HousingPrime by RC11, the Bartlett, 2020 by 刘紫东,

Acknowlegement Tutors: Philippe MOREL, Paul POINET We would like to thank the Bartlett Prospective Urban Design program that gave us the opportunity to learn architecture and urban design in a brand new way. We also want to thank our tutors Philippe and Paul who constantly gave us inspiration and guidance.

The Bartlett School of Architecture University College London 22 Gorden Street London WC1H 0QB Title: Housing Prime This Portfolio is the outcome of the Program B-Pro MArch. Urban Design, Research Cluster 11, Housing Prime 2019 - 2020 Tutors: Philippe Morel, Paul Poinet Students: 18107193 Shiyuan HUANG, 18127328 Yi LI, 19063065 Huiyu PAN, 19081914 Zidong LIU Submitted on 14th September 2020

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Contents 1. Background 1.1 Housing Crisis 1.1 Homeonership Rate 1.1.2 Homeowner Vacancy Rates

1.2 Biggest Victim 1.2.1 Altered Aspirations 1.2.2 The gig Alternative

2. Research 2.1 Introduction 2.2 Research 2.2.1 Life Cycle of Housing 2.2.2 Life of Agent Base 2.2.3 Working Strategy

2.3 New age of mobile terminal 2.3.1 Growing Market 2.3.2 Case Study

2.4 Design Proposal

3. Concept 3.1 Housing Decision Factors 3.2 Activity Sequence 3.2.1 Soane's Museum 3.2.2 Parallel World

3.3 Working Strategy

4. Design 4.1 Site Decision 4.1.1 Basic Theory 4.1.2 Data Collecting - Facilities 4.1.3 Data Evaluating 4.1.4 Screen-out Top 10 Results 4.1.5 Get the Potential Site

4.2 Community Generation 4.2.1 Basic Logic 4.2.2 Size Classifying 4.2.3 Designing Rules 4.2.4 Simulation Rules 4.2.5 Layout Data - Pool

4.3 Living Cluster Generation 4.3.1 Activities 4.3.2 Data Collection 4.3.3 Location Choosing 4.3.4 Aggregation 4.3.5 Generation Process 4.3.6 Neighbours' Relationship 4.3.7 Public Space 4.3.8 Overall Effect 4.3.9 Renderings

4.4 Public Cluster Generation 4.4.1 Shared Functions 4.4.2 Cluster Circulation 4.4.3 Shopping Mall . . ffice 4.4.5 Gym 4.4.6 Daily Routine

4.5 Community Generation 4.5.1 Topology Study 4.5.2 Force Simulation 4.5.3 Volume Generation 4.5.4 Circulation Optimization 4.5.5 Public Cluster Sequence 4.5.6 Green Line 4.5.7 Sturcture in Public Spcace 4.5.8 Renderings 4.5.9 Expansion to Urban Scale

5. The App 5.1 Global Concept 5.1.1 Working Flow 5.1.2 Developing a new world as a stack

5.2 Front End 5.2.1 Using Flow 5.2.2 Android

5.3 Back End 5.3.1 Dynamic Database

5.4 Digital Twin 5.4.1 Introduction

5.5 3D Visualization 5.5.1 Web

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Chapter 1

Background Housing Crisis Biggest Victim

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a serious global phenomenon

2 0 1 4

rs O t he

1.1 Housing Crisis

2 0 1 5

> 6 5

The housing crisis is a serious and common phenomenon all over the world, it may lead to a severe problem to society and cause social instability. As a result, we would like to do some research on it and try to solve the problems.

2 0 1 6 5 5 - 6 4

1.1.1 Homeonership Rate 2 0 1 7

7 0 6 9

4 - 5 4 5

6 8

6 7

2 0 1 8

6 5 3 5 - 4 4

6 4 6 3

< 3 5

6 2 6 1 7 0 6 0 6 9 5 9 6 8 5 8 6 7 P ercen t

6 6

2 0 1 9

Homeownership Rates by Age of Householder: 2014 to 2019 Source: U.S. Census Bureau, Current Population Survey/Housing Vacancy Survey, January 30, 2020. 1 9 9 8

2 0 0 1

2 0 0 4

R eces s i o n

6 5

2 0 0 7

H o m eo w n ers hi p R at e

1 9 9 8

2 0 0 1

2 0 0 4

P ercen t

R eces s i o n

1 9 9 8

2 0 0 7

6-2019

H o m eo w n ers hi p R at e

2 0 0 1

2 0 0 4

R eces s i o n

P ercen t

6-2019

1 9 9 8

2 0 0 1

.org>

2019

.org>

2 0 0 7

2 0 1 0

2 0 1 6

2 0 1 9

2 0 0 7

2 0 1 3

2 0 1 6

The homeownership rate was lowest for those householders ages under 35 years of age, which is less than 40 percent and those householders between 35 to 44 years of age are also a bit low compared to others.

2 0 1 9

Seas o n al l y A d j u s t ed H o m eo w n ers hi p R at e

2 0 1 0

Homeowner Vacancy Rate

2 0 0 4

R eces s i o n

2019

2 0 1 3

Seas o n al l y A d j u s t ed H o m eo w n ers hi p R at e

6 3 Quarterly Homeownership Rates and Seasonally Adjusted Homeownership Rates 6 2 16 2 1 Source: U.S. Census Bureau, Current Population Survey/Housing Vacancy Survey, January 30, 2020, 16 1 0 Recession data: National Bureau of Economic Research, <www.nber.org>

1.1.2 Homeowner Vacancy Rates 2 0 1 9

2 0 1 0

6 4

15 0 9 5 98 8 7 6 5 4 3 1 2 2 1 1 1 1 0 0 9 8 7 6 5 4 3 2 1 0

2 0 1 9

P ercen t

6 6

2 0 1 0

Homeowner Vacancy Rate

2 0 1 3

2 0 1 6

2 0 1 9

Rental Vacancy Rate

2 0 1 3

2 0 1 6

2 0 1 9

Rental Vacancy Rate

Quarterly Rental and Homeowner Vacancy Rates for the United States: 1996-2019 Source: U.S.Census Bureau, Current Population Survey/Housing Vacancy Survey, January 30, 2020. Recession data: National Bureau of Economic Research, <www.nber.org>

Although the number of houses and homeownership rates are constantly increasing in recent years, the homeowner vacancy rates have remained high due to some reasons, which results in the occupancy rate of houses has not been improved. As a result, the problem of the housing crisis has not been solved. HOUSING PRIME 8

Average household size, 2008 and 2018 (average number of persons in private households) Source: Eurostat LFS Survey

In spite of the fact that there is a slowing trend of population growth worldwide, the reduction of average household size in the past decades has led to a continuous increase in the demand for housing, which as a result causes a more serious housing crisis. 9 HOUSING PRIME

1.2 Biggest Victim

people from age 25-44

Source from:The Deloitte Global Millennial Survey 2019 Please indicate if you have any of the following ambitions. Base: All millennials 13,416, all Gen Zs 3,009.Millennials in junior roles 2,706, senior roles 4,101, parents 6,036, not parents 7,380. Gen Zs in junior roles 773, mid-level roles 444, parents 268, not parents 2,741

1.2.1 Altered Aspirations Every generation is shaped by its circumstances, and millennials are no exception. They’re no less ambitious than previous generations: More than half want to earn high salaries and be wealthy. But their priorities have evolved, or at least been delayed by financial or other constraints. aving children, buying homes, and other traditional signals of adulthood “success markers” do not top their list of ambitions. Instead, travel and seeing the world was at the top of the list (57 percent). Generally, though, millennials believe their ambitions are within reach. Two-thirds who want to reach senior levels in their careers believe it’s attainable. Seven in 10 who want to see the world think it s possible. hree- uarters who want to buy homes are confident they ll be able to. nd 3 percent of those desiring families don’t believe barriers will prevent it. HOUSING PRIME 10

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Source from: The Deloitte Global Millennial Survey 2019 Additional information: Would you consider joining the gig economy? Base: All millennials 13,416, all Gen Zs 3,009. Q43. Which of the following best explain why you have joined or would consider being part of the gig economy? Base: All millennials/Gen Zs who have joined or would consider 11,266/2,433. Q44. Which of the following best explain why you would not consider being part of the gig economy? Base: All millennials/Gen Zs who have not joined or would not consider 1,172/276

1.2.2 The Gig Alternative Millennials indicated they would not hesitate to do freelance or contract work. Overall, the gig economy appeals to four in five millennials and Gen s. verall, almost half of millennials believe gig workers can earn as much as those in full-time jobs, and the same number think gig workers have a better work/life balance. But 51 percent said the unpredictability would be stressful. (They were not asked about the stress inherent in full-time jobs.) HOUSING PRIME 12

Wework, WeHai Road, ShangHai

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1.1 Housing Crisis 1.2 Biggest Victim 1.2.1 Age 1.2.2 Gig Economy

Chapter 2

Research Introduction Research New age of Mobile terminal Design Proposal

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The world is an APPLICATION ecosystem.

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2.1 Introduction The application program, which usually based on mobile devices, is to recompress and condense the world copied from the original version, and finally, the world seems to be reduced to a small square shortcut icon. It should be argued that the urban environment needs to respond to the life and work style of this new generation. The world seems to be reduced to a small square shortcut icon, Author

This means that how architecture becomes a new media embedded in the application ecosystem could be a crucial point in this new age of media.

2.2 Research 2.2.1 Life Cycle of Housing It is not surprising that the problem of transforming the resident value into their dwelling environment is a broad concern. The reason is that though the value of users plays a significant role in reali ing the high- uality housing. Satisfaction happens when a resident’s expected performance is met, for example, in the past, the architecture would have led the whole process of design, but now they seems to play participant role in a team and cooperate with others (Cole-Colander,2003). Also, the architecture construction industry is already in the face of the unusual situation that progressively it is the customer who is in management and guiding the radical changes. For instance, In the UK industry, customer-led action continues to emerge to drive change. However, with the popularity of client-led initiatives, there are increasing concerns that the design quality, beauty, and space quality would become secondary. This is because overshadowing the affection of professionals in the project could lead a lack of design expertise. In addition, how could general residents participate in the housing design process would be another crucial problem. specially, with the trend of floating population, such as the young people who have more likelihood to become the major resident group in the future housing market.

Life circle of housing and four steps the customers mainly particapted

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21 HOUSING PRIME

offices

Global WeWork locations, as of 1/18/2019

Year Memberships

2010 450

2012 4000

2014 15000

2016 87000

2018 401000

2019 527000

The Change of WeWork Memberships, as of 1/18/2019

2.2.2 Life of Agent Base WeWork under sharing economy

Global Expanding

“The sharing economy is the value in taking underutilized assets and making them accessible online to a community, leading to a reduced need for ownership of those assets.”

WeWork has explored 550 offices till 2019, especially in developed countries, like merica, and has the most offices in ew ork. Besides, the amount of membership keep increasingly rising.

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2.2.3 Working Strategy After the study of the business model of We Work, we found that the difference between WeWork and traditional real estate is regarding space as a service platform.

location assessment

Through the process of the whole program, from the site choosing, the designing process, to the final building process, We Work has a corresponding team to deal with it, saving time and resources. For example, they choose the location with the help of a data company, to calculate the quantity of each kind of leisure facilities before the fieldwork by the agent.

data of amenties coffee shops restaurants fitness nightlife hotels

location strategy attract members

heat map sign a lease

With the help of Factual, WeWork could make the quick decision on site choosing, by collecting the data of amenities and locate them firstly, including coffee shops, restaurants, fitness, nightlife, and hotels. Secondly, they generate the heat map with their own specific software, which could not only reflect the closest amenities but also, more importantly, show the quantity and density of each kind of amenities.

3D scan space info square footage window mullions door sizes slab flatness thickness ductwork piping

BIM

factual

different layout testing

design strategy optimize space

layout design

Fieldlens

WeWork uses the special tools to scan the building, which takes only one hour a floor and gets so much information of the layout that the error range is narrowed down in the following layout designing, which means it could help to arrange desks as much as possible. While WeWork designs a new office layout, they could predict the necessary uantity of facilities according to e isting offices, of which the usage condition will be collected generally. For example, they could predict the number of meeting rooms, and even the number of phones. The data accumulation happens through the whole process of projects and operates like a circulation.

frames & assemblies mapping Machine Learning

usage prediction neural net meeting room usage e isting offices layout final layout

construction strategy fi chaos +

building management real-time info post-construction management

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In the building management life cycle, the mangers, the foremen, the architects, the owners, and supers could communicate in real-time on the platform - FieldLens, on which the conversations could be documented, and the issues could be tracked and assigned. Hence the real-time communicational constructing is more efficient than the traditional one.

25 HOUSING PRIME

Worldwide mobile app revenues from 2011 to 2017 (in billion U.S. dollars)

Total worldwide in-app purchase revenues from 2011 to 2017 (in million U.S. dollars

Growing Market s you can see in the first graph below, the biggest increase was in 2012, when the annual revenue jumped from $8,32B to $18.56B. For Android, it was the year of significant growth as well, in 3 of 2012 Android got 75% of the global smartphone market. The growth was fueled by more and more OEM adopting Android to run their mobile devices.

2.3 New age of mobile terminal

Mobile app business is powered by three models – paid, in-app purchases, and advertising. Originally the app ecosystem debuted with the paid model and. The reason for revenue, driven by the paid model, to decline was the in-app purchase model introduction. With inapp purchases, mobile app users could install an app for free, try it to see if it’s something they really need, and later pay for additional functionality or to remove ads inside an app.

Source from: Gther, TechCrunch @ Statista 2015 Additional information : worldwide 2011 to 2013,Smartphone apps

Averege In-App Spending per Active U.S.Iphone - Top 5 Categories

Source from:Sensor Tower Store Intelligence Figures based on US consumer spending in iphone apps for Jan 1 , 2017 through Dec 31.2018 .Includes premium apps and in-app revenue HOUSING PRIME 26

While mobile games led in terms of the total amount spent on average, other app categories experienced greater yearover-year growth in 2018 according to our findings. Ultimately, as the market reaches saturation, owners of these devices continue to spend more than ever on average—and in ever-increasing amounts across all categories, particularly where in-app subscriptions are factors. For developers, this should be a clear sign that categories in addition to mobile gaming do hold great revenue potential. For Apple, we see a definite direction for the future growth of apps within its services business revenue, even if faced with slowing hardware sales.

Key word: interior top 60 Searching with key word as 'interior' in app store, 11/22/2019

2.3.1 Growing Market There are Top 60 Apps of interior and home design, and we categorized them based on 3 dimensions. as we can see, the 3D apps occupy 50% of these apps, so the customer would prefer the 3D type which could Help them get more intuitive feedback. It's no doubt that apps for design home have quite a potential market, and inspired us to design a platform to help people design by themselves. 27 HOUSING PRIME

Roomle

iagram Using flow of oomle

2.3.2 Case Study

App for intrior design HOUSING PRIME 28

ere is the specific case which combines the and 3 design. s the diagram shows, this app includes several different parts, such as the product lists, customers could choose their preferred one and order it immediately. and people could also share their design with media-platforms. So, Roomle creates their own ecology and help people have better user experiences. However, oomle still missed the construction part which is the most difficult part of generals design, so we think in our app we should pay more attention to the real building. 29 HOUSING PRIME

2.4 Design Proposal

The output of applications could become cities HOUSING PRIME 30

In the project, we would like to transform this process into the urban design with the new media age. Firstly, gathering the data from clients through an open platform, such as applications, and then integrating the data as input in the housing simulator, so customers would obtain the unique house as feedback. Finally, each house would be regarded as the new input for the community simulator. Overall, our proposal is that Building an application of which digital outputs will become cities. 31 HOUSING PRIME

Building an APPLICATION of which digital outputs will become cities.

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Chapter 3

Concept Housing Decision Factors Activity Sequence Working Strategy

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3.1 Housing Decision Factors A wide range of factors is influencing the housing decisions of people, which can be classified as 6 dimensions: built and natural environment, psychological and psychosocial, economic, social, and time and space-time. All these important factors give us a framework of guidelines of housing design.

Geographic location

Independence

Preconceptions

Potential adaptability

Satisfaction

Building type

Security Control over envrionment

Knowledge of housing options

Proximity of services Adapted

Dwelling size Public transport

Convenient

Aspirations Social roles Family roles

Psychological & Psychosocial

Public facilicies

Traffic/car facilities Green space

Healthcare

Mortgage

Built & Natural Evironment

Housing market Equity

Function mixity

Accessibility

Housing costs

Economic

Investment

Storeys

Housing Decision Factors

Housing taxes

Housing value

General conditions

Social Activities

Tigger event

Maintenances

Density

Services Time & Space-Time

Years in dwelling Anticipation

Timing

Social network

Residential experience Routine Habits

Social

Proximity to family & friends

Neighbors Relation

Number of children Pressure

ousing ecision Factors, classified by the meaning and e perience of home dimensions. Source Factors influencing housing decisions among frail older adults systematic review HOUSING PRIME 36

37 HOUSING PRIME

3.2 Activity Sequence a tower is a city

In Koolhaas’s metropolitan theory, each floor in a skyscraper represents a parallel world. People’s activities are organized freely in it. What we hope to achieve is to realize the rational logic behind the chaotic appearance.

Perspective of Soane's Museum Source: https://travelbetweenthepages.com/2016/12/03/virtual-history/

Sir John Soane greatly enriches the interior space by using multiple layers of walls and exhibits. We found that architects organically combine these complex elements through a sequence of daily activities

Ground Floor Plan of Soane's Museum

3.2.1 Soane's Museum Although the number of houses and homeownership rates are constantly increasing in recent years, the homeowner vacancy rates have remained high due to some reasons, which results in the occupancy rate of houses has not been improved. As a result, the problem of the housing crisis has not been solved. HOUSING PRIME 38

Section of Soane's Museum Source: https://travelbetweenthepages.com/2016/12/03/virtual-history/

In the vertical direction, Sir John Soane also organize activities through daily circulations. We can find many uni ue flow lines, corresponding to the uni ue lifestyle of architects. ere, customi ation becomes a reality. 39 HOUSING PRIME

3.2.2 Parallel World

Section of Soane's Museum Source: Koolhaas, Rem. Delirious New York : a Retroactive Manifesto for Manhattan / Rem Koolhaas. New ed., Monacelli Press, 1994.

n Koolhaas s metropolitan theory, each floor in a skyscraper represents a parallel world. eople s activities are organized freely in it. What we hope to achieve is to realize the rational logic behind the chaotic appearance. HOUSING PRIME 40

Design Concept, Draw by author

We accept the surrealistic landscape in the urban complex and the free layout of functions. But at the same time, we tried to organize the activities in a more democratic way. We tried to use a tunnel (wormhole) to traverse these parallel universes. 41 HOUSING PRIME

3.3 Working Strategy Site Decision

Housing Crisis

Public Facilities

Current Situation Age Homeonership Type Homeowner Vacancy Rate

Accessibility

Site 2

Security

......

Housing Price

Site N

Site Preference

Household Size

Customization Design

Research WeWork

Resource Allocation

AirBnB

Bussiness Module

Agent-based Platform

Agent-based Design

Age Needs

Activity Sequence

Data Collection

Cluster Generation

Buget

Housing Unit 1 Material

Housing Unit 2

Interior Design Decoration

......

Type Household Size Private

Housing Unit N Cluster Kitchen

Function

Community

Preference

Community Generation

Victim analyse

Mobile Terminal

Housing Generation

Design Concept

Age

Users

Site Selection

Routine Habits

Target Group

Type

Site 1

Topology

Causes

Green Space

Working

Tendency

Shared

Market

Toilet Gym

Housing Decision Factors

Built & Natural Environment

Gender

Economic

Age

Psychological & Psychosocial

Daily Rountine

Garden

Activity Sequence

Social

Shopping Center

Time and Space-time

Working Space Common Space

Socioeconomic and Health

Park Fitness

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Chapter 4

Design Process Site Decision Housing Unit Generation Living Cluster Generation Public Cluster Community Generation

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Housing Unit

Cluster

Community

Site

Housing Unit

Cluster

Community

Site

New York

London

Paris

Shanghai

LONDON

4.1 Site Decision

simulate the process of developers' site-choosing t the very beginning of our project, we need to find a suitable site for our community. he first thing we do is to map all the lands for sale with the coordinates we get from the real estate agent website by Python. HOUSING PRIME 50

Lands in London 51 HOUSING PRIME

4.1.3 Data Evaluation

AMENITY

Quantify the number of facilities around each land and get the rank of the lands.

LEISURE TRANSPORT SPORT

Facilities around Each Land 400

400

200

-200

-400

-200

Site_0

200

400

rank:

-400

-200

Site_1

200

400 -400

rank:

-200

Site_2

200

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rank:

102

-400

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Site_3

200

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rank:

-400

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Site_4

200

rank:

400 -400

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Site_5

200

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rank:

-400

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Site_6

200

rank:

400 -400

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Site_7

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rank:

-400

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Site_8

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rank:

107

-400

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400

200

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Site_10

200

rank:

400 -400

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Site_11 rank: 89

-400

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Site_12 rank: 28

-400

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Site_13 rank: 49

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Site_14 rank: 107

-400

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400 -400

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Site_15 rank: 37

200

Site_16 rank:

400

-400

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200

400 -400

Site_17 rank: 85

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Site_18

200

400

rank: 109

-400

400

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Site_20 rank: 96

-400

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Site_21 rank: 93

-400

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400 -400

Site_24 rank: 69

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Site_26 rank: 77

-400

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400 -400

Site_27 rank: 134

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Site_28

200

rank:

400

-400

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Site_29 rank: 58

-400

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Site_31

200

rank:

400

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Site_35

200

rank:

400 -400

400

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Site_40 rank: 94

-200

200

Site_41 rank:

400 -400

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Site_42 rank: 39

-200

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Site_43 rank: 113

-200

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Site_44 rank: 126

-200

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Site_47

Site_46 rank: 53

200

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rank: 110

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Site_48

200

400 -400

rank: 67

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Site_49 rank: 47

400

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400 -400

Site_51 rank: 23 HOUSING PRIME 62

-200

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Site_52 rank: 76

-400

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Site_53 rank: 135

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Site_55 rank: 63

-400

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Site_56 rank: 61

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Site_57

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rank:

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Site_58

200

rank:

400 -400

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Site_59

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rank: 108

-400

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Site_60

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rank:

400

Site_9

-400

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rank:

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Site_50 rank:

Site_19

rank: 100

Site_39 rank:

-200

Site_61

200

400

rank:

63 HOUSING PRIME

400

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Site_62 rank: 42

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400 -400

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Site_63 rank: 19

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Site_64 rank: 32

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Site_65 rank: 101

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Site_66

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rank:

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Site_67

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rank:

400 -400

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Site_68

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rank:

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Site_69

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rank:

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Site_70

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rank:

400 -400

400

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Site_72 rank:

400 -400

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Site_74 rank: 41

Site_73 rank: 90

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rank: 44

Site_75

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rank: 64

Site_76

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rank: 36

Site_77

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rank: 83

Site_78

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400 -400

rank: 62

Site_79

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Site_80 rank: 103

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Site_83 rank: 33

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Site_84 rank: 34

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Site_85

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rank: 121

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Site_86

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rank:

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rank:

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Site_89

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rank:

400 -400

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Site_90 rank: 119

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Site_92 rank: 50

400

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Site_96 rank: 116

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Site_97

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rank:

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Site_98

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rank:

400 -400

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Site_99

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rank:

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Site_100 rank: 127

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400 -400

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Site_101 rank: 54

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Site_102 rank: 117

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Site_103 rank: 80

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Site_104 rank: 73

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Site_108 rank: 123

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Site_109 rank: 79

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Site_110 rank: 38

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Site_113 rank: 11

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Site_114 rank: 27

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Site_116 rank: 45

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Site_120 rank: 115 HOUSING PRIME 64

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Site_121 rank:

400

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Site_123 rank: 10

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Site_125 rank: 16

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Site_126 rank: 78

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Site_127 rank: 81

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Site_132 rank: 43

200

400

Site_117 rank: 13

400

-400

-200

-400

-200

200

-400

-200

200

400 -400

Site_118 rank: 112

400

-200

200

400

-200

200

400

-200

200

400

Site_106 rank: 18

400

-400

200

Site_94 rank: 86

400

-400

Site_81 rank: 104

400

-400

-200

Site_71 rank: 21

-200

200

400

Site_119 rank: 87

400

Site_137 rank: 29 65 HOUSING PRIME

HOUSING PRIME 66

No. 6 Land No. 7 Land No. 8 Land No. 9 Land

No. 4 Land

uc at io

re en Sp ac

tio n C afe &B ar Su Sp pe or rM t ar ke t

Ed uc a

a Re rket st au ra Sp nt or t Ed uc at io n

Su pe rM

re en Sp ac

Source from Google Earth

ar ke t nS

re e

Source from Google Earth

l ce

ica

No. 3 Land

n ar

&B ed

M re e

afe

at io

Source from Google Earth

ke t

rM ar

Source from Google Earth

No. 2 Land

M e Ed dica uc l a Re tion st au ra nt C afe &B ar Su pe rM ar ke t

re en Sp ac

C afe &B ar Re st au Ed ran uc t at Su io n pe rM ar ke G t re en Sp ac e M ed ica l

Source from Google Earth

Ed uc at io n C afe &B ar Su Sp pe or rM t ar ke t

ke t

rM ar

re e

No. 1 Land

at io

afe

Source from Google Earth

M Su ed pe ica rM l ar ke C afe t &B ar Re st au ra nt Ed uc G atio re en n Sp ac e Source from Google Earth

re e

rM a C rke afe t &B ar Ed uc at io G n re en Sp ac e

ica

4.1.4 Screen-out Top 10 Results

Source from Google Earth

No. 5 Land

Source from Google Earth

No. 10 Land

67 HOUSING PRIME

4.1.5 Get the Potential Site Among the top 10 sites, we could specify the different advantageous aspects to suit different customers. For example, the land surrounded by more sports space is suitable for a sports fan.

HOUSING PRIME 68

69 HOUSING PRIME

4.1.5 Get the Potential Site

Proposed Site

HOUSING PRIME 70

71 HOUSING PRIME

Housing Unit

Cluster

Community

Site

4.2.1 Basic Logic Based on the household type, we proposed the standard rooms options for customers and the classified size including minimal, standard, and maximum.

Single

Couple

Couple with Children Single Parent Family Couple with Children Single Parent Family &Grandparents &Grandparents

n he tc Ki

Ro th Ba

ffi

Following the basic needs of the house, the customers could choose other functions, like the office room, bath, and kitchen, according to which, we could give an improved proposal of the house.

4.2 Housing Generation

simulate the designing process of architects According to the demands of customers, we give the proposed ideas of housing based on the different household types and our study. HOUSING PRIME 74

75 HOUSING PRIME

4.2.2 Size Classifying We classify the size of each room based on the different demands of different household types, according to our former research. Specifically, for each room, we proposed three scales, including minimal, standard, and maximum scale.

Single

Couple

Couple with Children

Single Parent Family

Couple with Children & Grandparents

Single Parent Family & Grandparents

Master Bedroom

Regular Bedroom

Study Room

Bath Room

Kitchen

Living Room

< ? < min

max Room Size Based on Housinghold Types

HOUSING PRIME 76

77 HOUSING PRIME

4.2.3 Designing Rules Connectivity Study in

Liv

The most important factor of the housing layout is connectivity between each room and others respectively.

Design Rules - Simulation

o gR

We try to simulate the housing design process in real life, by set rules by rules in grasshopper.

r do rri Co

m oo edr B r ste Ma

n che Kit

oo edr

B lar

ula

dro

e rB

om hro t a B om hro t a B

o dro

e rB

a gul Re

e ffic

If there is no living room: Master bedroom - centred layout

m oo

Connectivity

Privacy Study

g ivin

We classify the rooms to three types of space, according to the privacy study, including private space, public space, and the in-between space (service space).

r do rri Co

m oo edr B r ste Ma

n che Kit

dro

e rB

la egu

om hro t a B

o dro Be

gul

If there is living room: Living room - centred layout

om hro t a B

o dro

e rB

a gul Re

e ffic

If there is only 1 bedroom: Master bedroom f the num of bedroom One master bedroom at least

m oo

... ...

Privacy

Share - or - not We define the rooms into two types, one which could only be unshareable, another one which could choose shareable or unshareable according to customers' preferences.

m o o g R n i L iv

rB s te M a R

r u la eg l ar g u e R

m ro o ed

f the num of bedroom Corridor in need rri d C o

m o o t hr a B

o m d ro B e

l ar g u e R

o r en ch K it

m ro o ed

d B e

m ro o

m ro o at h

f the num of bath

... ...

_ 2

Basic Rules

_ 1

om Ro e c Offi

Share or Not

HOUSING PRIME 78

79 HOUSING PRIME

4.2.4 Simulation Rules Boundary - Modulization Firstly, we modularized the housing generation boundary, regarding the 4.5 X 4.5 meter square as the basic unit, to form a series of boundaries, including 4.5 X9, 9 X 9, and 9 X 13.5.

4.5

Input: Generation Boundary Wall Side Window Side Entrance Side

rooms

Locate the Rooms Roughly 4.5

rooms

Give the strength of linkage

9.0

Calculate the optimal view 9.0

9.0

rooms

Output: The House Unit Layout

9.0

13.5

rooms

Side - Specifying

en wa tra ll s nc ide e

wi nd ow en wa tra ll s nc ide e

sid e wi nd ow

en wa tra ll s nc ide e

wi n sid dow e

en wa tra ll s nc ide e

wi n sid dow e

sid e

Secondly, for each type of housing boundary, we specify two window sides and two wall sides individually, and set the entrance point, in order to make the generation process more reasonable and save the time of calculating.

Video: the Generation Process HOUSING PRIME 80

81 HOUSING PRIME

4.2.5 Layout Data - Pool According to the different household type and demands, we could generate a varity of layouts based on the basic rules to optimize the view of living room , which constituted the data pool of housing layout.

Single_L_1_MB_1_OR

Single_L_1_MB_1_OR_1

Single_L_1_MB_1_OR_1 _Ba_1

Single_L_1_MB_1_OR_1_Ba_1_K_1

Single_L_1_MB_1_Ba_1

Single_L_1_MB_1_Ba_1_K_1

Single_L_1_MB_1_K_1

Couple_L_1_MB_1_RB_1

Couple_L_1_MB_1_RB_1_OR_1

Couple_L_1_MB_1_RB_1_OR_1_Ba_1

Couple_L_1_MB_1_RB_1_Ba_1

Couple_L_1_MB_1_RB_1_Ba_1_K_1

Couple_L_1_MB_1_RB_1_K_1

CoupleWithChildren_L_1_MB_1_RB_1

CoupleWithChildren_L_1_MB_1_RB_1_ OR_1

CoupleWithChildren_L_1_MB_1_RB_1_ OR_1_Ba_1

CoupleWithChildren_L_1_MB_1_RB_1_ OR_1_Ba_1_K_1

CoupleWithChildren_L_1_MB_1_RB_1_ Ba_1

CoupleWithChildren_L_1_MB_1_RB_1_ Ba_1_K_1

SingleParentFamily_L_1_MB_1_RB_1

SingleParentFamily_L_1_MB_1_RB_1_ OR_1

SingleParentFamily_L_1_MB_1_RB_1_ OR_1_Ba_1

SingleParentFamily_L_1_MB_1_RB_1_ OR_1_Ba_1_K_1

SingleParentFamily_L_1_MB_1_RB_1_ Ba_1

SingleParentFamily_L_1_MB_1_RB_1_ Ba_1_K_1

SingleParentFamily_L_1_MB_1_RB_1_ K_1

CoupleWithChildrenAndGrandparents_ L_1_MB_1_RB_2_Ba_2_K_1

LEGEND L: Living Room MB: Master Bedroom RB: Regular Bedroom OR: ffice oom Ba: Bath Room K: Kitchen

CoupleWithChildrenAndGrandparents_ L_1_MB_1_RB_1_K_1

HOUSING PRIME 82

CoupleWithChildrenAndGrandparents_ L_1_MB_1_RB_2_OR_1_Ba_2

CoupleWithChildrenAndGrandparents_ L_1_MB_1_RB_2_OR_1_Ba_2_K_1

83 HOUSING PRIME

Feedback The customers could give their feedback then we could regenerate the layout to satisfy their specific demands. he interiour could also be disigned by themselves.

The Axonometric View of One House Unit

HOUSING PRIME 84

85 HOUSING PRIME

Housing Unit

Living Cluster

Community

Site

4.3 Living Cluster Generation simulate the designing process of architects

Basically, there 2 types of clusters, one is public clusters, another is living clusters. In the living cluster, we provide users a variety of functions to choose and classify them into different groups based on their choices.

HOUSING PRIME 88

89 HOUSING PRIME

4.3.1 Activities

Market

Grocery

Garden

Picnic

Coffee

Fitness

Pet

Playground

Exhibition

Work

HOUSING PRIME 90

91 HOUSING PRIME

4.3.3 Location Choosing This is an algorithm that helps to locate the housing units in the cluster automatically and generate the routes inside the cluster.

Shared Functions

Preferences

function

bedroom

kitchen

center

corner

washing

working

buzzing

quiet

toilet

living room

social

alone

Back private

Next share

Back private

Next share

Units Location Generator

Major road

Housing units

Minor road

Window side

Floor plan

4.3.2 Data Collection Through the application, customers are asked to input their personalities and preferences for housing. Then, designers would use the algorithm to locate housing units based on their needs. For instance, residents who enjoy socializing will stay closer to the public space. HOUSING PRIME 92

93 HOUSING PRIME

4.3.4 Aggregation Feedback

fter getting the final result of the housing units location on each floor, the aggregation will become an individual living cluster. The shared functions are located in the center, near the entrance of the living cluster. 6F

Customers are able to see the global structure of the living cluster and the circulation routes inside the cluster. According to customers' feedback, designers are required to modify the space.

2F Units Location Generator

Single

Single with Children and Grandparents

Couple

Couple with Children and Grandparents

Single with Children

Shared Functions

Couple with Children

GF HOUSING PRIME 94

95 HOUSING PRIME

4.3.5 Generation Process There are 4 steps of living cluster generation, which are location choosing, space optimization, general housing units design, and optimized housing units.

Location choosing

HOUSING PRIME 96

Space optimization

Step 1

Step 2

When the final modification of the housing units' location has been made, the next step is to optimize the public space inside the cluster in order to improve the spatial quality for residents.

The inner space in the cluster is optimized based on the circulation routes on each floor.

97 HOUSING PRIME

4.3.5 Generation Process There are 4 steps of living cluster generation, which are location choosing, space optimization, standardized housing units design, and optimized housing units.

Standardized housing units

HOUSING PRIME 98

Optimized housing units

Step 3

Step 4

Then, the customers will be provided standardized housing units, designed according to their household type, to see the overall effect in the cluster.

he final step is to provide customi ed housing units and optimized room space based on their neighbor and public space.

99 HOUSING PRIME

HOUSING PRIME 100

GF 101 HOUSING PRIME

4.3.6 Neighbours' Relationship Customers are able to check their basic housing unit information in the application, and the relationships with their neighbors and public space.

Info. : Unit No. 37 Household Type: Study Room: Bath Room: Kitchen:

Single Share Private Private

Info. : Unit No. 33 Household Type: Study Room: Bath Room: Kitchen:

Couple Private Private Private

Info. : Shared Function Function Type: Study Room, Kitchen, Gym

Info. : Unit No. 29 Household Type: Study Room: Bath Room: Kitchen:

Couple & Children

Share Private Share

Standarded Housing Units

Optimized Housing Units

HOUSING PRIME 102

103 HOUSING PRIME

4.3.7 Public Space fter finished the space optimi ation, a variety of activities can be arranged in the living cluster s public space, just like a small ecosystem. Residents are able to enjoy their spare time inside the housing unit and outside.

Public space & activities

HOUSING PRIME 104

105 HOUSING PRIME

4.3.8 Overall Effect

Info. : Living Cluster No.1 Storeys of cluster : Number of units : Amount of residents : Shared functions:

HOUSING PRIME 106

7 45 108 Kitchen, Gym, Study room, Café, Stage

107 HOUSING PRIME

4.3.8 Section Plan Here are the section plans of the living clusters with standardized housing units design compared with optimized housing units.

Section 1-1

HOUSING PRIME 108

Section 2-2

109 HOUSING PRIME

4.3.9 Renderings

HOUSING PRIME 110

111 HOUSING PRIME

4.3.9 Renderings

HOUSING PRIME 112

113 HOUSING PRIME

4.3.9 Renderings

HOUSING PRIME 114

115 HOUSING PRIME

Living Cluster

Public Cluster

Community

Site

4.4 Public Cluster Generation Circulation Organization

Working

Garden The green space goes through the entire building, which enables people a place to relax and socialize with others.

Working space is aim for our target groups, who are dedicated to their works. It is easy for them to access to.

Living Housing units is the main space for each clusters, they shared some common spaces with neighbors.

Fitness/Gym Gyms are located in each clusters and common spaces.

Market 4.4.1 Shared Functions Besides living clusters, we create some types of public clusters. They are professional for one collective function. HOUSING PRIME 118

The market/shopping center is located in the lower part of the building, providing residents daily supplies.

119 HOUSING PRIME

4.4.2 Cluster Circulation

When we try to deal with the internal relations of the cluster, our idea is to start from the circulation. After that, we arranged corresponding function rooms along the corridor. Step 1

Step 2

Step 3

Step 4

We have also developed a method for generating layouts on one floor.

Step 1

Step 2

Step 3

Step 4

Typology of Circulation

We set up different types of streamlines, corresponding to different types of functions. Only need to input the function type and volume geometric data, the computer will automatically generate the corresponding streamline. HOUSING PRIME 120

e t we introduced the influence of streamlines

121 HOUSING PRIME

4.4.2 Cluster Circulation

Input:

utline of floor Programme of rooms

Get:

Circulation of floor

Position of big rooms

Position of small rooms

Output: Layout of floor

HOUSING PRIME 122

123 HOUSING PRIME

4.4.2 Cluster Circulation

Input:

utline of floors Programme of rooms

Get:

Circulation of floors

Position of big rooms

Position of small rooms

Output: Layout of floors

HOUSING PRIME 124

125 HOUSING PRIME

4.4.2 Cluster Circulation

Input: Layout of floors

Get:

Shape of floors dig holes

Path & Stairs

Activity areas

Furniture Details

Output: Whole Project Perspective

HOUSING PRIME 126

127 HOUSING PRIME

4.4.3 Shopping Mall

Step 1

Step 2

Step 3

HOUSING PRIME 128

129 HOUSING PRIME

4.4.3 Shopping Mall

HOUSING PRIME 130

131 HOUSING PRIME

. .

ffice

Step 1

Step 2

Step 3

HOUSING PRIME 132

133 HOUSING PRIME

. .

ffice

HOUSING PRIME 134

135 HOUSING PRIME

4.4.5 Gym

Step 1

Step 2

Step 3

HOUSING PRIME 136

137 HOUSING PRIME

4.4.5 Gym

HOUSING PRIME 138

139 HOUSING PRIME

4.4.6 Daily Routine

Let's brush our teeth.

It's time to get up!

Honey, could you please give me some flour?

HOUSING PRIME 140

Taste good!

141 HOUSING PRIME

I like pasta.

Let's call it a day!

HOUSING PRIME 142

143 HOUSING PRIME

Let's go out.

Baby, hurry up!

Cheers, darling!

What a nice painting!

Dance tonight!

Nice call!

HOUSING PRIME 144

145 HOUSING PRIME

Living Cluster

Public Cluster

Community

Site

4.5 Community Generation a community defined by ralations

When we talk about communities, we focus on the relationship between functional groups. Attraction, repulsion, occupation, and order. We use physical force in nature to simulate the process of community generation.

Force Simulation Process

4.5.1 Topology Study

4.5.2 Force Simulation

opology research is the first step of our research and the foundation of the entire design. Because topology is uantifiable. nly when we reduce the possibility of the plan to an order of magnitude, the choice becomes possible.

We use physics to simulate the abstract interrelationships of planar elements. When two elements are closely connected, they should be relatively close. So we exert an attraction on them. In this way, we can get a reasonable position of the element.

HOUSING PRIME 148

149 HOUSING PRIME

4.5.2 Force Simulation

Step 1

Step 2

Step 3

Step 4

Here shows the trajectory between the elements. The small balls of different volumes represent functional volumes of different areas.

hrough physical simulation, we can finally get a reasonable position of each element HOUSING PRIME 150

Balls of different volumes represent functions of different areas. More important functions will occupy more space 151 HOUSING PRIME

4.5.3 Volume Generation

Step 1

Step 2

Step 3

Step 4

We need to modulari e the abstract small ball into a specific spatial form.

When designing this algorithm, we focused on how to avoid the coincidence problem between points due to too close distance. HOUSING PRIME 152

From Point to Box

We plug this algorithm into three dimensions and give it a specific boundary. ach color volume represents a cluster with a specific function. 153 HOUSING PRIME

4.5.4 Circulation Optimization We randomly generate dozens of results, and then use genetic algorithms to evaluate, select, and evolve solutions.

Results from genetic algorithm HOUSING PRIME 154

155 HOUSING PRIME

4.5.4 Circulation Optimization

Selection and Evolution

Total Length of Circulation

Public Cluster Circulation

Our goal is to minimize the total value of the circulation R, because the shorter the streamline, the more compact the layout and the reasonable functional arrangement. HOUSING PRIME 156

157 HOUSING PRIME

4.5.5 Public Cluster Sequence

Step 1

Step 2

Step 3

Step 4

Path of Building

Generate a plane inside the community by streamlines

We plug this algorithm into three dimensions and give it a specific boundary. ach color volume represents a cluster with a specific function.

According to the circulation automatically generated by the algorithm, the public clusters can be automatically generated in sequence one by one.

HOUSING PRIME 158

159 HOUSING PRIME

4.5.6 Green Line

Shopping Mall

Gym

ffice

Green Line

Public Floor Plan

We use the center space to put the different threads of the clusters together in order to cater to the residents' activity sequence. HOUSING PRIME 160

We return to the original concept of wormholes, garden as a unique cluster, the path formed by connecting them becomes a green line, bringing vitality to the entire building 161 HOUSING PRIME

4.5.7 Sturcture in Public Spcace

Input: Mass of each cluster

Get:

utline of each floor

Circulation of each floor

Room layout

Furniture layout

Output: The whole project

According to the circulation automatically generated by the algorithm, the floor plan can be automatically generated in sequence one floor by one floor. hen the system will automatically run to get the layout and details of everything in the whole building.

HOUSING PRIME 162

163 HOUSING PRIME

HOUSING PRIME 164

165 HOUSING PRIME

4.5.8 Renderings

HOUSING PRIME 166

167 HOUSING PRIME

HOUSING PRIME 168

169 HOUSING PRIME

HOUSING PRIME 170

171 HOUSING PRIME

4.5.9 Multi-Tower - Expansion to Urban Scale

Step 1

Step 2

Step 3

Step 4

HOUSING PRIME 172

When facing the situation of multiple towers, we connect the green space of multiple towers to form a green corridor on the urban scale.Specifically, we first use wool algorithm to integrate the paths of towers, and then generate more detailed floor plans through the method we studied before. 173 HOUSING PRIME

4.5.9 Multi-Tower - Expansion to Urban Scale

After generated the community, we want to make more project expansion to the urban scale and build a closer connection between our projects and the city.

HOUSING PRIME 174

175 HOUSING PRIME

1.1 Housing Crisis 1.2 Biggest Victim 1.2.1 Age 1.2.2 Gig Economy

Chapter 5

The App Global Concept Front End Back End Digital Twin 3D visualization

Housing Prime

PRIME

We shape our applications, then they shape us.

PRIME

Housing Prime

5.1 Global Concept Data from customer

5.1.1 Working Flow

Digital Twin

Process Conceptual/ Detailed Design Basic demand and preferences (size, function, style, community)

Construction/ Manufacturing

Site decision Housing storage

3D - Model Software

Model Based Instrctions

Prime factor

Tracking the process of construction

Media - platform Delivery

SCADA/ Cloud Service

Using/ Sustainment Tracking the process of construction

New house for customer Generally, the project would like to reshape the flow of customers to obtain their new house, and divided it into 3 sections, from conceptual design to construction and finally to using stage, for each step would follow with the digital twin. Firstly, the app could gather the data from clients and analyze them, for understanding individuals’ preferences and help them to make a decision about their housing. HOUSING PRIME 182

Secondly, based on the ideal housing created by customers, the prime fabric would be capable of construction with the Model-Based Instructure. Besides, they could also share the housing design by them to the media-platform, to create their own design community. The last is the using stage, clients would receive their new house as simple as receiving a box from Amazon, and obtain the creative living experience with advanced technology. 183 HOUSING PRIME

5.1.2 Developing a new world as a stack With the project of Seattle Central Library, Koolhaas defines the library in the information age, not just a cultural institution about books, but a place where all new and old media coexist and interact. However, it is necessary to transform the pre-internet OMA style layering to the post-internet web infrastructure stacking in the new age of media. As the diagram shows that the developing app stack aims to rebuild the relationship among users, databases, through various services technologies. For example, for the customer's side, they could deliver the request and obtain the feedback through the android frontend. To achieve this process with the Internet, it also needs the cloud server, which just like a bridge between the front-end and Database. The data from the app could have dynamic interaction with 3D software, such as Grasshopper and GISMO, according to the request from clients, finally, it could give a suitable 3d model as feedback to clients, and the preferences of customers could also be the data to support the customization design.

“Full Stack “Full StackArchitecture” Architecture”

Pre-internet OMA style layering with the developing App stack

HOUSING PRIME 184

185 HOUSING PRIME

Using Guide

Site decision part

Housing decision part, you could choose some basic information, such as the household type

5.2 Front End 5.2.1 Using Flow Then you could participate in some basic housing design, such as choosing the function, material

The goal of any interface is to be intuitive enough that it would require minimal familiarization to be reliably implemented. The using flow would be divided into four sections which customers could choose and design, and then is followed by the site decision, house decision, house design, and shared platform. The using flow would be divided into four sections, First of all, customers would get some tips for using this app, and then is follwed by the site decision, house design, and shared platform.

Finally, you could share the design to other mediaplatforms, and inspire others to join your design community Using flow

HOUSING PRIME 186

187 HOUSING PRIME

City

Location Preferences

Site Decision It should be indicated that the Back-end section, including the algorithm housing generation and Dynamic database, could be a crucial part. First of all, for site decision, Housingprime would provide the site evaluation system through GISMO, which is a free and open-source Grasshopper plugin for GIS environmental analysis. HOUSING PRIME 188

189 HOUSING PRIME

Function

Hobby

Household Type

Time

Housing Decision In addition, they could choose which function they want in their home, and if they want to share some of them with others, for instance, we would offer a collective kitchen in a cluster with many people who want to share the kitchen with others. HOUSING PRIME 190

191 HOUSING PRIME

Options

Comments

Basic Design

Housing Design Customers will receive severial options to match their data. They can choose 1-2 options they are satisfied with and give comments, such as the window location of certain rooms. Finally, the server will select the final plan for the client. Besides, clients also have chance to do some interior design by themselves. HOUSING PRIME 192

193 HOUSING PRIME

Delivery

Media-platform

Share Clients could build their own living community with a new network, share their design to mediaplatform, or find friends with similar interests in the community. HOUSING PRIME 194

195 HOUSING PRIME

Layout file <Button android:id="@+id/btn_Location"

Pop Window : A new dialog will pop up after the user clicks android:layout_height="wrap_content" android:layout_width="150dp" android:text="Location" android:layout_gravity="center" android:layout_marginTop="50dp" />

Java file mCities=new PopupWindow(view,mLocation.getWidth(), ViewGroup.LayoutParams.WRAP_CONTENT); mCities.setOutsideTouchable(true); mCities.setFocusable(true); mCities.showAsDropDown(mLocation);

@Override public void onClick(View v) { View view=getLayoutyouInflater().inflate (R.layout.acticity_location_cities,root:null); TextView textView=(TextView) view.findViewByID(R.id.tv_NY); TextView.setOnClickListener(new View.OnClickListener() { @Override public void onClick (View v) { mCities.dismiss();

5.2.2 Android Nowadays, Android is a very versatile development platform, and its market share is constantly increasing. According to Gartner (2014) data, tablet shipments in 2013 increased by 53.4%, reaching 184 million units, of which Android devices (Mobile phones, tablets, etc.) accounted for 38% of shipments. It is easy to conclude that Android development leads a considerable market share, so we would like to focus on Android development in this project.

5.3 Back End 5.3.1 Dynamic Database The ECS application scenarios of cloud servers are very extensive. They can be used as web servers or application servers alone, or they can be integrated with other Alibaba Cloud services to provide rich solutions. Users only need a laptop or a mobile phone to achieve everything they need through network services. In the past, these resources are distributed locally, but now they are on remote servers. It seems that "Cloud" could help us complete the search, storage, calculation and push work, so some people think that cloud computing is actually a service concept. Besides, we also should not lose sight of the data with different types should be stored in the corresponding databases, which give appropriate feedback from back-end to customers and allow the information from customers could help the design process. It should be argued that MySql as is the most popular Database Management, could be an appropriate solution for it. HOUSING PRIME 198

Elastic Computer Service of Alibaba 199 HOUSING PRIME

Binary Search Trees

AVL trees

B trees

MySql B + trees

Right Child < Parent < Left Child B + tree index is an implementation of B + tree in the database. It is the most common and the most frequently used index in the database. HOUSING PRIME 200

Right Child < Parent < Left Child Height difference<=1

Each node has at most m children Except for the root node and the leaf node, every other node has at least Ceil (m / 2) children. If the root node is not a leaf node, there are at least 2 children ....

There is a chain pointer between all leaf nodes Data records are stored in leaf nodes Non-leaf nodes only store key-value information

201 HOUSING PRIME

5.4 Digital Twin 5.4.1 Introduction The proposal of intelligent manufacturing has led to the transformation of manufacturing methods, and has become the commanding height of a new round of global manufacturing competition. Digital Twin as an advanced technology, is a digital expression of a physical product to facilitate deeper thinking and management of the physical world. This means that Digital Twin plays a vital role in the production line upgrade and efficiency improvement of the manufacturing industry, furthermore, in order to tackle the issue about the poor quality of housing construction, and to reduce the housing price (Schleich, 2017), the digital twin could be potential strategy. So that in this project, based on the Digital Twin, for the concept design to mass production optimization, Housing-prime would like to allow the housing generation run in the virtual way through 3D software, and the construction process would follow with housing design logic and details of the digital object. Besides, it also provides a practical way for customers to track the construction process, and provide after-sales service, such as Real-time understanding accessibility of community facilities. The diagram of Digital twins HOUSING PRIME 202

203 HOUSING PRIME

5.5 3D Visualization

5.5.1 Web Search

144 FPS (0-144)

HOUSING PRIME 204

movementSpeed

0.52

lookSpeed

0.015

Besides, visualization and editing of the model can be also achieved through this platform with the support from Three.js and OpenGL ES, and they target web and phones respectively.

144 FPS (0-144)

Frames Per Second

12 MB (10-16)

Refresh Time

0 MS (0-59)

Used internal memory

205 HOUSING PRIME

144 FPS (0-144)

movementSpeed

0.52

lookSpeed

0.015

Unit Scale

Detail

Wood

Steel

Glass

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Users can have a more intuitive understanding of their house through the human perspective function, such as materials, indoor partitions, and furniture placement. And real-time interaction allows users to make some changes to the interior according to their needs. They can easily move furniture, light-weight enclosures, and change floor materials.

207 HOUSING PRIME

144 FPS (0-144)

movementSpeed

0.52

lookSpeed

0.015

Cluster Scale

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Users can use the keyboard and mouse to move around in the web-based virtual model, to help users understand their living environment

209 HOUSING PRIME

144 FPS (0-144)

Global Scale

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movementSpeed

0.52

lookSpeed

0.015

For Global scale, you can have a macro understanding of the urban environment around your house,Such as surrounding public facilities, traffic conditions, etc.

211 HOUSING PRIME

If you want to get more information,please click on :

https://youtu.be/Q2L0C1mNNyY

Overall, human could have extension through media-platform, the internet help people reshape their life and the relationship between them and the world.The app give customers a nwe way to consider their home and even help them particapate into into the deisgn process, rather than just passively accept the plan from real-state or so-called professionals. This means that public have a chance to get the real sence of belonging of home by the result of their own work towards their house. HOUSING PRIME 214

215 HOUSING PRIME

HousingPrime by RC11, the Bartlett, 2020

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