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Spatially-Explicit Simulation of Urban Growth through Self-Adaptive Genetic Algorithm and Cellular Automata Modelling


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This paper presents a method to optimise the calibration of parameters and land use transition rules of a cellular automata urban growth model using a self-adaptive genetic algorithm (SAGA). Optimal calibration is achieved through an algorithm that minimises the difference between the simulated and observed urban growth. The model was applied to simulate land use change from non-urban to urban in South East Queensland’s Logan City, Australia, from 1991 to 2001. Performance of the calibrated model was evaluated by comparing the empirical land use change maps from the Landsat imageries to the simulated land use change produced by the calibrated model. The simulation accuracies of the model show that the calibrated model generated 86.3% correctness, mostly due to observed persistence being simulated as persistence and some due to observed change being simulated as change. The 13.7% simulation error was due to nearly equal amounts of observed persistence being simulated as change (7.5%) and observed change being simulated as persistence (6.2%). Both the SAGA-CA model and a logistic-based CA model without SAGA optimisation have simulated more change than the amount of observed change over the simulation period, however, the over estimation is slightly more severe for the logistic-CA model. The SAGA-CA model also outperforms the logistic-CA model with fewer quantity and allocation errors and slightly more hits. For Logan City, the most important factors driving urban growth are the spatial proximity to existing urban centres, roads and railway stations. However, the probability of a place being urbanised is lower when people are attracted to work in other regions.
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Land 2014, 3, 719-738; doi:10.3390/land3030719
ISSN 2073-445X
Spatially-Explicit Simulation of Urban Growth through
Self-Adaptive Genetic Algorithm and Cellular
Automata Modelling
Yan Liu 1,*, Yongjiu Feng 2 and Robert Gilmore Pontius Jr. 3
1 School of Geography, Planning and Environmental Management, The University of Queensland,
St Lucia, QLS 4072, Australia
2 College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China;
3 Graduate School of Geography, Clark University, Worcester, MA 10610, USA;
* Author to whom correspondence should be addressed; E-Mail:;
Tel.: +61-7-3365-6483; Fax: +61-7-3365-6899.
Received: 21 April 2014; in revised form: 3 July 2014 / Accepted: 8 July 2014 /
Published: 18 July 2014
Abstract: This paper presents a method to optimise the calibration of parameters and land
use transition rules of a cellular automata (CA) urban growth model using a self-adaptive
genetic algorithm (SAGA). Optimal calibration is achieved through an algorithm that
minimises the difference between the simulated and observed urban growth. The model
was applied to simulate land use change from non-urban to urban in South East Queensland’s
Logan City, Australia, from 1991 to 2001. The performance of the calibrated model was
evaluated by comparing the empirical land use change maps from the Landsat imagery to
the simulated land use change produced by the calibrated model. The simulation accuracies
of the model show that the calibrated model generated 86.3% correctness, mostly due to
observed persistence being simulated as persistence and some due to observed change being
simulated as change. The 13.7% simulation error was due to nearly equal amounts of
observed persistence being simulated as change (7.5%) and observed change being
simulated as persistence (6.2%). Both the SAGA-CA model and a logistic-based CA model
without SAGA optimisation have simulated more change than the amount of observed
change over the simulation period; however, the overestimation is slightly more severe for
the logistic-CA model. The SAGA-CA model also outperforms the logistic-CA model with
fewer quantity and allocation errors and slightly more hits. For Logan City, the most
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important factors driving urban growth are the spatial proximity to existing urban centres,
roads and railway stations. However, the probability of a place being urbanised is lower
when people are attracted to work in other regions.
Keywords: self-adaptive genetic algorithm (SAGA); cellular automata (CA); urban land
transition; simulation accuracies; Logan City
1. Introduction
Cellular automata (CA) models have been increasingly applied to simulate the systematic
spatio-temporal processes and the stochastic behaviour of land use change and urban growth [1–18].
Central to a CA-based urban model is the definition of the model’s transition rules, which determine
how the state of a cell changes over time [9–11,15]. CA models are intuitive and capable of
incorporating both the spatial and temporal dimensions of land evolution into the simulation process.
According to [19], the transition rules of urban CA models can be classified into five categories,
including (1) strictly orthodox transition rules [9,20]; (2) rules based on key drivers [5,21]; (3) rules
based on artificial intelligence [22]; (4) fuzzy logic transition rules [6,11,12,16,23]; and (5) other types
of transition rules [24–26]. Of these five categories, rules based on key drivers have been the most
widely applied, which require strict definitions of a number of variables and parameters representing
various spatial and non-spatial factors driving urban growth.
A diverse range of statistical methods have been developed to select such variables and parameters;
these include logistic regression [5], spatial logistic regression [27], multi-criteria evaluation (MCE) [28]
and hybrid models [29]. While the integration of spatial statistics and cellular automata has laid the
foundation of modelling land use dynamics, it can become a challenge to define suitable transition
rules or the relevant parameter values and to construct the architecture of the models [11]. For instance,
the MCE and logistic regression methods do not remove the effects caused by the multi-collinearity
amongst the spatial variables [11,15]. Principal components analysis (PCA) can extract independent
components of information for the set of independent variables, but PCA does not guarantee that the
principal components are relevant for the dependent variables [30]. Consequently, results generated by
CA models incorporating spatial statistics may not mimic the actual land use patterns, making them
ineffective in capturing the spatial dynamics of urban land use change [6,15].
A CA model can be developed using a hybrid method due to the advantages of its spatial
explicitness implied by the key drivers and the powerful computation capacity inherited from artificial
intelligence [31]. The development of genetic algorithms (GA) has provided researchers with new
ways to identify and to search for suitable transition rules and their defining parameters in urban
modelling [31–36]. A GA can be used to search for an optimal solution to a problem based on the
mechanics of natural genetics and natural selection [37]. Compared with other evolutionary methods,
such as particle swarm optimisation [15], the unique feature of the GA exists in its operators, including
selection, crossover and mutation. Substantively, a GA is a randomised method rather than a simple
random operation, because historical information is used to speculate on new candidate solutions [37].
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In practice, the GA technique has been applied to address various optimisation problems of
geographical systems [38–43]. In CA-based urban research, the general method of integrating cellular
automata with GA was initially proposed in [44] and subsequently adopted to parameterise the Markov
cellular automata model for land use change simulation [45]. The physical meanings of the “genes” in
the CA model were defined in [33], which were then applied to retrieve the geographical CA transition
rules. A modified GA was developed to search for optimal parameters and neighbourhood rules for a
Markov chain model to simulate the spatio-temporal urban landscape change processes in China’s
Daqing City [45]. Their results show that the combination of the GA and Markov model is capable of
capturing the spatio-temporal trend in the landscape pattern associated with urbanization for their
region. More recently, GA was applied to calibrate the SLEUTH model of urban growth, which not
only reduced the computation time in model calibration, but also improved the simulation accuracy of
the model [34]. In another application, a pattern-calibrated and GA-optimized CA model was
developed, which incorporates the percentage of landscape, patch metric and landscape division into
the fitness function of the GA model [46]. The GA optimisation technique has also been combined
with statistical techniques to calibrate an urban CA for a small urban settlement of northwest Spain;
their work shows that the model can be adapted to urban areas with various characteristics
and dynamics [47].
This paper presents a method for optimising the land use transition rules and parameters of a
cellular automata urban growth model using a self-adaptive genetic algorithm (SAGA). The model was
applied to simulate the spatio-temporal pattern of non-urban to urban land conversion in Southeast
Queensland’s Logan City, Australia. Section 2 presents the study area and the data collected and
processed for the modelling practice. Section 3 introduces the modelling approach, which includes a
CA urban model, followed by the SAGA used to optimise the spatial parameters of the CA transaction
rules. The optimisation process is guided through a fitness function to minimize the differences
between the simulated and the observed urban growth. Section 4 presents model outcomes followed by
discussions and conclusion in the last two sections.
2. Study Area and Data
Logan City in South East Queensland, Australia, was selected as the case study site to apply the
SAGA-CA model to simulate its land use evolution from non-urban to urban during the 1991–2001
period. Logan City is situated between the state capital city of Brisbane to the north and Gold Coast
City to the south. It also borders Redland City to the northeast, the City of Ipswich to the northwest
and the Scenic Rim to the southwest (Figure 1). Originally established in 1978 as a local government
area, Logan City’s land size tripled in 2008, due to major changes to the local government
in Queensland [48]. The current land size is 913 km2 with a total population of 278,000 in 2011 [49].
Two land cover maps were collected from the Department of Natural Resources and Mines of the
State of Queensland and used in this study, one representing the land cover in 1991 and one in 2001.
These land cover maps were the product of the Statewide Landcover and Trees Study (SLATS)
program funded by Queensland Government [50]. Landsat TM imageries acquired between June and
September 1991 were used for the baseline land cover data, with a partial update of the baseline land
cover dataset for 2001. The raw imagery has a nominal spatial resolution of 30 m. These raw imageries
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were pro-processed through several stages of automated and semi-automated image, classification
together with visual interpretation, field calibration and validation [51,52]. The final product was
resampled to a spatial resolution of 25 m for release to the public [50,51].
Figure 1. Logan City in South East Queensland, Australia.
Three land categories were identified from the SLATS land cover data in 1991 and 2001: urban
(consisting of settlement areas with more than a population of 200, delineated using an analysis of
digital cadastral boundaries based on lot size and population statistics), non-urban (consisting mostly
of pasture land, woody vegetation and bare lands), and excluded (consisting of crop land and water)
(Figure 2). As the focus of this research is on land use change from non-urban to urban states, the
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presence of water bodies and crop land were considered as a hard constraint to urban growth; these
areas were excluded from being urbanised. However, spatial proximities to water and crop land were
taken as factors impacting urban growth. Table 1 lists the topographical and spatial factors, as well as
the neighbourhood and stochastic factors used in the CA urban model.
Figure 2. Logan City land use change from 1991 to 2001.
Other data collected include a 1-second DEM from the national Shuttle Radar Topographic Mission
(SRTM) data and data on urban centres, main roads, railway lines and stations collected from
the relevant local government authorities. A land slope data layer was generated from the DEM.
The spatial proximity factors to urban centres (d_centres), to main roads (d_road), to railway lines
(d_rail), to railway stations (d_railstn), to agricultural land (d_agri) and to water bodies (d_rivers) were
generated using the Spatial Analyst Tools in ArcGIS from the relevant data sources. In addition,
an external impact factor (I_external) measuring the impact of other urban centres in the neighbouring
regions (including Brisbane, Ipswich, Gold Coast and Redland cities) on Logan City’s urban growth
was also considered in the model. This factor was quantified using the Journey to Work data from
Logan to the surrounding regions; the earliest data available is in the 2006 census [53]; hence, this data
was used as a proxy to quantify the external impact to urban growth of the city. A large external
impact factor value indicates that more people have travelled to work outside the region rather than
within the region. All data were processed to raster grids at 25-m spatial resolution to match with
the spatial resolution of the SLATS land cover data; the values of the spatial variables were normalised
to a range between 0 and 1 inclusive (Figure 3).
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Table 1. Variables used to compute land conversion probabilities.
Variable Meaning Data Extraction
Impact of urban centres surrounding Logan City
(including Brisbane, Ipswich, Gold Coast and
Redland cities) on the urban growth within
the region
Calculated using Journey to Work data from Logan to
the neighbouring regions from the 2006 Census
d_centres Distance to urban centres within the region Measured in GIS from the urban centres data layer
d_agri Distance to agricultural land Measured in GIS from the land use data layer
d_road Distance to main roads Measured in GIS from the transportation network
data layer
d_rail Distance to railway lines
d_rail_stn Distance to railway stations
d_rivers Distance to rivers Measured in GIS from the land use data layer
SLOPE Land slope Extracted from the DEM
DEM Land elevation
Probability of a cell changing its state from time
t to the next time within a square
5 × 5-cell neighbourhood
Calculated using the focal function in GIS according to
Equation (3) presented in Section 3.1
R Stochastic disturbance of unknown errors Generated randomly using Equation (4) presented in
Section 3.1
Figure 3. Spatial variables used in the SAGA-CA model in Logan City, Australia.
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3. Method
The modelling framework consists of a generic CA urban growth model, which is linked to an
optimisation module using SAGA to search for an optimal set of transition rules and parameters. The
optimal set of rules and parameters will then be used by the CA model to simulate the dynamic process
of urban growth (Figure 4). Each iteration of the model represents one year.
Figure 4. The SAGA process searching for the optimal CA transition rules.
3.1. Cellular Automata Model
The generic CA urban growth model was initially configured using the logistic regression
approach [54], where the land use conversion probability of a cell at location i from time t to the next
time (t + 1) is represented as:
PFNConR=×× ×
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i represents the overall land use conversion probability of cell i from time t to the next time;
i is a land use conversion probability at location i from time t to the next time that is determined
by the topographical features of the area and its spatial proximity to facilities and services.
The topographical features impacting urban land conversion include slope and elevation, while
the spatial proximity factors include distances to town centres, roads, railways, rivers and prime
agricultural land. Therefore, Ft
i can be written as:
a (2)
where: a = aj (j = 0, 1, …, k), a0 is a constant; aj (j = 0, 1, …, k) are parameters representing the impact
of the j-th factor xt
ij (including both topographical and spatial factors) on land use conversion
probability at location i at time t. The values for aj were initially generated using the logistic regression
method [54]; they were subsequently optimised using the SAGA optimisation approach.
i is a land use conversion probability at location i from time t to the next time due to
neighbourhood support. In this research, a square neighbourhood with m × m cells was adopted; the
probability that a cell develops from one state to another was defined as a proportion of the
accumulative state of urban cells within the m × m neighbourhood. For the case study applied in this
research, the neighbourhood size is 5 × 5 cells.
mm t
where the processing cell i is not considered as a neighbouring cell, i.e., j i.
Con represents a condition where simulated urban growth cannot occur at certain locations, such as
large water bodies and land used primarily for farmland, which is prevented from undergoing urban
development by land use planning regulations. Con takes on Boolean values of either 0 or 1, with 0
indicating that the cell is excluded from being urbanised and 1 indicating that the cell can change from
non-urban to urban during a time step.
R is a stochastic disturbance factor on urban development, which is defined as [1,25,26]:
=+ (4)
where r is a random real number that ranges from 0 to 1, while a is a real number that ranges from 0 to
10 and controls the effect of the stochastic factor on urban development.
3.2. Optimisation through Self-Adaptive Genetic Algorithm
3.2.1. Encoding the Chromosomes
Chromosomes are the abstract representations of candidate solutions. A chromosome is a set of
parameters that defines a proposed solution to the problem the GA is trying to solve. In the CA urban
modelling practice, all possible CA transition rules impacting on urban growth are considered as
chromosomes. Each chromosome is coded by a simple string as:
[ , ,...., ]
ββ β
=C (5)
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where C represents a candidate solution; k is the total number of topographic and spatial variables (as
in Equation (2)); β0 is a constant, that is, a “gene” in a candidate solution; β1 to βk represent
the evaluation score of each variable in a candidate solution. The optimal candidate solution values of
β0 to βk are the parameter values of aj (j = 0, 1, …, k)) used in Equation (2).
Initially, a number of chromosomes are randomly generated to form the possible solutions for the
SAGA to begin its searching process. After many generations of selection, crossover and mutation
operations, only those chromosomes that acquire better fitness values remain, resulting in the
emergence of an optimal chromosome structure.
3.2.2. The Fitness Function
A fitness function is used to evaluate and quantify the optimality of a solution during the optimisation
process [55]. The fitness function is an objective function to quantify the difference between simulated
and observed urban growth patterns. An optimal set of solutions is achieved when the fitness function
value, that is, the difference between the simulated and observed urban growth pattern, is minimised. It
is created by selecting sample cells, within the cellular urban space, to minimize the differences
between the simulation results produced by the model and the observed urban growth patterns
identified from remotely-sensed imagery. The optimisation objective is written as:
() ( )
Minimise D P U
C (6)
Where: D(C) is the fitness function for the candidate solution C; M is the number of samples that were
selected from the cellular urban space and used to retrieve the CA transition rules; Pt
i is the global
conversion probability of the state of cell i during time t to t + 1, as defined in Equation (1) for
candidate solution C; and Ui is the reference conversion of cell I, which can only take one of the two
values, 0 or 1, with Ui = 0 meaning that the state of the cell i persists as non-urban and Ui = 1 meaning
the state of the cell changes from non-urban to urban for candidate solution C.
The simulation process of urban growth can be calibrated by dynamically updating the various
parameters of the transition rules to minimize the value of the fitness function, so that the simulated
urban patterns match the observed patterns of urban growth. The calibration process of the model is
completed once the fitness function reaches a stable value over generations. At that point, the model’s
transition rules and their defining parameters can be considered optimised for operation in the CA model.
3.2.3. Selection, Crossover and Mutation
GA uses natural selection rules, including selection, crossover and mutation, to search for and
optimise a solution from a set of chromosomes. Selection is the key operation in which individual
genomes are chosen from a population of candidate solutions for later breeding, including
recombination and crossover. Individual solutions are selected through a fitness-based process during
each successive generation, where solutions with better fitness values are more likely to be selected.
The smaller the difference between the simulated and observed results, the better the fitness value.
Crossover is an exchange of genetic material between homologous individuals for final genetic
recombination. Mutation is a genetic operator used to maintain genetic diversity from one generation
of a population of chromosomes to the next.
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However, standard GA commonly uses fixed and non-interactive selection, crossover and mutation
rates, which can be problematic, because such operators cannot be modified during the search and
optimisation process [37,56,57]. A SAGA can overcome this problem, because SAGA keeps
population diversity and ensures the existence of all possible solutions in the solution domain; SAGA
also ensures the identification of an optimal solution and improves the performance of the local and
premature convergences [58,59]. In addition, the SAGA improves the search speed and precision of
the standard GA and, hence, accelerates the search and optimisation process for the problem solutions.
The three operations used in the SAGA are illustrated as follows (Figure 5).
Figure 5. The selection, crossover and mutation operations. Here, A, B, C and D are
candidate solutions; Pc and Pm are the crossover and mutation probability, respectively.
Specifically, the selection operator used in this research was a ranking method that retains the best
set of individuals that remain unchanged in the next generation of the selection operation [60].
The crossover and mutation operators are defined through a probability measure, which changes
in accordance with the fitness values [59]. The crossover probability Pc and mutation probability Pm
are defined as:
(1 2)( )
1 ........... min
1.................................................... min
(1 2)( )
1 ...... min
Pc Pc fmin favg
Pc f favg
fmax favg
Pc f favg
Pm Pm fmin favg
Pm f favg
fmax favg
................................ min
where: Pc1 and Pc2 are the maximum and minimum crossover probabilities, the values of which were
set to 0.95 and 0.45, respectively; Pm1 and Pm2 are the maximum and minimum mutation
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probabilities, the value of which were set to 0.1 and 0.001, respectively; fmin and fmax are the
minimum and maximum fitness values, respectively; and favg is the average fitness value.
3.3. Model Implementation
A total of 25,000 sample pixels were selected randomly from the candidate region, meaning
the entire study area minus the existing urban areas, agricultural land and water bodies in 1991.
Therefore, the candidate region is areas that potentially can be developed into urban land use from
1991 onwards. Each sample includes a land use change variable from 1991 to 2001, as well as
the external impact factor, distance factors, slope and DEM, as listed in Table 1 and shown in Figure 4.
The land use change variable is a bi-value (with 0 or 1 value) indicating whether the state of a land cell
has changed from non-urban to urban during 1991–2001 (in which case, the value is 1) or not
(in which case, the value is 0). These sample data were used to build the logistic regression model
(termed logistic-CA hereafter), which generates the initial values of aj (j = 0,1,…,k). This initial set
of parameters was then used to form the possible solutions for the SAGA to construct the fitness
function and begin its searching and optimisation process.
4. Results
4.1. The Optimal Chromosome/CA Transition Rules
Figure 6 shows the fitness track in the evolutionary computation of the SAGA module, which
initially converges rapidly to the best fitness line. After 200 generations, the fitness value stabilized
with the best fitness value of 47.1 and a mean fitness value of 47.2 (Figure 6).
Figure 6. Fitness track of the SAGA model.
Table 2 lists the optimal solution by the SAGA in comparison with the initial setting of parameters
generating the logistic-CA model. This optimal solution determines the initial input data for
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the SAGA-CA model to compute the conversion probability of cells from non-urban to urban, as
designated in Equation (2).
Table 2. Optimized CA parameters by the SAGA in comparison with parameters generated
by the logistic regression model.
Variables Parameters
Logistic SAGA
a0 (Constant) 0.77 0.82
a1 (I_external) 1.02 1.19
a2 (d_centres) 0.98 1.13
a3 (d_agri) 0.93 0.98
a4 (d_road) 1.09 1.05
a5 (d_rail) 0.41 0.58
a6 (d_railstn) 1.15 1.11
(d_rivers) 0.75 0.68
a8 (SLOPE) 0.34 0.34
a9 (DEM) 0.91 1.12
The optimal chromosome (i.e., the optimal set of CA parameters) demonstrated the different
impacts of the various factors on urban land use conversion in Logan City. According to Equation (2),
aj (j = 0, 1, …, 9) is a string of parameters representing the constant, I_external, d_centres, d_agri,
d_road, d_rail, d_railstn, d_rivers, slope and DEM, respectively. A negative parameter of aj leads to a
larger Ft
i value, that is, a higher probability for a cell to convert from a non-urban to an urban state.
Likewise, a positive aj value results in a smaller Ft
i; hence, a lower probability for the cell to convert
into an urban state the next time. The parameter values optimised by the SAGA show that the external
impact factor has the largest positive value, with a1 (I_external) = 1.19, which is larger than the 1.02
generated by the logistic regression model. This positive value indicates that a place with a large
number of people commuting to work out of the place is associated with a smaller probability of urban
growth at that place. This is an opportunity cost for a place in Logan to be urbanised, as more people
commute to work outside the region. On the other hand, the distance from existing urban centres
correlates negatively with the probability of urban growth. This is reflected by the lowest negative
value of a2 (d_centres) = 1.13; such an effect is stronger in the SAGA-CA model than the logistic
regression-based CA model, given the value of 0.98 generated by the logistic regression approach.
Likewise, elevation, land slope and distances to roads and railway stations are also associated negatively
with the probabilities of simulated urban growth, with a9 (DEM) = 1.12, a8 (SLOPE) = 0.34,
a4 (d_road) = 1.05 and a6 (d_railstn) = 1.11, respectively. However, close proximity to agricultural
land, rivers or railway lines tends to decrease the probability of simulated urban growth with a3
(d_agri) = 0.98, a7 (d_rivers) = 0.68 and a5 (d_rail) = 0.58 after optimisation by the SAGA. Hence,
the closer a cell is to rivers or railway lines, the less likely the cell is to be developed as an urban state.
4.2. Simulation Accuracies of the SAGA-CA Model
Using the 1991 land use map as the initial input data, the SAGA-CA model was operated with a set
of land use transition rules, optimised by the SAGA, to generate a series of land use patterns. Each
Land 2014, 3 731
iteration of the model represented one year. Hence, the result representing urban land use pattern in
Logan City in 2001 was generated after 10 iterations of the model.
The simulated urban growth map during 1991–2001 produced by the SAGA-CA model was
compared with the reference map of urban growth during 1991–2001 generated from the SLATS land
cover data [50]; this was realised through the comparison of three maps using the approach introduced
in [61]. This produces a set of measures to evaluate the correctness and error of the simulation output
(Figure 7a). Here, hits (H) indicate that growth areas from 1991 to 2001 shown on the reference maps
were simulated as growth; misses (M) indicate that growth areas shown on the reference maps were
simulated as persistence (i.e., no change in simulated state over the two time points); false alarms (FA)
indicate that the persistence shown on the reference maps were simulated as growth; correct rejections
(CR) indicate that persistence shown on the reference maps was simulated as persistence; and excluded
indicates the union of water and agriculture at the base year in 1991. Figure 7b illustrates the
simulation correctness and error by the logistic-CA model without SAGA optimisation. To assess the
simulation accuracies of the model, existing urban areas, agricultural land and water bodies in 1991
were excluded from the calculations. According to the SAGA-CA model, many of the false alarms are
near the misses; this is different from the result generated by the Logistic-CA model, where many false
alarms are rings around the excluded region. Figure 8 shows the size of the misses, hits, false alarms
and correct rejections for the candidate region by the SAGA-CA model in comparison with the result
by the logistic-CA without SAGA optimisation.
Overall, the SAGA-CA model generated 86.3% correctness, mostly due to observed persistence
being simulated as persistence (79.2%) and some due to observed change being simulated as change
(7.1%). The 13.7% simulation error was due to either observed persistence being simulated as change
(7.5%) or observed change being simulated as persistence (6.2%). The observed change (OC) accounts
for 13.3% of the candidate region, whereas the simulated change (SC) by the SAGA-CA model accounts
for 14.6% of the region. Compared to the logistic-CA model, the SAGA-CA has resulted in slightly
less overall error, 13.7% versus 15.1%. Figure 8 shows that both the logistic-CA and SAGA-CA models
have simulated more change than the amount of observed change, while the overestimation is slightly
more severe for the logistic-CA model.
The simulation errors generated by the model can also be presented through the measures of error
due to allocation (A) and error due to quantity (Q) [61–65]. The error due to quantity measures how
much less than perfect is the match between the observed and simulated quantity of change. The error
due to allocation measures how much less than maximum is the match in the spatial allocation of
the changes, given the specification of the quantities of the changes in the observed and simulated
change maps [65]. Amongst 13.7% of the total errors between the SAGA-CA simulated and the actual
land use change for the 1991–2001 interval, 1.3% was due to quantity disagreement; the other 12.4%
were due to allocation disagreement. The quantity disagreement occurred due to the SAGA-CA model
simulating slightly more growth than the reference growth. The results also show that the SAGA-CA
model outperforms the logistic-CA model with fewer quantity and allocation errors and slightly more
hits (Figure 9).
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Figure 7. Simulation correctness and error based on the reference growth versus the simulated
growth during 1991–2001. (a) The simulation correctness and error by the SAGA-CA
model; (b) the simulation correctness and error by the logistic-CA model without
SAGA optimisation.
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Figure 8. Misses, hits and false alarms in the non-urban area of 1991, where SC denotes
simulated change and OC denotes observed change from 1991 to 2001.
Figure 9. Quantity error, allocation error and hits in the non-urban area of 1991.
5. Discussion
Cellular automata techniques have been developed to explore urban land use evolution over the last
two decades, and the reliability of the models has been studied [16,35,62]. The simulation accuracies
of an urban CA model can be affected by the methodologies used in retrieving the transition rules, the
spatial and thematic resolution of the model, as well as the physical, socio-environmental and
institutional situations of the areas under study.
Firstly, the simulation accuracy of the model is subject to the characteristics of the region under
study. In this research, three thematic categories (i.e., urban, non-urban and excluded) were extracted
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from a secondary data source (i.e., the SLATS land cover data) to simulate the process of land use
change from non-urban to urban. These three land use categories are highly generalised from the more
complex land use types on the ground.
The simulation accuracy of the CA-based urban models is also sensitive to the resolution of
cells [15,66]. Regardless of other factors impacting on the simulation accuracies of the CA model,
a model configured at 250-m resolution can generate lower simulation accuracies compared to models
configured at 30-m cell resolution [15]. The low simulation accuracy of the model at a coarse scale is
usually due to the isolation of urban cells at such a scale, where only a small number of isolated urban
cells can be identified in the initial input data [15].
In addition, the methodology adopted in retrieving the CA’s transition rules is crucial for plausible
simulation results. A number of new methods have been developed to capture land use dynamics
and to improve simulation accuracy. By integrating the SAGA method into a typical logistic
regression-based CA model, the integrated model is capable of taking into account feedback
from individual “genes” during the modelling process, leading to the identification of a set
of optimised transition rules.
6. Conclusions
Spatially-explicit simulation of urban land use change has attracted widespread interest in recent
years with the focus on the spatio-temporal dynamics of the urban system and its land use evolution.
Many CA-based urban models have been developed and applied in various situations to simulate the
dynamic change of urban land use over time. However, it remains challenging for urban modellers to
identify suitable transition rules reflecting the driving factors on land use change in the modelling practice.
This paper contributes to the field by developing an urban CA model with its transition rules
optimised by a SAGA. It builds on the evolutionary computation technique [58,59] by implementing
a set of essential driving forces to urban land use change and using SAGA to optimize the design
configuration, similar to evolutionary processes. Consequently, a set of optimised transition rules
and their defining parameters were identified and used to simulate the spatio-temporal process of land
use evolutions. For the application of the model in Logan City in Queensland, Australia, most of
the areas changed from non-urban to urban occurred around the existing urban centres, due to
the effect of existing urban centres attracting more growth within their proximity. Likewise, the spatial
proximity to roads and railway stations are also important factors attracting urban growth. On the other
hand, the probability of a place being urbanised is lower when more people are attracted to work
in other regions, mostly in Brisbane, Ipswich and the Gold Coast cities. In addition, spatial proximities
to agricultural land, to rivers and railway lines also discourage an area from developing into urban land
use. The application of the SAGA-CA model to Logan City demonstrates that the SAGA technique
can increase simulation accuracy compared to a conventional logistic method, because the SAGA
technique optimizes the transition rules of an urban CA model for simulating urban growth.
Author Contributions
All authors contributed to the writing of the paper. Data processing, analysis and modelling were
performed by Yan Liu and Yongjiu Feng. Reviewers’ comments were responded to by all authors.
Land 2014, 3 735
Conflicts of Interest
The authors declare no conflict of interest.
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... Experimental results indicate that this algorithm, even dealing with large-area LUO problems, is capable of generating optimal alternative solutions approximate to the true Pareto front. Yan et al. [74] presents a method to optimize the calibration of parameters and land use transition rules of a cellular automata (CA) urban growth model using a self-adaptive GA (SAGA). Optimal calibration is achieved through an algorithm that minimizes the difference between the simulated and observed urban growth [75,76]. ...
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... Thus, the urban fringe is characterized by highly dynamic and spatially heterogeneous areas [18][19][20]. This process takes place in fringe areas, because it's relatively low land prices with limited transport, shopping, and entertainment infrastructure [21,22] compared to that in central urban areas [20]. ...
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In this paper we present a cellular automata (CA) model based on nonlinear kernel principal component analysis (KPCA) to simulate the spatiotemporal process of urban growth. As a generalisation of the linear principal component analysis (PCA) method, the KPCA method was developed to extract the nonspatially correlated principal components amongst the various spatial variables which aff ect urban growth in highdimensional feature space. Compared with the linear PCA method, the KPCA approach is superior as it generates fewer independent components while still maintaining its capacity to reduce the noise level of the original input datasets. The reduced number of independent components can be used to better reconstruct the nonlinear transition rules of a CA model. In addition, the principal components extracted through the KPCA approach are not linearly related to the input spatial variables, which accords well with the nonlinear nature of complex urban systems. The KPCA-based CA model (KPCACA) developed was fi tted to a fast-growing region in China’s Shanghai Metropolis for the sixteen-year period 1992–2008. The simulated patterns of urban growth matched well with the observed urban growth, as determined from historical remotely sensed images for the same period. The KPCA-CA model resulted in signifi cant improvements in locational accuracy when compared with conventional CA models and acted to reduce simulation uncertainty.
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Cellular automata CA stand out among the most commonly used urban models for the simulation and analysis of urban growth because of their ability to reproduce complex dynamics, similar to those found in real cities, from simple rules. However, CA models still have to overcome some shortcomings related to their flexibility and difficult calibration. This study combines various techniques to calibrate an urban CA that is based on one of the most widely used urban CA models. First, the number of calibration parameters is reduced by using various statistical techniques, and, second, the calibration procedure is automated through a genetic algorithm. The resulting model has been assessed by simulating the urban growth of Ribadeo, a small village of NW Spain, characterized by low, slow urban growth, which makes the identification of urban dynamics and consequently the calibration of the model more difficult. Simulation results have shown that, by automating the calibration procedure, the model can be more easily applied and adapted to urban areas with different characteristics and dynamics. In addition, the simulations obtained with the proposed model show better values of cell-to-cell correspondence between simulated and real maps, and the values for most spatial metrics are closer to real ones.
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This article presents a novel cellular automata (CA) approach to simulate the spatio-temporal process of urban land-use change based on the simulated annealing (SA) algorithm. The SA algorithm enables dynamic optimisation of the CA's transition rules that would otherwise be difficult to configure using conventional mathematical methods. In this heuristic approach, an objective function is constructed based on a theoretical accumulative disagreement between the simulated land-use pattern and the actual land-use pattern derived from remotely sensed imagery. The function value that measures the mismatch between the actual and the simulated land-use patterns would be minimised randomly through the SA process. Hence, a set of attribution parameters that can be used in the CA model is achieved. An SA optimisation tool was developed using Matlab and incorporated into the cellular simulation in GIS to form an integrated SACA model. An application of the SACA model to simulate the spatio-temporal process of land-use change in Jinshan District of Shanghai Municipality, PR China, from 1992 to 2008 shows that this modelling approach is efficient and robust and can be used to reconstruct historical urban land-use patterns to assist with urban planning policy-making and actions. Comparison of the SACA model with a typical CA model based on a logistic regression method without the SA optimisation (also known as LogCA) shows that the SACA model generates better simulation results than the LogCA model, and the improvement of the SACA over the LogCA model is largely attributed to higher locational accuracy, a feature desirable in most spatially explicit simulations of geographical processes.
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This paper presents a model integrating GIS, cellular automata (CA) and genetic algorithm (GA) in urban spatial optimization. The model involves three objectives of the maximization of land-use efficiency, the maximization of urban spatial harmony and appropriate proportion of each land-use type. CA submodel is designed with standard Moore neighbor and three transition rules to maximize the land-use efficiency and urban spatial harmony, according to the land-use suitability and spatial harmony index. GA submodel is designed with four constraints and seven steps for the maximization of urban spatial harmony and appropriate proportion of each land-use type, including encoding, initializing, calculating fitness, selection, crossover, mutation and elitism. GIS is used to prepare for the input data sets for the model and perform spatial analysis on the results, while CA and GA are integrated to optimize urban spatial structure, programmed with Matlab 7 and coupled with GIS loosely. Lanzhou, a typical valley-basin city with fast urban development, is chosen as the case study. At the end, a detail analysis and evaluation of the spatial optimization with the model are made, and it proves to be a powerful tool in optimizing urban spatial structure and make supplement for urban planning and policy-makers.
This paper discusses the use of genetic algorithms to enhance the efficiency of transition rule calibration in cellular automata urban growth modeling. The cellular automata model is designed as a function of multitemporal satellite imagery and population density. Transition rules in the model identify the required neighborhood urbanization level for a test pixel to develop to urban. Calibration of the model is initially performed by exhaustive search, where the entire solution space is examined to find the best set of rule values. This method is computationally extensive and needs to consider all possible combinations for the transition rules. The rise in the number of variables will exponentially increase the time required for running and calibrating the model. This study introduces genetic algorithms as an effective solution to the calibration problem. It is shown that the genetic algorithms are able to produce modeling results close to the ones obtained from the exhaustive search in a time effective manner. Optimal rule values can be reached within the early generations of genetic algorithms. It is expected that genetic algorithms will significantly benefit urban modeling problems with larger set of input data and bigger solution spaces.
This research analyses the suburban expansion in the metropolitan area of Tehran, Iran. A hybrid model consisting of logistic regression model, Markov chain (MC), and cellular automata (CA) was designed to improve the performance of the standard logistic regression model. Environmental and socio-economic variables dealing with urban sprawl were operationalised to create a probability surface of spatiotemporal states of built-up land use for the years 2006, 2016, and 2026. For validation, the model was evaluated by means of relative operating characteristic values for different sets of variables. The approach was calibrated for 2006 by cross comparing of actual and simulated land use maps. The achieved outcomes represent a match of 89% between simulated and actual maps of 2006, which was satisfactory to approve the calibration process. Thereafter, the calibrated hybrid approach was implemented for forthcoming years. Finally, future land use maps for 2016 and 2026 were predicted by means of this hybrid approach. The simulated maps illustrate a new wave of suburban development in the vicinity of Tehran at the western border of the metropolis during the next decades.
Landscape metrics have been widely used to characterize geographical patterns which are important for many geographical and ecological analyses. Cellular automata (CA) are attractive for simulating settlement development, landscape evolution, urban dynamics, and land-use changes. Although various methods have been developed to calibrate CA, landscape metrics have not been explicitly used to ensure the simulated pattern best fitted to the actual one. This article presents a pattern-calibrated method which is based on a number of landscape metrics for implementing CA by using genetic algorithms (GAs). A Pattern-calibrated GA–CA is proposed by incorporating percentage of landscape (PLAND), patch metric (LPI), and landscape division (D) into the fitness function of GA. The sensitivity analysis can allow the users to explore various combinations of weights and examine their effects. The comparison between Logistic-CA, Cell-calibrated GA–CA, and Pattern-calibrated GA–CA indicates that the last method can yield the best results for calibrating CA, according to both the training and validation data. For example, Logistic-CA has the average simulation error of 27.7%, but Pattern-calibrated GA–CA (the proposed method) can reduce this error to only 7.2% by using the training data set in 2003. The validation is further carried out by using new validation data in 2008 and additional landscape metrics (e.g., Landscape shape index, edge density, and aggregation index) which have not been incorporated for calibrating CA models. The comparison shows that this pattern-calibrated CA has better performance than the other two conventional models.