Environ Earth Sci DOI 10.1007/s12665-015-4365-z

ORIGINAL ARTICLE

Tourism positioning using decision support system (case study: Chahnime—Zabol, Iran) Malihe Erfani1 • Shahram Afrougheh2 • Tahereh Ardakani3 • Asiyeh Sadeghi4

Received: 5 November 2013 / Accepted: 25 March 2015 Ó Springer-Verlag Berlin Heidelberg 2015

Abstract In recent decades, social–economic growth and developments have led to human communities toward the creation of tourism opportunities, which are likely to be considered as ways of spending leisure and reducing the impacts of tensions resulting from a rigorous urban and industrial life. Of the variety of regions in Iran, the southeastern regions may be considered as one of the tourism spots in Iran and Zabol, with the ruins of Burnt City having an ancient civilization, a freshwater lake called Hamoon Lake and people with a rich Aryan culture playing a significant role in providing the desired ecotourism. In spite of its importance, specific studies have not been conducted for development and planning of suitable tourism areas. Therefore, the current research has zoned the regions through multi-criteria evaluation using ecological criteria and few social–economic factors. Applied criteria in this paper include soil, distance to surface water

resources, slope, aspect, geological formation sustainability, vegetation and distances and limits, including distances from ponds, surface water resources, roads, political borders, rural and urban residential areas, historical centers and welfare facilities. Criterion standardization was done by means of fuzzy theory and the map of constraints was prepared through Boolean theory. To weigh the criteria, pairwise comparisons were performed in the form of the analytic hierarchy process. Then, the given weights were linearly combined and the layers mixed and the suitability index of lands was calculated. In this area, seven zones were identified for advanced tourism. Keywords Sustainable coastal tourism  Zoning  Multicriteria evaluation  Fuzzy logic  Chahnime region

Introduction & Malihe Erfani [email protected] Shahram Afrougheh [email protected] Tahereh Ardakani [email protected] Asiyeh Sadeghi [email protected] 1

Department of Environmental sciences, Faculty of Natural Resources, University of Zabol, Zabol, Iran

2

Department of English, Arak branch, Islamic Azad University, Arak, Iran

3

Department of Environmental sciences, Faculty of Natural Resources, University of Ardakan, Ardakan, Iran

4

Department of Environmental Sciences, Faculty of Natural Resources, University of Tehran, Tehran, Iran

Ecotourism or nature tourism is likely to be introduced as one of the most popular and most beneficial sciences related to natural resources and environment, used as tools to increase the national income of less-industrialized countries (Amani 2004). Considering the definitions presented by the ecotourism society, ecotourism is defined as a purposeful trip into nature to recognize the natural and cultural history of the environment without changing the ecosystems and destroying the environment, while developing economic activities leading to the correct exploitation of environmental resources and providing job opportunities to the local residents (Saremi Naieni 1998). In this respect, tourism practices which have been appropriately planned may be associated with the additional benefits for the local communities and regional economy as well as improved management and protection of nature (Salam et al. 2000).

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Presently, finding a suitable location or a desired one for developing an activity in a specific geographical area can be represented as one of the important stages of executive projects, especially at national and massive levels. To promote tourism all conditions and requirements should be considered, but not conducting sufficient pre-executive investigations concerning these conditions will result in inappropriate consequences. For example, among these problems, costs and inefficiency of projects have to be mentioned. However, through implementing a successful positioning, all the effective factors in creating the activities at the regional levels may be studied, and suitable locations demonstrated as the outputs of the positioning process are indicated to the managers and final decision makers. These individuals are to select suitable alternatives based on the existing policies and the preferences of the obtained results. Other results which should be discussed in addition to profitability involve the avoidance of selecting hazardous locations and the prevention of pollution and environmental destructions. As it can be found, the accurate, correct and comprehensive implementation of the positioning process is of significant importance in the projects (Malczewski 1999). Since the positioning process has a locational nature, geographical information systems applied as powerful tools of locational data analysis and management are more likely to create a suitable environment for achieving the above-mentioned goals; on the other hand, a variety of decision-making methods have been developed and considerably contribute to the designers and decision makers to make correct and inclusive decisions. If these methods are used for the locational analysis of geographical information systems, the knowledge of skilled experts concerning the analyses can be comprehensively utilized; in other words, the application of decision-making models and experts’ knowledge will enhance the ability of GIS to help the mentioned people to make locational decisions (Amini Faskhodi 2006). Various instances in this respect can be stated, e.g., Karimi (2000) obtained the optimum points to expand coastal tourism using RS and GIS in a manner such that effective elements such as slope, aspect, height, soil type, rivers, roads, communication networks, hotels and coastal tourism centers and negative elements of tourism development including soil erosion, land use changes, water pollution and natural landscape changes as well as development of urban constructions in positioning tourism installations were first extracted. Then, they were weighed by the means of the CITIC method (criteria importance through inter-criteria correlation) and, finally, by multi-criteria evaluation these factors were combined and suitable coastal tourism zones were identified in Noshahr and Chalos. Tsaur et al. (2007) have gathered few factors in relation with sustainable tourism development for Green Island in Taiwan and afterward,

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using Delphi technique, final elements or indices with regard to the characteristics of the studied region have been presented to evaluate sustainable tourism development. The next stage is to compute the factors’ weights by the use of the analytical hierarchy process. Finally, each criterion or the factor’s executive assessment was done under the establishment of fuzzy sets. Findings indicated that the studied region needed to be significantly investigated to achieve the goals of tourism development. Attributes selected by Nahuelhual et al. (2013) for identifying important potential recreation areas in Southern Chile were singular natural resources, scenic beauty, accessibility, tourism attraction capacity and tourism use aptitude. Each of them was represented by specific spatial criteria, validated and weighted by the experts with participatory methods (Delphi method and analytic hierarchy process). Remoteness was investigated as an indicator for the recreation potentials in the EU by Paracchini et al. (2014). Recently, some researchers such as Weyland and Laterra (2014) and Kienast et al. (2013) used landscape metrics as indicators of outdoor recreation potentials. Although the significance of beaches in tourism is quite evident, the effective factors which may be involved have not been completely acknowledged yet. To investigate the effectiveness of the factors, the obtained map of suitability can be compared with the actual demands of tourism. So, the success rate of used factors and procedures can be evaluated. Because the consideration of this industry is associated with plenty of positive impacts on national economy, this section’s development is able to alter the uniaxial state of the country’s economy. Thus, some experts considered tourism as mother industry whereas tourism requires less investments as compared to the other shares and regarding 0.09 percent Iranian share out of worldwide tourists and 0.07 percent share of it from worldwide tourists’ income. In this respect, investment, planning and management may be inevitable (Torabi 2005). In spite of lots of benefits associated with this industry and the improvements of economic and social conditions in different countries, if its development is not accompanied with planning and making policies according to the environmental views while emphasizing sustainable developments, negative effects will inevitably affect the environment. Thus, the sustainable development process of the above-mentioned industry is more likely to be disturbed (Jafarzadeh and Nabizadeh 1997). Due to the variety of topographic, climatic and fauna and flora conditions, Iran has been considered as one of the ten big countries in the world with regard to tourism attractions, because of its ancient culture and history. But for attracting tourists, it is ranged as the 120th country

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(Mikaieli 2000). Therefore, the Chahnime region, with its water resources in the dry area of Sistan, rich cultural background and ancient monuments, has always attracted nature-friendly tourists. After drying of the Hamoon wetland—the country’s largest freshwater lake—Chahnime as a source of permanent water has doubled in attracting tourists (Erfani et al. 2011). A visitor’s decision to use a particular recreational site is influenced by individual taste as well as the characteristics of the desired site (Paudel et al. 2011). According to the tourism opinions of Sistan, water resources are considered as the most important factor to attract tourists (Erfani et al. 2011). However, sustainable nature-based tourism has not been achieved in this area. Therefore, this paper aims to recognize the natural potential of the region for tourism, review the economic, social and cultural issues related to the discussion and evaluate tourism power to suggest suitable tourism areas and criteria based on natural capacities. Basically, it tries to propose the environmental criteria concerning the Chahnime region for acquiring sustainable tourism, which may be achieved through the accurate planning as well as environmental considerations in the desired region.

Materials and methods Case study Sistan and Balochestan Province is located in the southeast of the country and is regarded as the largest province in Iran with an area of 181,785 km2 at the northern latitude of 25° 30 to 31° 290 and eastern longitude of 58° 490 to 63° 200 . The province comprises two sectors, ‘Sistan’ in the north and ‘Baluchestan’ in the south. The Sistan region is perched in the north of the Sistan and Balochestan Province. This area is bordered by Afghanistan, Zahedan City, Southern Khorasan Province and Lut Desert in the east and northeast, south, west and northwest, respectively (Fig. 1). The mean precipitation of the area is 65 mm, and maximum, mean and minimum absolute temperature rates are 49, 24 and -7 °C, respectively. Domarten index was equal to 1.85 and it has a desert climate. Chahnimes has three natural pits besides Hirmand River and they have been created during Quaternary terraces (Khak Sefidi and Noura 2008). These reservoirs are used to store a portion of excess water of the river for being consumed in the low-water and dry seasons. In February 1999, Hirmand River was dewatered. The Chahnimes are of area 46 and 30 km2, 5 km from Zahak and Zabol cities, respectively (Noori et al. 2007). Chahnime1 is perched on the border of Afghanistan parallel to Sistan River and

extends till 6 km of Zahak City. Chahnime 2 extends from the border of Afghanistan to the middle of Chahnime 1. Chahnime 3 is also located in the west of Chahnime 2. These reservoirs have freshwater and in addition to agricultural and industrial usages, they supply drinking water to Zabol, Zahak, Hirmand and Zahedan (Consulting Engineers of Bandab 1992). The natural vegetation of the region is not fully rich due to the harsh climatic conditions and desert climate. 30 Plant species have been identified, including 8 families and 21 genus. The dominant species in the region were mostly tamarisk, camel’s thorn, bamboo, ashnan and haloxylon. 37 % of Sistan vegetation may consist of tamarisk, haloxylon and bamboo, and other species are grasses, bushes and forbs. Over 300 bird species including terrestrial, aquatic and water-dependent species have been recorded in Sistan. Among them, few species are more likely to be endangered. Fish species involve two groups of native and nonnative ones. The most popular native fish are Schizothorax zarudnyi, whitefish and disco fish, while non-native fish are carp, amur and silver carp. The richness of other animal species in the region is high. Jackals, foxes, forest cats, porcupines and mongooses can be observed in the region. Processing a weighted linear combination method Weighted linear combination is the most often used technique for tackling the spatial multi-attributed decisionmaking process. It can be introduced as a multi-attributed procedure based on the concept of a weighted average. The decision maker directly relates some weights of relative importance to each attribute. A total score is then obtained for each alternative by multiplying the importance weights assigned for each attribute using the scaled value given to the alternative on that attribute and summing the products over all attributes. When the overall scores are calculated for all the alternatives, the alternative with the highest overall score is chosen. The GIS-based linear combination method involves the following steps: 1. 2. 3.

4.

5.

Define a set of evaluation criteria (map layers) and a set of feasible alternatives. Standardize each criterion or each map layer. Define the criterion weights; in other words, a weight of relative importance is directly assigned to each criterion map. Construct the weighted standardized map layers; it refers to multiplying the standardized map layers by the corresponding weights. Generate the overall score for each alternative using the add overlay operation on the weighted standardized map layers. The overlay technique allows the map

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Fig. 1 Case study across Sistan and Balouchestan Province, Iran

6.

layers which result from the evaluation of criterion (input maps) to be aggregated in order to determine the composite map layer (output maps). Rank the alternatives (locations or pixels) according to the overall performance scores; the alternative with the highest score (rank) is the best one (Malczewski 1999).

Step 1: determination of criterion In this study, internal resources have been first reviewed. Afterward, the experiences and records of other countries (Beedasy et al. 1999; Banerjee et al. 2000; Miller 2001) as well as various experts’ viewpoints on the environmental parameters and factors affecting the coastal tourism positioning were collected. It was impossible to refer to all the given criteria with regard to the regional characteristics and lack of access to some existing data and information. Also, lots of data including economic, cultural and social factors (crime, variety of ceremonies and tourism share) could not be mapped and were not of locational nature. After specifying the limits of the case study (Chahnimes 1, 2

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and 3), the geographical database of the desired area was provided through gathering primary information and data. Using multi-criteria evaluation, criteria must be defined to meet the discussed aim and it should be understood whether the goals will be achieved on the basis of those criteria. Thus, accurate decisions are made according to the given criteria and also should be measured. The criteria are to be classified into two groups. named factors and constraints (Eastman 2003). A factor is a criterion that enhances or detracts from the suitability of a specific alternative for the activity under consideration and is most commonly measured on a continuous scale. A constraint serves to limit the desired alternatives (Eastman 2006). In the current research, two environmental and economic–social factors along with the criteria involve physical features of the area such as slope, aspect, geological formation sustainability, vegetation, and distances and limits including distances from ponds, surface water resources, roads, political borders, rural and urban residential areas, historical centers and welfare facilities (Fig. 2) (Table 1 included continuous criteria and Table 2 included the example of constraint criteria).

Environ Earth Sci Table 1 Standardizing continuous factors with fuzzy set membership

Table 2 Standardizing vegetation factor by fuzzy logic

Factor

Type of fuzzy function

Control points a

b

c

d

Distance from facilitated center

Monotonically decreasing

500

500

500

Distance from political borderline

Monotonically increasing

1000

2000

2000

1000 2000

Distance from road

Symmetric

100

500

500

5000

Distance from rural center

Symmetric

1000

3000

3000

10,000

Distance from city centers

Symmetric

3000

5000

5000

50,000

Distance from rivers

Monotonically decreasing

100

100

100

300

Distance from historical area

Monotonically decreasing

30

30

30

200

Distance from ponds

Monotonically decreasing

100

100

100

1000

Class

Land covering

Features

BL

Wild land and rock outcrop

Pasture land with canopy density less than 5 % and outcrop

PF

Planted forest

–

180

F3

Low-density forest

Forest with canopy density of 5–25 %

120

RB

Bed stream

–

30

IF

Irrigated farming and gardens

–

90

SHR

Camp

Shrubbery with a canopy density of more than 10 %

20

Among all possible ideological and economic criteria, these criteria were selected as the most effective ones which have great impacts on the suitability of tourism positioning. To extract the information layers, geometric correction of satellite IRS and LISS_III images of 2006 in relation with the studied region was conducted in the preparation stage using digital topographic maps (1:25,000) by means of 25 control points and RMSE that was lower than 0.5. For all the mentioned maps, coordinate systems of UTM and ellipsoid reference of WGS84 were utilized. This region is located in the northern zone 41. The pixel size of the applied maps was 30 9 30 m. Step 2: standardization of criterion After drawing the criteria maps, it should be noted that all the criteria maps cannot be compared with each other because they may be measured in different scales (e.g., distance unit, geologic one, etc.) so that during the decisionmaking process, it will be necessary to standardize the benchmark maps that are of various scales and limits. To match the measuring scales and changing them into comparable and standard ones, the fuzzy method was implemented. The fuzzy set theory, which was first introduced by an Iranian scientist named Lofti A. Zadeh from California University in 1965, refers to a mathematical assumption designed for modeling and formulating the

Score 0

mathematics in the processes (Lootsma 1997). In this paper, to perform fuzzy standardization, a linear function was applied. In linear functions in the environment of IDRISI, at least two to four points’ positions called a, b, c and d have to be specified in the linear function graph to perform fuzzy standardization of the map layers (Eastman 2003). Table 1 demonstrates the threshold amounts and fuzzy function types for the standardization of factors’ map in this study. Table 2 shows a fuzzy example of discrete criterion (i.e., vegetation). Also, Fig. 3 presents an example of Fuzzy map. Boolean logic may be one of the simplest methods to standardize the limits in a manner that the limits are presented by two-value maps so that values 0 and 1 indicate the unsuitable and suitable regions for the desired development, respectively (Kue et al. 2006). To combine the two-value maps, logical expressions of AND, OR have been utilized; Table 3 shows the limitations that are to be standardized in this manner. Step 3: weighting of criteria As already stated, the selection of appropriate index enables us to compare the options or alternatives correctly. However, the evaluation task will be complicated when several indices are to be evaluated. Evaluation complexity may be enhanced if some criteria from different types are placed in the same space at the same time. Therefore, the

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Environ Earth Sci Fig. 2 Flow diagram of recreation criteria

evaluation and comparison will not be simple analyses that enable the mind to deal with and a powerful tool is more likely to be required for doing practical analysis. Various methods such as least squares, logarithmic least squares, scoring and eigenvectors are utilized to calculate the weights for conducting the multi-criteria evaluation. In this paper, the eigenvector method based on pairwise comparisons has been implemented in the form of AHP (Tsaur et al. 2007). This process is expressed as a mathematical method to determine the significance and priority of criteria during the evaluation and decision-making processes and involves the following stages. At first, criterion matrices are formed through preparing some questionnaires. Thus, with regard to the hierarchical structure of various levels, the relative importance of features is compared by the related experts (individuals who are skilled in tourism and desired evaluations of the country as well as the region). Experts have responded to the questions presented in the questionnaire ranging from 1 to 9 according to Table 4. A series of pairwise comparisons is to be done concerning the relative significance of the criteria to perform the evaluation process. Table 5 demonstrates the results, and the weights were specified on the basis of the eigenvector method. Relative weights and criteria obtained for every criterion are regarded as the main inputs in the form of data to analyze the multi-criteria evaluation in GIS. To determine the accuracy and precision rates of weighting, a compatibility index was utilized. If the compatibility index is equal to 0.1 or lower than that, the

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weighting is correctly done. Otherwise, relative weights given to the criteria should be changed and the weighting process has to be repeated. Steps 4 and 5: modulation of criteria Multi-criteria evaluation aims to select the best alternative (location or pixel) based on their ranking through evaluating several main criteria. There are lots of methods including weighted linear combination (WLC) and approaches of value/utility function (Malczewski 1999) to analyze the evaluation process. Weighted linear combination (WLC) is one of the most common methods for making multi-criteria decisions on locations and is also called simple additive weighting and scoring. It is based upon the concept of weighted average. The decision maker directly weighs the criteria according to the relative significance of each criterion. Then, the relative weight is multiplied by the amount of that criterion and their sum is considered as the final suitability rate for every alternative using Eq. 1: X S¼ w i xi ; ð1Þ where S is the suitability rate, Wi is every criterion’s weight and Xi is the standardized value of every criterion. When the constraints are expressed in the form of Boolean maps, the suitability of Eq. 2 will be given as (Eastman 2003): X S¼ wi xi PCj ; ð2Þ

Environ Earth Sci Table 3 Constraints of Boolean logic Constraints

Interval with zero score (m)

Interval with one score (m)

Distance from political borderline

0–100

[100

Distance from wetland

[1000

0–1000

Table 4 Scales for pairwise comparisons [adapted from Saaty (1980)] Intensity of importance

Verbal judgment of preference

1

Equally important

3

Moderately important

5 7

Strongly important Very strongly important

9

Extremely important

2, 4, 6, 8

Intermediate values between adjacent scale values

Table 5 AHP weight derivation of evaluation criteria Criterion

Weight

River

0.03

Geology

0.02

Soil

0.02

Aspect

0.04

Slope

0.01

Vegetation

0.07

Pond

0.18

Welfare facilities

0.2

Road

0.02

Urban residential areas

0.002

Rural residential areas Monuments

0.005 0.04

Political borders

0.01

Incompatibility coefficient

0.07

areas lower than 25 ha should be separated by the means of site selection function and the final map of suitable regions will be achieved. Step 6: ranking the alternatives

Fig. 3 Maps showing distance from: river (a), political borderline (b), roads (c)

where P is the symbol of multiplication and Cj is the standardized value of every constraint. The WLC function results in a map on which regions with 70 % suitability and

Finally, the suitability of each pixel calculated using Eq. 3 and every spot may be prioritized according to the given area average: hX i S¼ ðs  aÞ=A ; ð3Þ where s is the pixel suitability of i,j in the identified zone, a is each pixel’s area and A is the identified zone’s area.

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Environ Earth Sci Table 6 Zonal land suitability and area of seven zones for extensive recreation Number of zones

Area (ha)

Summation of suitability

Average (zonal) land suitability

Minimum suitability

Maximum suitability

1

17.07

27,498.8

100.72

100

101.88

2

390.06

702,516.2

112.56

100

146

3

279.12

603,211

135.06

100

172

4

328.21

709,388

135.04

100

163

5

97.68

162,999

104.28

100

110.88

6

175.25

299,165

106.69

100

112

7

57

94,634

103.77

100

107.77

According to the overall performance scores, the alternative (in this case, it is a spot) with the highest score (rank) is regarded as the best (Malczewski 1999). In Table 6, geometric and area averages of every pixel are indicated in the final suitability map.

Results Increasing demand of tourism in the coastal areas of Chahnime, Sistan and Baluchestan Province, Iran, as one of the major tourist attractions near Zabol, Zahak Hirmand and Zahedan cities has led to tourism. However, given the tourism potential within the region, it is important for tourism managers and planners to follow sustainable development principles. In this regard, determining sites capable of tourism development and finding an optimal site among the alternatives are two main challenges that are faced. Therefore, this study aims to both identify and prioritize suitable sites for coastal tourism. For this purpose, the methodology was employed in four stages: determination of criteria, standardization of criteria, weighting of criteria [analytic hierarchy process (AHP)] and modulation of criteria. Suitable sites for coastal tourism development were determined using geographical information system (GIS). In this regard, the ecological and socio-economic criteria with 13 sub-criteria were considered as the main factors to identify the intended sites. The resulting map from the site selection function is shown in Fig. 4 and has the suitability of 0–172, so that locations with values less than 160 are rejected as unsuitable and those having values of 160 and higher are completely suitable. The threshold of 160 was chosen based on experts’ opinions. By utilizing the site selection function, it has been observed that 1344.39 ha of the studied region in seven zones can be of widespread tourism power. Because two of the zones located in the border region were excluded, finally, five suitable zones remained for tourism (see Fig. 5). However, it is essential to note that when countries with major existing tourist resorts are compared with the

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Fig. 4 Map of site selection function

selected sites, investigations showed that the selected areas whose number of tourist resorts is consistent indicate that the chosen criteria for site selection were suitable to determine the potentials for tourism in this place. A low number of the selected areas also prove the case because the less the criteria, the more are the numbers and areas of the selected zones. But the present study does not follow this idea. It focuses on suitable criteria of the area as bases for analysis.

Discussion and conclusion The study has shown that the use of GIS has greatly facilitated the tourist managers’ ability to find suitable sites for tourist activities. About five tourist sites were selected

Environ Earth Sci

Fig. 5 Selected five zones

by applying criteria such as proximity to urbanized and rural areas, ponds and rivers, road network and so on. GIS applies the process of making preliminary decisions to find the desired sites more efficiently, as it easily finds suitable sites in a quick way and then recommends them for further study. Use of multi-criteria evaluation in location of tourism activities has not a considerable background in the coastal regions, especially by the use of GIS in the country. In this study, fuzzy logic and Boolean logic have been implemented for the standardization of criteria and constraints during the evaluation process. It has been verified on the basis of previous researches (Orak 2002) that fuzzy logic acknowledges that there are objects that cannot be clearly defined as those belonging to one category or the other, but they have a degree of membership to a category (Zadeh 1965). Thus, fuzzy logic provides a good framework to work with the uncertainty data. Boolean logic is able to separate the regions into two groups of suitable and unsuitable ones leading to the destruction of information and inaccuracy of analyses as well as pairwise comparisons in the form of analytic hierarchy process to weigh the criteria providing the weighting of quantitative and qualitative criteria together.

The efficiency of this method has been confirmed by Razmi et al. (2004), Feiz Nia et al. (2004), Sheri Vastava and Nathawat (2003) and Tsaur et al. (2007). To modulate the criteria, the WLC model was utilized which has been changed into a mathematical in a manner that they can be implemented in the software environments having macroprograms. This research shows that 1344.39 ha of the study area in seven zones may be suitable for tourism. Zones 3 and 4 are located north of Chahnime with the shortest distance to Zabol. In addition, it had a suitable road network and better conditions regarding the ecology. These zones were of the highest priority for tourism. After them, zones 5, 6 and 7 in the south of Chahnime had appropriate conditions for tourism due to suitable road networks and afforestation. Zones 1 and 2 were omitted because of location at the border. Although the suggested zones are based upon the locational criteria and physical parameters, they are not the final ones for implementing tourism plans. In fact, these zones may be potential locations for tourism development and require to be studied in detail, while such problems as flood prevention along with the consideration of water flow and landslides as well as erosion must be investigated in this regard. Many criteria can be factored into the creation of a map which allows the detailed study possible, if necessary data are available and have mapping capability. It is recognized that the availability of data really dictates the level of details of a GIS analysis. Further analysis can be carried out if necessary data are available and standard and in the right format. Another advantageous capability of GIS is that more criteria can be added easily as more information becomes available. The results of this study indicate that combining MCE with AHP in GIS can be employed as a suitable tool by the decision makers to determine the appropriate sites for coastal tourism development. This study also demonstrates that a well-structured spatial decision support system (SDSS) can provide a comprehensive framework to assist the decision makers in developing sustainable tourism. A compensation method was used through compensating the low score of one criterion with the help of a high score of another factor and, practically, the effects of constraints are covered. Thus, it will be proposed that noncompensation methods like ELECTER are applied. Meanwhile, to increase the precision of suitable locations for establishing this application, base maps with higher precision and accuracy have to be prepared by the related departments. Finally, in relation with the land suitability analysis for tourism development, multi-objective allocation method of lands can be utilized in addition to multicriteria evaluation.

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