RECONSTRUCTING URBAN COMPLEXITY From Temporal Descriptions of Historical Growth to Synthetic Design Models
Kinda Al_Sayed
[email protected]
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Space Group Bartlett School of Graduate Studies
16/01/2014
UNIVERSITY COLLEGE LONDON
Understanding
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Decoding urban complexity
Design Reconstructing urban complexity
1980
Generative experiments (Hillier and Hanson, 1984) Integration is static and choice is dynamic (Hillier et. al., 1987) Cellular automaton with agent modelling (Batty, 1991) Changes in the shape of cities (Hillier & Hanson, 1993) Demand and supply agent model (Krafta, 1994) Centrality as a process (Hillier, 1999)
Self-Organization and the City (Portugali, 2000) Centrality and extension (Hillier, 2002) Multi-layer agent model (Krafta et. al., 2003) Self–organisation in organic grid (Hillier, 2004)
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2011
Add hoc models in architecture and urban design
MODELLING SPATIAL DYNAMICS IN URBAN SYSTEMS
MODELLING
UNDERSTANDING
TOWARDS MODELLING SPATIAL DYNAMICS IN URBAN SYSTEMS
Space Syntax versus Complexity Science Cities are Simple!
Cities are Complex!
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“The tension between chaos and order often keeps cities on the edge of chaos _ a situation that enables cities to be adaptive complex systems and withstand environmental changes.” (Portugali, 2012) A city is “a network of linked centres at all scales set into a background network of residential space. We then show that this universal pattern comes about in two interlinked but conceptually separable phases: a spatial process through which simple spatial laws govern the emergence of characteristically urban patterns of space from the aggregations of buildings; and a functional process through which equally simple spatio-functional laws govern the way in which aggregates of buildings becomes living cities. It is this dual process that is suggested can lead us in the direction of a ‘genetic’ code for cities.” (Hillier, 2009)
"Chaos is aperiodic long-term behavour in a deterministic system that exhibits sensitive dependence on initial conditions" (Strogatz: 323). Let V be a set. The mapping f: V → V is said to be chaotic on V if: 1. f has sensitive dependence on initial conditions, 2. f is topologically transitive (all open sets in V within the range of f interact under f), 3. periodic points are dense in V. (Devaney 50)
http://otp.spacesyntax.net/methods/urban-methods-2/interpretive-models/
http://en.wikipedia.org/wiki/File:Logistic_Bifurcation_map_High_Resolution.png
Hillier, B. (2009). The genetic code for cities–is it simpler than we thought?. in proceedings of complexity theories of cities have come of age at tu delft september 2009
Strogatz, Steven H. Nonlinear Dynamics and Chaos. Cambridge MA: Perseus, 1994. Devaney, Robert L. An Introduction to Chaotic Dynamical Systems. Menlo Park, CA: Benjamin/Cummings, 1986.
"A chaotic map possesses three ingredients: unpredictability, indecomposability, and an element of regularity "(Devaney: 50).
Premise
Cities show an autonomous behaviour, where local processes appear to reinforce natural patterns of growth and differentiation.
Positive feedback
Reinforcing feedback
+
addition
deletion
edges
mergence
middle
Urban System
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Subdivision
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On order, structure and randomness: where do urban systems fall?
Metric Mean Depth MMD Radius 1000metric
Normalised Angular integration Radius n
Searching for clues in the historical growth patterns of Barcelona and Manhattan Goal/purpose/rule
Assumption Y = 99.877e0.1622x R² = 0.9657
A model can be outlined from the process of growth and structural differentiation in cities
Input
Output spatial system
Mapping and externalising growth dynamics in historical growth patterns
simulations and short term predictions
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Condition
Expansion affordances Space to expand people to occupy Will determine whether positive or negative dynamic changes
Looking for invariants in the transformations of street networks
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Method/Product Temporal Mapping of Historical Growth
A dynamic model
Mapping transformations in-between synchronic states of the growing system
That implements generative rules
DECODING AND ENCODING GROWTH DYNAMICS Method/Product Temporal mapping of historical growth
Extract an invariant that marks growth patterns
Mapping transformations in-between synchronic states of the growing system
State A+2
Infer invariants from urban growth patterns
State A+1 State A
State T = transformation (A, A+1)
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Make assumptions on how they contribute to urban growth
Emergence
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Generative growth is a bottom up activity. Given the condition of spatiotemporal configurations in the street network, a generative mechanism operates to allow for the emergence of new elements and patches.
PREFERENTIAL ATTACHMENT
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where configurational increase in the network accessibility is likely to occur, new elements/patches tend to attach to existing street structures
CHANGE WILL NOT LEAD TO CHANGE
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A matrix of maps plotting changes in integration (radius 500m) over time
CHANGE TRANSFERS
1855-1891
1920-1970
1970-2010
1891-1920
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1806-1855
Waves of change in integration values transferring from the core of Barcelona towards the edges
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Preferential attachment Angular choice is generative globally
PRUNING
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Once the growing structure reaches its maximum boundaries, patches with low local integration will tend to disappear
PRUNING Weak local structures are trimmed down
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Colour range 3colours at 130
Manhattan (current state)
Manhattan (gaps filled)
Self-organisation
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Self-organisation mechanisms are likely to have a role in maintaining a part-whole structural unity. As a side effect of this process, a fractal structure emerges in the form of monocentric patchwork patterns that have certain metric proximity. the overall distance between patches approximates one and half the radius that defines them.
DISTANCE CONSERVATION BETWEEN PATCHES Clusters were derived directly from MMD radius 1000 metric Manhattan
Manhattan
—
B
Barcelona
Distances between each two neigbouring patches linking their peaks higher MMD R 1000 values marking patchwork patterns in the physical street network
MMD Radius 1000
MMD Radius 2000 1780
ManhattanManhattan —
BarcelonaBarcelona
Distances between each two neigbouring patches linking their peaks
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higher MMD R 1000 values marking patchwork patterns in the physical street network
Al_Sayed K. (2013). The Signature of Self-Organisation in Cities: Temporal patterns of clustering and growth in street networks, International Journal of Geomatics and Spatial Analysis (IJGSA), Special Issue on Spatial/Temporal/Scalar Databases and Analysis, In M. Jackson & D. Vandenbroucke (ed), 23 (3-4).
MMD RadiusMMD 1000Radius 1000
MMD RadiusMMD 2000Radius 2000 1780
1780 2000
1780 2000
1780
2000
2000
2000
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SPATIAL BEHAVIOUR IN CITIES
Cities grow naturally wherever an emergent bottom-up activity is possible
Cities deform to differentiate the uniform grid either by intensifying the grid where more through-movement is expected or by pruning weak local structures.
In a process of preferential attachment, city structure records a certain memory wherever integration change takes place and recalls this memory to attach to new elements.
This process is continuously updated once the system reconfigures its local settings.
The system is apt to to fit within a certain distribution and tends to conserve metric distance between patches.
Structural differentiation aims to adapt the grid to match organic city structures.
Spatial structures in cities can be considered as independent systems that are self-generative and selforganised.
Angular choice R 6000 metres against MMD R 500
Angular choice Rn metres against MMD R 1000
Angular choice Rn metres against MMD R 2000
Distinguishing two layers in the spatial structure: a background & a foreground Overlaying two maps; Angular choice map R 6000 metres and Patchwork map, metric mean depth within radius 1000
Barcelona
INVARIANTS OF GROWTH OR RULES FOR URBAN PATTERN RECOGNITION
FIRST : Skewed distribution of angular depth
SECOND : street structures are moderately intelligible
THIRD : Choice overcomes the cost of depth
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FOURTH : Conserved distance between patches
Research questions
What type of mechanism is needed to convert an explanatory reading of architectural phenomena into a synthetic and yet creative design approach?
How far can we automate an urban design process?
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Is a systematic design approach counter-creative?
In search for a sensible approach…
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DESIGN FILTERS IN SPACE SYNTAX (HILLIER, 1996)
Genotype
Generic function of space (movement and occupation)
Phenotype
Cultural identity (locality & time)
Phenotype of Phenotype
Individual cultural identity
Cultural identity?
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REDEFINING DESIGN FILTERS Generic function/ genotype
The affordances of a spatial structure as a physical domain for movement and occupation
Direct Phenotype
parameters that can be interpreted numerically
Indirect Phenotype
Qualitative properties (aesthetic, identity…)
A PRIORITISED-STRUCTURED MODEL OF DESIGN THINKING Space
Space-dependant parameters
Other quantitative parameters
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Qualitative criteria
1. The first set of design filters or rules are mainly based on quantifiable spatial attributes of street networks. 2. The second set of design filters is dependent on the first set of spatial measures. It accounts for the relationships between street spaces and general formal and functional attributes of urban regions. 3. The third set of design filters will have weak dependencies on the first set but will be constrained by quantifiable and well-defined variables (environmental measures, natural lighting, …etc). 4. The fourth design filter is where singularities can be presented to reflect on design idealism, individual or communal cultures.
To inform our synthetic urban design model we need to extract rules from real urban systems
We need rules that define The first filter
The second filter
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We will only talk about the first two filters, and probably explore the role of a designer in defining the final solution
First filter Rules for defining the invariant patterns that characterise street networks Temporal mapping
Invariants
A generative model
Mapping transformations in-between synchronic states of the growing system
that help recognising urban growth patterns
That implements simple generative rules
State A+2
Extract invariants
State A+1
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State A
State T = transformation (A, A+1)
Use the invariants to assess structures generated by the model
For the first filter we define rules that capture the growth of street networks and rules to filter best performing street configurations
A SIMPLE GENERATIVE GROWTH MODEL
Simple generative rule (Centrality and extension, Hillier, 2002)
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Do not block a longer alignment if it is possible to block a shorter alignment
Generative Networks: comparing growth iterations to real and random systems
Iteration 1
Iteration 2
Iteration 3
Iteration 4
Barcelona
random structure
7747.37
8109.46
1756.161
3564.98
IIterations
Choice Rn
∑TD/NC
15726.2
11366.29
MMD R500metric
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Distribution
KSL test
0.09
0.15
0.088
0.1
0.035
0.04
Skewness
-1.32
-1.15
-1.46
-1.5
0.28
-0.18
2
R =0.13
2
R =0.35
Intelligibility
R =0.12
Synergy
R =0.37
2
R =0.17
2
R =0.1
2
R =0.36
2
2
R =0.38
2
2
R =0.56
2
R =0.80
R =0.33 R =0.62
2
2
Observed invariants that help recognising urban patterns
FIRST : Skewed distribution of angular depth
SECOND : street structures are moderately intelligible
THIRD : Choice overcomes the cost of depth
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FOURTH : Conserved distance between patches
Evaluating growth iterations
Iteration 1
Iteration 2
Iteration 3
Iteration 4
Barcelona
random structure
7747.37
8109.46
1756.161
3564.98
IIterations
Choice Rn
∑TD/NC
15726.2
11366.29
MMD R500metric
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Distribution
KSL test
0.09
0.15
0.088
0.1
0.035
0.04
Skewness
-1.32
-1.15
-1.46
-1.5
0.28
-0.18
2
R =0.13
2
R =0.35
Intelligibility
R =0.12
Synergy
R =0.37
2
R =0.17
2
R =0.1
2
R =0.36
2
2
R =0.38
2
2
R =0.56
2
R =0.80
R =0.33 R =0.62
2
2
Second filter
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Rules for defining the relationship between street networks and Form-Function
Method for mapping space-form-function
Pixelmapper* binning Spatial and urban data
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For the second filter we define latent variables that capture the relationship between spatial structure and form-function parameters * Al_Sayed, Space Syntax as a parametric model (2011)
ANNs model to forecast form-function attributes by means of spatial factors
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in 3 hidden nodes
Output
responses
Activation functions stored
Form-function
Spatial factors
Input
ANNs model to forecast form-function attributes by means of spatial factors Applied and validated against Barcelona
— Streets wider than 30m
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↑Street width predictions
◯ Building heights above 35m ↑Estimated building height
◯ Block centroids ↑Block density predictions
◯ Superstores ↑Estimated retail activity
Tested against Manhattan
— Streets wider than 30m ↑Street width predictions
☐ high-rise density above 100m ↑Estimated building height
◯ Block centroids ↑Block density predictions
◯ Superstores ↑Estimated retail activity
Used to forecast form-function attributes
Predicted form-function attributes
Block density
High-rises
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Higher values of each measure
Commercial zones
Street width
___ Routes with high choice values [SLW]
Reconstructing urban complexity
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from predictions to formalised design propositions
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Design variations
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Publications
Al_Sayed, K. (2014) Thinking systems in Urban Design: A prioritised structure model. In Explorations in Urban Design. M. Carmona (ed), (Farnham: Ashgate, 20XX), Copyright © 2014
Al_Sayed K. (2013). Synthetic Space Syntax: A generative and supervised learning approach in urban design, In Proceedings of the 9th International Space Syntax Symposium, Edited by Y O Kim, H T Park, K W Seo, Seoul, Korea.
Al_Sayed K. (2013). The Signature of Self-Organisation in Cities: Temporal patterns of clustering and growth in street networks, International Journal of Geomatics and Spatial Analysis (IJGSA), Special Issue on Spatial/Temporal/Scalar Databases and Analysis, In M. Jackson & D. Vandenbroucke (ed), 23 (3-4).
Al_Sayed, K. (2012) A systematic approach towards creative urban design. In Design Computing and Cognition DCC’12. J.S. Gero (ed), pp. xx-yy. © Springer 2012.
Al_Sayed, K., Turner, A. (2012) Emergence And Self-Organization In Urban Structures, In Proceedings of AGILE 2012, Avignon, France.
Al_Sayed, K. (2012) Urban Pattern Recognition In Generative Structures, In Proc. of AGILE’s workshop on Complexity Modelling for Urban Structure and Dynamics, Avignon, France.
Al_Sayed K., Turner A., Hanna S. (2012). Generative Structures In Cities, In Proc. of the 8th International Space Syntax Symposium, Edited by Margarita Greene, José Reyes, Andrea Castro, Santiago de Chile: PUC, 2012..
Al_Sayed K., Turner A., Hanna S. (2010). Spatial Morphogenesis in Cities: A Generative Urban Design Model, Pro. of the 10th International Conference On Design And Decision Support Systems In Architecture And Urban Planning, Edited by In: Harry JP Timmermans, Eindhoven.
Al_Sayed K., Turner A., Hanna S. (2009). Cities as emergent models: The morphological logic of Manhattan and Barcelona, In Proc. of the 7th International Space Syntax Symposium, Edited by Daniel Koch, Lars Marcus and Jesper Steen, Stockholm: KTH, 2009.
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