Lecture on Urban Growth

FLOODRESILIENCE

FLOODRESILIENCEGROUP


LECTURE 2: URBAN GROWTH William Veerbeek w.veerbeek@fl oodresiliencegroup.org

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5

10

15

20

25

30

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


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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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

Lecture on Urban Growth

Education

Transcript of Lecture on Urban Growth