lecture on urban growth
DESCRIPTION
TRANSCRIPT
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LECTURE 2: URBAN GROWTH William Veerbeek w.veerbeek@fl oodresiliencegroup.org
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5
10
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Africa
Asia
Austra
lasia
Europe
N. Ameri
ca
S. Ameri
ca0
Expo
sed
popu
latio
nURBAN FLOODING
EXPANSION (Asia) VS STASIS (Europe)
Ho Chi Min City, 2007
Mumbai, 2007 New Orleans, 2005
OECD, 2008
Population exposed to extreme water levels (2005)
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CAUSE
FLOOD RISK
HAZARD
EXPOSURE
SENSITIVITY
EFFECT
1. DRIVERS
FLOOD VULNERABILITY:
HAZARDFrequency of a fl ood event• Physicial characteristics of a fl ood • event
EXPOSUREExtent of the event• Aff ected people, assets, items, etc.•
SENSITIVITYConsequences of the event• During (coping capacity) and after • (recovery capacity) the event
Vulnerability Framework
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CAUSE
VULNERABILITY
HAZARD
URBAN DEVELOP-
MENT
HOW DOES URBAN DEVELOPMENT AFFECT FLOOD VULNERA-BILITY?
HAZARDSurface runoff (pluvial fl ooding)• Encroachment (pluvial, fl uvial, coastal fl ooding)•
SUSCEPTIBILITYConcentration of people, assests•
SENSITIVITYRate of Casualties, injuries, health risks• Damage rate•
1. DRIVERS
CLIMATE CHANGE
Vulnerability Framework
EXPOSURE
SENSITIVITY
EFFECTTangible• Intagible• Direct• Indirect•
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2. URBAN GROWTH FIGURES
GENERAL FIGURES:1800: 3% of the world population lived in cities• 2007: 50% of the world population lived in cities • Diff erent patterns (compare London, Lagos and Tokyo)•
World bank, 2000
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2. URBAN GROWTH FIGURESLargest cities (2006) ranked by population size
0 5 10 15 20 25 30 35 40
Tokyo
Mexico City
Mumbai (Bombay)
New York
São Paulo
Delhi
Calcutta
Jakarta
Buenos Aires
Dhaka
Shanghai
Los Angeles
Karachi
Lagos
Rio de Janeiro
Osaka, Kobe
Cairo
Beijing
Moscow
Metro Manila
Istanbul
Paris
Seoul
Tianjin
Chicago
Lima
Bogotá
London
Tehran
Hong Kong
Chennai (Madras)
Bangalore
Bangkok
Dortmund, Bochum
Lahore
Hyderabad
Wuhan
Baghdad
Kinshasa
Riyadh
Santiago
Miami
Belo Horizonte
Philadelphia
St Petersburg
Ahmadabad
Madrid
Toronto
Ho Chi Minh City
2020 2006
GENERAL FIGURES 2030 (2000):4 billion people live in cities (UN, 2004)•
DEVELOPING COUNTRIES100% growth• of urban areasAnnual decline of density of 1.7% (World Bank, 2005)• Cities tripled occuplied space• New inhabitant takes • 160m2 (avg)
INDUSTRIALIZED COUNTRIES11% growth• of urban areasAnnual decline of density of 2.2% (World Bank, 2005)• 2.5x amount of occuplied space• New inhabitant takes • 500m2 (avg)
City mayors, 2009
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2. URBAN GROWTH FIGURESLargest cities (2006) ranked by land area
0 2000 4000 6000 8000 10000 12000
New York Metro
Tokyo/Yokohama
Chicago
Atlanta
Philadelphia
Boston
Los Angeles
Dallas/Fort Worth
Houston
Detroit
Washington
Miami
Nagoya
Paris
Essen/Düsseldorf
Osaka/Kobe/Kyoto
Seattle
Johannesburg/East Rand
Minneapolis/St. Paul
San Juan
Buenos Aires
Pittsburgh
Moscow
St. Louis
Melbourne
Tampa//St. Petersburg
Mexico City
Phoenix/Mesa
San Diego
Sao Paulo
Baltimore
Cincinnati
Montreal.
Sydney
Cleveland
Toronto
London
Kuala Lumpur
Brisbane
Rio de Janeiro
Milan
Kansas City
Indianapolis
Manila
San Francisco//Oakland
Virginia Beach
Jakarta
Providence
Cairo
Delhi
Denver
land area [sqKm] density [people sqKm]
EXPLORATIONS IN DENSITY:Large diff erences between urban area and • density
DEVELOPING COUNTRIES100% growth• of urban areasAnnual decline of density of 1.7% (World • Bank, 2005)Cities tripled occuplied space• New inhabitant takes • 160m2 (avg)
INDUSTRIALIZED COUNTRIES11% growth• of urban areasAnnual decline of density of 2.2% (World • Bank, 2005)2.5x amount of occuplied space• New inhabitant takes • 500m2 (avg)
COMPARE:Rotterdam (rank: 101): 2500 ppl/sq KmMumbai (rank:1): 29650 ppl/sq Km
City mayors, 2009
SPRAWL
DENSE
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1. AUTONOMOUS POPULATION GROWTH
2. RURAL > CITY MIGRATION
3. CITY > CITY MIGRATIONStill marginal compared to other factors
3. CAUSES OF URBAN GROWTH
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1. AUTONOMOUS POPULATION GROWTH Decline in most Western countries (babyboom), growth in Africa and some other countries
3. CAUSES OF URBAN GROWTH
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3. CAUSES OF URBAN GROWTH
2. Rural to Urban Migration:Economic progress, opportunity• Macro economic factors (industrialization, technological advancements)•
Rural-Urban Migration in China 1950-2030 Rural-Urban Migration per Region 1950-2030
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4. CAUSES OF URBAN GROWTH
3. Economic attraction / GlobalizationIntra-urban migration•
Connectivity of Urban Agglomerations:Assumption: The stronger the connectivity and directionality the stronger the urban de-velopment per capita
Connectivity can be subdivided per industrial sector• Connectivity and sectoral diversitiy tell indicate economic resilience•
Wall & v.d. Knaap, 2007
A
B
C
E
D
headquarter
subsidiary
city
100
50
100
20050
100
10
450
200
200
10
850
500
Map of global city-fi rm networks.Amsterdam: 8th, Rotterdam: 68th
Connectivity
Wall & v.d. Knaap, 2007
Global dataset = 9243 connections2/3 of global GDPFirms lead to urban patterns
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5. SPATIAL URBAN GROWTH PATTERNS
EXPANSION (Asia) VS STASIS (Europe)
GANGZHOU, China 1990-2000
1990
Urban expansion
World Bank, 2005
YIYANG, China 1990-2000
HYDERABAD, India 1990-2000 LONDON, UK 1990-2000
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5. SPATIAL URBAN GROWTH PATTERNS
CAIRO 1984-2000 Population growth: 10.1 million (1984) to 13.1 million (2000)
Can this expansion be classifi ed into diff erent types?
CAIRO 1984-2000Cairo 1984
Urban expansion
World Bank, 2005
AnnualMeasure 1984 2000Population 10.1 million 13.1 million 1.58%Built-Up Area (sq Km) 366.50 369.65 2.77%Average Density (persons /sq Km) 27727 22965 -1.16%Built-Up Area per Person (sq m) 36.07 43.54 1.17%Average Slope of Built-Up Area (%) 4.11 4.03 -0.12%Maximum Slope of Built-Up Area (%) 20.65 20.80 0.04%Buildable Perimeter (%) 0.66 0.67 0.06%Contiguity Index 0.62 0.61 -0.9%Compactness Index 0.22 0.22 0%Per Capita GDP USD 2.413 USD 3.281 1.92%
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5. SPATIAL URBAN GROWTH PATTERNS
1. Infi ll: New development • within remaining open spaces in already built-up areas.
Infi ll generally leads to • higher levels of density and increases contiguity of the main urban core.
CAIRO 1984-2000Infi ll
World Bank, 2005
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5. SPATIAL URBAN GROWTH PATTERNS
1. Infi llCHARACTERISTICS:
Compact city•
Small footprint•
Relatively modest infrastructural needs•
Often only a fraction of total development•
Not always controlled development•
Sao Paolo, Brazil Mumbai, India
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5. SPATIAL URBAN GROWTH PATTERNS
2. Extenstion: New non-infi ll development extending the urban footprint in an • outward direction.
Extenstion generally leads to an • increased ara of contiguity.
CAIRO 1984-2000Extension
World Bank, 2005
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5. SPATIAL URBAN GROWTH PATTERNS
2. ExtensionCHARACTERISTICS:
Often low density, sprawl•
Large footprint•
Relatively high infrastructural needs•
Often majority of total development (together with Leapfrog development)•
Not always controlled development•
El Paso, United States Los Angeles, United States
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5. SPATIAL URBAN GROWTH PATTERNS
3. Leapfrog development: New development• not intersecting the urban footprint leading to scattered development.
Leapfrog generally leads to an • increased level of fragmentation.
CAIRO 1984-2000Extension
World Bank, 2005
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5. SPATIAL URBAN GROWTH PATTERNS
3. Leapfrog developmentCHARACTERISTICS:
Often low density, sprawl•
Largest footprint (since often indepent from morpholical constrains)•
Highest infrastructural needs (far away from centers)•
Often majority of total development (together with Leapfrog development)•
Often planned new residential areas•
(Can become foundation for network cities)•
Las Vegas, United States Newman & Kenworthy, 1989
Houston
Los Angeles
Washington
New York
Melbourne
Sydney Toronto
Paris
London Vienna
SingaporeTokyoMoscow
Hong Kong
Europe
Australia and Canada
United States of America
Far East and Russia
80000
70000
60000
50000
40000
30000
20000
10000
00 50 100 150 200 250 300
Petroleum
use p/a (average per capita)
Density (persons per hectare)
Relation between densitity and petrol consumption
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5. SPATIAL URBAN GROWTH PATTERNS
BUILT-UP AREA
30 TO 50% URBAN>50% URBAN <30% URBAN
LARGEST CONTIGUOUS
DEVELOPMENT
ALL OTHERDEVELOPMENT
LINEAR SEMI-CONTIGUOUS
DEVELOPMENT(100M WIDE)
ALL OTHERDEVELOPMENT
MAIN CORE SECONDARY CORE FRINGE RIBBON SCATTER
Classifi cation of urban areasMain Core (Central Business District)•
Secondary Core (Neighborhood centers)•
Fringe (Suburbs)•
Ribbon (Suburbs along main infrastructure)•
Scatter (Secondary towns)•
LeapfrogExtension, Leapfrog
Extension, Leapfrog
Infi ll, Extension
Infi ll, Extension
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5. SPATIAL URBAN GROWTH PATTERNS
Classifi cation of urban areasMain Core (Central Business District)•
Secondary Core (Neighborhood centers)•
Fringe (Suburbs)•
Ribbon (Suburbs along main infrastructure)•
Scatter (Secondary towns)•
Example: Chengdu, China, 1991-2002(!)
Boston University, 2000
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6. CONSEQUENCES
Increase of impervious areas > surface runoff Strong relationship between land-use and level of imperviousness. •
Urbanized areas result in large runoff coeffi cients.•
LAS VEGAS 2001Extension
Veerbeek, 2008
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6. CONSEQUENCES
Relating urbanization to imperviousnessRelation is not always straightforward•
Local diff erences resulting from urban typologies•
PHOENIX 2001
Veerbeek, 2008
SEATTLE 2001
Veerbeek, 2008
LAS VEGAS 2001
Veerbeek, 2008
Is SEATTLE the GREENEST CITY?
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6. CONSEQUENCES
CausesIMPERVIOUSNESS:
Building footprint•
Paving private gardens•
Roads, parking•
Unknown Moscow, Russia
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6. CONSEQUENCES
CausesIMPERVIOUSNESS:
Paving private gardens•
Halton (Leeds suburb) 1971-200413% increase of impervious areas
12% increase in runoff
75% due to paving of residential front gardens!
Perry & Nawaz, 2008
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7. URBAN GROWTH MODELING
Quantitative vs SpatialQUANTITATIVE GROWTH MODELING:
Statistical regression and extrapolation to future•
SPATIAL GROWTH MODELING: Spatial representation of urban growth (past, future)•
FIRST MODELS BASED ON REGIONAL ECONOMY: Central place hierarch (Weber, 1909)•
Power distribution of settlements (Allen, 1954)•
Equlibrium states (Alonso,1964)•
Theoretical models describing ‘ideal cities’ in equilibrium
MODELS HAVE DIFFICULTY DESCRIBING REAL URBAN GROWTH
Clarke et al, 1997
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7. URBAN GROWTH MODELING
Dynamic urban growth modelsDiff use Limited Aggregation (fractal)•
Markov models (conditional probability)•
GEOGRAPHIC AUTOMATA•
CELLULAR AUTOMATA‘A regular array of identical fi nite state automata whose next state is determined
solely by their current state and the state of their neighbours.’
Cells•
Cell states•
Cell space (n-dimensional, n > 0)•
Transition rules•
Neighborhood•
Iteration•
Starting position•
FLOODLOOOFLOODLOOFLOODLOOFLOODLOOFLOODRESILESIESIRESILESIRESILESIRESILESIRESILIENCEENCENCEIENCEENCIENCEENCIENCEENCIENCEGGRGROGRRGR
1-d CA with rule 30, Wolfram, 2005
0123456789101112131415
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7. URBAN GROWTH MODELING
CELLULAR AUTOMATADeterministic yet intractable•
Capable of simulating complex behavior•
Simplicity•
E.g. GAME OF LIFE (Gardner, 1970)
Remarkably complex behavior generated by 4 simple rules•
Game of Life, Gardner, 1970
LONELINESSA cell with less than 2 adjoning cells dies
OVERCROWDINGA cell with less more than 3 adjoning cells dies
REPRODUCTIONA cell with more than 3 adjoining cells comesalive
STASISA cell with exactly 2 adjoning cells remainsthe same
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7. URBAN GROWTH MODELING
FROM CELLULAR AUTOMATA to URBAN GROWTH MODELINGGeographic automata (Benenson & Torrens, 2004)
Cell states • > Land cover/use classes
Cell space • > Region
Transition rules • > Rules for urban development
Neighborhood • > Infl uence of current urban extent
Iteration • > Time
Starting position • > Urban extent at some point in time
IS URBAN GROWTH DETERMINED BY UNIVERSAL LAWS?Maybe, but at least local conditions diff er
Extending cell states by properties (GIS Data)•
Defi nining more complex transition rules•
John Holland, 1995:
(...)”A city is a pattern in time. No single constituent remains in place.”
“The mystery (of urban economical balance) deepens when we observe the kaleidoscopic nature of large cities.
Buyers, sellers, administrators, streets, bridges, and buildings are always changing, so that a city’s coherence is
somehow imposed on a perpetual fl ux of people and structures.”
Maxe et al, 1998
Berlin actual data Berlin simulated
1875
1920
1945
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7. URBAN GROWTH MODELING
WHY COULD THERE BE UNIVERSAL GROWTH LAWS?CITIES SHOW A HIGH LEVEL OF SELF-ORGANISATION
Spontaneous order•
robust•
adaptive•
PROPERTIES
organisation based on local interactions (decentralised)•
high level of redundancy•
system state is emergent• Flocking of birds, NASA, 2005
ALLIGNMENT
COHESION
SEPERATION
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7. URBAN GROWTH MODELING
URBAN GROWTH MODELINGSLEUTH MODEL
GIS information as additional input data•
Thus: spatially heterotropic•
Infl uence of transition rules determined by weights•
Control over growth rate•
What is a good prediction?NEED FOR EVALUATION CRITERIA
SLOPE
LAND COVER
EXCLUSION
URBAN TRANSPORTATION
HILLSHADE
Clarke et al, 1997
NASA, 2005
Simulation of Washington DC, 2005
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7. URBAN GROWTH MODELING
EVALUATION CRITERIACOMPARING SIMULATED DATA TO ACTUAL DATA
X• 2 Criteria (classifi cation errors)
Fractal dimension (amount of space fi lled by •
shape)
Human interpretation•
ACCURACYCURRENTLY AROUND 80% (X2 Criteria)
Parameters
Neighborhood (computational load)•
Cell states/properties (complexity)•
Global rules•
Transition rules (bottom-up vs top-down)•
Yang et al, 2008
Shenzhen actual data Shenzhen simulated
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7. URBAN GROWTH MODELING
STATE-OF-THE-ART1. Capping growth rate using a Constrained CA
Mixing quantitative growth and spatial growth•
Rank list of candidate cells•
2. Neighborhood size variation
size•
using n-hood hierarchy•
3. Regression of transition rules instead of defi nition
machine learning (e.g. neural network)•
Von Neuman Moore Von Neuman r=2
output evaluationactual data t0 application oftransition rules
actual data t1
growth model (cells,neighborhoods,transition rules)
adjustment transitionrules
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8. URBAN GROWTH MODELING
FROM CELLULAR AUTOMATA to URBAN GROWTH MODELINGGeographic automata (Benenson & Torrens, 2004)
Cell states • > Land cover/use classes
Cell space • > Region
Transition rules • > Rules for urban development
Neighborhood • > Infl uence of current urban extent
Iteration • > Time
Starting position • > Urban extent at some point in time
IS URBAN GROWTH DETERMINED BY UNIVERSAL LAWS?Maybe, but at least local conditions diff er
Extending cell states by properties (GIS Data)•
Defi nining more complex transition rules•
John Holland, 1995:
(...)”A city is a pattern in time. No single constituent remains in place.”
“The mystery (of urban economical balance) deepens when we observe the kaleidoscopic nature of large cities.
Buyers, sellers, administrators, streets, bridges, and buildings are always changing, so that a city’s coherence is
somehow imposed on a perpetual fl ux of people and structures.”
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8. CONCLUSIONS
URBAN GROWTH IS A MAJOR DRIVER IN FLOOD VULNERABILITY1. Increased number of people/assets2. Infl uence on runoff behavior
NOT EVERY TYPE OF URBAN GROWTH IS SIMILAR1.Infull, extension, leapfrogging2. Main Core, Secondary Core, Fringe, Ribbon, Scatter
SPATIAL URBAN GROWTH MIDELING IS VITAL TOOL1.Providing insights in future vulnerability2. Diffi cult since growth characteristics are locally defi ned