RENIA
RENIA KAGKOU PORTFOLIO Harvard University Graduate School of Design Master of Architecture in Urban Design, 2018 Master in Design Studies, Urbanism , Landscape & Ecology, 2018 Pratt Insitute School of Architecture Bachelor of Architecture, 2014
2018
R
01
RESEARCH agricultural geographies | graduate research is pacific limitless[ed]? | Urban Ttheory Lab wood urbanism | Publication
02
SPATIAL ANALYSIS
03
URBAN PLANNING & DESIGN
metroboston rent analysis | MIT 11.521 inequality in NYC | GSD 5407 california's energy landscape | Harvard CGA integrative manhattan | GSD 1221 water stress | GSD STU
recipro[city] | undergraduate thesis
4
RENIA KAGKOU | WORKSAMPLE
Crop Distribution in the United States 57.73% Grazing & Pasture 37.04% Commodity Crops 5.23% Specialty Crops Synthetic Map of Agricultural Production overlay of grazing, commodity crops and specialty crops
agricultural geographies
5
AGRICULTURAL GEOGRAPHIES 2017 An exploration based on Spatial Analysis and Land Use Modeling Mapping 2: Geosimulation | Robert Gerard Pietrusko [instructors], Ashley Thompson [partner] Independent Research | Robert Gerard Pietrusko [instructors]
This project tries to investigate the territorial dimension of urban metabolic processes focusing on the structure of the broader landscapes of food systems within the United States. It offers a complementary way of understanding urban metabolism which is not reduced to flows and networks but is directly connected to the ground conditions of the cultivation processes of various crops. Through a series of cartographic representations, systems analysis and simulation models that benefit from the recent proliferation of data and modeling tools, the research explores how a series of regulatory, economic and environmental parameters affect the formal, spatial and material attributes of the geography of food production and the structure of its land use patterns.
6
RENIA KAGKOU | WORKSAMPLE
FOOD SYSTEMS IN THE UNITED STATES REGULATIONS | POLICIES
MARKET | ECONOMICS
FEDERAL FARM PROGRAMS
DEMAND
GOVERNMENT PAYMENTS & SUBSIDIES
AGRIBUSINESSES
ENVIRONMENTAL FACTORS TEMPERATURE SOIL NUTRIENTS SOLAR RADIANCE
GOVERNMENT COMMODITY CREDIT CORPORATION LOANS
WATER
$ $$
$ $$$
$
INDUSTRIAL
GRAZING & PASTURE
SPECIALTY CROPS
COMMODITY CROPS
CROPS
PERISHABLE
PRODUCTION | SUPPLY
NON PERISHABLE
LOCAL | REGIONAL
BIOFUEL
LIVESTOCK
LIVESTOCK PRODUCTION COMMODITY EXPORTS COMMODITY IMPORTS FOOD PROCESSING & ASSEMBLY
INTERNATIONAL TRADE
PROCESSING
GROCERY MANUFACTURING FIRMS
PRODUCT IMPORTS BROKERS
PRODUCT IMPORTS
WHOLESALERS
FOOD SERVICE OUTLETS
CONSUMERS
CHAINS & RETAIL
CONSUMERS
CONSUMERS
CONSUMERS
DIRECT MARKETS
CONSUMERS
CONSUMERS
CONSUMERS
CONSUMPTION
CONSUMERS
DISTRIBUTION
FOOD SERVICE WHOLESALE ORGANIZATIONS
US Food Systems Diagram AGRICULTURAL PRODUCTION ENVIRONMENTAL FACTORS WATER AVAILABILITY
TEMPERATURE
SOIL CONDITION
SOLAR RADIANCE
ELEVATION
LAND SUITABILITY
GRAZING & PASTURE
SOCIAL & ENVIRONMENTAL OPTIMIZATION
COMMODITY CROPS
SPECIALTY CROPS
MARKET EFFICIENCY INDUSTRIES GLOBAL TRADE DEMAND
EFFICIENCY IN USE OF INPUTS RESILIENCE
The project grounds geographies of food systems not only by cataloging and representing the factors of agricultural production but also by comparing how different factors and their combinations are reflected upon variable patterns of the organization of agricultural land.
REVENUE
EQUITY
LOCATION KEY INPUTS & LABOR PROXIMITY TO MARKETS CONCENTRATION OF BUYERS
REGULATORY INPUTS
CAPITAL INPUTS
LABOR
GOVERMENT SUBSIDIES
FEDERAL FARM PROGRAMS
CORPORATION LOANS
REGULATORY FACTORS
Factors of Agricultural Production
LAND & BUILDINGS MACHINES & EQUIPMENT
FUELS, OIL & GASOLINE FERTILIZERS & CHEMICALS
FIXED COSTS
VARIABLE COSTS
ECONOMIC FACTORS
Factors analyzed: Federal Funding, Credit Loans , Government Payments, Cost of Land and Buildings, Equipment and Machinery Expenses, Fuel and Gas Expenses, Cost of Fertilizers and Chemicals and Labor Costs,
30
10 10
CROP %
CROP %
20
0 0
10
LAND & BUILDING EXPENSES ($)
20
30
40
50
60
70
80
10,000
20,000
30,000
7
agricultural geographies
0
0
40,000
50,000
60,000
70,000
80,000
90,000 +
FEDERAL FARM PROGRAMS & GOVERMENT PAYMENTS ($)
90
FEDERAL FUNDING & GOVERMENT PAYMENTS
10
CROP %
LAND SUITABILITY
0 0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
90,000 +
40
FEDERAL FARM PROGRAMS & GOVERMENT PAYMENTS ($)
90
FEDERAL FUNDING & GOVERMENT PAYMENTS
30
40
28.3%
20 71.7%
% OF CROPS THAT RECEIVE PAYMENTS 30
10
28.3%
31.8%
20
54.8%
45.2% 68.2%
CROP %
71.7%
% OF CROPS THAT 0 RECEIVE PAYMENTS 0
% OF CROPS THAT RECEIVE PAYMENTS
% OF CROPS THAT RECEIVE PAYMENTS 50,000
100,000
150,000
200,000
250,000
300,000
350,000
400,000
57.73% GRAZZING
GOVERMENT COMMODITY CREDIT CORPORATION LOANS
37.04% COMMODITY 5.23% SPECIALTY
Federal Funding, Credit Loans & Government Payments Grazing & Pasture Commodity Crops Specialty Crops 28.3%
31.8%
45.2%
Overlay of Regulatory Factors 50,000
100,000
150,000
200,000
250,000
300,000
350,000
400,000
450,000 +
GOVERMENT COMMODITY CREDIT CORPORATION LOANS ($)
% OF CROPS THAT RECEIVE PAYMENTS
40
54.8%
68.2%
71.7%
0 0
450,000 +
GOVERMENT COMMODITY CREDIT CORPORATION LOANS ($)
CROP DISTRIBUTION
10
% OF CROPS THAT RECEIVE PAYMENTS
% OF CROPS THAT RECEIVE PAYMENTS
40
GOVERMENT COMMODITY CREDIT CORPORATION LOANS
30
30
40
40
20
20
30
30
10
10
CROP %
CROPS %
20
20
0 0
500,000
LAND & BUILDING EXPENSES ($)
1,000,000
1,500,000
2,000,000
2,500,000
3,000,000
3,500,000
4,000,000
4,500,000 +
0
0
EQUIPMENT & MACHI
10
10
EQUIPMENT
LAND & BUILDINGS COSTS
0
CROPS %
0 0
500,000
LAND & BUILDING EXPENSES ($)
1,000,000
1,500,000
2,000,000
2,500,000
3,000,000
3,500,000
LAND & BUILDINGS COSTS
Example of Economic Factora [equipment, machinery fixed costs]
4,000,000
4,500,000 +
0
50,000
EQUIPMENT & MACHINERY EXPENSES ($)
100,000
150,000
200,000
250,000
300,000
350,000
400,000
450,000 +
Equipment & Machinery Expenses EQUIPMENT & MACHINERY COSTS Grazing & Pasture Commodity Crops Specialty Crops
40 40
30 30
20 20
10
CROP %
10
CROP %
80
CROP %
70
CROP %
60
0
LAND SUITABILITY
Environmental Factors [soil nutrition, temperature, precipitation]
0
0 10
20
30
Land Suitability LAND SUITABILITY Grazing & Pasture Commodity Crops Specialty Crops
40
50
60
70
80
90
40
30
20
8
RENIA KAGKOU | WORKSAMPLE
Currently policies and regulations are focussed on global market efficiency and incentivize the production of commodity crops (wheat, corn, alfa-alfa, soy) that are used and traded as a primary commodities for larger manufacturing processes, such as food processing, livestock production, trade and biofuel . SUPPLY
CROP YIELD
SUPPLY
CROP YIELD
ENVIRONMENTAL FACTORS
LAND SUITABILITY
REDUCES NEED FOR CAPITAL INPUTS
ENVIRONMENTAL FACTORS
COMPARATIVE ADVANTAGE SUPPLY
AGRICULTURAL EXPANSION & INTENSIFICATION CROP YIELD
LAND SUITABILITY
REDUCES NEED FOR CAPITAL INPUTS
COMPARATIVE ADVANTAGE
AGRICULTURAL EXPANSION & INTENSIFICATION
SUPPLY
ENVIRONMENTAL FACTORS
LAND SUITABILITY
CROP YIELD
REDUCES NEED FOR CAPITAL INPUTS
AGRICULTURAL EXPANSION & INTENSIFICATION
COMPARATIVE ADVANTAGE
ENVIRONMENTAL FACTORS
LAND SUITABILITY
REDUCES NEED FOR CAPITAL INPUTS
COMPARATIVE ADVANTAGE SUPPLY
AGRICULTURAL EXPANSION & INTENSIFICATION CROP YIELD
ENVIRONMENTAL FACTORS
LAND SUITABILITY
REDUCES NEED FOR CAPITAL INPUTS
COMPARATIVE ADVANTAGE
AGRICULTURAL EXPANSION & INTENSIFICATION
LOW COMMODITY PRICES LOW COMMODITY PRICES
REGULATORY FACTORS
AGRICULTURAL LOW COMMODITY POLICY SUPPORT
EMPHASIZE ON OPENING NEW MARKETS
LOW COMMODITY AGRICULTURAL PRICES POLICY SUPPORT
REGULATORY FACTORS EMPHASIZE ON OPENING NEW MARKETS
REGULATORY FACTORS
NEW MARKETS
PRICES
AGRICULTURAL POLICY SUPPORT
EMPHASIZE ON OPENING NEW MARKETS
LOW COMMODITY
REGULATORY FACTORS
FOOD PROCESSING INDUSTRY SUPPLY
EXPORTS
BIOFUEL
CROP YIELD
NEW MARKETS
FOOD PROCESSING INDUSTRY SUPPLY
EXPORTS
BIOFUEL
CROP YIELD
FARM PROGRAMS FOOD PROCESSING & SUBSIDIES INDUSTRY SUPPLY NEW MARKETS FARM PROGRAMS & SUBSIDIES
FARM PROGRAMS & SUBSIDIES NEW MARKETS
AGRICULTURAL PRICES POLICY SUPPORT
NEW MARKETS
AGRICULTURAL POLICY SUPPORT
FARM PROGRAMS & SUBSIDIES
FARM PROGRAMS & SUBSIDIES
ECONOMIC FACTORS
ECONOMIC FACTORS EMPHASIZE ON EFFICIENT MARKET OUTCOMES
ECONOMIC FACTORS
EMPHASIZE ON EFFICIENT MARKET OUTCOMES
REVENUE
FOOD PROCESSING INDUSTRY SUPPLY
PROCESSING REVENUEFOOD INDUSTRY SUPPLY
EXPORTS FOOD PROCESSING INDUSTRY SUPPLY
AVAILABILITY OF CAPITAL REVENUE
INVESTMENT IN AVAILABILITY OF FIXED COSTS CAPITAL INVESTMENT IN INVESTMENT IN REVENUE FOOD PROCESSING FIXED COSTS VARIABLE COSTS INVESTMENT IN AVAILABILITYFOOD OF PROCESSING INDUSTRYEXPORTS SUPPLY BIOFUEL INDUSTRY SUPPLY FIXED COSTS CAPITAL
EXPORTS
EMPHASIZE ON EFFICIENT MARKET OUTCOMES
EMPHASIZE ON EFFICIENT MARKET OUTCOMES
AVAILABILITY OF CAPITAL
AVAILABILITY OF CAPITAL
INVESTMENT IN FIXED COSTS
BIOFUEL
INVESTMENT IN INVESTMENT IN VARIABLE COSTS FIXED COSTS
AGRICULTURAL EXPANSION & INTENSIFICATION CROP YIELD AGRICULTURAL EXPANSION & INTENSIFICATION
AGRICULTURAL EXPANSION & INTENSIFICATION CROP YIELD
AGRICULTURAL EXPANSION & INTENSIFICATION
AGRICULTURAL EXPANSION & INTENSIFICATION
BIOFUEL
CROP YIELD
EXPORTS CROP YIELDBIOFUEL
CROP YIELD
INVESTMENT IN VARIABLE COSTS EXPORTSAGRICULTURAL BIOFUEL EXPANSION & INTENSIFICATION INVESTMENT CROPINYIELD VARIABLE COSTS
AGRICULTURAL EXPANSION & INTENSIFICATION CROP YIELD AGRICULTURAL EXPANSION & INTENSIFICATION
INVESTMENT IN AGRICULTURAL EXPANSION VARIABLE COSTS & INTENSIFICATION
AGRICULTURAL EXPANSION & INTENSIFICATION
EMPHASIZE ON EFFICIENT MARKET OUTCOMES
ECONOMIC FACTORS ECONOMIC FACTORS
BIOFUEL
CAPITAL INPUTS
EXPORTS BIOFUEL
CROP YIELD
EXPORTS
CAPITAL INPUTS
EMPHASIZE ON OPENING NEW MARKETS
REVENUE
BIOFUEL
CAPITAL INPUTS FOOD PROCESSING INDUSTRY SUPPLY CAPITAL INPUTS
CAPITAL INPUTS FOOD PROCESSING INDUSTRY SUPPLY
EMPHASIZE ON OPENING NEW MARKETS
REGULATORY FACTORS
EXPORTS
Factors Comparison
Having spatialized the all the variables of agricultural production, the second part of the research aims to examine the dynamic interplay of the three variables (capital inputs, regulatons, environmental) and investigate if it corresponds to meaningful correlations. Indeed this investigation reveals a series of contradictions. The exploration of these contradictions could of course be attributed to a multitude of social technical and economic factors. Highlighting the spatiality of these contradictions could allow for further research on understanding and optimizing these factors.
9
agricultural geographies
high suitability low yield
high suitability high yield
high yield low suitability
FACTORS COMPARIS
Environment - Yield Nexus
120,000
120,000
100,000
100,000
100,000
80,000
80,000
80,000
80,000
60,000
60,000
60,000
60,000
40,000
40,000
40,000
40,000
20,000
20,000
20,000
TORS COMPARISSON
COMMODITY YIELD
GRAZING YIELD
20,000
0 0
10
LAND SUITABILITY
20
30
40
50
60
70
80
0
90
0
10
LAND SUITABILITY
20
30
40
50
60
70
80
GRAZING YIELD
120,000
100,000
SPECIALTY YIELD
120,000
0
90
0
10
LAND SUITABILITY
20
30
40
50
60
70
80
0 0
90
10
LAND SUITABILITY
ALL CAPITAL INPUTS
CAPITAL INP
ENVIRONM 120,000
120,000
120,000
100,000
100,000
100,000
80,000
80,000
80,000
60,000
60,000
60,000
40,000
40,000
40,000
20,000
20,000
20,000
120,000
80
0
10
LAND SUITABILITY
20
30
40
50
60
70
80
SPECIALTY YIELD
0
90
0
90
0
10
LAND SUITABILITY
20
30
40
50
60
70
80
80,000
0
90
0
10
LAND SUITABILITY
20
30
40
50
60
70
80
90
60,000
CAPITAL INPUTS: 2,000,000 - 2,200,000 40,000
ENVIRONMENT - YIELD NEXUS
Grazing & Pasture Commodity Crops Specialty Crops
20,000
GRAZING YIELD
70
COMMODITY YIELD
GRAZING YIELD
100,000
0 0
500,000
CAPITAL INPUTS ($)
LAND SUITA
120,000
120,000
120,000
120,000
120,000
120,000
100,000
100,000
100,000
100,000
100,000
100,000
80,000
80,000
80,000
80,000
80,000
80,000
60,000
60,000
60,000
120,000
100,000
80,000
10
RENIA KAGKOU | WORKSAMPLE
FACTORS COMPARISS
120,000
120,000
120,000
100,000
100,000
100,000
100,000
80,000
80,000
80,000
80,000
60,000
60,000
60,000
60,000
40,000
40,000
40,000
40,000
20,000
20,000
COMMODITY YIELD
GRAZING YIELD
20,000
0 0
10
LAND SUITABILITY
20
30
40
50
60
70
80
0
90
0
10
LAND SUITABILITY
20
30
40
50
60
70
80
20,000
GRAZING YIELD
ORS COMPARISSON
SPECIALTY YIELD
120,000
0
90
0
10
LAND SUITABILITY
20
30
40
50
60
70
80
0 0
90
10
LAND SUITABILITY
ALL CAPITAL INPUTS
2
CAPITAL INPUTS ENVIRONMENT
120,000
120,000
120,000
100,000
100,000
100,000
80,000
80,000
80,000
60,000
60,000
60,000
40,000
40,000
40,000
20,000
20,000
20,000
120,000
80
0 0
90
10
LAND SUITABILITY
20
30
40
50
60
70
80
SPECIALTY YIELD
COMMODITY YIELD
GRAZING YIELD
100,000
0
90
0
10
LAND SUITABILITY
20
30
40
50
60
70
80
80,000
0
90
0
10
LAND SUITABILITY
20
30
40
50
60
70
80
90
60,000
CAPITAL INPUTS: 2,000,000 - 2,200,000
high inputs high yield
high yield low inputs
40,000
20,000
GRAZING YIELD
ENVIRONMENT high inputs - YIELD NEXUS low yield
Capital Inputs - Yield Nexus
0 0
500,000
CAPITAL INPUTS ($)
1,00
LAND SUITABILI
120,000
120,000
120,000
120,000
100,000
100,000
100,000
100,000
100,000
100,000
80,000
80,000
80,000
80,000
80,000
80,000
60,000
60,000
60,000
60,000
60,000
60,000
40,000
20,000
20,000
20,000
20,000
20,000
0 0
0
500,000
CAPITAL INPUTS ($)
1,000,000
1,500,000
LAND SUITABILITY: 60 - 90 CAPITAL INPUTS ($) 0
500,000
1,000,000
1,500,000
2,000,000
2,000,000
2,500,000
2,500,000
3,000,000
3,000,000
3,500,000
3,500,000
4,000,000
4,000,000
4,500,000 +
0
4,500,000 +
100,000
80,000
60,000
40,000
20,000
20,000
0
0
SPECIALTY SPECIALTY YIELD YIELD
40,000
40,000
COMMODITY COMMODITY YIELD YIELD
40,000
GRAZING GRAZING YIELD YIELD
40,000
40,000
120,000
500,000
CAPITAL INPUTS ($) 0
500,000
CAPITAL INPUTS ($)
1,000,000
1,000,000
1,500,000
1,500,000
2,000,000
2,000,000
2,500,000
2,500,000
3,000,000
3,000,000
3,500,000
3,500,000
4,000,000
4,000,000
4,500,000 +
0
0
4,500,000 +
0
500,000
1,000,000
1,500,000
2,000,000
2,500,000
3,000,000
0
500,000
1,000,000
1,500,000
2,000,000
2,500,000
3,000,000
CAPITAL INPUTS ($)
CAPITAL INPUTS ($)
3,500,000
3,500,000
4,000,000
4,000,000
4,500,000 +
GRAZING YIELD
120,000
120,000
0 0
4,500,000 +
500,000
CAPITAL INPUTS ($)
1,00
LAND SUITABILI
ALL LAND SUITABILITY
120,000
120,000
120,000
100,000
100,000
100,000
80,000
80,000
80,000
60,000
60,000
60,000
40,000
40,000
20,000
20,000
120,000
100,000
0
500,000
CAPITAL INPUTS ($)
1,000,000
1,500,000
2,000,000
2,500,000
3,000,000
3,500,000
4,000,000
0
4,500,000 +
60,000
20,000
SPECIALTY YIELD
0
4,500,000 +
0
500,000
CAPITAL INPUTS ($)
1,000,000
1,500,000
2,000,000
2,500,000
3,000,000
3,500,000
4,000,000
40,000
0
4,500,000 +
0
500,000
CAPITAL INPUTS ($)
1,000,000
1,500,000
2,000,000
2,500,000
3,000,000
3,500,000
4,000,000
4,500,000 +
20,000
GRAZING YIELD
LAND SUITABILITY: 30 - 60
0 0
500,000
CAPITAL INPUTS ($)
120,000
Grazing & Pasture Commodity Crops Specialty Crops
1,00
LAND SUITABILI
120,000
120,000
100,000
100,000
100,000
80,000
80,000
80,000
60,000
60,000
60,000
40,000
40,000
40,000
20,000
20,000
20,000
Y
4,000,000
COMMODITY YIELD
GRAZING YIELD
80,000
40,000
ECONOMIES OF SCALE, AFTER AN INPUT LEVEL THE YIELD DOES NOT INCREASE ALTHOUGH THE INPUTS INCREASE. QUESTIONS RAISE: ARE LOW INPUTS/HIGH YIELDS ON HIGHLY SUITABLE LAND? OR ARE HIGH INPUTS LOW YIELD RETURNS ON VERY UNSUITABLE LAND?
CAPITAL INPUTS
11
agricultural geographies
1.50
1.50
1.50
1.25
1.25
1.25
1.0
1.0
1.0
0.75
0.75
0.75
0.5
0.5
0.5
120,000
100,000
80,000
60,000
0
CROP YIELD
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
40,000
0.25
0.25
0
90,000
0
CROP YIELD
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
90,000
20,000
0 0
CROP YIELD
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
90,000
Regulatory Inputs - Yield Nexus
GRAZING YIELD
GRAZING %
0
high yield low inputs
SPECIALTY %
high inputs high yield
0.25
COMMODITY %
high inputs low yield
0 0
100,000
REGULATORY INPUTS
LAND SUITA
120,000
120,000
100,000
100,000
80,000
80,000
80,000
80,000
0
100,000
200,000
200,000
300,000
300,000
REGULATORY INPUTS ($) LAND SUITABILITY: 60 - 90 LAND SUITABILITY: 30 - 60
400,000
400,000
500,000
500,000
600,000
600,000
700,000
700,000
800,000
800,000
900,000 +
0
120,000
120,000
100,000
100,000
100,000
200,000
300,000
200,000
300,000
400,000
400,000
500,000
500,000
600,000
600,000
700,000
700,000
800,000
800,000
900,000 +
60,000
40,000
500,000
600,000
700,000
800,000
900,000 +
LAND SUITABILITY: 30 - 60 0
120,000
900,000
900,000 +
100,000
200,000
300,000
400,000
500,000
600,000
700,000
800,000
0
900,000 +
0
100,000
REGULATORY INPUTS
1,200,000
Grazing & Pasture Commodity Crops Specialty Crops
1,500,000
1,800,000
2,100,000
2,400,000
2,700,000+
120,000
100,000
80,000
40,000
60,000
60,000
20,000
40,000
SPECIALTY YIELD
40,000
0 0
20,000
100,000
REGULATORY INPUTS ($)
200,000
300,000
400,000
500,000
600,000
700,000
800,000
900,000 +
0 0
300,000
CAPITAL INPUTS ($)
600,000
900,000
1,200,000
1,500,000
1,800,000
2,100,000
2,400,000
2,700,000+
0
20,000
SPECIALTY YIELD
400,000
COMMODITY YIELD
GRAZING YIELD
0
600,000
800,000
60,000
20,000
300,000
700,000
80,000
40,000
CAPITAL INPUTS ($)
600,000
80,000
COMMODITY YIELD
20,000
40,000
300,000
500,000
100,000
60,000
200,000
400,000
100,000
40,000
60,000
0
0
60,000
100,000
300,000
120,000
80,000
0
200,000
REGULATORY INPUTS ($)
80,000
REGULATORY INPUTS ($)
100,000
120,000
100,000
20,000
0
REGULATORY INPUTS ($)
0
900,000 +
120,000
80,000
GRAZING YIELD
100,000
REGULATORY INPUTS ($)
LAND SUITA
100,000
900,000 +
20,000
0
0
0
REGULATORY INPUTS ($)
80,000
800,000
0
900,000 +
120,000
700,000
20,000
0
100,000
REGULATORY INPUTS ($)
200,000
300,000
400,000
500,000
600,000
700,000
800,000
900,000 +
0 0
20,000
GRAZING YIELD
100,000
SPECIALTY SPECIALTY YIELD YIELD
COMMODITY COMMODITY YIELD YIELD
GRAZING GRAZING YIELD YIELD
0
REGULATORY INPUTS ($)
40,000
20,000
20,000
0
0
40,000
20,000
20,000
60,000
40,000
40,000
20,000 90,000
60,000
40,000
40,000
80,000
60,000
60,000
40,000
100,000
80,000
60,000
60,000
120,000
100,000
100,000
60,000
80,000
120,000
100,000
80,000
000
120,000
120,000
100,000
GRAZING YIELD
120,000
300,000
CAPITAL INPUTS ($)
600,000
900,000
1,200,000
1,500,000
1,800,000
2,100,000
2,400,000
2,700,000+
0 0
100,000
REGULATORY INPUTS
LAND SUITA
120,000
120,000
100,000
100,000
100,000
80,000
80,000
80,000
60,000
60,000
60,000
40,000
40,000
40,000
20,000
20,000
20,000
SUBCIDIES -
12
RENIA KAGKOU | WORKSAMPLE
Urban Agglomerations urban areas in relation to water system
Agricultural Production Land specialty and commodity crops in relation to water system
Building on the basis of these observations, the second part of the research focuses on the Colorado River Basin and speculates three scenarios of policies and regulation of agricultural productivity.
agricultural geographies
ENVIRONMENT
AGENT SYSTEMS
REGULATIONS
CAUSAL LOOPS
ATMOSPHERIC
ECONOMICS (+) EVAPORATION
B
(-)
B (-) POPULATION GROWTH (+)
(+)
SURFACE WATER INFRASTRUCTURE DEVELOPMENT (+)
WATER DEMAND (+)
- CAPTURE - STORAGE
PRECIPITATION
TEMPERATURE
DECLINE
(+)
RISE
(+)
(+) (-) FOOD & FODDER PRODUCTION
(+)
(-)
SURFACE WATER AVAILABILITY & ACCESS FOR USE (+)
- WITHDRAWAL - DIVERSTION
- RECREATION - AGRICULTURE - MUNICIPAL & INDUSTRIAL (+)
URBAN & RURAL CONSUMPTION (+) (+)
TERRESTRIAL
(+) HYDROELECTRIC POWER PRODUCTION & LARGE SCALE MINING
(+) (+) SOIL, LAND & HABITAT DEGREDATION (+)
WATER QUALITY DEGREDATION (+)
(+)
REINFORCING
(-)
BALANCING
Colorado River Basin Loop Diagram environment, regulations and economics 243,000 SQM | 1,450 M SUMMIT TO SEA
REGULATION LAW OF THE RIVER
ECONOMIC + ENVIRONMENTAL PRODUCTION
DISTRIBUTION
1915
USE + CONTROL
SOURCE: Rocky Mountain Western Slope
FRAME: Urban Agglomerations
80% AGRICULTURAL 20% MUNCIPAL + INDUSTRIAL (40M PERSONS)
1917 – League of the Southwest
GRAND LAKE, COLORADO 2000 M
1935
1944 – Mexico Water Treaty
1948 – Upper Colorado River Basin Compact
1955
UPPER BASIN
1931 – California Seven Party Agreement
-- SUPPLEMENTS EXTERNAL TRIBUTARIES --
1928 – Boulder Canyon Project Act
DENVER METRO BOULDER FORT COLLINS COLORADO SPRINGS PUEBLO CHEYENNE
GUNNISON RIVER 1 ASPINALL UNIT, CO BLUE MESA DAM COLORADO FRONT RANGE PROJECT LITTLE SNAKE RIVER WATER PROJECT
CENTRAL UTAH PROJECT STRAWBERRY VALLEY PROJECT
RIO GRANDE RIVER ALBUQUERQUE SANTA FE
YAMPA RIVER
2 FLAMING GORGE DAM, UT
SAN JUAN RIVER
ROCKY MOUNTAIN NATIONAL PARK
CURECANTI NATIONAL RECREATION AREA
BLACK CANYON OF GUNISSON
DINOSAUR NATIONAL MONUMENT ARCHES NATIONAL PARK
1500 M
CANYONLANDS NATIONAL PARK
3 NAVAJO DAM, NM SAN JUAN CHAMA PROJECT
INTER-BASIN WITHDRAWAL
1956 – Colorado River Storage Project Act
CRYSTAL DAM
GREEN RIVER
ARKANSAS RIVER SALT LAKE CITY PROVO
MORROW POINT DAM
CONSERVATION -- INAUGURAL STORAGE UNITS --
SOUTH PLATTE RIVER
1925
1945
MAJOR TRIBUTARIES
TRANS-BASIN WITHDRAWAL
1922 – Colorado River Compact
MAJOR RESEVOIRS
GRAND JUNCTION 4 GLEN CANYON DAM, AZ 1965
1975
GLEN CANYON NATIONAL RECREATION AREA
LAKE POWELL
1964 – Arizona v. Colorado
1000 M
LEE’S FERRY
1966 – National Historical Preservation Act 1968 – Colorado River Basin Project 1969 – National Environmental Policy Act 1970 – Criteria for Coordinated Long Range Reservoir Operations 1973 – Endangered Species Act & Mexico Commission 1974 – Colorado River Basin Salinity Control Act
HOOVER DAM, AZ/NV LAS VEGAS
LAKE MEAD DAVIS DAM, AZ
GRAND CANYON NATIONAL PARK LAKE MEAD NATIONAL RECREATION ARE + 34 NATIVE AMERICAN RESERVATIONS
1992 – Grand Canyon Protection Act
1995
-- SOLE SOURCE --
1985
LOWER BASIN
LAKE MOHAVE
PHOENIX TUCSON
PARKER DAM, AZ
500 M
LAKE HAVASU CITY LOS ANGELES SAN DIEGO YUMA / GILA RIVER IMPERIAL VALLEY COACHELLA VALLEY
LAKE HAVASU
COLORADO RIVER AQUADUCT
IMPERIAL DAM, AZ/NV
IMPERIAL RESEVOIR
ALL-AMERICAN CANAL
MORALES DAM, AZ 2005 2007 – Interim Shortage Guidelines
SEA LEVEL 2015
GULF OF CALIFORNIA, MEXICO 500M
1000M
1500M
Colorado River Basin Systems Diagram [Regulatory, Economic, Environmental]
2000M
2500M UPSTREAM
13
35000
30000
25000
14
RENIA KAGKOU | WORKSAMPLE
20000
15000
10000
5000
2015
0
35000
30000
25000
Land Suitability Analysis unsuitable
suitable
[1] Commodity Crop Incentives Scenario Model cash-crops are subsidized
20000
[2] Specialty Crop Incentives Scenario Model specialty crops (fruit and vegetables) are subsidized 15000
very suitable
10000
35000 5000
2015
25000 35000 20000 30000 15000 25000 10000 20000 5000 15000 0 10000
2015
[1] Current Condition cash-crops are subsidized
30000 0
2015
3000 0 2500 2000 1500 1000 500 35000
0
30000
2015
[2] Reverse Hypothesis specialty crops are subsidized
5000 3500
25000 35000
15000 25000 10000 20000
0 10000
2015
5000 15000
5000
0 2015
[3] Equal Hypothesis no crops are subsidized
20000 30000
[3] No Incentives Scenario Model cash-crops and specialty crops are not subsidized
Commodity Crops Specialty Crops
Crop Growth
3500 3000 2500
15m
5000
0m
0
Water Supply
0 0
0
-20000 -20000
-20000
-40000 -40000
-40000 0
0 -20000 -40000
0m 20m
15m15m
15m
25m 5m 20m 0m
40m
0m 50m 45m
35000
40m 35m
35m
30000
25000
30m
25000
20000
25m
30m 25m
20000
20m
15000
15m
10000
35m 60m35m 60m
35m 60m
60m 35m
25000 40000
25000 40000
30m 50m30m 50m
30m 50m
50m 30m
20000 35000
20000 35000
25m 45m25m 45m
25m 45m
45m 25m
15000 30000
20m 40m
15000 30000
20m 40m20m 40m
40m 20m
15m 35m
10000 25000
15m 35m15m 35m
35m 15m
10000 25000
10m 30m10m 30m
10m 30m
30m 10m
5000 20000
5000 20000
5m 5m 25m25m
5m 25m
25m 5m
0 15000
0m 0m 20m20m
0m 20m
20m 0m
15m15m
15m
10000 10000
10000
10000
10m10m
10m
50005000
5000
5000
5m 5m
5m
5m
0
0
0m 0m
0m
0m
Regional Labor
50000 50000
50000
45000 45000
45000
40000 40000
40000
35000 35000
35000
50000 45000 40000
15m
2015
Regional Revenue
70m70m
70m
60m60m
60m
50m50m
50m
45m45m
45m
40m 70m40m 70m
40m 70m
70m 60m 50m 45m
2100
10m
2015
2015
2100
0
0 15000
2100
70m 40m
30000 45000
2015
45m
40m 70m
30000 45000
2015 2015
45m
40m 70m40m 70m
2100
2015
2100
50m
45m45m
2100
50m
40000 35000 50000
2100
40000 35000 50000
2100
50m50m
40000 40000
2100
5m 70m 0m 60m
2100
060m
2100 2100
0m 60m 60m
2100
0 45000
2015
5000 5m 70m 70m 70m
2100
10m
5000 50000
2100 2100
10m
2100
20m
10000
5m 60m
2100
35000
5m 0m
40000
2015
45m
45000
15m 10m 70m
2015
0m 0m 50m
2015
2100
2100
2100
2100
2015
5m 5m 60m
15m
2015 2015
30m 10m
10m 10m10m 70m 50000
50000 5000 45000 0
35m 15m
2015
2100
2100
2100 2100
2015
2015
10000
2100
0m 0m 20m20m
40m 20m
2100
5m 25m
45m 25m
2100
5m 5m 25m25m
5000 20000
2100
10m 30m
2100
10m 30m10m 30m
10000 25000
50m 30m
2100 2100
15m 35m
60m 35m
2100
15m 35m15m 35m
70m 40m
2100
20m 40m
2015
20m 40m20m 40m
45m
2015
2015
25m 45m
2015 2015
25m 45m25m 45m
15000 30000
0m 50m
2015
2100
2100 2100
2100
2015
20000 35000
2015
2015 2015
2015
2015 2015
30m 50m
40000
5m 60m
35000 35000 50000 50000 30000 30000 45000 45000 25000 25000 40000 40000 20000 20000 35000 35000 15000 15000 30000 30000 10000 10000 25000 25000 5000 5000 20000 20000 0 0 15000 15000
0
2100
2100
2100
2015
2015
2015
2015
2100
-160000 -160000 -160000-160000
2100
-140000 -140000 -140000-140000
30m 50m30m 50m
2100
2100
2100
-120000 -120000 -120000-120000
2015
2015
2015 2015
2100
-100000 -100000 -100000-100000 -160000 -160000 -160000 -160000
2100 2100
-80000 -80000 -80000 -80000 -140000 -140000 -140000 -140000
25000 40000
0 15000
10m 70m
050m
35m 60m
2100 2100
-60000 -60000 -60000 -60000 -120000 -120000 -120000-120000
50m 0m50m
35m 60m35m 60m
2100
-40000 -40000 -40000 -40000 -100000 -100000 -100000-100000
15m
10000 70m
40m 70m
2100
-20000 -20000 -80000 -80000
20m
40m 70m40m 70m
35000 50000 30000 45000
2015
-20000 -20000 -80000 -80000
25m
15000
45m
2015
0 -60000 -60000
30m
20000
45m45m
2015
0 0 -60000 -60000
0
35m
60m 5m60m 500060m
15000
10m -140000 5m 5000 50000 50000 50000 0m -160000 0 45000 45000 45000 2015
0-160000 -20000 -20000
40000
25m
30000
2015 2015
-140000 5000 0 0
45000 0
2015
2100
2100
2100
2100
2015
10000
2100
2015
2015
10000
-40000 -40000
0
0 15000
15000 -12000015m
-40000 -40000
0
5000 20000
15000 -120000
2015
2100
0
10000 25000
25m 20000 -100000 20m
-140000 0 0 -160000 -20000 -20000
15000 30000
20000-100000
2100
500
20000 35000
-8000030m 25000
2100
-120000
-80000
2100
1000
25000 40000
40m -60000 30000 35m
30000 25000
30000 45000
35000
-60000
2015
1500
35000
2015
2015
3000
2015
2100
-140000 -140000
-20000 45000-160000 -20000 -160000 -160000 -160000 40000 -40000 -40000
35000 50000
10000 10000 10000 070m 50000 5000500060m 5000 45000 -20000 0 0 050m 40000 -4000045m 2015
2100
2100
2100
0
35000 35000 50000 50000 30000 30000 45000 45000 25000 25000 40000 40000 20000 20000 35000 35000 15000 15000 30000 30000 10000 10000 25000 25000 5000 5000 20000 20000 0 0 15000 15000
2015
2015 2015
2100
2015
-40000
10000 10m 50000 50000 50000 -140000 5m 5000 45000 45000 45000 0m -160000 0 40000 40000 40000
40m
2015
70m70m 10m
3500 0 50000-140000 -140000
-100000
25000
50000 5000
-120000 -120000 -120000 -120000
-80000
30m
15m
20000
45m
30000
10000
-100000 -100000 -100000 -100000 -160000 -160000 -160000-160000
2000
25000
50m
40000
35m
20m
0 0 -60000 -60000 -20000 -20000 -80000 -80000 -40000 -40000-100000 -100000 -60000 -60000 -120000 -120000 -80000 -80000 -140000-140000
2500
30000
60m
45000
35000
40m
15000
-40000
-60000
45m
15000 -12000015m
2100
-80000 -80000 -140000 -140000
35000
25m 20000 -100000 20m
2100
-60000 -60000 -120000 -120000
50m
-100000 20000
2100 2100
-40000 -40000 -100000 -100000
40000
-8000030m 25000
2015
2015
-20000 -20000 -80000 -80000
50000
60m
25000
10000 0 0 0 0-140000 5000 -140000 5000 -20000 -20000 -20000 -20000-160000 0 0 -160000
0 0 -60000 -60000
70m 70m
2015
40m -60000 30000 35m
10000 15000-120000
-40000 -40000
10 ACRES OF SPECIALTY $20,000 OF REVENUE 20 ACRE-FT OF WATER 10-20 JOBS PER DAY
2015
35000
2015
-100000
2100
20 ACRE-FT OF WATER 10-20 JOBS PER DAY
REVENUE
45000
2015
15000
2100
10 ACRES OF SPECIALTY $20,000 OF REVENUE 20 ACRE-FT OF WATER 10 ACRES OF SPECIALTY 10-20 JOBS DAY $20,000 OF PER REVENUE
WATER
50000
070m
60m 45000 -20000 50m 40000 -4000045m
30000 Agricultural Production Diagnostics -80000
-80000
-120000
50000
5m
0m
2015
SPECIALTY CROP SPECIALTY CROP
20000
2015
-60000
2015
ENCY
2015
ENCY
-20000 1045000 ACRES OF COMMMODITY -20000 30000 $3,500 OF REVENUE 40 -40000 ACRE-FT OF WATER 1040000 ACRES OF COMMMODITY 1-5 JOBSOFPER DAY $3,500 REVENUE -40000 25000 40 ACRE-FT OF WATER 35000 1-5 -60000 JOBS PER DAY
10 ACRES OF COMMMODITY $3,500 OF REVENUE 40 ACRE-FT OF WATER 1-5 JOBS PER DAY
10m
2100
5m
2015
0
2015
10m
0m -160000 0
15m
10000
5000
2015
AGRICULTURAL PRODUCTION DIAGNOSTIC WATER COMMODITY LABOR CROP REVENUE 0 WATER COMMODITY LABOR CROP REVENUE
0 35000 50000
20m
15000
2100
20m
LABOR
LABOR SOCIAL EFFICIENCY
25m
20000
2015
10m -140000 5m 5000
REVENUE ECONOMIC EFFICIENCY
25000
15
2100
10000
25m
30m
2100
30m
30000
The investigation is developed through three simulation scenarios for crop subsidies where each produces different social, environmental and economic outputs. Overall, this research allows SPECIALTY COMMODITY notCROP only to make spatial senseCROPof the variables that affect agricultural production but also offers a platform for further research on understanding and optimizing these factors both socially and environmentally. 2015
2015
15000 -12000015m 10000
2100
0
0
-160000
15000
2100
10000-140000 5000 5000 -160000
2015
2100
-140000
WATER ENVIRONMENTAL EFFICIENCY
2015
-120000
-120000 10000 15000
35m
20000
25m 20000 -100000 20m
40m
agricultural geographies 35m
35000
40m
25000
-8000030m 25000
25000 -100000 20000
2100
15000
30000
40m -60000 30000 35m
2100
-80000 -100000
35000
2100 2100
20000
30000 -80000
45m
40000
45m
2100
35000 -60000
50m
45000
50m
35000
60m
50000
60m
40000
2015
25000
2015
-60000
-40000
70m
45000
2015 2015
40000 -40000
070m
60m 45000 -20000 50m 40000 -4000045m
45000
2100
-20000 30000
70m
50000 50000
-20000
2100
0 0 35000 50000
IS PACIFIC LIMITLESS[ED]? Investigating exploitations of oceanic ecological production in the Pacific 16
RENIA KAGKOU | WORKSAMPLE
Renia Kagkou | Ashley Thompson
Harvard Graduate School of Design
Pacific Ports
Exclusive Economic Zones
Fishing Density
Fishing Vessel Density
Synthetic Map of Pacific Processes of Urbanization overlay of vessel density, EEZs & Pacific ports
© Renia Kagkou | Ashley Thompson
is pacific limitless[ed] | UTL
17
IS PACIFIC LIMITLESS[ED]? 2016 Investigating exploitations of oceanic ecological production in the Pacific Urban Theory Lab | Neil Brenner & Robert Gerard Pietrusko [instructors], Ashley Thompson [partner] The project uses AIS transmission vessel data provided by navama® project website: http://navama.com/?p=863
One of the only remaining ‘last frontiers’, the Pacific Ocean is a rare site in the 21st century still capable of supporting the exploration and appropriation of new territories and resources, what Moore terms ‘cheap nature’. Despite historic notions of a vast expanse of endless resource, the Pacific is in fact far from limitless. Analyzing the reorganization of the ocean as an operational landscape, we identify processes of urbanization, and specifically the exploitation of wild food-source by the commercial fishing industry driven by capitalist accumulation, commodification, and privatization. The investigation reveals the intensification and connectivities of the fishing industry to the agglomeration in relation to territorial rights and competitive access to open-seas, patterns of regional and international trade, and the role of states versus operations of transnational corporations to include considerations of labor and technology. The intensification of these practices demonstrates how commercial fishing has been rendered disassociated both with supply and demands of food systems, but also from the resource base, the marine biomass itself. Instead, fishing industries and the regulatory and governance frameworks within which they operate are dependent on achieving economies of scale to secure projected assurances to fish, synonymous with present and future profit. Furthermore, exploitative manipulation of these political and economic mechanisms are in fact jeopardizing access to and heath of wild fisheries with massive implications for the enduring stability of the Pacific Ocean as an enduring source of sustenance, livelihood, commerce, and culture.
18
RENIA KAGKOU | WORKSAMPLE
Global ports
Regulatory Fishery Bodies
Exclusive Economic Zones & Bathymetry
Territorial Rights and Access to Open Seas Exclusive Economic Zones and Regulatory Fishery Bodies
Pacific Fish Production
Pacific Fish Production Per Capita
Global Fish Consumption
Global Fish Consumption Per Capita
IS PACIFIC LIMITLESS[ED]? Investigating exploitations of oceanic ecological production in the Pacific Renia Kagkou | Ashley Thompson is pacific limitless[ed] | UTL
19
Harvard Graduate School of Design
Global Fish Trade
Population Density
Fishing Vessel Density
Global Fish Trade
© Renia Kagkou | Ashley Thompson
Operationalization of the Pacific Population Density Mapping relational geographies Fishing Density
200
150
100
50
0 1950 0 ft
Depth Ecologies Pacific Bathymetry in Section
100 ft
200 ft
300 ft
1960
1970
1980
1990
2000
2010
400 ft
Wild Catch vs Aquaculture Rise of aquaculture as wild fish decreases
Delineating the bounds of an increasingly limitless[ed] ocean, the result of this diminutive dynamic will result in overcapitalization that sinks profits to satisfy endless capital investment as all other marine social and ecological resources are inversely depleted. Investing more and more for less and less, shoring up profit margins on speculation and intensification until the system collapses, long after all the wild fish are gone – the ultimate exhaustion of cheap nature.
20
RENIA KAGKOU | WORKSAMPLE
Population < 10,000
10,000 - 20,000
20,000 - 50,000
50,000 - 100,000
200,000 - 500,000
500,000 - 1,000,000
1,000,000 - 2,000,000
2,000,000 - 5,000,000
Territorial Calibration in Relation to Population territorial zones are redistributed to respond to population values of urban agglomerations
is pacific limitless[ed] | UTL
21
Synthetic Map of Alter-Urbanization gradient oceanography of territorial and resource calibration
Major Fish Habitats Suitability Map mapping thermohaline circulation and fish ecosystems
Exclusive Economic Zones existing territorialization of the Pacific
Proposal for a new notional urban-oriented oceanography that incorporates spatial conditions deployed through gradient strategies of ocean surface, depth, and access parameters while accommodating the transient nature of a swimming biomass and the interdependent ecological systems that impact their lifecycle. We seek to disrupt this market-resource genocide by envisioning alternative processes of commercial fishing to redistribute territorial capital development through the intersection of national, commercial, and local privileges for biomass extraction across scale and function indicators predicated on the urban.
22
RENIA KAGKOU | WORKSAMPLE
WOOD URBANISM 2016 From the Molecular to the Territorial, Daniel Ibañez, Jane Hutton and Kiel Moe Visual Essay using Satellite Data and Remote Sensing GSD | Daniel Ibañez [partner]
By using false color composite techniques, this visual-essay reveals the patterns of urban transformation across very diverse landscapes looking at wavelengths and intensity of visible and near-infrared light reflected by the land surface. Near-infrared light reflected provides a quantity that correlates with the capacity of the lands to absorb and synthesize solar energy. If the land surface reflects very low values it corresponds to barren areas of rock, sand or snow, but also typically urban infrastructures such as roads and buildings. Moderate values represent shrub and grassland, while high values indicate temperate and tropical rainforests. This is process enables to quantify the concentrations of green leaf and vegetation. It provides a gradient (from blue to red) that measures what is alive (red) from what is not (blue). But also, it identifies where plants are thriving and where they are under stress. The selected images bring together eight geographies of the earth’s surface, rendering a particular urbanization process within and beyond urban agglomerations. By freezing a particular moment in time, each of them depicts a unique pattern within a larger, dynamic and ongoing process of transformation – deforestation, logging, land-grabbing, formal and informal plantations, urban agglomerations, etc. The visual essay proposes a threefold reading: First, a comparative reading of the different sites by scale – all satellite images are at 1:250,000; Second, it enables a comparative reading of the imprints on the ground and the patterns of urban transformation – from areas of regional specialization to urban agglomerations; And third, a comparative reading of the false composite images revealing the gradients and arrays of alive vegetation in contrast with lifeless surfaces.
wood urbanism
23
[Amazon 1.250000m.jpg]: The Amazon is being dramatically deforested in support of global urbanization. The connectivity infrastructures that enable the extraction of timber and the deployment of soy plantations and cattle are creating unprecedented patterns of deforestation in the ParĂĄ State, in Brazil.
[Barnapito 1.250000m.jpg]: The Amazon has become a site of land-grabbing providing economic surplus to international investment firms. Both the regulatory framework and the landscape has been subject of a radical transformation to enable massive agricultural crops of soy beans, in Bamapito region, State of Bahia in Brazil.
[Brunei 1.250000m.jpg]: Ancient rainforests in Indonesia are being massively plundered in support of global capitalism around the globe. These two areas of land-grabs located in the periphery of Brunei are three times the size of Paris and have been completely erased in support of paper industries.
[El Chaco 1.250000m.jpg]: El Chaco in Paraguay has been radically transformed in the last twenty years. Both the ecological and economical value of the area has been plundered though massive illegal logging, deployment of agricultural field and cattle farming.
[Irving 1.250000m.jpg]: Forest plantation by large timber corporation in the border between US and Canada. In the image we see Spruce-fir forest of J.D. Irving, Limited Sawmill Division.
[Kansas 1.250000m.jpg] Massive agricultural crops of corn, wheat and sorghum in Kansas State, USA. As many other areas of the mid-west, this huge center-pivot irrigation system enable the operationalization and intensification of land in support of food demand.
[NYC 1.250000m.jpg] New York City is the urban agglomeration and the landscape of consumption per excellence. In contrast to other urbanized areas of the earth, here the polarization between areas of biomass â&#x20AC;&#x201C;typically urban parks- and the areas characterized by the dense urban fabric is very distinct.
[Punchaw 1.250000m.jpg] Punchaw, in British Columbia, Canada is one of the most productive timber regions in North America. This region is easily recognizable by a scattered pattern of plantations and logging strategies.
24
RENIA KAGKOU | WORKSAMPLE
METRO-BOSTON RENT ANALYSIS 2017 Using Craigslist data to understand rental conditions Prepared for Metropolitan Area Planning Council (MAPC) MIT 11.521 | Joe Ferreira [instructor], Matthew Archer, Liana Banuelos & Omar De La Riva [partners]
Problem and Context Over the past two years, the MAPC has collected data on 1.95 million rental listings in Massachusetts to better understand housing affordability and spatial variation in rental prices in the Boston Metropolitan area. Recognizing and predicting these patterns can help the MAPC work with partner cities to propose effective policy that preserves affordability in communities throughout the region. Because the listing information was collected through a scraping process on websites such as Craigslist and PadMapper, the data includes listings that are duplicates and/or improperly geolocated. Drawing meaningful relationships and conclusions from this dataset, then, can be hindered by the excess, and often incomplete, information. .
MAPC Initial Data Summary Statistics
Histogram of MAPC Initial Data
MAPC Clean Data Summary Statistics
Histogram of Cleaned Data
MIT 11.521 | metroboston rent analysis
25
To aid in the MAPC efforts, our team set out to: 1. 2. 3. 4. 5. 6. 7.
Establish an effective, replicable method for de-duplication Distinguish between listings seeking housemates and whole units Determine average rent prices (using only whole units) Use average rents to re-run distinction between housemates and units Transform housemate listing prices to full unit prices Examine the effect of site specific and unit specific amenities on price Understand these characteristics spatially
Data Cleaning Graphic Representation values removed & final numbers
All Listings all listings (roommate all & full listings units) (roommate & full units) roommate and whole price
Whole Unit Listings only full price unit listings only full price unit listings only whole price
26
RENIA KAGKOU | WORKSAMPLE
Defining Neighborhood Size based on a transit, businesses & households
Studio Seasonality Postings variability per season
1 Bedroom
MIT 11.521 | metroboston rent analysis
Average Rent by Neighborhood rent accuracy comparison
Studio
1 Bedroom
2 Bedroom
3 Bedroom
Average Rent by Neighborhood & Type apartment type & accuracy comparison
27
28
RENIA KAGKOU | WORKSAMPLE
Indicators of Rent Price Per Bedroom Our team considered the following variables : 1. Number of Bedrooms 2. Proximity to Neighborhood Centers 3. Distance from MBTA Rail Stop 4. Amenities : Deck, Porch, Yard, Brick, Laundry, Parking, AC, Gas & Heat, Dishwasher, Hardwood Floors, Electricity Included, Utilities Included, No Brokerâ&#x20AC;&#x2122;s Fee, In Unit Gym, Concierge, Loft, Natural Light, Pets Allowed, Storage ,Internet
Concierge
Concierge Concierge
Deck
In Unit Gym
InInUnit UnitGym Gym
Yard
Spatial Location of Amenities
Deck Porch
PorchYard
Yard
MIT 11.521 | metroboston rent analysis
29
Model & Collinearity
A
A
Adjusted r2 = 0.5932 Results
30
RENIA KAGKOU | WORKSAMPLE
quartile representation[blocks] representation[calls]
calls from quartile 1
50,000 45,000
1
25%
25%
40,000
2
25%
24%
30,000
25% 25%
calls
3 4
calls from quartile 2
35,000
26% 25%
calls from quartile 3
25,000 20,000 15,000
calls from quartile 4
10,000 5,000 0 0
mean block income is $52500
500
1,000
1,500
2,000
2,500
3,000
income
3,500
4,000
4,500
5,000
5,500
total
6,000
Physical Disrepair 0
10
311 Call volume and Income normalized calls [*1000]
100
1200
15000 +
$35296
$52500
$75215
$250000
GSD 5407 | inequality in NYC
31
INEQUALITY IN NYC 2015
Water System Tunnel Condition Condition Traffic Signal /Missed Sweeping ate g/Inadequ Sweepin n - Missing g Sig Street Danglin Sign aged Street gn - Dam ition nd Si n Street t Light CoConditio l Stree Street chanica ter a e e - M g W ites dpip Standin lled Segee ta e l Stan S Squanica n ch itio er Me nd w r - Co Seint nkle alk la ce Spri Sidew mp an ty Co en afe t ter in S n d en Ma old itio ar t r C ol aff nd g C ea nio ho Sc k Co rkin l H ility Se Sc al Pa ntia ac e rF sid o Re ce n- an No ten in Ma
ew
Sid
er/
ew
/S
ot
Ro
H H ig Hig ighw hwa hw ay y S ay Sig ign ral S n C Fir onst Hig ign - - D Mis e h Fir Ala ructio way Damang sing l e r Fire Alar m - R n/Plu Con age ing m d d Fire Alarm - Re eplac mbin ition Ala - N ins em g p e Fire rm - M ew S ectio nt Ala odifi yste n rm - A catio m Ferr Ferry dditionn y Co Inqu mpla iry Elev int Elect ator ri DamagDead Treecal ed Tree Cu Cranes rb Condition and De rricks Constructi Bus Stop She on lter Placem ent Building/U Broken Parking Meterse Broken Muni Meter Bridge Condition Bike/Roller/Skate Chronic Bike Rack Condition
Ge
ne
Residential
epair
Public disorder
P Ra Pu ublic dio blic P ac R As ayp tiv a se ho mb ne P e M nge ly C ub at ho Po stin Pu - Te omp lic T eria od g A blic mp lain oile l dv As ora t t ert se ry is m Ove Overg Po eme bly rflo iso nt win rown n P Ove g Rec Tree/B anha PlaIvy rflow yclin ra nd n nc lin t g in Opin g Litte Baske hes g ion rB ts Open for the askets Flam May Noi e Pe or Noise - Stre se - Vehrmit et/Sid icle ewalk Noise House Noise - Park of Worsh Noise - He ip Noise - Com licopter mercial Noise Municipal Parking Facilit y Missed Collection (All Materials) Litter Basket / Request
Unclassified
nt lai mp nt et o k as aC e -B es un cem -A su Sa la int int pla pt cy Is ool/ Rep t o om nt ke pla as aC e Ad gen ch/P Card ter g om -B ues aun acem t-Ay Iss ol/S epl A ea efit Wa lin ard C int p t R pla B en led abe lac om ueAdso enc h/Po ard er t Req AgBeaecnefilet Cd WaatbelinPglacard C B ott ie L le P ues Req ion B ott ie L le t B alor ehic a r tion ic B alo eh lic lica C ity V laint t int C ity V laint t App int App C mp men ompla icense C mp imen ompla icense Co mpli er C ew L est Co nsum OH N Requ Co mpl er C ew L Residential Co A / D rature equest st DC A Lite ture R equest ent Co nsum OH N Reque t DC P Litera ture R quirem PhysicaloDisrepair C A / D rature eques DE Litera Savings Ret DFTA Income Reques t DC A Lite ature R equest HS terature n ption D e em Ex m DOF LiParking - Tax ction Issue DC P Liter ature R du equire DOF perty - Re DE A Liter Savings R t DOF Pro rature Request DOT Lite ature Request DFT Income Reques DPR Liter n F59 o tion S re Unclassified ti Inspec H EAP D Literatu ax Exemp Emergency Response Team (ERT) e DOF Parking - T Fire Safety Director - F58 tion Issu Forensic Engineering DOF roperty - Reduc HPD Literature Internal Code Request DOF P rature Request Investigatio te Li ns and Dis T Invita DO cipline (IAD e Request ) Labo tion DPR Literatur - F59 Lega ratory Misc. l Services Pr EAP Inspection Comm ovider Misce Compl OEM llaneo ents ai nt Emergency Response Team (ERT) Oth Lite us Ca Re er En rature tegorie Fire Safety Director - F58 Sp ques force Reque s Sp ecia t for In ment st Forens ic Engineering S ecia l Enfo form Public disorder Taxpecial l Natu rceme ation HPD Literature Request i Co Pro ral n mp ject Area t A Internal Co lim s In Dis A ir Q ent spe tric A nim ua Investig de ctio t (S A nim al lity n T NAD B nim a Ab eam ) Invitatio ations and Disciplin Co EST al l Fac use (SP e (IAD) llec /Sit in a ility IT) Labo n tio e Pa - N n T Saf rk o P Legalratory ru ety erm S c e k rv it Misc. ices P No C is ro omm vider C Mis e ompla OE cellane ents int Ot M Lite ous Ca Reqher Enf rature R tegories eque Sp ues orce Sp ecia t for In ment st Speecial l Enfor forma c N Tax cial atu eme tion i Co Pro ral n mp ject Area t A lim s In Dis An ir Qu ent spe tric An ima alit ctio t (S A im l y n T NAD B ni a Ab eam ) Co ESTmal l Fac use (SP lle /S in a ilit IT) ct ite P y ion S ar N Tr afe k o Pe uc ty rm kN it ois e
l na cle er icy cle s Int B hi le R lict Ve hic ns DP re lict Ve tio th De re ict di ou De erel y Con rly Y D irt rde nt D iso ing rmit shme int D nk e bli pla Dri rry P sta ning om Fe ood EPoiso hicle CReport F od e Ve icle Fo r Hir e Veh ps Fo Hir Was Foraffiti g Bees/ rials Gr rborin s Mate se ou e/U n Ha g d r a r Haza at Sto ampme Hazmeless Encrson Ass Hom eless Pe t as Hom Animal Kep Illegal Animal Sold Illegal reworks Illegal Fi Illegal Parking age Illegal Tree Dam Industrial Waste
cial Noise Parking Facility (All Materials) asket / Request
Research Questions 1. Who complaints, where and about what? 2. How differences in government response time vary across social groups and location in NYC?
$35296
$52500
$75215
$250000
Constituents Block Group Residents statified into 4 income quartiles residential
l na cle er icy le s Int B hic le R lict Ve hic ns DP ere elict t Ve ditio uth D er elic on Yo D er y C rly D irt rde nt D o g it me int Disrinkin Permablish g pla D erry Est onin Com rt F ood Pois hicle Repo F od e Ve icle Fo r Hir e Veh asps Fo r Hir s/W ls Fo ti Bee a ffi Gra oring Materi Harbzardous rage/Use ent Ha mat Sto campm tance Haz eless En rson Assis Hom eless Pe as Pet Hom l Animal Kept Illega imal Sold Illegal An works Illegal Fire ing Illegal Park Illegal Tree Damage Industrial Waste
servation Water Con rk Rules of Pa Vendingt Violation Lo Vacantblic Pu ing in n Urinat Conditiorty eon Prope lity ig P itary l Pvt Faci og Unsan ry Anima Animal hed D t ary Unleas plain raffic nita T ort Unsa Unsanit om p er C i Re int rovid P Tax mplaooingg tion i Co Tatt nnin ow orta Tax Ta Sn ing nsp Tra ok n t Sm ditio den n o t Co R en m ion t ce ita or f n n Sa gE lin yc c Re
GSD Spatial Analysis for the Built Environment | Andres Sevtsuk [instructor] Arianna Salazar, Lindiwe Rennert, Lucy Perkins, Omar De La Riva, Rida Qadri [partners]
APPLIANCE Asbestos Blocked Drive way Boilers DOOR/W INDOW Drinking ELEC Water FLOO TRIC RING/ Found Pro STAIRS GE HE NERAL perty Ind AT/HO In oor A T WA Le door ir Qu TER Mo ad Sewag ality e O ld P UTS P AIN IDE S LU T/P BU S AF MBIN LAS ILD UN CRIEETY G TER ING W S W A AN W ateTER ITAR ind r L Y C ow Qua EAK OND ITIO Gu lity N ar d
Water System Tunnel Condition l Condition
APPLIANCE Asbestos Blocked Drive way Boilers DOOR Drinkin/WINDOW ELE g Water FLO CTRIC FounORING/ST AIRS GE d Pro HE NERAL perty IndoAT/HOT In or A WA Le door ir Qu TER M ad Sewag ality e OUold P TS P AIN IDE SA LUM T/PL BUIL S F BI AS D U CR ET NG TER ING W NS IE Y W AT ANIT W ate ER AR ind r L Y C ow Qua EAK OND ITI Gu lity ON ar d
Visualizing Complaints using 311 data
physiscal disrepair
.
dropped
public disorder
Amenity Studied 200+ Complains Grouped residential, physiscal disrepair, public disorder .
32
RENIA KAGKOU | WORKSAMPLE
quartile representation[blocks] representation[calls] 25%
25%
2
25%
24%
3 4
25% 25%
26% 25%
calls from quartile 1
300,000
calls from quartile 2
250,000 200,000
calls
1
350,000
calls from quartile 3
150,000 100,000
calls from quartile 4
50,000 0 0
mean block income is $52500
500
1,000
1,500
2,000
2,500
3,000
income
3,500
4,000
4,500
5,000
5,500
total
6,000
Residential 0
10
311 Call volume and Income normalized calls [*1000]
50
100
200 +
$35296
$52500
$75215
$250000
GSD 5407 | inequality in NYC
quartile representation[blocks] representation[calls] 25%
25%
2
25%
24%
3 4
25% 25%
26% 25%
calls from quartile 1
300,000
calls from quartile 2
250,000 200,000
calls
1
350,000
calls from quartile 3
150,000 100,000
calls from quartile 4
50,000 0 0
mean block income is $52500
33
500
1,000
1,500
2,000
2,500
3,000
income
3,500
4,000
4,500
5,000
5,500
total
6,000
Public Disorder 0
20
311 Call volume and Income normalized calls [*1000]
40
60
80 +
$35296
$52500
$75215
$250000
34
RENIA KAGKOU | WORKSAMPLE
Physical Disrepair Response 0
100
Residential Response 200
300
400 +
311 Complaints Response Time measured in hours
311 Number of Calls Increase in number of calls is negatively correlated with property value. This could be because properties of higher value are less likely to have absentee landlords and have higher exposure to other complaint solving avenues than those of lower market value. Additionally, there is a negative relationship between quantity of physical disrepair complaints and real-estate values. This is probably because issues of physical disrepair in high property value areas are handled quickly, before residents feel motivated to call in and complaint whereas disrepair in lower property values lingers, warranting a call.
GSD 5407 | inequality in NYC
35
Public Disorder Response
311 Response Time Response time statistics was expected to show a negative relationship between income and response time because government is more responsive to richer block groups. (Brown & Coulter,1983) There is a positive correlation between average response time and real-estate value for residential complaints. This may be caused by difference in the type of residential complaint made from high property value areas and low property value areas. Complaints from low value areas may be deemed more problematic and, as a result, placed above high value area complaints with respect to hierarchy of response urgency.
36
RENIA KAGKOU | WORKSAMPLE
CALIFORNIA'S ENERGY LANDSCAPE 2016 Harvard Center for Geographic Analysis GIS Institute Training CGA | Scott Bell & Jeff Blossom [instructors],
low California Built Density intensity of the built environment
medium
high
CGA | energy landscapes
37
California's Energy Landscape A few years after the Western U.S. Energy Crisis, the State of California is in the midst of a clean energy rush with a goal for 33 percent renewable energy by 2020. This one week mapping exercise focuses on the regionâ&#x20AC;&#x2122;s energy landscape and efficiency. The projects analyses, at a county level, the production capacities of Californiaâ&#x20AC;&#x2122;s power plants in relation to energy consumption levels (some of the counties will present a surplus of energy whereas others a deficit). Part of the overall energy production infrastructure constitutes of wind turbines, photo-voltaic parks and hydroelectric dams. The exercise investigates the spatial configuration of such renewable energy plants within the State of California, in an effort to study how these power plants are associated with previously analyzed conditions of surplus and shortage.
60000
50000
Millions of kWh
40000
30000
20000
50-100 kWh 100-250 kWh 250-500 kWh 500-1000 kWh 1000-2000 kWh 2000-5000 kWh 5000-10000 kWh 10000-30000 kWh 30000-75000 kWh
10000
0
Counties
Electricity Consumption [kWh]
Non-Residential Electricity Consumption Residential Electricity Consumption
Non Residential Consumption
Residential Consumption
Non Residential Consumption
Residential Consumption
3000
2500
Millions of Thermes
2000
1500
1000
0-5 therms 5-10 therms 10-20 therms 20-40 therms 40-70 therms 70-170 therms 170-350 therms 350-1000 therms 1000-3000 therms
Gas Consumption [1 therm=29.3001 kWh] California Energy Consumption consumption per county
500
0
Counties
Non-Residential Gas Consumption Residential Gas Consumption
38
RENIA KAGKOU | WORKSAMPLE
16000
14000
12000
MWh
10000
8000
6000
4000
2000
0
Counties
Overall Per County Energy Production
1234 Power Plants
!
!
! ! !
!
509 Nonrenewable Power Plants 50312.132 MWh production
!
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0-38 MWh 38-86 MWh ! 86-130 MWh ! 130-180 MWh ! 180-230 MWh ! 230-280 MWh ! 280-320 MWh ! 320-370 MWh ! 370-420 MWh ! 420-470 MWh ! 470-510 MWh ! 510-560 MWh ! 560-610 MWh ! 610-2500 MWh
! ! !
! ! ! !
!
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! !
0-38 MWh 38-86 MWh ! 86-130 MWh ! 130-180 MWh ! 180-230 MWh ! 230-280 MWh ! 280-320 MWh ! 320-370 MWh ! 370-420 MWh ! 420-470 MWh ! 470-510 MWh ! 510-560 MWh ! 560-610 MWh ! 610-2500 MWh !
Nonrenewable Energy Electricity Power Plants [2009]
!
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Renewable Energy
725 Renewable Power Plants 25904.764 MWh production
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CGA | energy landscapes
39
For the study, solar energy, wind power, geothermal energy, hydroelectric energy and bioenergy were categorized as renewable and sustainable energy sources. While oil, gas, municipal solid waste, landfill gas and nuclear were categorized as nonrenewable sources of energy. Although, all â&#x20AC;&#x153;waste to energyâ&#x20AC;? modes of production could be considered renewable, municipal solid waste and landfill gas were excluded due to their harmful by-products.
20000
10000
0
MWh
- 10000
- 20000
- 30000
- 40000
2000-3000 MWh 1000-2000MWh 500-1000 MWh 0-500 MWh (-)500-0 MWh (-)1000-(-)500 MWh (-)2000-(-)1000 MWh (-)5000-(-)2000 MWh (-)10000-(-)5000 MWh (-)20000-(-)10000 MWh (-)30000-(-)20000 MWh
- 50000
Alameda Alpine Amador Butte Calaveras Colusa Contra Costa Del Norte El Dorado Fresno Glenn Humboldt Imperial Inyo Kern Kings Lake Lassen Los Angeles Madera Marin Mariposa Mendocino Merced Modoc Mono Monterey Napa Nevada Orange Placer Plumas Riverside Sacramento San Benito San Bernardino San Diego San Francisco San Joaquin San Luis Obispo San Mateo Santa Barbara Santa Clara Santa Cruz Shasta Sierra Siskiyou Solano Sonoma Stanislaus Sutter Tehama Trinity Tulare Tuolumne Ventura Yolo Yuba
- 60000
Counties
Electricity Surplus & Deficit [2009]
Net Values 2009
20000
10000
0
MWh
- 10000
- 20000
- 30000
Electricity Surplus & Deficit [2014] California Electricity Surplus & Deficit comparison between 2009 and 2014
- 40000
- 50000
- 60000 Alameda Alpine Amador Butte Calaveras Colusa Contra Costa Del Norte El Dorado Fresno Glenn Humboldt Imperial Inyo Kern Kings Lake Lassen Los Angeles Madera Marin Mariposa Mendocino Merced Modoc Mono Monterey Napa Nevada Orange Placer Plumas Riverside Sacramento San Benito San Bernardino San Diego San Francisco San Joaquin San Luis Obispo San Mateo Santa Barbara Santa Clara Santa Cruz Shasta Sierra Siskiyou Solano Sonoma Stanislaus Sutter Tehama Trinity Tulare Tuolumne Ventura Yolo Yuba
2000-3000 MWh 1000-2000MWh 500-1000 MWh 0-500 MWh (-)500-0 MWh (-)1000-(-)500 MWh (-)2000-(-)1000 MWh (-)5000-(-)2000 MWh (-)10000-(-)5000 MWh (-)20000-(-)10000 MWh (-)30000-(-)20000 MWh
Counties
Net Values 2014
40
RENIA KAGKOU | WORKSAMPLE
INTEGRATIVE MANHATTAN 2015 Harvard GSD Eelements of Urban Design Pre-term Mapping Wortkshop GSD 1221 UPD Fall | Robert Gerard Pietrusko [instructor], Claudia Tomateo [partner]
06-08 hours
08-10 hours
10-12 hours
12-14 hours
14-16 hours
16-18 hours
Annual Shading [1] low Shadow Analysis intensity of shading in Manhattan, NY
medium
high
GSD 1221 | integrative manhattan
41
Manhattan’s Two Surfaces The research focused on understanding Manhattan and its complexities through the visualization off its intangible surfaces as well as their methods of interaction. The assumption made is that outdoor comfort and urban microclimate conditions relates to the vibrancy of social interactions. The two main parameters are classified as “hardscape”; the preconditioned physical form of the city, and “softscape”; the social dynamics that include physical and virtual interaction between people. The two surfaces are overlapped and generate a new representation of Manhattan. The final diagram explores through sections the potential of a correlation between the two conditions of the city. In order to account for scale variations within the city one section is taken from Lower Manhattan and one from Midtown. Although in certain points there seems to be a strong correlation this cannot be generalized and lead to particular conclusions about the affects of urban microclimate and outdoor comfort on social vibrancy.
06-08 hours
08-10 hours
10-12 hours
12-14 hours
14-16 hours
16-18 hours
Annual Solar Irradiance[2] <1000 kWh/m^2 Daylight Analysis sun power per unit area in Manhattan, NY
1700 kWh/m^2
2300 kWh/m^2
42
RENIA KAGKOU | WORKSAMPLE
Building Heights[3] low
medium
high
Vegetation Desnity[6] low
medium
high
Additional Factors atrributes that were considered in the analysis
Building Age & Energy Use[4]
Population Density[5]
1765-1880 1881-1921 1922-1961 1962-2013
low
Commercial Desnity[7]
Public Amenities[8]
80 kBTU/sqf/yr 74 kBTU/sqf/yr 71 kBTU/sqf/yr 76 kBTU/sqf/yr
low
medium
high
medium
public spaces
transportation
high
GSD 1221 | integrative manhattan
06-08 hours
08-10 hours
10-12 hours
12-14 hours
14-16 hours
16-18 hours
43
Annual Social Media Check-Ins[9] Social Activity Analysis check-ins for Manhattan by MIT’s Civic Data Lab
Urban Microclimate [Hardscape] | Social Dynamics [Softscape] For the definition of the “hardscape” a different conception of microclimate is generated by breaking it down to its primary parameters. A set of attributes that contribute to the conditions of Manhattan’s urban microclimates are identified and analyzed. Shading, solar irradiance, building heights, building’s energy consumption and vegetation density and population density all contribute to small temperature variations in the city and affect human’s outdoor comfort. The attributes were merged in order to generate an overall definition and classification if the urban microclimate. For the “softscape” surface that represents social dynamics diverse factors that seem to affect interaction levels are taken into account. Major parameters are agglomeration of commerce, public amenities and geo-located social media activities.
44
RENIA KAGKOU | WORKSAMPLE
06-08 hours
08-10 hours
Urban Microclimate[1,2,3,4,5,6] & Social Dynamics[7,8,9] Comparative Analysis an overlay of urban microclimate with social media activities
Lower Manhattan Section Sectional Analysis comparison of microclimate and social media activity
10-12 hours
12-
-14 hours
GSD 1221 | integrative manhattan
14-16 hours
Midtown Manhattan Section Sectional Analysis comparison of microclimate and social media activity
16-18 hours
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Altitudes of Extraction water extraction & population analysis
GSD STU | water stress
WATER STRESS 2016 Analyzing water availability and withdrawal in Jakartaâ&#x20AC;&#x2122;s Urban Agglomeration Jakarta Studio | Felippe Correa [instructor],Mark Jongman-Sereno [partner]
No city is sinking faster than Jakarta because of a phenomenon called subsidence. This surface motion happens when extraction of groundwater causes layers of rock and sediment to slowly pancake on top of each other. The problem is particularly acute in Jakarta because most of its millions of residents take water through wells that tap shallow underground aquifers. Wells also provide about a third of the needs of business and industry, according to city data. As a result, the metropolitan area is sinking at an average rate of three inches a year, outpacing the one-third inch annual rise in mean sea level in the area, while the coast near Jakarta is sinking at a much greater rate of six inches a year. Jakarta Metropolitan Area topography & hydrology
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Indonesia Water Availability & Water Withdrawal water analysis around the Java Sea territory
GSD STU | water stress
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Built Environment Pressures The subsidence effect of Jakarta is worsened by the sheer weight of Jakartaâ&#x20AC;&#x2122;s urban sprawl. Economic development in recent decades has transformed the cityâ&#x20AC;&#x2122;s traditional low-rise silhouette into a thickening forest of high-rise towers whose weight crushes the porous ground underneath .
Kampung & New Development Pressure section of sediment and ground stress
Stratification of Subsidence view from below of Jakarta's overall sinking
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city block
city block
city block
city block
city block
city block
city block
city block
960 ft
city block
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Barcelona
Block Comparison in three different cities
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New York
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Athens
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440 ft
The projectparcel envisions an innovative public realm parcel parcel parcel 440 ft 65 ft within metropolitan Athens. The transformation of parcel parcel 88 440 ft ft vacant spaces into an interstitial network that 120 ft 160 ft 120 ft 160 ft parcel mitigates the boundaries between public and 120 ft 160 ft Midtwown New York City L’ Exampie Barcelona and to Barcelona Midtwownprivate, New York Cityimplements an adaptive design L’ Exampie 120 ft 160 ft address the problems of City the contemporary city Midtwown New York L’ Exampie Barcelona caused by the economic, institutional and social Midtwown New York City crisis. Today’s perplexing problems of the Athenian center require a cross-disciplinary approach at an urban scale. The choice of Exarchia for the application of the thesis proposal was based on the district’s high numbers of store closures since the 2008 economic recession, the exacerbated drug and criminality issues as well as its unique social parameters of a very active community. The intervention empowers collectiveness, culture and sustainability while responding pragmatically to the conditions of the city and the challenges of austerity. It utilizes the city’s urban voids to implement a network of flexible, multifunctional spaces through bottom-up initiatives.
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65 ft 88 ft
parcel 60 ft
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parcel 100 ft
65 ft 88 ft
Exarchia Athens
L’ Exampie Barcelona
thesis design proposal | recipro[city]
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RECIPRO[CITY] 2014 Thesis Design Proposal for an expanded public paradigm in Exarchia, Athens 10th Semester, thesis | Michael Su, Philippe Baumann [instructors]
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Kypseli
Exarchia
Historic Triangle
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Three Areas of Study Historic Triangle | Exarchia | Kypseli
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2011 Population Athens: 664,046 Suburbs: 3,074,160
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Athens: 867,023 Suburbs: 1,673,218
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1971 Population
Athens: 437,000 Suburbs: 43,000
City’s Form Development growth & population changes
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Past to Present During the 20th century, Athens went through a series of transformations that define today’s urban form. Unplanned erection of urban dwellings and the “system consideration” resulted in an unregulated, dense city grid. Private properties and the automobile dominated, while public areas were neglected. The resulting metropolitan environment was lacking quality of living while suburbanization competed for its population and commercial power.
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thesis design proposal | recipro[city]
Photos from Exarchia & Kypseli abandoned spaces [top], sidewalk conditions [middle], NTUA campus & Exarchia square [bottom]
Current Conditions The final hit for the Athenian center was the outbreak of the 2008 economic crisis. Protesting, loss of commercial activity and the defacing of public spaces devastated the streetscape. Austerity and the socio-economic instability triggered poverty, homelessness, racism and criminality. Today’s public realm is left unclaimed by the citizens. Aging building stock, squares that lack function as well as marginalized people constitute the pedestrians experience. Vertical segregation is the result of the diverse conditions between the streets and the “retiré” apartments [penthouses]. Eventually, the unutilized spaces become urban voids within the city’s fabric and dominate the streetscape conditions.
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Topography & Green
Street Typology
Athens: 2.5 sqm / resident
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Elevation Typologies 6m
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pilotis
Berlin: 13.0 sqm / resident
sidewalk
New York: 20.0 sqm / resident
Empty Lots
Abandoned Buildings
Vacant Ground Floors
Site Analysis Kypseli and Exarchia districts in relation to the Historical Triangle.
Exarchia by Numbers According to data collected from the NTUA School of Architecture for Exarchia, in 2013 there were 467 vacant ground floor stores, 86 abandoned buildings and 21 empty lots.
thesis design proposal | recipro[city]
a a
Parks & Squares Courtyards Empty Lots Abandoned Buildings Vacant Ground Floor
Master Plan of Interconnected Urban Voids within Exarchia & Kypseli on site personal research conducted in January 2014
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RENIA KAGKOU | WORKSAMPLE
Gestures of Penetration urban dwellings & neoclassical buildings
Exarchia Square
Conceptual Proposal Perspective [a-a] Interconnectivity of the public spaces formed by utilizing negative spaces
thesis design proposal | recipro[city]
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Connecting the NTUA with Exarchia Square penetration of ground floor urban voids
National Technical University of Athens
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RENIA KAGKOU | WORKSAMPLE
Negative Space
Capturing
Splitting
Unfolding
Composing Strategies Axonometric Diagrams of the Negative Space perspective view of the interstitial network
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Circulation Play Use Work Use Live Use
Allocating Program Integrated Program & Collectiveness The interventions form a multifunctional urban system between the square of Exarchia and the enclosed NTUA School of Architecture campus. Programs from both academia and the plaza are integrated within the â&#x20AC;&#x153;networkâ&#x20AC;? and diverse social activities are established. Vacant stores and basement near the campus incorporate lectures, critiques and other academic events that will foster inspiration and dialogue between the cityâ&#x20AC;&#x2122;s users and its planners. At different times of the day, the same spaces transform into workshop areas, open cinemas, event venues and exhibition spaces. Near Exarchia square, the decaying neoclassical buildings are preserved but also deconstructed encouraging urban agriculture, farmers markets and public dining areas. Participation in the production, sale and consumption of food can create opportunities for income and provide affordability. Above the streetscape, the first floor residential apartments that have been used by small private practices although they are lacking proper layout for an efficient work environment. These units are redesigned into affordable, open plan work spaces where creativity, innovation and the local economy will be empowered.
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RENIA KAGKOU | WORKSAMPLE
Plan ground floor public network
Urban Strategies: 1. Reprogramming of urban voids within the city that will reconnect them with the urban fabric. 2. Reactivating public spaces and improving the streetscape and ground floor conditions so that pedestrian circulation is prioritized. 3. Introducing a network of public platforms and communal work spaces that will allow ad hoc activities and will reinforce the multi-functionality of the urban dwelling and reduce vertical segregation 4. Redesigning courtyards and sidewalks with landscape elements that will enrich the respond to the challenges of pollution and bring nature within the concrete environment 5. Resolving the relation of the NTUA campus with the neighborhood through a new main entrance.
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Networkâ&#x20AC;&#x2122;s Ecosystem The public platforms create continuity between the interventions while a system of low and high planting cools the areas over the hot Athenian summer months, reduces air pollution caused by traffic and introduces soft textures to the intense concrete physical environment. Eventually, the blocks within the network diminish the boundaries between private and public use, encouraging a concept of sharing and offering resources. The Exarchia paradigm will create new initiatives for urban design and change the way Athenians interact with their city.
High Planting
Low Planting
Public Platform
Fabric & Circulation
Pedestrian Circulation Public Transportation Bus Stops Movement & Landscape Systems panels indicate interventions & planting cools the air and reduces C02
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RENIA KAGKOU | WORKSAMPLE
Long Section cutting through the networkâ&#x20AC;&#x2122;s live, work, play spaces
Revitalizing Vacant Ground Floors urban dwelling near the NTUA campus
thesis design proposal | recipro[city]
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03. Upper Levels 02. First Levels 01. Ground Levels
Revitalizing Abandoned Neoclassical Buildings neoclassical buildings near the square of Exarchia
2018
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