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M S ARCHITECTURE AND URBAN DESIGN

COLUMBIA UNIVERISTY GRADUATE SCHOOL OF ARCHITECTURE, PLANNING AND PRESERVATION

HATEM.ALKHATHLAN@GMAIL.COM +1 (973) 5105 946

HATEM ALKHATHLAN MAY 10 2020

PORTFOLIO


PROJECTS

THE VIBR A NT HEA RT OF JERSY CITY SUMMER

POST R ETAIL SCA PE COLLA BOR ATIV E M AIN STR EET

SEMESTER

FALL

R E-A RTICULATING CA PITA L SPRING SEMESTER

2

SEMESTER


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THE STA RBUCKS EFFECT

CLIM A K E WA R

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FALL

SEMESTER

FALL

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R ECOMBINA NT URBA NISM RIYA DH CITY SPRING SEMESTER

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SEMESTER


THE VIBRANT HEART OF JERSY CITY SEMESTER: SUM MER - UD STUDIO

LOCATION: JERSEY CITY

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SITE CH A LLA NGES

The Palisade & Fenced Green Areas

Underdeveloped Lands Along The Transport Infrastructure

Railyards & Brownfields Lands

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Increase Pressure on Lands Due To Growing Population

Poplulation

2010 247,639

Density

Poplulation

%7.2

Increase in density

2018 265,549

Density

2010 16,736

2018 17,910

P/sqm

P/sqm

%7.0

Increase in population

Vulnerable Areas To Strom Surge Average Annual Precipitation 1990-2018 4.0

2.5

1990

2018

1.0

%53

of Jersey and Hoboken is covered with impervious surface.

769

Gallons of water can be intercepted by increasing urban green canopy.

6,515 6

properties experienced storm surge flooding during Hurricane Sandy in 2012.


Inaccessible Green Areas

276

of the city land is green but inaccessible/fenced

Acres

= 208

Football Fields!

Areas with low walkbility to open green spaces

11%

1.2%

Total Green Area 1,024 Acre

Maintained Parks 105 Acre

3.4%

7.2%

Inaccessable Green Areas 1,024 Acre

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Protected Natural Parks 105 Acre


Strategy

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CONNECT Establishing a social corridor and connecting it with the rest of the city.

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ENGAGE AND EXPERIENCE Providing a series of experiences to engage people in different programs based on context.

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RESILIENCE Incorporating storm water management and ecological resiliency.


SITE 1 - Hoboken / NJ Transit Line

RESIDENTIAL

RESIDENTIAL

RESIDENTIAL

ECOLOGY

PEDESTRIAN BIKE TRACK

LIGHT RAIL STATION

PLAZA

RAILROAD

PLAZA

RAILROAD

MULTIFUNCTIONAL LAZA

ECOLOGY

LIGHTRAIL

CITY FAIR

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BIKE LANES

PLAZA SPORTS FIELD

PEDESTRIAN BIKE TRACK

RESIDENTIAL

RAILROAD

NEW PATH STATION

LIGHT RAIL STATION

SCHOOL

LIGHT RAIL STATION

PEDESTRIAN BIKE TRACK


SITE 2 - Dickinson High School

RESIDENTIAL

ECOLOGY

PEDESTRIAN BIKE TRACK

RESIDENTIAL

LIGHT RAIL STATION

RESIDENTIAL

PLAZA

MULTIFUNCTIONAL LAZA

RAILROAD

PEDESTRIAN BIKE TRACK

RESIDENTIAL

RESIDENTIAL

LIGHT RAIL STATION

CITY FAIR

PLAZA

FLOWER FIELD

HEALTH CARE

SCHOOL

BIKE LANES

RAILROAD

SPORTS FIELD

MULTIFUNCTIONAL LAZA

STORMWATER PARK

FLEA MARKET

CITY FAIR HIKE TRACK

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BIKE LANES

SPLASH PARK

LIGHT RAIL STATION

PEDESTRIAN BIKE TRACK

RESIDENTIAL

PLAZA

RAILROAD

NEW PATH STATION RESIDENTIAL

LIGHTRAIL

ECOLOGY

NEW PATH STATION

COMMERCIAL

PLAZA

RAILROAD

SCHOOL

LIGHTRAIL

RESIDENTIAL

RESIDENTIAL

SPORTS FIELD RESIDENTIAL

SCHOOL LIBRARY

LIGHT RAIL STATION

PEDESTRIAN BIKE TRACK


SITE 3 - Liberty State Park

RESIDENTIAL

ECOLOGY

PEDESTRIAN BIKE TRACK

RESIDENTIAL

LIGHT RAIL STATION

PLAZA

MULTIFUNCTIONAL LAZA

RAILROAD

NEW PATH STATION

CITY FAIR

LIGHT RAIL STATION

PLAZA

RAILROAD

LIGHTRAIL

BIKE LANES

PEDESTRIAN BIKE TRACK

RESIDENTIAL

SPORTS FIELD

SCHOOL

Family Park

(barbecue and gathring areas)

Multifunctional Plaza

RESIDENTIAL

Social Spaces

FLOWER FIELD

STORMWATER PARK

Event Space

RESIDENTIAL

FLEA MARKET

Water Park (connected to the river)

COMMERCIAL

RESIDENTIAL

COMMERCIAL

RESIDENTIAL

HEALTH CARE

SCHOOL

RESIDENTIAL

SCHOOL

SCHOOL

RESIDENTIAL

FAMILY PARK

RESIDENTIAL

SPLASH PARK

HIKE TRACK

HIKE TRACK

PARK

RESIDENTIAL

PARK

LIBRARY

EDUCATIONAL HUB

BOAT & DOCK

RAILROAD

MULTIFUNCTIONAL PLAZA

WATER PARK

11 LIGHT RAIL STATION

PLAZA

LOWINCOME HOUSING

COMMUNITY CENTER


RETAIL APOCALYPSE SEMESTER: FA LL - UD STUDIO

LOCATION: KINGSTON - HUDSON VA LLEY

Dead Retail Infrastructure Hudson Valley

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PROJECT DISCRIBTION

With the global transition from traditional shopping to e-commerce and the change in consumer trends, the main street retail, Big Box stores and malls which were a de facto social space for American small towns have transformed into redundant infrastructure. The repercussions of this have highly impacted social interaction, local economy, existing infrastructure, jobs and services. This gives an opportunity to redefine a new retail module that spurs from a bottom-up approach through main street revival and by providing experiences and awareness to consumers along with establishing a shared collaborative platform for small businesses by sharing space, energy, resources, waste management and storage. This sharing of assets not only reduces cost and carbon emission but also funnels local economy, promotes interaction, elevates jobs and social equity. It further encourages collaborative consumption in the Hudson Valley Region.

25% of the shopping malls in the Hudson Valley are in danger of closing in the next 5 years.

The Old Model: Individual Retail Stores

The New Model: Collaborative Making Spaces

Footprint

Infrastructure

Supporting CO2 Reduction

Lower Cost

Collaboration

Economic Impowerment

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Integrated Active Spaces

Interaction Social Engagment

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Experiences


KINGSTON - A NEW COLLA BOR ATIV E MODEL OF R ETAIL To investigate this phenomenon further we chose Kingston as a case study to look at this change. Kingston is a city in the lower hudson valley , about 91 miles from New York City. In the last decade the street retail vacancy has increased dramatically and the Hudson Valley mall located at the periphery is on the verge of shutting down. The city has several small businesses and entrepreneurs who have great potential but lack funding and infrastructure. The existing model of these micro businesses is based on the traditional

model where each of them depend on individual infrastructure which is less cost effective and increases the carbon footprint. When each individual business has its own supply chain of raw materials, customer shipping, processing machinery, it increases freight transportation, energy consumption and the overall cost of production and sale.

Refurbished Malls & Big Boxes, Regional Service Infrastructures

Railway, Collective Logistical Corridor

Refurbished Malls, Renewable Energy Infrastructure

Cornell St, Mid-scale Manufacturing

Store Vacancy Rate

Broadway, the Co-op Main Street 0%

Light rail, Public Transit Extension

Water Railway Existing Tram Tram Extention Extended Tram Existing Local Makers Stations

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SCAN TO WATCH THE VIDEO

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THE STARBUKS EFFECT SEMESTER: FA LL - GIS

LOCATION: NEW YOR K

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PROJECT DISCRIBTION

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. !

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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 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.

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Walking Distance

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<660 ft

1 min

<1320 ft

2 min

<1980 ft

5 min

<2640 ft 10 min

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50.000001 - 75.000000 75.000001 - 100.000000 100.000001 - 104.567308 Gent_Race5p_HousholdIncome Gent_Race_Above5Perc Gentrifide NYC_CensusTract selection 3 Gent_St NYC_CensusTract selection 2 NYC_CensusTract selection Starbucks_NYC_Clean Starbucks_NYC_OpenDateAvailable_Format

Export_Output

09_17w_

-100.000000 - -28.979592 -28.979591 - 5.000000 5.000001 - 37.168142 37.168143 - 155.238095 155.238096 - 266.037736

Race

Black_09_13 0 - 33 34 - 92 93 - 171 172 - 263 264 - 360 361 - 457 458 - 559 560 - 694 695 - 856 857 - 1028 1029 - 1237 1238 - 1561 1562 - 2091 2092 - 3061 3062 - 7240

Process of Identifying Gentrified Tracts

Race_2009to2013_2013to2017

White_09 0 - 258 259 - 542 543 - 1134 1135 - 2100 2101 - 4053

Household_Income

Change -100.000000 - 50.000000 50.000001 - 75.000000 75.000001 - 100.000000 100.000001 - 250.000000 250.000001 - 457.030402

Household_Income

09Income 0 - 15000 15001 - 50000 50001 - 75000 Legend

Legend

Rent_Change

Rent_Change

Change

Change

Change 17Income

-100.000000 - 50.000000 50.000001 - 75.000000 75.000001 - 100.000000 100.000001 - 200.000000 200.000001 - 471.074380 Race_Gen Gentrifide_Merge Gent_Inc_Race_Rent

Gent_Median_Gross_Rent

Gent_Median_Gross_Rent

Gent_Median_Gross_Rent

Per_Change

Per_Change

Per_Change 1211 - 1553

!

!

26.391016 - 25.000000 25.000001 - 50.000000 50.000001 - 75.000000 75.000001 - 100.000000 100.000001 - 104.567308 Gent_Race5p_HousholdIncome Gent_Race_Above5Perc Gentrifide NYC_CensusTract selection 3 Gent_St NYC_CensusTract selection 2 NYC_CensusTract selection Starbucks_NYC_Clean Starbucks_NYC_OpenDateAvailable_Format

09_17w_

-100.000000 - -28.979592 -28.979591 - 5.000000 5.000001 - 37.168142 37.168143 - 155.238095 155.238096 - 266.037736

Race

Black_09_13 0 - 33 34 - 92 93 - 171 172 - 263 264 - 360 361 - 457 458 - 559 560 - 694 695 - 856 857 - 1028 1029 - 1237 1238 - 1561 1562 - 2091 2092 - 3061 3062 - 7240

Median Gross Rent <40th percentile

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-100.000000 - 50.000000 0 - 15000 50.000001 - 75.000000 15001 - 50000 75.000001 - 100.000000 50001 - 75000 100.000001 - 200.000000 75001 - 120000 200.000001 - 471.074380 120001 - 230000

Race_Gen Median_Gross_Rent

Gentrifide_Merge Gent_Inc_Race_Rent 0 - 643 644 - 1210

17Rent

26.391016 - 25.000000 25.000001 - 50.000000 50.000001 - 75.000000 75.000001 - 100.000000 100.000001 - 104.567308 Gent_Race5p_HousholdIncome Gent_Race_Above5Perc Gentrifide NYC_CensusTract selection 3 Gent_St NYC_CensusTract selection 2 NYC_CensusTract selection Starbucks_NYC_Clean Starbucks_NYC_OpenDateAvailable_Format

26.391016 1554 - 2141- 25.000000 25.000001 2142 - 3500- 50.000000

50.000001 - 75.000000 Median_Gross_Rent

75.000001 - 100.000000 100.000001 - 104.567308 -1770.000000 - 113.000000 Gent_Race5p_HousholdIncome 113.000001 - 441.000000 Gent_Race_Above5Perc 441.000001 - 988.000000 Gentrifide 988.000001 - 2002.000000 NYC_CensusTract selection 3 2002.000001 - 3500.000000 Gent_St NYC_CensusTract selection 2 09Rent NYC_CensusTract selection 0 - 284 Starbucks_NYC_Clean 285 - 775 Starbucks_NYC_OpenDateAvailable_Format 776 - 1105 Export_Output 1106 - 1446 09_17w_ 1447 - 2000 -100.000000 - -28.979592 NYC_CensusTract -28.979591 - 5.000000 5.000001 - 37.168142 37.168143 - 155.238095 155.238096 - 266.037736

09_17

Median_Gross_Rent

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Export_Output

09_17w_

-100.000000 - -28.979592 -28.979591 - 5.000000 5.000001 - 37.168142 37.168143 - 155.238095 155.238096 - 266.037736

Race

Race

Black_09_13

Black_09_13

0 - 33 34 - 92 93 - 171 172 - 263 264 - 360 361 - 457 458 - 559 560 - 694 695 - 856 857 - 1028 1029 - 1237 1238 - 1561 1562 - 2091 2092 - 3061 3062 - 7240

>500 Population

Increase In Median Gross Rent >60th percentile

0 - 33 34 - 92 93 - 171 172 - 263 264 - 360 361 - 457 458 - 559 560 - 694 695 - 856 857 - 1028 1029 - 1237 1238 - 1561 1562 - 2091 2092 - 3061 3062 - 7240

Increase In College Educated >60th percentile

Race_2009to2013_2013to2017

Race_2009to2013_2013to2017

Race_2009to2013_2013to2017

White_09

White_09

White_09

0 - 258 259 - 542 543 - 1134 1135 - 2100 2101 - 4053

0 - 258 259 - 542 543 - 1134 1135 - 2100 2101 - 4053

0 - 258 259 - 542 543 - 1134 1135 - 2100 2101 - 4053

Household_Income

Household_Income

Household_Income

Change

Change

Change

-100.000000 - 50.000000 50.000001 - 75.000000 75.000001 - 100.000000 100.000001 - 250.000000 250.000001 - 457.030402

-100.000000 - 50.000000 50.000001 - 75.000000 75.000001 - 100.000000 100.000001 - 250.000000 250.000001 - 457.030402

-100.000000 - 50.000000 50.000001 - 75.000000 75.000001 - 100.000000 100.000001 - 250.000000 250.000001 - 457.030402

Household_Income

Household_Income

Household_Income

09Income

09Income

09Income

0 - 15000 15001 - 50000 50001 - 75000 75001 - 120000 120001 - 230000

0 - 15000 15001 - 50000 50001 - 75000 75001 - 120000 120001 - 230000

0 - 15000 15001 - 50000 50001 - 75000 75001 - 120000 120001 - 230000

Household_Income

Household_Income

Household_Income

17Income

17Income

17Income

0 - 15000 15001 - 50000 50001 - 75000 75001 - 120000 120001 - 230000

0 - 15000 15001 - 50000 50001 - 75000 75001 - 120000 120001 - 230000

0 - 15000 15001 - 50000 50001 - 75000 75001 - 120000 120001 - 230000

Median_Gross_Rent

Median_Gross_Rent

Median_Gross_Rent

17Rent

17Rent

17Rent

0 - 643 644 - 1210 1211 - 1553 1554 - 2141 2142 - 3500

0 - 643 644 - 1210 1211 - 1553 1554 - 2141 2142 - 3500

0 - 643 644 - 1210 1211 - 1553 1554 - 2141 2142 - 3500

Median_Gross_Rent

Median_Gross_Rent

Median_Gross_Rent

09_17

09_17

09_17

-1770.000000 - 113.000000 113.000001 - 441.000000 441.000001 - 988.000000 988.000001 - 2002.000000 2002.000001 - 3500.000000

-1770.000000 - 113.000000 113.000001 - 441.000000 441.000001 - 988.000000 988.000001 - 2002.000000 2002.000001 - 3500.000000

-1770.000000 - 113.000000 113.000001 - 441.000000 441.000001 - 988.000000 988.000001 - 2002.000000 2002.000001 - 3500.000000

Median_Gross_Rent

Median_Gross_Rent

Median_Gross_Rent

09Rent

09Rent

09Rent

0 - 284 285 - 775 776 - 1105 1106 - 1446 1447 - 2000 NYC_CensusTract

Median Household Income

Rent_Change Household_Income

-100.000000 - 50.000000 50.000001 - 75.000000 75.000001 - 100.000000 100.000001 - 200.000000 200.000001 - 471.074380 Race_Gen Gentrifide_Merge Gent_Inc_Race_Rent

Export_Output

Median Gross Rent

75001 - 120000 Legend 120001 - 230000

Median Rent Value <40th percentile

0 - 284 285 - 775 776 - 1105 1106 - 1446 1447 - 2000 NYC_CensusTract

0 - 284 285 - 775 776 - 1105 1106 - 1446 1447 - 2000 NYC_CensusTract

>500 Population

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.

Increase In Median Rent Value >60th percentile

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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.

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Increase In College Educated >60th percentile


Site 1 - Brooklyn Percentage of Change In Median Rent From 2009 to 2017

The first site that we chose to examine is located in 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.

+100% ! ! !

100% to 75.1

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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.

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25% to 0

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Percentage of Change In Median Income From 2009 to 2017 +100% ! ! !

100% to 75.1

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%48.15

%41.13

Increase in Median Rent

Increase in Median Income

25% to 0

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Percentage of Change In White Population From 2009 to 2017 +100% ! !

%9.91

%61.68

Increase in White Population

Increase in Education of bachelorâ&#x20AC;&#x2122;s degree or higher

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Established Starbucks in the study period. (2009-2017) Established Starbucks before/after study period.

!

Identified Gentrified Tracts.

! !

25% to 0

Starbucks Boundary (Voronoi diagram) Gentrified Tracts from 2009 to 2017 & Starbucks Locations

Percentage of Change In Higher Education From 2009 to 2017 +100%

!

!

!

! !

!

!

100% to 75.1

!

G

G

!

!

!

!

!

!

!

G !

!

75% to 50.1

!

G !

!

!

50% to 25.1

!

!

!

! !

Starbucks Opening Date:

25% to 0

!

6/30/12

9/24/13

!

0

0.0275 0.055

Miles 0.11

19


SITE 2 - MANHATTAN - HARLEM Percentage of Change In Median Rent From 2009 to 2017

The second site is located in Manhattan, Harlem, which has one Starbucks stores opened in May 2008, in addition to 3 other stores opened before the study period 2009-2017.

+100%

!

100% to 75.1

!

75% to 50.1

G

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.

!

50% to 25.1 25% to 0

!

Percentage of Change In Median Income From 2009 to 2017 +100%

!

100% to 75.1

!

75% to 50.1

G !

50% to 25.1

%36.03

%31.49 Increase in Median Rent

Increase in Median Income

25% to 0

!

Percentage of Change In White Population From 2009 to 2017 +100%

%19.64

%15.7

Increase in White Population

Increase in Education of bachelorâ&#x20AC;&#x2122;s degree or higher

! !

75% to 50.1

G !

+ .

100% to 75.1

50% to 25.1

Established Starbucks in the study period. (2009-2017) Established Starbucks before/after study period.

25% to 0

!

Identified Gentrified Tracts. Starbucks Boundary (Voronoi diagram) entrified Tracts from 2009 to 2017 & Starbucks Locations

Percentage of Change In Higher Education From 2009 to 2017 +100%

!

!

!

100% to 75.1

!

G

G

!

!

75% to 50.1 50% to 25.1

!

Starbucks Opening Date:

!

5/22/08

0

0.0275 0.055

Miles 0.11

20

25% to 0


Conclusion The study was completed by looking at the Starbucks SITE 1 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.

Change from 2009 to 2017

Census Tracts Adjacent To Study Area the Study Area

City

Median Rent

31.49%

10.40%

26.58%

Median Household Income

36.03%

33.15%

14.97%

Change from 2009 to 2017

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%

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 SITE 2 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 visualized ,nearly half lie between the rent range of 1400-2100 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.

21


CLIMAKE WAR

THE GREATEST SECURITY THREAT

SEMESTER: FA LL - THE CITY A ND W H AT IT IS

LOCATION: GLOBA L

MORE GREENHOUSE GASES

INDUSTRIAL ACTIVITY LAND USE CHANGE TRANSPORT ET C.

OC EA N AC IDIFIC AT IO N GLOBAL W ARMING

Other Po llutio n Re lease o f trappe d methane

Drought

CLIMATE CHANGE

Slowing of the Gul f St ream

More and more powerful storms Glacial Retreat

Sink Holes

Pola r Vo rt ex

Melting Icecaps

Re duced Albedo Vo lcanic / seismic Activity

Water Shortages

Land Shortage

Food Sho rt ages

Migration Animal / Plant

Economic Impacts

s ea Level Rise

CRISI S EMERGENCY

The Multiplier Effect on Climate Change Data Source: Dave Sag, Medium.com

22


PROJECT DISCRIBTION

The United Nations warned a “climate apartheid” that would divide the world between those who have the means to adapt to higher temperatures and those who don’t, or those who can refugee migrants from other countries and those who don’t, being this a major geopolitical decision that needs to be consider as the humans consequences of Climate Change. But how this can affect conflict between individuals, armed groups, and national armies? In 2014, the Intergovernmental Panel on Climate Change (IPCC) was muted about whether climate change would contribute to a rise in conflict, the argue supported by some experts in how non-climate factors raise conflict risk. However, other experts argue that increased temperatures and declining rainfall have a substantial effect on violence at different levels. In 2017, an expert elicitation process to reconcile these differences. With the task of reaching evidence of violence related to climate change over the past century and what would be in the future. They found that climate change has contributed modestly to the risk of conflict and is less critical than other contributing factors that cause conflict and is the most uncertain (see figure 2). But if the world does not react, the risk of climate-induced violence is fivefold under the current scenario. Behind any conflict, there are a mix of political, economic, social, and environmental factors, and with the climate change “multiplier effect”, it can add to already stressed societies and fuel those most affected societies to start violent protests.

Climate change doesn’t directly cause conflicts and crisis; it exacerbates underlying problems in societies around the world, it is a multiplier effect, a major contributing factor, and both the Pentagon and NATO have defined climate change as a “threat multiplier”, think of it as a domino effect. In this section, we will look at how different climate change impacts builds on each other, causing a multiplier effect that eventually leads to crisis or conflicts. For example,higher temperatures cause increased evaporation in plant soils, which affects plant life and can reduce rainfall even more, causing severe droughts, the food costs spikes, and the threat of shortages begins to grow, which put farmers and communities that depend on agricultural land at risk, leading a mass migration to urban areas and cities, where most of these displaced populations struggle to find jobs, leading to higher unemployment rate, livelihood insecurity, adding higher pressure on infrastructures, housing, and lands, which can aggravate political grievances and encourage anti-government violence in the form of civil conflicts, riots, and violent protests.

23


MORE GREENHOUSE GASES

INDUSTRIAL ACTIVITY LAND USE CHANGE TRANSPORT ETC.

DIFICATION

Europe

ght

The IPCC expect “mutiple stress and systemic failures due to climate change” in the mediterranean. This will incease energy costs and damage toursim from 2050.

Reduced Albedo

olcanic seismic Activity

Migration

OCEAN ACIDIFICATION GLOBAL WARMING

Other Pollution Release of trapped methane

More and

Slowing of the Gulf Stream

more powerful storms

Glacial Retreat

Sink Holes

Asia

Drought

CLIMATE CHANGE

Melting Icecaps

Polar Vortex

Reduced Albedo Volcanic / seismic Activity

Land Shortages

Water Shortages

Sea Level Rise

Food Shortages

Migration

The majority of the people directly affected by sea level rise will be in southern and eastern Asia. However, water is also expected to affect most of Asia.

Animal / Plant

Economic Impacts

CRISIS EMERGENCY

+

+

+

+ MORE GREENHOUSE GASES

INDUSTRIAL ACTIVITY LAND USE CHANGE TRANSPORT ETC.

IFICATION

OCEAN ACIDIFICATION GLOBAL WARMING

Other Pollution

ht

Africa

Reduced Albedo

canic eismic tivity

gration

Food Security will be a major issue in Africa. Crops and livestock will be affected by both drought and flooding. There will also be moe soil erosion from storm.

Release of trapped methane

Drought

CLIMATE CHANGE

More and storms

Sink Holes

Australia

Slowing of the Gulf Stream

more powerful Glacial Retreat

Polar Vortex

Melting Icecaps

Reduced Albedo Volcanic / seismic Activity

Water Shortages

Land Shortages

Food Shortages

Migration Animal / Plant

Economic Impacts

Sea Level Rise

CRISIS EMERGENCY

Extreme wather is predicted. The Great Barrier Reef will continue to degrade, with warmer water leaching more coral.


25


HISTORICAL CASE

by a third. After the drought, the conurty faced the most severe crop failure, pushing more than 1.5 million people to flee from the countryside to the urban centers that had already influxes of refugees from Iraq. The government did little to help people with employment or services in these chaotic instant suburbs. According to some reports, it was largely in these areas that the uprising began.5

The world’s earliest documented water conflicts happened 4,500 years ago, near the junction of the Tigris and Euphrates rivers, when the armies of Lagash and Umma, city-states battled after Umma’s king drained an irrigation canal leading from the Tigris, leaving behind 60 soldiers dead on the bank of the canal.

“Drought can lead to devastating consequences when coupled with preexisting acute vulnerability.” (Shahrzad Mohtadi, Climate change and the Syrian uprising, 2012)6

The empire consisted of two distinct regions; an irrigated alluvial plain between the rivers in the south, and productive rain-fed agricultural regions in the north and the. The Akkadian Empire suddenly collapsed at 4,170 ± 150 years BP, leaving refugees from the north moving to the southern lowlands.1

According to a study published by PNAS, Syria became vulnerable because of the rapid population growth from 4 million in the 1950s to 22 million by 2010. The government encouragement of water-intensive export crops, illegal drilling of irrigation wells dramatically drained groundwater that might have provided reserves during dry years.

RECENT CASE: SYRIA According to NASA, the Tigris–Euphrates river comprising Turkey, Syria, Iraq, and western Iran had the highest loss of water, faster than any other place in the world; It lost 117 million acre-feet of stored freshwater as a result of poor water management policies and declining rainfall. 60% of the loss were attribute to pumping of groundwater from underground reservoirs.2 “A 2006 drought pushed Syrian farmers to migrate to urban centers, setting the stage for massive uprisings “ Smithsonian Magazine According to FAO, the farming infrastructure, including irrigation canals and grain depots, has been destroyed.3 The devastating drought beginning in 2006 forced many farmers to abandon their fields and migrate to urban centers. Some researches pointed that the migration fueled the civil war in Syria. “You had a lot of angry, unemployed men helping to trigger a revolution,” Aaron Wolf, water management expert at Oregon State University. “Climate change added to all the other stressors, it helped kick things over the threshold into open conflict. And a drought of that severity was made much more likely by the ongoing human-driven drying of that region.” Richard Seager, a climate scientist at Columbia University. The continuous increase in temperatures in the region has increased evaporation of moisture from soils, causing substantial droughts in the 1980s and 1990s (see figure. 8). However, 2006-10 was the worst and longest drought in Syria’s history.4 The Agricultural production, typically a quarter of the country’s gross domestic product, plummeted

26


G

G G

G G GG G G G G G G G G G GG GG G G G GG G G G G G G G G G G G G GG G GG GG G G G

%89.9

of agricultural land relies on rainfall (World Bank, 2010)

GGG

G G

G

G G G G G G G G G G

G G

G

G

G

G

G G

G G

G

G G

G G GG G G G

%60

G

G G

G

of agricultural land was affected by 2007-08 drought (IRIN, 2009)

G

G G

G

G G

G G G GGG G GGG

Syria’s Internal Migration After The War:

G

G

Urban Centers

G G GG G G G G G GG G G G G

+ Internal displaced persons (IDP)

3.5 3.0 2.5

Did The Drought In Syria Fuel The War?

2.0 1.5 1.0

1988-1993 Drought

1971 Syria implements Polices to increase agricultural yields and groundwater withdraws

1998-2000 Drought

0.5

2005-2010 Drought

1995 Syria achieves Self-sufficiency in wheat production 2001 Khabur River begins to dry up in northeast Syria

0

2007-2008 Driest winter on record

geo.nyu.edu

Internally Displaced Persons Net Urban Influx (in millions)

Data Source: Geographical Information System -

Syria’a internal migration after the war

2011 Syrian War

2007 Wheat, rice, and food prices have doubled 2005 Apartment Prices in Damascus have more than doubles

Syria’s timeline from 1970s to 2011. Data Source: Climate Central

Syria Climate Change Multiplier Impacts

Increasing Food Prices Rising Temperatures

Increasing Aridity

Drought Between 2006 and 2009

Causing 200,000 deaths

Outbreak of Civil War

Affecting 22 Million People The Multiplier effect on Syria. Data Source: Climate Central

6 Million Displaced

Crop Failures

27

Migration of 1.5 Million

Social Stresses

Escalated Into Multifaceted Conflict

Violent Uprising Began in 2011


RE-ARTICULATING CAPITAL SEMESTER: FA LL - UD STUDIO

LOCATION: A DDIS A BA BA - ETHIOPIA

Ambassador site, Addis Ababa

28


PROJECT DISCRIBTION

The city center in Addis Ababa is going through rapid development including a large influx of people and capital into the making of the CBD. The government and foreign developers are enforcing a generic vision of a ‘modern developed city’. Although it’s perceived as progressive, this development is in fact fragmenting the city, destroying ecosystems and widening socioeconomic gaps. Today’s development is monofunctional. Most of the capital is invested in real estate projects and hard infrastructure leading to privatized spaces. We can see it embedded in the Sheger project objectives of Real estate value, Tourism. These western ideologies of development failed to address the current challenges of Addis Ababa. In the diagram on the right ,we see the potential to reverse this urbanism to a multifunctional capital system which supports social and ecological capital as well.

Through several micro- interventions we saw the potential to recharge the aquifer, clean water, mitigate floods and collect water for public use.To create an inclusive social realm we aim to empower the local economy, enable accessibility throughout the city and improve the housing conditions. What if a “world class” city center is envisioned to leverage social and ecological capital in support of ecosystem restoration, an inclusive public realm and a local economy? Throughout the slide the social capital is red and ecological capital is green

SCAN TO WATCH THE VIDEO

29


PATCHES A NA LYSIS: FORCES A ND ACTORS The site is located in the city center and it is very diverse, throughout our site visits we observed 4 main patches, each one has different characteristics.

K EBELE HOUSING The Kebele housing located in the north part of the site, it is in poor quality, with homes made of wood and mud and without proper sanitation and infrastructure. It can be considered as sulms, and the new development in the city center puts pressure on the kebele housing to be displaced. However, we see this as an opportunity to improve the quality of the Kepele housing,to not displace the people who live thier, and to create a better inclusive housing in the city center.

FILWOH A HOT SPRINGS Located in the heart of Addis Ababa, an ecologically sensitive site, choked by the new developments.It is barely accessible to the public, fenced and not utilized.

MESK EL SQUA R E The largest public space in Addis Ababa,it hosts the main celebrations in the city, however, it is surrounded by wide streets, creating division and segregation from the surrounding land marks and cultural sites. The square is close to the river, but it is missing the connection and integration as most of the river surroundings.

30


31


PH ASING We envision this development through multiplicity of interventions rather than generic vision of a masterplan. We see the change as a process that is initiated by trigger points, then expands to a growth phase and finally becomes gradually a holistic network. The project begins with 3 triggers, by creating social nodes in the kebele housing , restoration and cleaning the river in the hot springs, and improving accessibility in the meskel square. These triggers will grow over time , overlap and create a self supporting network.

32


33


STR ATEGIC PLA N FOR SUSTAINA BLE A ND INCLUSIVITY To achieve the goals of an inclusive city centre we proposed gradual development from triggers,growth to a network of social realm, and rich ecology. To support this growth, the proposal will add additional 21 acres of green cover to the city. It will create about 600 green jobs through environmental custodianship. Additional +200,000 gallons of water holding capacity that will also be available for public use and recreation and double the kebele housing units in the city centre. This will improve the quality of life and create a healthy ecosystem.

34


K EBELE HOUSING |

IMPROVING CONDITION THROUGH SOCIAL AND ECOLOGICAL NETWORKS

Local materials

Stormwater Collection

Green Jobs

Training Playgrounds

Teaching

Trading

Fishing

Filtering

Stormwater reuse

Trade

Biogas

FILWOH A |

Gray water treatment

Custodians

Community Garden

Teaching

Retention ponds

Filtration + Irrigation

Stormwater storage

Training

Trade

INTEGRATING ECOLOGICAL CAPITAL TO THE DEVELOPING CITY

+25% Affordable Housing Government Policy

Commercial

Green Jobs

Riparian Vegetation

Public Bath

Pocket Parks Public Space

Bird Nesting Habitat

Bikelane

Bioswale

Geothermal Energy

Constructed Wetlands

Integrated Development

Permeable Surfaces

MESK EL SQUA R E |

Improved Connectivity

Flood Mitigation Retention Ponds

Constructed Wetlands

Inclusive Public Facilities

Geothermal Energy

IMPROVING CONNECTIVITY AND FLEXIBILITY Farmer market Trading unit+solar

Cultural path

Irrecha festival ponds

Museum connection

Riparian vegetation

Permeable pavement

Bike lane

Nature trail

Recharge wells

35


RECOMBINANT URBANISM: RIYADH SEMESTER: SPRING - R ECOMBINA NT URBA NISM

36

LOCATION: RIYA DH


PROJECT DISCRIBTION

SCAN TO WATCH THE VIDEO

37


Profile for GSAPP_Digital Publishing

Hatem AlKhathlan '20 MSAUD Columbia GSAPP  

Hatem AlKhathlan '20 MSAUD Columbia GSAPP