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Lidia DiappiDpt architecture and urban studies Politecnico di milano
Transport Infrastructures and Sprawl: a cause –effect relationship?
XV Riunione scientifica SIET- Venezia 18-20 september 2013
Scattered Urbanisation and RoadsMany theories and models (LUTI models in particular) are all based on the assumption that the road, and the generated accessibility, is the true engine of urbanization .
RESEARCH QUESTIONTo what extent the actual pattern and dynamics of urbanization are explained by the proximity to the roads?
The discovery of The discovery of complexity in complexity in planning implies to planning implies to change our change our cognitive approach cognitive approach to reality to reality
Linear approach
Complex approach
Th
e k
now
led
ge o
f th
e c
ity
The new point of view:
macro scale phenomena
are often the result of
emergent properties at
micro scale
Th
e c
ity a
s a c
om
ple
x s
yst
em
Three eggs diagram, Cedric Price
The extraction of location rules in urban The extraction of location rules in urban sprawl:sprawl:
Combining NN investigation capabilities with spatial logic of CACombining NN investigation capabilities with spatial logic of CA
Centrifugal and centripetal forces
Case study: the urbanization in the South Park of Milan: a model
The aim of this work is to investigate and built up a dynamic model of the process of urban sprawl .
The Land Use Transition Rules are identified by a Neural Network learning of Data concerning the spatio/temporal evolution.
The Rules are then applied in order to produce a possible scenario.
The approach enables to identify the most relevant variables affecting the process.
The AREAThe agricultural park South Milan
43.600 Ha.
e
Neural networksa
nd cellular automata
NEW TOOLS IN DISCOVERING
RULES OF CHANGE
The objects referred to in
macrostructural models of cognitive
processing are seen as approximate
descriptors of emergent properties of
the microstructure PDP Research group, 1986
Two assumptions:
• Cellular Automata: A local change in land use is function of the neighbouring land uses
• A bottom up (inductive) approach : knowledge endogenously built up through data processing which discover “ a posteriori” the rules of change (Neurocomputing)
Melegnano
Rozzano
Binasco
Abbiategrasso
Trezzano / Cesano
San Giuliano / San Donato
Pantigliate / Paullo
The Cell Grid
1980
1994
X 2703 Cells (500 x500 mt)
Cell land uses a T2
%Residence% Industry% CommerceDist. Road
The DATA
Nhb land uses T1
% Residence% Industry% CommerceDist. Road
% Residence% Industry% CommerceDist. Road
Cell Land uses T1
The Process
c11 c12 c13 c14
c21 c22 c23 c24
c31 c32 c33 c34
c41 c42 c43 c44
SOM clusters : a map (1980-1994)
0
100
200
300
400
500
600
700
c11 c12 c13 c14 c21 c22 c23 c24 c31 c32 c33 c34 c41 c42 c43 c44
cluster
nu
me
ro d
i c
ell
e
cl 80 94
cl 94 08
Number of Cells by Cluster
c11
CR
80
CP
80
CC
80 RD
NR
80
NP
80
NC
80
CR
94
CP
94
CC
94
c12
CR
80
CP
80
CC
80 RD
NR
80
NP
80
NC
80
CR
94
CP
94
CC
94
c13
CR
80
CP
80
CC
80 RD
NR
80
NP
80
NC
80
CR
94
CP
94
CC
94
c14
CR
80
CP
80
CC
80 RD
NR
80
NP
80
NC
80
CR
94
CP
94
CC
94
c21
CR
80
CP
80
CC
80 RD
NR
80
NP
80
NC
80
CR
94
CP
94
CC
94
c22
CR
80
CP
80
CC
80 RD
NR
80
NP
80
NC
80
CR
94
CP
94
CC
94
c23
CR
80
CP
80
CC
80 RD
NR
80
NP
80
NC
80
CR
94
CP
94
CC
94
c24
CR
80
CP
80
CC
80 RD
NR
80
NP
80
NC
80
CR
94
CP
94
CC
94
c31
CR
80
CP
80
CC
80 RD
NR
80
NP
80
NC
80
CR
94
CP
94
CC
94
c32
max
CR
80
CP
80
CC
80 RD
NR
80
NP
80
NC
80
CR
94
CP
94
CC
94
c33
CR
80
CP
80
CC
80 RD
NR
80
NP
80
NC
80
CR
94
CP
94
CC
94
c34
CR
80
CP
80
CC
80 RD
NR
80
NP
80
NC
80
CR
94
CP
94
CC
94
c41
CR
80
CP
80
CC
80 RD
NR
80
NP
80
NC
80
CR
94
CP
94
CC
94
c42
CR
80
CP
80
CC
80 RD
NR
80
NP
80
NC
80
CR
94
CP
94
CC
94
c43
CR
80
CP
80
CC
80 RD
NR
80
NP
80
NC
80
CR
94
CP
94
CC
94
c44
CR
80
CP
80
CC
80 RD
NR
80
NP
80
NC
80
CR
94
CP
94
CC
94
CR80 Cell, residential, 1980
CP80 Cell, productive, 1980
CC80 Cell, commercial, 1980
RD Road distance
NR80 Neighbourhood, residential, 1980
NP80 Neighbourhood, productive, 1980
NC80 Neighbourhood, commercial, 1980
Codebooks range, values over the mean
Codebooks range, values under the mean
Codebook
CR94 Cell, residential, 1994
CP94 Cell, productive, 1994
CC94 Cell, commercial, 1994
CR80 Cell, residential, 1980
CP80 Cell, productive, 1980
CC80 Cell, commercial, 1980
RD Road distance
NR80 Neighbourhood, residential, 1980
NP80 Neighbourhood, productive, 1980
NC80 Neighbourhood, commercial, 1980
Codebooks range, values over the mean
Codebooks range, values under the mean
Codebook
CR94 Cell, residential, 1994
CP94 Cell, productive, 1994
CC94 Cell, commercial, 1994
c11C
R80
CP
80
CC
80 RD
NR
80
NP
80
NC
80
CR
94
CP
94
CC
94
Far from the roads nothing happens
Green cells
c14
CR
80
CP
80
CC
80
RD
NR
80
NP
80
NC
80
CR
94
CP
94
CC
94
CR80 Cell, residential, 1980
CP80 Cell, productive, 1980
CC80 Cell, commercial, 1980
RD Road distance
NR80 Neighbourhood, residential, 1980
NP80 Neighbourhood, productive, 1980
NC80 Neighbourhood, commercial, 1980
Codebooks range, values over the mean
Codebooks range, values under the mean
Codebook
CR94 Cell, residential, 1994
CP94 Cell, productive, 1994
CC94 Cell, commercial, 1994
Close to the road, BUT still nothing happens!
Green cells
c41
CR
80
CP
80
CC
80
RD
NR
80
NP
80
NC
80
CR
94
CP
94
CC
94
CR80 Cell, residential, 1980
CP80 Cell, productive, 1980
CC80 Cell, commercial, 1980
RD Road distance
NR80 Neighbourhood, residential, 1980
NP80 Neighbourhood, productive, 1980
NC80 Neighbourhood, commercial, 1980
Codebooks range, values over the mean
Codebooks range, values under the mean
Codebook
CR94 Cell, residential, 1994
CP94 Cell, productive, 1994
CC94 Cell, commercial, 1994
Residential Land Use
Infilling of consolidated urban areasOrNew settlements in open areasRoads are not so close…
c31
CR
80
CP
80
CC
80
RD
NR
80
NP
80
NC
80
CR
94
CP
94
CC
94
c42
CR
80
CP
80
CC
80
RD
NR
80
NP
80
NC
80
CR
94
CP
94
CC
94
c33
CR
80
CP
80
CC
80
RD
NR
80
NP
80
NC
80
CR
94
CP
94
CC
94
CR80 Cell, residential, 1980
CP80 Cell, productive, 1980
CC80 Cell, commercial, 1980
RD Road distance
NR80 Neighbourhood, residential, 1980
NP80 Neighbourhood, productive, 1980
NC80 Neighbourhood, commercial, 1980
Codebooks range, values over the mean
Codebooks range, values under the mean
Codebook
CR94 Cell, residential, 1994
CP94 Cell, productive, 1994
CC94 Cell, commercial, 1994
c32
max
CR
80
CP
80
CC
80
RD
NR
80
NP
80
NC
80
CR
94
CP
94
CC
94
• Close to prexisting commercial settlements.
• No surrounding urbanization.
• Good accessibility to the roads
Commerce
And the industrial settlements?
c34
CR
80
CP
80
CC
80
RD
NR
80
NP
80
NC
80
CR
94
CP
94
CC
94
c44
CR
80
CP
80
CC
80
RD
NR
80
NP
80
NC
80
CR
94
CP
94
CC
94
Infilling of preexisting industrial zones close to main roads
New industral settlm. In areas scattered unurbanised and poorly accessible
But also….
00 < X <= 1010 < X <= 20
20 < X <= 3030 < X <= 40
40 < X <= 50
50 < X <= 60
60 < X <= 70
70 < X <= 80
80 < X <= 90
90 < X <= 100 c11
c12c13
c14c21
c22c23
c24c31
c32c33
c34c41
c42c43
c440.00.10.20.30.40.50.60.70.8
0.9
1.0
Frequency
Growth %
Clusters
a - Residential
00 < X <= 1010 < X <= 20
20 < X <= 3030 < X <= 40
40 < X <= 50
50 < X <= 60
60 < X <= 70
70 < X <= 80
80 < X <= 90
90 < X <= 100 c11
c12c13
c14c21
c22c23
c24c31
c32c33
c34c41
c42c43
c440.00.10.20.30.40.50.60.70.8
0.9
1.0
Frequency
Growth %
Clusters
b- Industrial
00 < X <= 1010 < X <= 20
20 < X <= 3030 < X <= 40
40 < X <= 50
50 < X <= 60
60 < X <= 70
70 < X <= 80
80 < X <= 90
90 < X <= 100 c11
c12c13
c14c21
c22c23
c24c31
c32c33
c34c41
c42c43
c440.00.10.20.30.40.50.60.70.8
0.9
1.0
Frequency
Growth %
Clusters
c - Commercial
The transition probabilities
c11 c12 c13 c14
c21 c22 c23 c24
c31 c32 c33 c34
c41 c42 c43 c44
Classi di dinamica 80-94Classi di dinamica 94-08
198019942008 confine
ferrovieautostradestrade principalialtre strade
0 - 1010 - 2020 - 3030 - 40
50 - 60
70 - 80
90 - 100
40 - 50
80 - 90
60 - 70
Residence
Incremento 80-94
Incremento 94-08
Residence
confine
ferrovieautostradestrade principalialtre strade
0 - 1010 - 2020 - 3030 - 40
50 - 60
70 - 80
90 - 100
40 - 50
80 - 90
60 - 70
198019942008 confine
ferrovieautostradestrade principalialtre strade
0 - 1010 - 2020 - 3030 - 40
50 - 60
70 - 80
90 - 100
40 - 50
80 - 90
60 - 70
Industry
Industry
Incremento 80-94
Incremento 94-08
confine
ferrovieautostradestrade principalialtre strade
0 - 1010 - 2020 - 3030 - 40
50 - 60
70 - 80
90 - 100
40 - 50
80 - 90
60 - 70
198019942008confine
ferrovieautostradestrade principalialtre strade
0 - 1010 - 2020 - 3030 - 40
50 - 60
70 - 80
90 - 100
40 - 50
80 - 90
60 - 70
Commerce
Incremento 80-94
Incremento 94-08
Commerce
confine
ferrovieautostradestrade principalialtre strade
0 - 1010 - 2020 - 3030 - 40
50 - 60
70 - 80
90 - 100
40 - 50
80 - 90
60 - 70
Compactness Index
21
2
/)N(N
d
ZZ
T ij
ji
Zi, Zj – areas of cells i and j >0dij – distance among centroids i and jN – number of urbanized cells
0.0
50.0
100.0
150.0
200.0
250.0
300.0
350.0
400.0
450.0
500.0
resi prod comm tot
0.0
0.1
0.1
0.2
0.2
0.3
0.3
resi prod comm tot
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
resi prod comm tot
All urbanized Cells New urbanized cells
Perimeter/ Area Ratio
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
incr % 8094 incr % 9408
0
10
20
30
40
50
60
70
1980 1985 1990 1995 2000 2005
incrementoresidenza
incrementoproduttivo
incrementocommerciokmq residenza
kmq produttivo
kmq commercio
ConclusionsEclectic approach where the NN capabilities of investigation cope with a stochastic model able to produce sound scenario of urbanization based on fuzzy rules learned by the NN.
The main land use transitions concern: 1.infilling of already urbanized areas2.edges of urban centers and 3.emerging nuclei in the green areas, which gradually become bigger
Actractivness for central living and services and facilities offered by the urban centers seem to explain the expansion around the urban nucle (Ewing and Cervero, 2001, 2010). A kind a centrifugal force is shaping the urban form.
Conclusions/2 The new cells tend to root around clusters with same land use
The two phases- calibrated and simulated- show an initial period where urbanization occur in nuclei external to the urban centers with settlements extending in large slot sizes ans a second phase where infilling occurs with smaller size lots.
Proximity to the main roads doesn’t play a crucial role in the spatial logic of the process.
The challenge of complexity
THANK YOU !!
