in this way create a unique environment of unlimited possibilities. They are hubs of innovation, centers of revolutions and places of interaction. Above all, a city is a place, as the writer Richard Sennett put it, where strangers meet; where new ideas are formed in a public space. A common ground open to anyone and everyone. A place directly created by its inhabitants; a physical reflection of society itself.

This ability of cities to not only exist as a reflection of our society but also amplify and affect its future evolution should be enough to recognize their importance as the true core of our civilization, which inevitably brings us to the question;

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1.1.2. An Urbanising World

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41%

% 31

Our world is in the middle of drastic change, the likes of which our civilization has not experienced before; population growth in recent decades has been the highest in history with Asia and Africa transitioning towards higher living standards and development.

These processes have put enormous strain on our natural, as well as the built environment in ways we have never seen before. To accommodate this increasing development the City has become more important than ever before; within them, we are able to efficiently house our growing populations, enable them access to work, leisure, and other daily activities. As a result, urbanized regions are rapidly expanding and becoming the prime human habitat in which we are able to thrive. As part of the Old Continent and the Western developed world, Europe has been influenced by this evolution in our environment for quite some time. With a quick glance of a population distribution map of Europe we are able to effectively look into the future of our planet; In Europe, 72% of the total population live on only 17% of the total land area. This statistic becomes even more drastic if we look solely at urban areas where 41% live onArea 4% Population of the area. These are growing trends worldwide and in spite of the majority of Europe already being urbanized, the share of the urban 4 13% population continues to increase.

Population

Area 4 13%

% 31

41%

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72% live in urban areas.....on 17% of the land area.

Urban

Suburban

72% live in urban areas.....on 17% of the land area.

Urban

Suburban

Rural

Rural

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2000 The process of urbanization as the settling of large amounts of people on a small area of land has been a fairly new phenomenon; for most of history, humans have lived in rural settings of low-density. According to estimations, prior to 1600, the world share of the urban population was lower than 5 percent. As time passed, the trend of urban living has dramatically risen; 7 percent by 1800, 16 percent by 1900 and finally in the 20th century the urban condition spread across the globe and became the predominant way of life.

These trends of increasing global urbanization are clearly visible in the mapping of predominantly urban versus predominantly rural areas of the world. In a map from the year 2000, we can already see that the vast majority of the world lives mostly in urbanized areas; Europe, North, and South America and Australia are predominated by urbanized settlements with only the relatively undeveloped Africa and Asia lagging behind.

2050

Urbanization vs. GDP

In a later projection for the year 2050 100%based on assumed population growth and related trends in urbanization, we can see that prac80% tically the entire world will be living in urban environments with 60% the only exceptions being a handful of countries in central Africa 40% and small parts of Asia. Concluding from this it seems that a highly 20% urbanized environment - the city - really is the future of mankind and the optimal habitat for our survival.

1.000

100%

10 billion

80%

8 billion

60%

6 billion

40%

4 billion

20%

2 billion

10.000

Level of urbanization

100.000 GDP/capita

Level of urbanization

Urbanization vs. GDP

1.000

10.000

100.000 GDP/capita

1500

Urban population majority 1600

1700

1800

Urban population Rural population

1900

2000

Rural population majority

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1.1.3. Cities of today

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New York - largest city in America

Megacities are extremely large cities, typically with a population of 10 million people. At the time of writing there exist 47 megacities in the world, predominantly in Asia. A continental division would result in 3 in North America, 5 in South America, 4 in Europe, 3 in Africa and a whopping 32 in Asia, China is absolutely predominant with 15 megacities and India second with 6.

London - European urban sprawl

While densification is generally referred to as good urban practice, especially with the ever-increasing world population growth, megacities also bring a fair share of problems still needing to be resolved in the decades to come. Among these are widespread degraded slum areas, concentrations of criminal activity, large numbers of homeless people, aerial pollution, as well as in many cases urban sprawl and related traffic congestions. All of these represent obstacles to efficient and pleasant life in a megacity - ones that we urgently need to tackle.

Out of the plethora of the world’s megacities, four had been picked for analytical research; Firstly Los Angeles as the global symbol of urban sprawl and due to the fact that our time uses survey data was performed on citizens of the United States of America. This factor also influenced the second choice, which was New York, as the largest city in America. Next, we decided to pick a European counterpart to the urban sprawl of Los Angeles and decided on London due to its sprawling suburbs and sheer size. Lastly, due to the Asian predominance in megacities, we felt we required at least one representative and chose Dhaka as the densest city on Earth and the complete opposite of urban sprawl. These would be our four benchmark megacities of today used to understand the contemporary urban situation, as well as a useful comparison to our project.

Dhaka - densest city on Earth Los Angeles - symbol of urban sprawl

Megacities of the World Bangalore Bangkok Beijing Bogota Buenos Aires Cairo Chengzhou Chengdu Chennai Chongqing Delhi Dhaka

Guangzhou Hangzhou Harbin Istanbul Jakarta Jinan Karachi Kinshasa Kolkata Kyoto-Osaka Lagos Lahore

Lima London Los Angeles Manila Mexico City Moscow Mumbai Nagoya Nanjing New York Paris Rhine-Ruhr

Rio de Janeiro Seoul Shanghai Shantou Shenzhen Sao Paulo Tehran Tianjin Tokyo Wuhan Xi’an

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Population density and Urban sprawl

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City area population density

The cities chosen for analysis vary greatly in size as well as their number of inhabitants. Another difference between the results from the division of the city center proper versus the broader metro areas.

Looking at the city center areas the most populous city by far is Dhaka with a total of 14.4 million inhabitants, followed by London and New York with 8.8 million and 8.6 million respectively, while Los Angeles takes the last place with only 4 million inhabitants.

The physical boundaries of the aforementioned cities again vary greatly from 306 km² for Dhaka, 780 km² for New York, 1213 km² for Los Angeles and 1578 km² for London. The combined result of both factors is the population density per square kilometer; one of the most important indicators describing the urban fabric of a city.

Dhaka has by far the highest population density of the analyzed cities with a density of 47.000 inhabitants per square kilometer, making it a truly hyperdense urban environment. The centers of London and New York find themselves in the medium to high-density range, while Los Angeles is last with a measly 3300 inhabitants per square kilometer.

Looking at the broader metro areas the most populous city is again Dhaka with a total of 20 million inhabitants, followed by New York and London with 14.5 million and 14 million respectively, while Los Angeles again takes the last place with 13.3 million inhabitants.

Again the physical size of the metro areas varies greatly from 2160 km² for Dhaka, 9100 km² for New York, 12500 km² for Los Angeles and 8300 km² for London. Combined, both factors again add up to the population densities of metro areas. Dhaka is again the densest with 7950 pers/km², with Los Angeles again being last with only 1060 inhabitants per square kilometer.

The consistent statistics of Los Angeles’ low density together with its physical size - it has the largest metro area - strongly point towards an intense urban sprawl. This is also confirmed by the proportion of the metro area compared to the city center; a whopping 1034%.

15 mil

10 mil

Dhaka 47000 /km² London 5600 /km²

New York 11000 /km²

5 mil

Los Angeles 3300 /km² 0

500

1000

1500

2000

km²

Metro area population density

25 mil

20 mil

New York 1560 /km²

Dhaka 7950 /km²

15 mil

London 1680 /km²

10 mil

Los Angeles 1060 /km²

5 mil

0

2000

4000

6000

8000

10000

km²

.10.1

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Built Builtdensity densityvs. vs.population populationdensity density Built density

Built Builtdensity densityanalysis analysisper perkm³ km³

Analyzing built density has shown to be quite an unenviable task, FAR FAR as2.00 there is practically no publicly accessible data regarding average 2.00 Dhaka Dhaka land use of global cities. To tackle this issue and area of one square kilometer of a generally representative part of the city was cut out London and modeled in 3d to act as a base for of each of the fourLondon cities spatial analysis. These locations included Inglewood in Los Angeles, New New York York Westminster in London, Brooklyn in New York and Central Dhaka.

1.00 1.00 The results of the analysis showed a linear increase between coverage and floor area ratio, showing that Los Angeles is by far the Los Los Angeles Angeles least dense city out of the four, pointing towards considerable urban sprawl. Another important takeaway was the total built volume; 0.20.2 0.30.3 0.40.4 0.50.5 cov cov something we would use for later comparison of contemporary cities to our project. Again, Los Angeles was last on the scale with a 0.15% of built volume per km3, translating to 1.500.000 cubic meters of built area. Lastly, we calculated the amount of built area per person according to the population density in that area. In this case, London seems to be most wasteful, but this could also be misinterpreted, due to Westminster being part of inner city London - an area Built Built density density vs. vs.population population density with many commercial buildings and fewerdensity dwellings.

2.00 2.00

POPPOP : : CO CV O:V: 11101000 /km FAFRAR 0.40.40 /0km ³ ³ 55 : : VO VLOL 1.51.5 33 : : 0.406.46 %%

POPPOP : : COCVOV 4747 0000 / : : km FAFRAR 0.20.2 /km 77 ³ ³ : : VOVLOL 0.50.5 11 : : 0.105.15 %%

Los LosAngeles Angeles- Inglewood - Inglewood

London London- Westmister - Westmister

POPPOP : : CO CV O:V: 141545 /km FAFRAR 0.30.0300/0km ³ ³ : : 33 VO VLOL 1.21.2 22 : : 0.307.37 %%

dha dha

ldnldn

New NewYork York- Brooklyn - Brooklyn

nyny

1.00 1.00

106m²/pers m²/pers LA LA 106 139m²/pers m²/pers LDN LDN 139 la la

0 0

POPPOP : : CO CV O:V: 474070 /km FAFRAR 0.50.0500/0km ³ ³ : : 55 VO VLOL 2.02.0 22 : : 0.601.61 %%

0.10.1

0.20.2

0.30.3

m²/pers NYC NYC 8585m²/pers

0.40.4

0.50.5

cov cov

Built Built area area per per person person (area (area in in m²m² per per person) person)

m²/pers DHA DHA 4343m²/pers

Dhaka Dhaka- Central - Central

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Urban mobility

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Analysis of Urban Mobility A direct consequence of the aforementioned sprawl is drastically reduced urban mobility due to long travel distances and congestion.

An important side effect of this is also increased use of the car resulting in a large production of co2 gas and thus extreme unsustainability as well as pollution.

LA 29 NYC 17 DHA 16

Analyzing commuting times in relation to population density we see that low-density cities such as Los Angeles and hyperdense cities such as Dhaka perform the worst. The reason for the former is large distances due to urban sprawl, while for the latter the primary reason is the sheer volume of commuters overloading the network in every way.

From comparisons between the four cities, we see that Los Angeles has by far the average daily commute distance - a clear indicator of extreme urban sprawl. This results in an extremely large percentage of car use as the primary mode of transport, which leads to unbearable congestion and also extreme co2 emissions.

Commuting distance

LDN 15

(average distance in km)

LA 84 DHA 26 NYC 22 Commuter car use

LDN 13

(% of commuters)

DHA 63

Population density vs. commuting time

LA 47 LDN 40

Low density

Medium-High density

Hyperdensity

60 min

Dhaka

Commuting time (average minutes)

LA 11300

Los Angeles

40 min

DHA 5400

London

NYC 3200

New York

CO2 Emmisions

20 min

(average g per return trip)

0

2.500

NYC 34

5.000

10.000

20.000

40.000

pop/km²

LDN 2200

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1.1.4. Los Angeles; the Symbol of Sprawl All of the analysis performed pointed towards a single “winner” - a city of extremely low population, as well as built densities, with a small center and an enormous supporting metro area around it. A city of extreme distances, long commutes and excessive use of the car as the predominant means of transport with no regard for public transport. Los Angeles; the international symbol of sprawl.

This sprawling nature of Los Angeles is not a new phenomenon; it is deeply ingrained into the cities’ DNA and has steadily increased its influence as the city grew throughout history finally resulting today in the concrete carpet spreading as far as the eye can see.

The influences contributing to its emergence have been many, but most important among them being strip development, the shopping mall and the private car as the symbol of freedom and of societal standing. The resulting cocktail is a city spilled over an enormous distance with an intricate web of roads and highways acting as always overburdened arteries trying to keep it all alive and running, usually to limited success.

Although the earliest uses of the term “urban sprawl” appear to be describing London in the 1950s, the actual physical phenomenon is a quintessentially American invention. On a cultural level, it symbolises a society of free individualism and limitless wealth, while spatially assuming an unlimited supply of land and resources.

These seemingly idealistic aspirations and intentions manifest as a somewhat different, even disappointing, physical reality. To live in sprawl means driving to work, driving to dinner, driving to meet with friends and driving to the supermarket. Most of the time the “driving” is even not actual driving but actually waiting in a traffic jam, wasting time, polluting the environment and, in most cases, being extremely frustrated.

Convenience aside, sprawl also causes exorbitant land use and the destruction of natural landscapes. It is the spatial reflection of our mindset towards space and the environment, an emblem of our carelessness and towards the world, we live in.

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Urban sprawl in Los Angeles

Los Angeles urban area growth

What is today the megacity of Los Angeles was once only a tiny pueblo counting no more than 650 residents. The year was 1821 and the city just fell under Mexican rule as New Spain achieved its independence from the Spanish Empire.

Throughout the next decades, this small settlement that would one day form the Downtown area of Los Angeles slowly grew in size and population. It was not until the turn of the 20th century that the surrounding areas were starting to be settled in small population pockets described by Aldous Huxley as “nineteen suburbs in search of a metropolis� that would eventually expand, merge and sprawl out in the process of creating the megacity that is the contemporary Los Angeles.

The cities’ growth started rapidly accelerating around World War I, following which it steadily densified until World War II and then finally burst out as a full grown sprawling metropolis from 1970 onwards. In recent history, the spread has considerably slowed and the city is expanding at a much lower speed. Nevertheless, the urban population remains on the rise, requiring careful management and planning on an urban scale. Recently the authorities are focusing much effort into densification of the city within existing limits, which is a sensible strategy, albeit its success is limited, due to the incredible strain put on the infrastructure supporting this urban machine.

1910

1930

1950

1970

1990

2010

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Built density in Los Angeles The built densities of contemporary cities are hard to evaluate as in the majority of cases there is no comprehensive data describing a specific cities’ urban morphology and its parameters. Los Angeles is no exception; excluding the standard values of population densities per square kilometer which are measured on a regular basis in every city in the world, we found no additional data on the density of built tissue in L.A. In order to work around this problem the urban area of the Los Angeles inner city was split into two distinct types: firstly a low-density morphology describing the condition of urban sprawl and secondly a high-density area found in the city center and consisting predominantly of skyscrapers. In the second step, a specific part of Los Angeles was chosen as a representational cut-out of 1 km² for each of the two morphologies; a section of the Inglewood neighborhood was chosen as a representative of low density and the central Financial district as representative of high density.

Both representative sections were modeled in three dimensions based on building height data and subsequently used for data extraction about built density. The differences in both areas were drastic; all of the spatial parameters, from coverage (COV) to floor area ratio (FAR) and subsequent volume indicated significant differences in spatial density. The central Financial district exhibited extremely high built densities, but unfortunately only covers approximately a mere 40 square kilometers; a measly 3.3% of the entire city area. All of the remaining space is covered by a sprawl morphology which, as shown in the earlier chapter, is of extremely low built density which leads to an unstoppable urban sprawl and all its accompanying problems.

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Built density analysis per km³ POP : COV : FAR : VOL :

470 0 0.27 /km³ 0.51 0.15 %

96.7

%

of a

rea

Los Angeles - Inglewood

POP : COV : FAR : VOL :

330 0 0.48 /km³ 8.96 2.69 %

Los Angeles - Financial district

3.3% of a

rea

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Mobility in Los Angeles Built density analysis per km³

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Los Angeles and its expanses of urban sprawl also present other POPdisplacing parks and other nature. difficulties except high land use : COV 700negative impact on urban One of the most serious side effects : is 4the /k FAR 0 .

27 m³ : mobility.

VOL

:

0.51 0.15

96.7 of a

r

%

a % to travel 29 e The average commuter in Los Angeles needs kilometers to reach their desired destination, on average lasting 47 minutes per person per day. This situation significantly worsens when we take into account the negative effects of congestion caused by the overloaded road network: on average the commuting times increase by 45%, while in the morning peak this percentage jumps to 62% and in the evening to 82%. All of this is compounded by the fact that car use is a whopping 84% compared to other means of transport. This also has environmental consequences with the average Angeleno producing 11.300 grams of CO2 per year, requiring approximately 125 trees to offset.

Los Angeles - Inglewood The results of urban sprawl combined with needs for mobility are an enormous web of necessary road infrastructures in the shape POP and parking lots. In total the area of streets, highways, pavements : COV 330 taken by all roads in Los Angeles :is a whopping 0 /k 25% - a quarter of FAR m³ 0 .48 vehicles! : the city is a surface designated strictly for Incredibly this VOL 8 .

9 6 : trend shows no signs of slowing down and will continue of a to burden 2.69 rea % the city and its inhabitants for years to come.

47 MIN

Average commuting time per person per day

AVERAGE +45% MORNING PEAK +62% EVENING PEAK +84%

45%

Average additional time due to congestion

84%

Predominant means of transport

3.3%

This huge footprint left by cars is also measurable in the total surface used only for parking purposes. In Los Angeles County the total amount of surface covered by car parking is four times Manhattan and only in Los Angeles that surface is bigger than the entire neighboring city of Pasadena that counts 142 647 inhabitants.

Los Angeles - Financial district

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25% ROADS

Total parking area in L.A....

...equals the size of Pasadena

Total parking area in L.A county....

...equals four Manhattans

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1.1.5. Atlas of Infrastructure Infrastructures are the glue holding our contemporary world together by enabling everything from your daily commute to work, the delivery of your Amazon order and Christmas cards, to delivering drinking water to your kitchen tap and allowing you to instantly talk to your significant other at the other side of the world. Infrastructures are everywhere we look, and even more so in places, we usually do not see.

These networks stretch across the globe, swallowing the surface of the planet, as well as permeating the ground. They vary in size from a single sewer drain through road interchanges and parking lots up to swathing expanses of terraformed artificial islands and deep mines. Functionally they encompass incredible variety like roads, parking lots, oil platforms, underground pipes, electricity poles, gas pipelines, railroads, subway stations, etc. In spite of all of these differences, they have one thing in common: they are the heart of our world and with every car, train or piece of information they pump through their veins they make sure everything is running as expected. It is due to this effect that we barely notice them in spite of their omnipresence; as long as everything is in order they are invisible to us and as soon as start breaking down they fall into the center of our attention.

It is at this exact moment we can begin to realize our overdependence on Infrastructures and their unrelenting grasp on our world.

Not only do they take vast amounts of space we can only obtain by cutting down forests, paving parks and in general destroying nature, but they also act as our self-imposed oppressors limiting our freedom to themselves; we can only call where there is signal, drive where there is a road and land an airplane where there is an airport.

At this point we ask ourselves: could we imagine a future reality where infrastructure, once the cornerstone of the city and contemporary society, in general, loses its purpose and retains meaning merely as a monument of the past, while we as a society and Planet Earth as our home are freed from this oppression? This is the dream we are working towards.

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Mass transport infrastructures

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Goods infrastructures

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Road infrastructures

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Production infrastructures

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Terraforming infrastructures

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1.2. Where are we going?

1.2.1. Evolution of Sustainability Since antiquity, our civilization has had an intense connection to nature and our planet in general. Throughout most of history, this relationship has been a symbiotic one; man and nature coexisted and thrived. Unfortunately, in recent history, this co-existence has transformed into a parasitic relationship in which the human race is exploiting the Planet and slowly destroying the same environment that enabled our existence. Fortunately, all is not lost! In the last decade, this exploitation has come into public scrutiny and triggered a newfound collective ideology of sustainability - striving towards an economic use of resources and nature preservation.

With the development of Ecology as a discipline at the beginning of the 20th century, the path had been laid for the later emergence of the sustainable movement. From beginnings such as the publication of the book Silent Spring in 1962, environmental thought was rapidly gaining momentum, culminating in 1970 with the first Earth Day. In the following decades, sustainability has exponentially gained importance in the contemporary world through discoveries such as the Ozone hole and the Global warming phenomenon. A recent landmark for sustainability has been the 2015 Paris Climate Agreement in which 195 nations agreed to combat climate change and to work towards a sustainable low carbon future. Looking at recent trends one thing is sure: sustainability is only going to have a greater impact in the future of our society. As the Millenial generation grows older, conscious consumption is projected to become the norm, the possibility of agreeing on a global carbon tax becomes realistic, and sustainable fuels gradually but persistently displace fossil fuels as the core of our economy. Our society will become more and more sustainable and eventually, with technological advancements, we will become entirely circular.

53

54

Timeline of Sustainability

55

First Earth Day

Low carbon economy predominates Millenium development goals

Millenium development goals

London clean air act

Conscious consumption predominates

Ozone hole Montreal protocol

Beginnings of ecology

Paris climate agreement Global carbon tax

Silent Spring book on climate

Kyoto protocol

Kyoto protocol

Sustainable environment 1900

1950

2000

2000

2050

2100 Sky City

56

1.2.2. Evolution of Food Food is one of the most essential things for our survival and its production has always been one of our greatest areas of focus. As population numbers in the world continue to increase, the amount of sheer land use required for the production of sufficient food has become one of the biggest problems in the food industry.

Through the history of food production, we have greatly increased our efficiency in production through various technologies; one of the first improvements from conventional farming had been factory farming quickly followed by the Green Revolution which brought new ways of increasing yields and helping us manage the constantly increasing demand. As innovations have progressed an obvious trend is emerging that is slowly freeing us from the shackles of the ground; construction of artificial growth environments which can be much more precisely controlled and thus more efficient. The roots of this approach lie in the invention of the greenhouse as a controlled piece of environment for optimizing growth. gradually this has advanced into hydroponics and the latest system: aeroponics.

These improvements bring numerous benefits besides reduced land use; increased growth speed, significantly higher yields, drastically reduced resource use such as water and finally spatial efficiency due to the possibilities of vertical/stacked farming.

The mentioned tendencies and evolution considered, is it beyond possibility to envision a future in which humankind becomes entirely independent of the ground for food production which enables us to theoretically produce food in thin air?

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58

Timeline of Food production

59

Land Dependence Land Dependence

Smart Farming

Green Revolution

Smart Farming

Green Revolution Lab Cultured Meat

Vertical Farming Factory Farming

Factory Farming

Hydroponics

Vertical Farming

Hydroponics

Genome Editing

Greenhouse Farming Land-based farming

3D Printed Food

Land-based farming

1900

Genome Editing

Greenhouse Farming

Aeroponics

1900

Lab Cultured Meat

1950

1950

2000

3D Printed Food

Aeroponics

2000

2050

2050

2100 Sky City

2100 Sky City

60

1.2.3. Evolution of Assembly The act of assembly has been present in human society from the moment we first adapted our environment to ourselves and laid one stone on top of another to make a wall or a place to sit. Needless to say, the ways in which we assemble things have drastically evolved since and are its own legitimate science in the world of today.

One of the first crafts in which assembly of objects and especially the way in which they connect was practiced and studied is woodworking. This resulted in an array of varied traditional wood joinery techniques that were in a way the precursor to modularity; if the leg of a chair broke, you were able to instantly replace it with another, provided the same joint was used. Later modularity was slowly starting to emerge through the establishment of the international ISO standard and various new techniques such as precast concrete.

All of these inventions eventually culminated in the proliferation of ideas of modularity and adaptability in the utopian decades between 1950 and 1980. At this time the general belief in scientific progress and leaps in technology strongly affected various fields including architecture, resulting in dramatic speculations about the future of construction and built environment such as the Nakagin Capsule Tower built in Tokyo.

After the 1970s and the ultimate failure of technology to solve all of man kind’s problems, the striving for modularity and ultimate flexibility reduced to a degree but has been slowly re-emerging in recent years. The main proponents for it are construction efficiency, spatial efficiency and increased technological capabilities such as prefab CLT panels, or repurposing of shipping containers into a modular housing. These tendencies point towards a slow reemergence of modularity, which is only going to increase as our technological proficiency for these systems improves.

Everything considered, is it too far fetched to think of a future world where there is a globally accepted modular system of construction, where every part is able to connect to everything else allowing for extreme efficiency in construction, as well as maintaining infinite possibilities for spatial diversity.

61

62

Timeline of Assembly

63

Gemini docking system

Total building prefabrication

Traditional wood joinery International International docking system docking system

(ISO) International Standardization

Kiosk K67

Ready-made prefab housing

Panel housingPanel housing construction construction Precast concrete

Container architecture

Dymaxion House Brick wall Nakagin tower capsule

Modularity 1900

1950

2000

2000

2050

2100 Sky City

64

1.2.4. Evolution of Ownership Ownership has been a crucial part of human society throughout history; it has defined social status and thus played a crucial part in an individual’s life. While in the past direct ownership of commodities and property had been the ultimate measure of one’s status, today these structures are experiencing drastic change.

In contemporary society ownership of things is less and less valued; there has been a fundamental shift in society from commodities towards experiences; people want different things all of the time and do not want to be tied down by ownership of a specific object. This has given rise to a new and extremely powerful global economy: the Sharing Economy. The foundations for this drastic change had been laid by the invention of the Internet at the end of the previous century and its inherent capabilities of universal communication. This first resulted in the global sharing of data and virtual content on platforms such as Youtube and Facebook.

In the past two decades this phenomenon has slowly spilled from the virtual to the physical world and is now present everywhere; people do not buy music but the buy subscriptions of Spotify, when traveling they sleep in other people’s homes by using Couchsurfing or Airbnb, when commuting they order an Uber and instead of buying movies they watch them on Netflix. Today it is all about the experience and experiences, as opposed to classic ownership, are impermanent and transitory. Future trends, especially with the Millenial generation, show that the Sharing Economy is only beginning and will exponentially grow into the foreseeable future without an end in sight.

Considering this tendency and its unrelenting growth, is it impossible to imagine a future society where classic ownership is an exception, rather than the norm; where people share practically everything, not only bringing them more satisfaction but also drastically increasing our efficiency of consumption?

65

66

Timeline of Ownership

67

YouTube

YouTube

Personal Property

Spotify

The Internet

Public transport

Co-housing Uber Hotel

Social Media Social Media

Land Ownership

Netflix

Taxi

Sharing Economy 1900

Airbnb

1950

2000

2000

2050

2100 Sky City

68

1.2.5. Evolution of Artificial Intelligence Artificial Intelligence is one of the youngest strands of technological development, only being present since the mid 20th century. Since its establishment as a field of study, it has exponentially grown into one of, if not the main technological goals of the 21st century.

The starting point of A.I. was the invention of the Turing test as late as 1950, intended for testing the capability of machines to mimic the responses of humans. Soon after this the notion of machine learning as a mechanism for self-improvement of machines firmly established itself in the field, but significant progress was somewhat halted by two Artificial Intelligence winters. It was in the 1990s when the next big breakthrough happened with IBM’s Deep Blue chess computer, which managed to beat grandmaster and then world champion, Garry Kasparov. After this, a resurgence of A.I. happened that lasts until today and brought the first AI human-robot Asimo, the first virtual assistant Siri, the first self-driving car and eventually IBM’s Watson, a question-answering computer system based on deep learning.

Today the future tendencies of A.I., while still very vague, strongly point to increasingly rapid development and application of similar systems in smart home systems, advanced virtual assistants such as Amazon’s Alexa, increasing autonomy of all vehicles and widespread application in commercial systems such as Netflix to name just a few. Eventually, with the sufficient advancement of machine learning, Artificial Intelligence will start taking over complex tasks such as medical assessments and medicine development. Beyond the year 2050, the intelligence level of A.I. will start approaching that of a human and exceed it with the result of that still remaining unpredictable.

With all of these developments in mind, is it unrealistic to imagine a future reality where Artificial Intelligence reaches a scope of overarching proportions - a single neural network assisting and coordinating every process on Earth, acting as one virtual assistant, an infinite repository of knowledge at everyone’s fingertips?

69

70

Timeline of Artificial Intelligence

71

Honda Asimo Honda Asimo humanoid robot humanoid robot AI exceeds human learning capability

Self-driving commercial cars Shakey - first autonomous robot

Deep blue chess computer chess computer

Widespread AI use

IBM Watson supercomputer

Machine learning

All transport autonomous

Siri - first AI assistant

Turing test

A.I. Intelligence 1900

1950

2000

2000

2050

2100 Sky City

72

1.2.6. Evolution of Production Production technology has been one of the cornerstones of our society since the dawn of civilization. As with other core technologies of such importance, production has also experienced a drastic evolution in recent history.

The steam engine kickstarted the First Industrial Revolution at the end of the 18th century and ushered in the era of mechanized production. As time went on technology advanced from strength to strength resulting first in the Second Industrial Revolution with the beginnings of automation and mass production on the newly invented assembly line and later the Third Industrial Revolution, affected mostly by developments in information technologies such as computers and the internet, resulting in the emergence of robotics and drastically increased levels of automation.

Today, as we are slowly approaching the Fourth Industrial Age, one of the main emerging trends of today is the advent of mass customization, fueled by the invention of additive manufacturing, more commonly known as 3d printing. This technology brings potentials for revolutionizing the whole production process through its extremely high degree of product customizability at when the technology becomes commercially widespread, an extremely low price. Another area of improvement is its very high degree of automation while simultaneously allowing for unprecedented decentralization, eventually reducing the need for factories and distribution of produced goods. Lastly, material efficiency can theoretically be drastically improved by using only the minimally required amount of raw materials which points to the possibility for extreme sustainability of the production process.

Taking into account these mentioned trajectories, is it not possible to envision a reality in which production becomes super efficient and completely decentralized while enabling us to fully embrace mass customization as the only mode of production.

73

74

Timeline of Production

75

Mass production

Automated production

Mass customization

Automated production

Mechanized production Internet Of Things

Computer

Artificial intelligence

Combustion engine

Internet

Biotechnology

Distributed production

Standardisation

Electricity Robotics Nanotechnology Steam engine Automation

Automation

Assembly line

Additive manufacturing

Production efficiency 1900

1950

2000

2000

2050

2100 Sky City

76

1.2.7. Evolution of Materials From the dawn of civilization, man has tried to modify and adapt the environment he lived in through the use of various materials.

Through time, our knowledge of the world around us, and the matter it is created from has gradually increased and we have learned how to manipulate it with increasing success.

Through history, we have gradually advanced from first using only natural materials such as wood and stone towards increasingly complex ones like ceramics, metals and later concrete. As with other technologies the biggest leap would happen in the 20th century when many new materials appeared; glass, polymers resulting in plastics, composite materials, biomaterials, smart materials and much more.

Today the tendencies of materials research strongly point toward an increasing capability to modify matter on an ever smaller scale and consequently, instead of using merely what nature had given us, constructing our own super-materials with superior properties.

Excellent examples of this are the inventions of carbon fiber, vantablack, and carbon nanotubes, just to name a few. The field of research making these inventions possible and that will lead us into the next generation of material production is the field of nanotechnology, giving us the capabilities of manipulating matter on a molecular and atomic scale, allowing for practically unlimited possibilities.

Considering the tendencies of material research and our exponentially growing ability to control physical matter, is it impossible to predict a future in which we are able to produce extremely efficient supermaterials of extreme strength, lightness, pliability, etc. allowing us limitless possibilities in creating the world around us?

77

78

Timeline of Materials

79

Fibre glass

Steel

Fibre glass

Steel

Smart materials

Composites

Metals

Smart materials

Composites

Metamaterials

Metals

Metamaterials

Bio membrane

Polymers

Bio membrane

Polymers Bio composites

Semiconductor

Bio composites

Semiconductor Nanotechnology Nanotechnology

Organic materials

Organic materials

Plastics

Plastics

Biomimicry

Biomimicry

Material Manipulation Material Manipulation 1900

1900

1950

1950

2000

2000

2050

2050

2100 Sky City

2100 Sky City

1.2.8. Evolution of Energy Energy production and its efficiency are one of the main indicators of a civilization’s development level. Throughout history, our society has been on a consistent upward trajectory of energy production starting from wood as fuel, through fossil fuels and on our way to transitioning to more sustainable alternatives.

An important trajectory observable today is the gradual rise of efficiency in energy production resulting in an ever-increasing amount of energy at our disposal. This evolution started in the 18th century with the invention of the steam engine which kickstarted the era of fossil fuels which is still predominant today. Environmental drawbacks aside, fossil fuels have a very high degree of stored energy but are extremely unsustainable in the long run as after only a couple hundred of years we are already running out of them.

The main candidate to replace and drastically improve our energy yield is nuclear energy, but as opposed to the already established nuclear fission processes and their many drawbacks, nuclear fusion has the largest potential to fulfill all our future needs. It is based on the same processes that take place in our Sun and produces theoretically unlimited amounts of energy at practically no cost and zero pollution.

The second tendency is one of slowly increasing decentralization of energy production. Where we once only produced electricity in large thermal, nuclear or hydropower plants, today a trend towards self-sufficiency is emerging, creating a network where everyone is a producer as well as a consumer. This can be observed most strongly in the recent proliferation of solar panels, seemingly covering every roof and facade, as well as in the spread of wind power plants. The same phenomenon can to a smaller degree also be observed in compact nuclear reactors in ships, aircraft carriers, and spacecraft.

Taking these tendencies into account, is it impossible to envision a future in which our civilization has a practically infinite amount of energy at its disposal in the form of decentralized compact fusion reactors making every building a small self-sufficient power plant?

81

82

Timeline of Energy

83

Centralisation Nuclear fuels

Fossil fuels

Sustainable fuels

Commercial Photovoltaic panels Hydroelectric power plants Fission power plants

Solar power plants

power plants

Fusion power plants Thermoelectric power plants Wind power plants

Photovoltaic panels

Wind power plants

Hydrogen fuel cells

Nuclear fission Compact fission reactors

Amount of energy 1900

1950

2000

2000

2050

2100 Sky City

1.2.9. Evolution of Flight The ability of flight has been one of the ultimate dreams of humanity since antiquity. This notion of complete freedom of movement released from all constraints was an obsession of many people throughout history and it has brought us incredible advances in only a couple centuries time, setting us well on the path towards the ultimate goal.

The first predominant tendency one can draw today is the continuously increasing accessibility of flight for everyone. As aviation evolved from initial experiments conducted by enthusiasts through military and later private aviation for the wealthy, today it is one of the main modes of transport, yearly carrying millions of passengers worldwide. It is available to the rich as well as the poor and is thus extremely accessible. This accessibility is only increasing as the technology becomes more evolved, efficient and consequently dominant in the global market.

Another tendency which strongly related to that of accessibility is autonomy. From the advent of the first autopilot, aviation has slowly been on a consistent trajectory towards complete autonomy.

First major advances in this field have been made from the 1980s onwards in the form of military drones, a technology that has slowly trickled down and is today showing in the growing commercial drone market. This trajectory toward autonomy is expected to take the next step in the coming years with the introduction of autonomous air taxies by companies such as Uber and Airbus in selected trial cities worldwide, kickstarting the commercial market of autonomous distributed air transport. In time this advance will also slowly free air travel from cumbersome airport infrastructures allowing it to become almost as flexible as a car, being able to take off and land anywhere.

Considering these predominant trajectories of today, is it impossible to foresee a future where flight is possible for everything, not just people and cars, but also objects and buildings?

85

86

Timeline of Flight

Autopilot

87

Autopilot Flying car

Commercial flight

Flying car

Commercial flight Commercial Commercial autonomous UAV autonomous UAV

Helicopter

Propeller

Helicopter

Martin jetpack

Propeller

Martin jetpack

First UnmannedFirst Unmanned aerial vehicle aerial vehicle

Autonomous Air taxi

Autonomous Air taxi

Jet propulsion Jet propulsion Military UAV use

First airplane

Military UAV use

Commercial UAV use

First airplane Supersonic flight

Commercial UAV use

Supersonic flight

Autonomy Autonomy Accessibility, Accessibility, 1900

1900

1950

1950

2000

2000

2050

2050

2100 Sky City

2100 Sky City

88

89

2.

Sky City 2122

92

2.1 Introduction to Sky City

93 Mount San Antonio 3069m

Sky City is situated around 1300 - 1800 m from the level of the sea. It occupies a volume of 1 Km³ for a population of 20 000 people.

Sky City displays a new form of urban life. Changes in mobility and movement determine a shift to a new way of living.

Inside Sky City, people’s desires are fully satisfied in a short amount of time. The freedom of movement created by the possibility of flying shortens distances, letting people be a few minutes away from their beloved ones. This change in the way of moving affects every aspect of everyday life, producing positive effects on citizens who are not forced anymore to commute for an insane amount of time.

Treeline 2900m Altitude

2000 meters

-

1750 meters

1500 meters

Sky City has been thouroughly studied under every aspect of its urban dimensions. Consequently, the following chapters are divided per topic, focusing only on one of the three main elements that define a city: the Users, the Objects and the Flows.

Users are at the basis of the evolution of urban patterns. A city develops accordingly to users behaviours and needs.

Sky Citizens have been created upon the basis of the American Time User Survey. Ten different citizens archetypes constitute the main groups of Sky City citizens. They are not fixed, but they are based on percentages that allow a unique special variation for every citizen, as in a real life.

A new form of architecture takes shape in Sky City. Objects have substituted buildings, and they are now detached from the ground and able to fly as well. Clusters of pods form the objects by detaching and attaching to each other, and this system is governed

by Artificial Intelligence.

1250 meters

1000 meters -

Sky City Altitude Temperature Humidity Air pressure&density Oxygen Population

1300 - 1800 m Ø 6 C (min), Ø 16 C (max) Ø 70,9% 79-80 kPa 16,3 - 17,3% 20 000 inhabitants

-

750 meters

Los Angeles 500 meters

Altitude Temperature Humidity Air pressure&density Oxygen Population

87 m Ø 13 C (min), Ø 22 C (max) Ø 75% 101,59 kPa 16,3 - 17,3% 4 million inhabitants

250 meters

Eventually, this new system is organized and controlled by flow rules. The last main aspect that is analyzed in the book, in fact, concerns the flow management of all elements inside the City, defining how and when they have to move to ensure a safe and organised city.

0 meters

Los Angeles

94

95

3.

Who lives in Sky City?

98

3.0. Why Focus on Users?

When imagining a city in the sky, questions of congestion, density, efficiency and speed immediately spring to mind.

These are quantifiable aspects of life, and it’s easy to imagine future improvements and progress. However, when designing a city, there is always the question why. Why should commuting be faster, why should the city become more dense? Questions like these could be answered in economic terms, i.e. designing for economic growth. They could be answered from a mobility point of view; facilitating efficient movement for all. While these are valid approaches, in the end it boils down to the inhabitants of the city, the users. Only by designing for real people and adapting to their daily lives can an aggregation of buildings truly become a city.

The question is then, who will live in Sky City? How can we make it a truly vibrant, bustling setting for everyday life?

Since no-one currently reside in the sky, we had to create a society where each individual has their own habits, their own family and group of friends. The challenge was to simulate a population where every single individual is unique, but where recognisable patterns can be discerned from the population as a whole.

This would then lay the foundation of every design decision made, every pod, every object, all the flows - everything done with the users in mind.

99

100

101

3.1. Archetype Creation 3.1.1 Introduction Taking Los Angeles as a starting point for Sky City and using the American Time Use Survey as a base for the creation of the agent database, it is logical to base the demographics of Sky City on American society. The United States Census Bureau and the Bureau for Labor Statistics provide data about the distribution of age, family life, employment and job occupation and the mutual dependence between them.

Using this data makes the agent database representative for current society, and it also raises questions of how future society might differ from the present. Do people retire at a later age? Does family size change? Are there new ways of living together possible in Sky City?

First, the database covers data of the entire United States, without distinguishing between people living in urban of rural conditions.It is therefore in no way possible to filter out data for the ‘urban’ population of the U.S. The data is also ten years old, and from a time of economic crisis, which would influence employment numbers.

Second, the dataset only contains data of people older than 15, meaning the time use of children is not available.

Gender

Age Male

15-24

Female

25-64 65+

3.1.2. American Time Use Survey, 2008 The data used for creating our agent-based system comes from the American Time Use Survey (ATUS). This dataset contains data of more than 50.000 United States citizens. The dataset is easily accessible online and contains very detailed information about time usage of the American population. A few side notes are to be made about the data from the ATUS.

Household type

Household size One person

1

Couple

2-3

Couple + children

4-5

Single parent + children

6+

Extended with family members Extended with non-family members

Employment

Company size Not part of labor force

1-4

Part-time job

5-9

Regular worker

10-19 20-99

Data sources include the ATUS, UN Department of Economic and Social Affairs, US Census 2008

100-499 500+

102

3.1.3. Explaining and Modifying the ATUS Data While the ATUS contains vast amounts of information, for the purpose of creating the archetypes, we only selected the following categories: - UserID - Start time of activity - End time of activity - Duration of activity For instance, for one specific respondent; User 47899 activity at 11.43: Waiting associated with relaxing/leisure. For 7 minutes.

The dataset attaches a UserID to each survey respondent. For each UserID we have information about which activity is performed, the start and end times, and the duration of the activity.

103

There are a lot of activities listed in one minute time slots. They are very specific, and include such activities as “Waiting for your food” to “Travelling for the care of a pet.” In total, the ATUS contains 410 different activities.

Simplification of the data is needed to make it more comprehensible and easier to employ.

At first the original 410 activities were clustered into eight categories. One of the categories, the “Home activity”, is much bigger than any of the others, and was subdivided into five categories for the sake of differentiation. The result was a list of 13 activities.

The 1-minute time slots are sorted into 15 minutes slots.

This means that when an acitvity from the ATUS lasts less than 7.30 minutes, the activity will be rounded to

410 activities

Atus Time Slots

New Time Slots

duration

start time

duration

start time

3 min

13:03

/

/

16 min

17:54

15 min

18:00

58 min

21:26

60 min

21:30

All sub-categories are clustered into Macro Activities

13 macro categories

Civic Education Eating/ Home Drinking Job

Home House Caring Religion Shopping Sleep Leisure Activity

Social

Sports

Work

104

3.1.4. Extract the ATUS to Create Archetypes

105

In order to be able to visualize and understand behavioural patterns of the agents, they have been organized into Archetypes. The archetypes are generated by creating clusters of common interests and time spent on activities, extracted from the modified ATUS. The clustering is based on activity duration rather than start times, as we believe people are better defined by how much time they spend on an activity rather than when they do it.

This clustering is done using an algorithm known as K-means, a so-called polythetic unsupervised machine learning cluster algorithm with hard boundaries. It calculates clusters of the respondents with similar interests (polythetic) instead of looking at just one common activity (monothetic). The surveyed Americans will belong to one cluster only (hard boundaries) and thus be represented by one archetype, also known as the centroid (the geometric centre of the cluster) of the K-means cluster algorithm. This process eventually results in 10 different archetypes with different schedule preferences. This is then transferred to the demographics of Sky City, where the sizes of the clusters represent percentages of the agents in the simulation.

The 10 clusters shown in three dimensions

The optimal amount of clusters is determined by the so-called elbow method, frequently used in k-means. A graph is drawn, and the 320 300 280 260 240 220 2.5

5.0

7.5

10.0

12.5

15.0

17.5

20.0

American Time Use Survey: 52.000 people

10 clusters

10 archetypes

3.1.5. The 10 Archetypes

106

The Demographics of Sky City’s population is as follows:

Although the whole population is subdivided into these Archetypes, it does not mean that within an Archetype users behave identically or have identical schedules. Every Sky City user has a unique schedule, just like in real life. This uniqueness was fundamental to achieve an acceptable simulation that follows how a population actually moves in urban space. However, for the sake of understanding how they behave, users in the same Archetype have the similar tenedencies of performing high probability activities for their archetype.

23

01

23

2

23

22

22

0

1

00

23

22

1

22

22

1

20

20

19

19

17

5

18

18

1 10

10

11

12

12

14

11

13

18

18

9

20

20

19

17

17

1 14

12

19

19

9 10

11

16

17

5

1 10

17

18

9

5

0

0

0

0

5

08

08

08

08

08

16

07

07

07

07

1

06

06

06

11

05

14

9

5

1

10

04

20

2

2

02

02 3

2

00

2

23

22 1

The retired

The workaholic

The homemaker

The stay-at-home parent

The unemployed

The volunteer

The part-time

1 10

14

13

12

The regular worker

The gamer

The student

Total population: 25.000 per cube

107

108

3.1.6. Simulating Archetypes

109

Static Schedules In this Grasshopper simulation, the first step was to program one archetype going about their daily schedule. The larger coloured spheres represent activities, and the small dots in between are users on their way to the next activity. With the first simulation, we can observe the group of agents moving in sync, having the exact same schedule. In this case the Student archetype, with activities such as Education, Home Activity and Social being present in the schedule. The sphere gets an offset with a size determined by the number of users performing that specific activity.

Home Activity

07.18: Students start studying

Work

Sports

Socializing

Sleeping

Shopping

Religion

Education

Civic

Initiating simulation

13.00: Students take a break

1: Find the activity from the activity probability graph 2: Find the duration from the duration probability graph

00:00

06:00

Why do we need probabilities?

Archetype 8 duration

70

60

50

40

30

20

10

00:00:00 00:15:00 00:30:00 00:45:00 01:00:00 01:15:00 01:30:00 01:45:00 02:00:00 02:15:00 02:30:00 02:45:00 03:00:00 03:15:00 03:30:00 03:45:00 04:00:00 04:15:00 04:30:00 04:45:00 05:00:00 05:15:00 05:30:00 05:45:00 06:00:00 06:15:00 06:30:00 06:45:00 07:00:00 07:15:00 07:30:00 07:45:00 08:00:00 08:15:00 08:30:00 08:45:00 09:00:00 09:15:00 09:30:00 09:45:00 10:00:00 10:15:00 10:30:00 10:45:00 11:00:00 11:15:00 11:30:00 11:45:00 12:00:00 12:15:00 12:30:00 12:45:00 13:00:00 13:15:00 13:30:00 13:45:00 14:00:00 14:15:00 14:30:00 14:45:00 15:00:00 15:30:00 15:45:00 16:00:00 16:15:00 16:45:00 17:00:00 17:30:00 17:45:00 18:15:00 18:30:00 19:00:00 20:00:00 21:00:00 21:45:00 22:00:00

0

CIVIC

Time

EATING_DRINKING

EDUCATION

Duration

HOME JOB ACTIVITY

HOME LEISURE

Work

Other

Sports

Socializing

Sleeping

Shopping

Religion

Caring

House Activity

Home Leisure

Home Job

The two different probabilities are defined in tables. Every archetype has a separate probability table, for both activity and duration. At the start of the schedule generator, there will be 96 empty timeslots. Each timeslot represents a quarter of an hour, as every day has 96 quarters in it. The generator will start at the 00:00 mark. It subtracts the percentage of each of the 13 main activities at the 00:00 mark from the PoA. For instance, if a new activity starts at 00:00 archetype A has a 70% change of going to sleep, 20% change of starting a house activity and 10% change of doing a sports

2 80

Education

How do the PoA and PoD work?

23:45

90

Eating/ Drinking

Realistically speaking, it is still possible that a user could have the same schedule every day. But the probability of schedule changes, should be present in order to create a more realistic simulation of a city. The influence of the probability for every archetype differs as well. For instance, the regular worker is unlikely to have a completely different schedule every day, but slight changes are relevant.

To include this probability factor, we need the probability for each activity the user does at a certain time mark and the probability of the duration. This is why the probability of activity (PoA) and the probability of duration (PoD) are created.

18:00

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Imagine if 1000 users were generated, all assigned to one archetype. At this point, an archetype just represents a fixed list of activities and duration linked to a specific time of the day. This would mean that all of htese users would behave exactly the same through the day, and every day of the week. To prevent this from happening, there should be parameters included which provide diversity to every user within an archetype and diversity of each daily schedule.

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Duration Probability

3.1.7. Probability of Activity and Duration

Activity Probability

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activity. All the remaining activities are therefore 0%, because the sum of all the activities together should be 100%. These percentages change every time mark according to the probability graphs.

After the generator has defined the activity of a certain timeslot, it will define the duration of this activity. This will be generated by the probability of duration. The PoD shows the probability of an activity duration for all the main activities. For instance, when the activity of sleep is considered in the PoA, there is a 50% change of sleeping for 7 quarters, 20% for 9 quarters and 30% for 17 quarters (actual percentages are much more specific).

All the percentages of probability for both the activity and the duration of the activity are based on real datamining and analysis from the ATUS.So now that the activity and duration are defined, the generator will fill the amount of empty timeslots according to the duration with the name of the activity. After that, it jumps to the next empty timeslot and does the same steps again. It will go through these steps until all of the 96 quarters are filled. Therefore we created a schedule that has the same characteristics for users within an Archetype on a daily basis, but still maintains uniqueness and possibilities of disruption.

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Each of the 10 archetypes has a different probability of performing an activity at different times of the day:

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3.1.8. Simulating Archetypes

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Adding Probability to the Schedule

Simulating Two Archetypes

The two different probabilities are defined in tables. Every archetype has a separate probability table, for both activity and duration. At the start of the schedule generator, there will be 96 empty timeslots. Each timeslot represents a quarter of an hour, as every day has 96 quarters in it. The generator will start at the 00:00 mark. It subtracts the percentage of each of the 13 main activities at the 00:00 mark from the PoA. For instance, if a new activity starts at 00:00 archetype A has a 70% change of going to sleep, 20% change of starting a house activity and 10% change of doing a sports activity. All the remaining activities are therefore 0%, because the sum of all the activities together should be 100%. These percentages change every time mark according to the probability graphs.

Adding an extra layer of complexity, another archetype was added to the simulation, the Workaholic. With both the Students and the Workaholics’ schedule being based on probability, they would perform different activities most of the time, but not all. Activities such as Work and Education dominate in the daytime, while agents of both archetypes go to do sports in the evening.

Initiating simulation

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3.1.9. Future Changes

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In order to compare life in a current city and life in Sky City, the project needed a sound and reliable foundation which is the ATUS, as we previously mentioned. From the thourough analysis of people’s habits and activities, it emerged that the average American citizen wastes great part of the day sitting in traffic, waiting for the bus or cleaning the house. In fact, in a lifetime, up to a whole year is spent on commuting to work. Not only is this time wasted, but, according to multiple experts, a worker’s personal and societal well-being is negatively affected by long commuting times. Longer commutes are linked with increased rates of obesity, back and neck pain, high cholesterol, high blood pressure, divorce, depression and,

Current day traffic Sky City traffic

Every day, on average, Americans spend: 52:00 min for commuting 21:00 min sitting in traffic 20:00 min waiting for bus/train 13:00 min cleaning up after meals 20:00 min cleaning the house 11:00 min doing laundry

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eventually, death.

Because the flying system developed in this project significantly reduces commuting and thus waiting time. Moreover, it is safe to assume that innovation in technologies will decrease the time spent on household activities and in Sky City, we speculate that those months and years of life nowadays dissipated will become ours again. Sky city is more efficient and the gained time is distributed on the schedule according to one’s favourite activity and physical activity.

Indeed, each archetype is more inclined to participate to certain activities. For example, a student will tend to look for social interactions so he will find more time for it in Sky City than he did on earth.

But someone else might really like watching movies: each individual is different and they will all find more time to spend with their loved ones or play quidditch rather than being stuck in traffic jam.

In Sky City, flying does not require human effort, and as a consequence humans will be more sedentary. To counteract this, everyone will have to exercize more. In order to determine how much physical activity is required to stay fit in Sky City, the following estimate is inspired by the training undergone by astronauts when in the International Space Station: Sky City Regimen: 1. 150 min of basic Cardio a week 2. >30 min of Sports a day. Astronauts train for 2 hours a day because they are not subjected to the force of gravity. Since in Sky City gravity is still very much present, 2 hours a day might be an overstatement.

These number may nowadays sound unrealistic for every human being. However, since Sky Citizen will gain a great amount of extra time (from the reduction of the activities of commuting and waiting), physical activity will be surely consinstently present in every Archetype’s schedules.

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The Student Archetype

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The Sky City Student Archetype

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General Schedule Example: (Grey indicates travel time)

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When removing these mentioned activities from schedules of all archetypes, each user gained on average 95 min of extra time daily.

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The first Archetypes that were created included a list of activities that will not be performed (or will be strongly reduced) in Sky City: for example, dropping off/ picking up, home cleaning and maintenance, vehicle repair and services, travel time, heating and cooling, security procedures and, in general, waiting.

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Imagine not having to commute to work anymore: what would you do with your time? Everyone would probably spend it performing different activities. Looking at the probabilities we could speculate how each Archetype would spend their extra time. For each archetype, a list of top activites was produced, determining how their schedule fills up.

For example, you can observe the difference between the two graphs: in the Sky City schedule, the Student Archetype will most probably use the extra time for Sports and Socializing.

Sky City Schedule Example:

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3.2. The Sky City Archetypes the unemployed

the student

TOP ACTIVITIES: - socializing - sports - education

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TOP ACTIVITIES: - house activity - home leisure - sports

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TOP ACTIVITIES: - caring - shopping - house activity

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TOP ACTIVITIES: - working

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the stay-at-home parent

the gamer

TOP ACTIVITIES: - house activity - home leisure -sleeping

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the homemaker

the regular worker TOP ACTIVITIES: - working - socializing - sports

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20,4 %

the workaholic

the part-time worker TOP ACTIVITIES: - house activity - socializing - sleeping

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the retired

the volunteer

TOP ACTIVITIES: - civic - socializing - sports

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3,9 %

TOP ACTIVITIES: - civic - shopping - house activity

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Every day, the student is dedicating his time to education, while trying to maintain a social and healthy life.

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The gamer is mostly at home, focusing on leisure activities, with less time for activities like work or sports.

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The Volunteer

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Archetype 5 skycity

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The workaholic is very career-driven, and prioritises work over other activites, sometimes making time for late night socialising or early morning housework.

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3.3. Presenting Archetype Schedules 3.3.1 Schedule Changes

Regular Changes

Sky City is full of surprises. Schedules changed on Earth and schedules are changing in Sky City.

There are many factors that have an impact on the schedule of a person. The most predictable schedule changes are the changes that happen regularly.

Every day in Sky City looks different. The schedule is generated based on probability, and will never be identical. Meaning that every single citizen will have a unique schedule that changes slightly every day. The schedules are not rigid, and might change at any time depending on a variety of factors There are three different types of changes: regular changes, emergency changes and behaviour changes.

The most common example of a regular change is the weekend: Sky City looks different on a weekend, because people’s schedules differ from a normal weekday: they sleep longer, they don’t work and spend more time in leisure activities. The different archetypes will have different preferred activites to do in their free time.

Other examples of regular changes, are summer and christmas holidays or special events. Climate can change a schedule as well.

People spend more time at home in the winter, because it is darker

100 %

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The schedule is created based on probability

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The schedule changes during the week

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Emergency Changes

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Schedules can change suddenly because of different factors, such as unforseen events that are irregular and unpredictable.

Like any city, Sky City is a place for social interactions. If you unexpectedly meet your neighbour, you can start to talk instead of following your normal schedule. Schedules may change depending on the people around, the same way as in any city.

A schedule can change based on health. A Sky City Citizen can get sick and the therefore change the schedule from a day at work to a day in bed or a visit at the doctor.

There could even be unexpected emergencies, such as a fire starting, which would change the schedule of people close by.

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Everyone in Sky City has a list of acquaintances. If these come in proximity, they might influence each other’s schedule.

Therefore the schedule changes that happen based on social behaviour become part of the simulation.

Objects can potentially be broken and change the schedule.

Objects that have a higher priority (for example ambulance) can cause a traffic jam that then again causes a schedule change.

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A citizen is sick and changes his schedule accordingly

join Compearty! the

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3.4. Social Behaviour Changes 3.4.1. Introduction In order to simulate the dynamics of a real city, social behavior in public settings is fundamental. In fact, a city where Users behave independently from each other’s influence at all times is extremely fictitious. As many experts point out: individual actions are governed by the “collective consciousness” or “group mind”.

As many experts point out: “When people are placed within a group aggregate, depending on the strength of their personal bias, they inevitably alter their social identity”.

Social Psychology: Group Processes The study of why Social Behaviour occurs is a complex subject: it entails a profound knowledge in diverse fields. However, the result of Social Behaviour is something human beings encounter daily: from a colleague asking you to join him for lunch break, to influencing your friends into changing their minds about a certain subject.

3. Correlation: differnet individuals usually look for commonalities between themselves and their peers.

4: Continuing DIversity: individuals tend to defend the uniqueness of their social group.

Social Behaviour in Sky City “Consolidation” is a very interesting concept to include in the simulation of Sky City, and it has become a fundamental part of the analysis of how the Users population moves and acts within an urban context.

However, “consolidation” is a word that comes directly from the vocabulary of the Psychological profession, and it is really difficult to transform into a scientific and quantitative parameter.

But the concept at the basis of it is really simple: when I am bored at work and my colleague friend asks me to join for a coffee break, I will most likely join.

The following study is extracted by the research of A. Hogg and Scott Indale, who studied and experimented group processes at the Florida Atlantic University.

During a study panel, these experts gathered a group of students for a consistent period of time and studied the dynamics of influence between them in various activities. The result of this experiment can be synthesized in these four group processes: 1. Consolidation: conforming to what other people think or do 2. Clustering: moving spatially towards people that we perceive as similar to us in ethnicity, age, economic status, cultural status etc...

A spontaneous citizen changes his schedule when he sees an old friend

3.4.2. Overwriting the Schedule

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Introduction In urban life situations are encountered where people change their schedule depending on their surroundings. Like in an office if someone initiate a lunch break and his or her co-workers might join in. This has an impact on the way urban infrastructure is used, to stay in the example a group will move together towards a restaurant and make use of it as a group. In a small scale this is a considerable impact.

2. And IF an user from the Acq List of User A are inside the sphere of user A, with a radius of X meters (let’s call this user inside the sphere User B) 3. And IF the current activity of User B isn’t sleep or work (because it isn’t realistic when User A spontaneity goes working or sleeping with User B) 4. Then User A will follow User B and do the activity of User B (which basically overwrites User A’s own schedule)

Social Influence

When the activity of User B has ended, it will depend on the next activity of User A and User B if they follow each other again.

If User A is going to do an activity which has a probability higher than 40%, they will separate. If User B is going to do an activity which is Sleep or Work, they will separate.

For the sake of a schedule change it is assumed, that only meaningful connections are considered. Therefore, not the median of the network of 472 persons but the Dunbar number of 150 friends (or “clan”) based on cognitive capacity as personal maximum is taken.

These “friends” create a group that we call “Aquaintances”.

When the distance between two persons is less than 13 meters (threshold for facial communication), social influence can occur.

We call this threshold the sphere of influence. These distances are further explained in chapter 3.5.

To make the Users in Sky City further realistic, we created the possibility of Social Influence. For the social influence to work, this is the data we have and we need: - UserID (number) - Archetype (number) - Schedule (list) - HH Group (number) - Work Group (number) - Acquaintences Group (number) - Acquaintances List (number) The principle of the social influence is: If a User comes across someone he knows, he will take part of that Person’s activity if his own activity isn’t that important.

Alex ’s p

ted sc da

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f re o he

In this part we separate two factors: the User that gets influenced (User A) and the User that influences the other (User B). Every user can be either one, depending on the following statements.

up

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1. IF the current activity of User A has a probability lower than 40% 1: Lindsay approaches Alex

2: Alex adapts his schedule to Lindsay’s

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Acquaintances In order to give the users a more realistic behaviour, they have been provided with an individual Acquaintances List, which is a list of UserIDs that an individual user can get influenced by. So every User has its own unique Acquaintances list.

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3. Acquaintences Group (can be considered a group of friends) We have divided the amount of people in Sky City into different groups of friends, each of which consists of 15 people. So there are ‘population of SkyCity’ / 15 = x amount of friend groups in the Sky City. The number 15 is based on Dunbar’s number, which also takes into account that our brain is cognitively capable of knowing around 150 people that can influence us, which is why the number of Acquain tances we defined is 150 in total.

The creation of this list is based on the following 4 principles: 1. Household group which have the same Home ID ( family or roommates). This derives from the demographics describing the % of amount of people in a house (mentioned at the beginning of the chapter).

4. Random (randomly added people) For instance, 9.800 Sky Citizens (49% of total population) have 3 members in their HouseHold (HH). Hence, there are around 3.266 HouseHolds with 3 people in them. Format to define HH group: x, with x = amount of users in this group. So basically, the HH number is the Home ID as well. Every User with the same HH number goes inside each other’s Acq List.

The amount of randomly added people depends on the previous steps, because the sum of the list has to be 150. So the amount of randomly added people is: 150 – number of users already in list. In addition to this, we created statements that if the randomly added user is already in the list, the system has to generate again. Because someone can’t be twice in a User’s Acquaintances List. The same statement is made for the User itself, because a User can’t be inside his own Acquaintaces List. 50% of this random selection of Users are from the same Archetype of the User we are calculating the acquainces list for.

2. Work group which have the same Work ID (colleagues) As previous example, based on demographics:% of amount of people in a company. Define the amount of companies and their size. Every user has their colleagues in their list, with an maximum amount of 25 colleagues.

So what if there are more than 25 colleagues in the company they work on? If a company has for instance 300 people in it, it will divide these 300 into groups of 25 people. So you get 300/25= 12 different groups inside a company. As a result, all the users in this company will get a group number within this company. So a User will get a company number, in the format WP_XX_YY where: XX defines how many people there are in this company, and YY defines the company number. All the people with the same company group number will be added to their Acq List

In conclusion, the people in the Acquaintances list generated from the HH number, the Work number and the Acquaintences group are in each other’s list. However, the people in the Acquaintances list generated randomly from the Archetype and generated totally randomly aren’t necessarily in each other’s list.

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3.4.3. Simulating Social Behaviour

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In this simulation, we follow one user, a student named Ludi Storm.

The surrounding users are either Ludi’s acquaintances (shown in colour) or regular users. Ludi is the black dot, highlighted with a dashed circle. Whenever an acquaintance enters this circle, Ludi’s sphere of influence, she might change her activity. As can be seen in the third and fourth screenshot, she’s on her way to do sports at 08:37, then meets her friend Erik Cloud, changes her mind and starts socializing instead.

Home Activity Work

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Initiating simulation

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3.5. Social and Visual Distance 3.5.1. Introduction: Humans are linear, and forward oriented mammals. Our sensory receptors (eyes,ears and nose) allow us to orientate and have a sense of our environment and other humans in the front and peripherally up and down.

Theatres: 25-35m In theatres it is important for the spectator to be able to fully observe body language as much as being able to hear emotions in the pitch of the actor’s voice. Theater ticket prices generally increase the closer you get to the stage. Make-up and costumes are used to amplify facial expressions and body language.

threshold for facial communication possibility

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full body + face gestures and expressions

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short conversations + expression of emotions

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full face

The most developed of our senses is vision. The eye perceive other people and objects differently depending on their distance from you. At 100m one is able to identify an outline and the closer you get, the more you can see, or even feel as the ears and nose become involved, making it a multisensory experience.

Relevant threshold distances: Reading body language: 50-70m

would be 35 m but 100m also suffice in this case. Sound amplification is very common.

Reading facial expression: 22-25m Finally, proper communication can be achieved at distances of less see, listen (and smell) each other.

3.5.2. Visual distance: Your expereience when observing events depends on these distances. They also determine the limitation in size of the venues needed. To expand these limits some events incorporate large screens.

Sports events: 100 m Because movement alone is the most relevant.

Concert halls: Ideally 35m Without voice amplification, the ideal distance to hear one’s voice

main features of a person still recognizable

than 7,5 meters where all the senses are involved and people can

Designing social spaces is also affected by the 100m radius of vision. In a public space 50-70m are enough to get an overview of the social situation which enables potential further sensory involvement at 25m.

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3.5.3. Social distance:

Cone of vision:

The physical distance kept between humans vary according to the relationship that one has to the other. Cultural differences may also play a role. The following distances are found in a sociology study:

Humans have specific cones of vision, both horizontally and vertically. In the fifth floor of a high rise building one would for example not be able to see someone on the street when looking out the window.

The cones of vision are illustrated below.

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Intimate distance: 0-45cm The distance kept between lovers.

Personal distance: 45-120cm The distance kept between friends, communicating.

Social distance: 120-370cm The distance kept inside a circle of friends allowing each individual to communicate with everyone.

Social distance: Anything above 370cm The distance kept between strangers and people encountered in other social situations when one doesn’t know the other.

62˚

50˚

25˚ 25˚

Intimate space: 15-45 cm

Personal space: 45-120 cm

-10˚

-25˚

-35˚

-75˚ -62˚

Social space: 120-360 cm

Public space: 360+ cm

4.

Where do they live?

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4.1. Introduction 4.1.1. Towards a Sky City To project where architecture is going to be in the future, its history needs to be understood. Therefore, we will shortly reflect on the last hundred years, to see which global effects have had a major influence on the topic of architecture and urbanism.

We will start at the second industrial revolution, also known as the technological revolution, which started around the final 3rd of the 19th century and ended in the beginning of the first world war, in 1914.

This period which has been characterized by a rapid industrialization, has brought forth innovations in manufacturing, such as the establishment of a machine tool industry, the development of methods for manufacturing interchangeable parts, the invention of an inexpensive industrial process for the mass production of steel. Advancements in manufacturing and production technology enabled the widespread adoption of preexisting technological systems such as the telegraph, railroad networks and generic gas, water and sewage systems. Two other notable technological innovations of the period were the electrification of factories and the introduction of the production line.

(Wikipedia, 2019) Modernist architecture emerged at the end of the 19th from revolutions in technology, engineering, new building materials and the desire to reinvent society and the build environment. With the newly gained possibilities the use of cast iron, plate glass and reinforced steel structures could become stronger, lighter and taller. After the second world war, le Corbusier got commissioned his first work in ten years in 1947. He called it the ‘Unité d’Habitation’ and was built in Marseille. Following his design principles, he lifted the building of the ground and organized 337 duplex apartment units, or as he called them ‘Machines for living’.

Another pioneer in modern architecture was Ludwig Mies van der Rohe, his architecture strove toward: ‘minimal framework of structural order balanced against the implied freedom of unobstructed free-flowing open space. One of his iconic buildings, is the Seagram building in

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Chicago built in 1958, the same year that Oscar Niemeyer finished the president’s residence in Brasília. Contrary to these modernists, Mies worked mainly with steel and glass, his ‘less is more’ approach gives the work a generic feeling. But inspecting the building from a closer angle reveals that ‘God is in the details’.

Kenzo Tange was invited to what would become the last CIAM meeting, here he exposed the work of his students to the biggest gathering of modernist of the time. His presentation, focused on the tower-shaped city and the build ‘Sky House’, it would become the first display of the Metabolist Movement. The key concept of this movement was the fusion of architectural megastructures with those of organic biological growth. The Nakagin Capsule tower was erected in 1972 and completed in 30 days. Prefabricated in a factory, the 140 high tech capsules where plugged into two cores and where designed in such a way that they were interchangeable. Sadly, the capsules never went into mass production.

As of the beginning of the 21st century the need for a greener approach to how we deal with the built environment and the planet has started shifting the way we built. Today, we are using technological solutions to become more sustainable and these can fall into two categories either they are: High Tech or they are Low Tech systems. Two trends that are worth mentioning are that architecture is truly becoming a ‘machine for living’, the emphasis being that, nowadays almost everything can be computed, made virtual or automated. The other trend being a new approach to how we work with materials, the most innovative of these being the concept of circularity. Where materials never would go to waste, a good example of this second trend is the Wikkelhouse. Which is being fabricated in a factory in Amsterdam, in simplistic terms, the architecture consists of modules which are being wrapped in cardboard. The modules can be organised in different programs.

Reflecting on these historic effects, a framework in which key moments are highlighted to indicate that certain trends will likely continue in the future. For example, rapid industrialization is a key factor for the architecture movement to survive, the lack of this caused the Nakagin Tower capsules to become irreplaceable. Interchangeable parts were

first introduced on products in the technological revolution, however they haven’t yet been introduced in the field of architecture on a mass scale. The Wikkelhouse is a new sustainable approach which is intending to launch a new type of generic module that can be mass produced and consists of interchangeable parts. Two, very important elements of Modernism are the so-called machines for living, and the desire for unobstructed free-flowing open space. Are increasingly being used in architecture and we expect this trend to continue into the future. The last two take-aways from the research is the increase in prefabrication and the advancements in circular design.

Today

Sky City

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4.2. Environment 4.2.1. Sprawl and Density in the Sky City By gravitating off Earth’s surface, we release ourselves from the burden which is gravity. Although vital for the functioning of our planet, on the human scale it limits us to horizontal movement. In the Sky City we have been liberated and our now able to live and construct in the sky.

Which raises a few questions like, can we enforce densities that would lead to a more productive city? Or can we an increase in density lead to much more pragmatic variety? And will this increase generate more synergy, social encounters? These changes in urbanity can surely lead to more possibilities for architecture?

As learned from the previous research, Los Angeles high density suburbs compensate for the comparatively low density of the cities urban score and it’s inability to produce a mass transit system has given the city of one of the worst congestions. Also because of the automobile the city has integrated into a series of satellite cities to form the Greater Los Angeles.

For the design of Sky City, a cube has been used to scope the work and make it comparable. The cube has been set to 500 x 500 x 500 m or 0.125km3. The starting point for the density study is the data which was used for the KM3 research and is based on Dutch cities but will be considered as generic for this research.

The data which considers the third dimensionality of space has been simplified to avoid over-complexity, but informs us about quantities that build up a generic city. For the Sky City we have taken this data and modified it according to the expected necessities of an elevated city.

Summary Cube

Units 0.125 km3

Population

1,000

Pop. Density

125 /km3

Built Density

20%

Leisure Greenery Agriculture 2 1,216,030 m2 26,000 m 3 (Forest) m 6,080,150 m3 312,000 427,000 m2 1.25% 3 24.39% 35,868,000 m Housing 71.95% 54,110 m2 162,330 m3 Water 0.65% 39,790 m2 159,160 m3 Work 0.64% 7,580 m2 Energy 68,220 m3 2 Shopping 0.27% 4,000 m 2,680 m2 164,000 m3 Parking 0.66% 10,720 m3 4,580 m2 0.04% 3 Service 13,740 m 5,580 m2 0.06% 3 22,320 m 0.09%

Waste 50 m2 200 m3 0.00%

KM3 density on the ground.

Summary Cube

Units 0.125 km3

Population

1,000

Pop. Density

313 /km3

Built Density

1%

Housing 54,000 m2 162,000 m3 Leisure 26,000 m2 23.48% Greenery 312,000 m3 27,900 m2 45.23% 111,600 m3 16.18% Shopping 3,000 m2 3 Service 12,000 m 6,000 m2 1.78% 3 Work 24,000 m 7,580 m2 3.48% 68,220 m3 9.89%

Sky City with a population of 1,000 citizens and a built density of 1%.

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To understand how the Sky City would work in terms of density and population, calculations have been made that quantify the amount of space needed per capita. By using this a starting point the mass and size of the city can be visualized and better understood. Therefore we have modified the initial data to project what the Sky City would need in terms of build mass. This elimination process however has been done through thoughtful discussion and research that focus on trends and tendencies that we expect to see in the future.

We eliminated the volumes for Agriculture, as we expect that once we have taken off we could still access Los Angeles agricultural land for luxurious commodities like organic grown food, and assuming that we will also have laboratories and other artificial environments to produce food like cultured meats. Because of the expected emergence of a yet to be determined energy source, power plants and the need for an energy grid will become redundant. Water management will become integrated into individual systems which will provide for the self sufficient units. The expected developments in autonomous vehicles should be enough to relinquish the notion of parking. And lastly, as we are moving towards a circular economy we wont have waste.

The Sky City gives us the possibility to build more efficient cities. Not having to rely on large industries such as agriculture or energy. The tendency to use built as dense as possible has been considered, but raises fundamental questions in terms of the necessity to live so close to each other. Especially when in the case of Los Angeles, there is an abundance of space. As we have seen in the research before, Los Angeles when compared to other cities has a disproportionate amount of space and relatively low density. Therefore we opted toward a more decentralized approach whereby the Sky City takes the same approach that is already present. Emphasizing the wishes of the current citizens which is a low density, relatively large amount of solar radiation and green space.

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Summary Cube

Units 0.125 km3

Population

25,000

Pop. Density

3,125 /km3

Built Density

20%

Leisure 650,000 m2 7,800,000 m3 45.23%

Housing 1,350,000 m2 4,050,000 m3 23.48%

Greenery 697,500 m2 35,868,000 m3 16.18%

Shopping 75,000 m2 300,000 m3 Service 150,000 m2 1.78%

600,000 m3 Work 3.48% 189,500 m2 1,705,500 m3 9.89%

Sky City with a built density of 20% and population of 25,000.

Summary Cube

Units 0.125 km3

Population

100,000

Pop. Density

12,500 /km3

Built Density

80%

Leisure 26,000 m2 31,200,000 m3 45.23% Greenery 2,790,000 m2 11,160,000 m3 16.18%

Housing 54,110 m2 16,200,000 m3 23.48%

Shopping 300,000 m2 3 Service 1,200,000 m 1.78% 600,000 m2

2,400,000 m3 Work 3.48% 758,000 m2 6,822,000 m3 9.89%

Sky City with a built density of 80% and a population of 100,000.

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4.2.2. Life in the clouds Sky Angeles floats between 1300-1800 meters above Los Angeles.

The location is low enough to ensure a habitable environment for vegetation, animals and humans, without casting large shadows on the ground below. By taking to the sky, and leaving behind all of the problems attached to th earth’s surface, Sky City introduces a new spatial dimension. The eradication of a ground plane frees Sky City from the limitations of a horizontal boundary, and enables a flexible, adapting self organised system, which can take advantage of climatic conditions from all angles.

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Altitude Mount San Antonio 3069m 3000 meters

2500 meters

Health and wellbeing There is a great deal of clinical evidence accumulating in medical journals about the specific therapeutic benefits of residing and / or exercising at low intensity (in strict aerobic mode) at moderate altitude (between 1000 and 1800 m) because of effects of hypoxia - that is, the depletion of oxygen in the air and the decrease in the amount of oxygen in the blood it produces. Thus, increased effects compared to the adaptations observed in the plain have been reported to normalize the arterial pressure.

2000 meters

Sky Angeles

1500 meters

Recent studies have also shown that hypoxia, by its anorectic appearance, was associated with a greater loss of fat mass in obese patients.

Finally, altitude could delay or counterbalance several pathologies associated with aging. Living and aging at moderate altitude has overall positive effects on many mortality risk factors.

The risk of dying from an infarction was reduced by 22% for 1000 meters of altitude in addition, that of attack brain fatality of 12%. The inhabitants of Davos, for example, are thus significantly less exposed to cardiovascular disease than Oberland.

Altitude Temperature Humidity Air pressure Oxygen Average Wind

1300 - 1800 m Ø 6 C (min), Ø 16 C (max) Ø 70,9% 79-80 kPa 16,3 - 17,3% 25 mph

1000 meters

Los Angeles

500 meters

93 meters

Altitude Temperature Humidity Air pressure Oxygen Average Wind

93 m Ø 13,1 C (min), Ø 21,3 C(max) Ø 70,9% 101,32 kPa 20.9% 7 mph

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Treeline 2900m (Mount San Antonio)

Indian Poke

American Bistort

Altitude: 1600m-2000m

Altitude: 1600m-2000m

2000 meters

Subalipine Fir

Broadleaf Lupine

Sitka Valerian

Altitude: <900-1300m

Altitude: 1600m-2000m

Altitude: 1600m-2000m

Sunlight: 4 hours 1500 meters

Douglas Fir Altitude: <600-800m Sunlight: 4 hours

1000 meters

Cascade Bilberry

Western Moss Heather

Altitude: 1600m-2000m

Altitude: 1600m-2000m

Grand Fir Altitude: <600-800m Sunlight: 4 hours

Ponderosa Pine Altitude: <600m

Oregon Boxleaf

Pink Mountain-heath

Altitude: 900m-1300m

Altitude: 1300m-1900m

Sunlight: 6 hours 500 meters

Sky Habitat Green space is required for Sky City to meet the health and wellbeing requirements of users. The future of green space requires incorporating greenery within urban density. In order to positively impact human health, green space will make up 30% of Sky City. Plants chosen for the climate are used with various greenery pods to create flexible arrangements for high density zones.

Certain plants grows at distinct altitudes depending on changes in environmental conditions. Altitude, temperature, humidity, soil composition, solar radiation, wind can determine the species of plants. In Sky City, we can assume that more plants of the alpine or subalpine zone will be abundant. The different conditions required by certain species such as hours of sunlight per day and Co2 and oxygen levels, affect its ideal position to allow optimum growth and will inform positions of priority.

In order to enable a habitable and self-sufficient city, it is important to ensure a prosperous ecosystem whereby humans, plants and animals can coexist in harmony. Therefore the altitude of Sky City is liveable for vegetation and pollenating insects, especially bees.

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2500 meters

2000 meters

1500 meters

1000 meters

Average Wind: 28 mph

Humidity: Ă&#x2DC; 70,9%

500 meters

Climate Conditions One of the solutions used for climate control is a technology termed Nano AirSkin. It arrives from the core technology principles of the windmill, through advances in material technology and nanotechnology. AirSkin is a flexible membrane that shields the facade of Sky City structures, absorbing the high and low energy winds. The membrane is a strong and flexible grid structure, creating a woven pattern of micro wind-turbines that absorb the wind energy and stores it. The advances made in material technology allows for the creation of these durable, strong and tiny energy harvesters, and the developments made in battery technology facilitates the possibility to efficiently store the output, so the city can use it when needed.

The second important response to the winds is the continual restructuring of the pods and objects in Sky City. By allowing a master-AI to calculate optimal outer city structure configurations, the city can shield or open different areas and spaces, to the appropriate needs, at any

given time. The configuration allows for increased power generation and better wind protection, while also regulating temperature of inner public spaces, by the natural directing of wind currents.

In terms of water management, the Sky Angelesâ&#x20AC;&#x2122; location provides optimum conditions to channel, extract and filter rain and water vapour. Oceans are the biggest source of creating moist air with high levels of humidity. By implementing atmospheric water generating strategies, this creates a stable water cycle that can provide water for the whole city, all year round.

Humidity condenses on the surface of pods which are covered with an aqua phobic material. This repels water in the atmosphere, causing it to run down until it is collected on the bottom edge of the pod. From there it is channeled into a biofilter and becomes usable, integrated into the Sky Water Systems.

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4.2.3. Sun Radiation

169 Built density: 20% Amount of objects: 1

This experiment shows how the sun radiation performs in a 3D world.

The following 5 cases are all in the same volume (20% built density) and placed in the 500x500x500 m cube. With the increasing of subdivision, the total sun radiation in the system will increase radically first and becomes steady. But the average sun radiation on each surface will decrease as what shows in the following graph.

Also, the ratio of objects which get the lower sun radiation will increase since more and more objects will be trapped in the center when we are keeping increase subdivision

Dimension of each object: 292 m Total sun radiation: 4.1e+8 kwh/yr Average sun radiaton: 805 kwh/m2/yr Value distribution: Built density: 20% Amount of objects: 27 Dimension of each object: 98 m Total sun radiation: 7.7e+8 kwh/yr Average sun radiaton: 501 kwh/m2/yr Value distribution:

Built density: 20% Amount of objects: 125 Dimension of each object: 59 m Total sun radiation: 9.0e+8 kwh/yr Average sun radiaton: 352 kwh/m2/yr Value distribution:

Total sun radiation ( kwh/yr )

Built density: 20% Amount of objects: 343 Dimension of each object: 42 m Total sun radiation: 9.7e+8 kwh/yr Average sun radiaton: 271 kwh/m2/yr Value distribution: Average sun radiaton ( kwh/m2/yr )

Amout of Objects

Total/average sun radiation changes with the increasing of city cube subdivision

Built density: 20% Amount of objects: 729 Dimension of each object: 32 m Total sun radiation: 9.9e+8 kwh/yr Average sun radiaton: 214 kwh/m2/yr Value distribution:

The image shows the sun radiation performance in 5 cases which are all in the same volume (20% built density). Right graph shows the percentage of surfaces get different value of sun radiation in each case.

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4.3. Sky Architecture 4.3.1. Sky Architecture Intro Architecture and urbanism dramatically changed in the 1960’s after the first Computer-aided design system went mainstream. Although this would not be possible without the mathematician Euclid of Alexandria. Who, in 350 B.C., wrote his treatise on mathematics “The Elements” expounded many of the postulates and axioms that are the foundation of Euclidean Geometry which is the basis for todays CAD software. In the 1960’s Citroen’s de Casteljau made fundamental strides in computing complex 3D curve geometry, this work continues to be one of the foundations of 3D CAD software. However it would take until 1987 for a 3D CAD software to become common practice.

When Parametric Technology Corp. Launched their first UNIX workstation, the Pro/Engineer, which heralded greater use of future-based modeling methods and parametric linking of the parameter features.

Fast forward to today, CAD software has integrated so much so that it is even possible to use your phone to project augmented or virtual objects. Architectural students are required to make 2D drawings and 3D models and most recently are encouraged to partake in scripting and additive manufacturing.

For the design of the Sky City we used a generative design which is an iterative process that involves CAD software to generate a certain amount of outputs that meet a certain constraints. More specifically we used the Wasp plugin, which is a set of Grasshopper components, developed in Python.

Sky Architecture

4.3.2. The Voxelised Grid: Why voxels?

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A voxel is a unit of measurement which comprise three dimensional objects. (Collins Dictionary, 2019).

Voxelisation is the conversion of continuous geometry into smaller three-dimensional pixels (voxels). (Cohen, Kaufman and Yagel, 1993).

500M

In Sky City, a voxelised grid is used to create three dimensional pods and objects. Voxels of 1.2m x 1.2m are used: the dimensions of a cube surrounding a seated human. Multiples of this size allow pods of human dimensions to be designed within within the 500m x 500m grid.

M

2.4M

500

1.2M

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1.2M

500m x 500m Sky City voxelised grid

1.2M

Voxels: in a pod, sitting human dimensions are multiplied to create comfortable spaces

4.3.3. Elements, Pods, Objects, Hybrids

ELEMENT

The architecture of Sky City is composed of pods: the smallest building units. Pods are created using elements, which are furniture units including their circluation space. These units are functional spaces for the Sky Citizens which cater to the basic activities of the citizens.

Pods aggregate with and detach from one another according to a set of rules (see 4.5), which allows them to form aggregations of pods known as objects. Groups of objects are known as hybrids.

POD

Element:

+

Single furniture unit and its corresponding circulation space.

Pod: The smallest building unit in Sky City: a space created using elements.

These spaces are stationary and moving in Sky City.

Object: A group of pods aggregated together, creating a series of attached functional spaces.

=

OBJECT

Hybrid: A series of objects grouped together, forming a neighbourhood.

HYBRID

EL

P

O

EM E

D

O

BJ

H

YB

NT

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EC T

RID

Pods aggregating to form objects within Sky City.

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1.2 M

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Pods within bounding boxes

Specific pods attaching to generic pods

4.3.4. Bounding Boxes

4.3.5. Generic Pods and Specific Pods

When building pods, a maximum bounding box was first created using dimensions obtained from Neufert standards according to the needs of users in each pod.

A generic pod serves as a simple space for commonplace activities.

These pods attach to specific pods: unusual pods which serve specific purposes.

The pods were then created within these bounding boxes using voxel dimensions of 1.2m x 1.2m..

An example of a generic pod includes a bathroom pod, many of which may attach to a specific pod, such as an exhibition pod. The number of generic pods attaching to a specific pods depends on the occupancy of the object.

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4.3.6. Parameters Parameters are used in pods as well as in objects to determine preferences of influences that affect a certain pod or object. Based on these preferences the location, design and aggregation of pods and objects is controlled. The different parameters that will be used in this book are: Activity The activity describes what a pod or object is (most) used for. Pods and objects are chosen by and for Sky Citizens based on the activity they need or want to perform at a certain time.

Sunlight The sunlight parameter describes the amount of daylight the people inside a pod or object usually prefers to receive to the people in other pods and objects.

Privacy The privacy parameter describes the amount of privacy that people inside of the pod or object usually would want to have relative to the people in other pods and objects.

Activity

Sunlight

Privacy

Noise The noise parameter describes the amount of noise that people inside of the pod usually want to experience relative to the people in other pods and objects.

Noise

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4.3.7. Circulation Despite the common use of corridors in nowadays architecture, the principle of having a corridor for circulation is actually relatively young.

Until the 17th century it was very common to pass through other rooms when trying to reach your next destination. The corridor provides a higher amount of privacy for the rooms surrounding it, but it comes with a cost ... A lot of unnecessary space is occupied by a place that nobody actually means to be in, often leaving an unpleasant space as a result.

Traditional circulation

Sky City is based on an architectural system of pods that all have their specific activity assigned to. Getting from one pad to the other pad is quite different from moving from room to room in a traditional building.

Instead of using corridors and stair to provide the necessary space for circulation, often the pods themselves will move to get people around in their objects. In a sense this is very similar to the way people moved around in building before invention of the corridor, but without the disturbance of nearby pods.

It must be noted that it is also possible to pass through other pods to reach your destination. Our prediction is that bath means of circulating inside objects are allowed and practiced in Sky City depending on object rules, personal relationships and personal preferences.

In a further development of objects (especially larger ones), rules could be assigned to paths of circulation to create moving architecture that takes an objectâ&#x20AC;&#x2122;s pods properties, such as view, into account.

Sky City circulation

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4.3.8. Materials The envelope of the built elements in Sky City have to withstand extreme conditions whilst incorporating a wide range of performance requirements to enable life in Sky City. Material science has developed a wide range of materials with interesting characteristics relevant for use in Sky City. A first requirement for these materials would be for them to be as light as possible, making it less energy consuming to keep the entire city afloat. Second a high degree of flexibility should allow the building element to adapt to changing conditions.

Envelope We can think of the surface of the building as a membrane that keeps all the external influences out, but allows people to go through it.

Accessibility is made easy with bio-membrane portals. The membrane acts as a penetrable skin, allowing passage between exterior and interior. The portal solution facilitates efficient use of the space within the pod, and is specialized for the weather and climatic conditions of Sky City. It opens upon receiving a specific amount of pressure, and restructures itself back to a protecting skin, after the user has passed through.

A portal does not merely serve as an entrance, but also as a boundary to control trespassing and accessibility. It can be implemented as a threshold between the public and the private. Data regarding the approaching user is checked and assesed in order to grant access. The membrane is implemented with nano technology and creates skinprints of the user, checks if the conditions are met and therefore allows or prevents the user to enter the space. The portals are adaptable and can move in every direction to allow users through.

The implementation of this supermaterial allows adaptability in terms of transparency, thermal insulation, ventilation, and self-maintenance, allowing the user to have total control to define their desired space.

Accessibility is made easy with bio-membrane portals. The membrane acts as a penetrable skin, allowing passage between exterior and interior.

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4.3.9. Aggregation The understanding of the Discrete Mathematics led to the creation of the aggregation algorithm, which relies on certain constraints to produce certain outputs. The outputs being pods or objects, the constraints can be divided into rules and parameters. The rules provide a programmatic constraint that influences the pod and object composition. While the parameters also have influence on the composition, their role enforces certain qualities to be integrated in the final output.

Furthermore a quick reminder towards some key terminology, it is vital to understand that for the creation of generic pods, elements are required as the input for the Pod Maker. And that for the creation of objects, generic pods are required as input for the Object Maker.

Hybrids will be discussed further on as they are created by the same Object Maker script.

The Sky City pods and objects are based on the activities that take place inside the space, these have been defined by the American Time Survey, which produces the Sky City Schedule. But both pods and objects are not limited by these categories as they can be generic or hybrids. Generic pods can connect within a hierarchical set of rules, at a moments notice to accommodate the users wishes to transition into another activity.

The plugin which is directed at representing and designing with discrete elements. The description of each individual element or generic pod includes parameters for the aggregation process (geometry, connections location and orientation). The set of connections define the topological graph of the element or generic pod, which is then used to define the possibilities of aggregation with other parts to form generic pods or objects.

The core of the framework relies on a set of aggregation iterations, allow the generation of specific structures from the combination of different elements of generic pods. Each of these iterations is composed of strategies for the selection of aggregation rules. Which can be described as an instruction to orient one element or generic pod over a selected connection of another element or generic pod.

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4.4. The Pod Maker 4.4.1. Introduction Pod Podmaker Podmaker is responsible for designing the fundamental building units of Sky City, known as pods. The pods are mainly a result of the composition created by circulation volumes, furniture units and openings. pod entrances enable pod-to-pod connections, which enable multiple pods to connect to form objects. The chief aim was to create a pod generator which outputs usable living spaces for Sky City. We also needed to create pods which were able to be used within the general Sky City simulation.

Aiming for ergonomic spaces, we decided to focus on circulation, especially when determining locations of openings and pod to pod connections. To research and produce our architecture generator we relied on Rhinoceros, Grasshopper and the Wasp-plugin. We began by defining our elements, then we defined our connection points and rules for aggregation between elements. The aggregation process comprises the element geometry, the connection locations and the orientation. Thus, the script generates the possible variations of pods, based on the previous steps.

We then manually selected the most suitable pods based on the circulation percentage with the least number of corners. The Podmaker script is then used by the Object script to create the architecture of Sky City.

4.4.2. Podlist Sky Cityâ&#x20AC;&#x2122;s Objects are made out of composition of different kind of Pods. Each Pods are designed for one activity, such as cooking, sleeping, eating. There are 28 different kinds of activity Pods currently in the Sky City. These different Pod not only have different volume but also varies in design due to difference in sunlight requirement, noise that generates, and connectability to other Pods. Bounding box of each activity Pods indicate the maximum or general volume of the Pods, however within that bounding box, there can be different design possibilities. This allows for the system of Pods aggregation forming Object to work, while still having room for different designs of Pods to occur.

Possible outcomes of the Podmaker script, using 1.2m x 1.2m voxels.

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Podlist

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Sports

Education PODS: -Dancing Pod - Walking Pod - Gym Pod - Dressing Pod

PODS: - Presentation Pod - Study Pod

ACTIVITY

LEISURE

Shopping

ACTIVITY

PUBLIC

Sleeping PODS: - Kiosk Pod - Display Pod - Trade Pod

PODS: - Sleeping Pod

ACTIVITY

SHOPPING

Household

ACTIVITY

DWELLING

Socializing PODS: - Living Pod - Cooking Pod - Bathroom Pod - Eating Pod

ACTIVITY

DWELLING

Work

PODS: - Relax Pod - Tree Pod - Grass Pod - Outdoor Seating - Bar Pod - Exhibition Pod

LEISURE

PODS: - Medical Pod - Storage Pod

ACTIVITY

ACTIVITY

Civic PODS: - Workshop Pod - Stage Pod - Audience Pod - Workstation Pod - Conversation Pod - Discussion Pod

ACTIVITY

WORK

Religion PODS: - Praying Pod

ACTIVITY

PUBLIC

PUBLIC

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4.4.3 Podmaker Simulation Key Furniture Bounding Box

Vertical Connection

Circulation Bounding Box

Pod Entrance

Vertical Entrance / Exit

Connection to Circulation

1.2m 12 x 1.2m = 20.8 m3 1.2m

1.2m 6 x 1.2m = 10.4 m3 1.2m

1.2m 2 x 1.2m = 3.5 m3 1.2m

1.2m 2 x 1.2m = 3.5 m3 1.2m

1.2m 3 x 1.2m = 5.2 m3 1.2m

This simulation was responsible for designing the fundamental building units of Sky City, known as pods. The pods are mainly a result of the composition created by circulation volumes, furniture units and connection to next pod. Pod entrances enable pod-to-pod connections, which enable multiple pods to connect to form objects. These key indicates forthcoming diagrams for this design process.

1.2m 25 x 1.2m = 43.2 m3 1.2m

1.2m 6 x 1.2m = 10.4 m3 1.2m

1.2m 1.2m 2 x 1.2m = 3.5 m3 2 x 1.2m = 3.5 m3 1.2m 1.2m

1.2m 10 x 1.2m = 5.2 m3 1.2m

Pod creation process starts with defining furnitures and faces of it which a circulation space can connect. Smallest circulation space of this aggregation process defined as 2 voxels which is 3.456 m3.

Circulation spaces let a user to have a certain amount of always inherit the pod entrance. Process reduce space to anthropometric elements combine those again to iterative results.

1.2m 6 x 1.2m = 10.4 m3 1.2m

1.2m 1.2m 3 x 1.2m = 5.2 m3 3 x 1.2m = 5.2 m3 1.2m 1.2m

1.2m 10 x 1.2m = 5.2 m3 1.2m

192

How are Pods Created?

193

There are sample furnitures for each pod to initiate design process which specific to type of pod. Bounding Box of these furnitures are created and faces defined to the script where a circulation can attach. Different type of furnitures come together as desired spaces increases. Integration of furnitures, always through circulation spaces.

Couch Type I

Couch Type II

Couch I BBox 20.8 m3

Couch II BBox 10.4 m3

Couch III BBox 15.5 m3

Chairs + Coffee Table BBox 10.4 m3

Couch BBox 20.8 m3

Couch BBox 10.4 m3

Couch BBox 15.5 m3

Couch BBox 10.4 m3

Circulation BBox 5.2 m3

Circulation BBox 6.9 m3

Circulation BBox 12.1 m3

Circulation BBox 13.8 m3

Couch BBox 10.4 m3

Couch BBox 15.5 m3

Circulation BBox 6.9 m3

Circulation BBox 15.5 m3

Couch BBox 10.4 m3

Couch Type III

Chairs + Coffe Table

Circulation BBox 10.4 m3

194

Sleeping Pod

Sleeping Pod Alterations

Single Bed BBox 10.4 m3

Double Bed BBox 20.8 m3

Single Bed BBox 6.9 m3

Double Bed BBox 13.8 m3

Single Bed BBox 6.9 m3

Double Bed BBox 13.8 m3

Circulation BBox 3.5 m3

Circulation BBox 6.9 m3

Single Bed BBox 10.4 m3

Double Bed BBox 13.8 m3

Circulation BBox 6.9 m3

Circulation BBox 13.8 m3

195

196

4.4.4. Pod Catalogue

197

Bathroom Pod

Elements Cooking Pod

CONNECTABLE SURFACES

USE DWELLING

0

6

NOISE 0

100 db

SUNLIGHT 0

1500 lux

POD VOXELS 12 DESCRIPTION A space for cooking.

Usually within a residential object for multiple people to use.

198

199

Cooking Pod

Elements Cooking Pod

CONNECTABLE SURFACES

USE DWELLING

0

6

NOISE 0

100 db

SUNLIGHT 0

1500 lux

POD VOXELS 8 DESCRIPTION A space for going to the bathroom.

A Pod with a toilet and or shower and sink.

200

201

4.5. The Object Maker

a. Entrance connection

4.5.1. Shaping the city

Connection rules

The object maker in Sky City is an algorithm that controls the growth of objects. Based on parameters on pods and objects, the most suitable location in an object is selected for a new pod to attach. Parametric design is the new architectural solution to designing a flexible everchanging city.

The voxel grid system is used to define a pod by using four face types. Information from the Pod Maker is used the define four types of faces. We have entrance connections, wall connections, top/bottom connections and windows.

Three types of rules are used to control aggregation: Connection rules

b. Wall connection

Cluster rules Conditional rules Sequence of aggregation is determined by user schedules and real-time desires of the citizens of Sky City.

+

POD

+

POD

+

POD

...

c. Top/bottom connection

=

d. Window

OBJECT

Living pod Total voxels: 60 Entrance connection faces: 8 Window faces: 30 Wall connection faces: 8 Top/bottom connection faces: 4

202

4.5.2. Cluster Rules Pods are defined by the activity which they are used for. However, some activities cause friction when near each other because of conflicting parameters. To avoid conflict, pods have cluster rules which define in what way pods can or cannot attach to each other.

In de image on the right page the cluster diagram is shown. A line drawn between two pods means that is possible for the two pods to connect.

a. Entrance connection allowed by clustering

b. Entrance connection not allowed by clustering

4.5.3. The Object Catalogue This catalogue contains a collection of objects that can be found in Sky City. The catalogue contains information about pod types that can be found in certain object, Aggregation and interaction rules for those pods, the objects hours of use and general aggregation parameters.

It must be noted that an object in Sky City is never fixed and thus far from a rigid body. Even though a certain set of pods is indicated for each object, different recipes are possible. Furthermore, single objects are very likely to change their ‘pod recipe‘ from day to day or even second to second.

Thus, objects can be born, grow, shrink, change, split, unite or jump out of existence. The catalogue is merely showing an ‘average‘ state of a certain object.

Clustering diagram

203

204

Object Name

Activities leisure, relax, socialize, work, cook, sleep

Apartment

205

Object specific rules - preferably light from the south for the living pods

Pods Bathroom pod 5%

Cooking pod 10%

Eating pod 15% Living pod 15% Study pod 5%

Hours of Use

Conversation pod 5% Relax pod 5% Sleeping pod 40%

0

4 Mon

8 Tues

Wed

12 Thur

16 Fri

20 Sat

24 Sun

Connection rules living pod study pod

Sunlight Preference cooking pod

Low

High

Privacy relax pod

bathroom pod

Isolation

Aggregation

Noise

conversation pod

eating pod sleeping pod

Silent

Noisy

Porosity Dense - 100%

Poros - 0 %

Pods

Aggregation

Apartment

Design

Bounding box

40 pods

160 pods Sleeping Pod

Cooking Pod

Bathroom Pod

Bounding box

Living Pod

Design

206

Eating Pod

Tree Pod

Grass Pod

Outdoorseating Pod

207

80 pods

208

Activities work

Object Name

Office

209

Object specific rules - short ways from between the pods inside an office

Pods Discussion pod 24%

Presentation pod 13%

Eating pod 5%

Hours of Use

Storage pod 2% Bathroom pod 2%

Conversation pod 15% Relax pod 4%

Workstation pod 35%

0

4 Mon

8 Tues

Wed

12 Thur

16 Fri

20 Sat

24 Sun

Connection rules bathroom pod presentation pod

Sunlight Preference eating pod

Low

High

Privacy storage pod

workstation pod

Isolation

Aggregation

Noise

relax pod

conversation pod discussion pod

Silent

Noisy

Porosity Dense - 100%

Poros - 0 %

Pods

Aggregation

Office

Design

Bounding box

25 pods

100 pods

Discussion Pod

Bounding box

Workstation Pod (9X)

Design

210

Conversation Pod

Relax Pod

Presentation Pod

211

50 pods

212

Activities leisure - sports, socialising

Object Name

Park

213

Object specific rules - none

Pods

Hours of Use

0

4 Mon

8 Tues

Wed

12 Thur

16 Fri

20 Sat

24 Sun

Connection rules walking pod

Sunlight Preference Low outdoor seating pod

tree pod

High

Privacy Isolation

Aggregation

Noise Silent grass pod

relax pod

Noisy

Porosity Dense - 100%

Porous - 0 %

Aggregation

Pods

Park

Design

Bounding box

50 pods

150 pods

Walking pod

Bounding box

Grass pod

Design

214

Relax pod

Tree pod

Outdoor seating pod

215

100 pods

216

Object Name

Activities work, Education

BK city (University)

Object specific rules -Eating pod and kiosk pod cluster -Audience pod and stage pod cluster ...

Pods Workshop pod 6% Presentation pod 8%

Study pod 20%

Discussion pod 8%

Eating pod 5%

Hours of Use

Kiosk pod 1%

Conversation pod 8%

Storage pod 5%

Exhibition pod 8% Audience pod 10% Relax pod 3% Stage pod 2% Outdoor seating pod 2% Greenery pod 1% Workstation pod 12% Bathroom pod 1%

0

4 Mon

Connection rules

Presentation pod

Workshop pod

Discussion pod

Study pod

8 Tues

bathroom pod

Conversation pod

Low

Storage pod

Relax pod

Stage pod Audience pod Greenery pod

Kiosk pod

Thur

16 Fri

20 Sat

24 Sun

High

Privacy Isolation

Outdoor seating pod

Wed

12

Sunlight Preference Eating pod

Workstation Pod

Exhibition pod

217

Aggregation

Noise Silent

Noisy

Porosity Dense - 100%

Poros - 0 %

Pods

Pods

219

Design

Design

Bounding box

Bounding box

BK city (university)

Workstation Pod (4X)

Presentation Pod

Discussion Pod

Conversation Pod

Study Pod

Design

Bounding box

Bounding box

Audience Pod

Design

218

Bathroom pod

Eating Pod

Relax Pod

Tree Pod

Grass Pod

Outdoorseating Pod

Orange hall pod

Aggregation 1 + 150 pods

Bounding box

BK city (University)

1 + 600 pods

Design

220

Orange hall pod

221

1 + 300 pods

223

4.5.4. Conditional rules Bk City Through the creation of additional rules for the aggregation of an object it is possible to gain control over the aggregation process and thus control over the physical appearance and functionality of an object.

In the example shown on the right, a priority field of aggregation is created to ensure that the orange hall pod always receives a sufficient amount of daylight. New pods will always try to aggregate in location that have the highest priority.

Isometric

In fact, creating the rules is part of the new definition of the term â&#x20AC;&#x153;architectâ&#x20AC;&#x153;.

The priority field can be used in multiple ways to control aggregation of objects.

Daylight aggregation

Top

222

Aggregation field High priority

Low priority

Neighbouring object aggregation

Isometric

225

Top

224

Aggregation field High priority

Low priority

226

227

4.5.5. Hybrid Objects The build environment is not compromised by singular or modular architecture. Instead it is a mix of multiple typologies which have managed to coexist in a combined space. In the same sense, hybrids are a composition of multiple typologies.

Since the connection between pod and pod is free within the rules, the classification of objects is essentially an “official recommendation” for pod aggregation. In some special cases, the aggregation of pods will cross the classification of the object into a hybrid object, which is equivalent to a complex in the city, but with more possibilities.

Park object 100p

Aparment object 100p

Office object 100p

The process of hybridization usually occurs first among pods with more public features in each object categories, these pods act as “bridges” to merge the connection rules among different objects.

Living Pod

Apartment × Park Hybird object 200p Office× Apartment Hybird object 200p

Walking Pod

Grass Pod

Workstation Pod Relax Pod

Apartment object

Park object

Office object

Apartment × Office × Park Hybird object 400p The image shows an example of how three different kinds of objects make up different hybird objects. Hybird objects will inherit features from objects.

228

4.5.6. Reflections

229

2. Porosity ( surface/volume )

To measure the advantages of the Sky City. We have developed a tool which can quantify and compare multiple parameters, such as space efficiency, porosity, terraces and sun radiation. We have chosen to compare an apartment and have therefore deconstructed a contemporary building and computed a sky city object. In the following comparison we can see how the Sky City object outperforms the apartment building in the chosen framework.

0.49 Bounding box: 41804 m3

Apartment

(Increase 165%) (Increase 294%)

1.30 164540 m3

3. Terraces

Floors: 6 Unit: 47 Area: 4350 m2 Volume: 16008 m3 Capacity: 141

Apartment (Ver. SkyCity ) Pod amount: 471 Volume: 13233 m3 Capacity: 141

To compare Sky City Objects with buildings nowadays, an apartment building is chosen as an example to analyze. This apartment is taken apart according to different pods and aggregate again with the same capacity but in a Sky City way. In the following comparation, we can see how the porosity, terraces surface and sun radiation performance of an Object are improved in Sky City.

1. Space efficiency ( Functional volume/total volume ) Circulation 17.2% Outdoor

1889 m2

(Increase 121%)

4178 m2

4. Sun radiation

Living

Grass Tree Sleeping Cooking Bathroom

Eating

83.8%

(Increase 19.3%)

100%

4.8e+6 kwh/yr

(Increase 39.6%)

6.7e+6 kwh/yr

230

231

232

233

234

235

236

237

5.

What is the city?

242

5.1. Sky Urbanism 5.1.1. Introduction Flow represent everything that moves in a continuos and progressive way. Flows are not always easy to define.

A city is affected by many various forms of flows. A city is dense, caothic, fast, noisy, populated, smelly, but it does is crossed by lots of people, cars, buses, and all of these represent a flow. Flows are the dinamic tensions inside the urban scene that let the city machine to start up and to live, to breathe and to grow.

Flows are the big box that contains everything. They are the result of all the city elements integrations. Their regualtion allow to set a mechanism of ocntrol inside the city and to fix rules of organisations. In every city we can group main cathegories of flows: goods flow, people flows, mobility flows. In Sky City all these flows are still present but in different ways.

People flows describe the movement and managing of people that in a 2D city could be marginal compared to mobility flows, but in Sky City Users flow respresent a crucial element to organize.

Mobility flows take into account both car movement and trains and other means of transport. In Sky City objects become the means of transport, giving birth to the fundamental system of Sky City, object flow, and its suburdinate, pods flow.

Good flows are still grouped in the same category, but when something can travel detached from the grounf, everything changes.

In this chapter all these types of flows that consitute Sky City will be analyzed and the rules of governance will be highlighted.

243

244

From Today Towards Tomorrow

245

The primitive act of founding a city is to track its main roads. Roads and streets are the signs in which people, cars and trams move.

However, the notion of road and street is still strictly related to a two-dimension way of moving and thinking. Linking to the case study of Los Angeles, it can be noticed how commuting time affect everyday life. People want to live in a quiet place, surrounded by green and nature to breathe fresh air and find peace and calm after work, but at the same time they need to move to work and they want the commodities offered in a city center.

This combination of things seems impossible, but not in a Sky City.

In a place free from space limitations a new kind of urbanism is set.

The terms “road” and “street” lack of meaning in an environment not related to earth. In Sky City every movement is possible in every direction. Consequently, a new degree of freedom of movement is added.

As in a 2D city, also in 3D city movement defines its shape and

commute

its mechanisms. Roads and streets give place to “paths”, so the movement is not focused on main routes, but we talk about origins and destinations. The movement takes places in “corridors”, as we will see later on in the chapter, with the peculiarity of temporality.

Paths constantly appear and disappear, allowing movement in every direction. By doing so, there are no more fixed point, but everything changes constantly.

An object that occupies a certain place can be in a completely different position in the evening. In this way the real dynamic component of the movement burst out englobing everything, from objects to people. Objects move accomplishing people desires.

In Sky City people don’t have to choose between a quiet place for their house or the chaotic life of city center, because in Sky City commuting time is equal to zero. People are inside objects that move toward their next location while they are finishing doing what they were doing, without having to wait for the bus, the train or driving their cars. People can also decide to fly freely 66 through the space to change location, but time is so shortened that distances are not a problem anymore.

vs. outskirts

+ big house + cheaper + nature - distance - commuting

city

- apartment - expensive - concrete + proximity + no commute Today

Sky City

246

Sky Urbanism System Sky CIty is embedded in a cube of 1000x1000x1000m. Inside this cube users, objects and pods are the agents that define Sky Urbanism. Each agent has a typical behaviour that sets the relationships of interactions among same agents or other ones.

247

Environment

At the basis of movement of the agents there are usersâ&#x20AC;&#x2122; schedules that set their desires and their needs. These ones are the input for every kind of flow. For this reason Sky City is strongly based upon their citizens desires.

The behaviour of each agent is affected by paramethers that regulate the system, imposing hierarchies and governace. The behaviour can be highlighted in the simulations trials, where it takes shape in the cityâ&#x20AC;&#x2122;s different patterns.

Agents

Behaviour

248

5.1.2. Sky City Environment

249

The cube of 1000x1000x1000 m represents Sky City dimensional space. That doesnâ&#x20AC;&#x2122; tmean that outside this space there couldnâ&#x20AC;&#x2122;t be something else. The aggregation of multiple cities creates a megalopolis. All planet earth is populated by megalopolis. Sky Megalopolis highlight patterns of freedom of movement and position. Moving from the top to the bottom, in the different sequences of scales it is noticable how the relationship among agents are diverse. In a Megalopolis configuration many factors need to be considered that are not taken into account in Sky City, such as the interrelations among external objects, the possibility of growth of the city and the parameters of sky urbanisation. In this context, the focus is only on the three main scales: urban (city), neighboorhood (block) and local (street), to better highlight the behaviour of the agent in the new Sky Urbanism.

1012 Km3

Planet Earth

106 Km3

Megalopolis

L

0,125 Km3

M

0,001 Km3

S

1-6 Km3

City

Block

Street

- 3 1-9 Km

Object

- 3 1-18 Km

Matter

Total dimensions: Total population: Total programme:

1*1*1 km 20.000 citizens 1.500.000 m2

Environment Scales and Resolutions

251

500m 1000m

levels explained

In the City scale the level of complexity is quite low, it only focus on the main general aspects of the urban setting, highlighting the main features of the different behaviours. They are readable by tracking the iterations among aagents that define the typical shape of the city. This shape is subject to changing throughout the day and the year, leading the city to reconfigure itself over times. Priorities flows affect the City in this level and are highly visible in the reshaping of it.

City scale Size: 500*500*500m Agents:

000 0

mm

10Objects 0m

m 0m 105000

L

City scale

m

100

Block scale

M

Size: 100*100*100m

Agents: Objects

Agents: Objects Users Pods

120m 100m

1000m

Size: 1000*1000*1000m

M

Block scale Size: 120*120*120m Agents:

11200 0mm

0m

100

City scale

1Objects 0m

Pods Users Goods

m m 110200

M

Block scale

S

Size: 1000*1000*1000m

Size: 100*100*100m

Size: 10*10*10m

Agents: Objects

Agents: Objects Users Pods

Agents: Objects Users Pods Goods

100m

1000m 00

m

0m

100

L

City scale

10

0m

m

100

M

10m

Street scale

S

Street scale Size: 10*10*10m

10

10m

150

On a Block scale, more details start to pop up. The relationship among activities objects is visible and pods occupy a role in this Environment scale. Users and pods flows, in fact, are clealry visible in their evels explained behaviour and typical patterns.

Lowing down the resolution, on a street scale another type of agent is added, the goods agent. In this level of detail it is noticable 10 the system of production and distribution of goods and how they 00 m interact with other agents. Moreover, at this scale the relationship between users and pods in clearly visible in certain aspects related to entrance and exit.

L This scale allows also the focus on objects attaching and detaching from pods, deeply described in the previous chapter.

L

100m

The three chosen scale levels are used in the simulation process to verify data inputs and quantify outputs, in order to make compariEnvironment sons and deepenings.

10m 10m

250

Block scale

1100 mm

m 10m

S

Street scale

Agents: Objects Pods Users Goods

252

A Self-Organising System

253

The basis of all contemporary cities is an urban master plan that defines and structures the whole city as a framework in which development can take place. Such an approach works sufficiently well in the stationary city of today where connections between fixed objects and various parts of the city are physically materialised in the shape of concrete pavements, roads and other infrastructures.

In the Sky City such an approach is fundamentally flawed due its inherent capacity for constant movement and consequent temporality. For that reason we propose, instead of a master plan, a master programme, since this includes a time dimension. The city is no longer bound by rigid formal rules describing streets here and blocks there, but is instead a complex web of spatial and human relations, each affecting the other and creating a free flowing Sky Urbanism, continuously adapting to reach an equilibrium.

The entire system of building movement is derived from user schedules and their activities, which are in turn derived from user archetypes. This ensures that the city directly responds to the users immediate needs and is dependent solely on their behaviour.

Randomised system

This approach of desribing the city as a set of relations instead of formal characteristics is a crucial aspect of Sky City. To emphasise this, all urban simulations have been performed from initial randomnes, with only relationships defined. This ultimately let to an emergent behaviour of Sky Urbanism - a free flowing urbanism perfectly tailored to the needs of the citizens.

Sky Urbanism

254

5.1.3. Sky City Agents

255

Agents are the absolute core of the Sky City; with their diversity they represent and create the city, from buildings to humans, delivery pods down to a single tree. In the system agents are used as an abstract representation of more complex objects, while still containing all of their most important attributes and behaviours.

What is

In Sky City there are four types of agents: Object Agents, Pod Agents, Users Agents, Goods Agents. To all of this agent is associated a certain type of behaviour.

The whole of the Objects Agents define objects flows. Users, pods and goods agents, instead, share a quite similiar behaviour, but they are differentiated by speed limits.

Every agent has a centerpoint that lies in the centre of its phisycal volume and determines its location in space, as well as a direction and speed of movement, which describes where it is moving and how fast. To ensure safety, every agent also has a minimum safety halo of 1 m, which is present at all times, as well as an additional speed safety halo extending from it and depending on the agentsâ&#x20AC;&#x2122; speed and deceleration. Lastly, Object agents also have a privacy halo that defines a space around the building in which the movement of other agent is not prefered, but also not strictly prohibited.

Object Agent Object agents represent Buildings as aggregations of multiple Pod agents. They form the base of Sky Urbanism through their interrelation and consequent emergent behaviour in space. They are extremely slow, with almost unperceptible accelerations to ensure comfort of users at all times.

Pod Agent Pod agents represent small spatial capsules intended for a specific activity. They are primarily building blocks for objects, but can also be used for commuting by users. They are efficient over long distances, or when time is not an issue, due to low accelerations limited by safety.

MOVEMENT DIRECTION

PHISYCAL VOLUME AGENT CENTERPOINT MIN. SAFETY HALO SPEED SAFETY HALO PRIVACY HALO (OBJECTS)

User Agent User Agents represent Sky Citizens living in objects and moving across space. In Sky Urbanism simulations they are primarily simulated as a type of commuting - a user with a jetpack. This mode of transport is very fast in short distances, being able to accelerate rapidly with its speed only being limited by comfort.

Goods Agent Goods agents represent delivery pods flying between objects in Sky City and delivering goods to users. Due to being small and autonomous, they are extremely maneuverable and can accelerate fast to a very high speed, allowing for very efficient distribution of goods.

256

Speed and Acceleration

257

Speed and acceleration are two of the more important parameters affecting the movement of agents in space. They affect speed of movement, one of the main evaluation parameters of Sky City, as well as the agents’ safety and maneuverability in space.

The maximum speed of agent movement is defined by various influences depending on the type of agents. Objects speed limit of 5 km/h is defined as a speed at which the entry/exit of users in and out of objects remain comfortable. Users’ limit of 200 km/h is likewise limited by comfort of wind resistance due to free flight.

Lastly Pods and Goods as small enclosed spaces are theoretically allowed to travel up to 1200 km/h; just below the sonic limit, which ensures no uncomfortable sonic booms occur in Sky City.

Possibly even more important then speed, acceleration and deceleration are crucial parameters of movement that especially affect humans due to our strong perception of them, as well as our phisycal limitations and safety. The acceleration of objects relates to a non-perceptable speed of 0.01g in effect making them seem stationary. Pods’ limit of 0.2g is defined by an acceleration that is still safe for a human freely standing inside, ensuring that he/she does not get dangerously thrown around due to movement. The maximum allowed acceleration of users of 2g is limited primarily by our physical constraints with larger forces becoming dangerous to untrained humans. Lastly the acceleration limit of goods is extremely fast due to the lack of any physical or spatial constraints.

OBJECTS 5

USERS 200

PODS 1200

GOODS 1200

0 km/h

5

100

20

OBJECTS 0.01

350

200

PODS 0.2

1250

900

2500

USERS 2

GOODS 50

0g

0.01

0.5

0.2

3

1.5

10

6

100

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Safety Halo and Maneuverability

259

Safety Halo is one of the most important attributes of any agent in Sky City. A safety halo is a minimum virtual distance from the agent to all other agents in space that ensures safe movement for itself as well as others.

Minimum safety halo is a required safety distance calculated at every point in time based on the agentsâ&#x20AC;&#x2122; current speed and possible deceleration. This distance ensures enough space for an agent to decelerate in a safe manner in the event of an unexpected obstacle in its trajectory.Speed of an agent and its possible deceleration also affect the maneuverability it has in space; every change of direction at speed results in lateral acceleration forces that can likewise be dangerous for the travelling agents and humans inside. The maneuverabilty of an agent in this way also affects its ability to safely steer away from any incoming obstacles and retain a safe trajectory.

Observing the compared safety halos and maneuverabilities of all agents we can see that while pods are allowed to travel much faster than users, they are much less maneuverable and require larger halos to ensure safety; this makes travelling as a user a more efficient and faster mode of transport. On the other hand goods, due to a lack of phisycal restraints on speed and acceleration combine best of both worlds; high speed and maneuverability, allowing them to distribute goods across space extremely efficiently.

The safety halo and maneuverability are thus used to prevent crashes and unsafe situations inside the system by ensuring that if an unexpected event occurs and danger is imminent the agent has the time to safely decelerate and/or avoid the incoming obstacle.

0

5

20

50

100

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400

800

1200

km/h

260

Spatial Speed Limits

261

As it is describe before, each agent has a specific speed and halo volume linked to the speed.

The maximum speed reached is different for each agent. Objects agents are the slowest, reaching a peack of 5 km/h.Users and pods agents move faster. They rely only on few agents, that it means that their movement is freed from complex schedules interlinking and dependency. Due also to their dimensions, they occupy less space inside the city, that allows them to move faster, but also to find more tracks possible among the objects where to move.

Goods are the faster agents of the system. They are not tied to any physical restriction of movement. Due to their function, their speed can be the higher possible, to reach rapidly every corner of Sky City.

As well as the previous two agents, thanks to their size they can move everywhere easily and in this case its also supported by the little turning radius they need.

2:00

1:00

0

1000 Sky City

5

20

2000

50

5000

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400

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km/h

262

5.1.4. Sky City Behaviour

Priority flows

To each of the agent correspond a certain type of behaviour. The specific behaviour outputs a type of flow, based upon its own rules and parameters. Flow types are structured in a hierarchy in which the upper influence the lower.

Priority flows are on top of the pyramids. This type of flow is not restricted to just one type of agent, as it embedds all of them. It’s on the top due to the fact that its objects have the priority to pass first to the others, for special reasons and situations.

Objects flow is the second one and the basis of the system. Object flow is a predictive flow: the object starts to move time before the change of location and it is all based on schedules of single users.

Object flow is the basis system upon which the others are related to. This flow, in fact, establish the position of every object in time.

As neither users nor goods can fly in objects’ halos, the temporary position of the objects defines where is possible and not possible to fly.

Consequently, there is users and pods flows. They are not a predictive system, but a reactive system. They move after an immediate need or desire. Their fly movement is locked in the immediate near to objects, as they can’t fly inside safety halos.

Objects flow

Users flow

Pods flow

Last of all, there are goods flows. Their movement follows users and pods flows, with the exception that they are faster.

Goods flow

263

264

Evaluation Criteria

265

All simulations, though, are evaluated according defined criteria, in order to compare and better understand them. The criteria are linked to the previous research part, to track the outputs of the simulation taking into account the status quo popped up from the research to highlight its positive and negative features. Consequently, the criteria set are population density, built density, commuting time and intensity satisfaction.

Population density indicates the number of people per Km3 and can track the possible condensation of the spread population of Los Angeles into a smaller space unit to verify is effectiveness and livability.

Built density is measured in voxels per cube kilometer. The built land coverage of Los Angeles is converted into the same unit, in this way the two amounts of built space can be compared to verify effectively the maximization or minimization of space use.

Population density

Built density

Commuting time

Location score

Commuting time is indicated in minutes and it represent a different outcome to follow, thighs to the movement discourse mentioned before.

Location score is represented by a percentage and indicates the grade of satisfaction of an object to be in a certain location at a certain time. It is a parameter that contributes to the movement of an object, as it is described later on, since a low location score turns out into a desire of changing location toward a less or higher level of intensity.

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Systems Diagrams

5.2. Object Flows

267

5.2.1. System Overview Objects flows govern the movement of buildings in time across the space of the city according to four basic behaviours.

Flows represent attraction of objects to reduce commuting of users between them. Intensity governs clustering or dispersing behaviour of each object. Distribution ensures optimal coverage of specific object types such as Police stations. Separation prevents collisions of objects through mutual evasion.

The system is, excluding priorities, on top of the spatial hierarchy which allows its agents to move in an unrestricted way. The simulated agents are only Object agents, with the goal of the system finding their optimal location at avery point in time. Lastly the system is predictive, meaning it pre-calculates neccessary movements based on the usersâ&#x20AC;&#x2122; schedules a certain time period in advance allowing for optimal functioning of the city.

Flows Behaviour 5000

0

Hierarchy

Agents

Control

Objects

Predictive

0.01

Flow behaviour

Intensity behaviour

Distribution behaviour

Separation behaviour

P O U G

1st level

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5.2.2. Flow Behaviour

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def: attraction due to users moving between objects A flow represents the attraction of two objects toward each other as a direct response to the need of users to move between them in an attempt to reduce commuting times. A single flow always exists between a pair of objects between which a user needs to commute eg. home to office. Often a single object can be affected by multiple flows simultaneously in which case in tries to optimise equally to all, eventually reaching a equilibrium between all.

This need of users to change their position in the city at a specific time is based entirely on the usersâ&#x20AC;&#x2122; schedules and thus causes the city to adapt accordingly. These flows are dynamic in time, as well as in their strength based on the proportion of people moving between a set of objects at a specific moment. The flow system is predictive based on user schedules and ensures that optimization begins before the movement of a user actually takes place so that when the user needs to change location, the objects are already close to each other.

Schedule

Flows Behaviour

A flow between two objects attracts them

The (ideal) result is objects next to each other

The (ideal) result is objects next to each other

The object optimizes towards all flows equally

Flows

home home

home - work

work work User user x

work work

work - rest.

restaurant

rest. - work

work work work home home

work - home

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5.2.3. Intensity Behaviour

271

def: attraction or repulsion depending on desire to cluster Intensity behaviour determines where in the space of Sky City in relation to other objects a specific object would like to be positioned.

This parameter affects clustering and/or scattering of objects based on their occupancy and the activities performed inside. Like in the current cities of today certain objects like offices and restaurants are attracted to areas of high intensity usually in the city center, while others like homes prefer to locate themselves in calmer parts of the city. Intensity behaviour tries to simulate this behaviour and optimise the structure of the city and position of its objects at all times throughout the day.

Intensity Behaviour

Intensity of Activity

Intensity Landscape

location

Intensity Produced

Intensity guides clustering of objects in space

Intensity Desired

object Occupancy

The term Intensity relates to the notion of Urban Intensity as the volume of user interaction in a certain space. In Sky City this is directly translated into a property of every activity; each activity has a designated Intensity of Activity value between 0 and 10 that approximates the amount of user interaction (and thus intensity) inherent to that activity. Examples could include partying as one of the most intense and sleeping as one of least intense activities. education high school

I.A. sleeping religion prayer

0

sports exercise

social park

home work at home

work office

5

social restaurant

work meeting

5000

0

This creates an intensity landscape in space

shopping social partying

social sport event

10

Objects project intensity into space

5000

0

The object either clusters or seeks privacy

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273

Every object in Sky City has a certain amount of total Intensity Produced by all its users at a specific point in time. This value is calculated by the sum total of all users currently in the building and the activities they are performing. In this way a small house where people are enjoying a calm lunch has much less intensity than a large bar full of people partying. This Intensity Produced is the value each object projects into its surrounding space of Sky City and together with all other objects creates the Intensity Landscape of the city; A sum total of all Intensities Produced creating areas of high and low intensity acting as a map of lively and calm areas within Sky City.

I.P. Object object 105

Users

Activity

I.A.

user 213

work

5

user 5482

home

2

user 84

sleep

0

user 1535

home

2

user 4197

sport

7

Total 16

I.P.

I.D.

Total users 5

I.L.

I.L. I.P.

The last part of Intensity Behaviour is the Intensity Desired, which is based on the assumption that, with rare exceptions in mind, most people like to be located next to other people doing a similar activity at the same point in time. For example most people that are enjoying an afternoon nap like to be in an area where similarly calm activities are performed. Conversely a group of students having a house party prefer to be as near as possible to bars and clubs where other similar people are gathering at that time. Consequently Intensity Desired is calculated as the average intensty of the users inside an object, is distributed by a Gaussian distribution to simulate deviation in personal choice and lastly is remapped from a value between 0 and 10 to match the values in the Intensity Landscape; in this way an object with an Intensity Desire of 10 always searches for the maximum possible intensity in the city.

Finally, this value is used to orient an object within the Intensity Landscape by guiding it towards the exact intensity it desires at that point in time.

O.

Object

16

IP / O

object 105

O.

3.2

I.P.

Remap

Gaussian dist.

I.P.

-x

3.2

+x

0

I.D. 0

5

10

10%

80%

I.P.max

10%

I.P.

I.P.max

Loc. Search

Move

I.L.

Loc.

Loc

I.D. 0

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5.2.4. Distribution Behaviour

5.2.5. Separation Behaviour

def: equal spatial distribution according to user density

def: repulsion to prevent collisions between objects

Distribution is a behaviour of certain object types to distiubute themselves equally across the city in proportion to population density. Examples of such objects include police stations, schools, hospitals, etc. The behaviour is based on the distribution parameter assigned to a object type which defines if that object type tends to distribute itself equally across the city according to population density. This parameter is important to simulate behavior of object which are less or negligibly affected by flow and intensity parameters mirroring their behaviour in the current city.

Separation behaviour affects all agents in the system equally and ensures safety by anticipating and preventing collisions in the most optimal way. The system takes into account an agents speed, possible deceleration, safety halo and anticipates possible collisions.

As two agents are dangerously approaching each others safety halo limit, the separation behaviour applies a proportionally increasing separation vector that ensures a collision can never occur.

Object type

Distribution

dwelling

no

hospital

yes

Database

factory

no

object database

nightclub

no

supermarket

yes

school

yes

office

no

...

...

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separation force 100% privacy halo safety halo object separation force 0% object

safety halo

privacy halo

Separation Behaviour

ution viour

Random distribution of object type

Optimal distribution according to pop. density

Two agents on a path towards collision

Automatic adjustment to avoid collision

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5.2.6. From Users to Sky Urbanism Despite Object flows being spatially hierarhically higher than User flows, it is important to note that the vast majority of the system is derived directly from users and their schedules. Flows behaviour is a direct derivative of users through them changing location in the city according to their personal schedule. Distribution is based on user density in the city, ensuring equal access to services like schools for everyone. Intensity relies both on the presence of users at a certain location and their performed activity to guide clustering (or dispersing) activity of objects. All of these measures ensure that the city directly responds to users needs and their behaviours and is in effect constructed by them.

User Schedules

User Activities

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User Movement

User Locations

Flows

Distribution

Intensity

Object Types

Object Movement

Separation

Object LOCation

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5.2.7. Subsystem Memory Behaviour

5.2.8. Subsystem Densification Behaviour

A city in which everything constantly moves presents a challenge to human orientation and comfort due to a lack of long term familiarity.

To counteract this, Memory behaviour is responsible for preventing sudden drastic change in Sky City by adding inertia to the movement of buildings. This inertia causes every object to recognise its current neighbors and location in space and guides him to daily return into the vicinity of the same objects. In this way drastic change is possible only through a longer period of time, giving Sky Citizes time to recognise change and adapt.

Sky City takes great care to optimise itself to its inhabitants, their location and thus increase their life quality at all times of the day.

Densification behaviour ensures that valuable space in Sky city is never wasted through forced clustering of unoccupied objects. In this way if an object is empty at a point in time it seeks out other unoccupied objects in its vicinity and clusters with them in order to save space. As soon as the object is occupied again it immediately restores its privacy to ensure the needs of citizens within are met.

ation viour

Memory Behaviour

Objects sense their immediate neighborhood

During the day the neighborhood disperses

Occupied objects strive to achieve privacy

When empty, nearby objects move closer

These connections remain despite dispersal

Finally it regroups to a similar state

The result is densifying and spatial efficiency

When occupied they return to their initial state

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5.3. User Flows 5.3.1. System Overview Users flows govern the movement of users across the space of the city. This movement can be performed by a user either flying independently, or within a pod. In both cases in is governed by three basic types of behaviour.

Flows represent the need or wish of a sky citizen to travel to a specific destination in the city. Bundling ensures spatial optimisation by grouping nearby users travelling in the same direction.

Separation prevents collisions of users through mutual evasion, while also evading objects.

The system is hierarchically below Objects flows, meaning its agents are obliged to avoid Object agents by flying around them.

The simulated agents are User agents and Pod agents, with the goal of the system enabling the agents to reach their destination in the shortest possible time. Lastly the system is active, meaning it directly responds to the usersâ&#x20AC;&#x2122; wishes thus allowing freedom of movement and choice in Sky City.

Flows Behaviour

Flow behaviour

Hierarchy

2

Agents

0.2

Control

P O U G

2nd level

Users

Pods

Reactive

Separation behaviour

Bundling behaviour

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5.3.2. Flow Behaviour

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User flow behaviour is one of the most important aspects in Sky City - it organises movement from origin to destination be it between objects, or only users wandering in space. Flow behaviour is based on user schedules which are controlled by users themselves, allowing for total freedom of movement at any point in time either as user agents, or as user-occupied pod agents. Additionally, flow behaviour also governs unoccupied pod agents and as neccessary moves them autonomously across Sky City based on their demand.

The system works on a origin to destination principle; origin is the current location of an agent, while the destination is defined either as direct user input, derived from a users schedule, or in the case of unoccupied pods based on global demand. When both parameters are known, the system guides the agent towards the destination using the shortest path possible. user schedules

00:00

18:00

06:00

users

current location

final destination system

lows viour 12:00

User agents are able to move freely, everywhere in space

They move to their destination by the shortest path possible

24-hour diagram of user flows

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5.3.3. Bundling Behaviour

Bundling Space Optimisation

User bundling behaviour controls grouping of user agents based on their similar path of movement in space. Bundling is controlled by two conditions that if met result in grouping of two users; firstly the users need to be travelling in a similar direction and secondly they need to be in each others proximity. When criteria are met bundling occurs resulting in the merging of halos for space savings and higher allowed speed decreasing travel time. This behaviour essentially creates temporary virtual highways in Sky City, dramatically increasing movement efficiency.

Commuting and general movement of users and pods in Sky City is an extremely important behaviour which also tends to consume a lot of space, as well as potential chaos through a lack of structure in patterns of flight. Bundling behaviour of user agents has important spatial consequences for space usage optimisation in Sky City as well as acting as a structuring mechanism.

dling viour

285

Users which are travelling as a bundle are allowed to move closer together to the distance of the minimum safety halo of 1 meter, regardless of their speed. The Speed Safety Halos of each user are then merged into a single Bundle Speed Safety Halo, which only affects other agents outside the bundle. These halo optimisations greatly reduce the space required for travel in Sky City and thus drastically increase quality of life through providing structure, as well as enabling greater densities.

Due to bundling occasionally increasing total travel time because of marginal travel distance increases, all bundling agents also receive a maximum speed bonus, allowing them to reach their destination in at least the usual time, if not faster.

Users move in space towards their destination

When conditions are met bundling occurs

agents safety halo speed safety halo total space used

without bundling Condition 1: Similar direction of travel

Condition 2: Users are in near proximity

Bundling spatial efficiency

with bundling

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287

5.4. Goods Flows 5.4.1. System Overview Users flows govern the movement of goods across the space of the city. This movement is performed by a goods pod when distributing goods and is governed by three basic types of behaviour.

Flows represent the required movement of a goods pod from origin to destination object. Bundling behaviour of goods is stronger than in user flows ensuring the largest spatial optimisation possible.

Separation prevents collisions of users through mutual evasion, while also evading objects.

This system is hierarchically the lowest meaning its agents are obliged to make way for all other agents in the system. The simulated agents are Goods pod agents, with the goal of the system enabling the agents to reach their destination in the shortest possible time. Lastly the system is predictive, meaning it pre-calculates neccessary movements based on the users requirements a certain time period in advance allowing for optimal functioning of the city.

Flows Behaviour

Flow behaviour

Hierarchy

Agents

Control

Goods

Predictive

50

P O U G

3rd level

Separation behaviour

Bundling behaviour

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5.4.2. Flow Behaviour

5.4.3. Extreme Bundling Behaviour

Flow behaviour of goods is the movement of goods agents across the city between objects based on the requirements of users occupying them. A goods flow guides a goods pod towards its destination in the shortest path possible. Occurence of a goods flow is controlled entirely by user schedules and their consequent activity in that point in time in space. This triggers certain requirements for goods such as food delivery, water delivery, or waste disposal.

Extreme bundling behaviour of goods is a variation of regular bundling behaviour governing user movement. The main difference between them are lower bundling requirements, specifically a larger acceptable angle and larger minimum distance. This results in a much higher amount of bundling leading to higher spatial savings in Sky City. This also increases the travel distances for goods flows but this is easily offset by the very fast acceleration and top speeds of goods agents.

Flows viour

Bundling Behaviour

Goods agents move strictly from object to object.

They move to their destination by the shortest path possible

Goods pods move in space towards their destination

When conditions are met bundling occurs

Condition 1: Similar general direction of travel

Condition 2: Agents are in relative proximity

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5.5. Priority Flows 5.5.1 Priority behaviour Priority flows govern states of exception within the system and override all other rules for agent movement. This is the only subsystem that replaces the otherwise equal movement rights of agents.

Priorities are at the top of the spatial hierarchy and as such override all other agent movements and behaviours - agents with a priority status are free to move everywhere while other agents move away.

Any system agent can in certain circumstances become a priority; fire in an object, ambulance pod, user terrorist, goods pod crash.

Lastly the system is active as it reacts to any occuring emergencies in the city and accordingly assigns priority status to corresponding agents.

ystems Intro

Hierarchy

0.01

Agents

0.2

Priority Behaviour

Priority as an stationary event: fire in object

All other agents move away from the area

Priority as a moving event: ambulance pod

Agents move and make way for priority

Control

P O U G

Override

Objects 2

Pods 50

Users

Goods

Reactive

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5.6. Simulation 5.6.1 First Simulation System The first simulation system involves all types of agents and set up the start of the city.

0.01

For the sake of clarity, in the following simulations there are showhed only three types of agents: objects, pods and users.

The simulations will show first the city at a lower scale, so at a street scale, and then the city at a neighboorhood scale.

The inputs of the simulations are usersâ&#x20AC;&#x2122; schedules, that define the amount of pods in the cube and the position of objects.

Flows Behaviour

Agents

Objects

2

Users

Agents

Flow behaviour

Separation behaviour

Flow behaviour

Separation behaviour

Flows Behaviour

0.2

Pods

Total amount of activities in schedule for all users

M L

Total amount of pods that needs to be added to the cube

dwelling work

socializing sports

shopping education

religion

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How to change from POD to POD?

1. Current activity pod brings Carla to next activity pod

In the simulation, every pod is equal to a one person activity space.

Each pod has its own specific activity, which cannot be changed.

To explain the different ways a user can change from pod to pod, we will follow the user Carla in here journey from a work-pod to a socializing pod.There are three different ways for Carla to reach her next destination in the schedule:

295

1

2 Carla

1

Empty socializing-pod attaches to socializing-object

2

Carla travels in her current work-pod to the socializing object and occupies the empty sociolizing pod, her former work-pod becomes empty and leaves

1. Current activity pod brings her to next activity pod 2. Carla flies on her own (without pod) to next activity pod 3. Next activity pod comes to her current activity pod

2. Carla flies on her own (without pod) to next activity pod

There are three different ways of travelling to reach your next destination.

1

2 3 Carla

1

Empty socializing-pod attaches to socializing-object

2

Carla flies on her own to the sociolizing-object and occupies the empty

3

Carla former work-pod, which is empty now, leaves

3. Next activity pod comes to Carlaâ&#x20AC;&#x2122;s current activity pod

Carla

1

1

Empty socializing-pod attaches to Carlaâ&#x20AC;&#x2122;s current work-object. Carla occupies the sociolizing-pod and her former work-pod leaves the work-object.

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5.6.1 First Simulation System Neighbourhood scale - 50 Users

M

298

299

300

301

5.6.1 First Simulation System City scale - 1000 Users

L

302

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5.6.1 First Simulation System City scale - 2000 Users

L

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5.6.2 Second Simulation System The second simulation system involves only objects. It adds two more behaviours described previously in objects behaviour: intensity and distribution behaviour.

The simulations proposed are three, at the same scale. The first one shows only flow, separation and distribution behaviour. The second one shows separation, distribution and intensity behaviour. The last one is an optimization of the previous two.

Agents 0.01

Flows Behaviour

Objects Flow behaviour

Distribution behaviour

5000

0

Separation behaviour

Intensity behaviour

S

dwelling work

socializing sports

shopping education

religion

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5.6.2 Second Simulation System City scale - Simulation without intensity behaviour Random starting point of the simulation.

L

L Sky City after 22 days: recognizable patterns become evident

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L Sky city at night

L Sky City at 9:00 AM

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315

L Sky city after lunch

L Sky City going back home

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5.6.3 Second Simulation System City scale - Simulation with intensity, separation and distribution behaviour.

Random starting point.

L

L Sky City at 9:00 AM

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5.6.4 Subsystem Simulation Neighbourhood scale Simulation showing users behaviour

M

M

320

321

5.6.4 Subsystem Simulation Neighbourhood scale Simulation showing aggregation behaviour of users

M

M

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5.6.4 Subsystem Simulation Neighbourhood scale Simulation showing collision behaviour of users

M

M

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5.6.4 Subsystem Simulation Neighbourhood scale Simulation showing separation behaviour of users

M

M

6.

Reflections

I am still permanently stuck in the lower levels of the city and seeing almost no sun; apparently the so called “Artificial Intelligence” still has some learning to do! I could run Sky City better myself! #endrant

We may feel like passengers, but we’re actually the drivers in this city!

I’ve got more time to be around friends and family; more time… to be home

My great grandfather was labeled by society as disabled, and was dependent on a wheelchair. In Sky City, there is no such thing as being disabled. This is freedom.

Dear Santa, grandma told me that a long time ago people did not believe you existed because nobody could fly and you had to take what she called the “train” but your reindeers were not allowed and the elves were kinda useless so your company closed and you got very depressed. I am happy you can fly now.

Do you want to improve even more our amazing City, maybe with new pods and new experiences?

If so, AI party is what you have to vote for in the elections! We believe in changes, we need you!

My home is never really my own, so generic and no possibilities to have it be something special

Man… the thing I like are the surprises… everytime I open a door another pod is on the other side.

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1.1 Cities of yesterday

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6.3 Work methodology Sky City is an experimental research project led by 40 people of The Why Factory studio.

Sky City proposes a city where people and buildings can fly. When people and buildings can fly there are lots of questions that raise up: What happens if people and buildings can fly? What happens to everyday life? What happens to social relationships? What happens to the ground?

But apart from all these questions, one main big question has lead our research: What if?

“What if” sums up all the questions related to this topic. By this research we don’t want to propose a solution of a Sky City but a possibility of how life will be in the Sky. It doesn’t mean that is the only way of thinking about future life, but it’s our way of responding to our questions.

“This is the bit we are interested in. Not in trying to predict the future but in using design to open up all sorts of possibilities that can be discussed, debated, and used to collectively define a preferable future for a given group of people: from companies, to cities, to societies. Designers should not define futures for everyone else but working with experts, including ethicists, political scientists, economists, and so on, generate futures that act as catalysts for public debate and discussion about the kinds of futures people really want.” (Speculative Everything, Dunne & Raby) Working with 40 people is not so easy as someone can expect, but the great richness of working with so many people is that each of them has particular interests and ideas. working with 40 people is about

relationship and about respect, it’s the very first simulation of a city.

The first simulation of Sky City was not in a computer, but was among these 40 people and their ideas, their discussions and the specific questions they raised up.

Sky City is first of all the 40 people who created it.

So we decided to stop trying to solve problems, but to raise problems.

We constantly questioned each other, because even from the most precise and banal question like “How do you walk your dog in Sky City?” great ideas come up, because you won’t come to that question if you are embedded in normal way of designing linked to the ground.

Sky City is a was of escaping the border of a earth-like reality to fulfill the human dream to reach and conquer the sky. Human beings are by nature, conquers, and the sky is the last and desired frontier we want to reach.

By gathering together around a table full of questions and dreams we tried to order them, to answer them step by step. From our questions we build up a new kind of urbanism, a new kind of living, a new kind of community.

Sky City is the result of our thoughts. Of course it won’t be complete, of course all questions can’t be answered, we have started from the ones we considered most urgent and important.

But all of the questions have led to Sky City, because progress relies on questioning, and new researches can just start with a simple but deep question: “What if?”.

What if people live in the Sky?

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7.

MoleculAir City

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7.1. Moleculair: Beyond Sky City As the technological advancement of urban development in Sky City moves beyond pods and objects, humans will find the ability to create the ultimate flexible architecture.

Moleculair assembly creates the city and the things in it, allowing humans to live in an environment in which they are fully self-sustainable and only rely on the sun and oxygen.

Arriving to the point of molecular assembly might very well be argued to be the last invention of mankind. Since it potentially has the ability of generating anything at any given time. This would outweigh any naturally occurring systems, leaving humans with a never before seen level of independency.

In Moleculair City, molecules join and detatch as needed to create organic formations which are livable, edible and wearable, sustaining life in the sky. Molecular objects themselves become part of the process of the moleculair cycle: Atom Assemblers arrange atoms to create the objects of Moleculair City, creating a cyclical process.

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Material scale

Solid to gas

Gas to solid

Final molecule created during the atom assembling process Molecular scale

Molecule Composition The Atom Assembler in Molecular City produces chemical reactions to create strong particles which are then assembled into structures.

Some of these particles include Buckminsterfullerenes: multipurpose molecules which can be used in the production of solar energy production.

Material scale

The process of creating these involves a reaction from solid to gas, and back to solid again. The molecular disassemblage and atom assemblage process is at a stage where all molecules can be broken down into their atomic form and reassembled by the Atom Assembler into any type of new molecule .

The creation of solids: in this example, Buckminsterfullerenes are created via the rearranging of isotopes

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Travel and Energy Generation

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The evolution of travel and transportation will see transport vehicles reduce in scale over time. In Moleculair City, transport occurs via human motion through the sky, similar to Sky City, but also via the motion of molecular objects.

Molecular objects in which humans reside serve as multi-purpose spaces which enable long-distance transport across multiple cities.

Development towards these transport clusters sees a gradual reduction in size from pods into molecules, creating the ultimate movement method.

Travel will likely occur at the speed of nanoseconds using precise locating systems in order for every structure and molecule to be geolocated via satellites or other positioning systems.

Energy

Transport cluster concept in Moleculair City

Energy in Moleculair City is required to fuel human life.

As with all things in Moleculair City, energy generation will occur at a molecular level via PV and Reactor molecules which can generate, transmit and receive energy. Created by the Atom Assembler, these molecules are not visible to the human eye and designed to allow sunlight through, enabling them to become part of all shelter structures.

Sunlight may be able to filter through particles of Moleculair City for maximum energy production

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Technology in Moleculair City

343

The future of humans and the planet relies on a circular production process. The Atom Assembler in Moleculair City is a vision of the ultimate sustainable production machine. Using a similar concept to 3D printing, the Atom Assembler will enable the circular production and distribution of products across Moleculair City.

Atom assembly will be used to create flexible spaces for humans to shelter in according to their needs: individuals can occupy single homes which can be modified and changed where necessary via the accumulation or disassembly of molecules.

Clothing and products are created in the same way, with production focusing on what humans need over what they want. Any unsued item is fed back into the Atom Assembler for disassembly and reuse.

Food in Moleculair City is also produced by the Atom Assembler.

The desired goods will be produced using atom arrangement, eliminating agriculture and traditional food production By the time we reach Moleculair City, we envisage a new production technology. Using a similar concept to 3D printing, the â&#x20AC;&#x2DC;Atom Assemblerâ&#x20AC;&#x2122; is a device which arranges atoms to produce the molecular structures of a desired product. This will revolutionise the way in which a city operates, especially in relation to the distribution of goods.

The evolution line will sharpen more toward a complete decentralized and customized way of production thanks to the Atom Assembler and the molecular aggregation technology, when production will be held by assemble and disassemble of molecules, to reach a full circular economy process.

Assemblage of atoms for food production: atoms are assembled to produce all products of Sky City.

8.

Appendix

1.1 Cities of yesterday

346

347

8.1 Evaluation criteria summary

Population

Commuting time

Time gained

LA

1572,21

SC

20 000

LA

30 min

SC

0,4 min

LA

0 min/day

SC

95 min/day

Efficiency of space

LA SC

Built density

Built density

66 %

LA

0,15 %

SC

20 %

LA

1%

SC

20 %

349

VOXELS: 27 USE: DWELLING

VOXELS: 12 USE: DWELLING

CONNECTABLE SURFACES:

CONNECTABLE SURFACES:

0

0

6

NOISE: 0

100 db

Cooking Pod

Living Pod

8.1. Pod Catalogue

SUNLIGHT:

6

NOISE: 0

100 db

SUNLIGHT: 1500 lux

0

1500 lux

VOXELS: 8 USE: DWELLING

VOXELS: 27 USE: DWELLING

CONNECTABLE SURFACES:

CONNECTABLE SURFACES:

0

0

NOISE: 0

100 db

Eating Pod

6

SUNLIGHT: 0

6

NOISE: 0

100 db

SUNLIGHT: 1500 lux

0

1500 lux

VOXELS: 8 USE: DWELLING

VOXELS: 36 USE: WORK

CONNECTABLE SURFACES:

CONNECTABLE SURFACES:

0

0

6

NOISE: 0

100 db

SUNLIGHT: 0

1500 lux

Workshop Pod

Bathroom Pod

0

Sleeping Pod

348

6

NOISE: 0

100 db

SUNLIGHT: 0

1500 lux

351

VOXELS: 8 USE: WORK

VOXELS: 2 USE: WORK

CONNECTABLE SURFACES:

CONNECTABLE SURFACES:

0

0

6

NOISE: 0

100 db

Audience Pod

Stage Pod

350

SUNLIGHT:

0

100 db

1500 lux

0

1500 lux

VOXELS: 8 USE: WORK

CONNECTABLE SURFACES:

CONNECTABLE SURFACES:

0

0

6

NOISE: 0

100 db

SUNLIGHT:

Conversation Pod

VOXELS: 12 USE: WORK

0

Discussion Pod

NOISE:

SUNLIGHT:

1500 lux

6

NOISE: 0

100 db

SUNLIGHT: 0

1500 lux

VOXELS: 72 USE: WORK

VOXELS: 12 USE: LEISURE

CONNECTABLE SURFACES:

CONNECTABLE SURFACES:

0

0

6

NOISE: 0

100 db

SUNLIGHT: 0

Dancing Pod

Workstation Pod

0

6

6

NOISE: 0

100 db

SUNLIGHT: 1500 lux

0

1500 lux

353

VOXELS: 8 USE: LEISURE

VOXELS: 64 USE: LEISURE

CONNECTABLE SURFACES:

CONNECTABLE SURFACES:

0

0

6

NOISE: 0

100 db

Gym Pod

Walking Pod

352

SUNLIGHT: 1500 lux

0

0

100 db

1500 lux

VOXELS: 12 USE: LEISURE

VOXELS: 27 USE: LEISURE

CONNECTABLE SURFACES:

CONNECTABLE SURFACES:

0

0

6

NOISE: 0

100 db

SUNLIGHT: 0

6

NOISE: 0

100 db

SUNLIGHT: 1500 lux

0

1500 lux

VOXELS: 16 USE: LEISURE

CONNECTABLE SURFACES:

CONNECTABLE SURFACES:

6

NOISE: 0

100 db

SUNLIGHT: 0

1500 lux

Outdoor Seating Pod

VOXELS: 1 USE: LEISURE

0

Grass Pod

NOISE:

SUNLIGHT:

Tree Pod

Relax Pod

0

6

0

6

NOISE: 0

100 db

SUNLIGHT: 0

1500 lux

355

VOXELS: 64 USE: LEISURE

CONNECTABLE SURFACES:

CONNECTABLE SURFACES:

0

0

6

NOISE: 0

100 db

SUNLIGHT:

Kiosk Pod

0

Exhibition Pod

VOXELS: 27 USE: LEISURE

1500 lux

0

100 db

SUNLIGHT: 1500 lux

VOXELS: 27 USE: SHOPPING

VOXELS: 8 USE: SHOPPING

CONNECTABLE SURFACES:

CONNECTABLE SURFACES:

0

0

6

NOISE: 0

100 db

SUNLIGHT: 0

6

NOISE: 0

100 db

SUNLIGHT: 1500 lux

0

1500 lux

VOXELS: 8 USE: SHOPPING

VOXELS: 27 USE: PUBLIC

CONNECTABLE SURFACES:

CONNECTABLE SURFACES:

0

0

6

NOISE: 0

100 db

SUNLIGHT: 0

Medical Pod

Trade Pod

6

NOISE:

0

Display Pod

Bar Pod

354

6

NOISE: 0

100 db

SUNLIGHT: 1500 lux

0

1500 lux

357

VOXELS: 27 USE: PUBLIC

VOXELS: 12 USE: PUBLIC

CONNECTABLE SURFACES:

CONNECTABLE SURFACES:

0

0

6

NOISE: 0

100 db

Study Pod

Storage Pod

356

SUNLIGHT:

NOISE: 0

100 db

SUNLIGHT: 1500 lux

0

1500 lux

VOXELS: 72 USE: PUBLIC

VOXELS: 27 USE: PUBLIC

CONNECTABLE SURFACES:

CONNECTABLE SURFACES:

0

0

6

NOISE: 0

100 db

SUNLIGHT: 0

Praying Pod

Presentation Pod

0

6

6

NOISE: 0

100 db

SUNLIGHT: 1500 lux

0

1500 lux

358

8.1. Object Catalogue

Object specific rules - cooking pod, storage pod, bar pod always adjacent - pods for customers able to disperse according to desired level of privacy

Object Name

Restaurant Eating pod 32%

Pods Eating pod Bathroom pod Outdoor seating pod Relax pod 4% Relax pod Conversation pod 5% Conversation pod Cooking pod Bar pod Outdoor seating pod 30% Storage pod workstation pod

359

Bathroom pod 3% Cooking pod 12%

Hours of Use

Storage pod 4% Workstation pod 2% Bar pod 8% 0

4 Mon

8 Tues

Wed

12 Thur

16 Fri

20 Sat

24 Sun

Activities leisure - socialising, work

eating pod

Functional diagram

Sunlight Preference

bathroom pod

relax pod

Low

conversation pod

cooking pod

Privacy Isolation

Outdoor seating pod

storage pod

Workstation Pod

Aggregation

Noise Silent

bar pod

High

Noisy

Porosity Dense - 100%

Porous - 0 %

360

Object Name

Object specific rules - cooking pod, storage pod, bar pod always adjacent - pods for customers able to disperse according to desired level of privacy

Bar

361

Relax pod 23%

Pods Conversation pod Relax pod Eating pod Bathroom pod Cooking pod Conversation pod 45% Bar pod Storage pod

Eating pod 5% Bathroom pod 4% Cooking pod 4%

Hours of Use

Storage pod 4%

Bar pod 15% 0

4 Mon

8 Tues

Wed

12 Thur

16 Fri

20 Sat

24 Sun

Activities leisure - socialising, work

relax pod

Functional diagram conversation pod

Sunlight Preference eating pod

Low

High

Privacy Isolation

Aggregation

bathroom pod

bar pod

Noise Silent storage pod

Noisy

cooking pod

Porosity Dense - 100%

Porous - 0 %

362

Object Name

Object specific rules - cooking pod, storage pod, bar pod always adjacent - pods for customers able to disperse according to desired level of privacy

CafĂŠ Relax pod 10%

Pods Cooking pod Bathroom pod Conversation pod 24% Eating pod Outdoor seating pod Conversation pod Relax pod Bar pod Outdoor seating pod 30% Storage pod

363

Bar pod 10% Storage pod 4%

Hours of Use

Cooking pod 8% Bathroom pod 4% Eating pod 10% 0

4 Mon

8 Tues

Wed

12 Thur

16 Fri

20 Sat

24 Sun

Activities leisure - socialising, work

relax pod

Functional diagram

Sunlight Preference bar pod

conversation pod

Low

High

Privacy Outdoor seating pod

storage pod

Isolation

Aggregation

Noise bathroom cooking pod pod

eating pod bathroom pod

Silent

Noisy

Porosity Dense - 100%

Porous - 0 %

364

Object Name

365

Object specific rules - sleeping pods very private, reception pod very public

Nightclub Outdoor seating pod 5%

Pods dancing pod bar pod outdoor seating pod conversation pod bathroom pod

Conversation pod 25%

Hours of Use

Bar pod 15% Bathroom pod 5%

0

Dancing pod 50%

4 Mon

8 Tues

Wed

12 Thur

16 Fri

20 Sat

24 Sun

Activities leisure - socialising, work

conversation pod

Functional diagram

Sunlight Preference Low bathroom pod

Outdoor seating pod

High

Privacy Isolation

Aggregation

Noise

bar pod

Dancing pod

Silent

Noisy

Porosity Dense - 100%

Porous - 0 %

366

Object Name

367

Object specific rules - none

Gym Bathroom pod 2% Dresing pod 10%

Workstation pod 10% Walking pod 10%

Pods Walking pod Dancing pod 10% Conversation pod Relax pod Bar pod Medical pod Gym pod Gym pod 48% Dancing pod Dressing pod Bathroom pod Workstation pod Activities leisure - sports, work

Conversation pod 5%

Medical pod 4%

0

4 Mon

Workstalivng tion pod pod

Functional diagram

Hours of Use

Relax pod 5% Bar pod 2%

Bathroom pod

8 Tues

Low conversation pod

relax pod

Medical pod

Sat

24 Sun

High

Aggregation

Noise Silent

Gym pod

Fri

20

Privacy Isolation

Dancing pod

Thur

16

Sunlight Preference

Walking pod

Dresssing pod

Wed

12

Noisy

bar pod

Porosity Dense - 100%

Porous - 0 %

368

Object Name

Object specific rules - none

Pool

369

Pool Dresing pod 10%

Pods Dressing pod Bar pod Kiosk pod Conversation pod Bathroom pod Pool pod

Bar pod 4% Kiosk pod 10%

Hours of Use

Conversation pod 5% Bathroom pod 1%

Pool pod 70% 0

4 Mon

8 Tues

Wed

12 Thur

16 Fri

20 Sat

24 Sun

Activities leisure - sports, work

bar pod

Functional diagram

Sunlight Preference Kiosk pod

Dresssing pod

Low

High

Privacy Isolation

Aggregation

Noise conversation pod

Pool pod

Bathroom pod

Silent

Noisy

Porosity Dense - 100%

Porous - 0 %

370

Object Name

Object specific rules - spa, pool, dressing room pods cluster

371

Spa Dresing pod 10%

Pods Pool pod Greenery pod Bar pod Dressing pod Relax pod Eating pod Bathroom pod Spa pod

Relax pod 20%

Bar pod 4%

Hours of Use

Greenery pod 5% Eating pod 4% Bathroom pod 1% Pool pod 10%

Spa pod 46%

0

4 Mon

8 Tues

Wed

12 Thur

16 Fri

20 Sat

24 Sun

Activities leisure, work

relax pod

Functional diagram

Sunlight Preference eating pod

Dresssing pod

Low

High

Privacy Bathroom pod

bar pod

Isolation

Aggregation

Noise Pool pod

greenery pod Spa pod

Silent

Noisy

Porosity Dense - 100%

Porous - 0 %

372

Object Name

Object specific rules - trade pod, display pod for public access - workstation pod, storage pod, bathroom pod, eating pod not for public access and cluseter accordingly

Grocery Store Pods Living pod Cooking pod Bathroom pod Display pod 70% Eating pod Sleeping pod Conversation pod Relax pod Bar pod Storage pod

Trade pod 10%

373

Hours of Use

Eating pod 4% Bathroom pod 1% Storage pod 5% Workstation pod 10% 0

4 Mon

8 Tues

Wed

12 Thur

16 Fri

20 Sat

24 Sun

Activities work, sleep

Trade pod

Functional diagram

Sunlight Preference eating pod

Display pod

Low

High

Privacy Isolation

bathroom pod

Workstation pod

Noise Silent

storage pod

Aggregation

Noisy

Porosity Dense - 100%

Porous - 0 %

374

Object Name

Supermarket Pods living pod cooking pod bathroom pod eating pod sleeping pod conversation pod relax pod bar pod storage pod

Object specific rules - trade pod, display pod for public access - workstation pod, storage pod, bathroom pod, eating pod not for public access and cluseter accordingly

Supermarket

375

Trade pod 20%

Hours of Use Eating pod 4% Bathroom pod 1% Storage pod 10%

Display pod 60%

Workstation pod 5% 0

4 Mon

8 Tues

Wed

12 Thur

16 Fri

20 Sat

24 Sun

Activities work, sleep

Trade pod

Functional diagram

Sunlight Preference eating pod

Display pod

Low

High

Privacy Isolation

bathroom pod

Workstation pod

Noise Silent

storage pod

Aggregation

Noisy

Porosity Dense - 100%

Porous - 0 %

376

Object Name

377

Object specific rules - sleeping pods very private, reception pod very public

Boutique Hotel Pods Living pod Cooking pod Bathroom pod Eating pod Sleeping pod Conversation pod Relax pod Bar pod Storage pod

Eating pod 6% Bathroom pod 1% Cooking pod 8% Living pod 10% Sleeping pod 50%

Hours of Use

Storage pod 2% Bar pod 8% Conversation pod 8% Relax pod 7%

0

4 Mon

8 Tues

Wed

12 Thur

16 Fri

20 Sat

24 Sun

Activities work, sleep

livng pod

Functional diagram

Sunlight Preference

storage pod

cooking pod

Low

bathroom pod

bar pod

Privacy Isolation

eating pod

relax pod

sleeping pod

Aggregation

Noise Silent

conversation pod

High

Noisy

Porosity Dense - 100%

Poros - 0 %

378

Object Name

Object specific rules - very random, unpredictable assembly of pods

Casino

379

Casino Bar pod 5%

Pods Bathroom pod Workstation pod Conversation pod Relax pod Bar pod Trade pod Casino pod

Conversation pod 8%

Hours of Use

Relax pod 5% Bathroom pod 1% Storage pod 1% Workstation pod 10% Casino pod 70% 0

Activities play

4 Mon

bathroom pod

Functional diagram

8 Tues

Wed

12 Thur

16 Fri

20 Sat

24 Sun

Sunlight Preference

storage pod

workstation pod

Low

High

Privacy Isolation

Aggregation

conversation pod

casino pod

Noise Silent bar pod

Noisy

relax pod

Porosity Dense - 100%

Poros - 0 %

380

Object Name

381 Object specific rules - big traffic within the object in certain moments, almost no traffic in others

Cinema

Kiosk pod 2% Display pod 4% Bar pod 2% Relax pod 10% Stage pod 10%

Pods Bathroom pod Stage pod Audience pod Conversation pod Relax pod Bar pod Kiosk pod Display pod

Conversation pod 10%

Hours of Use

Bathroom pod 2%

0

Audience pod 60%

4 Mon

Activities watch movies

bathroom pod

Functional diagram display pod

8 Tues

Wed

12 Thur

16 Fri

20 Sat

24 Sun

Sunlight Preference stage pod

Low

High

Privacy kiosk pod

audience pod

Isolation

Aggregation

Noise bar pod

conversation pod relax pod

Silent

Noisy

Porosity Dense - 100%

Poros - 0 %

382

Object Name

Object specific rules - preferably light from the north

383

Factory Pods Bathroom pod Eating pod Workshop pod Workstation pod Storage pod Specific pods

Workshop pod 55%

Eating pod 10%

Hours of Use

Bathroom pod 5% Conversation pod 15%

Storage pod 20%

Activities work

0

Workstation pod 10%

4 Mon

bathroom pod

Functional diagram

specific pod

8 Tues

Wed

12 Thur

16 Fri

20 Sat

24 Sun

Sunlight Preference

eating pod

Low

High

Privacy Isolation

Aggregation

Noise storage pod

workshop pod

workstation pod

Silent

Noisy

Porosity Dense - 100%

Poros - 0 %

384

Object Name

Object specific rules - preferably light from the north for the exhibition pods

385

Museum Exhibition pod 50%

Pods Bathroom pod Eating pod Stage pod Audience pod Workstation pod Bar pod Exhibition pod Trade pod Storage pod

Trade pod 5% Bar pod 1%

Hours of Use

Storage pod 10%

Bathroom pod 1% Eating pod 5% Workstation pod 20%

Audience pod 6% Stage pod 2% 0

4 Mon

8 Tues

Wed

12 Thur

16 Fri

20 Sat

24 Sun

Activities Leisure, Work bathroom pod

Functional diagram

Sunlight Preference

storage pod

eating pod

Low

stage pod

trade pod

Privacy Isolation

audience pod

exhibition pod

workstation pod

Aggregation

Noise Silent

bar pod

High

Noisy

Porosity Dense - 100%

Poros - 0 %

386

Object Name

Object specific rules - short ways from between the pods inside an office

387

Office Discussion pod 24%

Presentation pod 13%

Pods Bathroom pod Eating pod Workstation pod Conversation pod 15% Conversation pod Discussion pod Relax pod Relax pod 4% Storage pod Presentation pod

Hours of Use Eating pod 5% Storage pod 2% Bathroom pod 2%

Workstation pod 35%

0

4 Mon

Activities work

bathroom pod

Functional diagram presentation pod

8 Tues

Wed

12 Thur

16 Fri

20 Sat

24 Sun

Sunlight Preference eating pod

Low

High

Privacy storage pod

workstation pod

Isolation

Aggregation

Noise relax pod

conversation pod discussion pod

Silent

Noisy

Porosity Dense - 100%

Poros - 0 %

388

Object Name

Object specific rules - preferably light from the south for the living pods

389

Row House Bathroom pod 5%

Pod Living pod Cooking pod Bathroom pod Eating pod Sleeping pod Conversation pod Relax pod Study pod

Cooking pod 10%

Eating pod 15%

Hours of Use Living pod 15% Study pod 5% Conversation pod 5% Relax pod 5% 0

Sleeping pod 40%

Mon

Activities leisure, relax, socialize, work, cook, sleep

living pod

Functional diagram

4

study pod

8 Tues

Wed

12 Thur

16 Fri

20 Sat

24 Sun

Sunlight Preference cooking pod

Low

High

Privacy relax pod

bathroom pod

Isolation

Aggregation

Noise conversation pod

eating pod sleeping pod

Silent

Noisy

Porosity Dense - 100%

Poros - 0 %

390

Object Name

Object specific rules - preferably light from the south for the living pod

391

Single House

Bathroom pod 5%

Cooking pod 10%

Eating pod 15%

Additional pods Living pod Cooking pod Study pod 5% Bathrrom pod Eating pod Sleeping pod Conversation pod Relax pod Sleeping pod 40% Study pod

Living pod 15% Conversation pod 5% Relax pod 5%

0

4 Mon

Activities leisure, relax, socialize, work, cook, sleep

living pod

Functional diagram

Hours of Use

study pod

8 Tues

Wed

12 Thur

16 Fri

20 Sat

24 Sun

Sunlight Preference cooking pod

Low

High

Privacy relax pod

bathroom pod

Isolation

Aggregation

Noise conversation pod

eating pod sleeping pod

Silent

Noisy

Porosity Dense - 100%

Poros - 0 %

392

Object Name

393 Object specific rules - big traffic within the object in certain moments, almost no traffic in others

Theatre

Kiosk pod 2% Display pod 4% Bar pod 2% Relax pod 10% Stage pod 10%

Additional pods Bathroom pod Stage pod Audience pod Conversation pod Dressing pod Relax pod Bar pod Display pod Trade pod Storage pod

Conversation pod 10%

Hours of Use

Bathroom pod 2%

0

Audience pod 60%

4 Mon

8 Tues

Wed

12 Thur

16 Fri

20 Sat

24 Sun

Activities watch plays bathroom pod

Functional diagram

storage pod

Sunlight Preference

stage pod

Low trade pod

audience pod

Privacy Isolation

display pod

conversation pod

Aggregation

Noise Silent

bar pod

High

Noisy

dressing pod relax pod

Porosity Dense - 100%

Poros - 0 %

394

Object Name

Object specific rules -none

395

Public Office Pods Bathroom pod Eating pod Relax pod Conversation pod Discussion pod Workstation pod Presentation pod Storage pod

Hours of Use

0

4 Mon

8 Tues

Activities Work

Conversation pod

Functional diagram

Sunlight Preference Eating pod

Relax pod

Low Privacy bathroom pod

Discusion pod

Isolation Noise

Storage pod

Workstation pod Presentation pod

Silent

Porosity Dense - 100%

Wed

12 Thur

396

Object Name

Object specific rules -Dressing pod and Gym pod cluster -Storage pods cluster

Fire Station Additional pods Bathroom pod Workshop pod Presentation pod Workstation pod Conversation pod Dressing pod Gym Pod Medical pod Storage pod

397

Hours of Use

0

4 Mon

8 Tues

Wed

12 Thur

16 Fri

20 Sat

24 Sun

Activities Work

Bathroom pod

Functional diagram storage pod

Sunlight Preference Workshop pod

Low

Presentation pod

Medical pod

Privacy Isolation

Workstation pod

Dresssing pod

Conversation pod

Aggregation

Noise Silent

Gym pod

High

Noisy

Porosity Dense - 100%

Poros - 0 %

398

Object Name

399

Object specific rules -Conversation pods detach the object to become satellite when in use -Presentation pod and Workstation pod cluster

Police Station Pods Presentation pod Workstation pod Bathroom pod Workshop Pod Conversation pod Dressing pod Storage pod

Hours of Use

0

4 Mon

8 Tues

Wed

12 Thur

16 Fri

20 Sat

24 Sun

Activities work

Workstation pod

Functional diagram storage pod

Sunlight Preference Presentation pod

Low

High

Privacy Isolation

Aggregation

Workshop pod

Dressing pod

Noise

conversation pod

Bathroom pod

Silent

Noisy

Porosity Dense - 100%

Poros - 0 %

400

Object Name

Object specific rules -Maximum distance from sleeping pod to medical pod is 100 meter -Medical pods desire a distance to other medical pods of 150 meter, but not outside the region that is populated.

-Presentation pods, workstation pods and discussion pods cluster -Selling pods, kiosk pods, eating pods and chating pods cluster in he center of the object. Center of object desires daylight.

-Storage pods attach to medical pods -Outdoor sitting pods move to the outside of populated space

Hospital Pods Bathroom pod Cooking pod Eating pod Sleeping pod Presentation pod Discussion pod Workstation pod Conversation pod Relax pod Outdoor Seating pod Medical pod Storage pod Kiosk pod Trade pod Praying pod

Hours of Use

Activities work, sleeping, Civic

0

4

8

12

16

20

24

Functional diagram Mon

Presentation pod

Sleeping pod

Discussion pod

Wed

Thur

Fri

Sat

Sun

Sunlight Preference

Eating Pod cooking pod

Workstation Pod

Tues

Low

High

Privacy bathroom pod

Conversation pod Praying pod Relax pod

Isolation Noise Silent

Outdoor seating pod

Aggregation

Noisy

Trade pod

Medical pod

Kiosk pod Storage podd

Porosity Dense - 100%

Poros - 0 %

401

402

Object Name

Object specific rules -Conversation pod and Eating pod cluster in 1 cluster - Sleeping pods and Living pods cluster in pairs

Elderly Home Pods Living pod Bathroom pod Cooking pod Eating pod Sleeping pod Conversation pod Medical pod Storage pod Praying pod

403

Hours of Use

0

4 Mon

8 Tues

Wed

12 Thur

16 Fri

20 Sat

24 Sun

Activities Sleeping, Home activity, Socializing

Livng pod

Functional diagram

Sunlight Preference

Storage pod

Cooking pod

Low

Bathroom pod

Medical pod

Privacy Isolation

Eating pod

Praying pod

sleeping pod

Aggregation

Noise Silent

conversation pod

High

Noisy

Porosity Dense - 100%

Poros - 0 %

404

Object Name

Object specific rules -Eating pod and kiosk pod cluster -Audience pod and stage pod cluster -Workshop pods cluster -Study pod, exhibition pod, relax pod, chatting pod and workstation pod desire to be situated on the outside of the object.

-Relax pods act as satellites, being near the object but in a quite zone.

University Pods Bathroom pod Eating pod Study pod Workshop pod Presentation pod Discussion pod Workstation pod Stage pod Audience pod Conversation pod Exhibition pod Outdoor seating pod Relax pod Greenery pod Storage pod Kiosk pod

Hours of Use

0

4 Mon

Activities work, Education

8 Tues

Wed

12 Thur

16 Fri

20 Sat

24 Sun

Functional diagram Presentation pod

Workshop pod

Discussion pod

Study pod

Sunlight Preference Eating pod

Workstation Pod

bathroom pod

Conversation pod

Low Privacy Isolation

Storage pod

Relax pod

Stage pod

Outdoor seating pod Exhibition pod

Audience pod Greenery pod

Kiosk pod

High

Aggregation

Noise Silent

Noisy

Porosity Dense - 100%

Poros - 0 %

405

406

Object Name

Object specific rules -Exhibition pod desire maximum spread over populated space -Presentation pods, workstation pods and discussion pods cluster

Secondary School Pods Cooking pod Bathroom pod Eating pod Study pod Workshop pod Workstation pod Discussion pod Presentation pod Conversation pod Relax pod Outdoor seating pod Greenery pod Exhibition pod Storage pod

407

Hours of Use

0

4 Mon

Activities Work, Education

8 Tues

Wed

12 Thur

16 Fri

20 Sat

24 Sun

Functional diagram

Presentation pod

Workshop pod

Study pod

Sunlight Preference Eating pod

Discussion pod

bathroom pod

Workstation Pod

Low Privacy Isolation

Conversation pod

Cooking pod

Relax pod

Storage pod Outdoor seating pod

Exhibition pod

Greenery pod

High

Aggregation

Noise Silent

Noisy

Porosity Dense - 100%

Poros - 0 %

408

Object Name

Object specific rules - All pods attach to pods of the same type

409

Preaching Area Pods Bathroom pod Conversation podd Praying pod Storage pod

Hours of Use

0

4 Mon

8 Tues

Wed

12 Thur

16 Fri

20 Sat

24 Sun

Activities Religion

Functional diagram

Sunlight Preference Storage pod

Bathroom pod

Low

High

Privacy Isolation

Aggregation

Noise Convesation pod

Praying pod

Silent

Noisy

Porosity Dense - 100%

Poros - 0 %