2. FLOODRESILIENCE
URBAN FLOODING
EXPANSION (Asia) VS STASIS (Europe)
OECD, 2008
Population exposed to extreme water levels (2005)
30 Ho Chi Min City, 2007
Exposed population
25
20
15
10
5
0
a
ia
pe
ica
a
a
ric
ic
si
As
ro
er
er
la
Af
Eu
Am
ra
Am
st
N.
Au
S.
Mumbai, 2007 New Orleans, 2005
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Page 2
3. FLOODRESILIENCE
1. DRIVERS
FLOOD VULNERABILITY:
HAZARD
• Frequency of a flood event
• Physicial characteristics of a flood
event
FLOOD RISK
EXPOSURE
• Extent of the event
• Affected people, assets, items, etc.
EXPOSURE
CAUSE
SENSITIVITY
• Consequences of the event
• During (coping capacity) and after HAZARD EFFECT
(recovery capacity) the event
SENSITIVITY
FLOODRESILIENCEGROUP
Vulnerability Framework
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Page 3
4. FLOODRESILIENCE
1. DRIVERS
HOW DOES URBAN DEVELOPMENT AFFECT FLOOD VULNERA-
BILITY?
HAZARD
• Surface runoff (pluvial flooding)
• Encroachment (pluvial, fluvial, coastal flooding)
VULNERABILITY
SUSCEPTIBILITY
• Concentration of people, assests
EXPOSURE
SENSITIVITY CAUSE
• Rate of Casualties, injuries, health risks
• Damage rate
HAZARD
• Tangible EFFECT
• Intagible
CLIMATE
• Direct CHANGE
• Indirect SENSITIVITY
URBAN
DEVELOP-
MENT FLOODRESILIENCEGROUP
Vulnerability Framework
FLOODRESILIENCEGROUP
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5. FLOODRESILIENCE
2. URBAN GROWTH FIGURES
GENERAL FIGURES:
• 1800: 3% of the world population lived in cities
• 2007: 50% of the world population lived in cities
• Different patterns (compare London, Lagos and Tokyo)
FLOODRESILIENCEGROUP
World bank, 2000
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6. FLOODRESILIENCE
Largest cities (2006) ranked by population size
2. URBAN GROWTH FIGURES 0 5 10 15 20 25 30 35 40
Tokyo
Mexico City
GENERAL FIGURES 2030 (2000): Mumbai (Bombay)
New York
São Paulo
• 4 billion people live in cities (UN, 2004) Delhi
Calcutta
Jakarta
Buenos Aires
DEVELOPING COUNTRIES Dhaka
Shanghai
Los Angeles
• 100% growth of urban areas Karachi
Lagos
• Annual decline of density of 1.7% (World Bank, 2005) Rio de Janeiro
Osaka, Kobe
• Cities tripled occuplied space Cairo
Beijing
• New inhabitant takes 160m2 (avg) Moscow
Metro Manila
Istanbul
Paris
Seoul
INDUSTRIALIZED COUNTRIES Tianjin
Chicago
• 11% growth of urban areas Lima
Bogotá
• Annual decline of density of 2.2% (World Bank, 2005) London
Tehran
Hong Kong
• 2.5x amount of occuplied space Chennai (Madras)
Bangalore
• New inhabitant takes 500m2 (avg) 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 FLOODRESILIENCEGROUP
City mayors, 2009
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7. FLOODRESILIENCE
Largest cities (2006) ranked by land area
2. URBAN GROWTH FIGURES 0 2000 4000 6000 8000 10000 12000
New York Metro
Tokyo/Yokohama
EXPLORATIONS IN DENSITY: Chicago
Atlanta
Philadelphia
• Large differences between urban area and Boston
Los Angeles
density Dallas/Fort Worth
Houston
SPRAWL
Detroit
Washington
Miami
DEVELOPING COUNTRIES Nagoya
Paris
• 100% growth of urban areas Essen/Düsseldorf
Osaka/Kobe/Kyoto
Seattle
• Annual decline of density of 1.7% (World Johannesburg/East Rand
Minneapolis/St. Paul
Bank, 2005) San Juan
Buenos Aires
• Cities tripled occuplied space Pittsburgh
Moscow
• New inhabitant takes 160m2 (avg)
St. Louis
Melbourne
Tampa//St. Petersburg
Mexico City
Phoenix/Mesa
INDUSTRIALIZED COUNTRIES San Diego
Sao Paulo
Baltimore
• 11% growth of urban areas Cincinnati
Montreal.
• Annual decline of density of 2.2% (World Sydney
Cleveland
Bank, 2005) Toronto
London
Kuala Lumpur
• 2.5x amount of occuplied space Brisbane
Rio de Janeiro
DENSE
• New inhabitant takes 500m2 (avg) Milan
Kansas City
Indianapolis
Manila
San Francisco//Oakland
COMPARE: Virginia Beach
Jakarta
Rotterdam (rank: 101): 2500 ppl/sq Km Providence
Cairo
Mumbai (rank:1): 29650 ppl/sq Km Delhi
Denver
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land area [sqKm] density [people sqKm]
City mayors, 2009
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8. FLOODRESILIENCE
3. CAUSES OF URBAN GROWTH
1. AUTONOMOUS POPULATION GROWTH
2. RURAL > CITY MIGRATION
3. CITY > CITY MIGRATION
Still marginal compared to other factors
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9. FLOODRESILIENCE
3. CAUSES OF URBAN GROWTH
1. AUTONOMOUS POPULATION GROWTH
Decline in most Western countries (babyboom), growth in Africa and some other countries
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10. FLOODRESILIENCE
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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11. FLOODRESILIENCE
4. CAUSES OF URBAN GROWTH
3. Economic attraction / Globalization
• Intra-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
Connectivity B
Map of global city-firm networks.
100 200
Amsterdam: 8th, Rotterdam: 68th
A
50
450
10
100
C
200 D
50
200
100 10
headquarter
subsidiary
E city
850
Global dataset = 9243 connections
2/3 of global GDP FLOODRESILIENCEGROUP
500
Firms lead to urban patterns
Wall & v.d. Knaap, 2007 Wall & v.d. Knaap, 2007
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12. FLOODRESILIENCE
5. SPATIAL URBAN GROWTH PATTERNS
EXPANSION (Asia) VS STASIS (Europe) 1990
Urban expansion
GANGZHOU, China 1990-2000 YIYANG, China 1990-2000
HYDERABAD, India 1990-2000 LONDON, UK 1990-2000
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World Bank, 2005
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13. FLOODRESILIENCE
5. SPATIAL URBAN GROWTH PATTERNS
CAIRO 1984-2000 Population growth: 10.1 million (1984) to 13.1 million (2000)
Can this expansion be classified into different types?
CAIRO 1984-2000
Cairo 1984
Urban expansion
Annual
Measure 1984 2000
Population 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% FLOODRESILIENCEGROUP
World Bank, 2005
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14. FLOODRESILIENCE
5. SPATIAL URBAN GROWTH PATTERNS
1. Infill:
• New development within remaining open spaces in already built-up areas.
• Infill generally leads to higher levels of density and increases contiguity of the main urban core.
CAIRO 1984-2000
Infill
FLOODRESILIENCEGROUP
World Bank, 2005
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15. FLOODRESILIENCE
5. SPATIAL URBAN GROWTH PATTERNS
1. Infill
CHARACTERISTICS:
• 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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16. FLOODRESILIENCE
5. SPATIAL URBAN GROWTH PATTERNS
2. Extenstion:
• New non-infill development extending the urban footprint in an outward direction.
• Extenstion generally leads to an increased ara of contiguity.
CAIRO 1984-2000
Extension
FLOODRESILIENCEGROUP
World Bank, 2005
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17. FLOODRESILIENCE
5. SPATIAL URBAN GROWTH PATTERNS
2. Extension
CHARACTERISTICS:
• 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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18. FLOODRESILIENCE
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 1984-2000
Extension
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World Bank, 2005
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19. FLOODRESILIENCE
5. SPATIAL URBAN GROWTH PATTERNS
3. Leapfrog development
CHARACTERISTICS:
• 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
Relation between densitity and petrol consumption
80000
Houston
70000
Petroleum use p/a (average per capita)
United States
60000 Los Angeles
of America
Washington
50000
New York
40000
Melbourne
Australia and
30000 Toronto Canada
Sydney
20000 Paris
Europe
Vienna
London
10000
Far East Singapore
Tokyo
Hong Kong
and Russia Moscow
0
0 150 200 FLOODRESILIENCEGROUP
250 300
50 100
Density (persons per hectare)
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20. FLOODRESILIENCE
5. SPATIAL URBAN GROWTH PATTERNS
Classification of urban areas
• Main Core (Central Business District)
• Secondary Core (Neighborhood centers) BUILT-UP AREA
• Fringe (Suburbs)
• Ribbon (Suburbs along main infrastructure)
• Scatter (Secondary towns)
30 TO 50%
>50% URBAN URBAN
Extension, Leapfrog <30% URBAN
Infill, Extension Extension, Leapfrog Leapfrog
Infill, Extension
LARGEST LINEAR SEMI-
CONTIGUOUS ALL OTHER CONTIGUOUS ALL OTHER
DEVELOPMENT DEVELOPMENT DEVELOPMENT DEVELOPMENT
(100M WIDE)
MAIN CORE SECONDARY CORE FRINGE RIBBON SCATTERFLOODRESILIENCEGROUP
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21. FLOODRESILIENCE
5. SPATIAL URBAN GROWTH PATTERNS
Classification of urban areas
• Main 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 FLOODRESILIENCEGROUP
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22. FLOODRESILIENCE
6. CONSEQUENCES
Increase of impervious areas > surface runoff
• Strong relationship between land-use and level of imperviousness.
• Urbanized areas result in large runoff coefficients.
LAS VEGAS 2001
Extension
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Veerbeek, 2008
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23. FLOODRESILIENCE
6. CONSEQUENCES
Relating urbanization to imperviousness
• Relation is not always straightforward
• Local differences resulting from urban typologies
Is SEATTLE the GREENEST CITY?
PHOENIX 2001 SEATTLE 2001 LAS VEGAS 2001
FLOODRESILIENCEGROUP
Veerbeek, 2008 Veerbeek, 2008 Veerbeek, 2008
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25. FLOODRESILIENCE
6. CONSEQUENCES
Causes
IMPERVIOUSNESS:
• Paving private gardens
Halton (Leeds suburb) 1971-2004
13% increase of impervious areas
12% increase in runoff
75% due to paving of residential front gardens!
Perry & Nawaz, 2008
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26. FLOODRESILIENCE
7. URBAN GROWTH MODELING
Quantitative vs Spatial
QUANTITATIVE GROWTH MODELING:
• Statistical regression and extrapolation to future
SPATIAL GROWTH MODELING: Clarke et al, 1997
• 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 FLOODRESILIENCEGROUP
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Page 26
27. FLOODRESILIENCE
7. URBAN GROWTH MODELING
Dynamic urban growth models
• Diffuse Limited Aggregation (fractal)
• Markov models (conditional probability)
• GEOGRAPHIC AUTOMATA
CELLULAR AUTOMATA
‘A regular array of identical finite 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 0
1
• Iteration 2
3
• Starting position 4
5
6
7
8
9
10
11
12
13
14
15 FLOODRESILIENCEG
FLOODRESILIENCEG R
FLOODRESILIENCEGROUP
LOO RESILIENCEGRO
O ESI ENC GR
ENCE
1-d CA with rule 30, Wolfram, 2005
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28. FLOODRESILIENCE
7. URBAN GROWTH MODELING
CELLULAR AUTOMATA
• Deterministic yet intractable
• Capable of simulating complex behavior
• Simplicity
E.g. GAME OF LIFE (Gardner, 1970)
• Remarkably complex behavior generated by 4 simple rules
LONELINESS
A cell with less than 2 adjoning cells dies
OVERCROWDING
A cell with less more than 3 adjoning cells dies
REPRODUCTION
A cell with more than 3 adjoining cells comes
alive
STASIS
A cell with exactly 2 adjoning cells remains
the same
FLOODRESILIENCEGROUP
Game of Life, Gardner, 1970
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29. FLOODRESILIENCE
7. URBAN GROWTH MODELING
FROM CELLULAR AUTOMATA to URBAN GROWTH MODELING
Geographic automata (Benenson & Torrens, 2004) Berlin actual data Berlin simulated
• Cell states > Land cover/use classes
• Cell space > Region 1875
• Transition rules > Rules for urban development
• Neighborhood > Influence of current urban extent
• Iteration > Time
• Starting position > Urban extent at some point in time 1920
IS URBAN GROWTH DETERMINED BY
UNIVERSAL LAWS? 1945
Maybe, but at least local conditions differ
• Extending cell states by properties (GIS Data)
Maxe et al, 1998
• Definining 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
FLOODRESILIENCEGROUP
somehow imposed on a perpetual flux of people and structures.”
FLOODRESILIENCEGROUP
Page 29
30. FLOODRESILIENCE
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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Page 30
31. FLOODRESILIENCE
7. URBAN GROWTH MODELING
Clarke et al, 1997
URBAN GROWTH MODELING
SLEUTH MODEL
SLOPE
• GIS information as additional input data
• Thus: spatially heterotropic
• Influence of transition rules determined by weights
• Control over growth rate NASA, 2005
LAND COVER
EXCLUSION
URBAN
Simulation of Washington DC, 2005
TRANSPORTATION
What is a good prediction?
NEED FOR EVALUATION CRITERIA
HILLSHADE
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32. FLOODRESILIENCE
7. URBAN GROWTH MODELING
EVALUATION CRITERIA
COMPARING SIMULATED DATA TO ACTUAL DATA Yang et al, 2008
Shenzhen actual data Shenzhen simulated
• X2 Criteria (classification errors)
• Fractal dimension (amount of space filled by
shape)
• Human interpretation
ACCURACY
CURRENTLY AROUND 80% (X2 Criteria)
Parameters
• Neighborhood (computational load)
• Cell states/properties (complexity)
• Global rules
• Transition rules (bottom-up vs top-down)
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33. FLOODRESILIENCE
7. URBAN GROWTH MODELING
STATE-OF-THE-ART
1. Capping growth rate using a Constrained CA
• Mixing quantitative growth and spatial growth
• Rank list of candidate cells
Von Neuman Moore Von Neuman r=2
2. Neighborhood size variation
• size
• using n-hood hierarchy
3. Regression of transition rules instead of definition
• machine learning (e.g. neural network)
adjustment transition
rules
growth model (cells,
application of
actual data t0 neighborhoods, output evaluation
transition rules
transition rules)
actual data t1
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Page 33
34. FLOODRESILIENCE
8. URBAN GROWTH MODELING
FROM CELLULAR AUTOMATA to URBAN GROWTH MODELING
Geographic automata (Benenson & Torrens, 2004)
• Cell states > Land cover/use classes
• Cell space > Region
• Transition rules > Rules for urban development
• Neighborhood > Influence 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 differ
• Extending cell states by properties (GIS Data)
• Definining 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
FLOODRESILIENCEGROUP
somehow imposed on a perpetual flux of people and structures.”
FLOODRESILIENCEGROUP
Page 34
35. FLOODRESILIENCE
8. CONCLUSIONS
URBAN GROWTH IS A MAJOR DRIVER IN FLOOD VULNERABILITY
1. Increased number of people/assets
2. Influence on runoff behavior
NOT EVERY TYPE OF URBAN GROWTH IS SIMILAR
1.Infull, extension, leapfrogging
2. Main Core, Secondary Core, Fringe, Ribbon, Scatter
SPATIAL URBAN GROWTH MIDELING IS VITAL TOOL
1.Providing insights in future vulnerability
2. Difficult since growth characteristics are locally defined
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Page 35
