1. William Veerbeek
DIN_arch
Dura Vermeer Business Development, Hoofdorp
Department of Artificial Intelligence, Vrije Universiteit Amsterdam
2. 1. Current Changes in Urban Development: Drivers
2. Vulnerability in the UFM context
3. Towards Vulnerability Indicators
4. Estimating Secondary Damage
5. Understanding the City from a Bottom-Up perspective
6. Urban modeling
7. Integration of Urban Models with Flood Modeling
8. Potentials
3. Rapidly Changing Conditions: Urban growth
e.g. urbanization:
-1800: 3% of world population lived in cities
-2000: 47% of world population lived in cities
5. Urban Conditions:
1. Increasingly Complex Conditions
2. Rapidly Changing Conditions
Halle (Ger): shrinking 25% after fall Berlin Wall
Las Vegas (US): 83.3% growth in 1990-2000
6. Increasingly Complex Conditions:
-Stakeholders (no classic top-down organization)
-Diffuse demands (heterogenous objectives)
-interconnectedness of problems/potentials
-scattered distribution of resources
-increase of available data
(private-public demands, public-private partnership, scale independent
economies, territorial indifference, power-distribution, remote-sensing
techniques, global financial markets, etc. etc. etc.)
7. Rapidly Changing Conditions:
-Economical conditions
-Social conditions
-Cultural conditions
-Spatial conditions
-Climate change
(globalization, evolving technologies, instable political conditions, indi-
vidualization, natural hazards, urban sprawl, labour distribution, ener-
gy production, evolving communications, social grouping, terrorism, etc.
etc. etc.)
8. CLIMATE CHANGE:
1. Cyclical Change, such as the seasonal variation and longer term cycles (El Niño);
2. Trend Breaking, being systematic changes such as climate change and also chang-
es in runoff as a consequence of land use changes;
3. Increase of variability in extreme events causing uncertainty in
mean impact level.
Green (2005)
9. URBAN CHANGE:
1. Densification decrease of infiltration of water because of ‘paved’ urban areas: changes in
runoff (clear in Rotterdam: flash floods)
2. Building in flood prone areas Developments along river banks, Netherlands
Growth along radial axes: Chengdu, China 1991-2002 (Boston University (2002))
10. CONCLUSION:
1. Probability-Centered Risk Assessment NO LONGER VALID
2. Focus on impact
Question: On what knowledge can we base Project appraisal?
Gaussian probability distribution becomes questionable, potential impact is increasing
11. From Vulnerability to Impact Assessment
VULNERABILITY: Susceptibility to hazards
location, runoff path, landuse, urban density, morphology, main flood defense
system, building conditions, infrastructure, utility network, soil conditions,
drainage system, emergency response protocols, responsibility distribution, etc.
1. Flood system related (you guys know all about that)
2.Urban related (physical, organizational, procedural)
Need for an evaluation function: what makes systems vulnerable?
13. SWARM: On a system level, a swarm is hardly vulnerable
System properties:
1. High Degree of Redundancy (Individuals)
2. Robust
3. Adaptive Behavior
4. Resilient
Organizational Properties:
1. Decentralized (no central command)
2. Systems behavior is emergent property
allignment cohesion seperation
14. Understanding System Properties from a BOTTOM-UP perspective
1. High Degree of REDUNDANCY
Overcapacity: sub-optimal solution to a problem posed by the envrionment
:-No Exclusive Dependency on a Single Part
:-Parts offer Some Degree of Similar Functionality
:-High degree of connectivity (use a network perspective)
2. ROBUSTNESS
Emergent property resulting from a high degree of redundancy
15. Understanding System Properties from a BOTTOM-UP perspective
3. Adaptive Behavior
Capacity to Adjust to New Conditions
:-Parts generate New Relations
:-Parts generate New Functionality to satisfy the System’s General Aim
:-Temporal Instability needed to ‘Regenerate’
4. RESILIENCE
-META PROPERTY COVERING BOTH ROBUSTNESS AND ADAPTIVITY
16. Nice Story, but what does that have to do with me?
:-Understanding Residual Risk from a Systems’ Perspective
:-Thinking of Flood Protection in Terms of Resilience
:-Designing for UFM in Terms of Resilience
:-Thinking from a Bottom-Up Perspective
EXAMPLE:
Identifying & Quantifying Vulnerability Indicators
17. Vulnerability Indicators: Robustness of networks
Relation of Potential Impact to Infrastructural Network
1. Potential Damage (Case Study Haarlemmermeer)
18. REDUNDANCY IN THE INFRASTRUCTURAL NETWORK
1. Branching Factor (#connections per node)
2. Length of Edges (euclidian distance)
Too general: need for pathfinder to check for local effects!
20. Pathfinder: Demo Environment
1. Generates all possible paths from all regular nods to dangle nodes
2. Creates General Statistics on Paths, Edge Use
3. Assignes Nodes to Activity Nodes and Assigns Paths
4. Calculates Flow
2
FLOW STATISTICS
Amount of Nodes in PATH STATISTICS !
(
Capacity saturation coefficient:
dBase: 7 Total amount of paths: 21
6
0.9505
Amount of Edges in Average path length : !
(
Average weighted flow per ac-
dBase: 7 2.7142856
19
96
95
tivityNode: 4752.5
Path list: Longest path: 4
0.
05
quot;1
)
5
Total available capacity:
------------- Shortest path: 1 5
!
(
50000.0 4277.2
2-0-1-3-5- ------------- 2
5
quot;
)
0
7
--------------------------------
2-0-5- EDGE FREQUENCIES !
(
quot;
)
0
quot;
)
Assigned path for node 0: 6-5-
4-1-0-5- Total amount of edges 7
0
Assigned path for node 1: 2-0-
4-1-3-5- Edge Frequencies used in
Assigned path for node 2: 2-0-
6-5- Paths: 3
3 quot;
)
Assigned path for node 3: 2-0-
2-0-1-3- Edge 0: 9
78
!
(
41
6
.
Assigned path for node 4: 2-0-
2-0-5-3- Edge 1: 7
62
quot;
)
5
4277
Assigned path for node 5: 4-1-
4-1-0-5-3- Edge 2: 9 5
. 25
quot;
)
Assigned path for node 6: 4-1-3-
4-1-3- Edge 3: 7 8
quot;
)
4
Assigned path for node 7: 4-1-
6-5-0-1-3- Edge 4: 9 quot;
) 1
!
(
0-5-
6-5-3- Edge 5: 9
18059.5
Assigned path for node 8: 2-0-1-
2-0-1- Edge 6: 7 4
-------------------------------- !
2-0-5-3-1- (
TOTAL FLOW of traffic/24h out-
4-1-
side region: 47525.0
6-5-0-1-
TOTAL FLOW of capital/year out-
6-5-3-1-
side region: 51624.0
2-0-
4-1-0-
4-1-3-5-0-
6-5-0-
6-5-3-1-0-
21. Pathfinder: Demo Environment
5. Run scenarios in which nodes/edges are disfunctional because of flood impact
6. check total impact on system (remember dependencies vs robustness!)
23. Pathfinder: Results
-Indication of Dependency of Economical Activity on Network (also utility, communication)
-Indication of Vulnerable Parts of Network and Economical Impact
-Suggestions for making Network more Robust (adding edges)
-Assessment from an Impact Wide instead of a Flood Probability side
-Yet, still Incomplete and Performance on Large Networks is bad
-Pathfinder generates information on One of the Many Vulnerability Indicators
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24. Remember this?
1. Densification decrease of infiltration of water because of ‘paved’ urban areas: changes in
runoff (clear in Rotterdam: flash floods)
NEED FOR URBAN GROWTH MODELS
accurate predictions:
-on growth rate
-morphology (growth direction)
-landuse
-effect of planning/policy changes
-simulation of scenario’s (disasters vs resilience)
PART II: STATE OF THE ART IN URBAN GROWTH MODELS
25. URBANITY:
“The mystery (of urban economical balance) deepens when we observe
the kaleidoscopic nature of large cities. Buyers, sellers, administra-
tors, streets, bridges, and buildings are always chan-ging, so that a
city’s coherence is somehow imposed on a perpetual flux of people
and structures.
(...)A city is a pattern in time. No single constituent remains in place
(...)What enables cities to retain their coherence despite con-
tinual disruptions and a lack of central planning?
John Holland (1995), Hidden Order, How Adaptation Builds Complexity, Cam-
bridge: Perseus Books
26. Paradigm:
A city is decentralized system, consisting of a vast amount of interacting
agents, structures and processes. Various degrees of self-organization
appear that create a certain sense of order and stability.
Tradition:
Spatial planning is traditionally top-down organized. This approach
used to be succesfull since the ‘behavior’ of cities was relatively stable.
27. Urban Growth paradigms:
-Cities can be treated as self-organizing systems
-Urban Growth shows some form of universality
-Many cities show the same morphological character
-Traditional urban theory fails on predicting growth
THERE IS NO UNIVERSAL THEORY FOR URBAN GROWTH
28. 1. H.A. Makse et. al., ‘Modeling Urban Growth Patterns with Correlated Percolation’ (1998),
phys. Rev. E58, 7054-7062
-DLA generates a fractal cluster
-morphlogy: tree-like dendrite structure
Critique on urban simulations using DLA:
1. Only 1 large cluster. Cities are composed of many clusters
2. density in real cities doesn’t decrease from center according to a power-law
3. morphology is not affirmed by real data
29. 1. H.A. Makse et. al., ‘Modeling Urban Growth Patterns with Correlated Percolation’
(1998), phys. Rev. E58, 7054-7062
1. Only 1 large cluster. Cities are composed of many clusters
Example networkcity:
-Randstad is composed of many different ‘seeds’
-note that the question of scale is important
Yet: also on a smaller scale this is true:
Nieuwegein is grown from several small villages
30. 1. H.A. Makse et. al., ‘Modeling Urban Growth Patterns with Correlated Percolation’
(1998), phys. Rev. E58, 7054-7062
2. density in real cities doesn’t decrease from center
according to a power-law
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31. 1. H.A. Makse et. al., ‘Modeling Urban Growth Patterns with Correlated Percolation’
(1998), phys. Rev. E58, 7054-7062
3. morphology is not affirmed by real data
cluster of 100 million particles created by DLA morphology of Berlin 1945
32. 1. H.A. Makse et. al., ‘Modeling Urban Growth Patterns with Correlated Percolation’
(1998), phys. Rev. E58, 7054-7062
Makse et. al. propose a extention on DLA called a
Correlated (site) Percolation Model:
1. Population density p(r) follows the relation:
- is the radial distance form the central core
- is the density gradient
2. There exist a correlation between occupied locations in
the city and the probability of developing empty locations
33. 1. H.A. Makse et. al., ‘Modeling Urban Growth Patterns with Correlated Percolation’
(1998), phys. Rev. E58, 7054-7062
1. Population density p(r) follows the relation:
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34. 1. H.A. Makse et. al., ‘Modeling Urban Growth Patterns with Correlated Percolation’
(1998), phys. Rev. E58, 7054-7062
2. There exist a correlation between occupied locations in
the city and the probability of developing empty locations
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(off course this applies to all the cells in the lattice)
35. 1. H.A. Makse et. al., ‘Modeling Urban Growth Patterns with Correlated Percolation’
(1998), phys. Rev. E58, 7054-7062
Influence of the degree of correlation on morpholgy
low correlation high correlation
medium correlation
36. 1. H.A. Makse et. al., ‘Modeling Urban Growth Patterns with Correlated Percolation’
(1998), phys. Rev. E58, 7054-7062
Comparison between CPM-simulation and real data
Berlin 1875
Berlin 1920
Berlin 1945
real data simulation
37. 1. H.A. Makse et. al., ‘Modeling Urban Growth Patterns with Correlated Percolation’
(1998), phys. Rev. E58, 7054-7062
Conclusions (Makse et. al.):
1. model produces correct quantitative distribution (core and neigboring towns)
2. Different sizes of clusters agree with real data
3. Fractal dimension (coverage) agrees with real data
Critique:
-Qualitative difference (see figures)!
-Only based on single central business center
-model seems scale-less
-fractal morphology doesn’t apply to every city (see Las Vegas later on!)
-no information on density (all occupied locations have same density)
-model gives very little topological information
38. Cellular Automata:
-simple system
-capable of extremely complex behavior
Cellular Automata:
A CA is an array of identically programmed automata, or cells, which inter-
act with one another in a neighborhood and have a definate state
array cell interact neighborhood state starting condition
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39. The Game of Life: simple rules, complex behavior
(John Conway 1970)
Loneliness: dies if number of alive neigh-
bor cells <= 2
Overcrowding: dies if number of alive
neighbor cells >= 5
Procreation: lives if number of alive
neighbor cells == 5
40. 2. Development of hybrid models using CA and fractals
-CA growth phase
-Redistribution based on fractal structure (compare to infrastructure!)
D.P. Ward et. al, ‘An Optimized Cellular Automata Approach for Sustainable urban Development in Rapidly
Urbanizing Regions (1999)
41. early urban growth models using CA:
-attention to transition rules
-use spatially isotropic lattices
(every cell within the lattice is treated the same; the environment is uniform which is
unrealistic)
mountains
river
sea
array cell interact neighborhood state starting
condition
42. 1994: Human Induced Land Transformation (HILT) model
-first Geographic Automata System (GAS) to use geographic
information as the envrionment for the CA
Kirtland et. al, ‘An Analysis of Human Induced Land Transformations in the San Fransisco Bay/Sacramento
area (1994)
43. 1997: Slope, Land-use, Exclusion, Urban Extent, Transpor-
tation and Hillshade model (SLEUTH)
K.C. Clarke and S. Hoppen (1997), ‘A
self-modyfying cellular automaton model of the historical
urbanization in the San Fransisco Bay area’ ,planning and Design 24, 247-261
Model includes:
-integration of GIS-layers as the operating environment
-different cell states (not binary as in game of life)
-complex set of transition rules
-set of coefficients that dictate outcome transition rules
-self-modifying rules
-calibration method
44. 2. K.C. Clarke and S. Hoppen (1997), ‘A self-modyfying cellular automaton model of the his-
torica urbanization in the San Fransisco Bay area’ ,planning and Design 24, 247-261
1. Integration of GIS-layers
2. Roads 3. Seeds
1. Slope 4. Excluded Areas
-all layers except (roads layer) are cell-based (pixels)
45. 2. K.C. Clarke and S. Hoppen (1997), ‘A self-modyfying cellular automaton model of the his-
torica urbanization in the San Fransisco Bay area’ ,planning and Design 24, 247-261
2. Different Cell-states
1. empty
2. seed cell
3. urbanized in current iteration
4. urbanized in a previous iteration (any)
(this can be extended to incorporate the age of a neighborhood
into the growth process)
46. 2. K.C. Clarke and S. Hoppen (1997), ‘A self-modyfying cellular automaton model of the his-
torica urbanization in the San Fransisco Bay area’ ,planning and Design 24, 247-261
3. Complex set of transition rules
Composite rules composed of:
-rules on interaction with GIS-layers
-rules on cell-states of neighboring cells
For every cell {
count the #neighbors in the neighborhood
for every cell {
calculate individual_urbanization_probabilites of parameters
}
probability_of_urbanization = sum(normalized_parameter_values)/5 //(5 parameters)
if probability_of_urbanization>0.5 { //probability > 50%
cell becomes urbanized
}
}
neighborhood used is classic MOORE (8 neighbors)
47. 2. K.C. Clarke and S. Hoppen (1997), ‘A self-modyfying cellular automaton model of the his-
torica urbanization in the San Fransisco Bay area’ ,planning and Design 24, 247-261
4. Set of Parameters
-diffussion (overall dispersiveness)
-breed (control of new development)
-spread (growth of urbanized areas)
-slope resistance (probability of urbanization depending on
slope values)
-road gravity (controls urban development alongside roads)
example spread:
if (#neighbors>2 || random_number<spread_coefficient) {
urbanize this cell
}
48. 2. K.C. Clarke and S. Hoppen (1997), ‘A self-modyfying cellular automaton model of the historica
urbanization in the San Fransisco Bay area’ ,planning and Design 24, 247-261
5. Self modifying rules
Control of growth rate by positive feedback loops:
-boost rapid urban growth (resulting in dispersed growth)
-dampen slow urban growth (resulting in concentrated growth)
Calculate growth_rate for a time cycle
// Rapid growth: boost coefficients by 10%
If growth_rate>high_growth_treshold{
DIFFUSION +* 1.1
SPREAD +* 1.1
BREED by +* 1.1
}
-self modifying rules influence effects of coefficients
-influence of positive feedback rules is moderated over time
49. 2. K.C. Clarke and S. Hoppen (1997), ‘A self-modyfying cellular automaton model of the histor-
ica urbanization in the San Fransisco Bay area’ ,planning and Design 24, 247-261
Results
Remember this!
Simulated growth pattern of Washington DC (2000) generated by SLEUTH-model
50. 2. K.C. Clarke and S. Hoppen (1997), ‘A self-modyfying cellular automaton model of the histori-
ca urbanization in the San Fransisco Bay area’ ,planning and Design 24, 247-261
6. Calibration method
Adapt the model to specific local conditions using real
world data!
2. E. A. Silva and K. C. Clarke (2004), ‘Calibration of the SLEUTH urban growth model for Lisbon
and Porto’ , Computers, Environment and Urban systems 26 , 525-552
AML AMP
Calibration phase final fine coarse final fine coarse
Score/resolution 784x836 392x418 196x209 347x563 173x281 86x140
Composite score 0.15 0.19 0.23 0.48 0.47 0.41
0.90 0.88 0.97 0.97 0.99 0.94
Compare
0.91 0.91 0.92 0.99 0.99 0.99
Population
0.78 0.99 0.98 0.98 0.99 0.98
Edges
0.85 0.85 0.93 0.99 0.95 0.97
Cluster
LeeSallee 0.35 0.34 0.32 0.58 0.57 0.53
Diffusion 16 20 1 20 40 1
Breed 57 51 100 20 1 100
Calibration: Optimization of coefficient values
(diffusion, breed, spread, slope resistance, road gravity and self-modification)
51. Typical problem of cell based models: what is the cell representing?
(a house, a plot, a neighborhood, an urban cluster?)
Growth simulation of the Baltimore-Washington region for a period of 200 years
52. Geographical Automata Systems: Problems and tendencies
1. Representation
2. Expressiveness of transition rules/parameters
3. Automated feature extraction from remote sensing data
4. Extending traditional models with new attributes/rules
53. 1. Representation
Adaptive neighborhoods: usable since transition rules are totallistic
(neighborhoods as graphs with different branching factors)
classic Moore neighborhood graph representation GIS representation
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54. 1. Representation
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In practice, an adaptation of a Von Neumann neighborhood works best since most parcels share a border
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55. 2. Expressiveness of transition rules/attributes
Using ‘abstract’ attributes (e.g. diffusion index) is not very usefull for
policy makers since they cannot influence these parameters in practise.
Advice: use regression using actual statistics to determine the influence
of attributes on phenomena like diffusion, polycentricity, etc.
56. 3. Automated feature extraction from remote sensing data
Automatically assigning values to attributes from satalite information
Partly solved: landuse can be assigned by using infrared imaging techniques
57. 4. Extending traditional models with new attributes/rules
EMPHASIS ON SCENARIO’S AND EFFECTS OF POLICY!
requires additional transistion rules, cell properties, etc.
example: Urban Flood Management
(incorporating flood data into the system)
58. Yet, there are many other phenomena happening in urban
space that require attention and research:
slum fragmentation
J. Barros and F. Sobreira (2002), ‘City of Slums: Self-organization across sclaes’ ,Centre for Advanced Spatial Analysis.
59. Veerbeek, et al (2004), ‘Extending the set of decisive factors in development plans’ ,EO-Wijers stichting.
e.g. policy and prizes
Potential development speed for the Rhine-Ruhr region
60. e.g. traffic-landuse relations
‘A Model of Fast Food Restaurant Chains’
In this model of urban development different strategies of unit location
for competing fast food restaurant chains are explored based on real
GIS data of Budapest (based on multi-agent system).
61. One of the key factors seems to be the integration of vari-
ous phenomena. Yet this builds up the complexity of the
models and might compromise their accuracy.
In a gaming environment this is already done: SIM CITY
In the coming decades the emphasis in urban research
will be on understanding the relation of various phe-
nomena within the urban tissue, so the future scenario’s
can be simulated and evaluated.
62. Literature:
Michael Batty (2004), Cities and Complexity, Understanding Cities with Cellular Automata, Agent-Based Mo-
dels and Fractals, Cambridge: MIT press
Itzhak Benenson and Paul M. Torrens (2004), Geosimulation, Automata-based modelling of urban pheno-
mena, New York: Wiley
63. CONCLUSIONS
1. Urban Environment are becoming Increasingly Vulnerable
(Climate Change, Increasing Density, Current Risk-Centered Approaches)
2. Indicating and Mapping Urban Vulnerability is Vital
(limited knowledge, theory, models)
3. Answer: Increasing Resilience on Different Scale Levels
(Chris’ lecture on Holistic Aproaches)
4. Integration of Urban and Flood models, Scenario’s
