Comparison between CBR and CA methods for

J. Geogr. Sci. 2012, 22(4): 716-736 DOI: 10.1007/s11442-012-0958-6 © 2012

Science Press

Springer-Verlag

Comparison between CBR and CA methods for estimating land use change in Dongguan, China DU Yunyan1, GE Yong1, V. Chris LAKHAN2, SUN Yeran1, CAO Feng1 1. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Science and Natural Resources Research, CAS, Beijing 100101, China; 2. Department of Earth and Environmental Sciences, University of Windsor, ON N9B 3P4, Canada

Abstract: Many studies on land use change (LUC), using different approaches and models, have yielded good results. Applications of these methods have revealed both advantages and limitations. However, LUC is a complex problem due to influences of many factors, and variations in policy and natural conditions. Hence, the characteristics and regional suitability of different methods require further research, and comparison of typical approaches is required. Since the late 1980s, CA has been used to simulate urban growth, urban sprawl and land use evolution successfully. Nowadays it is very popular in resolving the LUC estimating problem. Case- based reasoning (CBR), as an artificial intelligence technology, has also been employed to study LUC by some researchers since the 2000s. More and more researchers used the CBR method in the study of LUC. The CA approach is a mathematical system constructed from many typical simple components, which together are capable of simulating complex behavior, while CBR is a problem-oriented analysis method to solve geographic problems, particularly when the driving mechanisms of geographic processes are not yet understood fully. These two methods were completely different in the LUC research. Thus, in this paper, based on the enhanced CBR model, which is proposed in our previous research (Du et al. 2009), a comparison between the CBR and CA approaches to assessing LUC is presented. LUC in Dongguan coastal region, China is investigated. Applications of the improved CBR and the cellular automata (CA) to the study area, produce results demonstrating a similarity estimation accuracy of 89% from the improved CBR, and 70.7% accuracy from the CA. From the results, we can see that the accuracies of the CA and CBR approaches are both >70%. Although CA method has the distinct advantage in predicting the urban type, CBR method has the obvious tendency in predicting non-urban type. Considering the entire analytical process, the preprocessing workload in CBR is less than that of the CA approach. As such, it could be concluded that the CBR approach is more flexible and practically useful than the CA approach for estimating land use change. Keywords: artificial intelligence; case-based reasoning; land use changes; spatial relationship; cellular automata; Dongguan coastal region, China

Received: 2012-01-16 Accepted: 2012-03-20 Foundation: National 863 High Technology Programs of China, No.2011BAH23B04; The State Key Laboratory of Resource and Environment Information System, No.088RA500KA; National Natural Science Foundation of China, No.41071250 Author: Du Yunyan, Ph.D, specialized in geographical case-based reasoning and coastal GIS research. E-mail: [email protected]

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DU Yunyan et al.: Comparison between CBR and CA methods for estimating land use change in Dongguan, China

1

717

Introduction

Due to the direct relationship between land use changes (LUC) and important global processes such as loss of biodiversity, biochemical and hydrological cycling, and land productivity, research in this field has increased steadily over the past two decades (Burnicki et al., 2010). Several studies on LUC, using different approaches and models, have yielded good results. For instance, Markov chains, multivariate statistics, optimization, system dynamics modelling, and Conversion of Land Use and its Effects (CLUE)/Cellular Automata (CA) have been used to study LUC in different situations (Zhang and Zhang, 2005; Huang and Cai, 2005). Applications of these methods have revealed both advantages and limitations. For instance, Markov chains are useful for predicting short-term LUC (Guo and Ou, 2004), but the method is not effective for integrating spatial knowledge. Statistical modeling with multivariate linear regression is applicable for explaining the variations of one variable, but a linear regression model created from data from one region cannot be applied directly to other areas (Shi et al., 2000). Optimization modeling provides sufficient information to support decision-making, but cannot represent dynamic LUC processes (Chomitz and Gray, 1996; Konagaya et al., 1999). Systems dynamics modeling is effective for partially explaining complicated system functions and their relationships to structure in LUC. However the scale effect, which is one of the most important issues in studying LUC, cannot be integrated into systems dynamics modeling (He et al., 2004). CLUE/CA modeling is more applicable to simulating a variety of simultaneous LUC processes in complicated dynamic systems. This is, however, contingent on the use of previous LUC results (Chen et al., 2000; Li and Yeh, 1999; 2000; 2001a). The use of the Bayesian network approach is effective for simulating LUC between two periods in a region, but it requires time-consuming computations and more extensive assumptions such as a complete dataset, no preferred selection, and discontinuous variables (Lin et al., 2001). Since the late 1980s, CA has been used to simulate urban growth, urban sprawl and land use evolution (Zhou et al., 1999). The CA approach is a mathematical system constructed from many typical simple components, which together are capable of simulating complex behavior. CA can build specific models for specific problems, and extract rules according to different principles to simulate complex geographic processes by altering the cell state (Wolfram, 1984). Some researchers created several CA models to simulate urban development. For example, Couclelis (1985; 1988; 1989) established the CA model framework for urban development simulation incorporated into a geographic information system (GIS). Fractal theory has been incorporated into the CA model to investigate urban development and sprawl (Batty and Lonley, 1994; Batty and Xia, 1994). Xie (1996) simulated urban development in Buffalo, New York, USA, with GIS technology and the CA model. This was one of the first systematic applications of CA to urban sprawl. Clarke et al. (1997; 1998) established a self-modified CA model which adjusted model parameters during the simulation. The model allows the accumulation of probabilistic estimates based on Monte Carlo methods. White and Engelen (1993) used the CA model to simulate spatial structure change in urban land use in the United States. The CA model produced fractal structures for each land use category and in the regions of a city. The results indicated that American cities have a similar fractal dimension. Wu (1998b) used multi-criteria evaluation (MCE) to define CA

718

Journal of Geographical Sciences

rules and established a simulation prototype system integrated by GIS, CA and MCE. The system simulated the urban development of Guangzhou, China successfully. Li et al. (2000; 2002a; 2002b; 2005; 2006) developed the CA approach to produce varying methods such as the constrained CA model, ANN-CA model, PCA-CA model, GA-CA model and the CBR-CA model. These models were tested by examining the urban development of the Pearl River Estuary area in China. Case-based reasoning (CBR), as an artificial intelligence technology, has been employed to study LUC by some researchers since the 2000s. It relies on knowledge from previous cases to explain new situations. Compared to the above methods, CBR is relatively simple and flexible. It is efficient in simplifying knowledge derived from previous research, thereby improving solutions for new situations and for knowledge accumulation for further reasoning (Du et al., 2002). CBR uses a problem-oriented analysis method to solve geographic problems, particularly when the driving mechanisms of geographic processes are not yet understood fully. Some researchers used the CBR method in the study of LUC. For instance, Li and Yeh (2004a) used CBR to detect LUC in the Pearl River Delta, China. Their research indicated that the classification accuracy with the CBR method was better than that with the supervised classification method. Chen et al. (2007) employed a CBR method to classify multi-temporal SAR images with the aid of ancillary information. The study site was located in Beijing, China. Multi-temporal ENVISAT ASAR images from 2004 to 2005 were used in the experiments. The results demonstrated the promise of CBR method in SAR image classification with overall classification accuracy of 80%. Du et al. (2010) proposed a three-component model (“problem”, “geographic environment”, and “outcome”) to represent the LUC cases with complicated spatial relationships. The CBR method was tested by examining LUC in the Pearl River Estuary area, China. The approach yielded similar prediction accuracy as that derived from applying the Bayesian network approach to the same data. The CBR-based method was an effective and explicit solution to represent and solve LUC problems. However, LUC is a complex problem due to influences of many factors, and variations in policy and natural conditions. Hence, the characteristics and regional suitability of different methods require further research, and comparison of typical approaches is required. Thus, in this paper, a comparison between the CBR and CA approaches to assessing LUC is presented. The CA approach is a mathematical system constructed from many simple identical components, which determine the state of the cell with rules extracted by different principles to simulate complex change processes (Wolfram, 1984). CA simulation is extended to LUC applications, especially urban growth, urban sprawl and land use evolution. For the above reasons, CA is chosen for comparison with CBR in order to determine the suitability of CBR for solving LUC problems. The paper is organized as follows: In Section 2 the CBR approach for LUC prediction is described including previous studies, fundamental theory and modeling. In Section 3 the CA approach for LUC prediction is presented by describing the fundamental principles, mechanisms and estimation algorithm. The experimental procedures of the two approaches for LUC analysis are presented in detail in Section 4. A discussion of the experiment results is presented in Section 5, and Section 6 provides a conclusion.


2 2.1

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CBR approach for LUC estimation Theory of the CBR approach for LUC

Du et al. (2009) proposed an enhanced CBR which was applied to LUC prediction. In traditional CBR model, an unresolved problem or situation is called the target case, and the past problem or situation is called the base case. The principle of CBR is to use the base case retrieved by the guide of target case to direct the solution of the target case (Shi, 1998). The enhanced CBR is based on two improvements. The first is the improvement of the traditional dual mode (“problem” and “outcome”) in the case structure. Because spatial differentiation must be considered when trying to solve geographic problems, this research proposes a three-component representation model whereby a new component, “geographic environment”, is added. Secondly, prior to the similarity computation in the traditional CBR, the retrieval of inherent spatial relationships between geographical cases is incorporated into the retrieval process. The retrieval algorithm is based on rough set theory, a method used to extract information on the basis of learning, reasoning, and inferring knowledge from incomplete or uncertain data (Wang, 2001). Based on the aforementioned improvements, Figure 1 presents a flow chart of the CBR-based LUC estimation method. First, the specific meanings of the three components need to be understood, and suitable quantitative indices need to represent the LUC cases

Note: of the terms A, P, N1, N2, D1, D2, …, D5 are described in Section 4.2.1

Figure 1

Flow chart of the CBR approach for LUC estimation

720


selected. Second, based on the “4R” (Retrieval, Revise, Reuse and Retain) cycle proposed by Aamodt and Plaza (1994), the LUC estimation with the CBR approach is conducted as follows: (1) Define the land parcel to be estimated as the new case. Retrieve the most similar case from the previous cases with the retrieved spatial relationships and similarity computations; (2) reuse the changed result in the most similar parcels to predict change in the new parcels. During this step, constrain the inherent spatial relationship between the cases. Retain the new case to expand the case library according to the specificity of the estimated parcel. 2.2 2.2.1

CBR model for LUC Case representation model

In this study, a land use parcel is defined as a case, and the three-component model (“problem”, “geographic environment” and “outcome”) is used to represent LUC cases. “Problem” is defined as the prediction of LUC during a certain period of time in the study area. Quantitative indices such as area, perimeter, and fractal dimension of land parcels are used to describe the “problem” component in the cases. “Geographic environment” includes those geographic factors which may affect possible LUC. Multiple quantitative indices are calculated and used to define spatial relationships between land parcels and geographic environment. For example, proximity of a land parcel to the nearest road or river and to other parcels is introduced into the “geographic environment” component. The final case component, “outcome”, refers to the LUC result during a certain period of time, as for example the change from agricultural to developed land during a five-year period. Hence, LUC cases can be defined by the following equation: Casei={Si, SA1i, SA2i, …, SAji, SR1i, SR2i, …,SRli, Landy1i → Landy2i} (1) i=1, 2,…, K; j=1,2, …, M; l=1,2, …, N;

S i = {( xi1 , yi1 ), ( xi2 , yi2 ),..., ( xim , yim )} where i is the case number, Si is the shape and size of case i represented by the coordinates of land use parcel boundaries, SA1i , SA2i ,

SR1i , SR2i ,

, SAji are the attributes (total M) of case i,

, SRli are quantitative indices (total N) of spatial relationships between case i

and geographic environment factors, and Landy1i and Landy2i are the case “outcome”, i.e. LUC during a certain period of time. 2.2.2

Spatial relationship retrieval algorithm

Li and Yeh (1999; 2000) indicated that LUC is affected or controlled by geographic environment and surrounding land use categories. For example, parcels next to the city center or main transportation routes are more likely to be converted into urban land. In contrast, the likelihood of ecologically sensitive areas being converted into urban land is much lower. These spatial relationships can be retrieved and incorporated into the “geographic environment” component as quantitative indices. Key decision-making spatial relationships, once retrieved, will therefore improve CBR reasoning performance and expand its application to the study of geographic phenomena. Cao et al. (2009) indicated that inherent spatial relationship rules can be retrieved using rough set theory. The model is briefly described below. A decision table (S) can be used to describe the rough set-based processes through which


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core spatial relations can be extracted: S = (U , AV , ,f )

(2)

where U is the collection of all geographic cases under investigation. A = C ∪ D,C ∩ D =φ . C and D refer, respectively, to a set of the condition and decision attributes in the decision table. A consists of all spatial relations and the reasoning results. V = ∪Va , where Va is the a∈A

domain of attribute a and a∈ A . f :U × A →V is an information function used to describe the attribute values of a geographic case. U, C, and D are given, respectively, by U ={x1 ,x2 ,xn } = {Case1, Case2,…, Casen}

(3)

C ={C1 ,C2 , ,Cm } = {Distance relations, Direction relations …}

(4)

D ={d1 ,d 2 ,...,d n } = {Geographical result}

(5)

The decision table is simplified using the algorithm proposed by Pawlak (1991), and is summarized as follows. The positive region of D with respect to C is given by POSC ( D) =

∪ CX

(6)

X ∈U / D

where X is a subset of U(X⊆U) and U/D represents an equivalence class of the decision attributes. CX refers to the lower approximation of X with respect to C. POSC(D) is a collection of all elements in U, which can be uniquely categorized into an equivalence class of D by the partition of a spatial relations C. For an index a in set C ( ∀a∈C ), if POSC ( D) = POSC −{a} ( D)

(7)

then index a is D-dispensable in C. The subset B⊆Cwill be called a D reduct of C, if and only if every index in C is D-dispensable and POS B ( D) = POSC ( D) . The reduct set B is then used to generate decision rules using the method proposed by Pawlak (1991). Let Xi and Yj be the equivalence classes of U/B and U/D, respectively. des(Xi) represents the description of equivalence class Xi, i.e. the value description of the spatial relations in set B. des(Yj) refers to the description of equivalence class Yj, i.e. the value description of the geographic result of a geographic case. Decision rules can then be generated and expressed as iij : dex( X i ) → des (Y j ), Y j ∩ X i ≠ φ (8) 2.2.3

Case similarity computation and reasoning

The representation model proposed by Du et al. (2010) includes spatial relationships between cases, and between cases and the environment. Hence, instead of using the nearest neighbor method to compute similarity between cases as done in previous studies, this research calculates case similarity with the equation: SimilarityCase (i , j ) = w1 × Sr (Case (i , j )) + w2 × Sa (Case (i , j )) + w3 Ss (Case (i , j )) (9) where w1, w2 and w3 are weights assigned to similarity coefficients, and w1 + w2 + w3 = 1. Sr (Case (i , j )) , S a ( Case ( i , j )) and S s (Case (i , j )) are the similarity coefficients between cases i and j for spatial relationship, attributes, and shape, respectively.

722


For a specific geographic problem, different weights are assigned to the three similarity coefficients on the basis of their potential influences on the “outcome” of the new case. For instance, if spatial relationships play a more important role in determining the “outcome”, a higher value will be assigned to w3. In contrast, greater values will be assigned to w1 and w2 when there is no significant impact of spatial relationships on the “outcome.” For the study of LUC, land parcel shape is considered to be unrelated to case “outcome”, and as such it is not used. S a ( Case ( i , j )) is calculated using the traditional Euclidean distance method,. Similarity coefficients of adjacency topology and continuous spatial relationships can be calculated by various methods (Du et al., 2005; Guo et al., 2005). An adjacency index to describe the topology relationship has been derived (Du et al., 2010). A vector space algorithm (Niu, 2006) is used to calculate the spatial adjacency coefficient. Case reasoning is then performed after the similarity coefficients are calculated. Prior cases with similarity coefficients greater than an arbitrarily set threshold are first selected as the candidate cases, and the “outcome” of these prior cases is then analyzed. Then the one with maximum probability is chosen from the selected candidate cases thereby satisfying the constraint of the inherent spatial relationship. If none of the candidate cases satisfies the constraint of the inherent spatial relationship, the case with the greatest similarity coefficients is selected directly. The land use category corresponding to the “outcome” with maximum probability is accepted finally as the “outcome” of the current case.

3 3.1

Cellular automata approach for LUC estimation Theory of CA

The logical theory of automata was presented in the early work by von Neumann (1951). Mathematicians subsequently extended and described CA from the perspectives of set theory and topology (Culik et al., 1990; Xie, 1994). The definition of CA based on set theory is presented as follows (Zhou et al., 1999): Suppose d is the spatial dimension, k is the state of cell and the value range is inside a finite set S, r is the neighborhood radius of cell, Z is an integer set indicating one-dimensional space, and t is time. In a simple example, assume d=1. In this case the whole cell space is a one-dimensional space. SZ is the distribution of state set S in integer set Z. The dynamic evolution of CA is the change of state combined with time, and can be described as: F : StZ → StZ+1

(10)

This dynamic evolution is also controlled by the local evolution rule f of the cell. This local function f is usually called local rules. In the one-dimensional space, cell and neighborhood can be stated as S2r+1, and the local function can be expressed as: f : St2 r +1 → St +1

(11)

The input set and output set of the local rule f are all finite sets, and are finite reference tables. For example, when r =1 the formation of f is similar to these: [0,0,0]→0; [0,0,1]→0; [0,1,0]→1; [1,0,0]→0; [0,1,1]→1; [1,0,1]→0; [1,1,0]→0; [1,1,1]→0. Applying these local functions to cells in the cell space the global evolution can be expressed as:


F (cti+1 ) = f (cti−r , cti , cti+r )

723

(12)

i t

where, c indicates the cell in site i at time t. 3.2

Construction of the CA model for LUC estimation

In this study a cell in the LUC raster map is defined as a cell in the CA model in order to build the CA model. In the CA model the rules are described as the function of the state of the cell itself and its neighborhood cells at time t, at which the state at time t+1 can be calculated (Shi, 1998). Rules are the core of CA models, but the rule definitions are sometimes complex. The main definition methods are heuristic, such as matrix (White and Engelen, 1993), multicriteria evaluation (MCE) (Wu and Webster, 1998) and grey cell (Li and Yeh, 2000). All of these methods are impacted by subjective factors and are different in form. In this study, the multicriteria evaluation (MCE) method is used to define the rules (Wu, 2002). Both global and local impact factors are considered in urban simulation with CA models. In particular, the MCE-CA model assumes that the suitability of a site is a function of independent variables such as travel distance to the city, elevation and slope. The dependent variable is a binary (categorical) one, specifying whether the land has been developed or not in the observation period. In this case both dependent and independent variables are grids derived in a GIS environment and exported subsequently to fit a regression model. According to the logistic model, the probability of a site experiencing land conversion is computed as (Wu, 2002): exp( z ) 1 Pg ( sij = urban) = = (13) 1+ exp( z ) 1+ exp(− z ) where Pg is the observed global probability, sij is the state of the cell (i, j), and z is a vector that describes the development features of the site: z = a + ∑bk xk

(14)

k

where a is a constant, bk is a coefficient of the regression model, and xk is a set of site attributes. In addition, global influences from development factors, and local influences on site (cell) development from adjacent land use types should be incorporated into the measure of development suitability. In this study, the neighborhood function is calculated in a conventional ad hoc way, i.e. through a 3×3 kernel. The neighborhood potentiality of cell transition is defined as (Wu, 2002): Ω = t ij

∑ con(s 3×3

ij

= urban)

3×3−1

(15)

where Ωtij is a neighborhood evaluation function, with reference to the development density within the 3×3 neighborhood, and con( ) is a conditional function which returns true if the state sij is urban land use. In this simulation, the neighborhood is defined as the eight immediately neighboring cells. It must be noted that Ωtij is denominated by time t, which means that neighborhood density changes during the simulation. The joint probability is calculated as the product of global probability, cell constraint, and

724


neighborhood potentiality. Cell constraint refers to factors which exclude land development on cells such as a water bodies, mountainous areas and planning restriction zones. It is possible to use an evaluation score of land suitability instead of a binary one (suitable/unsuitable). The joint probability is expressed as (Wu, 2002):

pct = pg con( sijt = suitable)Ωtij

(16)

where, con ( ) converts the state of suitable land into a binary variable. Again, note that the joint probability pct is denoted with time t, indicating it changes with iterations. Urban expansion is impacted by various political, human, and occasionally random events, such as the migration, thereby making it a complicated process (Guo et al., 2004). To make the simulation reflect uncertainties in the urban system, a stochastic factor should be incorporated into the improved GeoCA model (Li et al., 2008a). The stochastic factor can be represented by: Ra =1+ (−lnγ )α

(17)

where γ is a stochastic factor ranging from 0 to 1, α is the parameter controlling the impacts of the stochastic factor, ranging from 1 to 10. By incorporating a series of constraints and a stochastic factor, the total development probabilities can be further represented as follows (Li et al., 2008a): p t = pct × Ra

(18)

The change of the cell state can be judged by calculating the development probabilities. After the comparing the development probabilities and the threshold, the state of the cell in the next iteration is decided. This process is described by: ⎧⎪Developed , p t (ij ) > pthrehold S (ij ) = ⎨ t ⎪⎩Undeveloped , p (ij )≤ pthrehold t +1

(19)

where S t +1 (ij ) is state of cell (i, j) at time t + 1, and pthrehold is the threshold of the development probability, ranging from 0 to 1. Based on the above equations, the LUC can be simulated on the basis of the cell state change.

4 4.1

A case study Study area

This study focuses on LUC in the Dongguan coastal region, China (see Figure 2) for the time period 1995–2000. The coastal areas of China, especially the Pearl River Delta, have gained a rapid economic growth with the continuing implementation of the opening strategy of the coastal cities in China since about 1980, and huge changes of the land-use have taken place under the acceleration of industrialization and urbanization in these areas (Cui et al., 1994; Liu et al., 2003; Shi, 2001; Zhang et al., 2003; Xiong et al., 2009; Xu et al., 2008; Zhan et al., 2009; Wu et al., 2010; Dong et al., 2010; Liu et al., 2010). The city Dongguan, located at the east coast of the Pearl River Estuary, is one of the fastest growing cities among the Pearl River Delta city group with a land area of 2465 km2. In 1995, Dongguan had a resident population of about 3.36 million, and the gross domestic product (GDP) reached 29.63 billion yuan. By 2000, the population rose to 6.45 million and the GDP increased to


Figure 2

725

Land use change in the Dongguan coastal region from 1995 to 2000

82.03 billion yuan (Statistical Yearbook of Dongguan, 2010). Rapid urbanization had led to land-use change from 1995 to 2000 in Dongguan. Therefore, to explore and reveal the land-use change pattern and the underlying driving mechanisms in Dongguan is quite significant in studying the economic development of this area and even the entire delta (Cui et al., 1994; Shi, 2001). Data used in this case study were from interpretation of Landsat TM images which were

726


acquired in 1995 and 2000. There are 2464 LUC parcels in the research region (Figure 2 and Table 1). Statistical analysis reveals that there are 38 LUC categories. Between 1995 and 2000 the study area lost rural settlement sites and pond and reservoir, but gained streams and canals and plain non-irrigated land. The eight major change categories are listed in the legend of Figure 2. Major rivers, reservoirs, highways, and light-duty roads are plotted in Figure 2 to illustrate the spatial relationship between land use categories and related geographic environment. Because CA is appropriate for LUC prediction in urban regions, the data in the CA simulation experiment and the test cases in the CBR experiment, which are same, are from the Dongguan coastal region, located in southwest Dongguan. This simulation region (Figure 3) has 306 parcels and contains 124 LUC parcels and 38 LUC categories which comprise 41% of the total case histories.

Figure 3

4.2

Land use change in the experimental region from 1995 to 2000

CBR quantitative estimation for LUC

(1) Description of CBR model for LUC Three components (problem, geographic environment, and outcome) were constructed for the cases. The first component, “problem”, estimates LUC in the Shenzhen-Dongguan region from 1995 to 2000. This change was described quantitatively by variation in perimeter (P) and area (A) of land parcel polygons. The second component, “geographic environment”, refers to the distribution of geo-


727

graphic features, including major rivers, reservoirs, cities, highways, and light-duty roads. Previous studies indicated that LUC is affected by a series of factors including distance, surrounding land use categories, and natural attributes (Li and Yeh, 1999; 2000). Results from several studies indicated that proximity to the city center (Li and Yeh, 1997), rivers (Li and Yeh, 2001a), reservoirs (Li and Yeh, 2001b), and roads (Li and Yeh, 2001a; Wang et al., 2008) have significant influences on potential LUC. Consequently, two topology indices and six variables were used to describe the “geographic environment” component. The two topology indices were major surrounding land use categories in 1995 (N1) and in 2000 (N2), and the six variables were related to distance: (a) to the nearest town (D1), (b) to the nearest built-up land (D2), (c) to the nearest river (D3), (d) to the nearest reservoir (D4), and (e) to the nearest highway (D5). The LUC result is the “outcome” of the cases (i.e. estimating major land use categories in 2000 based on land use categories in 1995). As a result, cases can be represented by equation (20). Casei ={ID,Pi , Ai N1i ,N 2i ,D1i ,, D2i , D3i , D4i , D5i , Land y1995, Land y 2000,}i =1,2,...,k

(20)

(2) Spatial relationship retrieval and case library creation The eight spatial indices in equation (20) were calculated for each of the land parcels in Figure 2. Distance indices (D1, D2, D3, D4, D5) to major geographic features were calculated by executing a VBA-based algorithm in ArcMap. The two adjacency indices ( N1i , N 2i ) were derived by examining the topology relationships in the GIS database. Using equation (10), adjacency indices were calculated for all land use categories. To simplify the calculation only the three major land use categories with the largest adjacency indices were introduced into the case library, and then used to calculate similarity coefficients. Three land use categories were required for each case, otherwise the character * was used to ensure the total number of surrounding land use categories was three. The LUC case library was then constructed for the 2464 LUC parcels (Table 1) in the study area. 306 of these parcels were selected to test the CBR approach. The remaining Table 1 ID

LUC case library for the experimental region from 1995 to 2000 P

A

N1 *

N2 *

D1

D2

D3

D4

D5

Land1995 Land2000

1

6705

421

bg

bh

0

723

3256

1213

1085

g

b

2

16667

540

gl

hl

0

407

2391

406

1534

g

l

3

8034

421

bhl

bhl

294

0

2924

210

1783

b

b

4

10082847

42669

aeghl

abdehl

0

0

0

0

0

g

h

5

699836

4997

ghkl

hkl

0

0

4273

291

267

l

l

6

8929

485

ghl

hl

0

0

2756

601

2523

l

h

7

75628

1405

hil

dhl

1785

0

10988

900

4198

h

d

8

8094

483

ce

dl

4096

657

13319

0

5617

e

l

…

…

…

…

…

…

…

…

…

…

…

…

2464

53418981

179236

0

0

0

0

0

h

d

2465

3374085

17568

0

1309

2913

0

0

l

l

acdeghijl adehijl aegl

aehl

* a: Dense forest; b: Sparse forest; c: Other forest; d: Lakes; e: Pond and reservoir; f: Tidal flat; g: Cities and residential land; h: Rural settlement sites; i: Other built-up land; j: Other land use; k: Hilly rice field; l: Plain rice field; m: Hilly nonirrigated land.

728


2,158 history cases were used in the similarity and reasoning calculation. Detailed estimation accuracy was given. (3) Similarity coefficient calculation and reasoning Prior cases that were similar were retrieved using a similarity-calculation algorithm. The topological adjacency relationship was first calculated using equation (11), and then similarity coefficients were calculated using equation (9). Weights were assigned to similarity coefficients depending on the impacts of different indices on the “outcome”. (4) CBR results A test, performed on 306 selected cases, indicated that the estimation accuracy was 52%. Similar cases were obtained for all test cases with a threshold set at 80%. 4.3

Cellular automata estimation for LUC

This experiment used the data shown in Figure 3, which was the same as the test cases used in the CBR experiment. Based on the CA model described in Section 3.2, the geographical CA simulation software GeoSOS developed by Li et al. (2008b) was used to simulate LUC in the experimental region. Before the CA simulation, the raster data should be transformed from the vector LUC data that had been used in the CA experiment. The cell size is set at about 97 (m) in the transformation. There are 70,085 cells totally in the raster form of the 306 parcels. The simulation steps will be presented as follows: (1) Model construction The objective of the experiment was to estimate LUC for each cell in the experimental region from 1995 to 2000. The variables selected for the CA experiment were similar to those in the CBR experiment. They can be expressed as: X={D1, D2, D3, D4, D5, UrbanNums, Suitable, Landy1995, Landy2000} (21) where Landy2000 is the estimate category in 2000, and Landy1995 is the land use category in 1995. UrbanNums is the number of urban cells in the 3 × 3 window, Suitable is the land development constraint, and the five distance variables (D1, D2, D3, D4, D5) were the same as in the CBR experiment (see Section 4.21). And the raster forms of D1, D2, D3, D4, D5 data are necessary in the simulation and can be got by using the AcrGIS tool. (2) Determining model parameters The weights of the five distance variables in the CA experiment were the same as those in the CBR experiment. Because GeoSOS can simulate the transformation from non-urban types to urban types rather than the changes between different non-urban types and different urban types in this paper. So it is necessary to choose LU categories as the urban and non-urban ones. In this paper, “Cities and residential land”, “Rural settlement sites” and “Other built-up land” were set as the urban types, and the other LU types were set as the non-urban types. (3) Setting simulation quantity The normalized raster data were input into GeoSOS, and the appropriate simulation quantity to conduct the CA experiment was set. The CA simulation is a dynamic process and it will not stop unless the number of changed cells reaches to a threshold. This threshold is called simulation quantity in CA experiment. In this case example, the number of actual changed cells was 27,857. So the simulation


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quantity (number) of the cells to be changing in this experiment was set at 27,857 in this paper. (4) Test result Results indicate that 49,575 of the 70,085 cells were estimated correctly, with a total accuracy of 70.7% (see Table 2B). Table 2A

The validation result of CBR estimation (raster form) compared with the actual LUC Actual

Validation Accuracy (%)

39262

4080

90.6

Urban

3642

23101

86.4

In total

42904

27181

89.0

The validation result of CA simulation (raster form) compared with the actual LUC Actual

5.1

Urban

Non-urban

Table 2B

5

Estimated Non-urban

Estimated Non-urban

Urban

Validation Accuracy (%)

Non-urban

24583

18759

56.7

Urban

1751

24992

93.5

In total

26334

43751

70.7

Comparison between CBR and CA Accuracy comparison

(1) CBR accuracy analysis 160 of the total 306 test cases were estimated correctly, with an overall accuracy of 52%. The similarity between problem cases and prior cases was >80%. Similar prior cases were found for each test case when a threshold value of similarity was set at 75%. To further analyze the underlying relationship between estimation accuracy and LU category, the test cases were classified according to the Land Use (LU) category in 1995 and the LU category in 2000. As shown in Table 3, there are 13 LU categories in 1995 totally. Only two LU categories (“Hilly nonirrigated land” and “Sparse forest”) have accuracies up 70%. And four LU categories (“Dense forest”, “Plain nonirrigated land”, “Pond and reservoir” and “Hilly rice field”) have accuracies between 50% and 70%. However, seven LU categories (“Rural settlement sites”, “Other forest”, “Other built-up land”, “Other built-up land”, “Lakes”, “Tidal flat” and “Other land use”) have accuracies down 50%. In the statistical results for the year 2000 (Table 4), there are 14 LU categories in 2000 totally. Only four LU categories (“Sparse forest”, “Hilly rice field”, “Cities and residential land” and “Hilly nonirrigated land”) have accuracies up 70%. And two LU categories (“Pond and reservoir” and “Dense forest”) have accuracies between 50% and 70%. However, eight LU categories (“Plain nonirrigated land”, “Rural settlement sites”, “Streams and canals”, “Other built-up land”, “Other forest”, “Lakes”, “Tidal flat” and “Other land use”) have accuracies down 50%. In both Tables 3 and 4, accuracies of three LU categories (“Sparse forest”, “Hilly rice field”, “Cities and residential land” and “Hilly nonirrigated land”) are 0 because the number

730 Table 3

Journal of Geographical Sciences Comparison of estimation accuracy with different LU categories in 1995 Land use in 1995

Table 4

Number of Test cases

Past cases

Validation accuracy (%)

Dense forest

50

236

56

Sparse forest

16

88

94

Other forest

27

153

34

Lakes

2

2

0

Pond and reservoir

55

286

67

Tidal flat

1

1

0

Cities and residential land

20

74

30

Rural settlement sites

44

414

46

Other built-up land

34

110

35

Other land use

1

1

0

Hilly rice field

3

27

67

Plain nonirrigated land

51

353

57

Hilly nonirrigated land

2

17

100

Total

306

1762

52

Comparison of estimation accuracy with different LU categories in 2000 Number of Test cases

Past cases

Validation accuracy (%)

Dense forest

41

200

56

Sparse forest

23

123

74

Pond and reservoir

63

336

67

Rural settlement sites

63

262

41

Other built-up land

18

89

44

Other forest

4

23

25

Streams and canals

24

226

34

Plain nonirrigated land

59

439

48

Cities and residential land

4

18

75

Lakes

1

1

0

Land use in 2000

Tidal flat

1

1

0

Other land use

1

1

0

Hilly rice field

2

26

100

Hilly nonirrigated land

2

17

100

Total

306

1762

52

of past cases of these LU categories is only one. In this study, the CBR experiment was based on the vector format, while the CA experiment was based on the raster format. For comparison with the CA experiment, the vector estimate results of the CBR experiment were transformed into the raster results. The size of the cell was set at 97(m) that is same to the one in the CA simulation, because it becomes inappropriate when it was either too small or too large. If the cell size was too large, the number of the cells would be too low, thereby severely demoting the estimation accuracies


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of the LU categories with fewer cells. And if the cell size was too small, too many cells would unnecessarily enhance the estimation accuracy, thereby being superfluous and having a chill effect on the experimental efficiency. Additionally, because the data formats of estimation results of CBR and of simulation results of CA are different (the former one is a vector one while the latter one is a raster one), the estimation (simulation) results of the two approaches cannot be compared immediately. Considering the transformation convenience and data fidelity, the vector results of CBR prediction was then transformed into the rater forms. At the same time, the results of CA simulation only have two LU categories (urban and non-urban), while the results of CBR estimation have 12 LU categories. It can be supposed that the 12 LU types are the subtypes of the two types (urban and non-urban) in this paper. (E.g. “Cities and residential land”, “Rural settlement sites” and “Other built-up land” are subtypes of “urban”.) So the differences among the same subtypes should be ignored and the subtypes should be combined into the two types (urban and non-urban), when the vector results of CBR estimation are transformed into the raster form to be compared with that of CA simulation. By comparing raster forms of CBR estimation results that experienced the combination process of subtype and the actual raster data for the year 2000, 61,478 of the 70,085 cells were estimated correctly, with a total accuracy of 89.0% (see Table 2A). Figure 4 shows the raster form of the estimation results with the CBR approach.

Figure 4

The spatial distribution of CBR result

The estimation accuracies of five LU categories were all 100%, with the exception of “arable land”, whose accuracy was 16%. It can be concluded that the estimation accuracies of “arable land” for 1995 and 2000 were both low. One possible explanation is that the transition from “arable land” to other LU categories is affected by both natural (e.g. hydrological) and anthropogenic (e.g., behavioral) factors.

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(2) CA accuracy analysis The overall estimation accuracy with the CA method was 70.7% (see Table 2B). Figure 5 shows the LUC simulation results. Li and Yeh (2004b) used a CA model to simulate LUC from 1997 to 2001 in Dongguan, Guangdong Province. The simulation accuracy was 72.4%; almost similar to the CA accuracy presented in this paper.

Figure 5

The spatial distribution of CA result

The accuracy of the urban simulation is high at 93.5%, while that of the non-urban simulation is low at 56.1%. “24583” and “18759” in Table 2B indicate there were 24583 cells simulated to non-urban type and 18759 cells simulated to urban type in the actual non-urban cells. And “1751” and “24992” in Table 2B indicate there were 1751 cells simulated to non-urban type and 24992 cells simulated to urban type in the actual urban cells. So “24583” and “24992” in Table 2B indicate the number of the correct simulated cells of non-urban and urban types. (3) Comparison The total estimation accuracy with the CBR approach is slightly higher than that with the CA approach. Figures 4 and 5 present raster results of the estimation with CBR and CA approaches, respectively. For the non-urban type, the accuracy of CBR estimation is obviously higher than that of CA simulation (the former is 90.6% and the latter is 56.1%). For the urban type, the accuracy of CBR estimation is lower than that of CA simulation (the former is 84.6% and the latter is 93.5%). The differences of the accuracy between CBR estimation and CA simulation are resulted from the different quantities of the actual non-urban cells that are estimated (simulated) into urban type by these two approaches. It can be concluded that CA might have more tendencies and potential than CBR to simulate (estimate) urban cells. In the east part of the experimental region (right part of the map on Figures 4 and 5), the


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pattern of the estimation results of CBR and CA methods is similar. However, in the west part of the experimental region, the two methods produced almost opposite pattern of estimation results. As shown in Figure 4, the falsely estimated cells, mainly located in several parcels in Figure 3, are the parcels with large area, large perimeter length, and irregular shape, as well as long distance to the nearest river or the road. Thus, based on the similarity computation method, it is difficult to retrieve a reasonably similar case for these parcels from the case library shown in Figure 2. It is possible that the total prediction accuracy would rise after cutting these falsely estimated parcels into smaller ones with comparatively regular shape. 5.2

Methodological comparison

A methodological comparison was also conducted to supplement the accuracy analysis. In this study, based on different data formats, the two approaches are different in the selection variables. In comparison to the CA, the CBR approach considers the impacts of “perimeter” and “area”. In addition, the CA model developed here has a limitation. It can stimulate the conversion of other LU categories to “residential land” rather than the conversion among other LU categories. For example: “arable land”→“mudflat”. The applications of CA in LUC assessment are limited to urban regions. Besides, the CBR approach considers all the LUC categories, and is useful in different regions. Moreover, by comparing the distribution of the cells in the estimation results, a conclusion might be made that CBR approach could keep the stability and completeness of the parcel in the shape (Figure 4); a result which cannot be obtained with the CA approach. In the results of the CA simulation (Figure 5), the stability and completeness in the shape can not be well represented in the discrete form. The results of CA estimation are more similar to urban development without many influences of the urban planning, while the results of CBR estimation are more similar to the results of urban development under certain planning objectives. Compared with CBR, the CA approach requires more data preprocessing, such as normalization of raster data. In addition, the CA model requires setting the appropriate simulation quantity. Too small or too large a simulation quantity will result in large errors. If the simulation quantity is too small, the difference between the simulation results and the original data is not obvious. If the simulation quantity is too large, many cells will be changed in the simulation, which easily produces the estimate results against the actual situation. Here the simulation quantity is determined on the basis of actual numbers of changed cells in the experimental region. In practical applications, it is impossible to obtain the actual numbers of changed cells when predicting future LUC. The primary method to determine the simulation quantity is based on the relationship between numbers of changed cells, and the time interval in past years.

6

Conclusions

To address deficiencies in the traditional CBR approach to analyzing LUC, an enhanced CBR approach is used to examine the LUC of Dongguan coastal region in China in this pa-

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per. To validate the CBR, it is compared to the CA method. The experimental results demonstrate that CBR is able to satisfy the application needs in estimation accuracy. Considering the complexity of the computation process, it is a simple, flexible and practical method to quantitatively simulate, and analyze complex geographic phenomenon. In addition, due to its dynamic updating of cases and self-learning, the CBR approach has advantages in addressing rapidly-changing resource and environmental problems. However, LUC is a complex problem affected by natural and anthropogenic factors. The variables selected for the experiments documented here are all natural factors. Anthropogenic (e.g. behavioral, policy, economical, social) factors should be considered in future studies. In addition, the CBR approach needs to be applied to other case study regions to validate its reliability and wider applicability. The results indicate that the accuracies of the CA and CBR approaches are both >70%. Considering the entire analytical process, the preprocessing workload in CBR is less than that of the CA approach. Moreover, it is difficult to determine the appropriate simulation quantity in the CA approach for practical applications. As such, it could be concluded that the CBR approach is more flexible and practically useful than the CA approach for estimating land use change.

Acknowledgments The authors would like to sincerely thank the Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (RESDC), for providing the land use data used in this research.

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