Neuralisms Shenzhen

Andrew Witt / Robert Pietrusko

Neuralisms Shenzhen

Andrew Witt / Robert Pietrusko

Neuralisms Shenzhen

Provocations Shenzhen is China’s city of the future, a manufacturing and product development hub unlike any in the world. It is a place where innovation in the material world is increasingly enabling an entirely new vision of culture. Perhaps there is no better place to anticipate the lifestyle of the future—and how the city is integrated with the countryside—than the place where that future is being manufactured. This studio will consider AI, data science, remote sensing, and other new modes of encoding and representing landscape and architecture as the raw material for a fictional and imaginative lifestyle futurism emerging in Shenzhen. Leveraging technologies of deep learning, neural imagination, and recombinant gaming, the studio will also draw on speculative fictions to imagine types and territories for the lifestyles of Shenzhen’s coming golden age. Shenzhen is a planned city par excellence, created almost ex nihilo forty years ago from a framework of overlapping national and local mandates and policies. The result of this strict and fastidious planning logic is an archipelago of architectural, urban, and landscape mono-cultures, blocks of single types set in surprising juxtapositions to each other. Taking these individually homogeneous and collectively diverse blocks as fuel, we will embrace ways to recode, remix, and recombine these strict distinctions into hybridized and interleaved inter-territories and between-types. The projects consider the unique relationship of Shenzhen to the larger Pearl River Delta

region, playfully rethinking near and far, locality and territory. In a sense, each project becomes a network of territories shrunk to a microcosm, a specific view of the entire region distilled to an intensified block.

Faculty Researchers Andrew Witt Robert Pietrusko Studio TAs Minyong Kim Shiyi Peng Studio Participants Meric Arslanoglu Alia Bader Qiushi Deng Jorge Ituarte-Arreola Ana Gabriela Loayza Nolasco Yueheng Lu Koby Moreno Gem Chavapong Phipatseritham Zheng Ren Naksha Satish Ye Chan Shin Qiao Xu Sherly Tongtong Zhang

Contents

Preface 2

How can AI Shape the City? Mohsen Mostafavi

New Networks 72

Maps

Systems

Spatial Outcome

104

AI Applications

110

Projects

Essays 9

Neurocomputing the City Andrew Witt Neuralisms Shenzhen Andrew Witt

New Resources Introduction 22

History of Urbanism in Shenzhen

Timeline: Zoning and Planning

Artificial Intelligence Across Scales

154

Maps

166

Systems

180

Spatial Outcome

186

AI Applications

192

Projects New Nature

230

Maps

244

Systems

256

Spatial Outcome

262

AI Applications

270

Projects Conclusion

300

Conclusion

309

Colophon

Mohsen Mostafavi

Until a few years ago, all the buzz of future urbanization was about the concept of the smart city—except no one knew what that was, let alone how to make it happen. Slogans can be useful as long as some form of result matches their power as a message; otherwise, they become just another unrealized promise. Together with their crucial symbolic role, that significant capacity explains their utility for politicians across the globe—until of course, people stop believing in their promises. In any case, in its most promising version, the smart city turned out to be mainly a data-driven urban management tool with the primary goal of a more efficient mobility system. Not a bad idea at all, if only its many promoters had made a genuine commitment to its many possibilities. The conversation has now moved on and more people seem interested and intrigued by the potential of artificial intelligence (AI) as a game changer in the coming years. But again, most of the emphasis as far as the built environment is concerned seems to be placed on its operational capabilities and utility. Yet perhaps the promise of these tools is rather in how AI might help us imagine, design, and construct new environments that can best reflect the desires, needs, and values of a society. Such challenges in the relationality between design and AI are the primary goals of the latest research studio conducted at the Harvard University Graduate School of Design with support from our collaborators at AECOM Asia Pacific. The location of our research for this project is the city of Shenzhen in China. Shenzhen’s geographical position and proximity to Hong Kong, and its history of rapid growth and transformation as

How can AI Shape the City?

an advanced technology hub, make it an ideal location for this investigation. There are two key advantages in using AI as a critical component and complement to innovative forms in design and planning. The first advantage is configurational in character. The use of AI can enable a multi-scalar and multi-thematic approach to the analysis and investigation of a possible design area. Configurational knowledge involves the accumulation and juxtaposition of various forms of data and knowledge that affect a geographical territory over time. It allows future design projects to consider dynamic interrelationships—physical, operational, and environmental—between the two scales: territory and a site-specific location. The second benefit of using AI might be described as projective in character. It relates to the role of AI in shaping how designers plan, organize, and devise speculative proposals for an area in the light of the data at the territorial scale. The use of projective methods could enable the investigation, development, and potential evolution of a project and its impact analysis within the larger territory. The use of such dynamic multi-scalar approaches is by necessity contingent on various forms of disciplinary knowledge, from environmental sciences to transportation systems, from the social sciences to planning and design. It requires new forms of collaboration and affiliation between the disciplines that AI might support.

Equally valuable, an approach for elevating the citizens’ engagement and participation could also be incorporated in Shenzhen’s possible futures. What role can social media play in linking people and places, virtual and physical spaces? How can AI help shape the physical environment and make it respond to changing patterns of use over time? Making Shenzhen a flexible and responsive city is the exciting challenge that lies ahead.

Essays

Andrew Witt

Neurocomputing the City

In their 1973 article “Dilemmas in a General Theory of Planning,” theorists Horst Rittel and Melvin Webbe articulated the notion of the “wicked problem,” a highly multivariable challenge that defied a single optimal solution [2]. Architects, landscape architects, and urban designers live and breath wicked problems across experiential, spatial, and ecological scales. Yet even by those standards, contemporary urban challenges grow inexorably more interconnected and wicked, entailing ecological impacts, social needs, political pressures, spatial particularities, and myriad other externalities. These problems often sprawl across urban and even regional boundaries, implicating the city in wider world networks. Compounding these methodological challenges are the intensifying ways in which disciplines --- architecture, landscape architecture, and urban design, but also engineering, business innovation, technology, and others --- are intersecting and mutating in novel ways. These disciplinary mutations constantly expand the

Andrew Witt

The future city demands the tools of the future. Cities are negotiating invisible and turbulent forces of disruptive new technologies, ecological uncertainty, and constrained resources in realtime. The analytic tools of the past derived from exact rules and clear lines are being tested and found wanting. In their book “Realtime: Making Digital China,” Clément Renaud, Florence Graezer Bideau, and Marc Laperrouza observed that “The construction of highly predictable systems is concomitant with the rise of great uncertainties on a planetary scale.” [1] To confront challenges that are both unpredictable and nuanced, designers need a new arsenal of tools that allow them to reprogram the mind of the city itself.

Speculating on scenarios of the future city requires an omnivorous and ecumenical attitude toward both disciplines and technique. Computational encodings provide an increasingly powerful way to bridge the two. Indeed, computation is becoming a kind of moderating and modulating medium between disciplines, a common and integrating language of knowledge and method. Yet strictly analytic computational techniques such as parametric and performance-driven design are cracking under the strain of the competing and sometimes conflicting demands of contemporary urbanism. Neural networks offer a path beyond the limitations of procedural tools, and a new way to venture integrative solutions to complex urban conditions. As tools, neural networks are of a qualitatively different order than the computational techniques that have dominated design in the last twenty years.They are nonlinear and relational, ideal for synthesizing and generating highly intricate and interdependent design proposals. Their power lies in their capacity to automatically draw connections and patterns from highly complex or apparently unstructured information. Long the province of arcane corners of computer science, they have recently exploded onto nearly every aspect of human activity. Neural networks are a type of pattern recognition that is as

Neurocomputing the City

knowledge expected of designers, as working across disciplines becomes an indispensable aspect of addressing the multifaceted problems of today’s city.

The future city is a fertile context for the application of neurocomputational techniques. Cities increasingly confront systemic issues that span physical, political, and ecological boundaries and exceed individual disciplines. The speed of contemporary urban evolution also demands systems that can analyze and respond instantly to changing conditions, and that can map those conditions surgically and operationally. Neural networks allow the forensic mapping of conditions of the city from raw, unstructured satellite imagery through a process analogous to facial recognition. By applying such techniques to a corpus of monitoring imagery, the resulting database provides granular inventories of urban conditions that are more optically nuanced than typical planning surveys. Combined with generative artificial intelligence tools trained on optimal design scenarios, neurocomputation offers a new and more elastic answer to difficult urban questions.

Andrew Witt

close as possible to human visual cognition. Drawing their cues from the structure of biological vision, they are tuned and sensitized as they are exposed to sequences of images, schemas, diagrams, and other visual organizations. A new kind of statistical image processing, they are trained a posteriori on thousands of images rather than a priori on explicit rules. This means they are exceptionally adept to responding to context: they can interpret subtle contextual clues such as microclimate, differences in vegetation, apparently minor spatial differences, or even patterns of human behavior with specific and calibrated design interventions..

Certain architects are beginning to embrace the hallucinatory capacities of generative neural techniques for purely formal invention. Yet the rigorous application of neural networks analytically across multiple scales of the city in an integrated and coherent way is even newer territory. Such a practice redefines typical boundaries between disciplines. It fuses architecture, landscape, and urbanism in a symbiotic nexus with imaging, scanning and neurocomputing. To program the mind of the city, designers can now embrace new methods synthesizing data analytics, strategic planning, and spatial design to envision fundamental future transformations of urban space. Neurocomputing opens the door to not only to new cities and new urban life but, more fundamentally, to new conceptions of the very disciplines of design.

Neurocomputing the City

The accelerated feedback cycle between scanning and generating creates a new opportunity to adapt design strategies to the limitless idiosyncrasies of modern cities in perpetual transition. Zoning, once a static practice, can become continuous, fluid, and adaptive. Instead of designing specific proposals for particular sites, neural networks allow us to articulate a body of ideal scenarios that can then be adaptively prototyped across the city with new versatility and nuance.

Clément Renaud, Florence Graezer, Marc Laperrouza, “Introduction,” in Realtime: Making Digital China, Clément Renaud, Florence Graezer, Marc Laperrouza eds. (Lausanne: EPFL Press, 2020), 9. Horst W. J. Rittel and Melvin M. Webber, “Dilemmas in a general theory of planning,” Policy Sciences 4 (1973):155-169.

Andrew Witt

INTRODUCTION

The ambition of the studio is to develop techniques and proposals that show how AI can be used to enable a more localized, sustainable, and adaptable planning process. By using a combination of satellite imagery analysis and AI generative methods, planners can move beyond individual proposals to networks of related proposals across the city. Twenty-first-century cities like Shenzhen have grown into diverse, complex, and interconnected organisms. That interconnectedness brings an expanding range of constraints and demands —for new types of civic engagement, for new ways of integrating nature with the city, and for more sustainable use of resources. Instead of blunt “terrain vague” redevelopment strategies that can create enormous impacts and waste, we propose that development strategies be more calibrated to specific contexts and responsive to these interconnected demands. Big data and smart city approaches are a start but are limited. These approaches have generated a torrent of information, but not always corresponding insight. Cities need ways to not only gather data but test scenarios in a more calibrated way. Artificial Intelligence and data science approaches can create more adaptable, flexible, and localized solutions to planning and design problems. By using techniques from industrial surveillance — including methods of satellite image analysis — planners can get more precise and more localized insight into how to develop particular sites. AI can also be used generatively,

Introduction

Neuralisms Shenzhen Trans-scalar Design and Artificial Intelligence

Introduction

to create rule sets and organizations that can be easily applied to a range of sites more flexibly and easily than typical parametric approaches.

Neuralisms Shenzhen

History of Urbanism in Shenzhen

Situating Shenzhen The Pearl River Delta

Shenzhen 2,079 / KM2

Special Economic Zone 4,855 / KM2

Futian District 9,000 / KM2

Shenzhen lies at the mouth of the Pearl River Delta in Southeast China, adjacent to major cities such as Guangzhou, Hong Kong, and Macao. Unlike its neighbors, Shenzhen is the densest city in China and fifth most dense city in the world, with an average population of 6500 residents per square kilometer. This density occurred from acute growth coinciding with China’s 1979 economic liberation and Shenzhen’s 1980 designation as a Special Economic Zone. In 2010, Shenzhen’s boundary expanded to over twice its original size, claiming new territory to house its economic operations.

24 0

50 KM

1. Mumbai, India 2. Kolkata, India 3. Karachi, Pakistan 4. Lagos, Nigeria 5. Shenzhen, China 6. Seoul, South Korea 7. Taipei, Taiwan 8. Chennai, India 9. Bogota, Colombia 10. Shanghai, China

11,359 / KM2

14,350,000

9,163 / KM2

14,350,000

6,956 / KM2

9,800,000

9,046 / KM2

9,800,000

6,577 / KM2 6,391 / KM2 5,808 / KM2 5,506 / KM2

12,000,000

520 KM2 741 KM2 468 KM2

17,500,000 5,700,000 5,950,000

5,177 / KM2

7,000,000

5,135 / KM2

10,000,000

486 KM2 533 KM2

1053 KM2

372 KM2 416 KM2 520 KM2 750 KM2

Special Economic Zone District Structure of Shenzhen

Special Economic Zone 1980 1. NANSHAN - QUASI-”SILICON VALLEY”

- High tech industrial parks + Major shopping centers - Major global brands: Tencent, Baidu, Microsoft, Huawei and ZTE - One of the richest districts of Shenzhen - local output > 320 billion RMB)

2. FUTIAN - CBD + GOVERNMENTAL DISTRICT - Modern center - Convention and exhibition centers + Location of top 5 hotels - Government and municipal center + corporate and commercial centers - Futian railway station of the Guangzhou–Shenzhen–Hong Kong express rail link

3. LUOHU - HISTORIC CENTER - Land border with Hong Kong + immigration control - Old fishing village and market town - Commercial centers + markets + nightlife

4. YANTIAN - LEISURE - Natural and scenic landscape - Famous beaches & theme parks

Special Economic Zone 2010 5. BAO’AN - SERVICE - International airport - Service sector industry

- Ferry terminal - Major sports venues - Concentration of electronic factories

6. GUANGMING - FARMING - Farming district

- Tourist farming

7. LONGHUA - PRODUCTION AND MANUFACTURING - Manufacturing, trade, and information technology - North railway station of the Guangzhou–Shenzhen–Hong Kong express rail link - Residential base for residents working between Hong Kong and Futian

8. LONGGANG - MANUFACTURING - Factories

- Museum and cultural villages

9. PINGSHAN - AUTOMOBILE MANUFACTURING - Headquarters of BYD automotive : electric car and bus manufacturer

10. DAPENG - LEISURE - Scenic landscape famous for its beaches - Home to Dapeng ancient fortress

Since its establishment in 1980, the Special Economic Zone (SEZ) has been the center of Shenzhen’s urban structure. As the zone’s financial importance grew, so did its influence on urban development, planning, and policy decisions. Today Shenzhen contains 10 economic zones, each facilitating a particular market sector relating to commercial, industrial, and cultural operations. While a large majority of these sectors serve manufacturing and high-tech needs, the fastest-growing industries are resource extraction and farming.

6 8

5 10

4 3

1 2

25 KM

0.4 % - Culture Related 10.5 % - Logistics

12.7 % - Finance

37 % - High Tech

Industries 0.1 % - Primary

34 %

Resource extraction, mining and farming 41.2 % - Secondary

Manufacturing and Production

Economic Sectors

9.3 %

58.7 % - Tertiary

Service, Business and Technology

6.4 %

Urban Development Shenzhen 1964

Urban Development Tracing Shenzhen’s Development History

AGRARIAN REFORM 1950 Law established on Agrarian Reform. Land of landowners and rich farmers being re-distributed to millions of peasants

COLLECTIVIZATION OF LAND 1958

China's household registration system divides citizens into rural and urban categories, preventing large-scale population movements and ensuring social stability.

All land is collectivized and farmers are organized into the (large) People's Communes.

FOUNDING OF PEOPLE’S REPUBLIC OF CHINA

1949 Mao’s mandate to encircle cities in the countryside helped the Communist Party to seize Chiang Kai-shek’s power and found the People’s Republic of China

1952 China's first five-year plan entails a forced supply of cheap agricultural supplies to cities, although the per capita allocation remains low to discourage urbanization.

1945

1950

1955

1960

Different from any other cities in the world, Shenzhen has been meticulously planned from its inception. It is a pioneer for China’s economic opening-up, and greatly privileged from the trend of globalization and market economy in the late 20th century. Modern Shenzhen was brought to life in 1980, when the Central Committee of PRC designated Shenzhen as a “Special Economic Zone,” one of the first four cities to practice market capitalism within the planned economic model. Preferential policies, including business autonomy, taxation, foreign exchange management, and so on, make it the first portal for foreign investments. Shenzhen’s success relies on both support from the central government and its superior location—right next to Hong Kong, at the throat of the Pearl River Delta. The city started as a connection point between Hong Kong and the mainland, yet its GDP surpassed its neighbor in 2018. Nowadays, Shenzhen has the third-largest economic output, and the seventh-largest population of all chinese cities. Its economy is mainly upheld by four pillar industries: high-tech, finance, logistics, and culture. Given its past, Shenzhen has no plan of slowing down its pace in the future. The new Qianhai Modern Service Industry Cooperation Zone was established in 2010 following other industrial zones such as Shekou Industrial Zone (1979), High-Tech Industrial Development zone (1996), and Shenzhen Software Park (2001) to foster development in the service industry.

SOCIALIST PUBLIC OWNERSHIP

As it begins to move from a planned to a market economy, China's urban population is 18%.

Deng Xiaoping implements step-by-step economic reforms: "The Four Modernisations"

1982 The Constitution of 1982 provided for the 'socialist public ownership' of the means of production, which takes two forms state-ownership and collective-ownership.

1979

1983

The introduction of the "Household Responsibility System" in agriculture improves China's food security considerably.

A new wave of changes slowly grants farmers the rights to sell produce directly to markets outside their hometowns and to take up work or set up their own enterprises in the cities. This promotes migration to rural-urban areas.

1977 Hua Guofeng begins "Open Door" scheme, which is later integrated into Deng Xiao's Four Modernisations• programme.

1965

1970

1975

1980

1985

1978

Urban Development Tracing Shenzhen’s Development History

1990

The urban population is 26% of the total population

1998

The new housing reform has abandoned China's old system of linking housing distribution to jobs, with the housing market experiencing rapid growth.

NATIONAL URBANIZATION STRATEGY

LAND TAXATION 1984

2001

The 10th Five-Year Plan (2001-05) lists urbaniza as a national strategy a notes that increasing urbanization rates will transform the economic system and create virtuo cycles of sustainable socio-economic growth.

The restructuring of the tax policy affected the distribution and increase of taxes on land added. This has increased central government control over local income tax on land income.

1985

1990

1995

2000

200

URBAN HOUSHOLD REGISTRATION 2012

2005 The number of people who are not registered at their current place of residence is 150 million, representing 10% of the total population. 80% of these are migrant workers from the countryside.

The government of Shenzhen draws up a pilot regulation that requires migrant workers to receive urban household registration via an assessment program.

2013 The extent of pollution in China's urban centers is illustrated by unprecedented heavy smog. Prime Minister Li Keqiang told the annual session of national legislators and political advisors that a people-focused "New Type of Urbanization" would promote better integration of migrant workers into urban society and encourage growth.

2011 The 12th Five-Year Plan (201 1-1 5) sets out goals to meet the increasing urban population by building 36 millions of affordable housing units.

ation and

c ous

For the first time in 2011, China's urban population exceeds the rural population by 51.3 %.

2005

2010

2015

2020

2015

2035

2020

Urban Development Shenzhen Today

Shenzhen’s transformation from fishing village to economic powerhouse is not a linear trajectory. Economic development and urban planning have proceeded hand in hand influencing each other in the conception of a new metropolis. This journey has seen several big shifts in its development course, evolving with new forms and approaches. Tracing this trajectory, one can observe a fundamental change in the conception of the relationship between the part and whole of the city of Shenzhen. The nature and extent of territory of Shenzhen has been drawn and redrawn with the changing economic, social, political, and environmental relationship it shares with the Pearl River Delta and China at large. Along with this larger transformation is the change in the finer grain of the city and the building types that have emerged as a response to the quick transformations of the city. As an example, we see urban villages that have emerged as an adaptive typology to accommodate the new density and provide access to opportunity. Development impetus in various forms has led to different emergent typologies along with the larger shift in the city. With the last phase of development being fueled by innovation and new technologies, urban development has also begun embracing new approaches, technique, and technologies in imagining the city and urban life in new ways. This studio raises questions about the emerging trends in planning, with the AI technologies taking over.

Introduction

Timeline: Zoning and Planning, in Theory and Practice Evolutions and Convergences: Urban, Economic, and Technological Developments

Zoning and Planning, in Theory and Practice: Evolutions and Convergences

The individual student projects imagine a new reality taking on a facet of urban operations. While these individually question the nature of emerging urban and architectural typologies for the technology-mediated city, the larger shift in urban planning is visible as a collective pattern.

Planning and Zoning Trajectories—Past Tracing Dominant Global Trends

700

650 27.0 600

Euclidean Zoning

Redlining

Introduced in the early 20th century, dividing towns or communities into areas according to permitted land uses.

Euclidean zoning abusive tool used against minority planning develop

550 60 500

26.5

450 50

400

26.0

350

300 25.5 30 250

200 20 25.0 150

100 10

24.5

24.3

1900

1910

1920

1930

g became an d to discriminate groups in city pments.

American Civil Rights Movement

Inclusionary Zoning

Decades of racist urban planning practices was one of many forces that drove the American Civil Rights Movement during the 1950’s.

Inclusionary zoning required a share of new construction to be affordable by people with low to moderate incomes.

Form-Based Code

American Suburbanization Suburban lifestyle became much more attractive for generations surviving the hardships of the great depression and WWII, causing a mass exodus away from city centers.

"#$(%&)* "#$(+&'

"#$ %&'

World War II

Form-based code regulated land development to achieve a specific urban form, one which catered to the experience of the pedestrian instead of the automobile driver.

Introduction of Machine Learning The first instances of machine learning started during the 1950’s, but was not fully embraced by the technology center until the 1980’s.

Environmental Movement The environmental movement began in the 1970’s, primarily focusing on water and air pollutions and environmental disasters .

1940

1950

1960

1970

1980

Planning and Zoning Trajectories—Present Tracing Dominant Global Trends

700

Clustered Linear Model (1980 - 1985)

Multiple Axes Network (1985 - 1990)

650 27.0 600

550 60 500

26.5

Socialist Market Economy

An economic system and model of development based on the predom public ownership and state-owned within a market economy.

450 50

400

26.0

350

Climate Change Movement

As scientific knowledge advanced, issues relatin to the environment not only considered present conditions but also future scenarios in which the earth’s climate would be drastically altered.

300 25.5 30 250

World-Wide Web The introduction of the world-wide web made the internet publicly accessible, opening up a new domain for communication.

200 20 25.0 150

100

Shenzhen Construction Boom

As Shenzhen’s economy and population grew, construction had to keep up to meet manufacturing and housing demands.

24.5

24.3

1980

1985

1990

1995

40 Polycentric Development (post 1990)

f economic minance of d enterprises

Global Value Chain

Fin-Tech Investments

Production is broken into activities and tasks carried out in different countries, behaving as a large-scale extension of division of labor.

Technology is integrated into the offerings by financial services companies in order to improve their use and delivery to consumers.

Deep Learning Models The term “Deep Learning” was introduced by Rina Dechter, describing a subset of machine learning based on articifical nerual netwroks with representation learning.

Shift to Adaptation Planning

Smart City Movement Quickly embraced by modern culture, the internet provided new methods and data (such as big data and crowd-sourcing) to better understand how cities behaved.

A new focus on adaptation planning emerged upon realizing the limitations of mitigation and resiliency strategies.

Material Shortages After decades of extreme construction, developers in Shenzhen have begun to run out of building materials, resorting in illegal and unsafe construction practices.

2000

2005

2010

2015

2020

Planning and Zoning Trajectories—Future Tracing Dominant Global Trends

700

650 27.0 600

Generative Zoning Generative zoning allows urban planners and designers to respond to multiple conditions at once. Instead of proposing a static masterplan, generative zoning defines the rules through which the city is organized and formed. Generative zoning is also able to respond to emergent conditions by updating these parameters into the intial model.

550 60 500

26.5

Updated Log

450 50

Updated Logic 400

26.0

350

Generated Organization v. 1.0 300 25.5 30 250

200 20 25.0 150

Virtual Experiences

Digital technologies pertaining to mobility, augmented reality, and telecommunication have transformed the ways in which peopl move through and experience Shenzhen’s public spaces.

100 10

24.5

24.3

2020

2025

2030

42 Updated Logic

gic

2035

Generated Organization v. 1.2

Bio-Adaptive Infrastructures

Circular Material Futures

Rising temperatures, storm frequencies, and loss in biodiversity call for an integration of ecological systems into the city’s technical infrastructures, allowing the city to adapt to seasonal and long-term changes in climate.

Material and housing shortages force a reimagination in how Shenzhen constructs, organizes, and recycles physically occupiable spaces to accomodate changes in technology and mitigate material waste.

2040

2045

New Context

ns le

New Context

Generated Organization v. 1.1

2050

Artificial Intelligence Across Scales

Artificial Intelligence comprises a staggering array of methods, techniques, and practices that range from statistical modeling to neurosimulation. Though it draws on ancient myths and speculations of intelligent or even enchanted objects, the pragmatic science of artificial intelligence has its roots in electrical engineering research of the 1950s around neural networks. Indeed, artificial intelligence has recently become almost synonymous with neural networks, as research into their far-reaching applications has exploded in the last decade. The varieties of artificial intelligence are not all equally relevant to design, and those that promise the most impact deal with pattern recognition and generation from images. Image processing neural networks can be used both analytically and generatively in design. Analytically, neural networks can process and classify imagery, particularly satellite or perspectival photos. This allows traffic, spatial arrangements, vegetation, and other elements of the city to be precisely identified and inventoried. Other neural methods, such as style transfer and object generation, open new creative doors for design. These techniques allow designers to train a network to generate images in a certain visual style or having certain qualitative characteristics. The remarkable versatility of generative neural networks allows them to integrate disparate types of data and project solutions in synthesized and polyvalent models, to tackle urban problems that span scales.

Introduction

An Overview of AI in Design AI Tools Map

Big Data

Strategy

IOT

Computer vision

Time Series Robotics

Recommendation

Spatial Navigation Roads

Scene

Scene Network

Image Reconstruction

Object Generation

Static

Scene Reconstruction

Multi-view

Scene Generation

Stereoscope

Scene Analysis

Image Representation 2.5D Depth Perception

Generative Learning

Image Processing

Video

Information (4 + D) Moving Object 3D Image Series 3.5 D

Spatial Navigation Drones - Aerial

Object Detection

Instance Segmentation

Image Synthesis

Image 2D Text

Image Classification

Image to Text

Text to Image

Text Classification

Image Generation

Style Transfer

Image Segmentation

Natural Language Processing

Sound

Language

Artificial Intelligence Across Scales

Big Data Processing for Prediction

Natural Language Understanding

Natural Language Generations Machine Translations

Voice Recognition

MEDIUM

TASK

Detection

Segmentation

Diagram courtesy of Gia Jung and Claire Djang

Synthesis

Generation

AI Tools for Planning Yolo and Pix2Pix

Introduction

Unit of Generation

Training: Neural Network

Scanning Input: Satellite Image

X = 76.09 Y = 92.33

X = 04.20 Y = 12.16

X = 26.21 Y = 55.44

Results Geospatial Coordinates

YOLO / Identification ‘You Only Look Once’ (YOLO) is a real-time object detection system that uses deep learning to classify certain objects within an image and determine where they are located on it. Being trained on several images, YOLO is used to recognize those same objects when new images are provided by creating bounding boxes and class probabilities for those boxes. In this studio, YOLO was used as a scanning system to recognize various optically differentiated types and territories. Here, the satellite image of the city serves as the larger image field in which the detection system located certain types, qualities and organizational patterns that can be visually differentiated along with their usual data-based categorization. This system has been used to identify site locations resources and objects and their proximities to create new urban relationships. Here, we are able to achieve a fine resolution of approaching urban space, yet also able to comfortably scale the process across the entire city.

Artificial Intelligence Across Scales

Coding: Schema

Training: Neural Network

Generation: Input: New Schema

Results Satellite Image

Pix2Pix / Generation Pix2Pix is a shorthand for an implementation of a generic image-to-image translation using conditional adversarial networks. Given a training set that contains pairs of related images (“A” and “B”), a Pix2Pix model learns how to convert an image of type “A” into an image of type “B,” or vice-versa. It is a generative technique that is used to produce imagery of various kinds. In this studio, we have deployed this technique in the context of spatial planning, at various scales. Some projects use it as a proxy for organization of activities, while others have applied it to generate a simulated visual experience. Across projects, one can see a range of approaches in integrating this image-generative process with the traditional design methods. In most cases, the output is not only a one-time image; it also loops back to make the process iterative.

Unit of Generation

AI Tools for Planning Processing and Interpolation

Introduction

Unit of Generation

Image Processing

Traditional Data

New Mapping

Image Processing / Abstraction Image processing is a method for performing some operations on an image in order to get an enhanced image or to extract some useful information from it. It is a type of signal processing in which the input is an image and output may be an image or characteristics/features associated with that image. It performs the operation based on an algorithm that specifically abstracts the image. The studio projects have used this technique to deal with the raster data in new ways. Through various forms of abstraction of the image with techniques of hysteresis and pixel sort, new mapping of the existing condition is created to help analyze the context differently. This is akin to the remote sensing processes that perform visual operations on the collected data to create new cartographies.

Start State - Underlying Schema

Artificial Intelligence Across Scales

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1.0

End State - Underlying Schema

Latent Space Interpolation Image Interpolation is a process that helps the pixels to transform and rearrange when there is a change in size, distortion, or any other pixel-based transformation. Unlike the image space Interpolation, which is popular, here we have deployed the Latent space technique that creates the interpolated pixels based on what the neural network recognizes as a pattern and not the naked eye. This technique uses the Generative Adversarial Networks (GANs) to interpolate between points in latent space and generate images that morph from one start state to another end state. The studio projects create proposals that change with time from one assembly to another. This technique has been used to understand the underlying pattern of that transformation and decode transitions in optical changes of the image and corresponding spatial changes in the real space of the city. This technique has been used to simulate the various transitory states of the dynamic urban conditions.

Scanning the City YOLO Detection from Satellite Imagery

Stage 2: Identification

Optical Categories Designers rely on visual information about the city in order to interpret and imagine it. Through machine vision, designers can augment their capacity to optically scan, sort, categorize, and organize the hyper-local conditions of the city.

Villa

Bars

Construction Site

Stage 1: Scanning

Urban Village

Residential Towers

Office Towers

Introduction

Machine Vision Training Image Recognition

RGB: 0, 6, 217

RGB: 0, 180, 255

RGB: 0, 201, 255

Water

Background

Park

FAR = 0

FAR = 0.05

FAR = 0.1

RGB: 29, 255, 221

VILLA FAR = 0.4

RGB: 104, 248, 152

STADIUMS FAR = 0.7

CONSTRUCTION SITE FAR = 2.8

RGB: 184, 255, 56

LOW FAT BUILDINGS FAR = 1.8

RGB: 255, 68, 1

RGB: 255, 146, 4

URBAN VILLAGE FAR = 2.4

RGB: 29, 255, 221

PORT FAR = 0.3

RGB: 238,237,40

BARS FAR = 2.0

RGB: 246, 0, 1

RESIDENTIAL TOWERS FAR = 8

RGB: 217, 11, 124

OFFICE TOWERS FAR = 12

Optical categories FAR = 0

FAR = 12

Artificial Intelligence Across Scales

Generative Zoning New Software Methodology

Use of the raster data and optical lens as an added layer of data

Raster Data

Land use + quantity Rethinking the categories not only based on use but other properties as well

Generated Diagram Change in the unit of mapping and measurement, referring back to this idea of prototyping

Generated Scheme Effective zoning emerging from a generative process and not purely intuitive

Design Increased frequency of mapping and measurement - making the zoning process a dynamic one

Generative zoning is a process of using machine learning to automatically classify areas within the city by their visual, structural, or informational characteristics. Because it can programmatically draw on a wide range of data sources, generative zoning can be much more dynamic, flexible, and nuanced than typical zoning practices.

Artificial Intelligence Across Scales

Raster Image

Project Locations Neuralisms Shenzhen New Resources Extreme Village

New Nature Productive Boundaries Ana Gabriela Loayza Nolasco

Food Production Districts Gem Chavapong Phipatseritham

Biotechnical Transects Koby Moreno

Media Spaces Qiao Xu

Autonomous Future Incubators Meric Arslanoglu

Stock City Alia Bader + Naksha Satish

Mega Mobiles Jorge Ituarte-Arreola

New Networks

Resource Memorial Park Sherly Tongtong Zhang

Brain of apartments Yueheng Lu

Re-imagine Huaqianbei Qiushi Deng + Zheng Ren

Introduction

Ye Chan Shin

Media Spaces

Mega Mobiles

Autonomous Future Incubators

New Nature

Productive Boundaries

Food Production Districts Biotechnical Transects

Resource Memorial Park

Brain of Apartments

Extreme Village

Reimagine Huaqianbei

Texture Transition

Pix2Pix

New Objects

Grasshopper

Imagery

Processing

Form Generation

YOLO

Organizational Logic

Generation

Generative Logic

Design Operation

New Mapping

Abstraction

Resources

Identification

Sites of Intervention

New Networks

Stock City

New Resources

Artificial Intelligence Across Scales

Project Tools Software Applications

Artificial Intelligence Across Scales

New Networks

Neuralisms Shenzhen

New Nature

New Resources

Three key metaprojects, New Networks, New Resources, and New Nature, are the stage for playing out the implications of artificial intelligence at the urban scale. Each one presents distinct opportunities for integrating data, visual structure, and the larger trends of urban transformation into synthetic digital tools.

Static vs. Dynamic Planning Designing Logic instead of Form

Single Input

Data

Manual Data

Geospatial Information

Manual Data

Automated Data

Geospatial Information

Crowdsourced, Big Data

Designed Data Image Scanning

Analysis

Computation Parametric Tools

Generated

Machine Intelligence

Scale

Region

City

Static Planning Static planning proceeds in a more or less linear and sequential way from fixed inputs to defined outputs. While it may employ technological tools, it lacks the continuous feedback cycle that could make it truly responsive to the city’s evolving conditions.

Region

City

Block

District

Dynamic Planning In contrast, dynamic planning uses processes of generative zoning to create more varied, integrated, and adaptive organizational regimes. Smart technologies have been able to speed up access to certain types of information. This studio utilizes AI to create responsive mechanisms through feedback information loops that could update changes in a previously static process. Thus, planning is more dynamic in the way it interacts with the context. This studio applies tools that help us visualize elements and experiences in light of Dynamic Planning processes.

Introduction

Rel-Time Input

Parametric Design vs. Hyperprototyping Allowing the Design to Iterate on Its Own Computer Logic

Classification Human Logic

Algorithmic Rationale x = solar exposure y = tree

Annotation

Artificial Intelligence Across Scales

Imagery

Set of Rules

Parametric Method

Feedback

Generative Method Machine Intelligence

Optimized Human Logic

if sidewalk has no shade, plant a tree

Schematic

Set of Rules

Generated Imagery

Blueprint

Fixed Form

Dynamic Forms

Trees exist where there is no shade

Location, Scale, Orientation, Assembly, Texture

Parametric Design

Hyperprototyping

Shape

Parametric design has become more common as a method of design, but requires the explicit definition of specific rules. Situations outside those specific rules cannot be addressed.

Machine Intelligence

In contrast, hyperprototyping uses artificial intelligence processes to create more flexible methods of generative design that can integrate a wide range of inputs. Since formal rules don’t need to be explicitly defined, hyperprototyping has the potential to relate conditions across large and small scales in a more robust and fluid way. Applying this process at a smaller resolution but being able to deploy it across scales, this planning process creates a responsive mechanism at various scales and multiple resolutions. It uses machine intelligence derived from human logic, contextual constraints, and existing conditions to produce forms/environments that are optimized to suit dynamically changing contexts.

Each of the three initiatives of the studio—New Networks, New Nature, and New Resources—engage Shenzhen on a spectrum of interlocking scales. The initiatives influence and interconnect the worlds of media, microclimate, infrastructure, government, and more, fusing different aspects of urban life and operation in organic new relationships. New network projects connect across scales—from point locations to networks to create connected experiences. New Nature intertwines elements of nature and built environments to create sustainable habitats. And lastly, New Resources create material flows across resources at various scales—from furniture to territorial zones. To achieve this interconnection, we needed a conceptual device that could connect these diverse systems. For each initiative, there is a cross-boundary zone that stitches together disparate urban territories, ecological systems, and resources flows. The inspiration is the idea of the watershed: an area of broad and interconnected influence that ties distinct systems in a common whole. These various “sheds”—datasheds, water-sheds, and resource-sheds—become the basis for the application of AI tools to stitch together the city along new lines. Through these cross-boundary zones and the generative systems that connect them, we can develop plans that are more integrated and unified, stitching together new ecologies for the future city.

Introduction

Cross-Scalar Interventions Linking Regional, Urban, and Architectural Scales

New Nature Construction

Manufacturing Poles

New Resources

Microclimate

Mobility Networks

Media Parks

Work Transactions

Media Parks

Governance

New Infrastructure

Artificial Intelligence Across Scales

New Networks

Material-sheds

Watersheds

Data-sheds

NEW NETWORKS

Over the past 40 years, Shenzhen’s data infrastructure has allowed the city to develop at a rate previously unimaginable for cities with comparable populations and industries. While the city is closely connected to global markets, its urban fabric lacks the same level of resolution. Shenzhen’s public spaces, physically fragmented and far apart, fall short of their programmatic potential to fully engage with the energetic, complex, and fast-paced virtual operations of the city. New Networks addresses this issue by redefining, connecting, and extending the urban experience through urban and architectural designs. These interventions rework Shenzhen’s existing data infrastructure to integrate mobility networks, virtual logistics, and public spaces into novel, human-centered experiences.

Introduction

New Networks Introduction

New Networks

Mobility

Connected Experience

Logistics

Public Space

A Built Experience The realities that dictate the human urban experience are no longer limited to a static physical realm. Digital technologies influence one’s vision of, movement through, and goals within architectural and urban spaces. Designers of the built environment, therefore, can no longer successfully design form without considering this larger, more complex human experience.

NEW NETWORKS: MAPS

Global Networks Shenzhen’s Data Connectivity

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Shenzhen Intl Airport (Natl Scale 150) Thinking cities as multifunctional information machines that rise on the infrastructures with millions of networks pave the way to create alternative approaches in the design field. While Shenzhen can be identified by keywords such as “world’s tech incubator,” “Silicon Valley for hardware,” “electronics capital of the world,” and “the manufacturing hub,” the data connectivity map explains Shenzhen’s emerging characteristics and gives hints about the global position of the city.

Major Intl Airport (Natl Scale 150) Intl Airport (Natl Scale 100) Minor Intl Airport (Natl Scale 50) Undersea Telecommunication Cables Passenger Flight Paths

Shenzhen’s Data Networks Virtual Territorial Boundaries

Virtual Boundaries

Signal Strength

Low

76 0

10 KM

High

Terrain of Data Isobytes of High-Speed Data Zones

Data Hubs

Primary

Secondary

Tertiary

78 0

10 KM

AV Charging Network Emerging Mobility Nodes

Low

High

EV Infrastructure Density

TOTAL ANNUAL SALES (IN MILLIONS) 100

0 2012

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

80 0

10 KM

PHASE 01

PHASE 02

PHASE 03

EV TO TAKE 50% OF CHINA’S MARKET

LOW SPEED

PEAK HOURS HIGH SPEED

URBAN

NIGHT HOURS

SUBURBAN 2023

RURAL

2024

2025

2026

2027

INCLEMENT WEATHER 2028

2029

2030

2031

2032

2033

NEW NETWORKS: SYSTEMS

Data Connections Linking Local and Global Scales

SECONDARY STAT

DATA

VIRTUAL

TERTIARY STATION

DIGITAL INTERFACES

MEDIA

PUBLIC SPACES

AUTONOMOUS MOBILE STRUCTURES

CROSS ROADS

INFRASTRUCTURE

DRONES

MOT

PEARL RIVER DELTA

GUANDONG

PHYSICAL

GEOGRAPHY

SHENZHEN

GLOBAL

PHYSICAL CONNECTION EXISTING

PROPOSED

DATA CONNECTION VIRTUAL CONNECTION

PRIMARY STATION

PROPOSED DATA HUB

DATA MEDIA

INTERCHANGES

PORT INFRASTRUCTURE

LOGISTICS VENDING STATIONS

TORWAYS

WORLD

GEOGRAPHY

SCALE

CHINA

LOCAL

TION

Historically, Shenzhen is a city impulsed and transformed by its limital conditions. Today, it is swept by another movement where physical space is rapidly altered by means of virtual projection, from the human to the urban scale. Shenzhen therefore represents a novel system supported by access to data, connection speed, and new modes of transactions instigating new and unprecedented values in urban life. In addition, this underlying network of high-speed information brings new gadgets, devices, and tools—our coexistence with these tools is yet to be understood. New Networks capitalizes on the prevalence of data networks and seeks to give them a physical urban form, starting from the conceptualization of technology as new infrastructure and recognition of its organizational capacity. While major data center and telecommunication antennas can embody the importance of such networks, the city still needs to rearticulate its current dynamics to this new power: transportation, public space, society, and lifestyle—also charged with digital media and digital devices—will shape a new mode of urban hyper reality. This futuristic vision for Shenzhen centers data in the cultural landscape of China, in step with the hardware-centered economy that has been shaped over the last decades. Hence, data is not only an outcome of technological progress but a medium of urban and cultural liminality. In order to embrace dynamic technological production, prototyping and testing, need to go back and forth from the physical to the digital.

New Networks

Connecting Digital and Physical System Diagram

Territories Global Regional

Systems

Urban Local

Data

Port

Drones

Autonomous Mobile Structures

Media Interfaces

Logistics

Mobility

Public space

Data

Physical Stations Primary Station Infrastructure Intersections

Secondary Station Tertiary Station

Intervention Zones Redefining Urban Centers

BAO’AN Airport

Data Sheds

Shenzhen is a city with an unprecedented constant evolution. Former suburban fields and rural sites of Pearl River Delta have become part of the giant urban galaxies with rapid infrastructure development that activates intercontinental transportation corridors, energy and data networks, and special economic zones. At this point, Shenzhen creates an experimental platform for the adaptation of innovative technologies with the importance of its geolocation and its mega infrastructure. The intervention zones reflect the idea of connecting different functional centers of the city and redefining them in an interwoven system fostering interaction, entertainment, and manufacturing.

Exchange

90 C

10 KM

Flows

QIANHAI BAY

FUTIAN DISTRICT

Emerging Center

Old Center

Flows

New Hubs of New Networks

92 New Networks is a non-centric articulation of projects supported by digital networks of information that connect Shenzhen. Digital infrastructure—optic fibers and data points extend along Shenzhen’s urban fabric—making network connection available through a hierarchy of antennae. Megamobiles, media parks, and new lifestyle module factories satisfy space demands as they are dynamically distributed along the data network. Streaming, projection, and hologram communication are used to add virtual environments, allowing new ways of interaction, entertainment, and work.

Data Sheds Masses of data circulate through networks and define urban characteristics in the digital age while city dwellers place themselves at a transit point and their experience the built environment as unconscious voyager. Establishing a bridge between digital and physical elements offers exciting opportunities for innovative urban settings, which can interact with society. Through this perspective, the proposed system aims to connect different cores of Shenzhen, both physically and experientially, with an optimized network.

New Networks

New Hardscapes

BAO’AN

Exchange

Flows

QIANHAI BAY Emerging Center

Density

Port

Built blocks

FUTIAN DISTRICT Old Center

Programmatic interest

Delimitation

Data Sheds

Systems

Airport

New Hardscapes Transect B

5 KM

Data Hubs

Interactive Media Interventions

96 Mobility Elements

NEW NETWORKS: SPATIAL OUTCOME

New Networks

New Networks Project

Stock City Alia Bader + Naksha Satish

Public Space for New Media Qiao Xu

Providing the backbone data networks for Shenzhen’s future, Stock City proposes physical manifestations of virtual data transactions. As demand for data speed inexorably increases, datacenters and wireless towers move to the heart of the city. These structures grow and populate interstitial urban spaces, growing new structures to anchor next-generation data networks.

VR experiences, augmented reality, media walls, esports and more are blurring the distinction between digital and physical. When that happens, what are the implications for public spaces? This project proposes public spaces small and large as parks for new media, giving the physical public realm a window into the virtual one.

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Spatial Outcome

Mega Mobiles Jorge Ituarte-Arreola

Autonomous Future Incubators Meric Arslanoglu

Public transport has long enjoyed an iconic status in the Pearl River Delta; Hong Kong’s tramway is a notable example. Why not go bigger? Much bigger? Mega Mobiles proposes a moving entertainment district, roaming through the city and connecting it in new ways.

Shenzhen is already a leader in new mobility technologies like electric cars. As the Silicon Valley of hardware, it is an ideal place to rethink how hardware innovation happens. This project proposes reconfigurable, adaptive factories as incubators for the development of new mobility technologies.

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Spatial Outcome New Networks Project

Dynamic Structures

Media Parks

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Mega Mobiles

Autonomous Drones

NEW NETWORKS: AI APPLICATIONS

Generative Densities AI in New Networks Planning

105

Satellite

Schema

New Networks

Types and Density Optical types

Pix2Pix Training

Abstracted Schema

Density - Speed Interaction Dynamic Grasshopper

Speed Heat Map

Speed Operations

Schema

Pix2Pix Transition

Generated Condition

Intermediate Stages Transition

Pix2Pix Transition

New Schema

Start State

Pix2Pix Interpolation

End State

Set 1

Iterative Pairs

Input data layers

Schema and output

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AI Applications

Training Sets and Generated Outputs Results and loops

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NEW NETWORKS: PROJECTS

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New Networks City as Virtual and Physical Infrastructure

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113

New Networks

New Networks: Projects Experiences through Data

Stock City Alia Bader + Naksha Satish

Public Space for New Media Qiao Xu

As digital infrastructure takes on a more tangible presence in the city through data centers, relay stations, and telecommunications towers, there is an opportunity for these faceless technical elements to become playful elements of urban design. Taking the language of scaffolding in a city constantly rebuilding itself, they become a playground that mixes the physical space of the city with digital experiences.

Experientially, the most salient aspect of new public spaces may be their electronic interfaces. These can provide new ways to entertain and connect across the city, stitching together distinct places, neighborhoods, and aspects of the city.

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Projects

Mega Mobiles Jorge Ituarte-Arreola

Autonomous Future Incubators Meric Arslanoglu

Mega Mobiles combines concepts of mobility and urban experience. The constantly evolving design of the project and its daily updated routes based on neural networks reflect the quick transformation of Shenzhen while providing spontaneous experience to inhabitants of the city.

Materialization of Shenzhen’s emerging characteristics in the form of a spherical autonomous vehicle generates the backbone of the project. Taking advantage of spherical geometry provides flexible and dynamic spaces, which are ready for constant transformation in the urban context.

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Stock City Urbanism at the Speed of Data

Alia Bader + Naksha Satish

The project builds an AI tool to simulate how the invisible forces of digital finance and high-speed trading shape the visible structure of Shenzhen’s fabric. Based on this tool, we propose selective architectural interventions that provide flexible new data infrastructures for buildings and the city itself. Cryptocurrency, high-frequency trading, and e-commerce are reshaping social interactions and potentially the city itself. As the speed of transactions accelerates, urbanity is suddenly shaped by data infrastructure—optic fibers and data points. Milliseconds and nano bytes are now central to achieving economic competitiveness. At the intersection between visible and invisible forces, what are the implications on urban form and life? How can we, as designers, use these tools to shape and speculate on Shenzhen’s possible futures? The project uses emerging technologies that are already revolutionizing the way we perceive and design space. We simulate a certain transformation in the urban fabric that could be triggered based on the technology using artificial intelligence and neural network training processing. The data creates zones of advantage and disadvantage and a map of population density that constantly changes based on multiple factors. Here, we use the technological tools to make the invisible visible and produce a new schema for future development. The tool integrates values of the larger economic network into the physical structure of the city.

Right: Hybrid Drawing: Perspectival Plan & Section: Dynamic Structures that adapt density to speed of data, constantly redistribution zones of advantage & disadvantage for transactions in the city.

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GUAM

NEW YORK

LOS ANGELES

BUSAN

TOK OKYO YO

HAWAII

HONG KONG KONG

DAGUPAN FUTIAN

SHANGHAI MANILA

HO CHI MIN CITY HAIKU SINGAPORE JAKARTA DA NANG

PHUKET

BURMA

Stock City: Urbanism at the Speed of Data

SHENZHEN

MUMBAI

KERALA COLOMBO

KARACHI

DUBAI MUSCAT

DOH OHA A

BRUNEI

BODRUM CYPRUS

DJIBOUTI ADEN

JEDDAH

FRANKFURT CRETE

SICILY

BARI

ALEXANDRA SUEZ AQABA

PLYMOUTH

PARIS MARSEILLE

LONDON

BREST GIBRALTAR

KRUMHORN

ZEELAND

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SHENZHEN AIRPORT

FUTIAN

zhuhai

XIS

A NT

P LO

DACHAN PORT PROPOSED CENTER

QUIANHAI BAY CHIWAN BAY

HONG KONG

Top: Regional Plan: As Shenzhen does not have any marine optic fiber outputs, the most robust and fastest types of data cables, our project therefore aims to strengthen this infrastructure to establish new international connections with other competitive markets and reinstate its value at the global level.

Bottom: Master Plan: Our proposed intervention embraces foreseen narratives of Shenzhen’s expansion, though we attempt to requestion the meaning of a financial district in a future economy completely dependent on data. With that, our project defines a new significance to a conventional CBD, as we establish a powerful infrastructural axis.

120

Alia Bader + Naksha Satish Top: Site Plan: A New Data Hub: As a spatial outcome, we imagine the port to be redefined. The port, historically an urban edge and station for the distribution of goods, is here reimagined as a new interface between the world and the city. In our proposal, the port serves for the commodification of data in the production of transactions and services.

Bottom: Infrastructural Plan (crop): By mapping building arrays, we draw a relationship between the existing patterns of density and the layout of the proposed data infrastructure. As the pattern of replication grows, the network mutates accordingly to plug into these patterns. With this growth, in both data infrastructure and density, new points at the intersection will provide the opportunity for the erection of new structures.

121 The port as the interface for a new type of trade and the commodification of data: In this drawing, the large disk is a programmed structure that acts as an infrastructural transition between the point of output at the port with the organic edge of the city, as the data network seeps into the armature of the city.

122

123 Interstitial structures allowing for strategic expansion of occupiable space of existing buildings at a rapid pace: Sometimes these towers will hover and create a new roofscape. Other times, the towers will sit in between the interstitial spaces between buildings as per the residential block. Others will parasitically latch on to the existing structures where density is already high.

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125

Public Space for New Media An Interactive Media Park

Qiao Xu

The project proposes a strategy for adapting and activating existing public spaces into interactive digital media parks. Anticipating the future of collective entertainment, such parks augment existing ones through digital art, experiences, and games. These parks are also networked together through screens and projections, creating one megapark appropriate for China’s technology capital. The park thus becomes a new kind of interface to the city. Today, New York City has more than 500 street parks and small open squares. Though they are collectively only one-tenth of the size of Central Park, what makes them so successful is the sensible arrangement of scale and subdivision, as well as engaging small-scale public events. Although Shenzhen has a large number of public places such as the Citizen Plaza, Shenzhen Library Plaza, and the City Mall Plaza, many of them are so huge that they are not effectively used. Using a technological kit of parts, this project aims to improve the utilization of these plazas on a humanized scale while maintaining the original functions for large public gatherings. AI and neural network systems are used in the interfaces of the digital media of the park to connect the city from within and they also play important roles in the design process. Training satellite images in YOLO detects the existing enclosed urban spaces in Shenzhen, while scripted iterations generated by Hyster processing scripts can be read as a material or programmatic division.

126

Public Space for New Media: An Interactive Media Park

127 Top: YOLO and digital processing for the programmatic and spatial reconfiguration of public spaces in Shenzhen.

Bottom: Digital Park, Sectional Perspective.

128

Qiao Xu Top: The innovative space created by new media can be used to connect people and provide a new way of life. It can provide opportunities for friends and strangers to play games together. For instance, people can play virtual soccer games together from different locations. Large events can be broadcast live from media parks around the city and citizens can interact online.

Bottom: The light purple shapes are where the programs are located. The four big vertical rails are the truss columns that can hold screens, lights, and cameras. The circles are the small rail systems and movable panels. The rail system introduced in this project makes the space more flexible and adaptive. The green space under the trusses are softscape, grass and trees. The lightest area with paving strips is the open space for circulation. They are hardscape stone/concrete materials.

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Mega Mobiles An Autonomous Entertainment Project

Jorge Ituarte-Arreola

The project proposes a new kind of entertainment complex for Shenzhen: an autonomous and mobile megastructure that roams the city, connecting to existing entertainment districts as well as directly to buildings themselves. Taking its cue from the massive “megabuses” that have emerged recently in China’s cities, the project aims to introduce to Shenzhen an autonomous, adaptable, and modular urban design that infills missing programs throughout the city fabric. Using neural networks and agent-based software for autonomous mobility, the project uses aerial imagery and remote sensing to determine what is happening throughout the city. The proposal’s modular design hyperextends and attaches itself to certain nodes via routes that are calculated by remote sensing. The routes are updated as often as daily basis. It is a part of the city and fits in wherever it is required. The design is constantly evolving, changing to best suit the users of the city. It is the prototype for a network of autonomous Mega Mobile structures that simultaneously move throughout the city every day, docking themselves for service and when being underutilized.

Right: This is an AI urbanism; the Mega Mobile is not necessarily about the designer using AI, it is more so about imagining a city that has AI embedded, that begins to self-analyze parts of the city, all while operating at a time scale that has not been explored previously. Mega Mobiles is a response to the ever-changing fabric of the future city. It is the solution.

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Mega Mobiles: An Autonomous Entertainment Project

133 Top: Here we begin to establish the sites (nodes) in which a modular, autonomous structure could begin to dock itself in order to infill missing programs. These range from parks to business districts to airports, creating nodes for a temporary entertainment district.

Bottom: Here we look at the different aspects of the design. Nightlife + Fitness + Leisure + Shopping + Connections. These modular aspects can be switched in and out and fit to any site now adapted to the future and are what programs required.

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Jorge Ituarte-Arreola Top: The section diagram shows the mega mobile docked in a busy business district, connecting to the sidewalks and ground level, connecting the users of these high-rises to the entertainment center. The entertainment center provides high-tech workers access to green spaces, bars, event spaces, e-gaming, gymnasiums, and recreational spaces.

Bottom: The plan begins to explore the moving platform of the project as well as the built forms situated on top of the platform. Here you can begin to see how these forms each hold their own program; this plan shows the mega mobile moving through the city. A public plaza for spaces of higher densities.

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Shenzhen Inc. Autonomous and Agile Assembly Factory

Meric Arslanoglu

The project proposes a factory for autonomous assembly bots, self-driving delivery capsules that expand into a kit of parts for dynamic event architectures. It playfully takes the vending machine as a model, imagining the production of architectural bots on demand. It rethinks the relationship between agile development and manufacturing, and the way that assembly itself can occur with new autonomous machines. Shenzhen Inc. aims to create a way of dialogue between the urban ecologies and cutting-edge manufacturing technologies of the future. Shenzhen is well known as the “world’s tech incubator,” “Silicon Valley for hardware,” “the electronics capital of the world,” and “manufacturing hub.” Through the lens of remote sensing and neural imagination, the Shenzhen Inc. project investigates adaptation of autonomous manufacturing with the urban lifestyle and speculates about innovative packaging and assembly methodologies. The vending machine flowchart and agile management principles guide the responsive concept of the project and reflect the project’s manifestation of the constant transformation of Shenzhen. Inspired by Karl Sims’s “Evolving 3D Morphology,” the project develops an urban interface that interprets spontaneous digital dataflows from citydwellers with the integration of the vending machine workflow. It brings in new possibilities for creating adaptive and self-learning spaces to Shenzhen.

Right: Axonometric view of the Autonomous Factory shows relationships of various units, which includes an autonomous spherical drone, an adaptable kit for living in, and an autonomous manufacturing facility.

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Shenzhen Inc.: Autonomous and Agile Assembly Factory

139 Top: The sphere allows for agile development principles in spatial organization in which different units of the company can be arranged in an adjustable way.

Bottom: Sketches for understanding the spatial possibilities of spherical form.

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Meric Arslanoglu Flexibility for the reconfiguration of the factory setting is the crucial idea behind the spherical layout. In contrast to the traditional one-directional assembly line, which is highly resistant to the reconfiguration, the company embraces multidirectional ability and mobility features of the sphere for allowing flexible manufacturing layout. This layout is able to respond to changes over time.

Shenzhen Inc.: Autonomous and Agile Assembly Factory

141 Top: Spherical geometry has its advantages for mobility, efficiency in packaging, and flexible reconfiguration.

Bottom: Expansion of the spherical form with cam mechanism principles provides spatial possibilities for various functions.

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Meric Arslanoglu Expansion of the spherical autonomous vehicle creates a space for living.

143 Interaction of adaptable spaces in the urban context. The kit inside the spherical drones allows users to form a flexible habitation.

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NEW RESOURCES

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From a fishing village to the 26th most populated city in the world, Shenzhen’s transformation in just 40 years is unprecedented. However, behind the heat of construction and expansion is a massive demand for natural resources that has far outstripped what Pearl River Delta could supply. Looking to the future, it is time for Shenzhen to slow down and re-examine the costs and benefits of its development mode. This chapter, “New Resources,” exhibits a series of projects that together create new flows of materials and resources across the city to create new urban environments. Shenzhen is a city that has always adapted to change rapidly. The projects call for a series of transformations that need to be designed in a sustainable manner, to reduce waste and create a judicious resource flow. Through the proposition of New Resources, they put forth ideas for adaptive mechanisms and circular production cycles that create linkages at various scales. By integrating different flows of materials, labor and energy, the projects in this chapter call for more environmental responsibility in the era of the Anthropocene.

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Introduction

New Resources Introduction

New Resources

151

Resource Extraction

New Resources

Construction

Labor

Linear vs. Circular Resource Flow Model This chapter starts from looking at the current situation of intense global resource extraction, then enumerates problems on massive urban constructions and human resource flows between countries and cities. The three topics frame what New Resources wants to tackle in modern metropolises. In the case of Shenzhen, the three topics tightly relate to one another: The rapid growth rate of the city would never be realized without the support of massive resource extraction and huge immigrant labor inflow. Shenzhen is a perfect testing site for a new urban resource ecology.

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NEW RESOURCES: MAPS

Extracting the Earth Resource Distribution and Utilization around the Globe

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Petroleum Fields Mining Sites Extraction of natural resources has deeply altered our lifestyles and the appearance of our mother planet. For hundreds of years, economic and technological developments have largely relied on the support of available natural resources—fossil fuels, metals, minerals, and so on. This chapter joins the growing advocacy for the transformation towards a more circular, self-recovering development model.

Oil Pipelines Dredging Growth Opportunities = 500m

Resource Extraction Sand Dredging in the Pearl River Delta

1989 - 2011

1990 1988 - 2003

1985 - 2012

157

1992 - 2008

1995 - 2005 1990 - 2010

Sand is probably the most used natural material in the 21st century. It is critical to building materials, technological products, reclamations, beach nourishments, and so on. Shenzhen’s massive city-building process heavily relied on sand dredging from the PRD. Yet these dredging activities resulted in devastating environmental consequences and have been highly limited by the government since 2005. The conflict between rapid urban expansion and limited accessible construction resources in Shenzhen is an epitome of the global scale crisis.

1989 - 2005 1986 - 2011

1986 - 2005 1985 - 2008 2007 -

1987 - 2005

1992 - 2015

Quaternary Clastic Sediments Permitted Dredging Location 1992

2008 -

Permitted Dredging Period Cumulated Dredging Volume

2013 -

2012 -

Anticipated Dredging Volume

Shenzhen Built Area

Concrete Mixing Factories

1984

1994

0 - 1998

1986 - 2005 1986 - 2006

158

Shenzhen

2011 -

2008 -

50 KM

2004

2014

Geological Formation Shenzhen’s Abundant Underground Resources -1000m

159

Geological Section Jurassic Granite Unit Upper Paleozoic-Mesozoic Sedimentary Unit Quaternary Clastic Unit Upper Mesozoic Lava Flow Unit Urban Areas

Underneath the ground of Shenzhen are ample mineral resources. Many of them are excellent sources for building material, i.e. granite, marble, diabase, etc. In face of the impending resource shortage, Shenzhen has recently opened to areas to mining and geologic exploration.

500m 0m -500m -1000m

160

A‘

10 KM

A‘

Building Typologies Portraying Shenzhen 1979–2020

161

Shenzhen is a city that rebuilds itself at an incredible speed. During Shenzhen’s rapid growth in the past 40 years, its building typologies (especially residential buildings) went through several evolutions. By scanning through an aerial map of Shenzhen, we are able to identify these typologies and their likely year of construction.

0 M3

> 100,000 M3

Total Built-Up Volume

Urban Villages: 1979–2000

Slab Apartments: 1980s–1990s

- Some now illegal - 3–8 floors, no elevators, mostly brick/concrete structures

- 5–6 floors, no elevators, precast concrete structures

162 0

10 KM

High-Rises: 1990s–present

Villas: 2005–present

- Up to ~ 25 floors, multiple elevators, concrete structures

- 2–3 floors, brick/concrete structures

163

164

NEW RESOURCES: SYSTEMS

Projects in this chapter could be portrayed across different temporal and spatial scales. Yet all of them look at how new technologies could help reconfigure ways of extracting and utilizing resources in future cities. Diving into these projects, we could see that new ways of building, dismantling, and living are imagined for this city and its residents.

New Resources

167

Fluid Resources System Diagram

Scale

+ High Tech

168

Systems

Raw Materials

Geological Formation City

Recycle

City From a Grain

Hybrid With

Block

Scan

Extreme Villages

Reside In

Architecture

Manufacture

Neutral Interiors

Trash Reuse

Human Beings

Adapt To

Support

Today

Timeline

Building Material Reuse The Cycle of Sand - Concrete - Sand

169

Sand for Concrete

Marine Sand

Sand Purification

10-20mm

Shells; Chlorin

Sorting

5-10mm

0-5mm “Raw” Materials

Blasting The technology of sorting out leftover concrete from building trash and processing it for another round of usage is mature, and has been put in use in several countries. It is not a high-tech process, has moderate costs and could reduce the emission of carbon dioxide compared to extraction of new materials.

Recyclin

15% Water 10% Cement Casting

Blending

ng Process

Concrete Slab

Crushing

Feeding

Building Trash

170

171

P ea rl R iv er S ys te m

New Material Ecology Promoting a Smarter and Healthier Growth

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lF or

Tim

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ati

l eta

orm kF

Gla e ret

Co ck

Bri

Dia e

nit

nd f

ill

20 10 s

Co ns t

ct io

19 90 s

ild

Gra

This chapter of new resources proposes that a new network of material extraction, recycling, and exchange could be developed within Shenzhen. The relationship among urban materials would be reimagined, which would not only promote a healthier and smarter growth, but also inform new urban forms in multiple ways.

V ill

hris

H ig

S b

la A pa rt en t

V ill

ite

ra n

U rb

rbl G

Sh en

S ou th

an d

ar in

172

he nz he n

Q ua r tz it e

173

Urban Materiality Breaking down Shenzhen at Different Scales

The new relationship among urban materials would also inform new urban forms in Shenzhen at multiple scales: from the 6000m2 Pearl River alluvial plain to a 75m2 single apartment.

Villa

High-rise

Alluvial Plain

174

6000 m2

City 1997.74 m2

Block 0.66 km2

Building 720 m2

Interior 75 m2

Urban Village

Slab apartments

175

A New Resource Flow Model Rearrangement, Recycling, and Replacement

Instead of the single-source extraction model, the new model of urban resource flow takes into account types of substitutes, recycling of the used material, and a rational method to arrange material resources across the city. It aims to create a new material ecology that would promote a healthier and smarter growth in cities like Shenzhen.

176

177

Resource Exchange Envisioning 2020–2040

Urban Village Slab Apartments Granite Unit Diabase Unit Rock Extraction Site Building Trash Recycling Site Resource Exchange Route

2020–2040 Material resource exchange is a process that spans across years. By acquiring statistics about both buildings construction/ deconstruction schemas and viable geological resources, a thoughtful plan of resources arrangement between demand and supply could help Shenzhen to grow without impacting our planet as much.

2020 - 2025

178 0

0.5

2 KM

2025 - 2030

2030 - 2035

NEW RESOURCES: SPATIAL OUTCOME

181

New Resources

New Resources Projects

City from a Grain Sherly Zhang

Neural Interiors Yueheng Lu

This project looks specifically into the rearrangement of building trash and geological resources at an urban scale. It proposes active flows of resource exchange that enable the city to feed on itself and develop in a sustainable way.

Zooming in to a small scale, this project looks at the interior of different types of apartments in Shenzhen. It uses neural networks, advanced materials, and new hardware technology to generate a different living style.

182

Spatial Outcome

Reimagine Huaqiangbei Qiushi Deng + Zheng Ren

Urban Villages to Gardens Ye Chan Shin

This project puts its focus on the district of Huaqiangbei, the former “tech hub” of Shenzhen. Within the lens of urban materiality, it examines how the whole of a district can be “recycled” and “rearranged” to regain its vitality.

The future of Shenzhen’s urban villages is the focus of this project. It proposes a radical reuse strategy of the urban village typology and all the valuable construction materials from the dismantling process.

183

Spatial Outcome Project Proposal

Proposed Design Location

New Development

Rock Extraction Site

Urban Villages Renovation

Smart Furniture

184 Building trash Recycle

Geological Resource Extraction

NEW RESOURCES: AI APPLICATIONS

187

Single Furniture Individual Unit

Developed Unit

Data

Darkest/ Deepest

Object Response

Furniture Zone

New Resources

Scale

Intermediate Highest/ Embossed

Background

Built

Living

Furniture and Space Residence Level

Dining

Room

Unbuilt

Balcony

Circulation Only

Urban Blocks City Level

Area for Regeneration

Commercial Area

Surrounding Road

Residential Area

Green Space

Background

Generated Condition Result 1

188

AI Applications

Training Set

189

190

NEW RESOURCES: PROJECTS

193

194

195

New Resources

New Resources Projects

City from a Grain Sherly Zhang

Neural Interiors Yueheng Lu

This project proposes a new method of rearranging, recycling, and replacing existing building materials across multiple spatial and temporal scales. It also works on an urban experience park that makes visible the extraction and aggregation of natural resources to build 21st-century cities.

This project proposes that the future of interior living in Shenzhen embrace ultimate leisure and comfort by integrating new programmable materials, AI-powered responsive space configuration, and mixed-media technology. It is a reimagination of urban materiality at a personal level.

196

Projects

Re-imagine Huaqiangbei Qiushi Deng + Zheng Ren

Urban Villages to Gardens Ye Chan Shin

Based on a set of analyses via different tool kits, this project points out the fragmentation and disconnectivity of Shenzhen. Using Huaqiangbei as a site, this project utilizes available resources and materials to build a megastructure system and create new vistas for the city.

This project works on strategies for radical reuse of the urban village typology in Shenzhen. AI technologies are used to scan underlying structures of the buildings. Ultimately, the urban villages would be used as valuable construction material resources for the whole city.

197

City from a Grain The Geologic Processes of Construction

Sherly Zhang

Sand is critical not only to building materials like concrete and glass but also to silicon and other technology products. But due to overbuilding and intensive use there is a global sand crisis, and the shortage of sand is a preview of other, more severe shortages of construction materials to come. This project proposes an urban experience park that makes visible how natural resources are extracted and aggregated to build 21st-century cities. The park is a network of specific sites that selectively preserve the architecture of the recent past while excavating the geology below. It draws reciprocal connections between above ground urban developments and underground geological formations, showing that the built environment is intricately interwoven with processes that are far older than cities themselves. It aims to educate the public about the resources of the city and how they can be radically reused. Shenzhen is a city that grows and rebuilds itself at an unprecedented speed, outstripping the geologic resources that it demands such as sand and other minerals. In face of the impending resource shortage, it is also a city that has also opened areas to mining and geologic exploration. This project works on methods to rearrange, recycle, and replace existing building materials across multiple spatial scales and a temporal scale of 50 years. By revealing the hidden story of urban materiality, “City from a Grain” also advocates for more environmental responsibility in the era of the Anthropocene.

Right: Project Logic across scales

198

199 Shenzhen is a city that is rebuilding itself at an incredible speed. By scanning through the aerial image of Shenzhen, we are able to identify different residential typologies, which correspond to years of construction, and likely deconstruction. Given the active rebuilding process happening in the city, and the abundant resources buried under that city, the project envisions a typological process that rearranges building materials at the city scale to maximize efficiency and sustainability. With the assistance of neural networks, zoning patterns could also be generated at the scale of blocks that inspire future planning and design arrangements.

200

201

Towards a Material-friendly Future

“Conveyor Belt”

Architecture from the Recent Past

202

Post-extraction Landscape

The experience park is a network of three sites that selectively preserve the architecture of the recent past while excavating the geology below. When walking from one end to the other, the public would be able to draw connections between aboveground urban developments and underground geological formations, and get a better understanding of how much change humans have brought about in the era of the Anthropocene.

203

Neural Interiors Furnishing the Future

Yueheng Lu

Located in the Pearl River Delta, the high-tech industry and electronics manufacturing center has exhibited its great potential for rapid prototyping and producing modularized products. Therefore, the future of interior living in the City of Shenzhen is projected to embrace ultimate leisure and comfort by integrating new programmable materials, AI-powered responsive space configuration, and mixed-media technology. Apartment living is no longer bounded by physical space. By adding dynamic and virtual space to the traditional static rooms, the interior experience will be much more expansive and flexible. With the help of AR/VR and micro-environment controlling systems, the living room can be transformed into a virtual cinema where you spend quality time with family and friends; a bathtub could emerge in your bedroom with spectacular views as if you were bathing in the mountains of Hokkaido. Separate furniture pieces are replaced by a continuous topography of functional regions made from uniform programmable material. This new material is able to form anything from cozy seating to a smooth working surface due to its various granularity. By training multiple networks of Pix2Pix, a new interior landscape is imagined in two steps: generating the layouts of functional zones and the 3D forms of its associated furniture. This AI experiment has created a brain of the apartment that accommodates the ever-changing mind of its inhabitants.

Right: AI Generated Furniture Formed by New Programmable Material: Various seatings are automatically generated by pix2pix network pipeline, outline-depth map-modularized model.

204

Neural Interiors: Furnishing the Future

205 Top & Bottom: Imagine a New Type of Material: With the rapid development in programmable material, it is reasonable to imagine a future material with distributed electricity, water, heating & cooling system to transform traditional furniture at home.

Following page - Top: Perspective of generated furniture “transformer”: Sofa, side table, and working desk all integrated in one.

Following page - Bottom: Plan of Neural Apartment Unit.

206

Yueheng Lu

207 A speculation on the furniture occupying a self-organizing Neural Apartment Unit.

208

209

Reimagine Huaqiangbei Megastructures for Shenzhen’s Panorama

Qiushi Deng + Zheng Ren

The project proposes an analytic atlas of how humans and machines experience Shenzhen urbanism. It considers the new overlapping demands of autonomous machines with human aspirations for a future urban environment. Our project has two phases. In the first phase of research, we built a toolkit to analyze the city related to the views, like the Isovist and the building density, etc. Shenzhen’s views are fragmented and disconnected, with substantial variations by density. The toolkit will inventory human and machine views of the city, compare them, and find common ground between them. Building on this toolkit, in the second phase we design a network of structures that improve these view conditions, as well as making the city denser. We want to build a megastructure system to create a new image of Shenzhen as well as bold new vistas of the city.

Top Right: Interior collage of the experience of a new commercial megastructure.

Bottom Right: The future shopping experience is a saturated media space.

210

Reimagine Huaqiangbei: Megastructures for Shenzhen’s Panorama

211 Through computational Isovist vision analysis and typological classification, development priorities can be automatically generated.

Following page - Top: A reconceived hub of the shopping district.

Following page - Bottom: The electronics district is reimagined as a hub of media experiences.

212

Qiushi Deng + Zheng Ren

213

Urban Villages to Urban Gardens Radical Building Reuse in Shenzhen

Ye Chan Shin

This project proposes a strategy for radical reuse of the Urban Village typology in Shenzhen, slicing, deconstructing, expanding, and reconstructing it into a new network of interlocking residences and gardens. It uses AI techniques to scan the underlying structure of the buildings, slice and reassemble them based on structural logic. Ultimately, it aims to use the urban villages as a valuable construction material resource. During its explosive economic and urban development, thousands of undocumented urban villages were built in Shenzhen. Once considered illegal architecture and subject to demolition and redevelopment, the Urban Villages are now reconsidered as sustainable, essential, and hybrid places. However, the density of the urban villages has reached a peak, preventing dwellers from accessing light and nature. In this project, the urban village apartments are formally dissected into puzzle like structures, which enables us to explore spaces for light and nature. With cutting-edge technology in Shenzhen, scanning drones, construction cranes with a diamond cutting blade, and 3D printing construction, the project is not far-fetched. Like in Tangram Puzzles, the push-and-pull movement of dissected apartments creates space for the light and nature.

Right: Assembly Technique : Reassembly pushes construction techniques in Shenzhen to the max. Achieving almost-complete reuse of the heritage is possible by thinking, limiting the construction methods, and controlling the process.

214

Urban Villages to Urban Gardens: Radical Building Reuse in Shenzhen

215 Top: Sectional rendition of the recomposed space: The study of assembly shows possible variants in dynamic space composition from the existing pieces of the Urban Village. The building’s recomposition puts a new perspective on how we think of historical heritage as a material that can build a new urban fabric.

Bottom: Raw Data by Drone Scanning: With the technology of Shenzhen, massive drone scanning is possible. Drones are the most proper way to scan Urban Villages because they can fly between narrow buildings better than other scanning devices.

Following page: Data to X-ray of the Building by Pix2Pix: The X-ray training set was made by; first teaching TensorFlow to learn building facades by Pix2Pix schematic images drawn by a human. Secondly, from the schematic illustrations to the structural diagram.

216

Ye Chan Shin

217 Reassembled Urban Village: AI-generated Urban Village can save the most material and help us imagine dynamic space and the possibility for adaptive renovation.

218

219

220

NEW NATURE

223

After the establishment of Shenzhen as a Special Economic Zone (SEZ), the agricultural and industrial sectors went through dramatic transformation reflected in their processes, production trends, and territorial occupation in the city. Shenzhen’s economic and land structure turned from an agriculture-dominated one to an industry-dominated one. The city has turned into a platform and mechanism for economic development, R&D investment, and ubiquitous innovation, leading to high-quality production and particular rates of customization for the electronics market and other sectors. Now that industries have shifted from labor-intensive to technology-intensive ones, processes have become safer and eventually can be open to the public. Multiple production processes and modes of habitability underline current Shenzhen’s urban dynamics. While addressing food supply, consumer demand, maintenance, and industry innovation, a results-driven network of activities enables new relationships with the environment: resource extraction, greenhouse emissions, energy, and waste become tangible elements in the production sequence. The innovative and technology-centered context of Shenzhen along with its specific water, vegetation, geographic, and

224

Introduction

New Nature Introduction

The idea of a New Nature draws on local natural resources and technological advances to shape hybrid infrastructures to host Shenzhen’s productive, recreational, and living needs, enabled and conducted by AI generative process. These hybrids will perform in the realm of data and responsiveness, socioeconomic growth and sustainability, existing urban configurations and new territories.

New Nature

225

urbanistic conditions, calls for a reevaluation of production processes and mechanical systems. By rearranging and reconnecting traditional workflows, new modes of infrastructure can be addressed in order to transform the habitability and connectivity of Shenzhen’s land and cityscape. Climate regulation, species conservation, and sustainable production trends in the framework of a circular economy become naturalized values in the urban dynamic.

226

227

228

Introduction

Ecological Conservation

Adaptability

Sustainable Production

Resilience

The vulnerabilities of climate change put environmental, economic, and cultural systems all at risk. New Nature recognizes the inextricable links between these systems and addresses change through an adaptive design approach. Responsive biotechnical designs can accommodate physiological, ecological, and economic needs amidst future uncertainties, which are particularly exacerbated in a climate like Shenzhen’s.

NEW NATURE: MAPS

231

Global Landcover Conserved and Modified Natures

232 Intact Forest Cropland Major Waterways As global populations continue to increase, most of earth’s vegetative biomes have become highly altered in order to accommodate human needs. Intact forests, which house the majority of terrestrial biodiversity, are continually destroyed to make room for food and material crops, which now cover over 40% of earth’s land surface.

Urban Areas 5m

10 m

20 m

233

From an aerial image, Shenzhen may appear lush and green but lacks a connection between its ecological systems and built structure. Vegetated areas of land are often fragmented one and cut off from the watersheds that originally fed them. Nature has become something outside of and foreign to the urban pedestrian’s experience. During the summer, Shenzhen is primarily hot and humid, with daily highs reaching the mid-90’s and precipitation levels increasing six-fold. Climate change simply exacerbates these issues; temperatures will continue to increase and storms will become more frequent and violent. During this season, the urban heat effect is of significant health concern. Tree cover, pervious surfaces, and running water all provide opportunities for the city to create comfortable microclimates, manage stormwater, and promote biodiversity. Many of these strategies, however, are not fully realized within the urban landscape.

234

Maps

Layered Landscapes Shenzhen’s Environmental Systems

Local Watersheds Natural and Municipal Boundaries

235

Pearl River Delta

Dry Season (Oct - Mar): 45mm

Wet Season (Apr - Sept): 277mm

Stream / River District Boundary Shenzhen Boundary Watershed Catchment Boundary

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12)&%34' &,'+

236

0 1

20 KM

237

Biotopes Anthropogenic and Ecological Habitats

Stream / River Natural Areas (not Forest) Natural Areas (Forest) Buildings

Corridor Context

238 0 1

20 KM

239

Urban Heat Effect Land Surface Material and Temperature

Forested Land

Non-built Land

15oC

25oC

35oC

Land Surface Temperature

Plaza

Highway

240 0 1

20 KM

Tree-lined Street

Parking Lot

241

242

NEW NATURE: SYSTEMS

In 2006, Urban Growth Boundaries (UGB) were delimited to protect water resources and provide ecotourist circuits. Currently, an area of 974 km2 of the city is excluded from the scope of permitted urban development. UGBs imply a sense of limits between urban and natural areas; however, the density of built mass, programs, and land property generates diverse transitions from urban centers to protected natural areas along the boundaries. Even though protected natural areas were still affected by the city expansion, urban equipment reduced the vegetation land cover, impacting surrounding natural ecosystems. From this city expansion process, questions arise on the interpretation of limits and growth: how can the notion of UGBs be embodied in an invulnerable and integrative form? The idea of New Nature is developed around these boundaries by creating or reorganizing new hybrid infrastructures shaped as ecological corridors with a common landscape syntax. These transects will define the transition to nature reserves, extend into Shenzhen’s urban centers, and connect to the region’s watersheds. This is a system enabled by watershed flow along urban conditions and new programs relevant to Shenzhen’s sustainable life. The process diagram portrays the integrative nature of the New Nature ecological corridors planned to connect new infrastructure with both watersheds and urban boundaries. This diagram is also the result of industrial processes reorganized by the inclusion of technological advances, landscape manipulation, the understanding of urban fabrics as composites of energy and nature, and the integration of food production processes into community living.

New Nature

245

Stitching Ecologies Together Systems Diagram

Urban Center

New Nature

246

Systems

Active

City

Watersheds

Aquaponics

Block Cluster

Food Production Rings

Food

Horizontal Axis

Multiblock

Hydroponics

Energy Storage

Geo-textiles

Textile for food production

Textile for fashion and sports

Textile for construction

Textile for medicine and hygiene

Environmental Comfort

Electric Harvest

Vertical Integration

Biotechnical Transects

Single Block

Hydroponics

Water Reservoirs

Storm-water collection & management

Vertical and Horizontal Farming Fiber and Yarn Portfolio

Productive Boundaries

Bio-fabrication Fiber Processing

Edge Condition Boundary and Periphery

Research hubs

ly pp Su UGB , Water bodies Natural Reserve

Dynamic/ Responsive

Periphery

Hyperprototyping

Ecological Corridors Connecting Shenzhen’s Natural Resources

247

AREA 1 : Re-activation

AREA 2 : Re-development

AREA 3: Innovation and Research

B Plants Animals Plants Animals

Textiles

species

Techniques Techniques

Textiles

Natural NaturalSpecies

A water supply

Artificial material

Artificial Intelligence

NEW NATURE

Food production New Nature

Food Production

Biotechnical transects for comfort Bio-Techical Tran

248 E

10 KM

nsects for Comfort

Reservoirs

Textile Industry

Food Production

Each transect is assigned a relevant function to the city’s environmental comfort needs, production trends, and food supply. As a set of interventions in the city, they create designations for urban sectors with regard to future development trends: innovation, regeneration, and new developments.

249

New Nature

Intervention Zones

Boundary type

Urban centers

Boundaries

Urban center type

Ecological corridors perform as connectors from the urban boundaries to urban centers. They are formally embodied in block-specific redevelopments, paths, bifurcations, branches, and land extensions, responding to qualitative and quantitative features of the intervention areas. In this sense, they connect different boundary areas to different urban center types.

250

Systems

5 KM

251

New Nature Ecology Relationship between Projects

252 The ecological corridors are assembled as a continuation of the region’s watersheds, procuring their canalization and recycling along the intervention areas. They respond to a programmatic sequence of food production rings, biotechnical transects, and production boundaries. Water usage will depend on the urban condition and resource collection capacity of the applied infrastructure: water streams directly feed food areas and basins, rainwater collection is used for thermal comfort and recreation, and lagoon water recirculation is convenient for industrial processes. This system diagram explains the ecological corridor as a hybrid infrastructure shaped by natural resources and technological components around programmatic needs. Data sets on topography, heatmaps, stormwater, and density foreshadow the responsive nature of the corridor enabled by AI techniques.

253

New Nature Corridor Intervention Structure

Corridor D is located in Futian District, along Fuqian Road and extending to the north into Xiangmihu Road. Interventions are focused on the transformation of urban villas, environmental comfort ecosystems, and consolidation of urban boundaries through new programs addressing research and processing of textiles. Corridors are configured through a condition to object logic. Water collection points, bridges, electrical harvesting crops, factories, etc. such are planned or temporal physical responses to previously detected conditions as topography, radiance, rainwater, or density. Corridors will change along with detected conditions according to the AI generative process crafted for food production rings, biotechnical transects, and productive boundaries specifically.

1 Ground crops

Food Rings

2 Vertical farming building

3 Multiple ring building intersections

Vegetation corridors

Biotechnical Transects

254 5 Electrical harvesting

Rainwater collection ponds

Agricultural facades

Productive Boundaries

Rainwater collection ponds

NEW NATURE: SPATIAL OUTCOME

257

New Nature

New Nature Projects Situating the Interventions

Productive Boundaries Ana Gabriela Loayza Nolasco Territories of contrast between urban occupation (city) and vegetation-covered land (protected natural areas) are analyzed to identify both protected areas that were lost due to city expansion after 2005 and the current formal conformation of the sense of limits by the presence of built mass and porosity. Topographic and rainwater data are juxtaposed to identify possible new placements for a sustainable industry.

258

Spatial Outcome

Food Production Districts Gem Chavapong Phipatseritham

Biotechnical Transects Koby Moreno

Pattern identification is used to detect urban villas characterized in satellite imagery by irregularly outlined groups of orthogonal volumes. For Futian district, Uuban villas will be redevelopments where food rings imply a community space around which new vertical housing will be built.

A scanning method will identify urban tissue and the unused irregularities and deviations of the area’s orthogonal grid. Streets, empty fields, and bifurcations are read as flexible spaces that can sustain biotechnical and thermal regulatory infrastructure as well as ensure vegetation increase and conservation in urban spaces.

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New Nature Projects Situating the Interventions

New Nature generative AI processes consider datas agency of acting over spatial outcomes to host new dynamics. The analysis of urban conditions in urban centers and peripheral territories through density, height, mass occupation, and block configuration precede AI generative operations that transform a schematic interpretation of information into new assemblages. For instance, food production Rings are the result of finding the most suitable centers within a single block, and the productive boundaries are a reaction to topographic conditions to allocate new factories. As Artificial Intelligence generative processes translate data into objects, these temporal or permanent configurations deliver changes in the corridor’s spatial complexity, relationship with nature, and circulation paths. Similarly, spatial outcomes respond to programmatic content: from food production rings to productive boundaries, the program activates new dynamics such as community farming, educational experiences, and the consolidation of ecotourist circuits.

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Periphery Productive Boundaries

Bio-technical Transects

Watershed

Urban Condition

Community Led Farming

Conservation and environmental education Sustainable Farming

New Education Technologies Environmental Comfort

Technological Development

Ecotouristic circuit

Urban Condition Single Block

Multi-Block

Boundaries

Data Typology

Population Density

Radiance Data

Urban Tissue

Topography

UGB delimitation

Program

NEW NATURE: AI APPLICATIONS

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Generative Landscapes AI in Planning New Networks

The training sets respond to a specific logic for each project. Training sets connect existing conditions to a response type, usually in the shape of a pattern of reorganization or a regulatory object response. AI techniques and tools are applied in the identification and generation of new territory types. Hyper-prototyping introduces the combination of analytical, creative, and heuristic tasks through image processing and training sets. New Nature generative sequence consists of four common steps: land identification, abstraction, design (as a transformative logic) and generation, which target a multiplicity of land arrangements to accommodate new sustainable dynamics.

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Generative Landscapes AI in Planning for New Nature Scanned condition

Data

Food Rings Redevelopment zoning

Object response

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Food ring ground Food ring edification Food ring edification Buildings Blocks

Biotechnical Transects Heat Responsive

Temperature ++++

New vertical housing

Water basins

Temperature +++

Vegetation bridges

Temperature ++

Close to ground vegetation

Temperature +

Vegetationcovereded roads

Canopies

Productive Boundaries Expansion pattern

Buildings / blocks

Buildings

Water flow

Water collectors

Highest topographies Lowest Points

Start of expansion Production areas

Territory to protect

Territory to reorganize (UGB lost areas)

Crop Growing Facade

New Nature

AREA

Generated condition result 1

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AI Applications

Training Set

Gen 1 data responsive object

Gen 2 landscape texture

New Nature

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Food Rings Redevelopment zoning

schema Pix2Pix training

Gen 1A

Grasshopper

Interpolation generated condition

Biotechnical Transects Heat Responsive

generated condition Grasshopper

schema

Honeybee plugin

Pix2Pix training

Gen A Gen B landscape texture

Productive Boundaries Expansion pattern

topographic data rainwater data

schema

Pix2Pix training

landscape texture

training set 2 Gen A Gen B

generated condition

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AI Applications

Generated landscapes results

NEW NATURE: PROJECTS

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New Nature

New Nature Projects

Productive Boundaries Ana Gabriela Loayza Nolasco The experience of the natural reserve is imported into the tectonic aspects of the textile factories. In a human-to-object scale of interaction, crop growing cover/facades act as a vertical alignment with changing proportions that simulate an agricultural landscape but subtly and sequentially reveal the inner processes of the industry. Consequently, they are a showcase of the short and long stages of crop growing, which are delivered for processing to the continuous intensity of textile production.

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Projects

Food Production Rings Gem Chavapong Phipatseritham

Biotechnical Transects Koby Moreno

Rings provide an iconic standardized language in medium to high-density developed districts, where permanent users are encouraged to engage in the vertical food production system that surrounds urban crops and is surrounded by new highdensity housing blocks.

Transects perform in temporality according to weather, water presence, and vegetation growth, constructing a changing image of the city for the pedestrian, yet allowing reference in the start and end notions given by food production rings and urban boundary projects.

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Center / Periphery A Sustainable Textile Factory

Ana Gabriela Loayza Nolasco

The project proposes a new kind of vertically integrated textile eco-factory that combines the sustainable cultivation of textiles with their high-tech fabrication. Using both new technologies and a new approach to sustainability, the factory becomes a public showcase, with a landscape that is an experience park for industry. This textile industrial ecopark is a blockchain-connected sequence of processing raw material, building a fiber portfolio, designing, and mass-producing high-precision clothing. It fills the fashion market’s critical need for an elevated concept of integrated production. The project also addresses Shenzhen’s urban growth edges as a space to encode new manufacturing processes that combine agriculture and industry. These edges become a place to reinvent the fashion industry with environmentally responsible production chains. Using software to scan these boundaries, designers identify sites to allocate the facilities of the ‘new’ textile industry are identified. The architecture proposal trains neural networks (Pix2Pix) to produce new formal arrangements and functional clusters, which will host machinery and users in a periphery-center logic. In these clusters, the centers hold structuring activities in the production chain, and the periphery holds complementary or supporting activities. This hierarchy integrates architecture into the landscape and opens the fashion industry to ecotourism. The project connects the industry and landscape in a symbiotic artificial-natural relationship.

Right: Urban intervention at Area 1, sector A.

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Center / Periphery: A Sustainable Textile Factory

275 Top: System image: Expansion-based formal results are arranged according to the logistic needs of textile processing. The crop-growing facade performs as a cover that unifies production clusters.

Bottom: Cluster distribution and sequential layering in plan.

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Ana Gabriela Loayza Nolasco Top: Yarn portfolios and workshops.

Bottom: The center: Periphery order is applied to the factory assemblage by enclosing heavy processes and radially opening toward outer public areas using translucent materials, temporary partitions, or grilled walls.

277 Right: The location of textile production hubs along Shenzhen’s UGB is also intended to provide symbiotic entrances —between technology and agriculture— ecotourist circuits in the natural protected areas.

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Symbiotic Verticalities Urban Farms for Shenzhen

Gem Chavapong Phipatseritham

The project proposes a new kind of vertically integrated textile eco-factory that combines the sustainable cultivation of textiles with their high-tech fabrication. Using both new technologies and a new approach to sustainability, the factory becomes a public showcase, with a landscape that is an experience park for industry. This textile industrial ecopark is a blockchain-connected sequence of processing raw material, building a ‘fiber portfolio’, designing, and mass producing nigh-precision clothing. It fills the fashion market’s critical need for an elevated concept of integrated production. The project also addresses Shenzhen’s urban growth edges as a space to encode new manufacturing processes that combine agriculture and industry. These edges become a place to reinvent the fashion industry with environmentally responsible production chains. Using software to scan these boundaries, sites to allocate the facilities of the ‘new’ textile industry are identified. The architecture proposal trains neural networks (pix2pix) to produce new formal arrangements and functional clusters, which will host machinery and users in a ‘center-periphery’ logic. In these clusters, the centers hold structuring activities in the production chain, and the periphery holds complementary or supporting activities. This hierarchy integrates architecture into the landscape and opens the fashion industry to ecotourism. The project connects the industry and landscape in a symbiotic artificial-natural relationship.

Right: Situated in the center of Shenzhen’s megablocks, agricultural rings bring new vitality and resilience to urban food supply chains.

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Symbiotic Verticalities: Urban Farms for Shenzhen

281 A section of the urban farm ring stacks symbiotic aeroponic, hydroponic, and aquaponic systems.

Following page - Top: Aquaponics and IFF mimic a natural ecosystem: Exchanging the waste by-product from the fish as a food for the bacteria, to be converted into a perfect fertilizer for the plants, to return the water in a clean and safe form to the fish.

Following page - Bottom: Within the agricultural ring is community greenspace for raising small crop batches.

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Gem Chavapong Phipatseritham

283 The symbiotic farm ring is elevated above the urban groundscape.

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285

Threaded Infrastructures Biotechnical Utilities for a Resilient Shenzhen

Koby Moreno

The project proposes an extensive urban landscape system that weaves together utility infrastructure such as water, electricity, and data networks with biological structures such as root systems and water reclamation areas to create new resources for a resilient city. With the onset of increasing environmental and public health crises, single-function, static, and overly efficient infrastructure systems put large urban centers at risk. In order for Shenzhen to be more resilient, infrastructures and the utilities they facilitate must be thought of as living organisms, with the ability to change capacity, store resources, and tolerate stress. Inspired by textile manufacturing and geotextile technologies, this project imagines urban utilities, streetscapes, and civic spaces not as linear lines but interwoven fields of both technological and living elements. Formerly isolated utilities such as water, electricity, waste management, and telecommunications combined and rescaled to create a second set of resilience utilities that prioritize experience-centered human needs such as comfort, safety, and education. Constructed and biological technologies integrate according to formal and performative functions and blur the distinction between living and nonliving infrastructure.

Right: This project looks at Shenzhen through the lens of radiance, which is a measure of heat and sun exposure calculated using local weather data, building masses, topography, and tree cover.

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Threaded Infrastructuress: Biotechnical Utilities for a Resilient Shenzhen

287 Top: The placement of groundcover, canopy, and pavement types responds to the radiance analyses of each urban block in order to mitigate urban heat island effect.

Bottom: Vegetal species interweave with smart geotextiles to form a new type of urban green infrastructure, fusing biological and technological systems.

Following page: This design behaves as multilayered watersheds and micro-climate corridors that facilitate the movement, processing, and storage of electricity, stormwater, and runoff waste.

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Koby Moreno

Forested Hillsides Urban Peripheries Urban Core Coastal Edge Shenzhen Bay

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New Nature Section Organization Based on Elevation

A cascading interconnection between water reservoirs and Shenzhen Bay weaves through the heart of the city. As it does, it connects the urban periphery to a new biotechnical infrastructure at the urban core. Each of the interventions of New Nature is organized along this section. The section becomes a natural lens through which to rethink the relationship between nature and city.

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Seasonal Adaptation Responsive Spatial Design

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SEASONAL ADAPTATION

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293 Ecological infrastructures, using kinetic textiles and biological matter, are made visible and embedded into publicly accessible pedestrian paths. These Top: Sum,spaces dynamic tem est, recognize odi repereiciam and respond re commoditia to changes insus temperature, aut ut id eveles rainfall, aut-and estrum quatiur re vent qui doleni reres. biodiversity.

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Pedestrian Experience Connecting with Local Ecology

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AI techniques and tools are crafted and applied in the identification and generation of new territory types. Hyper-prototyping introduces the combination of analytical, creative, and heuristic tasks through image processing and training sets. New Nature generative sequence consists of four steps: Land identification, abstraction, design (as a transformative logic) and generation, which target a multiplicity of land arrangements to accommodate new sustainable dynamics.

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CONCLUSION

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Disciplines and tools evolve symbiotically, and each new technical innovation within design is met by corresponding cultural innovation. That evolution plays out in multiple contexts but becomes most tangible in the practice of individual designers. Today’s innovations, including data science, machine learning, and artificial intelligence, require designers to work collectively as never before. Gathering vast amounts of data, calculating the neural networks themselves, and understanding how multiple stakeholders could engage and consume such processes demand new commitments to collaboration and an open perspective on where disciplinary boundaries begin and end. A new collaboration among different parties, including governments, investors, the public, technical experts, and designers themselves can make the shifts toward a more integrated and adaptive city proposed by the studio possible. Designers have the opportunity to lead the way with a combination of innovative vision and social responsibility. Taking place at the disruptive moment of the global COVID-19 outbreak, the studio could not directly travel to Shenzhen for the typical site visit. We instead turned necessity into virtue and developed an entire way of working based on the notion of remote sensing. Gathering satellite imagery, GIS data, city planning documents, and more, we created a data proxy for the city that became the basis for our research. We developed scanning tools and techniques that created insight even at a distance. This allowed new readings of types and territories enabled by the information codified in digital imagery.

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Conclusion

The Practice of Design in the Age of Neurocomputing Neuralisms Shenzhen Studio

New Networks The modern city is crisscrossed with dozens of multiscalar networks that define urban development and the urban experience. Shenzhen is no exception. From its high-speed rail, to its new and expanding airport, Shenzhen’s infrastructure is layered and constantly evolving and is indeed what solidifies Shenzhen’s position for “the city of the future.” Due to its proximity with Hong Kong, Shenzhen serves as an interface between China and the world, and hence, highly engaged with global markets. However, due to the scale and speed of its growth, the local fabric, Shenzhen fails to engage with the dynamic and complex infrastructural systems of the city. Speculating on the future of infrastructural networks of the modern city, New Networks proposes a strengthened dialogue between the urban experience and infrastructural systems of the city. With data infrastructure as the foundation to all projects, the interventions of New Networks attempt to reorganize infrastructural hierarchies by redefining historic, governmental, and financial centers while also proposing new ones. In the process, New Networks reimagines the role of public spaces, mobility, logistics, and consumer experiences

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The new disciplinary approach fused with an embrace of technical innovation formed the basis of our three critical interventions.

New Nature The notion of nature, a culturally constructed term used to describe unchanged places outside the built environment, is entirely restructured in this studio. New Nature considers Shenzhen’s human and nonhuman requirements not as separate systems but as an interrelated and shared one. Production, cultivation, and conservation goals constitute a singular agenda, that utilizes the same land, resources, and labor. The spatial complexity in meeting both ecological and anthropogenic needs encouraged students to pursue unique generative and computational methods. Projects under this theme, designed for living, dynamic, and unpredictable systems, which forced students to ask: What does architecture and urban form look like when it must facilitate both human and nonhuman experiences? How might one work with nature to embrace its dynamism, both seasonally and in the future? Most important, how might a reintegration of our urban and natural environments shift values toward species, land, and resource conservation? New Resources Our evolving cities need new ways of understanding resources that are more resonant with our ecological responsibilities. Since the Industrial Revolution in the 18th century, humans have been relying on extractions of natural resources to improve

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Conclusion

while recognizing the potential of this new future in establishing new connections between Shenzhen and the world.

New Resources considers not only resources borrowed from nature, but also resources generated from the obsolete structures of the city itself, and how these two can be used to fuller effect with new technologies. The definition of “resource” has been greatly expanded and the model of resource utilization is portrayed as a cycle, rather than a linear trajectory. The project confronts critical questions about the city: How would a growing city metabolize itself? How would life look in a city with adaptive/responsive resource flows and programmable materials? How would designers adapt to this new material ecology? New Cities Through these three projects, across scales and across disciplines, the studio has woven disparate themes together with the tools of artificial intelligence. In doing so, it tries to create also a provisional idea of new directions for the discipline of design. Crossing borders and boundaries and exceeding the limits of the past, we will bring bright new opportunities for design and for our notion of the city itself.

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our living conditions—from the production of consumer goods to the construction of modern cities. Inefficient ways of extracting, processing, and using these materials brings hazardous consequences such as staggering waste, pollution, and intensifying climate change.

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Contributors

Andrew Witt Andrew Witt is an associate professor in Practice of Architecture at the Harvard GSD, teaching and researching on the relationship of geometry and machines to perception, design, assembly, and culture. Trained as both an architect and mathematician, he has a particular interest in a technically synthetic and logically rigorous approach to form. He is also cofounder, with Tobias Nolte, of Certain Measures, a Boston/Berlin-based design futures and technology incubator that combines imagination and evidence for scalable approaches to spatial problems. Their clients include Audi, BMW, Futurium (the German federal museum of the future), and the Dubai Futures Foundation. The work of Certain Measures is in the permanent collection of the Centre Pompidou and has been exhibited at the Pompidou, the Barbican Centre, Haus der Kulturen der Welt, Le Laboratoire, and Ars Electronica, among others. Witt’s personal work has been featured at the Storefront for Art and Architecture. In 2017 Certain Measures were finalists for the Zumtobel Award in both the Young Professionals and Applied Innovation categories. Witt is a fellow of the Canadian Centre for Architecture and the Macdowell Colony, a Graham Foundation grantee, a World Frontiers Forum Pioneer (2018), and Young Pioneer (2017), and a 2015 nominee for the Chernikov Prize. Witt has lectured widely, including at the Venice Biennale, Library of Congress, Yale, Princeton, MIT, The Bartlett, The Berlage, Stanford, UCLA, Berkeley, ETH, and EPFL, and his research has been published in venues such as Log, Project, AD, Detail, Harvard Design Magazine, FAZ Quarterly, Surface, Space, Linear Algebra and Its Applications, and Linear and Multilinear Algebra, and Issues in Science and Technology.

Witt received an M.Arch (with distinction, AIA medal, John E. Thayer Scholarship, Frederick Shelden Travelling Fellowship) and an M.Des (History and Theory, with distinction) from the GSD.

Robert Pietrusko Robert Gerard Pietrusko is an associate professor in the department of Landscape Architecture, where his teaching and research focus on geographic representation, simulation, narrative cartography, and the history of spatial data sets. His design work is part of the permanent collection of the Fondation Cartier pour l’art contemporain in Paris and has been exhibited in over 10 countries at venues such the Museum of Modern Art (MoMA), ZKM Center for Art & Media, and the Venice Architecture Biennale, among others. Prior to joining the junior faculty of the Harvard GSD, Pietrusko worked as a designer with Diller Scofidio + Renfro in New York and held research positions at Parsons Institute for Information Mapping at the New School and at Columbia University’s Spatial Information Design Lab. Pietrusko holds a bachelor of music in music synthesis (with honors) from the Berklee College of Music; a master of science in electrical engineering from Villanova University; and a master of architecture (with distinction) from the Graduate School of Design at Harvard University.

Mohsen Mostafavi Mohsen Mostafavi, architect and educator, is the Alexander and Victoria Wiley Professor of Design and Harvard University Distinguished Service Professor, and served as dean of the Harvard GSD from 2008–2019. His work focuses on modes and processes of

He was formerly the Gale and Ira Drukier Dean of the College of Architecture, Art and Planning at Cornell University, where he was also the Arthur L. and Isabel B. Wiesenberger Professor in Architecture. Previously, he was the chairman of the Architectural Association School of Architecture in London. He studied architecture at the AA and undertook research on counter-reformation urban history at the Universities of Essex and Cambridge. He has been the director of the Master of Architecture I Program at the Harvard GSD and has also taught at the University of Pennsylvania, University of Cambridge, and the Frankfurt Academy of Fine Arts (Städelschule). He is a consultant on a number of international architectural and urban projects. His research and design projects have been published in many journals, including The Architectural Review, AAFiles, Arquitectura, Bauwelt, Casabella, Centre, Daidalos, and El Croquis. His books include On Weathering: The Life of Buildings in Time (co-authored, 1993), which received the American Institute of Architects prize for writing on architectural theory; Delayed Space (co-authored, 1994); Approximations (2002); Surface Architecture (2002); Logique Visuelle (2003); Landscape Urbanism: A Manual for the Machinic Landscape (2004); Structure as Space (2006); Ecological Urbanism (co-edited, 2010 and recently translated into Chinese, Portuguese, and Spanish); Implicate & Explicate (2011); Louis Vuitton: Architecture and Interiors (2011); In the Life of Cities (2012); Instigations: Engaging Architecture, Landscape and the City (co-edited 2012); Architecture is Life (2013); Nicholas Hawksmoor: The London Churches (2015); Architecture and Plurality (2016); Portman’s America & Other Speculations (2017); and Ethics of the Urban: The City and

the Spaces of the Political (2017). Sean Chiao Sean Chiao is President, Asia Pacific, at AECOM (NYSE:ACM), the world’s premier infrastructure consulting firm delivering professional services across the project lifecycle—from planning, design and engineering to program and construction management. With over 30 years of experience in urban design and management, Mr. Chiao is responsible for leading operational performance and strategic growth in the Asia Pacific region; this includes 15,000 employees across Greater China, Southeast Asia, India, Australia, and New Zealand. Throughout his career, Mr. Chiao has played a pivotal role in the creation of awardwinning master plans for high-density new towns and the revitalization of existing urban landscapes, including the Kuala Lumpur River of Life; Delhi-Mumbai Industrial Corridor; Bonifacio Global City in Metro Manila; and Suzhou Industrial Park. In 2016, Mr. Chiao spearheaded the publication Jigsaw City, which showcases AECOM’s work and philosophies around forming new urban environments and experiences in Asian cities. An advocate for inspiring future generations of talent, Mr. Chiao orchestrated AECOM’s collaboration with Harvard Graduate School of Design. The partnership enables students to explore topical issues related to Asia’s rapid urbanization and empowers them to envision tangible, holistic solutions. Mr. Chiao is a fellow of the American Institute of Architects, a global trustee of the Urban Land Institute, and a member of Asia Society’s Executive Committee. He is also a member of Harvard University’s Master in Design Engineering (MDE) External Advisory Board.

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urbanization and on the interface between technology and aesthetics.

Colophon

Primary Investigator Mohsen Mostafavi Faculty Researchers Andrew Witt Robert Pietrusko Editor Andrew Witt Associate Editors Meric Arslanoglu, Alia Bader, Ana Gabriela

Loayza Nolasco, Koby Moreno, Naksha Satish, Sherly Tongtong Zhang Dean Sarah M. Whiting Series Coordinator Jeffrey S. Nesbit Copyeditor Elizabeth Kugler

Acknowledgments We wish to express special and heartfelt thanks to Sean Chiao, President of AECOM Asia Pacific, and the AECOM Chief of Staff, Office of the President, Asia Pacific Nancy Lin, for their insight, guidance, and support. We are particularly grateful for their provision of data and collaborative expertise in the development of the studio. We are also deeply grateful to Mohsen Mostafavi, Alexander and Victoria Wiley Professor of Design, for his insightful and wise guidance. We would also like to thank Sarah M. Whiting, Dean and Josep Lluís Sert Professor of Architecture, chairs of the respective departments of the GSD, Mark Lee of the Department of Architecture, Anita Berrizbeitia of the Department of Landscape Architecture, and Rahul Mehrotra of the Department of Urban Planning and Design, for their support of a highly multidisciplinary studio.

Image Credits The editors have attempted to acknowledge all sources of images used and apologize for any errors or omissions.

Finally, we would like to thank Michael Voligny from the Harvard Graduate School of Design Department of Development and Alumni Affairs and are appreciative of the support from Anne Mathew and Christina Burkot in the Office of Sponsored Research.

Copyright © 2020 President and Fellows of Harvard College. All rights reserved. No part of this book, text, images, and their authors may be reproduced in any form without prior written permission from the Harvard University Graduate School of Design.

Harvard University Graduate School of Design 48 Quincy Street Cambridge, MA 02138

Neuralisms Shenzhen - Harvard GSD Option Studio Report

Articles inside

Colophon

How can AI Shape the City?

Projects

Neurocomputing the City

Projects

Conclusion

Projects

Spatial Outcome