Scientific Research and Essays Vol. 6(15), pp. 1361-3171, 11 August, 2011 Available online at http://www.academicjournals.org/SRE

DOI: 10.5897/SRE10.1109

ISSN 1992-2248 ©2011 Academic Journals

Full Length Research Paper

Site selection for small gas stations using GIS

Mohammad Aslani* and Ali A. Alesheikh

Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology, Valiasr Street, Mirdamad Cross, Tehran 19967-15433, Iran.

Accepted 4 March, 2011

These days one of the main concerns of transportation managers is inter-city communications. In this regard, supplying the required fuel for vehicles and consequently, suitable site location for fuel stations is considered a main issue. The location of fuel stations could influence human life tremendously, and even minor biases in selecting the location could lead to remediable losses. In today’s world, the overall demand for fuel consumption in large cities has been augmented due to the population boom. This issue, along with the scarcity of adequate spaces, creates hurdles for the construction of fuel stations. The solution to this problem can be found in the construction of small size fuel stations. To develop such stations, many parameters are considered. Among them are: vicinity to populated areas and fire fighting stations, the remoteness from green areas, and also other gas stations. Geospatial information system (GIS) as the science of analyzing positional information could assimilate multiple numbers of these parameters simultaneously. One of the great difficulties in this analysis is determining the most suitable process to give weight to parameters. The main concern of this paper is to introduce a model for positioning the optimum location of a small size fuel station with the help of GIS. In this model, the two methods for weighting spatial layers, fuzzy analytical hierarchy process (FAHP) and analytical hierarchy process (AHP), are compared. Also, the results of different methods for integrating spatial layers such as Boolean, index overlay and fuzzy operators were compared. The location of this study is situated in Tehran, district 2, which is one of the most populated areas in the region. The collected spatial data was processed with GIS. Among various weighting methods, this study showed that FAHP is optimum. Index overlay also resulted in the best spatial layer integration. Key words: Geospatial information system, Boolean logic, Fuzzy logic, Index overlay, fuzzy analytical hierarchy process.

INTRODUCTION The rapid rate of urbanization in recent years created greater demand for vehicles and, as a result, more fuel consumption (Upchurch and Kuby, 2010). A gas station having four units of fuel pumps requires a minimum area of about 250 to 300 m

2. Identifying an appropriate

location is quite difficult in congested populated cities. Yet, the considerable area of each station leads to development at remote site, which can unnecessarily increase traveling distances and fuel waste. Most developed countries introduce small stations as the best solution to overcome these difficulties. Some factors, like

*Corresponding author. E-mail: moh_aslani@yahoo.com. Tel: +98 21 8878 6212. Fax: +98 21 8878 6213. population, economic, geographic and government policies, should be considered in choosing the optimum site for gas stations. Selecting a variety of factors causes increasing data layers. Thus, decision makers should work on many data layers at the same time. This issue forces decision makers to use a system that has high precision and high speed. One of the most important systems is the geographic information system (GIS). GIS assists us in the positioning of various locations (Alesheikh et al., 2008; Ghayoumian et al., 2004). It brings some tools to combine different layers of information (Hosseinali and Alesheikh, 2008).

Methods such as Boolean logic (based on zero/one digits), index overlay and fuzzy operators can be used for

combining various data layers. Because finding an optimal location for a fuel station is influenced by many

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Goal

C1 C2

C21 C22

A B

Criteria

Sub Criteria

Alternative

b21 b22

b11, b211, b221 b12, b212, b222

W1 W2

Figure 1. The hierarchical structure of a decision-making process.

factors, this problem can be considered as a spatial multi-criteria decision analysis (SMCDA) (Malczewski, 1999). Among decision processes, the analytical hierarchy process (AHP) invented by Saaty (1980) has been used several times in many different projects (Ying et al., 2007). Still, choosing an optimal location for a fuel station is affected by the uncertainty of the description and ranking of criteria (Vahidnia et al., 2009). One of the methods for modeling uncertainty is fuzzy logic (Zadeh, 1965). In this logic, members of a set are specified by their membership degree in the set (Zadeh, 1973). This problem can be considered as a fuzzy multi-criteria decision making (FMCDM) problem (Chang et al., 2008). One method for decision making using fuzzy logic is the fuzzy analytical hierarchy process (FAHP) (Kahraman et al., 2003; Kuo et al., 2002). FAHP has a hierarchy structure, is based on pairwise comparisons of individual judgments, reduces inconsistency and models uncertainty in decisions (Kahraman et al., 2004).

The main objective of this research is to find locations with high potential to build small fuel stations using GIS in Tehran. In this article, AHP and FAHP were used to weight the data layers. In addition, Boolean operator, index overlay and fuzzy operators were used for combining the spatial layers. MATERIALS AND METHODS Mathematical principle in weighting information layers For weighting the factor map, two methods are available: 1) data-driven and 2) knowledge-driven weighting.

Knowledge driven weighting The main difficulty in multi-criteria decision processes is the importance of various criteria and sub-criteria for decision makers and the relative importance of each criterion compare together (Zimmermann and Zysno, 2003). Weighting different information layers can be done with various methods by specialists (Saaty, 1999). In this research, two of these methods, the AHP and FAHP, are discussed in detail. Analytical hierarchy process The AHP model was developed by Saaty in 1980, to specify the relative importance of criteria in multi-criteria decision making problems. This process has the ability to judge qualitative criteria along with quantitative criteria (Boroushaki and Malczewski, 2008). The AHP method is suitable for problems which have a hierarchical framework. AHP is simple to grasp and easy to implement and modify. The AHP method is based on six steps: 1) Define the unstructured problem, identification of input/output parameters, 2) Representation of a structure by a hierarchy, 3) Paired comparison between elements at each level, 4) Calculations of the weight at each level, 5) Test the consistency of each matrix and 6) Priority of an alternative by a composition of weights (Hosseinali and Alesheikh, 2008).

Representation of a structure by a hierarchy The goal of the decision is at the top level of the hierarchy as shown in Figure 1. The next level consists of the decision factors which are called criteria for this goal (e.g. C1, C2) and sub-criteria (e.g. C21, C22). At the bottom level there are some alternatives which should be evaluated (e.g. A, B).

Paired comparison of elements at each level

The evaluation matrices were built up through pairwise comparison of each decision factor under the topmost goal. The comparison results can be presented in a square matrix (A) as:

Aslani et al. 3163

Table 1. Pairwise comparison scale (Saaty, 1980).

Preferences expressed in numeric variables

Preferences expressed in linguistic variables

1 Equal importance

3 Moderate importance

5 Strong importance

7 Very strong importance

9 Extreme importance

2, 4, 6, 8 Intermediate values between adjacent scale values

Table 2. The index RI of average random consistency.

n 1 2 3 4 5 6 7 8 9 10 11 12 13 14

R1 0 0 0.52 0.89 1.12 1.26 1.36 1.41 1.46 1.49 1.52 1.54 1.56 1.58

nnnna...2na1na

............

n2a...22a21a

n1a...12a11a

nn)ija(A

== (1)

where

ija denotes the ratio of the ith factor weight to the jth factor

weight, and n is the number of factors. Saaty’s linguistic scale, given in Table 1, was used to collect expert opinions on the pairwise importance of criteria/subcriteria (Saaty, 1980).

Calculations of the weight at each level

At each level of the hierarchy, relative importance is presented by eigenvectors that are estimated by the following equation:

0W)ImaxλA( =×− (2)

where A is the n x n criteria of pairwise comparison matrix

(Equation 1), I is the n x n identity matrix, max

λ is the largest

eigenvalue and W is a non-zero vector called eigenvector.

}nw...2w1w{TjW = (3)

Test the consistency of each matrix In order to assess the results, consistency ratio must be calculated. Saaty defined the consistency index (CI) as:

1n

nmaxλCI

−

−= (4)

RI

CICR = (5)

The consistency index of a randomly generated reciprocal matrix is called the random index (RI). The random index is obtained through Table 2. The consistency ratio (CR) is defined as the ratio of CI to RI. Thus, CR is a measure of how a given matrix compares to a purely random matrix in terms of their CI. If CR has a value less than 0.1, the comparisons are consistent, else they have to be carried out again (Vahidnia et al., 2009).

Priority of an alternative by a composition of weights The weight of each alternative is calculated as

∑=

×=N

1jjWijbW (6)

Where j

W is the weight of each factor, and ij

b is the weight of

alternative towards each factor.

Fuzzy analytical hierarchy process

Because FAHP uses linguistic expressions, it uses fuzzy logic for determining pairwise comparison matrixes even though AHP does not have the capability needed for modeling uncertainty in the decision makers’ opinions (Mikhailov, 2003). Thus, researchers have extended the AHP method and suggested methods that use fuzzy numbers to express the priority of elements, which has led to a more precise display of the relation between alternatives and criteria (Choi and Oh, 2000; Ertugrul Karsak and Tolga, 2001; Chiadamrong, 1999). Here, the methods presented by van Laarhoven and Pedrycz (1983), Buckley (1985), Boender et al. (1989) and Chang (1996) should be mentioned. In this article, the Chang method was used. Fuzzy triangular numbers were used to express the priority of elements (Equation 7). Each fuzzy triangular number can be specified by three parameters (L,m,u). Figure 2 shows a fuzzy triangular number.

≤≤−

−

≤≤−

−

=

otherwise;0

uxm;mu

xu

mxL;Lm

Lx

)x(Aµ (7)

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l m u

)(xAµ

1

X

Figure 2. A fuzzy triangular number A = (L, m, u).

Table 3. Triangular fuzzy numbers of linguistic variables.

Linguistic variables Fuzzy triangular numbers Reciprocal fuzzy triangular numbers

Extremely strong (9,9,9) (1/9,1/9,1/9)

Very strong (6,7,8) (1/8,1/7,1/6)

Strong (4,5,6) (1/6,1/5,1/4)

Moderately strong (2,3,4) (1/4,1/3,1/2)

Equally strong (1,1,1) (1,1,1)

Intermediate (7,8,9), (5,6,7), (3,4,5), (1,2,3) (1/9,1/8,1/7), (1/7,1/6,1/5), (1/5,1/4,1/3), (1/3,1/2,1)

L, m, and u are the lower, mean and upper bounds of the triangular fuzzy number. The membership function µ represents the degree

to which any given element x in the domain X belongs to the fuzzy number A. To perform pairwise comparisons, linguistic variables can be used from Table 3. When the expert judgments are expressed as triangular fuzzy numbers, the triangular fuzzy

comparison matrix A~

is:

=×=

)1,1,1(...)2nu,2nm,2nL()1nu,1nm,1nL(

............

)n2u,n2m,n2L(...)1,1,1()21u,21m,21L(

)n1u,n1m,n1L(...)12u,12m,12L()1,1,1(

nn)ijM(A~

(8)

where )iju,ijm,ijL(ijM = and )ijL/1,ijm/1,iju/1(1

ijM =−

For each row of the pairwise comparisons matrix, the value kS

which is a fuzzy triangular number, will be calculated as follows:

1n

1j

n

1i

n

1jij

Mkj

Mk

S~

−

∑=

∑=

∑=

⊗= (9)

where ⊗ denotes the extended multiplication of two fuzzy numbers.

In the second stage, the possibility degree for j

Si

S~~

≥ is

calculated by the following equation:

ij;n,...,1j,iiujl;

otherwise;0

)jljm()imiu(

jliu

jmim;1

)jS~

iS~

(V ≠=≤−+−

−

≥

=≥

(10)

Where )ju,jm,jl(jS~

and)iu,im,il(iS~

== .

Figure 3 shows the possibility degree for two fuzzy numbers. In the

end, the priority vector T

nwwwW ),...2,1(= for the fuzzy

comparison matrix A~

is calculated by the following equation:

n,...,1i,n

1K

)kj;n,...,1j|j

S~

kS~

(V

)ij;n,...,1j|j

S~

iS~

(V

iw =

∑

=

≠=≥

≠=≥

= (11)

Data-driven weighting

The data-driven methods reduce difficulties of incorrect decision making and prejudgments. In this method, weights of each map’s factor specify the correlation between the map’s factor and specific positioning layer. Consequently, the weight calculation is based on probability analysis. Data driven methods require sample work for application and assessment (Hosseinali and Alesheikh, 2008).

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1 i

S~

il

im

iu

jl

jm

ju

)~~

( jSiSV ≥

jS~

x

)( xµ

Figure 3. The degree of possibility )~~

( jSiSV ≥ .

The mathematical principle for combining spatial layers

Different models are implemented in GIS for event simulation (Bonham-Carter, 1994), and the combining model is regarded as one of them. These combining models are based on various functions. Subsequently, three different models; Boolean, Index overlay and Fuzzy logic, will be depicted. Boolean operation The weighting process of data is based on zero/one digits and the logical operators of AND, OR, XOR and NOT (Brown, 1990). Despite the simplicity of this model, all factors have equal weights, which is considered as its main drawback. In the Boolean model, the AND operator of different information layers implies an environment bearing both conditions, and the OR operator specifies environments that accept at least one of the conditions. Finally, the NOT operator rejects all possible condition. In this research, the Boolean AND was used. Index overlay model In this method, the weights are allocated to each factor, class and positioning unit according to expert’s views (Brown, 1990). After completion of the weighting process, factors were combined together according to the following relation, and then the output values of the map was computed.

∑=

∑==

n

1iiW

n

1iiWijS

Sv (12)

Where

iW = The weight of the ith factor map

ijS = The ith spatial class weight of the jth factor map

S = The spatial unit value in output map

In a binary base map, ijS could have zero/one values. This model

has more flexibility compared to the Boolean method in the case of combining input data and ranking outputs. In another sense, its linear nature and its incapability of specifying the exact class weight changes is regarded as its main drawback.

Fuzzy logic model In selecting the best location for small size fuel stations, a membership function can be selected. A variety of operators can be employed to combine the membership values together. There are five operators which are found to be useful, namely the fuzzy AND, fuzzy OR, fuzzy product, fuzzy sum and fuzz γ gamma operator.

Fuzzy AND operator

The characteristic of this kind of fuzzy AND operator is that the degree of membership of the final output map tends to be low (Equation 13). Pixels with low degree of membership in lightweight class will have low value in output map. Hence, this operator does not consider the influence of all parameter simultaneously.

,...)Cµ,Bµ,Aµ(MINnCombinatioµ = (13)

=Cµ,

Bµ,

Aµ Spatial unit membership values

=nCombinatio

µ Each spatial unit value in the output map

Fuzzy OR operator

This operator enters the maximum input factor maps membership values into the output map and create optimistic map from effective criteria (Equation 14). As a result, Fuzzy OR operator is used when there are sufficient positive factors and evidences in the study area.

,...)Cµ,Bµ,Aµ(MAXnCombinatioµ = (14)

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=Cµ,

Bµ,

Aµ Spatial unit membership values

=nCombinatio

µ Each spatial unit value in output map

Fuzzy product operator Fuzzy product operator multiples input factor maps membership values and presents the results on output map (Equation 15). Therefore, it has decreasing affects on results and is used when input factor maps debilitate each others (Moghaddam and Delavar, 2007).

∏

=

=n

1iiµ

nCombinatioµ (15)

iµ =The weight of the ith factor map

=µnCombinatio

Each unit value in the output map

Fuzzy sum operator

Contrary to Fuzzy Product, the values of membership tend to 1 so have increasing effects on each other (Equation 16). Therefore, a greater number of pixels resides in a proper class of the output map. Hence, this operator has less sensitivity to the site selection process.

)n

1i)iµ1(1(nCombinatioµ ∏

=−−= (16)

iµ = The weight of the ith factor map

=µnCombinatio

Each unit value in the output map

Fuzzy γ operator

This operator is the general form of the sum and product operator. With this operator, the site selection is specified according to the following relation:

γ−µγµ=µ 1

Xoduct

XSum

XOperationGamma

))(Pr

())(()( (17)

Determining correct value of gamma generates output map showing adoption between decreasing and increasing trend in sum and product fuzzy operations (Bonham-Carter, 1994). Figure 4 depicts the methodology used in this study.

Study area

The study area is located in Tehran, the capital of Iran. The area

extends from 42519151 ′−′oo

east and 94351435 ′−′oo

north

(Figure 5). Tehran has a population of about 7.5 million, which accommodates about 36% of Iran’s urban population. Its annual population growth rate is about 2%. The average traveling time of each vehicle is about 43 km per day, and the average fuel consumption is 0.1108 L/km. Based on an estimation for the year 2011, the average fuel consumption reaches 14.3 million L/day.

Considering the remarkable growth rate of fuel demand in coming years, the necessity of constructing more fuel stations is inevitable, but this necessity has certain obstacles such as space scarcity in congested city areas. Figure 5 depicts the GIS-ready layers at a 1:10000 scale, which were produced by National Cartographic Center of Iran and edited to be GIS ready data by K.N. Toosi University of Technology, Iran.

Factors and effective criteria

Based on diverse views of traffic experts, fuel station owners and the national oil company of Iran, effective parameters in the positioning small size stations can be categorized in the following classes:

1) Safety: Safety mostly refers to safety of stations, including their vicinity to fire fighting stations and their remoteness from earthquake fault line, high pressure electric posts and oil and gas lines. 2) Traffic: Gas stations should be located far from squares and one way streets to considerably reduce the traffic. 3) Accessibility: This class includes the ease of access to highways and main roads and the maintenance of a suitable distance from parking. 4) Environmental: Gas stations should be far from green areas, hospitals and schools.

Data preparation

Before entering data into analytical models, they must be prepared according to models execution routine. The four stages of data preparation are as follows:

1. Combining the data of different districts in Tehran into one unit layer, 2. Changing the structure of the data, 3. Making a distant map, and 4. Data classifying.

One of the major parameters that influence the data processing is data structures. The preparation of data begins with a vectorization process and consequently transferring these vectors into a Raster

structure to create a distant map. The main purpose of this change is the simplicity and higher ability of the Raster structure in combining different layers. The Raster cell size should be optimum in these changes. Large cell sizes lead to less accuracy of the calculation, whereas small cell sizes increase the volume of data and slow the calculation process.

In this study, based on the maximum accuracy of the map (0.3 mm on the map scale), the minimum size of cells was about 3 m. Considering the fact that the maximum size of a station is about 250 m

2, the maximum size of cells is 15 m. Therefore, a cell size of

3 to 15 m could be selected. In this research, a cell size of about 5 m was used. The output of this stage is the preparation of the factor maps. There is a vector structure highway layer that must be

converted to raster form using data conversion method and classified based on spatial data priority, using data classifying method according to expert views spatial units value. Data weighting In this research, the weighting of layers was performed by help of expert opinions by implementing two methods of AHP and FAHP.

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Selecting the location of study

Determination of Efficient Criteria and Factors

Data Preparation as Factor Maps

Weighting different layers (AHP, FAHP)

Implementation of Models

Models Evaluation

Selecting the Optimum Model

Figure 4. Models execution and evaluation steps.

The calculation of FAHP was executed by means of the extent analysis presented by Chang. Accordingly, the software for calculations was designed in the MATLAB programming environment. Information integration

In this stage, the parametric map should be combined with the help of various combining models such as Boolean, Index Overlay, AND, OR, Product, Sum and γ Fuzzy. Each and every parameter should be categorized in subclasses according to their distance and expert views, and consequently, appropriate weights should allocate to each class. Finally, with the help of the aforementioned method, the layers have to be combined. For the γ operator, 9 values of γ ranging from 0.1 to 0.9 with an increment of 0.1 were tested. Therefore, 29 different arrangements in the combination process can be created. As an example, the first scenario is based on AHP weighting and the Index Overlay combining method, and the second scenario is based on the FAHP weighting process and Index Overlay combining method. After combining layers, pixels

values should be normalized and then categorized into four classes of (0-0.25), (0.25, 0.5), (0.5, 0.75), (0.75, 1) (Figure 6). Specifying the minimum required area for small fuel stations Factors involved in the overall fuel demand play an important role in specifying required space of fuel stations. The demand function of fuel consumption is as follows:

)I,P,A,N,C(FR = (18)

In this equation, C= Cost of fuel; N= Number of vehicle; A= Average service life of vehicle; P= Average population; I= Society Income. Cost of fuel: One of the major factors influencing demand is the cost of the product. Number of vehicles: An increase in vehicle number would directly influence the fuel consumption level. Average service life of vehicles: A reduction in vehicle service life would introduce new technology at a faster pace and obviously could influence the average fuel consumption.

Average population: A larger population has more demand for traveling. Society income: Other factors also influence the overall demand such as vehicles durability, efficiency and so forth. To find the required area of small fuel stations, the essential number of pump nozzles for the city should be calculated.

First method

The first method is based on the world standard in developed countries, where each fuel pump should give service to at least 5000 people. Therefore, for this case study, 3000 pumps are required. The current number is about 1700, which is a deficit of about 1300 pumps (325 stations, assuming 4 pumps in one station).

Second method

Assuming the minimum time of fueling is about 5 min and each

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Figure 5. Selected study area.

vehicle needs to be fueled one time a week. Therefore, 2153 nozzles are needed. It means that 453 more nozzles are required, which can be accommodated with 114 small size stations. The finding of the first method seems to be more optimistic; hence, the result of the second method was used. Considering that the

minimum required area of each station is about 300 m2, 34200 m

2

overall are needed to meet the requirement.

RESULTS AND DISCUSSION

Here, all previously executed models are assessed for optimum selection. The area and accuracy are the two influential parameters in such assessments. The optimum model results in the maximum area and accuracy in its best class. Therefore, the best class of each model is considered. For the accuracy assessment, site selection criteria described earlier are applied. For this purpose, first, each factor map is categorized into three ranks of proper, moderate and weak. Then, the proper class for each factor map is intersected with the best class of the

final map. Finally, the ratios of the intersected area of the proper class for each spatial layer to the area of the best class for the final map are calculated. For instance, Table 4 illustrates these ratios for the final map which is generated by FAHP-Index overlay method. Based on the ratios of the suitable class area to the total class area, expert views and the scatterings of recommended locations, some results can be given as follows: 1) Comparing areas of the weighting method in AHP and

Aslani et al. 3169

Figure 6. Thematic maps of a part of Tehran. A) AHP-Index Overlay, B) FAHP-Index Overlay, C) AHP-Fuzzy Sum, D) FAHP- Fuzzy Sum, E) AHP-Fuzzy γ (0.7), F) FAHP-Fuzzy γ (0.7).

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Table 4. The ratios of suitable class area to total class area

Factors FAHP-index overlay

Distance to main road 0.484485946

Distant to highways 0.861385376

Distant to fire fighting stations 0.666467095

Distant to public parking 0.893330812

Distant to city squares 0.946329286

Distant to current fuel stations 0.879437839

Distance to natural gas stations 0.999933476

Distance to public parks and urban green areas 0.654195202

Distant to schools 0.711640267

Distant to hospitals 0.873198605

Distant to floodway 0.696483341

Table 5. Final weights of 9 methods.

Methods Value

FAHP-Index Overlay 0.782364662

AHP-Index Overlay 0.782351118

AHP-Fuzzy Sum 0.719441777

FAHP-Fuzzy Sum 0.709940486

Boolean AND 0.703103746

FAHP-Fuzzy OR 0.636754422

AHP-Fuzzy OR 0.636547751

AHP-Fuzzy AND 0.607450749

FAHP-Fuzzy AND 0.607137673

AHP Fuzzy shows that the method weighted with FAHP has more area in the desirable class. 2) The Fuzzy Product method has more accuracy and less area, but Fuzzy Sum has less accuracy and more area. 3) The accuracy of Fuzzy Gamma resides between those of Fuzzy Product and Fuzzy Sum, and this accuracy depends on the Gamma value. 4) Based on the aforestated, and considering the fact that all districts of Tehran require an equal number of fuel stations, 1600 m

2 of area are needed for constructing fuel

stations. It is obvious that the most desirable methods should meet this least requirement. 5) Considering the fact that the ultimate results of aforesaid methods are quite close to each other, the AHP method was used, and the results were calculated accordingly (Table 5). 6) Both methods of FAHP-Index Overlay and AHP-Index Overlay had more values, but FAHP-Index Overlay had more area in the excellent class. Thus, the FAHP-index Overlay is considered the best method.

CONCLUSION AND RECOMMENDATIONS

In Iran, the site selection for constructing fuel stations is carried out by means of traditional methods that are incapable of implementing all effective parameters. This deficiency leads to inappropriate site selection, which brings more inefficiency in this area. Therefore, in this

research, GIS was proven to be capable of removing the potential obstacle of positioning. To reach the point, the location for study was selected, and different models of positioning were implemented to find the best possible model and optimum location. The weighting of the parametric map plays a great role in the positioning process, area selection and accuracy.

Among the weighting methods, AHP and Fuzzy AHP have more flexibility. Also, among the integrating methods, Index Overlay has a higher degree of accuracy. More research of this field can be focused on the implementation of analytical hierarchical process (ANP) and Delphi for weighting spatial layers. It is also recommended that some data-driven methods such as genetic algorithms and artificial neural networks are applied to weight information layers. REFERENCES

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