the sleuth urban ca-based model: an evaluation - théoquant2007
DESCRIPTION
SLEUTH model has been developed by its author, Keith Clarke, as general model, suitable for all kinds of urban growth, in order to define a sort of DNA of urban systems (constituted by particular sets of model parameters). To be really general, we think that this model has to fit two general aspects: the urban sprawl and the rank-size rule. We present an evaluation of Sleuth model through European case studies, showing the calibrated set of parameters which fit each city we have analysed, and showing how this model can predict urban growth and in particular the dynamic process of the sprawl, through the output maps of the Sleuth software. Moreover it’s possible to apply this model not only at single cities, but also to a wide territory (due to scale invariance), in order to predict the evolution of a system of cities; to do this we considered an ideal territory, built by ourselves, respecting the rank-size rule, evaluating the ability of the model to fit this aspect. We will present also the sensitivity analysis conducted on the 5 parameters of the model (see below), in order to establish how these parameters influence the growth of urbanized areas. The goal is a contribution for the ambitious Project Gigalopolis, investigating the meaning of the parameters of the model, and the common aspects among different type of urbanized areas, in order to build a “DNA of city” through the analysis of the outgoings produced by Sleuth.TRANSCRIPT
The SLEUTH Urban CA-Based The SLEUTH Urban CA-Based Model: an evaluationModel: an evaluation
ing. Matteo Caglioniing. Matteo Caglioni
prof. Giovanni Rabinoprof. Giovanni Rabino
Università di PisaUniversità di PisaDipart imento di Ingegneria Civi leDipart imento di Ingegneria Civi le
Poli tecnico di MilanoPoli tecnico di MilanoDipart imento di Architettura e Dipart imento di Architettura e
Pianif icazionePianif icazione
CA-based modelCA-based model
X:X: number of cells of the grid (map) number of cells of the grid (map) S:S: number of possible states for the cells number of possible states for the cells N:N: number of cells which defines the number of cells which defines the
neighbourhoodneighbourhood f(…):f(…): function of state transition, which function of state transition, which
gives the state at time t+1gives the state at time t+1
SLEUTH CA-based modelSLEUTH CA-based model It is a probabilistic 2D cellular automata based model that simulates urban It is a probabilistic 2D cellular automata based model that simulates urban
growth through time.growth through time. Constituted by 2 modules (sub-models):Constituted by 2 modules (sub-models):
1. UGM 2. DELTATRON1. UGM 2. DELTATRON
1. The Urban Growth Model (UGM) simulates the effect of topography, 1. The Urban Growth Model (UGM) simulates the effect of topography, adjacency, and transportation networks on the patterns of urbanization adjacency, and transportation networks on the patterns of urbanization through time. It uses Boolean logic (urbanized/not urbanized)through time. It uses Boolean logic (urbanized/not urbanized)
2. The Deltatron Land Use/Land Cover Model uses CA-based rules, class 2. The Deltatron Land Use/Land Cover Model uses CA-based rules, class transition probabilities (Markov matrixes), and local topography in order to transition probabilities (Markov matrixes), and local topography in order to define land use changes.define land use changes.
SLEUTH CA-based modelSLEUTH CA-based model
4 sequential phases for each module4 sequential phases for each module
Time step: 1 yearTime step: 1 year 5 parameters to calibrate5 parameters to calibrate
UGMUGM• Spontaneous growth• New spreading centres• Edge growth• Road influence growth
DeltatronDeltatron• Initial Change• Cluster Change• Propagate Change• Age Deltatrons
19001900 1925 1950 1925 1950 1975 2000 1975 2000
SS lopelope
LL and Coverand Cover
EE xcludedxcluded
UU rbanrban
TT ransportationransportation
HH i l lshadeil lshade
SLEUTH CA-based modelSLEUTH CA-based model
Changes are driven by 5 parameters:Changes are driven by 5 parameters: DispersionDispersion (determines the smallest, spontaneous, global (determines the smallest, spontaneous, global
urbanization probability)urbanization probability) SpreadSpread (defines the part of the growth that starts from existing (defines the part of the growth that starts from existing
spreading centres)spreading centres) BreedBreed (defines the probability for each new urbanized cell to (defines the probability for each new urbanized cell to
become a new spreading centre)become a new spreading centre) Slope ResistanceSlope Resistance (urbanization decrease with slope)(urbanization decrease with slope) Road Gravity Road Gravity (urbanization follows road network)(urbanization follows road network)
Spontaneous growthSpontaneous growth
Urban settlements may occur anywhere on a landscape
f (diffusion coefficient, slope resistance)
Some new urban settlements will become centers of further growth. Others will remain isolated.
f (spontaneous growth, breed coefficient, slope resistance)
Creation of new spreading centersCreation of new spreading centers
The most common type of development It occurs at urban edges and as in-fill
f (spread coefficient, slope resistance)
Organic growthOrganic growth
Urbanization has a tendency to follow transportation network.
f (breed coefficient, road gravity coefficient, slope resistance, diffusion coefficient)
Road Influenced GrowthRoad Influenced Growth
TT00 T T11
For For nn time periods (years) time periods (years)
spontaneousspreading
center organicroad
influenced deltatron
f (slope resistance,
diffusion coefficient)
f (slope resistance,
breed coefficient)
f (slope resistance,
spread coefficient)
f (slope resistance, diffusion coefficient,
breed coefficient,road gravity)
pastpast
presentpresent
For For m m Monte Carlo iterations
Monte Carlo iterations
For For n n coefficient sets
coefficient sets
CALIBRATION:CALIBRATION:Predicting the presentPredicting the present
from the pastfrom the past
SLEUTH CA-based modelSLEUTH CA-based model
CalibrationCalibration (brute force calibration)(brute force calibration)
1) Set init ial condit ions:1) Set init ial condit ions:• coefficient values (D; S; B; SR; RG) • 6 kinds of input images
2) 2) Apply Growth Apply Growth Rules:Rules:
• UGM (4 phases)• Deltatron (4 phases)
3) 3) Self-Modif ication:Self-Modif ication:• Calculate growth rate (GR) • If (GR > CRITICAL_HIGH), modify coefficients
for BOOM state rapid growth• If (GR < CRITICAL_LOW), modify coefficients
for BUST state depressed growth
http://www.ncgia.ucsb.edu/projects/gig/ncgia.htmlhttp://www.ncgia.ucsb.edu/projects/gig/ncgia.html
Simulation of ideal casesSimulation of ideal cases
Validity of information we can get from model Validity of information we can get from model prediction is directly proportional with the ability prediction is directly proportional with the ability of the model to adapt itself to the system… its of the model to adapt itself to the system… its ability in reproducing reality.ability in reproducing reality.
In order to evaluate this model ability we analyse In order to evaluate this model ability we analyse two ideal cases:two ideal cases:
- Zipf’s Rank Size Rule- Zipf’s Rank Size Rule- Urban Sprawl- Urban Sprawl
Road NetworkRoad Network(from Fulong Wu’s studies about spontaneous and self-organized urban growth)
Urbanized areaUrbanized area
19901950 19701930
1950 1970
rank-size
1
10
100
1000
1 10 100 1000rango
dim
ensi
one
R2 = 0,9949
0
10
20
30
40
50
60
70
80
0 1 2 3
rango
num
ero
cent
ri
Rank Size Rule is verified with the following set of calibrated parameters:Rank Size Rule is verified with the following set of calibrated parameters:(DI=0, BR=2, SP=0, SR=7, RG=60)(DI=0, BR=2, SP=0, SR=7, RG=60)
Rank Size Rule is verified with the following set of calibrated parameters:Rank Size Rule is verified with the following set of calibrated parameters:(DI=0, BR=2, SP=0, SR=7, RG=60)(DI=0, BR=2, SP=0, SR=7, RG=60)
3200
3220
3240
3260
3280
3300
3320
3340
3360
3380
1991 1994 1997 2000 2003 2006 2009
anni
celle
01020
3040
506070
8090
100
area urbana [n° celle] nuclei urbani
rank-size, analisi parametrica: area urbanizzata
3000
4000
5000
6000
7000
8000
1991 1994 19 97 2000 2003 2006 20 09
anni
[cel
le]
valori da calibrazione di=10, br=0, spr=1, s.r.=1, r.g.=61
di=10, br=10, spr=1, s.r.=1, r.g.=61 di=10, br=10, spr=10,s.r.=1, r.g.=61
di=0, br=10, spr=0, s.r.=1, r.g.=61 di=0, br=0, spr=10, s.r.=1, r.g.=61di=25, br=0, spr=1, s.r.=1, r.g.=61 di=25, br=25, spr=1, s.r.=1, r.g.=61
di=25, br=25, spr=25, s.r.=1, r.g.=61
Sensitivity analysis for model parametersSensitivity analysis for model parameters
• Dispersion parameter determines the level of urbanization.Dispersion parameter determines the level of urbanization.• Breed and Sprawl parameters increase Dispersion effects.Breed and Sprawl parameters increase Dispersion effects.
(di=10, br=10, spr=10, s.r.=1, r.g.=61)(di=10, br=10, spr=10, s.r.=1, r.g.=61) (di=25, br=25, spr=25, s.r.=1, r.g.=61)(di=25, br=25, spr=25, s.r.=1, r.g.=61)
When DI, BR, SPR are higher than 25 we loose the hierarchical structure When DI, BR, SPR are higher than 25 we loose the hierarchical structure and we obtain something similar to urban sprawl.and we obtain something similar to urban sprawl.
Growth of the urban sprawlGrowth of the urban sprawl
19901950 19701930
Urbanization probability in forecast Urbanization probability in forecast
DI=2, BR=6, SP=26, SR=1, RG=1DI=2, BR=6, SP=26, SR=1, RG=1
Calibrated parameters show an higher Calibrated parameters show an higher value of spread coefficient.value of spread coefficient.
Sleuth model recognises the sprawl Sleuth model recognises the sprawl dynamics acting on territory. dynamics acting on territory.
Simulation of real casesSimulation of real cases
The model has been calibrated using historical The model has been calibrated using historical data coming from MOLAND project (Monitoring data coming from MOLAND project (Monitoring of Land-use Dynamics).of Land-use Dynamics). Palermo Palermo (1955, 1963, 1988, 1997)(1955, 1963, 1988, 1997)
Padova – Mestre Padova – Mestre (1955, 1963, 1989, 1997)(1955, 1963, 1989, 1997)
Helsinki Helsinki (1950, 1966, 1984, 1998)(1950, 1966, 1984, 1998)
Bilbao Bilbao (1956, 1972, 1984, 1997)(1956, 1972, 1984, 1997)
PalermoPalermo
1955 1963 1988 19971955 1963 1988 1997
PalermoPalermo
Excluded areaExcluded area
SlopeSlope
HillshadeHillshade
PalermoPalermo
growth rate - Palermo 1997 - 2017
0
0,5
1
1,5
2
2,5
3
3,5
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
years
[%]
urban area - Palermo 1997-2007
30000
35000
40000
45000
50000
55000
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
year
[ha]
PalermoPalermo
Padova - MestrePadova - Mestre
HelsinkiHelsinki BilbaoBilbao
Velocità di crescita urbana (normalizzata)
0
0,0020,004
0,0060,008
0,010,012
0,0140,0160,018
1 4 7 10 13 16 19
anni di simulazione
[1/a
nn
o]
Padova Mestre Palermo Helsinki Bilbao
Growth rate for European cities after 20 years of simulationGrowth rate for European cities after 20 years of simulation
Valori medi dei parametri nei diversi paesi
2
18 21
36
90
8
47
27
38
65
40 4247
20
42
10
31
71
2231
0
10
20
30
40
5060
70
80
90
100
Diffusion Breed Spread Slope Road
italia europa usa altri
• min DI and max RG for Italian cases, opposite to USA (for historical reasons and different space competition)
• BR maximum in Europe
• SP is higher when faster is the development (i.e. economical boom)
Observing different cases allows us to trace a kind of “DNA of cities” using particular sets of Observing different cases allows us to trace a kind of “DNA of cities” using particular sets of parameters:parameters:
• RG and DI are different for coastal/inland citiesRG and DI are different for coastal/inland cities
• SP is higher for growing and more populated cities (Mexico City, Tijuana, Houston, SP is higher for growing and more populated cities (Mexico City, Tijuana, Houston, Palermo)Palermo)
• BR high and DI low for strictly planned areas (Netherlands, Helsinki…)BR high and DI low for strictly planned areas (Netherlands, Helsinki…)
Parameter values Parameter values
UrbanisationUrbanisation DIDI BRBR SPSP RGRG SRSR
New metropolitan areaNew metropolitan area 25-4025-40 >50>50 >80>80 >50>50
urban sprawlurban sprawl 10-2010-20 10-3010-30 10-3010-30 >50>50
Strictly planned cityStrictly planned city <5<5 >90>90 <10<10 40-6040-60
Urban constrainsUrban constrains <5<5 100100 <10<10 <10<10
Metropolis with satellite citiesMetropolis with satellite cities 5-105-10 30-4030-40 10-3010-30 >90>90
Possible range of parameter values in order to describe different kind of urban growth Possible range of parameter values in order to describe different kind of urban growth (SR independent by cities).(SR independent by cities).
Conclusive remarksConclusive remarks
Sleuth model is really useful for simulation Sleuth model is really useful for simulation and comparison of urban growth.and comparison of urban growth.
It’s possible to use parallel computing to It’s possible to use parallel computing to solve the calibration problem (high solve the calibration problem (high execution time).execution time).
Conclusive remarksConclusive remarks
It’s just a descriptive model (parameters It’s just a descriptive model (parameters are shape indices).are shape indices).
It isn’t explicative, it doesn’t explain the It isn’t explicative, it doesn’t explain the shape of the city.shape of the city.
The same shape can derive from different The same shape can derive from different urban sprawl dynamics acting on territory.urban sprawl dynamics acting on territory.