Portfolio | Ting Zhang | 2020 by Ting Zhang

STORIES

2 0 1 3 - 2 0 2 0

TING ZHANG

_TITLE

B e gi n s t h e s t o r y . . .

_PROLOGUE

I like stories, those which tell how people meet each other and form their relationships, and I believe that the physical environment plays the essential role as the stage for those romantic plots. Coincidence or preordination is driven by the interconnection of several daily routines, and the points of those intersections are urban public spaces which accommodates peopleâ&#x20AC;&#x2122;s everyday life. Designing space is designing peopleâ&#x20AC;&#x2122;s lives.

I have always been thinking that designers are acting like entrepreneurs, trying to find the balance of interests from different groups and seek an optimal solution: we organize urban space, provide possibilities for people to build connections with other objects, and the process of design resembles a social practice, which puts every aspect of the society into consideration.

STORIES

TING ZHANG

_ TA B L E O F CO N T E N TS

My Stories... URBAN

DESIGN

SEEDING THE MACHAMBA

p. / 03

DISPERSING WELLNESS

p. / 31

WASTE FRONT

p. / 49

ENCOUNTER MARKET

p. / 61

ARCHITECTURE

UPHILL EXPLORATION

p. / 77

TRICK OF SIGHT LINE

p. / 91

AFLOAT SANCTUARY

p. / 103

SHELLS ON THE SHORE

p. / 115

KIOSK ON CAMPUS

p. / 125

RECIPROCAL STRUCTURE

p. / 129

RESEARCH

EXTRACTIVE URBANISM

p. / 135

INFLUENZA ACTIVITIES

p. / 173

STARBUCKS EFFECT

p. / 195

BIKE CAMPAIGN IN NYC

p. / 221

LIFE ON VEGETABLE LEAVES

p. / 225

URBAN DESIGN

Photo By Joy You-Chiao Wu

SEEDING THE MACHAMBA

Studio

Urban Design Studio III - Water Urbanism: The Great Rift Valley

Semester

Spring 2020, GSAPP, Columbia University

Location

Beira, Mozambique

Instructor

Kate Orff, Geeta Mehta, Thaddeus Pawlowski, Lee Altman, Dilip Da Cunha, Julia Watson, Adriana Chavez

Team

Joy You-Chiao Wu, Jaime Palacios Anaya, Ashwin Nambiar, Xinyue Liu

Role in Team

Research, Conceptual Design, Modeling, Rendering, Diagram

Link

https://storymaps.arcgis.com/stories/ f27ab8fa6c294c9ebb67e261d191f5b7

The city of Beira has an extensive and integrated system of traditional agriculture that is under threat. Our project conceives of this system as more than just agriculture - it is a productive and preventative flood infrastructure. We envision that this agricultural system could coordinate communities, organize the city, and be the key to recovery and ongoing resilience. The goals of this project are followed: Consolidating and organizing cooperatives at a city scale; Protecting social and ecological capital; Empowering women in agriculture; Diversifying income and create job opportunities; Integrating adaptive, nature-based infrastructure.

Photo By Jaime Palacios Anaya

URBAN DESIGN

FLOODING AS A CONSEQUENCE OF UNPLANNED SPRAWL

Urban Expansion into Low-Lying Agricultural Land 1942

1975

Since Mozambican Civil War, there is a rapid population growth along with urban expansion in the city of Beira. The fact that these settlements located in the relative lowland made them more vulnarable to flooding, when cyclones visit here regularly.

1990

2015

RESETTLEMENT PLAN DISREGARDING PEOPLE’S LIVELIHOODS

Resettlement

SEEDING THE MACHAMBA

Currently, the resettlement plan defines “risk zones” and resettles the people to further inland. Of the 71 sites assessed, 82% are located in Sofala and Manica provinces which represent 85% of the displaced population.

Fishery As Main Livelihoods

Relocating the people eventually results in them migrating back into the “risk zones” because of its proximity to the sea and the central business district locally known as the Baixa.

Small Business In The City

LACK OF ELEVATIONAL PROGRAMMING

URBAN DESIGN

Beira 2020

SEEDING THE MACHAMBA

Beira 1990s

THE ROOTS OF AGRICULTURAL GROUNDS

Since before the colonial times, small scale agriculture has been embedded in the livelihoods of the people from Beira. In 1987, Office of Green Zones recognized 10 agricultural neighborhoods, 88% of the agricultural land in the city was considered a machamba. People started organizing in women led cooperatives. In 1990, the General Union of Coops became an independent Coop Company. Lack of support from the government disincentivized members to work together, but their agricultural practices remained in their machambas.

MACHAMBA AS A

MAÂˇCHAMÂˇBA Agricultural garden, where produce is cultivated feminine noun [Mozambique]

(Swahili mashamba, plural of shamba, farm, plantation, cultivated land, field)

by a family mainly for self-consumption.

AN OPPORTUNITY

MACHAMBA AS A WATER-HOLDING SYSTEM

Elevational Strategies

• • •

High grounds are equipped with public programs and accommodating more people. Low grounds are designated as productive agriculture and preventative water retention lands. People in low grounds retreat to safe zones in the nearby highlands.

URBAN DESIGN

These strategies can only be implemented if they are community driven. Co-op could be the trigger point of the process. Machamba CO-OPS Based on this water-holding system we propose three community based organizing frameworks to facilitate urban transformation according to different social contexts. The overall objective is to strategically retreat to safer zones combined with community empowerment and economic development.

• • •

High Ground Coop - Provide welfare facilities / densify housing Low Ground Coop - Increase agricultural production and manage water holding systems. Mid Ground Coop - Boost economy by scaling agriculture / aquaculture production.

SEEDING THE MACHAMBA

Photo By Joy You-Chiao Wu

SEEDING THE MACHAM

COMMUNITY

CENTER

MARKET

APPLY FOR FUNDING

HIGH GROUND CO-OP

CO-OP COORDINATOR

CONSTRUCTIO UNION

RETAIL UNION

URBAN DESIGN

GOVERNMENT

ORGANIZE

MARKET STREET Supporting agricultural production Providing construction materials

COMMUNITY CENTER Providing agro-training, seed banks and construction training

ELEVATED

Equipped wit store water in

MBA IN HIGH GROUND

NEIGHBORHOOD

MACHAMBA

TECHNICAL SUPPORT

NEIGHBORHOOD KITCHEN

AGRICULTURE UNION

NGO

SEEDING THE MACHAMBA

HOUSING

th rain water harvest system to n the foundation

MACHAMBA GUIDE LINE Organizing new housing in the surrounding

NEIGHBORHOOD KITCHEN Using biogas and other green energy

DRAINAGE Draining overflow to retention areas

URBAN DESIGN

NETWORK OF THE MACHAMBA

DRY SEASON Neighborhood machamba as the agricultural social space for self sufficiency

A SYSTEM IN HIGH GROUND

SEEDING THE MACHAMBA

RAIN SEASON

EXTREME CONDITION

Machambas working as a water retention system

Community center as the safe gathering point, providing food, emergency healthcare, boats for transportation, as well as for protecting the seeds, tools.

Photo By Joy You-Chiao Wu

SEEDING THE MACHA

NEIGHBORHOOD

MACHAMBA

SHIFT HOUSING TO HIGH GROUND

CONSTRUCTION UNION

URBAN DESIGN

HIGH GROUND CO-OP

IFT TO HIGH GROUND RS S H E M R -FA NON

AGRICULTURE

MOUNDS Building mounds near the existing neighborhood machamba to organize new housing

MULTIPURPOSE GROUND FLOOR Used for raising livestock or boat storage

AMBA IN LOW GROUND

E UNION

AGRO-TRAINING HUB / MARKET

ORGANIZE

TECHNICAL SUPPORT

CO-OP COORDINATOR

LOW GROUND CO-OP

NGO

APPLY FOR FUNDING

GOVERNMENT

AGRO-TRAINING HUB

SEEDING THE MACHAMBA

HOUSING SHIFT T O MO U NDS

PRODUCT STORAGE & TRANSPORTATION

INTEGRATED AGRI-AQUACULTURE Equipped with proper irrigation system

URBAN DESIGN

NETWORK OF THE MACHAMB

DRY SEASON Machambas working as productive agriculture and providing food to local people

BA SYSTEM IN LOW GROUND

SEEDING THE MACHAMBA

RAIN SEASON Machambas working as water retention for the city of Beira, and the mounds forming a network for protecting people and their assets

Photo By Joy You-Chiao Wu

SEEDING THE MACHA

FOOD

INDUSTRY

APPLY FOR FUNDING

GOVERNMENT

MID GROUND CO-OP

CONSTRUCTION UNION

CO-OP COORDINATOR

URBAN DESIGN

INVESTMENT

ORGANIZE

FOOD INDUSTRY In order for Beira to boost its economy, it first needs to build a manufacturing base. Innovation relies on building infrastructure and capacity to become the driver for economic growth

CYPERUS PAPY

Land-based agric rebuild an eco-cy growth between

AMBA IN MID GROUND

FLOATING

MACHAMBA

TECHNICAL SUPPORT

AGRICULTURE UNION

culture and aquaponics will begin to ycle providing mutual promotion of the various species.

SEEDING THE MACHAMBA

YRUS + MISCANTHIDIUM ZONE

NGO

FLOATING MACHAMBA

Floating machambas production system can guarantee food production in different climate condition as well as food diversity. The system does not rely on available fertile land; and it uses less space than conventional crop production, generating high yields of both vegetables and fish for consumption.

Community Interaction with Residents in Hudson & Prattsville, New York

DISPERSING WELLNESS

Studio

Urban Design Studio II - Defining The Region: A Green New Deal for the Hudson Valley

Semester

Fall 2019, GSAPP, Columbia University

Location

Hudson Valley, New York

Instructor

Kaja KĂźhl, Anna Dietzsch, Jerome Haferd, Liz McEnaney, Justin Moore, Shachi Pandey, Raafi Rivero, David Smiley, Dragana Zoric

Team

Mansoo Han, Niharika Shekhawat, Shailee Shah

Role in Team

Preliminary Research, Conceptual Design, Modeling, Axonometric Drawing, Renders, Video Making, Community Interaction, Interview

Video Link

https://vimeo.com/380161906

USA produces an estimated 6,456.7 million tons of CO2 every year. Healthcare industry accounts for 10% of the total greenhouse gases generated and has experienced a 30% increase in the rate of gas emissions from 2006. In the Hudson Valley, geography of the region drives peopleâ&#x20AC;&#x2122;s health seeking behavior, residents travel up to 1.5 hours one way for their basic health needs. At the same time, many hospitals in the Hudson Valley have 50% percent vacant bed space, that can be more efficiently repurposed. The project challenges and changes the perspective of the current healthcare system from being a measure of cure to an extension of health and wellbeing of the community. Therefore, we reimagine dispersion of wellness through an additive typology that empowers the role of social infrastructure to spread a wellness network in rural areas, that substantially lower the environmental impacts of the healthcare sector, and create an equitable and sustainable model.

URBAN DESIGN

HIGH CARBON FOOTPRINT & LACK OF ACCESSIBILITY IN HEALTHCARE SYSTEM

CHRONIC DISEASE AND SOCIAL ISOLATION IN COLUMBIA & GREENE COUNTY

The healthcare industry accounts for 10% of the total greenhouse gases generated. We have two major issues that are often ignored of the healthcare system High carbon footprint and lack of accessibility (physical and monetary) that in turn makes people sicker and more dependent on health facilities in the long run. The Green New Deal aims to focus on “Providing all people of the United States with high quality health care” However, the measures do not solve for the high carbon footprint of healthcare and the accessibility factor. Of the total greenhouse gases generated in the USA, while the other 16% of the gas emissions derive from

84% of emissions in the industry are derived from manufactured products used in healthcare delivery as well as the energy and transportation required to acquire them the building systems of health facilities. Therefore, we understand that a large percent of emissions can be reduced through patient preventative healthcare and supporting measures that would make people healthier. In the Hudson Valley, the concentration of hospitals in

urban areas is much higher whereas the rural areas lack services to healthcare. Therefore, there is a huge health care disparity in terms of accessibility between the urban and rural areas. The base maps represents median household income, the areas that have higher income have more access to health facilities whereas low income, which are more rural areas lack health facilities completely. The geography of the region drives people’s health seeking behavior in Hudson Valley, residents especially from the rural areas are the most disadvantaged and travel up to 1.5 hours one way for basic health needs. At the same time, within the Hudson valley, hospitals show at least up to 30% percent vacancy of bed space in each county, areas that can be more efficiently repurposed.

DISPERSING WELLNESS

The project challenges and changes the perspective of the current healthcare system from being a measure of cure to an extension of health and wellbeing of the community.

In the Hudson Valley, the project focused on Columbia and Greene County as they have the lowest access to healthcare facilities but also the lowest physician to patient ratio and higher travel distances for health needs. As identified that 84% of the carbon emissions can be reduced through patient preventative healthcare, we look into the health profiles of these counties. In both counties, we see that there is an increase in the aging population and is projected to rise by 2040. There are two main health issues in the county - First, high number of chronic diseases such as obesity. We also see that the community in these regions consume less fresh vegetables, engage less in physical activities and has limited access to fresh food supermarkets. Second, high number of substance use in the middle aged population that also results in mental health issues and social isolation within the aging population in the rural areas.

URBAN DESIGN

HEALTHCARE DISTRIBUTION NETWORK

MOBILE HEALTH NETWORK

To respond to the issues of the healthcare system, we propose two strategies that would substantially lower the carbon footprint and reduce health disparities in the Hudson Valley. The first strategy is to extend health services in the rural area by adding health service center to public institutions. In the twin counties, Columbia Memorial Hospital serves as a sole healthcare provider. Libraries and fire stations are fairly dispersed in the region. As major health issues in the region are chronic and can be treated through preventative measures, through this strategy we can reduce the carbon impact but also provide health services as closer proximity. The second strategy is the mobile network that

would make health services more accessible scheduled health professional servicing rural areas but also distribution of medications, fresh food and wellness services.

As the Columbia Memorial Hospital is a major stakeholder in this process of transition. It acts as a promoter of community well being in Hudson and disperses wellness through an additive typology â&#x20AC;&#x153;Shedâ&#x20AC;? that empowers the role of social infrastructure to spread a wellness network in rural areas.

DISPERSING WELLNESS

Columbia Memorial Hospital

ÂŠ Jeremy Graham

HOSPITAL At Columbia Memorial Hospital in Hudson, we focus on the retrofit of the hospital. We reimagine the hospital as not a place for cure of disease but also as a center of dispersing wellness. Therefore, we propose â&#x20AC;&#x2DC;Hospital as a Parkâ&#x20AC;&#x2122;. We intend to make the space inviting not just for patients but also reimagine the space as a form of public space. 1 PROGRAM TRANSITION

URBAN DESIGN

The overall strategy is to repurpose not fully utilized building by cluster hospitals operational programs into the main functions building and reprogramming other two buildings - Research center to a daycare center, family care and Office building to a wellness pavilion.

2 COMMUNITY PAVILLION

In the wellness pavilion, we have a classroom and training space for librarians and firefighters, telemedicine helpline center, community based programs such as communal kitchen and conference room with a playground.

4 DOUBLE SKIN FACADE & ROOF GARDEN

To tackle the energy systems that can reduce 12% of the carbon emissions. We proposed to retrofit the facade of the hospital - a double skin facade to make it more energy efficient in terms of heating and cooling. The double skin facade is connected through a series of ramps that form a connection between the buildings. All the buildings have been converted to have farm gardens and solar panels on the roof that help achieve upto 50% of the carbon emissions reduction in the energy systems of the facility.

L AS PARK

DISPERSING WELLNESS

3 GREEN PARKING The parking lots on site are converted to green spaces. The park will also have an ongoing health fair where the mobile health vans will be stationed. Residents can get their routine health check up while at the park during scheduled times of the day.

COLUMBIA MEMORIAL HOSPITAL HUDSON, COLUMBIA COUNTY

URBAN DESIGN

Pocket Window

Stepped Green Roof

DISPERSING WELLNESS

Community Pavilion

Childrenâ&#x20AC;&#x2122;s Playground

Prattsville, Greene County, New York

URBAN DESIGN

RURAL AREA MODULAR SHED

SHED ATTACHED TO DIFFERENT INSTITUTIONS

PRATTSVILLE, GREENE COUNTY

URBAN DESIGN

Public Promenade

Storefront Healthcare

DISPERSING WELLNESS

Shed As A Living Room

Healthcare Center

Project Video: https://vimeo.com/380161906

URBAN DESIGN

REC

Claire Parde Columbia Healthcare Consortium

00:03:

DISPERSING WELLNESS

:48:03

Nancy Barton Prattsville Art Center

WASTE FRONT

Studio

Urban Design Studio I - Metropolitan Transformation

Semester

Fall 2019, GSAPP, Columbia University

Location

Sunset Park, Brooklyn, New York

Instructor

Tricia Martin, Nans Voron, Hayley Eber, Sagi Golan, Quilian Riano, Austin Sakong, Shin-pei Tsay

Team

Claudia Kleffmann, Vasanth Mayilvahanan

Role in Team

Preliminary Research, Conceptual Design, Modeling, Axonometric Drawing, Renders, Video Making

Video Link

https://vimeo.com/354209483

Sunset Park’s Waterfront is an Industrial area which has multiple underutilized NYC properties with a great connectivity, and it currently hosts SIMS, the facility which receives and sorts 100% NYC’s residential recyclable waste. But residential waste only represents 25% of NYC’s Waste Stream. The remaining 75% is Commercial Waste of which only 22% gets recycled. And this recycling scattered all over the city, costing a lot of money and polluting due to its distribution. This project proposes a Green Waste System that can manage and recycle NYC waste, by locating a series of processes in one specific area, therefore reducing transportation, money and time invested in recycling, creating an asset at Sunset Park that will give back to the community by providing jobs, education, public spaces and energy.

URBAN DESIGN

ALL NYC RESIDENTIAL RECYCLABLE WASTE GOES TO IN SUNSET PARK WATERFRONT ONLY 22% OF THE COMMERCIAL WASTE GET RECYCLED IN SMALL FACILITIES ALL OVER THE CITY

AN UNDERUTILLIZIED INDUSTRIAL BUSINESS ZONE IN SUNSET PARK SIMS MUNICIPAL RECYCLING

It currently receives a 100% of NYCâ&#x20AC;&#x2122;s residential recyclable waste and sorts it for later recycling (Paper, Glass, Metal, Plastic)

SOUTH BROOKLYN MARINE TERMINAL $115 million investment in infrastructure

WASTE FRONT

BUSH TERMINAL $136 million investment

BROOKLYN ARMY TERMINAL 90 acres of space available for anchor tenant

WHAT IF BY INTRODUCING A GREEN WASTE SYSTEM HERE WE CAN MAXIMIZE THE RECYCLING EFFICIENCY & CREATE AN ASSET FOR THE COMMUNITY 51

Sunset Park Waterfront, Brooklyn, New York

WASTE

An Integrated Waste Manage 1 EXISTING INFRASTRACTURE FOR WASTE COLLECTION PNEUMATIC TUBE SYSTEM

ELEVATED EXPRESSWAYS

SUBWAY TUNNELS

RAILROADS

7 COMMUNITY

Providing Skill Local Commu

5 INCINERATION PLANT URBAN DESIGN

Use Non-recyclable Waste to Produce Clean Energy and Clean Air

3 RECYCLING-METAL 720,000 sqft 197,140 tons/year

3 RECYCLING-GLASS 57,000 sqft 176,060 tons/year

3 RECYCLING-PAPER 1,115,000 sqft 1,613,000 tons/year

3 RECYCLING-PLASTIC 375,000 sqft 542,000 tons/year

2 SORTING

RESIDENTIAL WASTE INTAKE 100% 3,570,000 tons/year COMMERCIAL WASTE INTAKE 50% 3,875,000 tons/year

4 WAREHOUSING & DISTRIBUTION Existing Building 900,000sqft NYCEDC Owned Land Rail & Water Connectivity

FRONT

ement System in Sunset Park

6 MANUFACTURING

Existing Building 90 acres of space Waiting for Anchor Tenant NYCEDC Owned Land Rail & Water Connectivity Recent Increase In Garment Manufacture & Food Packaging

Y TRAINING CENTER

l Training and Public Space for unity

WASTE FRONT

DIGITAL ENGAGEMENT PLATFORM

URBAN DESIGN

WASTE FRONT

URBAN DESIGN

Educational Programs in the Recycling Facilities

WASTE FRONT

City-Wide Awareness Campaign Through Advertising on The Proposed Tubes

ENCOUNTER MARKET

Studio

Urban Design of Central Changsha City Blocks

Semester

Spring 2017, Hunan University

Location

Huangxing Road, Changsha, Hunan, China

Instructor

Min Jiang

Team

Sijia Peng

Role in Team

Preliminary Research, Conceptual Design, Modeling, Axonometric Drawing, Collage Perspective

Markets are vibrant urban spaces where people meet and get involved with each other. They are original functional places in cities and provide a basic reason for peopleâ&#x20AC;&#x2122;s encounter - commodities exchange. Changsha is a city in a rapid growth of urbanization, a lot of development happens in the old city center. Therefore, the organic texture and the memory precipitated by time are quickly replaced with neat modern square boxes. This project is about how to gather peopleâ&#x20AC;&#x2122;s experience in the original site where there is a large market and apply those experience to a new functional urban place under the requirement of the government for economic needs. The attitude for urban renovation here is to preserve the experience of the past and translate that into the concept of the new space.

Residential Block Roadside Farmerâ&#x20AC;&#x2122;s Market

URBAN DESIGN

The site is located near the commercial center of Changsha. Currently, it is an old residential block with a lot of small businesses set up at the ground level. There is a roadside farmerâ&#x20AC;&#x2122;s market which attracted a lot of customers. Yet the subway economy stimulate the development of this place.

Entrance of Underground Mall

Historic Commercial Street Entrance of the Site

Subway to downtown

Underground Mall

Small Businesses

Residential Block

Roadside Market

TOURING IN SITE

It’s a new day! I want have some noodle for my breakfast!

I have had this shop for many years

Go get some groceries home for lunch!

The restaurant upstairs looks good!

This is such a narrow alley

I must go through this hallway to get my home

I can see my her through the opposite window

This is the yard I grow my flowers

Have bought too many things, I must stop

Wish someday I can live in the high-rise housing This is dark here!

I walked underground from the subway station here

Just want a cup of milk tea, too many choices!

Oh The smell from the garbage station! I want to run away ENCOUNTER MARKET

God bless. They all get off at this station

I need to take the subway to work now, or I’ll be late

NO! I want to go upstairs Honey, it is raining outside. Let’s stay in the underground mall

EXTRACTING PEOPLE’S BEHAVIOR AND SCALE The project attempts to extract the experience of the old neighborhood and the market in the site analysis and research. The designer observes the scale of buildings, the relationship between human behavior and space from a human perspective. It is found that these behaviors are influenced by specific scales, and because of the unique spatial nature and function of the market, they can be roughly divided into two categories, namely, wandering and viewing. Walk Through Narrow Spaces

Walk Through Roofed Space

Walk Upstairs

Walk Through Homogeneous Space

游 WANDERING

Wandering means walking, crossing, and also going upstairs and downstairs and climbing.

3m 0.8m

20m

10m

Look Upward

URBAN DESIGN

Walk Upstairs + Look at Each Other

览 VIEWING Viewing means observing, including watching and being watched.

Look Down

6m 3m

3m 10m 10m

Look Through Two Layers of Interface

Look Left and Right

10m 5m

10m

TRANSFORMING After extracting space and human behavior, the designer distorts, stretches, and rotates the space without changing the scale and topological relationship, the resulting space is given new functions, including hide-and-seek libraries, greenhouse restaurants, ring-shaped manual classrooms, etc., which are used as event activators throughout the site.

Walk Through Roofed Space

Look at Each Other

游览 WANDERING VIEWING In Chinese TOURING is often used to describe the experience of scenic spots, cities or markets, and this action is formally composed of WANDERING and VIEWING.

Look Left and Right

Walk Through Roofed Space + Walk Upstairs

Walk Upstairs

Look Through Two Layers of Interface

Look Down

Walk Through Narrow Spaces + Look Down

Walk Upstairs

ENCOUNTER MARKET

Walk Through Narrow Spaces

Look Upward

Walk Through Roofed Space

URBAN DESIGN

The shops in the original southern block were preserved, and an overhead building was placed in the upper part of the block as a traffic space connecting the commercial street air corridor and high-rise buildings. In the building, you can walk above the old block to enjoy the old look of the block. This space also provides a venue for the flea market and the exhibition.

Iw picant t wit o ta h K ke AWS a !

is um use m er Uppat! gre

ity tiv ac bile y r ir ra mo Fa po ith m w e T ce re plauctu str

m eu us pace s M s d ica nt oo ot re r g Ex spaume n s Traconase as owc sh

The be slid fun e w ! oul d

Lift

a be ! to ents s v m e See-up pop

e nc tra of n E e be nc le Tu ntratangce e c a in r re sp Mappe rket a u he m

eri int he the t or d n k f t an rde Lin rke ga de maoftop Sli ro ftop o Ro

My funson i her s ha e! vin g Let andâ&#x20AC;&#x2122;s ge dan t dr ce! unk

ya njo e . to oor ant utd I wie o mov

a m ne Ci eet r i -a r str-air e n a en Op uppope em e l d b re cin lka ve Waof co ro

r h Ba ug es et ro ac re e th terf t S nc in s Dariou va SMALL BUSINESS

FOOD

EXHIBITION

MARKET

Design brand boutique, Creative Technology Flagship Store, Cultural brand flagship store (commercial street 1-3F), Renovated Old Store 1-3F

Local food, Boutique Restaurant (1-3F, Commercial Street) provides regular catering services for the business district

Small Crafts Gallery (Business Street 1-3F) provides space for cultural products and designer products Mobile Booth (Commercial Street Air Corridor)

Flea market (4-6F, ring shape) provides a large space for conventions and markets. Mobile outdoor market (central square) with movable brackets for temporary exhibition booths and food stalls

sa e in! r e o h i d t bit ear xhi I hat e gre

an ist

s on rc

fo ry e lle Spac a G al n ir io Sphibit ex

tio ec nnd o Lift cal cd an rti n Ve grou of ller y ga

er ower p nd ow Wi ng T ith i et w ng h c t re eri Wa er st cov f p r! up roo up owe le go he t ab k n l a t Wa I cough thr

ibi exhift! e l h t e oy th Enjn in o i t

the ! or reet f t t s spoper estn up b o A w sho

ith

w a az ge Pl staand n e e c nk an nd e Su orme staanc f c r d Pedien for auuare sq

ed at gr ion e t In ct re th tru ua Pa conse sq th ion f sitiew o n a Tr ith v w

Io danften cin com gw eh ith ere pee rs!

FOOD

OFFICE

EXHIBITION

MARKET

Greenhouse Organic Food Restaurant (Office Building 11-12F) supplies organic green food in the greenhouse and nearby farms, including dining area and food processing display area

(office building 10-14F) provides office rental for regular businesses including art education and market management etc.

Art gallery (office building 6-9F, spiral shape) serving art exhibition, as part of the business of art

The supermarket (B2) is located on the ground floor of the office building and provides convenient services for the surrounding residents.

ce r se pa fo ou on stor y H i g at fac kin ort d ac nsp fooe P h od tra it vis Fo tical ed wper u r n Vembi to s co ople pe

ea! taknner y i ll d sua ter I uk af l wa

Ih of ave t eve o c ryd hec ay k s foo afe d ty

Iâ&#x20AC;&#x2122;m now:P k er e! nev her ld mes u o w ga Mumying pla

r te en re C e m sto Ga ook oome B & me rpac d gaing snde n e u d d o a Hi h re surr t wi

Ic to an br forsee ting c the he g usto ir roc mer foo eri s d! es

n ear o lke t a ant a c I wing k a m

s g ble din rm getaoun a F ve rr nic ic su ga ganr the r O or fo sh ed s Freovidrant pr tau res

p ho ks he r Wo in tng i ted rk e b i ih wonsid h i ex wit le me le op co circ pe t Ouuter o

SMALL BUSINESS

Food

Office

Exhibition

Other Service

Design brand boutique, Creative Technology Flagship Store, Cultural brand flagship-store (commercial street 1-3F); Workshop (1F, Commercial Street)

Local food, Boutique Restaurant (1-3F, Commercial Street); Greenhouse Organic Food Restaurant (Office Building 5-7F, 11-12F) including dining area and food processing display area

Creative space (office 2-6F) provides office space for small startups; Office (office building) provides office rental for regular businesses

Small Crafts Gallery (Business Street 1-3F) provides space for cultural products and designer products; Mobile Booth (Commercial Street Air Corridor)

Library (B1) provides resources for nearby community citizens and primary school students; Gym (Office Building 13-14F); Parking (B1)

BOOKSTORE & HIDDEN GAME ROOM OFFICE

VERTICAL FARM

WALKING DISTRICT

WORKSHOP

FOOD FACTORY

URBAN DESIGN

SKY RESTAURANT

GALLERY

SUBWAY ENTRANCE

SUNKEN SQUARE

EXOTICA MUSEUM

MARKET OPEN-AIR CINEMA

STREET BAR

URBAN DESIGN

ENCOUNTER MARKET

ARCHITECTURE

UPHILL EXPLORATION

Studio

Graduation Design - Reconstruction of Songgang Jiarong Tibetan Tusi Village

Semester

Spring 2018, Hunan University

Location

Songgang, Barkam,

Instructor

Suqi Jiang

Team

Individual Work

Sichuan, China

Songgang is a mountainous area inhibited by Jiarong Tibetan. In order to build a deep understanding of this remote and mysterious region, an anthropological research using Participatory Rural Appraisal (PRA) was conducted before the design. People lived in the village on the top of the mountain for a long history, but recently moved to the foot with standard detached housing lined up by the government. They appreciated the promoted life but still missed their past time in the mountain. Based on their will of reconstructing their original village, this project proposed to bring them uphill participating in the exhibition and offer a possibility for them to recall their past memories through a spatial strategy, with a hope that the museum can be consistency of their stories, and correspond with their new life.

THE VILLAGE ON THE MOUNTAIN Songgang, Tibetan means “the official village on the mountain”, near the site there are heritages including Songgang Tusi Village Remains and Zhibo Towers.

Plateau

NEW

Region

VILLAGE Standard Housing

Street”

ARCHITECTURE

“Celestial

O LD

VILLAGE

AIN

SITE OF

Octagonal Watchtower

Remains of the Offical Village

Cultural Heritage

The official Village is an administrative and residential building of Tusi who held the power of the Jiarong Region before foundation of People’s Republic of China. It collapsed together with the past political regulation. The “Celestial Street” was an important trade center as part of Silk Road. Here on the top of the mountain lives beautiful Jiarong Tibetan people. In recent years, the villagers were moved to the foot into standard detached housing lined up by the government, in order to develop tourism.

INTERVIEW With the help of the village head, we organized a community meeting where we interviewed inhabitants of Songgang Village and heard about their everyday life, past and present, and their opinions about the reconstruction on the remains

The government is hoping to develop the local economy through tourism, here the ruin of Songgang Official Village and Songgang Sky Street is the core tourism resource. On behalf of the government, we aim to invest on the reconstruction of the ruin in order to attract more tourists from surrounding and all over the country. Zhao Chunxiu Village Head

I build walls for people who need new houses in my town and some surrounding areas. Whenever there is a job, I will be busy for a few months. We usually work together on the housing with a few young labors in the town, from picking stones to construction. We get stone from the mountain, break them into pieces and transport them to the site by ourselves. We do not need scaffolding for building a house. Now young people are not willing to learn the craftsmanship. They all went to cities for working, and I don’t know how to pass my skills.

A Mugun

Stonemason

UPHILL EXPLORATION

Belts are indispensable accessories for Tibetans. Jiarong Tibetan Girls usually start to learn weaving a belt at around twelve years old. It takes a week to finish a belt, and up to one month for unskilled people. Women who can make a belt with exquisite pattern and complicated craft gain respect in the town. This is also an important skill when people choose their brides. Aiying Retired

I paint traditional Tibetan patterns on the fruit plates and sell them in the market. People also like my painting on their furniture. The process of painting includes making templates, punching, pouring talcum powder, and coloring. I sell the handicrafts on the market for a living, but I need a bigger space to be my studio. Now my room is too small to fit the big furniture. Zhou Minkun Tibetan Painter

I make furniture and housing with wood structures like Tibetan temples. The wooden structure of the Tibetans is very different from that of the Han nationality. The pillars in the room do not pass through the slab, so that they can be replaced when the wooden pillars are damaged, and that’s why Tibetan buildings can last long. Nowadays I do not have much work to do because people all build concrete buildings and they buy furnitures from factories with mass production. Zeng Yu Carpenter

Jiarong Tibetan have precious traditional skills and culture which are now threatened by the modern development. The preservation of the site should not only include the built factors but also the social values and cultural heritage. And these are also part of the key to local people’s wishes and also to tourism.

CONCEPT In order to bring locals and visitors to explore the past and present of the Jiarong Tibetan life and bring more joy to the uphill road, a exploration trip was leading the design.

TIBETAN CLIMBING

CONFERENCE HALL

SEE FLOWER FESTIVAL CAMPING SITE

WOOD WORKSHOP FOR TIBETAN FURNITURE HARVEST DANCE SQUARE

TIBETAN DRAMA UPHILL EXPLORATION

TIBETAN PAINTING

TIBETAN WEAVING WORKS

TIBETAN STONE CONSTRUCTION

WALL

“CELESTIAL STREET”

ENTRANCE AND EXHIBITION OF TIBETAN ARTS

Through the anthropological research conducted in Songgang Village, we have learned the traditional crafts which could be exhibited to and participated with visitors. Therefore, the multi-functional boxes are inserted at different altitude on the uphill road for visitors and villagers to explore. In addition, the square for harvest dance and Tibetan festival camping were set on the mountain as well.

4 12 5

ARCHITECTURE

SITE PLAN 1. Tibetan Arts Normal Exhibition 2. Exhibition of Remains 3. TIbetan Painter Studio 4. Tibetan Weaving Workshop 5. Tibetan Drama Stage 6. Watching point 7. Tibetan Climbing Experience 8. HIstory Image Display 9. Washroom 10. Wood Workshop 11. Stone Wall Building Watching 12. Harvest Dance Square 13. See Flower Festival Camping Site

Walking uphill through the dilapidated walls of the original Tusi Village

UPHILL EXPLORATION

ARCHITECTURE

Tibetan Traditional Dance Between The Original Watchtowers

UPHILL EXPLORATION

PLAN The exhibition boxes are set upon contour lines which was previously influenced by the construction of the official village. Current remains of those walls are preserved as part of the exhibition and structure of the new building.

ARCHITECTURE

The original site is mostly preserved as they witnessed the history of the official village and peopleâ&#x20AC;&#x2122;s past lives. Through the weathered walls and towers, an image of historical Jiarong Tibetan life is presented upon peopleâ&#x20AC;&#x2122;s eyes. Together with the new inserted activity space including harvest dance square and see flower festival camping site.

UPHILL EXPLORATION

Exhibition of Remains of Original Village

Exhibition of Tibetan Weaving Works

ELEVATIONAL

1. Tibetan Arts Normal Exhibition 2. Exhibition of Remains 3. TIbetan Painter Studio

7. Tibetan Climb 8. History Im 9. Wash

4. Tibetan Weaving Workshop 5. Tibetan Drama Stage 6. Watching point

EXPLORATION

bing Experience mage Display h room

10. Wood Workshop 11. Stone Wall Building Watching 12. Harvest Dance Square

13. See Flower Festival Camping Site

1 3 11

1 1

2 1

TRICK OF SIGHT LINE

Studio

Architecture IV - Extension of The North Bohemia Museum in Liberec - Collaborative Design Studio with Czech Technical University

Semester

Spring 2016, Hunan University

Location

Liberec, Czech Republic

Instructor

Suqi Jiang, Hui Chen

Team

Sijia Peng

Role in Team

Preliminary Research, Conceptual Design, Modeling, Technical Drawing, Collage Representation

Inspired by Hitchcock’s movie Rear Window, this project discussed the basic behavior of watching and being watched. People’s behavior behind a window can be fascinating. People’s original wanting of watching others can be somewhat satisfied here. Using narrative logic and clear streamlines to reproduce the experience, people’s behavior is induced, the architectural elements of the old museum, and the behavior of people in the new museum participate in the completion of a detective film. The design is divided into selecting observation objects, designing the story plot, generating internal space, organization form and route. The old museum is the exhibit of the new museum, and the new museum is the stage for the observer of the old museum.

SITE The base is located in liberec, czech, there are historical buildings of the same style and cultural institutions such as art galleries.

Technical Museum Liberec

Kostel Sv. Antoina

Prehrada Harcov

Zoological Garden

Education & Culture Place

DinoPark Liberec Plaza Penzion U muzea

Touring Attractions

Cemeteryb Liberec

CONCEPT ARCHITECTURE

Before considering our concept, we have two questions: QUESTION 1 What is the most common behavior in a museum?

ANSWER 1 The Relationship Of PEOPLE and EXHIBITS

QUESTION 2 What is the most important problem we should solve here?

ANSWER 2 The Relationship Of THE NEW and THE OLD

After thinking about the two relationship, we have got our concept. Take The Old Museum As An Exhibit Of The New

Watch Peopleâ&#x20AC;&#x2122;s Action Through Windows Of The Old

Take The New Museum As A Stage Of The Old

Watch The Old Museum Through Windows Of The New

GENERATION OF SIGHT LINE

The concept of the design is based on people in new museum watching the historical feature and visitors on old museum watching peoples’ behavir in new building

th e th be e b ll t ui ow ld er in o g

f do

n tio bi n hi lk i x a e w le om op ro l al , pe the sm ce t of a sp d ou an

th e bi ate t r pe ion -sty l buople spae wi ildin obce ex nd gs se w hiow r v he e r s th o e ro ld ug h

e re eg 0d

1 te ta ro

in or , vi um ha se ing be u er e ’s m nt th g, le w e in op ne g ng er re Pe e din avi ath a to th clu le g ng, rs as in d , ayi sito m uan om st vi seu m ro d as u ew m n an en ld he rs io e se e o of t av th h d th t r . be to an pa um o ’s d r t se le te ate its a op rel the hib Pe e ex ar ace me sp co r. be the ge

e ac sp

n tio

bi hi ex n r oo de td gar ou he t in

ws in ar o do of rm wit an h tw

th e en old tra nc e

Wa tch

ing

Wa tch

ing

wi d a dogab ws le an

of ts s, ar of l p ro ld ia s ui rec a b ce s sp ch he a a e su t f d e. th g in o se c l e in les ws u pa a se ld b o e l s ric r s ui ga d ar ra to fte or b d in h tu is A rs sit ld n w ic ec h . to Vi e o rs, a ugh wh hit the um visi ace th we hro le, arc es use es, sp the to g t ang the cho m clu um of our in in in s e the he use ture e, t tive s ta ues lue in r t m pic tim pec tor cl e c its g fo ld le is ers visi old th hib hin e o ho t th p w the wn ex arc th e w . A new ne of do out f se ter e th eum t a the ws e nd d o en se us d a e ndo cam n a en to d m tan erv wi lly de the ol s s obs the ina gar ea, ist d gh . F air ar an rou um en- tion th se op ibi u m airs exh st or ur. do e to th

do n wi of ow ar

ws in ar o do of rm wit an h tw

STORY BOARD AND NARRATIVE SPACE

ARCHITECTURE

The space of new museum is closely designed with narrative logic. Visitors go through special space with the dramatic windows to find â&#x20AC;&#x153;cluesâ&#x20AC;?, which are hidden in the parts of the old museum that have special significance, such as roofs, towers, and gables.

The main concept about the extension of the museum is based on the relationship between the old one and the new one ,that taking the old museum as an exhibit of the new and taking the new one as a stage of the old .When it come to entities ,kind of inspired by the old detective story ,we put a plot in the new building, a plot that directed by its own designers but can be reread in many other ways by visitors .In the museum people will start their visit.

SCENARIOS OF THE NEW MUSEUM

TRICK OF SIGHT LINE

SITE PLAN 1:1000

SECOND FLOOR PLAN 1:1200 1. CLOAKROOM 2. EXHIBITION SPACE

THIRD FLOOR PLAN 1:1200 3. ACTIVITY SPACE 4. OFFICE

5. REPOSITORY

We reset the streamline of the museum. People start their visit from the new, then to the old. On the site, we set a translucent glass wall to guide people and some glass boxes to be exterior exhibition. The plan of the new museum is generated by sight and route. All the windows of the old museum are perpendicular to the line of sight. There are six scenes corresponding to different plots. 97

SECTION 1-1 1:1000

TRICK OF SIGHT LINE

Visitors in new museum watching different parts of old museum. Old building elements: the roof with dormant windows, the bell tower,old entrance hall,rows of windows and the gable. These elements are arranged in a certain order to form one of the exhibits of the new museum. At the same time, the sequence of exhibitions is arranged in tandem, and the space is organized by narrative clues.

SECTION 2-2 1:1000 Visitors in old museum watching different activities in new museum. Peoplesâ&#x20AC;&#x2122; activities in the new building: observing through the window,in and out of the small rooms,gathering together. After visiting the new museum, visitors enter the old pavilion and observe the behavior of the people in the new pavilion through a row of windows in the old museum.

ARCHITECTURE

WATCHING

From the old museum we can see the new buildings and the activities of the people in them, including the ongoing events in the glass box, the watchers of the window, and the exhibitions seen through the two-story windows. Visitors in the old museum will be curious about the activities they see through the windows and then go to the new building to participate.

100

BEING WATCHED

TRICK OF SIGHT LINE

From the new part and going through the little game we set here. The game, as we said is like many detective stories, you can search the clues by looking through several windows, each window points on its own elements. Sometimes a wall full of windows, sometimes a clock on the tower. You will be leaded to finish actions and use the clues you find to piece thing together. (And sightseeing the old museum.)

101

AFLOAT SANCTUARY

Studio

Competition Work - Amphibious Retrofit For Resilience Against Flood

Semester

Fall 2018

Location

Lvâ&#x20AC;&#x2122;an, Anhui, China

Instructor

Suqi Jiang, Hui Chen

Team

Ziyuan Zhu, Xin Wang

Role in Team

Conceptual Design, Buoyant Foundation Design, Joint Design, Architectural Comics, Technical Drawing, Physical Model

A buoyant foundation is used to resist the influence of flooding. Micro adaption is invented to the wall in the yard of traditional Anhui house, which is originally used for separating the families of offspring. Here we would like to bring the big family together when facing the disaster and providing great public space for normal use when there is no flood. This is also a general design for many houses in the Anhui Province where the villagers often build their house as Triple yard. So that people can preserve their life right in the place they live instead of moving outside the town which is far away when the water level rises.

THE HEAVIEST FLOOD IN RECENT 100 YEARS

ARCHITECTURE

About 2.63 million people in the east China region were affected by floods as rivers saw surging water levels due to heavy rains and influxes of water from upper reaches. As of 8 a.m. Wednesday, 12 people had been reported dead and one missing. More than 100,000 people have been evacuated.

104

INHABITANT IN FLOODING AREA

REALITY IN FLOODING AREA - LVâ&#x20AC;&#x2122;AN

ITERATION

AFLOAT SANCTUARY

105

ARCHITECTURE

COMPARISON - BEFORE AND AFTER

Life before construction without Flooding The Traditional house in Anhui Province is Triple yard with a wall in the yard to prevent fire, it is also a symbol of separated family from the big family.

Life before construction with major flooding People have been rescued from their homes, after heavy rains caused serious flooding in the eastern Chinese province of Anhui.

106

AFLOAT SANCTUARY

Life with Sanctuary without flooding The Sanctuary can be a basic activity space for families living in the separated yard, especially for children to have fun and playing games in order to attract people living far away.

Life with Sanctuary with major flooding When flood comes, water level get high, the sanctuary float with the water and provide a temporary living space for families. Communication and public activities also happens here.

107

108

AFLOAT SANCTUARY

109

110

ARCHITECTURE

AFLOAT SANCTUARY

111

112

ARCHITECTURE

AFLOAT SANCTUARY

113

SHELLS ON THE SHORE

Studio

Architecture VI - Community Stadium with Large-span Structure Design

Semester

Spring 2017, Hunan University

Location

Houhu, Changsha, Hunan, China

Instructor

Zhaohui Yuan, Guang Deng

Team

Sijia Peng, Zongyue Zhang

Role in Team

Conceptual Design, Modeling, Technical Drawing, Physical Model

This project studies large span structure for stadium buildings. With aid of grasshopper and rhino, a shell-like form was created to respond to the waterfront site.

116

ARCHITECTURE

SHELLS ON THE SHORE

117

118

ARCHITECTURE

SHELLS ON THE SHORE

119

120

ARCHITECTURE

SHELLS ON THE SHORE

121

ARCHITECTURE

Entrance

Open Space

122

SHELLS ON THE SHORE

123

KIOSK ON CAMPUS

Studio

Design Basis II - Campus Micro Stay Space Structure Design

Semester

Spring 2014, Hunan University

Location

Changsha, Hunan, China

Instructor

Wei Zhang

Team

Sijia Peng, Zongyue Zhang, Aoshuang Yi, Jiaqi Wang, Jiantao Huang

Role in Team

Conceptual Design, Jiont Design, Technical Drawing, Render, Construction

Award

Excellent Prize in National College Excellent Architectural Design Teaching Plan and Achievement Evaluation, 2014

RECIPROCAL STRUCTURE

Studio

Digital Architecture Laboratory Summer Workshop 2015

Semester

Summer 2015, Hunan University

Location

Changsha, Hunan, China

Instructor

Biao Hu

Team

Xiao Li, Mingqing Yang, Xinqi Lin, Zhimin Yang, Xun Yao, Kaixiang Wen, Xiaoyang Lou, Xiaotian Lu, Xueqing Hu, Yelin Liu

Role in Team

Conceptual Design, Jiont Design, Technical Drawing, Construction

130

131

RESEARCH

EXTRACTIVE URBANISM

Course

Conflict Urbanism

Semester

Spring 2020, GSAPP, Columbia University

Location

Mozambique

Instructor

Laura Kurgan

Team

Chris Zheng, Annie Wu, Zhou Wu

Role in Team

Research of LNG in Cabo Delgado, GIS, Visualization and Documentation

Link

https://centerforspatialresearch.github.io/conflict_ urbanism_sp2020/2020/04/30/Wu-Annie-Wu-Zhou-ZhangTing-Zheng-Chris.html

Mozambique’s booming extractive industries have spurred the country’s making of modernity in the post civil war era. Through the lens of urbanism - urban development, foreign investments, infrastructure construction, settlements and resettlements, etc. - this project looks at how the extractive boom is building the country’s economy while characterizing it with spatial and socio economic fragmentation across the national territory.

EXTRACTIVE URBANISM MODEL The conflict this project addresses is extractive urbanism, a model for the development of new cities. Many places rich in natural resources have reaped great benefits by simply exploiting and exporting themselves. Urban construction of these places is aiming to reinforce this low-cost moneymaking cycle, called extractive urbanism.

producing, etc. And the spatial byproducts of this economic model is the rising of Mozambique’s new cities. Tete, Palma, Cuamba, Montepuez, Mulevala, Manica, and Chibuto, etc, are all those names that are spatially aligned with the large mining and mining serving companies. But for these companies, only less than 40% of them are owned by Mozambicans.

Africa’s urban population will almost triple in the coming 35 years, with more than 1.3 billion Africans living in cities by 2050. But the initial driving force of these urban growths is more a desire to find a fiscal reservoir from external capital than a natural increasing demand. And it is in this particular development form of foreign investment orientation that African cities are rising from the ground up. Mozambique is one of the shining stars. The country’s economy has taken off from relying on foreign direct investment, which is now turning into controlling domestic resources and making profits by exporting them abroad.

Moreover, In terms of annual profits, most of the highest value-creating ones do not belong to Mozambicans. For the largest aluminum mining company Mozal in Mozambique, “Initial investment in Mozal amounted to 40% of GDP but only created around 1,500 direct jobs, with nearly one third held by foreigners”, and “it is estimated that from the US$1.2 billion revenue posted per year, only US$200 million enters the Mozambican economy ”. And the ratio of Mozambican people living under the basic poverty line is a stunning 46.1 percent.

RESEARCH

Nearly 70 percent of capital flows in exports come from the mining and mining supporting industries, logistics, energy-

136

EXTRACTIVE URBANIZATION

EXTRACTIVE URBANISM

137

RESEARCH

LARGE MINING COMPANIES

138

NATIONALITIES AND NET PROFITS OF MINING COMPANIES

EXTRACTIVE URBANISM

139

140

RESEARCH

EXTRACTIVE URBANISM

141

Coal Mining Site of Moatize Photo By Mining Global

CITY SCALE RESEARCH | COAL MINING IN MOATIZE, TETE

RESEARCH

In Mozambique, coal mining is the fastest growing industrial segment. Significant reserves of coking coal have been discovered in the Tete province, which have attracted numbers of prominent foreign mining companies. According to Business Monitor International Report, coal production in Mozambique has been rapidly increasing since 2009. And make Mozambique itself rank among the top 10 largest coal production and export countries in the world.

Foreign Investment in Coal Related Infrastructure Not only focusing on the development of mines in the country, key infrastructure would also be invested and constructed to facilitate export of mining commodities. There are two developing railroad projects which Brazilian Company Vale Mining has investedâ&#x20AC;&#x201D; the Sena railroad project and the Nacala corridor. Both are mainly serving for transporting coal or other products for export from the Moatize mine to the seaport like City Beira and Nacala for exports, instead of passenger use.

144

EXTRACTIVE URBANISM

145

LAND ACQUISITION CONFLICTS

RESEARCH

of the Tete Province land is licensed to mining companies [Including pending licenses]

Tete Province, located at northwestern of Mozambique, is a “commodity extraction frontier” rich in coal. It holds an approximately 23 billion tons of mostly untapped coal reserves, with the natural resource boom still in its early stages. Mining concessions and exploration licenses approved by the government cover around thirty-four percent of Tete province land. Including licenses pending approval, around sixty percent of the province’s land are covered. Zooming into Moatize District of Tete, around ninety percent of the district’s land is divided and licensed to FDI.

146

LAND ACQUISITION CONFLICTS

of the Moatize land is already licensed to mining companies

EXTRACTIVE URBANISM

01 02 03 04 05 06 07 08 09 10

Chandrakant Jadavji SOCSI Gilberto Ricardo Zefanias Valerio Matavel Sogeocoa Mocambique Coal India Africa [India] Midwest Africa Capitol Resources North River Resources Mavuzi [Australia] North River Resources Murrupula [Australia]

11 12 13 14 15 16 17 18 19 20

Sociedade Carvoeira de Samoa Zambezi Energy Corporation ECSI Black Gold Mining Moc. Vale Projectos e Desenvolvimento [Brazil] Riversdale Captal Mozambique Essar Recursos Minerals de Moc. Acacia Mineracao Mina Moatize ENRC Mozambique

21 22 23 24 25 26 27 28

Rio Minjova Mining and Exploration [Australia] Vale Mocambique [Brazil] Osho Gremach Mining Global Mineral Resources Moz. Bala Ussokoti Tora Investimentos Camal e Companhia Sungo Resources

147

RESOURCE ACCESS RIGHTS AND RESETTLEMENTS

RESEARCH

As two major extraction companies, Vale Mining and Rio Tinto have been producing coal from the Moatize mine since 2009 and planned to have further expansion. Due to the current extraction and expansion, many families have been displaced. Over a thousand families resettled approximately 60 km away from the Moatize coal mining site. The local population of Tete province has suffered from this coal boom led by the foreign companies, since largescale resettlements have been taking place. As a result, the

148

communities have faced disruptions in accessing food, water, and work. Living conditions have decreased drastically, as many farming households who had “previously been living along a river” and were therefore “self-sufficient”, have now been resettled to sites far away from the markets in Moatize, with agricultural land of “uneven quality and unreliable access to water”. Food insecurity and dependence on food assistance provided by the mining companies has become a serious issue for the families.

EXTRACTIVE URBANISM

149

Photo By REUTERS / Akintunde Akinleye

RESEARCH

CITY SCALE RESEARCH | LIQUEFIED NATURAL GAS (LNG) IN CABO DELGADO

152

In 2010, large reserves of natural gas were discovered in the Rovuma Basin, the offshore area of Cabo Delgado Province, northern Mozambique, which attracted a lot of foreign investment and will make Mozambique the third largest country of Liquefied Natural Gas (LNG). There are also future plans for pipeline and natural gas plants, yet it remains unknown who will invest in this development. However, the existing pipeline plan in Maputo is going to transport huge natural gas to South Africa.

EXTRACTIVE URBANISM

153

CABO DELGADO | LICENSED GAS AREAS

RESEARCH

The Gas Fields were divided into onshore and offshore 6 areas held by different foreign companies, and a lot of those are owned by foreign governments. In each area, Mozambique holds 10-15 percent shares, but none of the areas is operated by Mozambique. In fact, most of LNG will be shipped to those countries instead of being locally used. To support the LNG production, an onshore facility will be constructed, which is projected to influence over 10,000 People.

154

EXTRACTIVE URBANISM

155

AFUNGI LNG PLANT | RESETTLEMENT PLAN

are expected to lose access to their economic resource

RESEARCH

554 952

households are expected to be physically resettled

A Resettlement plan was made for the construction of the LNG plant, over 500 households are expected to be physically resettled and another 1000 are expected to lose access to their economic resource. The replacement village is located at the marginal area of the plant, but the replacement agricultural land will be 10-15 km away, and there is a delay in this process while the replacement village is being constructed now.

156

LOSS OF MAIN SOURCE OF LIVELIHOODS

of displaced households reporting at least one member primarily engaged in FISHING

EXTRACTIVE URBANISM

Among the displaced households, 51% are engaging in fishing, and in coastal villages, the number can be more than 80%. This map shows the vessel fishing and intertidal collecting points as well as the home ports. The restricted marine area will have a huge impact on those fishing grounds.

157

RESEARCH

2012 A provisional authorization of the Right to Use and Benefit from Land (DUAT) was awarded to Rovuma Basin LNG Land, Lda., for an area of 7,000 ha.

158

2017 The LNG mega project reached the final investment decision (FID)

2018 The final plan was approve ernment.

EXTRACTIVE URBANISM

ed by the Mozambican gov-

2002 Construction Began. Several Attacks happened which slowed down the construction process.

2020

159

EMERGING INSURGENCY

RESEARCH

Apart from the loss of livelihoods, there are also rising security concerns about the emerging attacks since 2017. A series of attacks by Islamist extremists on the civilians have causing dozens people killed. In 2019, they started to target LNG projects. The big companies have been seeking more troops from the government for protection. The ongoing conflict between the insurgents and the military forces have been bringing more pressure to the people who already have a relatively low socio-economic background in the poorest region. People are afraid of going to their fields, and the displaced households with a far allocated field will face potential starvation.

160

EXTRACTIVE URBANISM

161

Cahora Bassa dam, Tete, Mozambique. Photo By Quartz Africa

INFRASTRUCTURE RESEARCH | CAHORA BASSA DAM

RESEARCH

Similar to the extraction industries, large-scale supporting infrastructures in Mozambique are often built by foreign capitals. They are mostly done as a pointto-point model, in favor of lowering the cost of transportation and export of the extracted resources, thus often flying through a great area of territory without any connection to the surroundings.

Power Generation and Distribution The electricity system is one of the examples. In 2014, the country generated 2,626 MW electricity, of which 79% are contributed by the hydropower at Cahora Bassa dam in Tete Province. At 187 gigawatts, Mozambique has the largest power generation potential in Southern Africa from untapped coal, hydro, gas, wind and solar resources. Mozambique is a major exporter of hydropower, coal and natural gas, with the aim of becoming southern Africaâ&#x20AC;&#x2122;s energy hub. On the other hand, Mozambique faces substantial challenges in reaching its goal of universal energy access. Its energy access rate is among the lowest of sub-Saharan countries. In a planned future, Mozambique will still sell most of its energy output to South Africa and the Southern African Power Pool, with only a small portion remaining for domestic consumption.

164

EXTRACTIVE URBANISM

165

DOMESTIC POWER ACCESS Despite the outsized energy generation, thereâ&#x20AC;&#x2122;s a huge gap between the demand and the distribution. Extraction and export segments are among the top priorities of power supply while the whole system is struggling with the existing highly subsidized tariffs.

RESEARCH

There are 4.1 million households with no power access in Mozambique. The current access rate for residence is 29%. This number is 57% in urban areas and 15% in rural areas, yet only 36.5% of the countryâ&#x20AC;&#x2122;s people live in a city.

166

EXTRACTIVE URBANISM

167

INBALANCED ENERGY DISTRIBUTION

RESEARCH

At the heart of Mozambique’s energy system is Cahora Bassa, the giant hydroelectric dam on the Zambezi River in Tete province, opened in 1977. As a colonial legacy, this project is ﬁnanced by foreign investors and the guaranteed sale of electricity to South Africa at below-market prices, in exchange for its support in the colonial war. In the post civil war era, Cahora Bassa was taken over by the Mozambique government and rendered as a symbol of national identity - ‘o orgulho de Moçambique (the pride of Mozambique)’. But as a major source of revenue income, annually 70% of the dam’s output has been committed to South Africa’s Eskom utility under a long-term agreement through 2029. A large portion of that is later reimported to Mozambique to serve large extraction industries owned by foreign shareholders. Beyond that, the country plans to expand sales to Malawi, Zambia and Tanzania. This all happens with a majority of the domestic population still having no access to electricity.

168

EXTRACTIVE URBANISM

169

BIBLIOGRAPHY Amanam, Usua, “Natural Gas in East Africa: Domestic and Regional Use”, Stanford University, 2017. Kirshner, Joshua, Marcus Power, “Mining and extractive urbanism: Post Development in a Mozambican boomtown”, Geoforum, 61 (2015) 67–78. Noorloos, Femke, Marjan Kloosterboer, “Africa’s new cities: The contested future of urbanization” Urban Studies. 55(2018): 1223–1241. Perrotton, Florian and Massol, Olivier, “Rate-of-Return Regulation to Unlock Natural Gas Pipeline Deployment: Insights from a Mozambican Project”, USAEE Working Paper, 08(2018), No. 18-353, SSRN: https://ssrn.com/abstract=3225143. Rawoot, Ilham, “Gas-rich Mozambique may be headed for a disaster”, 2020. Santley, David, Robert Schlotterer, Anton Eberhard, “Harnessing African Natural Gas: A New Opportunity for Africa’s Energy Agenda?”, 2014.

RESEARCH

Shannon, Murtah, “Who Controls the City in the Global Urban Era? Mapping the Dimensions of Urban Geopolitics in Beira City, Mozambique”, Land 8(2019): 37. CFM- Ports and railways of Mozambique, EP financial statements”, 2012. “Final resettlement plan”, Mozambique gas development, 2016. “Mozambique power plant”, Africa Infrastructure Country Diagnostics (AICO), World Bank Group, 2009. “Mozambique: Mining Resettlements Disrupt Food: water, government and mining companies should remedy problems, add protections”, Human Rights Watch, 2013. “Oil and gas investments in Palma District, Mozambique: findings from a local context analysis”, Shared Value Foundation and LANDac, 2019. “Resources & Energy Statistics Annual”, Bureau of Resources and Energy, Mozambique, 2012. “Sub-Saharan Africa - Electricity Transmission Network”, World Bank Group, 2018. “‘What a house without food?’, Mozambique’s coal mining boom and resettlement”, Human Rights Watch, 2012. Center for International Earth Science Information Network, Columbia university, http://www. ciesin.org/. WorldPop, https://www.worldpop.org/.

170

EXTRACTIVE URBANISM

171

Photo By Mark Lennihan - AP

INFLUENZA ACTIVITIES AND NEIGHBORHOOD CHARACTERISTICS IN NY C 2016-2019

Course

Exploring Urban Data with Machine Learning

Semester

Spring 2020, GSAPP, Columbia University

Location

New York City

Instructor

Boyeong Hong

Team

Hanzhang Yang, Xiyu Chen

Role in Team

Methodology, Python Programming, Visualization, Documentation

Link

https://issuu.com/tingzg/docs/influenzafinalreport

The recent Coronavirus disease (COVID-19) outbreak in China, Italy, United States, and other countries, urged the researchers to think about how machine learning can equip the academic, government, developer, or even proletariat with a better understanding of the urban data, to prepare and mitigate the city and its people from pandemic hazards? Due to the lack of detailed COVID-19 case data, and its high contagiousness nature (Sanche et al., 2020), to better examine the relationship between pandemic diseases and urban data, we choose influenza illness as our research object. Each year, influenza illnesses in the U.S. lead to between 23,000 and 61,000 deaths (CDC, 2020) and cost an estimated $87 billion (Molinari et al, 2007). Currently, the NYC Department of Health and Mental Hygieneâ&#x20AC;&#x2122;s Syndromic Surveillance program carried monthly Influenza-like Illness (ILI) data to the public. However, the dataset only covers relatively coarse-grained spatial units â&#x20AC;&#x201D; ZIP Code equivalent areas and thus only telling a blurry story of influenza activities in New York City. By building machine learning cluster and regression models, the research examined the relation between neighborhood characteristics and the Influenza illness in New York City, from 2016 to 2019, on the ZIP Code scale, and predict census tract scale Influenza-like Illness rate by using nonlinear regression models.

RESEARCH

INFLUENZA-LIKE ILLNESS (ILI) OVERALL EMERGENCY DEPARTMENT VISIT RATE PER 100 PEOPLE BY ZIP CODE 2016-2019

2016

2017

2018

2019

0.5

174

1.0

1.5

2.0

2.5

3.0

3.5

What is the relationship between Influenza illness and neighborhood characteristics? What are the principal neighborhood characteristics related to the Influenza-like illness rate? How to predict fine-grained influenza illness activities based on existing coarse-grained records?

In a paper by Yang et al. (2016), the authors compared network models at different spatial scales to forecast the influenza outbreak in New York City, it also suggested that Influenza-like illness (ILI) data available from the NYC Department of Health and Mental Hygiene is a primary source to measure the influenza activities. For neighborhood characteristics, recent studies draw attention to how lower neighborhood socioeconomic status will adversely impact resident physical functioning and individual health (Feldman & Steptoe, 2004). In the mode of transportation to workâ&#x20AC;&#x2122;s relation to individual health, Muller et al. (2015) concluded that active transportation(walking, cycling) provides substantial health benefits. In the report regarding the influence of the 2007 pandemic flu in New York City, retrospective analysis shows that the minority ethnic groups are not well informed of the ongoing flu trend and relative responding method (Fuller et al, 2007).

INFLUENZA ACTIVITIES AND NEIGHBORHOOD CHARACTERISTICS IN NYC 2016-2019

Despite the importance of governments and public health agencies to mitigate hazards created by contagious diseases, little attention has been paid to the data-driven study of urban pandemic diseases. Previous research focuses on influenza activity prediction generally employed epidemiological models (Chretien et al., 2014), or forecasting influenza activitiesâ&#x20AC;&#x2122; peak time, peak height, and magnitude during an outbreak (Nsoesie et al., 2014). Previous literature on influenza activities which employed machine learning models generally focuses on monitoring influenza activities on social media (Aramaki et al., 2011; Pineda et al., 2015; Allen et al., 2016). However, there are little writings about how to improve the spatial resolution of influenza activities estimation. Data are usually aggregated into larger geographical units due to privacy, confidentiality, and administrative concerns. The machine learning regression model could help to improve the spatial resolution of urban data. In other fields of urban science, Kontokosta et al. (2018) combined machine learning and small area estimation to predict the building-level waste generation from less granular sample data.

175

DATASET Category

Variable

Dataset

Source

Temporal Coverage

Spatial Granularity

ILI Rate

ili_p100

Influenza-like illness Syndromic Surveillance Data

NYC Department of Health and Mental Hygiene

2016 - 2019

ZIP Code equivalent

Health Insurance

%HealthInsurance

2013 - 2017

ZIP Code Tabulation Areas; Census Tract

%PublicInsurance %Drive Alone %Carpool

Means Of Transportation

%Public Transportation %Taxicab %Walk %WorkAtHome %lessthan30

Travel Time

%30_60 %morethan60 %pop_age_under5

Age

%pop_age_5_18 %pop_age_18_65

RESEARCH

%pop_age_65over

2013-2017 ACS 5-year United States Census Estimates Bureau

%Total_White Race

%Total_Black %Total_Other %Households_with_children

Household

Average_household_size %households_public_assistance %housing units_Owner_occupied

Education

%Population_less_than_college %Unemployed_Population %households_income_less_25K

Income

%households_income_25-75K %households_income_75-150K %households_income_over150K

Health Facility Accessibility

Facility_access

Health Facility Map

New York State Updated Weekly Department of Health

Address and Coordinates

TreeDens %GreenCover Urban Form

%Walkup

MapPLUTO

NYC Department of BBL or Building Updated Quarterly City Planning Address

%Com %Res 311 Health Service Request

176

311_p

311 Service Request Data

NYC311

2010 - present

BBL or Coordinates

METHODOLOGY Data Cleaning

Data Preparation Transfer Data into Census Tract scale • • •

GIS proportional split Spatial join Pandas DataFrame group ZIP Code level data

Accessibility: Network analysis

Proportion calculation •

Divide feature by the population with each census tract

Use Network Analysis (LION dataset) to calculate the average distance from the centroid of the census tract to the 3 nearest health facilities access. Get the reciprocal to represent health facility accessibility

•

Build and test different clustering models to find the similarity among census tract neighborhood

•

Build and test different regression models to find the relationship between various neighborhood characteristics with ILI rate. Identify key features and forecast the future

Preliminary Exploration ILI Rate Visualization •

Get the basic information of Influenza like illness distribution in NYC

Correlation Analysis •

Show the correlation of features

Clustering Analysis K-Means Clustering

Gaussian Mixture Model (GMM)

Agglomerative Clustering

DBSCAN Clustering

INFLUENZA ACTIVITIES AND NEIGHBORHOOD CHARACTERISTICS IN NYC 2016-2019

•

Regression Analysis Ordinary Least Squares (OLS) Regression

Decision Tree Regression

Ridge Regression

Random Forest Regression

Lasso Regression 177

DESCRIPTIVE STATISTICS Category

Variable

Count

Mean

Std

Min

25%

50%

75%

Max

ILI Rate

ili_p100

8660

1.06

0.66

0.552

0.931

1.479

3.619

%HealthInsurance

8660

0.886

0.136

0.871

0.913

0.942

%PublicInsurance

8660

0.404

0.164

0.298

0.393

0.505

%Drive Alone

8660

0.244

0.171

0.103

0.206

0.356

%Carpool

8660

0.049

0.042

0.017

0.04

0.072

0.333

%Public Transportation

8660

0.537

0.179

0.423

0.568

0.674

%Taxicab

8660

0.007

0.015

0.009

0.157

%Walk

8660

0.087

0.094

0.031

0.062

0.106

%WorkAtHome

8660

0.039

0.046

0.013

0.03

0.053

%lessthan30

8660

0.286

0.138

0.203

0.262

0.34

%30_60

8660

0.416

0.141

0.329

0.406

0.511

%morethan60

8660

0.277

0.133

0.181

0.292

0.371

%pop_age_under5

8660

0.063

0.031

0.043

0.061

0.08

0.315

%pop_age_5_18

8660

0.142

0.059

0.108

0.143

0.178

0.449

%pop_age_18_65

8660

0.642

0.119

0.609

0.647

0.691

%pop_age_65over

8660

0.136

0.073

0.092

0.127

0.169

%Total_White

8660

0.421

0.3

0.134

0.383

0.7

%Total_Black

8660

0.25

0.298

0.019

0.09

0.429

%Total_Other

8660

0.31

0.221

0.132

0.255

0.472

%Households_with_children

8660

0.315

0.127

0.233

0.324

0.4

Average_household_size

8660

0.028

0.008

0.024

0.028

0.032

0.065

%households_public_assistance

8660

0.042

0.041

0.012

0.031

0.059

0.392

%housing units_Owner_occupied

8660

0.365

0.258

0.15

0.326

0.56

%Population_less_than_college

8660

0.644

0.224

0.546

0.704

0.807

%Unemployed_Population

8660

0.078

0.049

0.045

0.069

0.101

0.647

%households_income_less_25K

8660

0.242

0.142

0.139

0.211

0.319

0.843

%households_income_25-75K

8660

0.346

0.111

0.289

0.356

0.419

%households_income_75-150K

8660

0.248

0.104

0.184

0.254

0.318

%households_income_over150K

8660

0.142

0.127

0.053

0.109

0.191

Facility_access

8660

0.529

0.395

0.277

0.438

0.666

6.169

TreeDens

8660

2873

943

2334

2900

3509

5615

%GreenCover

8660

0.271

0.146

0.009

0.17

0.238

0.344

0.918

%Walkup

8660

0.587

0.346

0.27

0.661

0.924

%Com

8660

0.24

0.214

0.099

0.169

0.297

%Res

8660

0.718

0.217

0.658

0.784

0.861

311_p

8660

0.124

1.106

0.046

0.07

0.102

44.5

Health Insurance

Means Of Transportation

Travel Time

RESEARCH

Age

Race

Household

Education

Income

Facility

Urban Form

311 Health Service Request

178

CORRELATION TEST

ili_p100 %HealthInsurance %PublicInsurance %Drive Alone %Carpool %Public Transportation

0.8 0.4 0.0

%Taxicab %Walk %WorkAtHome %lessthan30

-0.4 -0.8

%30_60 %morethan60 %pop_age_under5 %pop_age_5_18 %pop_age_18_65 %Total_White %Total_Black %Total_Other %with_children household_size %less_than_college %Unemployed %income_less_25K %income_25-75K %income_75-150K %income_over150K %public_assistance %housingOwner Facility_access TreeDens %GreenCover %Walkup %Com %Res

% He a il % lthI i_p1 Pu ns 00 bl ur icI an n c % sur e Dr an % ive ce Pu bl Al ic o Tr %C ne an ar sp po or ol ta % tion Ta xic % W % ab or W kA a % tHo lk les m sth e an % % 30 % mo 30 po r _6 p_ eth 0 a a % ge_ n6 po un 0 % p_a de po g r5 % p_a e_5 po g _1 p_ e_1 8 ag 8_ e 6 % _65 5 To o ta ve % Ho % l_W r To h us eh % tal ite % Av old To _Bla Po er s ta c pu ag _w l_O k lat e_h ith_ th io o c e % % U n_l us hild r ho ne es eh re us m s_t old n % eho plo han _si ho l ye _ ze % us ds_i d_P coll ho eh nc o eg % use old om pul e ho h s_ e at u o in _le io % seh lds_ com ss_ n ho ol in e 2 % us ds_ com _25 5K ho eh in us old co e_7 -75K m in s_ e 5-1 gu p _ 5 ni ub ove 0K ts_ lic r1 O _as 50 wn si K er sta _ n Fa occ ce cil up ity ie _a d c T ce % ree ss Gr D ee en nC s % ove W r alk u % p Co m % Re 31 s 1_ p

311_p

INFLUENZA ACTIVITIES AND NEIGHBORHOOD CHARACTERISTICS IN NYC 2016-2019

%pop_age_65over

By performing correlation analysis, the result of Pearson standard correlation coefficient between each pair of columns is presented in the correlation map. The correlation map suggested that percentage of population enrolled in public health insurance, percentage of population using public transportation to work, longer commute time to work, percentage of teenager population, percentage of non-White population, low education level in population, and low household income contributed to higher ILI rate. While shorter commute time to work, percentage of elderly population, percentage of white population, high household income, percentage of owner-occupied housing, and percentage of green area in neighborhood contributed to the lower ILI rate.

179

ANALYSIS - CLUSTERING MODEL EVALUATION - MODEL SELECTION To better understand how Influenza activities affected New York City neighborhoods, we use machine learning clustering algorithms to identify clusters of neighborhoods with shared characteristics. In choosing the optimal clustering model, we compared K-Means Clustering, Agglomerative Clustering,

RESEARCH

KMeans Clustering n_clusters=4

Agglomerative Clustering n_clusters=4

180

Gaussian Mixture Model, and DBSCAN. The elbow test suggested that the optimal number of clusters is 4. In the comparison, K-Means clustering yielded the best result, which divided the 2165 census tracts into 4 clusters.

Gaussian Mixture Clustering n_components=2

INFLUENZA ACTIVITIES AND NEIGHBORHOOD CHARACTERISTICS IN NYC 2016-2019

DBSCAN Clustering eps=5

181

Kernel Density Plots of Standardized Features By Each Cluster

ili_p100 %HealthInsurance %PublicInsurance %Drive Alone %Carpool %Public Transportation %Taxicab %Walk %WorkAtHome %lessthan30 %30_60 %morethan60 %pop_age_under5 %pop_age_5_18

RESEARCH

%pop_age_18_65 %pop_age_65over %Total_White %Total_Black %Total_Other %with_children household_size %less_than_college %Unemployed %income_less_25K %income_25-75K %income_75-150K %income_over150K %public_assistance %housingOwner Facility_access TreeDens %GreenCover %Walkup %Com %Res 311_p

182

New York City Census Tract Clusters - KMeans

INFLUENZA ACTIVITIES AND NEIGHBORHOOD CHARACTERISTICS IN NYC 2016-2019

The reason we choose K-Means clustering is because the resulting cluster group 0 (in Red) highly collocated with census tracts experiencing high Influenza activities. The visualization shows that neighborhoods with following characteristics: high percentage of population enrolled in public health insurance, high percentage of households with children, low education level in population, low to medium household income (less than 75k) are among the most vulnerable areas of Influenza activities.

183

Kernel Density Plots of Standardized Features By Each Cluster

ili_p100 %HealthInsurance %PublicInsurance %Drive Alone %Carpool %Public Transportation %Taxicab %Walk %WorkAtHome %lessthan30 %30_60 %morethan60 %pop_age_under5 %pop_age_5_18

RESEARCH

184

New York City High Flu Rate Census Tract Clusters - KMeans

INFLUENZA ACTIVITIES AND NEIGHBORHOOD CHARACTERISTICS IN NYC 2016-2019

We perform the K-Means clustering again by using only census tracts with ILI rate above city mean. It well clustered the census tracts by neighborhood characteristics and ILI rate, which group 0 (in Red) have higher percentage of elders in population, and higher household income; group 1 (in Blue) have higher percentage of teenagers in population, and lower household income.

185

ANALYSIS - REGRESSION MODEL EVALUATION - MODEL SELECTION In finding the optimal regression model, we compared the performance of 3 linear regression models: ordinary least squares (OLS) regression, Ridge regression, and Lasso regression; and 2 non-linear regression models: decision tree, and random forest. While the Lasso regression failed to have a meaningful result, the OLS and Ridge regression returned a performance score (test set score) of 0.46, and 0.458, respectively. The poor performance of linear regression models turned us to non-linear regression models. The non-linear regression models performed better in solving the prediction problems. Both decision tree model and random forest model have a test set score of 0.85, which we believe it performed well in predicting the ILI rate.

LINEAR REGRESSION MODEL

RESEARCH

ORDINARY LEAST SQUARES (OLS)

0.437

0.56

0.457

0.56

Training Set Score

Test Set Score

Mean Squared Error - Training

Mean Squared Error - Test

RIDGE

0.56

0.458

0.56

Mean Squared Error - Training

Mean Squared Error - Test

LASSO

0.0

0.99

0.0

1.04

Training Set Score

Test Set Score 186

max_depth=23

0.932

Training Set Score

0.853

Test Set Score

max_depth=19, n_estimators=100

0.437

Test Set Score

DECISION TREE

RANDOM FOREST

Alpha=10

Training Set Score

NON-LINEAR REGRESSION MODEL

Mean Squared Error - Training

Mean Squared Error - Test

0.929

Training Set Score

0.857

Test Set Score

OPTIMAL MODEL - DECISION TREE AND RANDOM FOREST The depths of two non-linear models are tuned using a search in order to achieve optimal model performance, by comparing test set scores. At max maximum depth of the tree equals 23, we have optimal model performance for decision tree regressor. At max maximum depth of the tree equals 19, we have optimal model performance for random forest model. By showing importance of each features, both the decision tree and random forest regression model pointed out that percentage of white population, percentage of people enrolled in health insurance, and percentage of population commute with public transportation have significant impact on ILI rate, while the random forest model further discovered that high household income, and education rate also contributed to the difference of ILI rate between census tracts.

%HealthInsurance %PublicInsurance %Drive Alone %Carpool %Public Transportation %Taxicab %Walk %WorkAtHome %lessthan30 %30_60 %morethan60 %pop_age_under5 %pop_age_5_18 %pop_age_18_65 %pop_age_65over %Total_White %Total_Black %Total_Other

RANDOM FOREST

INFLUENZA ACTIVITIES AND NEIGHBORHOOD CHARACTERISTICS IN NYC 2016-2019

DECISION TREE

%with_children household_size %less_than_college %Unemployed %income_less_25K %income_25-75K %income_75-150K %income_over150K %public_assistance %housingOwner Facility_access TreeDens %GreenCover %Walkup %Com %Res 311_p

187

PROJECTED AVERAGE INFLUENZA-LIKE ILLNESS (ILI) OVERALL EMERGENCY DEPARTMENT VISIT RATE PER YEAR PER 100 PEOPLE BY CENSUS TRACT 2016-2019

RESEARCH

The random forest regression model gave its prediction of ILI rate to each census tract. We compared the ILI rate derived from NYC Syndromic Surveillance Data, which is on ZIP Code equivalent area level, and the predicted ILI rate on census tract level. The comparison proved (1) the prediction matched the original training data, and (2) the prediction, since having a finer granularity, can identify the ILI rate difference between census tracts within the same ZIP Code area.

0.0

0.5

1.0

1.5

2.0

2.5

MEAN

Projected Average ILI ED Visit Rate Per Year Per 100 People

188

3.0

PREDICTED INFLUENZA-LIKE ILLNESS (ILI) OVERALL EMERGENCY DEPARTMENT VISIT RATE PER YEAR PER 100 PEOPLE BY CENSUS TRACT

INFLUENZA ACTIVITIES AND NEIGHBORHOOD CHARACTERISTICS IN NYC 2016-2019

0.0

0.5

1.0

1.5

2.0

2.5

3.0

MEAN

Predicted Average ILI ED Visit Rate Per Year Per 100 People

189

RESEARCH

COVID-19 OVERALL CONFIRMED CASE RATE PER 100 PEOPLE BY ZIP CODE TILL JUNE 7TH 2020

The aim of this research is to develop a predictive model for influenza activities at the census tract level in New York City, to facilitate the better implementation of public health measures and interventions on a finer spatial scale. With the development of the regression predictor using machine learning models, the study also identified important neighborhood characteristics related to influenza activities, which can also facilitate urban decision making and policy implementation. The research is triggered by the COVID-19 outbreak in New York City. Through comparing the COVID-19 case map with our predicted ILI rate map, we can find some collocation between these pandemic activities. In the future, we believe that the research topic can expand to other diseases, by using our model of small area estimation.

190

PREDICTED INFLUENZA-LIKE ILLNESS (ILI) OVERALL EMERGENCY DEPARTMENT VISIT RATE PER YEAR PER 100 PEOPLE BY CENSUS TRACT

INFLUENZA ACTIVITIES AND NEIGHBORHOOD CHARACTERISTICS IN NYC 2016-2019

While we can use the ZIP Code level ILI rate data to validate our predictions, the accuracy of our models are unable to prove since ILI rate data on census tract level does not exist in statistics. We respect that the health agencies want to protect the privacy of patients, thus finer-grain data is concealed. Another limitation of our study is that although these contagious disease datas are updated monthly, or even daily on health agenciesâ&#x20AC;&#x2122; websites, the neighborhood characteristics, especially those from the American Community Survey, are updated less frequently and come in out-of-date. Without a live feed of urban data, we are not able to understand the current socioeconomic and urban activities.

191

BIBLIOGRAPHY Centers for Disease Control and Prevention. (2019). Public Health Laboratory Virologic Age Surveillance: Age Group Distribution of Influenza Positive Specimens Reported by Public Health Laboratories, National Summary, 2018-19 Influenza Season [Data file]. https://gis. cdc.gov/grasp/fluview/flu_by_age_virus.html Centers for Disease Control and Prevention, National Center for Immunization and Respiratory Diseases (NCIRD). (2019). People 65 Years and Older & Influenza. https://www.cdc.gov/flu/ highrisk/65over.htm Centers for Disease Control and Prevention, National Center for Immunization and Respiratory Diseases (NCIRD). (2020). Past Seasons Estimated Influenza Disease Burden. https://www. cdc.gov/flu/about/burden/past-seasons.html

RESEARCH

Feldman, P. J., & Steptoe, A. (2004). How neighborhoods and physical functioning are related: the roles of neighborhood socioeconomic status, perceived neighborhood strain, and individual health risk factors. Annals of Behavioral Medicine, 27(2), 91-99. Fuller, E. J., Abramson, D. M., & Sury, J. (2007). Unanticipated Consequences of a Pandemic Flu in New York City: A Neighborhood Focus Group Study. Mueller, N., Rojas-Rueda, D., Cole-Hunter, T., De Nazelle, A., Dons, E., Gerike, R., ... & Nieuwenhuijsen, M. (2015). Health impact assessment of active transportation: a systematic review. Preventive medicine, 76, 103-114. Molinari, N. A. M., Ortega-Sanchez, I. R., Messonnier, M. L., Thompson, W. W., Wortley, P. M., Weintraub, E., & Bridges, C. B. (2007). The annual impact of seasonal influenza in the US: measuring disease burden and costs. Vaccine, 25(27), 5086-5096. NYC Emergency Management. (2014). NYCâ&#x20AC;&#x2122;s Risk Landscape: A Guide to Hazard Mitigation. https:// www1.nyc.gov/assets/em/downloads/pdf/hazard_mitigation/nycs_risk_landscape_a_guide_to_ hazard_mitigation_final.pdf US Census Bureau. (2019). Resident population of the United States by sex and age as of July 1, 2018 (in millions) [Data file]. https://www.statista.com/statistics/241488/populationof-the-us-by-sex-and-age/ Yang, W., Olson, D. R., & Shaman, J. (2016). Forecasting influenza outbreaks in boroughs and neighborhoods of New York City. PLoS computational biology, 12(11).

192

DATA SOURCE

NYC Department of City Planning, Information Technology Division. (2020). MapPLUTO 20V1 (shoreline clipped) [Data file]. https://www1.nyc.gov/site/planning/data-maps/open-data/ dwn-pluto-mappluto.page NYC Department of Health and Mental Hygiene. (2020). Influenza-like illness Syndromic Surveillance Data [Data file]. https://a816-health.nyc.gov/hdi/epiquery/visualizations? PageType=tsi&PopulationSource=Syndromic&Topic=1&Subtopic=39&Indicator=Influenza-like%20 illness%20(ILI)&Year=202 NYC Department of Health and Mental Hygiene. (2020). New York City Locations Providing Seasonal Flu Vaccinations [Data file]. https://data.cityofnewyork.us/Health/New-YorkCity-Locations-Providing-Seasonal-Flu-Vac/w9ei-idxz New York State Department of Health. (2020). Health Facility Map [Data file]. https://health. data.ny.gov/Health/Health-Facility-Map/875v-tpc8 United States Census Bureau. (2018). 2013-2017 ACS 5-year Estimates [Data file]. https:// www.census.gov/programs-surveys/acs/technical-documentation/table-and-geographychanges/2017/5-year.html

INFLUENZA ACTIVITIES AND NEIGHBORHOOD CHARACTERISTICS IN NYC 2016-2019

NYC 311. (2020). 311 Service Requests from 2010 to Present [Data file]. https://data. cityofnewyork.us/Social-Services/311-Service-Requests-from-2010-to-Present/erm2-nwe9

193

Photo By Mark Makela / Getty Images

THE STARBUCKS EFFECT

Course

Geographic Information System (GIS)

Semester

Fall 2019, GSAPP, Columbia University

Location

New York City

Instructor

Leah Meisterlin, Carsten C. Rodin

Team

Hatem Alkhathlan, Sushmita Shekar

Role in Team

Methodology, GIS Processing, Network Analysis, Visualization, Documentation

Link

https://issuu.com/tingzg/docs/starbuckseffect

Zillow chief executive Spencer Rascoff and chief economist Stan Humphries, write that “Starbucks fuels gentrification and so is responsible for higher housing prices”. Understanding the growth and establishment of Starbucks could be an early indicator that housing prices are about to spike or Starbucks and other cafe’s use gentrified neighborhoods for establishing new investment by increasing the prices further and causing displacement. The aim of this project is to explore the census tracts having potential Starbucks stores and determining its relation to gentrification criteria like median rent, median income, race and educational attainment. The Starbucks Effect is a new phenomenon which comments that properties in close proximity to a Starbucks actually appreciate much faster than those in less established neighborhoods.

“WE FOUND THAT CHANGES IN THE LOCAL ECONOMY — SUCH AS A NEW COFFEE SHOP OPENING — CAN PREDICT GENTRIFICATION,” -HARVARD BUSINESS SCHOOL’S MICHAEL LUCA, LEE J.

Starbucks is one of the largest coffee chains in America and their expansion is strategically determined by GIS analysis and extensive location and user based analysis of neighborhoods and cities. Zillow chief executive Spencer Rascoff and chief economist Stan Humphries, write that “Starbucks fuels gentrification and so is responsible for higher housing prices”1. Understanding the growth and establishment of Starbucks could be an early indicator that housing prices are about to spike or it could be used to understand that Starbucks and other cafe’s use gentrified neighborhoods for establishing new investment by increasing the prices further and causing displacement.

to gentrification criteria like median Rent, median Income, Race and Educational attainment. The Starbucks stores will be studied at a gentrified neighborhood such as Harlem and Brooklyn within the and will be compared with the change of the city in the same period and census tracts adjacent to the study area. The locations will be studied over a time line of 5 years before and after 2013 and the pattern will be analyzed. A 10 minute walking radius will be considered for the boundary of the study. The Starbucks Effect is a new phenomenon which comments that properties in close proximity to a Starbucks actually appreciate much faster than those in less established neighborhoods, hence the research focuses on verifying this.

RESEARCH

The aim of this project is to explore the census tracts having potential Starbucks stores and determining its relation

(1)Kasperkevic, Jana. “In Gentrified Cities Which Came First: Starbucks or Higher Real Estate Prices?” The Guardian, Guardian News and Media, 3 Feb. 2015, https://www.theguardian.com/money/us-money-blog/2015/feb/03/starbucks-gentrification-real-estate-prices

196

197

US CITIES WITH THE LARGEST NUMBER OF STARBUCKS 02

Chicago

Seattle

184 stores

133 stores 05 Los Angeles

137 stores

New York City 241 stores

04 San Diego 141 stores

03 Houston

RESEARCH

148 stores

STARBUCKS AND HOME VALUE 1997-2007

2007-2013

92%

139%

Near Starbucks

The charts indicate that there is higher increase in percentage of home value between 1997 -2007 with the presence of Starbucks in the vicinity (10 minutes walking radius) All Properties

49%

67%

STARBUCKS STORE OPENING DATA 2005 2007 2010 2013 2016 2019

198

7353 11131 10648

The charts represents the total number of Starbucks stores opened in United States between 2005-2019

11475 13173 15041

RESEARCH QUESTION

WHAT ARE THE PATTERNS OF ESTABLISHMENT AND GROWTH OF STARBUCKS IN RELATION TO NEIGHBORHOOD CHARACTERISTICS? IS THERE A CHANGE IN ANY OF THE GENTRIFICATION CRITERIA -MEDIAN RENT, MEDIAN INCOME, EDUCATIONAL ATTAINMENT AND DISPLACEMENT OF BLACK POPULATION AND IN THE NEIGHBORHOOD BEFORE AND AFTER THE ENTRY OF STARBUCKS INTO A LOCALITY? WHAT IS THE AREA OF THE IMPACT? CAN THE ENTRY OF STARBUCKS IN A LOCATION BE AN INDICATIVE OF GENTRIFICATION?

The intent of the project focuses on understanding the role of GIS in predicting changes or the growth of a neighborhood. While our focus is on measurement and prediction, our results also suggest that businesses respond to exogenous changes in neighborhood composition. Gentrification has a wide range of parameters that influence change,for example transport infrastructure, public projects and commercial development. Hence Starbucks stores located at gentrified census tracts were studied and compared with New York city wide data to draw inferences and the criteria is based on four parameters median income, median rent, educational attainment and racial change.

THE STARBUCKS EFFECT

HYPOTHESIS

ASSUMPTIONS The gentrification criteria includes four parameters- Median rent, median income, educational attainment and racial change, the assumption made is that a potential change in these parameters can quantify gentrification. The challenge also lies in determining a timeline of the study. To identify whether a 5 year before and after timeline will be ideal to quantify gentrification or changes in a neighborhood. Defining an area or boundary of study might be hard to establish, hence the census tracts which lie within a 10 minute walkable distance from the Starbucks is used for the study.

199

METHODOLOGY

Data Collection

Starbucks Location Coordinates

Data Preparation

Display Coordinates

Scripted From Starbucks Website

Table From Website

Network Analysis

RESEARCH

LION Single Line Street

1 / 2 / 5 / 10 minutes

Starbucks Thiessen Polygons

Crop

NYC Borough Boundary

2017 ACS 5-Year Estimates

All Starbucks in NYC & Part of Starbucks with Opening Dates

Starbucks Walkable Distance Boundary

Table Join

NYC Census Tract

Since there is a different density of Starbucks in NYC, we use different walkable distance boundary as their service areas according to Thiessen Polygons

2009&2017

2009 ACS 5-Year Estimates

NYC Census Tract 200

Table Join

Population / Median Household Income / Median Gross Rent / Educational Attainment / Race Ratio

Each Starbucks Service Area in different walkable distance

Union

NYC Starbucks Service Area Union

Census Tract Centroid

Select By Location

NYC Starbucks Serving Population

Union, Select By Location, Field Calculation, Table Join...

Table Join

Starbucks Data

Create Thiessen polygons

Starbucks Opening Dates Of The World

Starbucks Pattern in NYC Define Study Area Of Starbucks

Characteristics of the neighborhoods with Starbucks

Starbucks & Gentrification Analysis Identifying gentrified neighborhoods in NYC according to the criteria from the reference

Median Income Census Tract 2017

Median Gross Rent Census Tract 2017

Population >500 Median Home value >40th percentile Median Household Income >40th percentile

Change 2009-2017

Increase in Median Home Value > 60th percentile Increase in Educational Attainment > 60th percentile Increase in Median Household Income > when adjusted for inflation

2017

Race Ratio Census Tract 2017

Displacement 2009-2017

THE STARBUCKS EFFECT

Educational Attainment Census Tract

Eligibility 2009

5% decline in population of racial /ethnic group Decline in % of population of more than two SD from National mean

Identified Gentrified Census Tract

Harlem

Brooklyn

Change of Median Household Income / Median Gross Rent / Educational Attainment / Race Ratio

201

STARBUCKS LOCATIONS DISTRIBUTION AND WALKABILITY ANALYSIS Thiessen polygons are generated from Starbucks points such that each polygon defines an area of influence around its sample Starbucks, so that any location inside the polygon is closer to that Starbucks than any of the other. Compare it to the network analysis of the Starbucks in NYC, We see a concentration of Starbucks in midtown, downtown Manhattan and Dumbo Brooklyn, where there is one Starbucks within every 1min walkable distance, while in uptown and other waterfront areas, the boundaries extend

RESEARCH

to 2 - 5 min walkability.

Uptown Manhattan

Midtown Manhattan

202

THE STARBUCKS EFFECT

Starbucks Thiessen Polygon Walking Distance <660 ft

1 min

<1320 ft

2 min

<1980 ft

5 min

<2640 ft 10 min

203

STARBUCKS SERVICE AREA WITH POPULATION

Starbucks serves approximately 32% of the NYC overall population. Most Starbucks located in the census tract which has a relative higher population, but there is still some dense neighborhood which there is no Starbucks.

RESEARCH

32%

204

THE STARBUCKS EFFECT

Starbucks Service Area Population 0

2600

2601 - 4393 4394 - 6622 6623 - 10500 10501 - 28937

Miles 4

205

STARBUCKS LOCATIONS IN RELATION TO MEDIAN INCOME Starbucks Census tracts in relation to median income range:

0 33500 Min

56900

81300

119300

233700 Max

RESEARCH

Of Starbucks census tracts lie with median income range of 56900119300 dollars compared to New York City median income of 60,765 dollars

Median Income (Dollars) Starbucks location 0-33500 33501-56900 56901-81300 81301-119300 119301-233700

206

STARBUCKS LOCATIONS IN RELATION TO MEDIAN RENT Starbucks Census tracts in relation to median rent range:

0 700 Min

1400

2100

2800

3500 Max

Of Starbucks census tracts lie with median rent range of 1400-2800 dollars compared to New York City median income of 1194 dollars

Median Rent (dollars) Starbucks location 0-700 701-1400 1401-2100 2101-2800 2801-3500

207

STARBUCKS LOCATIONS IN RELATION TO POPULATION DENSITY Starbucks census tracts in relation to population density:

89 0 Min

111 2600

4300

6600 10500 29000 Max

of Starbucks census tracts lie with population change of 2600-6600 people compared to New York Cityâ&#x20AC;&#x2122;s population of 19,542,209

RESEARCH

59%

Population Change Starbucks location 0-2600 2601-4300 4301-6600 6601-10500 110501-29000

208

STARBUCKS LOCATIONS IN RELATION TO EDUCATIONAL ATTAINMENT Starbucks Census tracts in relation to Educational Attainment:

43 0 Min

36 20

68%

30 40

74 60

160 80

100 Max

of Starbucks census tracts lie with census tracts having education attainment range of 60-100 compared to New Yorks range of 20.8%

Educational Attainment Starbucks location 0-20% 21-40% 41-60% 61-80% 81-100%

209

GENTRIFICATION CRITERIA

Median Rent

Median Household Income

Race

College Graduates

PROCESS Gentrification is a process of neighborhood change that includes economic and demographic change in a historically dis-invested neighborhood â&#x20AC;&#x201D;by means of real estate investment and new higher-income residents moving in - not only in terms of income level, but also in terms of changes in the education level or racial make-up of residents.

RESEARCH

In this section, we are examining the correlation between gentrification and the impact of opening a Starbucks location. We are looking at data from 2009 to 2017, Starbucks stores opened around the period of study. By using the criteria and methods from Governing.com, we have identified the gentrified tracts that moved from the bottom 40th percentile for median rent and median household income to the top 60th percentile, for residents holding a bachelorâ&#x20AC;&#x2122;s degree or higher, and for tracts that has more than 500 population.

1- Eligible tracts determined by tracts which are below the 40th percentile in both median household income and median rent. In addition, the tract must has 500 population or greater at the beginning of the period which is 2009. 2- Possible gentrification is determined by including all eligible tracts and then identifying tracts that were in the top 60th percentile for increases in both median income and median rent, in addition to the percentage of college graduates. 3- The census tracts meeting all of the above listed criteria were identified as undergoing, or having undergone, gentrification base on the criteria and method that was used.

PROCESS OF IDENTIFYING GENTRIFIED TRACTS

210

Median Gross Rent

Median Gross Rent <40th percentile

>500 Population

Increase In Median Gross Rent >60th percentile

Increase In College Educated >60th percentile

Median Household Income

Median Rent Value <40th percentile

>500 Population

Increase In Median Rent Value >60th percentile

Increase In College Educated >60th percentile

IDENTIFYING GENTRIFIED NEIGHBORHOODS Starbucks Locations Identified Gentrified Tracts.

211

NEIGHBORHOODS GENTRIFIED AFTER OPENING STARBUCKS

Selecting Starbucks opened between 2010 and 2012, we see a overlapping between the Starbucks service area and the identified gentrified neighborhoods. Of the 20 Starbucks opened at that time, 17 Starbucks located in gentrified neighborhoods, which shows that after the Starbucks

RESEARCH

opened, there is a gentrification happening.

212

THE STARBUCKS EFFECT

Starbucks Opened Between 2010 - 2012 Service Area

Miles 4

213

IDENTIFYING THE STUDY SITES In this map we are looking at the Starbucks stores which are located inside the identified gentrified tracts and were opened in the study period. Two sites were chosen to examine from these locations, the first site is located in Harlem, Manhattan, and another site located around downtown Brooklyn. The study area of the sites were defined by the walkability analysis, which means site 2 has a larger study area because it has two Starbucks opened in the study areas and has overlapped walkability reach.

RESEARCH

SITE 1 - MANHATTAN - HARLEM

Starbucks Opening Date: 5/22/08

SITE 2 - BROOKLYN

Starbucks Opening Date: 6/30/12 and 9/24/13

214

THE STARBUCKS EFFECT

Miles 4

215

SITE 1 - MANHATTAN - HARLEM The second site is located in Manhattan, Harlem, which has

Percentage of Change In Median Rent From 2009 to 2017

one Starbucks stores opened in May 2008, in addition to 3 other stores opened before the study period 2009-2017. This study area saw an increase of median rent from 2009 to 2017 by %35, a change from $812.2 to $1,244. The Median income also has increased drastically by %36, from $42,402 to $58,107. The percentage of residents age 25 and over holding bachelorâ&#x20AC;&#x2122;s degrees increased moderately by %15.70, and the percentage white population has increased by %19.64 while the black population has increased too by %5.69. Percentage of Change In Median Income From 2009 to 2017

31.49%

36.03%

Increase in Median Income

RESEARCH

Increase in Median Rent

Percentage of Change In White Population From 2009 to 2017

19.64%

15.70%

Increase in White Population

+ .

Increase in Education of Bachelorâ&#x20AC;&#x2122;s Degree or Higher

Established Starbucks in the study period. (2009-2017) Established Starbucks before/after study period Identified Gentrified Tracts. Starbucks Boundary (Voronoi diagram)

Identified Gentrified Tracts from 2009 to 2017 & Starbucks Locations

Starbucks Opening Date:

216

5/22/08

Percentage of Change In Higher Education From 2009 to 2017

0.0275 0.055

Miles 0.11

SITE 2 - BROOKLYN The first site that we chose to examine is located in

Percentage of Change In Median Rent From 2009 to 2017

Brooklyn, which has two Starbucks stores opened in June 2012 and September 2013, located 0.43 Miles from each other, in addition to 12 other stores opened before the study period 2009-2017. This study area saw q drastic increase in median rent from 2009 to 2017 by %48, it increased from $1,135 to &2,000. The Median income also has increased by %41, from $65,521 to $11,0191. The percentage of residents age 25 and over holding bachelorâ&#x20AC;&#x2122;s degrees jumped to %61.68, and the percentage white population has increased by %9.91, while the black population decreased by % -58.53. Percentage of Change In Median Income From 2009 to 2017

48.15%

41.13%

Increase in Median Income

Percentage of Change In White Population From 2009 to 2017

9.91%

Increase in White Population

+ .

THE STARBUCKS EFFECT

Increase in Median Rent

61.68%

Increase in Education of Bachelorâ&#x20AC;&#x2122;s Degree or Higher

Established Starbucks in the study period. (2009-2017) Established Starbucks before/after study period Identified Gentrified Tracts. Starbucks Boundary (Voronoi diagram)

Identified Gentrified Tracts from 2009 to 2017 & Starbucks Locations

Starbucks Opening Date:

6/30/12

9/24/13

Percentage of Change In Higher Education From 2009 to 2017

0.0275 0.055

Miles 0.11

217

CONCLUSION The study was completed by looking at the Starbucks stores which were opened around the study period and located in tracts that fits the gentrification criteria. We tracked values of rent and median income in addition to the change of the educational attainment within 10 minutes walking distance of Starbucks. Generally, most Starbucks located in Midtown and Downtown Manhattan are located within 1 minute walkable distance, while in the other part of the city, the service area can extend to 2 - 10 minutes walkable distance. By defining the study area of each Starbucks, 32% of the people in NYC can access to Starbucks. The study of Starbucks stores in relation to median household income indicates Midtown and lower Manhattan have the largest concentration of stores and more than half (50%) lie between the income range of 56900-119300 dollars per household. Only 12% of stores lie in low income area where the income is less than 33500.When the relationship between Starbucks stores and median household rent was

RESEARCH

visualized ,nearly half lie between the rent range of 14002100 dollars per household. More than 59%of Starbucks lie in census tracts showing population change of 2600-6600 people. The store locations are concentrated in areas with high educational attainment which ranges between 60-100. The first study site saw an increase from 2009 to 2017 in median rent and median income by 31.49% and 36.03%, while the change across the city was 26.58% and 14.97%. Furthermore, to compare the change with the to tracts slightly farther away, which located outside the study area and inside a 6,560 ft buffer, the area had an increase by 10.4% and 33.15%. In short, the tracts closest to Starbucks appreciated more than 21% in median income, and 2.88% in median rent compared to the increase to the tracts slightly farther away from the study area. The second study site also saw an increase in the same period in median rent and median income by %48.15 and %41.13, while the change across the city was 26.58% and 14.97%. Comparing the change with the to tracts located outside the study area and inside a 6,560 ft buffer from Starbucks, the area had an increase by 42.81% and 38.60%. In short, the tracts closest to Starbucks appreciated more than 5.34% in median income, and 2.53% in median rent compared to the increase to the tracts slightly farther away from the study area.

218

SITE 1 Change from 09-17

Study Area

Census Tracts Adjacent To the Study Area

City

Median Rent

31.49%

10.40%

26.58%

Median Household Income

36.03%

33.15%

14.97%

Change from 09-17

Study Area

Census Tracts Adjacent To the Study Area

City

Median Rent

48.15%

42.81%

26.58%

Median Household Income

41.13%

36.60%

14.97%

SITE 2

DATASET

Starbucks Opening Dates Starbucks Opening Dates,Starbucks Everywhere, 4 Dec 201, http://www.starbuckseverywhere.net/StoreOpeningDates.htm.

Starbucks Store Locations “Starbucks®.” Starbucks, https://www.starbucks.com/store-locator?map=39.635307,-101.337891,5z.

2017 ACS 5-Year Estimates 2009 ACS 5-Year Estimates Median Income

Data Access and Dissemination Systems (DADS). “American FactFinder.” American FactFinder, 5 Oct. 2010, https://factfinder.census.gov/.

Median Rent

Data Access and Dissemination Systems (DADS). “American FactFinder.” American FactFinder, 5 Oct. 2010, https://factfinder.census.gov/faces/nav/jsf/pages/index.xhtml

Population density

Data Access and Dissemination Systems (DADS). “American FactFinder.” American FactFinder, 5 Oct. 2010, https://factfinder.census.gov/faces/nav/jsf/pages/index.xhtml

Race

Data Access and Dissemination Systems (DADS). “American FactFinder.” American FactFinder, 5 Oct. 2010, https://factfinder.census.gov/faces/nav/jsf/pages/index.xhtml

LION Single line Street Base Map

THE STARBUCKS EFFECT

Educational Attainment

LION Single Line Street Base Map, https://www1.nyc.gov/site/planning/data-maps/open-data/dwn-lion.page.

REFERENCE Kasperkevic, Jana. “In Gentrified Cities Which Came First: Starbucks or Higher Real Estate Prices?” The Guardian, Guardian News and Media, 3 Feb. 2015, https://www.theguardian.com/money/usmoney-blog/2015/feb/03/starbucks-gentrification-real-estate-prices. “The Starbucks Effect: Gentrification and The Best Investments.” Pierre Carapetian Group, 26 Apr. 2019, https://pierrecarapetian.com/starbucks-effect-gentrification-best-investments/. Iversen, Kristin. “‘I’m Happy for the Gentrification’: On Starbucks and Sunset Park.” Brooklyn Magazine, Brooklyn Magazine, 22 Oct. 2015, http://www.bkmag.com/2015/10/22/im-happy-for-thegentrification-on-starbucks-and-sunset-park/. “Nowcasting Gentrification: Using Yelp Data to Quantify ...” Harvard Business School, 2018. https:// www.hbs.edu/faculty/Publication Files/18-077_a0e9e3c7-eceb-4685-8d72-21e0f518b3f3.pdf.

219

Photo By Matteo Miliddi

BIKE CAMPAIGN IN NYC

Course

Urban Informatics

Semester

Fall 2019, GSAPP, Columbia University

Location

New York City

Instructor

Anthony Vanky

Team

Chris Zheng, Zixuan Zhang

Role in Team

Methodology, Analysis, HTML, Visualization, Documentation

Link

http://www.columbia.edu/~tz2436/BikeCampaign.html

The City of New York in a parallel universe is starting a campaign to take all automobiles off the roads. It kicks off with bicycles taking over certain streets and avenues in Manhattan at a certain time of the day, and eventually every street in the whole city, 24/7. While most New Yorkers are embracing this more equal and healthy future, they also wonder what are the next steps to take in the coming days. To learn from the trend of bicycles, especially shared bicyclesâ&#x20AC;&#x2122; growth in New York City, we looked into the data of Citi Bike in 2018, using python and html to process and visualize, trying to forecast where the next bicycle lane will be built in the near future. And if we are going to launch a campaign which could be participated as many people as possible, what time is the perfect time?

RESEARCH

This map shows the top 100 Citibike routes in NYC. With the data of start stations and end stations of Citibike rides in 2018, we conducted network analysis to get the shortest bike paths of each ride to represent the most possible biking routes. Overlapping with the existing bike routes, we can see where people like to ride but lack of bike lanes.

222

SEASONAL FREQUENCY

By visualizing the amount of bicycle trips in a year, it is obvious that people are more likely to choose a bicycle during their trips in the warm summer, while in the winter, the number for riding is greatly reduced. Thus, bicycle travel is significantly influenced by weather factors.

WEEKLY FREQUENCY

BIKE CAMPAIGN IN NYC

Through the visualization of data, we speculate that citibike is the main choice for some people when commuting to and from work. In the whole week, the means of each day that people ride citibike are changing. Tuesday, Wednesday, and Thursday consist of the peak of citibike using, while at the beginning and end of the week, bike usage declines. However, on leisure Saturdays, bicycling is also a good option for people to travel.

DAILY FREQUENCY

The time-specific data contribute to understanding the regularity of peopleâ&#x20AC;&#x2122;s travel every single day. During the working days, the peak period of people using citibike is mostly concentrated in the morning and afternoon, which overlap with the peak periods of work commuting. However, on the weekends, the stress of work is eliminated, and people prefer to ride a bike on a relaxing afternoon.

223

Photo By Zhiheng Jiao

LIFE ON VEGETABLE LEA V E S

Workshop

Super Informal Mapping Workshop

Semester

Fall 2016, Hunan University

Location

Malaowei Market, Changsha, Hunan, China

Instructor

Jason Ho, Xuan Chen

Team

Tian Liu, Zhiheng Jiao, Sizhe Wang, Liang Zhao, Wenhan Dong, Zhiqiang Ma, Jiahong Lin, Huanyu Liu

Role in Team

Item Tracking, Field Research, Exhibition Design, Presentation

Malaowei Market is a spontaneous farmers’ market in Changsha, China. During the mapping workshop, an informal recycling and formal waste management system was discovered through tracking abandoned vegetable leaves. A lot of people are involved in this ordinary urban public space: Stale vegetable leaves are thrown on the ground by the green grocers which surprisingly attract many “garbage pickers”. Among them, some grocers use them to make pickles and offer a second sale to customers; snack vendors pick the edible part as a kind of garnish; farmers gather them to feed their domestic animals. The remaining garbage are then transferred between dumpsters and eventually buried in landfill.

Photo By Zhiheng Jiao

228

RESEARCH

HEIMIFENG Refuse Landfill

LIFE ON VEGETABLE LEAVES

Garbage Transfer Station

GUAPIAOSHAN Garbage Station

General Garbage Forklift

Vegetable Leaves Pickers

Malaowei Farmerâ&#x20AC;&#x2122;s Market

229

230

ACKNOWLEDGMENTS AND SPECIAL THANKS

I would like to express my deepest gratitude to all the people who worked with me on these projects. This portfolio would never be here without your collaboration. I have learned a lot from each of you, not only skills but also life. Special thanks to all the faculty of Columbia GSAPP and School of Architecture at Hunan University. You provided these precious opportunities for me to work on these projects and your inspiring instructions made them great. I am incredibly grateful to my uncle and aunt who supported me pursuing my academic dream and sponsored my tuition. I want to thank my friends who were always by my side and encouraged me to do lots of things and those who picked up my calls when I was exhausted. Lastly, I want to thank my mother and my father for their limitless support. 231

TING

ZHANG

tingzg.com linkedin.com/in/tingzg issuu.com/tingzg instagram.com/tinzzang

+1 (917) 855 2680 // tz2436@columbia.edu 4142 24th St APT 407, Long Island City, NY 11101

E DU CATI ON

SKI LL

M. S. ARCHITECTURE AND URBAN DESIGN 06/2019 - 05/2020 Graduate School of Architecture Planning & Preservation, Columbia University Courses: GIS, Exploring Urban Data with Machine Learning, Urban informatics, Conflict Urbanism

BACHELOR OF ARCHITECTURE School of Architecture, Hunan University

09/2013 - 06/2018

Studio project “Trick of Sight Line” got Third Prize in Student’s Works Exhibition for Exchange Programs of Architecture Schools of China, 2017; Studio project “Rest in Forest” got Excellent Prize in National College Excellent Architectural Design Teaching Plan and Achievement Evaluation, 2014

EXPER I EN CE URBAN DESIGN TEACHING ASSISTANT Graduate School of Architecture Planning & Preservation

09/2019 - 12/2019 Columbia University

Acted as software tutor for classmates to troubleshoot as in-studio support; Coordinated and maintained the studio shared resources (GIS data, reference materials)

JUNIOR ARCHITECT JWDA Architecture Design Co., Ltd.

Shanghai, China

03/2019 - 05/2019

Assisted with the adjustments on Shanghai Qixin Rd Commercial Office Complex Project, including construction documentation, modelling and diagrams. Involved in Lanzhou Silk Road International Intellectual Property Port Office Tower project, from concept to final design, including design, render-ready rhino modelling, diagrams, documentation

RESEARCH ASSISTANT Buoyant Foundation Project

University of Waterloo, Canada

06/2017 - 09/2017

Helped with the amphibious architecture conference ICAADE 2017; Preliminary research for NRC Flood Resilience Housing Project, including GIS processing and analysis; Involved in Farnsworth Afloat design for American Architecture Prize Competition, including render

Super Informal Mapping Workshop

Hunan University, China

11/2016

Researched the informal waste disposal system in a spontaneous market in Changsha, China, including interview, graphics and exhibition

Digital Architecture Laboratory Summer Workshop

Hunan University, China

L EAD ER SH I P |

Collaborative Design & Education, Hunan University

09/2017 - 06/2018

Organized two sharing events across the school, in charge of social media editing

Creative Director / Project Manager

Enactus HNU

09/2014 - 06/2016

Managed the project of Food Map of Changsha, designed two annual reports Involved in Project “Fragrance of Rice”, Third Prize, Enactus China Social Innovation Regional Competition

Vice-director of Publicity Department | Student Union, Hunan University 09/2014 - 06/2015 Designed posters for lectures, edited over 5 school news posted on the school website

L ANGUAG E English Chinese

AWAR D Academic Travel Scholarship Hunan University, 06/2016 Enactus China Elite Student Enactus Worldwide, 04/2016 Merit Student Hunan University, 12/2016 Excellent Student Cadre Hunan University, 12/2015

08/2015

Used Grasshopper to develop a self-supporting construction for a canopy in School of Architecture, Hunan University, including concept design, programming and diagram

Core Member

Modelling Rhino / Revit / Auto CAD / SketchUp Graphics Photoshop / Illustrator / InDesign Mapping ArcGIS / QGIS Programming Python / Grasshopper / HTML Data Analysis / Visualization Machine Learning / Pandas / D3Plus Video Editing Premiere / After Effects Rendering Vray

PUBLI CATION “To Every Age Its Art: Modernization Exploration of Central European Regionalism in Architecture under the Influence of Secession in the Early 20th Century” China Building Industry Press (Await Publication)

Author: Hui Chen, Ting Zhang, Xuan Lan, Ruowei Liao, Liang Zhao, Kexin Lin Including: In-site Research, Writing of Modernization in Czech Architecture

_TITLE

continue d...

_EPILOGUE

Honestly, I was once a pessimist towards the present technological society where people are drifting apart from each other. However, my encounter with architecture helps me recognize the power of space and ignites my passion to explore a better living environment and a promoted city life, which leads to my decision to further my exploration in this area, namely that provide a better setting for people to create their unique stories.