Lecture on Urban Growth

F
FLOODRESILIENCE
LECTURE 2: URBAN GROWTH   William Veerbeek    w.veerbeek@floodresiliencegroup.org




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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
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                                   ric




                                                                                             ic
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                                                                                            er
                                                        la
                                   Af




                                                                 Eu



                                                                             Am
                                                       ra




                                                                                        Am
                                                   st




                                                                          N.
                                                  Au




                                                                                       S.




    Mumbai, 2007                                                                                  New Orleans, 2005




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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




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                                                        Vulnerability Framework

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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

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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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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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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
                                                                                                                    FLOODRESILIENCEGROUP
                                                                                land area [sqKm]    density [people sqKm]

                                                     City mayors, 2009
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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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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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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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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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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




                                                                         FLOODRESILIENCEGROUP

  World Bank, 2005
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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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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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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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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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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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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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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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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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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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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




                                                                                       FLOODRESILIENCEGROUP

  Veerbeek, 2008
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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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FLOODRESILIENCE
6. CONSEQUENCES
   Causes
   IMPERVIOUSNESS:
   • Building footprint
   • Paving private gardens
   • Roads, parking




Unknown                       Moscow, Russia




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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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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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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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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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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
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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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
                                                                                   FLOODRESILIENCEGROUP



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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)




                                                                                                    FLOODRESILIENCEGROUP



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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
                                                                                                                                    FLOODRESILIENCEGROUP



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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
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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Lecture on Urban Growth

  • 1. FLOODRESILIENCE LECTURE 2: URBAN GROWTH William Veerbeek w.veerbeek@floodresiliencegroup.org FLOODRESILIENCEGROUP FLOODRESILIENCEGROUP Page 1
  • 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 FLOODRESILIENCEGROUP FLOODRESILIENCEGROUP 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 FLOODRESILIENCEGROUP 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 Page 4
  • 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 FLOODRESILIENCEGROUP Page 5
  • 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 FLOODRESILIENCEGROUP Page 6
  • 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 FLOODRESILIENCEGROUP land area [sqKm] density [people sqKm] City mayors, 2009 FLOODRESILIENCEGROUP Page 7
  • 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 FLOODRESILIENCEGROUP FLOODRESILIENCEGROUP Page 8
  • 9. FLOODRESILIENCE 3. CAUSES OF URBAN GROWTH 1. AUTONOMOUS POPULATION GROWTH Decline in most Western countries (babyboom), growth in Africa and some other countries FLOODRESILIENCEGROUP FLOODRESILIENCEGROUP Page 9
  • 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 FLOODRESILIENCEGROUP FLOODRESILIENCEGROUP Page 10
  • 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 FLOODRESILIENCEGROUP Page 11
  • 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 FLOODRESILIENCEGROUP World Bank, 2005 FLOODRESILIENCEGROUP Page 12
  • 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 FLOODRESILIENCEGROUP Page 13
  • 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 FLOODRESILIENCEGROUP Page 14
  • 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 FLOODRESILIENCEGROUP FLOODRESILIENCEGROUP Page 15
  • 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 FLOODRESILIENCEGROUP Page 16
  • 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 FLOODRESILIENCEGROUP FLOODRESILIENCEGROUP Page 17
  • 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 FLOODRESILIENCEGROUP World Bank, 2005 FLOODRESILIENCEGROUP Page 18
  • 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) FLOODRESILIENCEGROUP Page 19
  • 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 FLOODRESILIENCEGROUP Page 20
  • 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 FLOODRESILIENCEGROUP Page 21
  • 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 FLOODRESILIENCEGROUP Veerbeek, 2008 FLOODRESILIENCEGROUP Page 22
  • 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 FLOODRESILIENCEGROUP Page 23
  • 24. FLOODRESILIENCE 6. CONSEQUENCES Causes IMPERVIOUSNESS: • Building footprint • Paving private gardens • Roads, parking Unknown Moscow, Russia FLOODRESILIENCEGROUP FLOODRESILIENCEGROUP Page 24
  • 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 FLOODRESILIENCEGROUP FLOODRESILIENCEGROUP Page 25
  • 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 FLOODRESILIENCEGROUP 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 FLOODRESILIENCEGROUP Page 27
  • 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 FLOODRESILIENCEGROUP Page 28
  • 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 FLOODRESILIENCEGROUP FLOODRESILIENCEGROUP 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 FLOODRESILIENCEGROUP FLOODRESILIENCEGROUP Page 31
  • 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) FLOODRESILIENCEGROUP FLOODRESILIENCEGROUP Page 32
  • 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 FLOODRESILIENCEGROUP FLOODRESILIENCEGROUP 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 FLOODRESILIENCEGROUP FLOODRESILIENCEGROUP Page 35