Research Article The Development of a Tourism Attraction ...downloads.hindawi.com/journals/mpe/2015/643842.pdf · tourist attraction, a town, a certain region in a country, an entire

Research ArticleThe Development of a Tourism Attraction Model byUsing Fuzzy Theory

Jieh-Ren Chang and Betty Chang

National Ilan University No 1 Section 1 Shen-Lung Road Yilan City 26047 Taiwan

Correspondence should be addressed to Jieh-Ren Chang jrchangniuedutw

Received 28 September 2014 Accepted 9 March 2015

Academic Editor Jong-Hyuk Park

Copyright copy 2015 J-R Chang and B Chang This is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work is properlycited

The purpose of this study is to develop amodel to investigate the touristsrsquo preference Ten attributes of tourist destinations were usedin this study Fuzzy set theory was adopted as the main analysis method to find the touristsrsquo preference In this study 248 pieces ofdata were used Besides the evaluations for the factors the overall evaluations (namely satisfied neutral and dissatisfied) for everytourism destination were also inquired After screening 201 pieces of these data could be used In these 201 pieces of data 141 wereclassified into ldquosatisfiedrdquo with the tourism destination accounting for 7015 and 49 were ldquoneutralrdquo accounting for 2438 while 11were ldquodissatisfiedrdquo accounting for 547 Eight rules were obtained with the method of fuzzy preprocess Regarding the conditionattributes three of the original ten attributes were found influential namely level of prices living costs information and touristservices and tourist safety of the tourism destinations From the results of this study it is shown that top management of tourismdestinations should put resources in these fields first in order to allow limited resources to perform to maximum effectiveness

1 Introduction

With the rapid economic and social development theincrease in GDP every year and peoplersquos growing concerntoward recreations the tourism industry has been developingvigorously In many countries the tourism industry is a mainindustry that deserves our policy attention and obviouslyit has become a global socioeconomic phenomenon [1] Asuccessful tourism industry can enhance regional economicdevelopment as well as becoming a source of rich foreignexchange income [2]The tourism industry is one of themainindustries that determine the worldrsquos long-term economicgrowth [3]

The tourism industry has a far-reaching influence onmany aspects such as social and economic developmentculture city development and revival in particular it hasthe greatest influences on economics [4] The output value ofthe tourism industry accounts for US$ 6 trillion of the globaleconomy which is 9 of global GDP [5] UNWTO (2011) [4]further estimated that by 2020 the number of global touristswill reach 16 billion and 2 million people global tourismearnings will reach as much as two trillion US dollars The

data indicates that tourism can bring in an enormous amountof economic benefits [6]

However in modern society no tourism industry canescape from international competition due to globalizationIn this situation how to increase international competitive-ness of the tourism industry has become one of the greatestconcerns

This study investigated into tourist destinations A touristdestination (such as city or region) is no longer viewed as aplace that features unique natural landscape culture or artinstead it is seen as a compound product that satisfies thetouristsrsquo need [1] Now many countries are actively devel-oping their own tourist destinationsrsquo international competi-tiveness [2] However how to enhance tourist destinationsrsquoattractiveness to tourists relies on more than a single factorit requires an overall plan to increase the tourist destinationsrsquocompetitiveness in the international market [7]

The goal of this study is to establish amodel formanagingtourist destinations The management of all tourism desti-nations should focus on enhancing their attractiveness andquality as well as effectively using the limited resources inthe current environment [8] Therefore this study explores

Hindawi Publishing CorporationMathematical Problems in EngineeringVolume 2015 Article ID 643842 10 pageshttpdxdoiorg1011552015643842

2 Mathematical Problems in Engineering

various tourist destinations from the perspective of touristsIn addition how these tourism destinations attract touristsand the touristsrsquo evaluations are also included in this study[9]

Fuzzy model is similar to the thinking model of humanbeings [10 11] This study therefore uses fuzzy model toanalyze the preference rules of tourist destinations Herebythis research aims to develop a model for investigationof tourist destinations management It adopts fuzzy settheory as the main analysis method for tourism industryto find the touristsrsquo preference In the second part of thisstudy it does literature exploration on the competitivenessand attractiveness of tourism destination The third sectionfocuses on introduction of fuzzy set theory and fuzzy rulesextraction algorithm [12] The fourth section gives a possibleexplanation for the results Finally the authors draw aconclusion and the suggestions for future research in the lastsection

2 Review and Discussion of the Literature

Tourism refers to peoplersquos temporary movement from theirresidence or working environment to a destination and allrelated facility or services in the destination which are to beprovided to tourists are covered in the tourism industry [13]

Gunn [14] reported that the so-called destination refersto local residentsrsquo ldquolocationrdquo on the other hand it is a ldquoplay-groundrdquo for tourists from other areas A better explanationfor a playground is a tourist destination as it can be a specifictourist attraction a town a certain region in a country anentire country or even a bigger area on the planet [15]Cracolici and Nijkamp [1] believed a tourist destination is asupplier that satisfies tourists physically and mentally twoparts are included ldquostructuralrdquo and ldquononstructuralrdquo ldquoStruc-turalrdquo refers to the natural landscape and cultural resourcesin a tourist destination while ldquononstructuralrdquo means humanresources perceptions and so forth Accordingly if an areaplans to develop tourism the key point basically lies in howto present a destination that attracts tourists [16] and how tobecomemore appealing and competitive than any other areasrsquodestinations

The critical factor model of tourist destinationsrsquo com-petitiveness established by Cracolici and Nijkamp [1] basedon the concept of Crouch and Ritchie [8] encompassesphysiography culture and history market ties activitiesevents and the tourism superstructure Ten attractions oftourist destinations were compiled and used as the attributesin this study as follows (F1) reception and sympathy of localresidents (F2) artistic and cultural cities (F3) landscapeenvironment and nature (F4) hotels and other accommo-dation (F5) typical foods (F6) cultural events (concertsart exhibitions festivals etc) (F7) level of prices livingcosts (F8) quality and variety of products in the shops (F9)information and tourist services and (F10) tourist safety

3 Establishment of Fuzzy Decision Rules

31 Introduction of Fuzzy Concept Fuzzy theory has beenwidely studied and successfully applied in various fields

which has got remarkable achievements so far The fuzzy setdefined by Professor Zadeh is represented by characteristicfunction 120583

119860(119909) in mathematics in which the value of mem-

bership function is the degree of element 119909 belonging to afuzzy set 119860 Therefore the function matches the elements inthe universal set to another set that is between 1 and 0

120583119860 119883 997888rarr [0 1] (1)

where 119909 isin 119883 119883 indicates the universal set that is defined forthe specific problem while [0 1] refers to the range of realnumbers between 0 and 1 Accordingly this study will applythe two operating factors in the deduction of if-then fuzzyrules and membership function

The vague linguistics between ldquoyesrdquo and ldquonordquo could beall represented by membership function values which is thebasic concept of fuzzy set theory It aims to illustrate fuzzyphenomenon by clear and strict mathematic methods

In this study the tourism management of towns withcultural heritage is investigated Ten attributes about tourismmanagement of these towns were used for the study Inaddition fuzzy set theory is utilized to obtain the rules oftourist preference During the fuzzy deduction we collectvarious data from complicated environments and apply themin fuzzy deduction rules and membership functions to makethe final decisions

For the tourism management of cultural heritage townsten properties are investigated Besides the fuzzy set theory isalso used to obtain the rules of touristsrsquo preference To sumupthe fuzzy system theory is scientific advanced and practicaland can also provide correct guidance to our work A newlearning method for automatically deriving fuzzy rules andmembership functions from a given set of training instancesis proposed here as the knowledge acquisition facility [17]Notation and definitions are introduced below

Data preprocess and fuzzy rule establishment areincluded in the fuzzy learning algorithm A set of traininginstances are collected from the environment Our taskhere is to generate automatically reasonable membershipfunctions and appropriate decision rules from these trainingdata so that they can represent important features of thedata set In order to avoid the disturbance of ineffectiveinformation all the data should be preprocessed in advance[18]

The support set of fuzzy set119863 is a crisp set it includes allthe elements in the universe set119880 but the membership valuein119863must be greater than 0 as follows

supp (119863) = 119909 isin 119880 | 120583119863 (119909) gt 0 (2)

The center of a fuzzy set is defined as if the membershipvalues which correspond to fuzzy set119863 from every element insupp(119863) are finite (basically 1 is supposed to be themaximumvalue) In this situation the position of the maximum valueor the medium point of the maximum value is defined as thecenter of the fuzzy set as shown in Figure 1The typical centerof a fuzzy set is shown in Figure 1

Fuzzy set includes all the points in the set 119880 Concerningset 119863 when the membership value is equal to 05 it is thevaguest point

Mathematical Problems in Engineering 3

U

120583

D1D2 D3

Center Center Center

Figure 1 Typical centers in fuzzy set

In order to obtain the support set higher than a certainlevel 120572-cut is used to extract the support set and 120572-cut offuzzy set119863 is a definite set119863

120572as follows

119863120572= 119909 isin 119880 | 120583

119863 (119909) ge 120572 (3)

Fuzzy proposition includes two types namely atomicfuzzy proposition and compound fuzzy proposition Anatomic fuzzy proposition is a single fuzzy proposition asfollows

1199021is 1198861 (4)

where 119902 is a linguistic variable and 1198861is the linguistic value of

1199021A compound fuzzy proposition is using conjunctions

such as ldquoandrdquo ldquoorrdquo and ldquonotrdquo to joint atomic fuzzy propo-sitions to make fuzzy intersection set fuzzy union set andfuzzy compensate set For example 119902

1stands for ldquoinfor-

mation and tourist servicesrdquo 1199022stands for ldquolevel of prices

living costsrdquo 1198861and 119886

2stand for linguistic values ldquovery

goodrdquo and ldquobarely acceptablerdquo and then the compound fuzzyproposition will be as follows

1199021is 1198861and 119902

2is 1198862 (5)

Fuzzy rules are made of ldquoif-thenrdquo and fuzzy propositionsas shown in rule 119903

119903 If 1199021is 1198861and 119902

2is 1198862

Then 119910 is 1198871

(6)

In an ldquoif fuzzy propositionrdquo the questionnaire analysis isset as a condition attribute and in a ldquothen fuzzy propositionrdquothe questionnaire analysis is set as a decision attribute Whenlinguistic variable 119902

1is 1198861and 119902

2is 1198862 linguistic variable 119910

will be 1198871 therefore with fuzzy rules the linguistic causal

relationship can be inferred All the fuzzy rules can be puttogether to make a fuzzy rule database and this databaseincludes various corresponding fuzzy rules

Fuzzy inferences mean making inferences with all therules in fuzzy rule database There are three types in fuzzyinferences namely type 1 type 2 and type 3 which standfor singleton linguistic and linear inference rules In thisstudy linguistic inference rules were used and the methodproposed by Tsukamoto was applied

32 Deleting Ineffective Data In order to avoid the interrup-tion from ineffective data preprocessing is necessary beforedata analysis [19] There are many different methods thatcan be used for preprocessing However one preprocessingmethod may not be suitable for all of the fields In this studya novel preprocessing method of screening ineffective datafor questionnaires was proposed Here we define the effectivedata as honest data and ineffective data as dishonest dataSome attributes and data might be deleted to let decision-makers obtain precise and useful data in questionnaireanalysis process In this process it is supposed that datafrom some respondents can be neglected This type can beconsidered as a form of majority verdict which can obtainthe main consensus from the majority of the questionnairerespondents Concerning the data analysis in this study theanswers from questionnaires responded by tourists were usedfor data analysis The effective data are defined as responsesfrom the majority of tourists The ineffective data on theother side include dishonest data and data from respondentswith special preference

33 Establishing Questionnaire Rules Themethod of deletingineffective data will be reported in this part First of all theauthors assumed that most people have similar perceptionTherefore concerning a specific tourism destination it issupposed that the scoring toward a specific attribute from thequestionnaire respondents would be aggregated in a rangeIn the space of condition attribute every decision attributeforms a block space and has its own center those datawith bias might be far from the center and more likely tobe ineffective data In addition in the space of conditionattribute the intersection with different decision attributemight be small or empty this assumption is tomake sure thatthe classification of decision attribute is identifiable

With establishing fuzzy rules the authors can screen inef-fective data with the method of fuzzy inference Concerningthe content of the questionnaire there are 119899 subquestionitems in each of the questions and these 119899 subquestion itemsstand for condition attribute items as follows

119876 = 1199021 1199022 119902

119899 (7)

where 119899 isin 119873 and119873 stands for the set of positive integersThe overall evaluation a respondent made is the decision

attribute 119910 in a fuzzy rule Supposing that a respondentanswered a specific question item 119902

119901 the set of linguistic

values is as follows

119886119901= 119886119901

1 119886119901

2 119886

119901

119895119901 (8)

where 1 le 119901 le 119899 and 119901 isin 119873 119895119901is the number of the linguistic

values of a specific condition attribute and 119895119901isin 119873

After answering all the subquestions the respondentmust select a linguistic value from set 119861 as the overallevaluation where set 119861 is a set of linguistic values as follows

119861 = 1198871 1198872 119887

119894 (9)

where 119894 is the number of decision attribute linguistic valuesand 119894 isin 119873


The data of the answers from respondents were trans-ferred into fuzzy rules For example when the linguistic valueof the decision attribute inference is 119887

ℎ the first fuzzy rule will

be as follows

119903ℎ

1 1199021is 11988611199011

and 1199022is 11988621199012

and sdot sdot sdot and 119902119899is 1198863119901119899

997888rarr 119910 is 119887ℎ

(10)

where 1 le ℎ le 119894 and ℎ isin 119873Then all the fuzzy rules would be put together in fuzzy

rule database as follows

119877 = 1199031 1199032 119903

119894 (11)

The linguistic values of decision attribute in fuzzy ruleof 119877 could be classified into 119894 categories and every categorywould correspond to the linguistic values in set 119861 as follows

119903ℎ= 119903ℎ

1 119903ℎ

2 119903

ℎ

119896ℎ (12)

where 119903ℎ stands for the fuzzy rule classification of 119887ℎand the

number of rules is 119896ℎ

The previous part reported the principles of fuzzy rulesfor multiple condition attribute to single decision attribute Itis found from rule classification that the distribution space of119887ℎcorresponds to set 119876 in (7) as follows

119865ℎ= 119886minus1(119887ℎ) 119886minus2(119887ℎ) 119886

minus119899(119887ℎ) (13)

where 119865ℎis the linguistic value distribution space of 119887

ℎand

119886minus1(119887ℎ) stands for the distribution situation of 1198861 which is

corresponded from 119887ℎ

4 Results and Discussion

41 Overview of the Research Data In this study 248 dataused were retrieved Most of the respondents are the officeworkers and young persons in Taiwan In these 248 data201 of the tourist sites the respondents mentioned includethe sites in northern parts central parts southern partsand eastern parts of Taiwan And the other 47 ones areinternational tourist sites out of Taiwan In these datatouristsrsquo evaluations for each of the factors about the tourismdestinations were included Besides the evaluations for thefactors the overall evaluations (namely satisfied neutraland dissatisfied) for every tourism destination were alsoinquired After screening 201 of these data could be usedIn these 201 data 141 were classified into ldquosatisfiedrdquo withthe tourism destination accounting for 7015 and 49 wereldquoneutralrdquo accounting for 2438 while 11 were ldquodissatisfiedrdquoaccounting for 547 The numbers and percentages of dataclassified into each category were shown in Table 1 Theevaluation of the attribute ldquolevel of prices living costsrdquohas three fuzzy linguistic terms of levels (ldquogoodrdquo ldquobarelyacceptablerdquo and ldquopoorrdquo ) On the other hand the attributeldquotourist safetyrdquo has four fuzzy linguistic terms of levels (ldquoverygoodrdquo ldquogoodrdquo ldquopoorrdquo and ldquovery poorrdquo) The levels of thesetwo attributes were shown in Table 2 Through the method

Table 1 The numbers and percentages of overall evaluation

Satisfied Neutral Dissatisfied TotalNumbers of dataclassified into eachcategory

141 49 11 201

Percentage 7015 2438 547 10000

of fuzzy preprocess 8 rules were obtained These fuzzy ruleswere shown in Table 3 Concerning the condition attributestwo of the original ten attributes were found influentialnamely level of prices living costs (F7) and tourist safety(F10) of the tourism destinations

42 Fuzzy Rules Analysis The results of the fuzzy rules anal-ysis were shown in Table 3 According to fuzzy mathematicsonly two (F7 level of prices living costs and F10 touristsafety) of the 10 attributes were strongly influential attributesFrom these rules the following results can be obtained

(1) From Rule 2 and Rule 3 when F7 (level of pricesliving costs) received ldquogoodrdquo the overall evaluationswould be ldquosatisfiedrdquo if F10 (tourist safety) receivedldquogoodrdquo or ldquovery goodrdquo

(2) From Rule 1 and Rule 3 when F10 (tourist safety)received ldquovery goodrdquo the overall evaluations wouldbe ldquosatisfiedrdquo even if F7 (level of prices living costs)received ldquobarely acceptablerdquo

(3) From Rule 4 and Rule 5 when F7 (level of pricesliving costs) received ldquobarely acceptablerdquo the overallevaluations would be neutral if F10 (tourist safety)received the level of ldquogoodrdquo or ldquopoorrdquo

(4) FromRule 6 andRule 7 when F7 (level of prices livingcosts) received ldquopoorrdquo the overall evaluations wouldbe dissatisfied if F10 (tourist safety) received the levelof ldquopoorrdquo or ldquovery poorrdquo

(5) From Rule 6 and Rule 8 if F10 (tourist safety)received ldquovery poorrdquo the overall evaluations would bedissatisfied no matter F7 (level of prices living costs)received ldquobarely acceptablerdquo or ldquopoorrdquo

(6) ComparingRule 1 andRule 4 F7 (level of prices livingcosts) received ldquobarely acceptablerdquo in both rules andat this time F10 (tourist safety) would be a key forthe overall evaluations F10 (tourist safety) receivedldquovery goodrdquo in Rule 1 and the overall evaluations wereldquosatisfiedrdquo while in Rule 4 the overall evaluationswere ldquoneutralrdquo as F10 (tourist safety) received ldquopoorrdquo

(7) While comparing Rule 2 and Rule 5 F10 (touristsafety) received ldquogoodrdquo in both of these rules F7(level of prices living costs) would be a key forthe overall evaluations in this situation In Rule 2F10 (tourist safety) received ldquogoodrdquo and the overallevaluations were ldquosatisfiedrdquo in Rule 5 however theoverall evaluations were ldquoneutralrdquo as F7 (level ofprices living costs) received ldquobarely acceptablerdquo


Table 2 Levels of attributes

Attributes Numbers of levels Fuzzy linguistic terms of levels(form high level to low level)

F7 level of prices living costs 3 levels ldquoGoodrdquo ldquobarely acceptablerdquo and ldquopoorrdquo

F10 tourist safety 4 levels ldquoVery goodrdquo ldquogoodrdquo ldquopoorrdquo and ldquoverypoorrdquo

Table 3 The 8 rules derived from fuzzy analysis

F7level of prices living costs

F10tourist safety Evaluation

Rule 1 Barely acceptable Very good SatisfiedRule 2 Good Good SatisfiedRule 3 Good Very good SatisfiedRule 4 Barely acceptable Poor NeutralRule 5 Barely acceptable Good NeutralRule 6 Poor Very poor DissatisfiedRule 7 Poor Poor DissatisfiedRule 8 Barely acceptable Very poor Dissatisfied

(8) Comparing Rule 4 and Rule 7 as F10 (tourist safety)received ldquopoorrdquo in both rules F7 (level of prices livingcosts) would be a key for the overall evaluations Forexample F7 (level of prices living costs) receivedldquobarely acceptablerdquo in Rule 4 and the overall evalu-ations were ldquoneutralrdquo while in Rule 7 F7 (level ofprices living costs) received ldquopoorrdquo and the overallevaluations turned to ldquodissatisfiedrdquo consequently

(9) Comparing Rule 5 and Rule 8 when F7 (level ofprices living costs) received ldquobarely acceptablerdquo F10(tourist safety) played a crucial role for deciding theoverall evaluations In other words if F10 (touristsafety) received ldquogoodrdquo the overall evaluations wouldbe ldquoneutralrdquo On the other hand if F10 (tourist safety)received ldquovery poorrdquo the overall evaluations would beldquodissatisfiedrdquo

(10) From the comparison of Rule 1 Rule 4 Rule 5 andRule 8 it was found that F7 (level of prices livingcosts) received ldquobarely acceptablerdquo in each of the rulesIn Rule 1 for example the overall evaluations wereldquosatisfiedrdquo since F10 (tourist safety) received ldquoverygoodrdquo The overall evaluations of Rule 4 and Rule5 were ldquoneutralrdquo on the other hand as F10 (touristsafety) received either ldquogoodrdquo or ldquopoorrdquo In Rule 8however the overall evaluations were ldquodissatisfiedrdquowhen F10 (tourist safety) received ldquovery poorrdquo

In this section the rules in Table 3 were represented asin Figure 2 In Figure 2 the upper right corner (areas ofRule 1 Rule 2 Rule 3 and Rule 5) shows that when theattribute ldquotourist safetyrdquo was evaluated as ldquogoodrdquo or ldquoverygoodrdquo the attribute ldquolevel of prices living costsrdquo was alsoevaluated as ldquogoodrdquo or ldquobarely acceptablerdquo and the overallevaluations were satisfied or neutral The reason might bethat most tourists already had sufficient information aboutthe level of local living costs before they made decision for

Safe

ty

Verygood

Rule 1Satisfied

Rule 3Satisfied

Good Rule 5Neutral

Rule 2Satisfied

Poor Rule 7Dissatisfied

Rule 4 Neutral

Verypoor

Rule 6Dissatisfied

Rule 8Dissatisfied

Poor Barely acceptable GoodLevel of prices

x

x

x

x

Figure 2 Fuzzy rule base (for all tourists)

their destinations The tourist might therefore think the levelof price is agreeable On the other hand the lower left corner(areas of Rule 6 Rule 7 Rule 8 and Rule 4) shows that whenthe attribute ldquotourist safetyrdquo was evaluated as ldquopoorrdquo or ldquoverypoorrdquo the attribute ldquolevel of prices living costsrdquo was evaluatedas ldquopoorrdquo or ldquobarely acceptablerdquo and the overall evaluationswere dissatisfied or neutral It is believed that the poor safetymight impair touristsrsquo confidence To sum up tourist safety isthe attribute the tourists care about the most

In Figure 2 ldquoxrdquo stands for no rules in that exact areaAccording to Figure 2 no rules were found in the upper leftcorner these areas stand for destinations with high safetyand high price The reason for no rules here might be thatmost respondents are office workers and young persons theymade very different evaluations about these destinations andtherefore no consistent rules could be produced Besidesthere are no rules either in the lower right corner Thislower right corner area stands for tourist destinations withpoor safety Since tourist safety was the attribute the touristscare about the most very few tourists would select thesedestinations

43 Comparison of the Results from Tourists of DifferentAges Tourism is getting more and more popular in the21st century However tourists of different ages might havevarious demands and different preference regarding tourismdestinations In order to investigate the tourist preference ofdifferent ages the authors divided the data of tourists into twogroups one group is of tourists above 30 years old and theother group is of tourists of 30 years old and below

431 Results from Tourists of 30 Years Old and Below In thegroup of tourists of 30 years old and below there are 139 piecesof data collected from these tourists After programming


Table 4 Levels of attributes (tourists of 30 years old and below)


F7 level of prices living costs 3 levels ldquoGoodrdquo ldquobarely acceptablerdquo and ldquopoorrdquoF9 information and tourist services 3 levels ldquoGoodrdquo ldquobarely acceptablerdquo and ldquopoorrdquo


Table 5 The 13 rules derived from fuzzy analysis (tourists of 30 years old and below)


F9information and tourist services


Rule 1 Barely acceptable Barely acceptable Very good SatisfiedRule 2 Barely acceptable Good Good SatisfiedRule 3 Barely acceptable Good Very good SatisfiedRule 4 Good Barely acceptable Good SatisfiedRule 5 Good Barely acceptable Very good SatisfiedRule 6 Good Good Good SatisfiedRule 7 Good Good Very good SatisfiedRule 8 Barely acceptable Barely acceptable Poor NeutralRule 9 Barely acceptable Barely acceptable Good NeutralRule 10 Poor Poor Very poor DissatisfiedRule 11 Poor Barely acceptable Very poor DissatisfiedRule 12 Barely acceptable Poor Very poor DissatisfiedRule 13 Barely acceptable Poor Poor Dissatisfied

with fuzzy set theory three of the attributes were foundto be crucial namely ldquolevel of prices living costsrdquo (F7)ldquoinformation and tourist servicesrdquo (F9) and ldquotourist safetyrdquo(F10) The evaluations of both of the attributes ldquolevel ofprices living costsrdquo and ldquoinformation and tourist servicesrdquowere divided into three fuzzy linguistic terms of levels ldquogoodrdquoldquobarely acceptablerdquo and ldquopoorrdquo while the evaluation of theattribute ldquotourist safetyrdquo could be divided into four fuzzylinguistic terms of levels ldquovery goodrdquo ldquogoodrdquo ldquopoorrdquo andldquovery poorrdquo as shown in Table 4 Thirteen fuzzy rules werederived from fuzzy computing as shown in Table 5

According to the fuzzy rules obtained from the data oftourists of 30 years old and below the following results canbe obtained

(1) Comparing Rule 4 and Rule 9 F9 (information andtourist services) received ldquobarely acceptablerdquo and F10(tourist safety) received ldquogoodrdquo in both rules at thistime F7 (level of prices living costs) would play acrucial role in deciding the overall evaluations Forexample when F7 received ldquogoodrdquo in Rule 4 theoverall evaluation would be ldquosatisfiedrdquo while in Rule9 F7 received ldquobarely acceptablerdquo and the overallevaluation was then ldquoneutralrdquo

(2) Comparing Rule 4 and Rule 9 F9 (information andtourist services) received ldquobarely acceptablerdquo and F10(tourist safety) received ldquogoodrdquo in both rules at thistime F7 (level of prices living costs) would play acrucial role in deciding the overall evaluations For

example when F7 received ldquogoodrdquo in Rule 4 theoverall evaluation would be ldquosatisfiedrdquo while in Rule9 F7 received ldquobarely acceptablerdquo and the overallevaluation was then ldquoneutralrdquo

(3) Comparing Rule 1 Rule 8 andRule 9 both of F7 (levelof prices living costs) and F9 (information and touristservices) received ldquobarely acceptablerdquo in each of therules In this situation F10 (tourist safety) wouldbe a key for the overall evaluations In Rule 1 F10(tourist safety) received ldquovery goodrdquo and the overallevaluationwas ldquosatisfiedrdquo while in Rule 8 F10 (touristsafety) received ldquopoorrdquo and in Rule 9 F10 (touristsafety) received ldquogoodrdquo and the overall evaluations ofboth of Rule 8 and Rule 9 were ldquoneutralrdquo

(4) Comparing Rule 8 and Rule 13 F7 (level of pricesliving costs) received ldquobarely acceptablerdquo and F10(tourist safety) received ldquopoorrdquo in both rules at thistime F9 (information and tourist services) would playan influential role in deciding the overall evaluationsFor example when F9 received ldquobarely acceptablerdquoin Rule 8 the overall evaluation would be ldquoneutralrdquowhile in Rule 13 F9 received ldquopoorrdquo and the overallevaluation was then ldquodissatisfiedrdquo

According to the results of fuzzy analysis for touristsof 30 years old and below three (F7 level of prices livingcosts F9 information and tourist services and F10 touristsafety) of the 10 attributes were strongly influential attributesCompared with the results in the previous section there was


Table 6 Levels of attributes (tourists above 30 years old)


F7 level of prices living costs 4 levels ldquoVery goodrdquo ldquogoodrdquo ldquopoorrdquo and ldquoverypoorrdquo

F9 information and tourist services 4 levels ldquoVery goodrdquo ldquogoodrdquo ldquopoorrdquo and ldquoverypoorrdquo


an extra influential attribute namely information and touristservices (F9) In order to analyze the relationship amongthese three attributes 3 figures based on three differentlevels (good barely acceptable and poor) of information andtourist services were generated

Figure 3(a) shows the rule base of tourists of 30 yearsold and below when information and tourist services of thedestinations are good Only four rules were generated inthe upper right corner of Figure 3(a) These 4 rules are allevaluated as ldquosatisfiedrdquo with very good or good in safety andgood or barely acceptable in living cost On the other handtherewere no rules created in other areas in Figure 3(a) In thecondition of sufficient information tourists would try theirbest to avoid going to destinations with poor safety or poorlevel of prices Similar to the condition in Figure 2 no ruleswere found in the upper left corner and the lower right corner

Figure 3(b) shows the rule base of tourists of 30 yearsold and below when information and tourist services of thedestinations are barely acceptable Comparing Figure 3(b)with Figure 3(a) Rule 9 in Figure 3(b) is in the same positionas Rule 2 in Figure 3(a) However the overall evaluationof Rule 9 in Figure 3(b) is neutral and that of Rule 2 inFigure 3(a) is satisfied the authors therefore inferred thatgood information and tourist services of the destinationsmaypromote the image of a tourist site

Figure 3(c) shows the rule base of tourists of 30 years oldand below when information and tourist services of the des-tinations are poor Comparing Figure 3(c) with Figure 3(b)Rule 12 in Figure 3(c) is in the same position as Rule 8 inFigure 3(b) Nevertheless the overall evaluation of Rule 12in Figure 3(c) is dissatisfied and that of Rule 8 in Figure 3(b)is neutral it is therefore inferred that poor information andtourist services of a tourist site may degrade the overallevaluation of a destination On the other hand there wereno rules generated in other areas in Figure 3(c) Actuallyvery few people know destinations with poor informationBesides it is supposed that a tourist site with good safety andliving cost condition will soon be popular in this Internet eraand then those cases will be transferred into the section ofsufficient information such as the cases in Figures 3(a) and3(b)

432 Results from Tourists above 30 Years Old In thegroup of tourists above 30 years old there are pieces of34 data collected from these tourists After programmingwith fuzzy set theory three of the attributes were foundto be crucial namely ldquolevel of prices living costsrdquo (F7)

ldquoinformation and tourist servicesrdquo (F9) and ldquotourist safetyrdquo(F10)The evaluation of all the attributes ldquolevel of prices livingcostsrdquo ldquoinformation and tourist servicesrdquo and ldquotourist safetyrdquowas shown as four fuzzy linguistic terms of levels (ldquoverygoodrdquo ldquogoodrdquo ldquopoorrdquo and ldquovery poorrdquo) as shown in Table 6Fourteen fuzzy rules were derived from fuzzy computing asshown in Table 7

According to the fuzzy rules obtained from the dataof tourists above 30 years old the following results can beobtained

(1) Comparing Rule 8 and Rule 14 F7 (level of pricesliving costs) received ldquogoodrdquo and F10 (tourist safety)received ldquovery poorrdquo in both rules at this timeF9 (information and tourist services) would play acrucial role in deciding the overall evaluations Forexample when F9 received ldquogoodrdquo in Rule 8 theoverall evaluation would be ldquoneutralrdquo while in Rule14 F9 received ldquovery poorrdquo and the overall evaluationwas then ldquodissatisfiedrdquo

(2) Comparing Rule 4 and Rule 11 F9 (information andtourist services) received ldquovery goodrdquo and F10 (touristsafety) received ldquogoodrdquo in both rules at this timeF7 (level of prices living costs) would be a key forthe overall evaluations In Rule 4 F7 (level of pricesliving costs) received ldquovery goodrdquo and the overallevaluation was ldquosatisfiedrdquo while in Rule 11 F7 (levelof prices living costs) received ldquogoodrdquo and the overallevaluation of Rule 11 was then ldquoneutralrdquo

According to the results of fuzzy analysis for touristsabove 30 years old three (F7 level of prices living costs F9information and tourist services and F10 tourist safety) ofthe 10 attributes were strongly influential attributes Besidesthere are four levels in each of the three attributes as shownin Table 6 In order to analyze the relationship among thesethree attributes 4 figures based on four different levels (verygood good poor and very poor) of information and touristservices were generated

Figure 4(a) shows the rule base of tourists above 30 yearsold when information and tourist services of the destinationsare very good Seven rules were generated four rules ofsatisfied were in the upper right corner of Figure 4(a) and theother three rules are of neutral Comparing Figure 4(a) withFigure 4(b) Rule 7 in Figure 4(a) is in the same position asRule 13 in Figure 4(b) However the overall evaluation of Rule7 in Figure 4(a) is neutral and that of Rule 13 in Figure 4(b)is dissatisfied it is therefore inferred that better information


Safe

ty

Verygood

Rule 3Satisfied

Rule 7Satisfied

GoodRule 2

SatisfiedRule 6

Satisfied

Poor

Verypoor

Poor Barelyacceptable

Good

Level of prices

x

x

x

x

x

x

x

x

(a)

Safe

ty

Verygood

Rule 5Satisfied

GoodRule 9Neutral

Rule 4Satisfied

PoorRule 8Neutral

Verypoor

Rule 11Dissatisfied


Good

Level of prices

x

x

x

x

x

x

x

(b)

Safe

ty

Verygood

Good


Verypoor

Rule 10Dissatisfied

Rule 13Dissatisfied


Good

Level of prices

x

x

x

x

x

x

x

x

x

(c)

Figure 3 (a) Tourists of 30 years old and belowinformation and tourist services are good (b) Tourists of 30 years old and belowinformationand tourist services are barely acceptable (c) Tourists of 30 years old and belowinformation and tourist services are poor

Table 7 The 14 rules derived from fuzzy analysis (tourists above 30 years old)




Rule 1 Poor Very good Very good SatisfiedRule 2 Good Very good Very good SatisfiedRule 3 Very good Poor Very good SatisfiedRule 4 Very good Very good Good SatisfiedRule 5 Very good Very good Very good SatisfiedRule 6 Very poor Very good Poor NeutralRule 7 Poor Very good Poor NeutralRule 8 Good Good Very poor NeutralRule 9 Good Good Poor NeutralRule 10 Good Good Good NeutralRule 11 Good Very good Good NeutralRule 12 Very good Good Poor NeutralRule 13 Poor Good Poor DissatisfiedRule 14 Good Very poor Very poor Dissatisfied

and tourist services of a tourist site may promote the overallevaluation of a destination

It is found that very few rules are in Figures 4(c) and4(d) Similarly there are few rules found in Figure 3(c)(three rules)The authors inferred that destinationswith poor

information and tourist services have fewer tourists Thereis only one rule especially in each of Figures 4(c) and 4(d)because of lack of data from tourists above 30 years old Itis therefore concluded that tourists in this group (touristsabove 30 years old) seldom travel to destinations with poor

Mathematical Problems in Engineering 9Sa

fety

Verygood

Rule 1Satisfied

Rule 2Satisfied

Rule 5Satisfied

GoodRule 11Neutral

Rule 4Satisfied

PoorRule 6Neutral

Rule 7Neutral

Verypoor

Verypoor

Poor Good Verygood

Level of prices

x

x

x x x x

x

x

x

(a)

Safe

ty

Verygood

GoodRule 10Neutral

PoorRule 13

DissatisfiedRule 9Neutral

Rule 11Neutral

Verypoor

Rule 8Neutral

Verypoor

Poor Good Verygood

Level of prices

x

x

x x x

x

xx

x

x

x

(b)

Safe

ty

Verygood

Rule 3Satisfied

Good

Poor

Verypoor

Verypoor

Poor Good Verygood

Level of prices

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

(c)

Safe

ty

Verygood

Good

Poor

Verypoor

Rule 14Dissatisfied

Verypoor

Poor Good Verygood

Level of prices

x

x

x

x

x

x

x

x

x

x

x

x

x

xx

(d)

Figure 4 (a) Tourists above 30 years oldinformation and tourist services are very good (b) Tourists above 30 years oldinformation andtourist services are good (c) Tourists above 30 years oldinformation and tourist services are poor (d) Tourists above 30 years oldinformationand tourist services are very poor

information and tourist services In other words touristsabove 30 years old need good information and tourist serviceswhen they select destinations for tour

5 Conclusion

In this study F7 (level of prices living costs) and F10 (touristsafety)were found influential factors through fuzzy algorithmanalysis [20] From this research a fuzzy rule database oftourism destinations is established to provide a fuzzy systeminference decision-makingmodelThis decision-making rulemodel can be provided to the tourismmanagers as a referenceto establish tourism management Tourism planners can usethe ten attributes as a reference

However the budgets of some tourism destinations areoften limited This research simplified the ten constituentelements into two in other words two key attributes werefound While the budgets are limited the tourism destina-tions could use the resource in the most crucial attributes tocreate comparatively large benefit

From the rule analysis it can be speculated that whentourists visit a tourism destination they value ldquolevel of pricesliving costsrdquo (F7) and ldquotourist safetyrdquo (F10) of this area

In order to investigate the tourist preference of differentages the authors divided the data of tourists into two groups

one group is of tourists above 30 years old and the other groupis of tourists of 30 years old and below It was found thattourists of different ages showed their different preferencesin three fields namely ldquolevel of prices living costsrdquo (F7)ldquoinformation and tourist servicesrdquo (F9) and ldquotourist safetyrdquo(F10) In other words if the tourism industry would satisfytouristsrsquo demands and preferences especially for tourists ofdifferent ages they have to focus on information and touristservices as well

On the basis of the results of this study it is shown that topmanagement of tourism destinations should put resources inthese fields first in order to allow limited resources to per-form to maximum effectiveness for the positive evaluationsby tourists

Lastly this study still has parts that can be furtherresearched or improved In terms of the fuzzy linguisticsattribute F7 (level of prices living costs) is of 3 levelswhile attribute F10 (tourist safety) is of 4 levels and 8rules were produced If other attributes such as touristsrsquoage or gender are further considered more focused ruleswill be obtained which will assist in providing manage-ment of tourism destinations with more precise referencerules At the same time this can help decision-makers tomake future development plans for tourism destinations thatthey manage so as to cater to the preferences of differentgroups


Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

References

[1] M F Cracolici and P Nijkamp ldquoThe attractiveness and com-petitiveness of tourist destinations a study of Southern ItalianregionsrdquoTourismManagement vol 30 no 3 pp 336ndash344 2009

[2] W-WWu ldquoBeyond Travel amp Tourism competitiveness rankingusing DEA GST ANN and Borda countrdquo Expert Systems withApplications vol 38 no 10 pp 12974ndash12982 2011

[3] A Kyriakidis H Hancock S Oaten and R Bashir ldquoCapturingthe visitor economy a framework for successrdquo in The Travel ampTourism Competitiveness Report 2009 J Blanke and T ChiesaEds pp 65ndash77 World Economic Forum Geneva Switzerland2009

[4] World Tourism Organization UNWTO UNWTO Global Sum-mit on City Tourism 2011 2011 httpwwwunwtoorg

[5] World Travel Tourism Council Travel and Tourism 2011 2011httpwwwwttcorg

[6] I Hwon ldquoMining consumer attitude and behaviorrdquo Journal ofConvergence vol 4 no 2 pp 29ndash35 2013

[7] G Peng K Zeng and X Yang ldquoA hybrid computational intelli-gence approach for the VRP problemrdquo Journal of Convergencevol 4 no 2 pp 1ndash4 2013

[8] G I Crouch and J R B Ritchie ldquoTourism competitiveness andsocietal prosperityrdquo Journal of Business Research vol 44 no 3pp 137ndash152 1999

[9] J C Augusto V Callaghan D Cook A Kameas and ISatoh ldquoIntelligent environments a manifestordquo Human-CentricComputing and Information Sciences vol 3 no 1 p 12 2013

[10] C T Lin and C S G Lee Neural Fuzzy Systems Prentice-HallSingapore 1999

[11] M Malkawi and O Murad ldquoArtificial neuro fuzzy logic systemfor detecting human emotionsrdquoHuman-Centric Computing andInformation Sciences vol 3 article 3 2013

[12] O P Verma V Jain and R Gumber ldquoSimple fuzzy rule basededge detectionrdquo Journal of Information Processing Systems vol9 no 4 pp 575ndash591 2013

[13] A Matheison and G Wall Tourism Economic Physical andSocial Impacts Longman New York NY USA 1982

[14] C A Gunn Tourism Planning 1988[15] C RGoeldner and J R B RitchieTourism Principles Practices

Philosophies John Wiley amp Sons Hoboken NJ USA 2006[16] L Dwyer P Forsyth and P Rao ldquoThe price competitiveness of

travel and tourism a comparison of 19 destinationsrdquo TourismManagement vol 21 no 1 pp 9ndash22 2000

[17] M Lee ldquoDesign of an intelligent system for autonomousgroundwater managementrdquo Journal of Convergence vol 5 no1 pp 26ndash31 2014

[18] E Namsrai T Munkhdalai M Li J-H Shin O-E Namsraiand K H Ryu ldquoA feature selection-based ensemble methodfor arrhythmia classificationrdquo Journal of Information ProcessingSystems vol 9 no 1 pp 31ndash40 2013

[19] M Brahami B Atmani and N Matta ldquoDynamic knowledgemapping guided by data mining application on Healthcarerdquo

Journal of Information Processing Systems vol 9 no 1 pp 1ndash302013

[20] H Binh and S Ngo ldquoAll capacities modular cost survivablenetwork design problem using genetic algorithm with com-pletely connection encodingrdquo Human-Centric Computing andInformation Sciences vol 4 no 1 article 13 2014

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MathematicsJournal of


Mathematical Problems in Engineering

Hindawi Publishing Corporationhttpwwwhindawicom

Differential EquationsInternational Journal of

Volume 2014

Applied MathematicsJournal of


Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of


Mathematical PhysicsAdvances in

Complex AnalysisJournal of


OptimizationJournal of


CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of


Operations ResearchAdvances in

Journal of


Function Spaces

Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of Mathematics and Mathematical Sciences


The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014


Algebra

Discrete Dynamics in Nature and Society



Decision SciencesAdvances in

Discrete MathematicsJournal of


Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Stochastic AnalysisInternational Journal of


various tourist destinations from the perspective of touristsIn addition how these tourism destinations attract touristsand the touristsrsquo evaluations are also included in this study[9]

Fuzzy model is similar to the thinking model of humanbeings [10 11] This study therefore uses fuzzy model toanalyze the preference rules of tourist destinations Herebythis research aims to develop a model for investigationof tourist destinations management It adopts fuzzy settheory as the main analysis method for tourism industryto find the touristsrsquo preference In the second part of thisstudy it does literature exploration on the competitivenessand attractiveness of tourism destination The third sectionfocuses on introduction of fuzzy set theory and fuzzy rulesextraction algorithm [12] The fourth section gives a possibleexplanation for the results Finally the authors draw aconclusion and the suggestions for future research in the lastsection

2 Review and Discussion of the Literature

Tourism refers to peoplersquos temporary movement from theirresidence or working environment to a destination and allrelated facility or services in the destination which are to beprovided to tourists are covered in the tourism industry [13]

Gunn [14] reported that the so-called destination refersto local residentsrsquo ldquolocationrdquo on the other hand it is a ldquoplay-groundrdquo for tourists from other areas A better explanationfor a playground is a tourist destination as it can be a specifictourist attraction a town a certain region in a country anentire country or even a bigger area on the planet [15]Cracolici and Nijkamp [1] believed a tourist destination is asupplier that satisfies tourists physically and mentally twoparts are included ldquostructuralrdquo and ldquononstructuralrdquo ldquoStruc-turalrdquo refers to the natural landscape and cultural resourcesin a tourist destination while ldquononstructuralrdquo means humanresources perceptions and so forth Accordingly if an areaplans to develop tourism the key point basically lies in howto present a destination that attracts tourists [16] and how tobecomemore appealing and competitive than any other areasrsquodestinations

The critical factor model of tourist destinationsrsquo com-petitiveness established by Cracolici and Nijkamp [1] basedon the concept of Crouch and Ritchie [8] encompassesphysiography culture and history market ties activitiesevents and the tourism superstructure Ten attractions oftourist destinations were compiled and used as the attributesin this study as follows (F1) reception and sympathy of localresidents (F2) artistic and cultural cities (F3) landscapeenvironment and nature (F4) hotels and other accommo-dation (F5) typical foods (F6) cultural events (concertsart exhibitions festivals etc) (F7) level of prices livingcosts (F8) quality and variety of products in the shops (F9)information and tourist services and (F10) tourist safety

3 Establishment of Fuzzy Decision Rules

31 Introduction of Fuzzy Concept Fuzzy theory has beenwidely studied and successfully applied in various fields

which has got remarkable achievements so far The fuzzy setdefined by Professor Zadeh is represented by characteristicfunction 120583

119860(119909) in mathematics in which the value of mem-

bership function is the degree of element 119909 belonging to afuzzy set 119860 Therefore the function matches the elements inthe universal set to another set that is between 1 and 0

120583119860 119883 997888rarr [0 1] (1)

where 119909 isin 119883 119883 indicates the universal set that is defined forthe specific problem while [0 1] refers to the range of realnumbers between 0 and 1 Accordingly this study will applythe two operating factors in the deduction of if-then fuzzyrules and membership function

The vague linguistics between ldquoyesrdquo and ldquonordquo could beall represented by membership function values which is thebasic concept of fuzzy set theory It aims to illustrate fuzzyphenomenon by clear and strict mathematic methods

In this study the tourism management of towns withcultural heritage is investigated Ten attributes about tourismmanagement of these towns were used for the study Inaddition fuzzy set theory is utilized to obtain the rules oftourist preference During the fuzzy deduction we collectvarious data from complicated environments and apply themin fuzzy deduction rules and membership functions to makethe final decisions

For the tourism management of cultural heritage townsten properties are investigated Besides the fuzzy set theory isalso used to obtain the rules of touristsrsquo preference To sumupthe fuzzy system theory is scientific advanced and practicaland can also provide correct guidance to our work A newlearning method for automatically deriving fuzzy rules andmembership functions from a given set of training instancesis proposed here as the knowledge acquisition facility [17]Notation and definitions are introduced below

Data preprocess and fuzzy rule establishment areincluded in the fuzzy learning algorithm A set of traininginstances are collected from the environment Our taskhere is to generate automatically reasonable membershipfunctions and appropriate decision rules from these trainingdata so that they can represent important features of thedata set In order to avoid the disturbance of ineffectiveinformation all the data should be preprocessed in advance[18]

The support set of fuzzy set119863 is a crisp set it includes allthe elements in the universe set119880 but the membership valuein119863must be greater than 0 as follows

supp (119863) = 119909 isin 119880 | 120583119863 (119909) gt 0 (2)

The center of a fuzzy set is defined as if the membershipvalues which correspond to fuzzy set119863 from every element insupp(119863) are finite (basically 1 is supposed to be themaximumvalue) In this situation the position of the maximum valueor the medium point of the maximum value is defined as thecenter of the fuzzy set as shown in Figure 1The typical centerof a fuzzy set is shown in Figure 1

Fuzzy set includes all the points in the set 119880 Concerningset 119863 when the membership value is equal to 05 it is thevaguest point


U

120583

D1D2 D3




120572as follows

119863120572= 119909 isin 119880 | 120583

119863 (119909) ge 120572 (3)


1199021is 1198861 (4)









1199021is 1198861and 119902

2is 1198862 (5)


119903 If 1199021is 1198861and 119902

2is 1198862

Then 119910 is 1198871

(6)


1is 1198861and 119902








119876 = 1199021 1199022 119902

119899 (7)





119886119901= 119886119901

1 119886119901

2 119886

119901

119895119901 (8)




119861 = 1198871 1198872 119887

119894 (9)





be as follows

119903ℎ

1 1199021is 11988611199011

and 1199022is 11988621199012


997888rarr 119910 is 119887ℎ

(10)



119877 = 1199031 1199032 119903

119894 (11)


119903ℎ= 119903ℎ

1 119903ℎ

2 119903

ℎ

119896ℎ (12)




119865ℎ= 119886minus1(119887ℎ) 119886minus2(119887ℎ) 119886

minus119899(119887ℎ) (13)


ℎand







141 49 11 201

Percentage 7015 2438 547 10000























Safe

ty

Verygood

Rule 1Satisfied

Rule 3Satisfied

Good Rule 5Neutral

Rule 2Satisfied


Rule 4 Neutral

Verypoor

Rule 6Dissatisfied

Rule 8Dissatisfied


x

x

x

x










































Safe

ty

Verygood

Rule 3Satisfied

Rule 7Satisfied

GoodRule 2

SatisfiedRule 6

Satisfied

Poor

Verypoor


Good

Level of prices

x

x

x

x

x

x

x

x

(a)

Safe

ty

Verygood

Rule 5Satisfied

GoodRule 9Neutral

Rule 4Satisfied

PoorRule 8Neutral

Verypoor

Rule 11Dissatisfied


Good

Level of prices

x

x

x

x

x

x

x

(b)

Safe

ty

Verygood

Good


Verypoor

Rule 10Dissatisfied

Rule 13Dissatisfied


Good

Level of prices

x

x

x

x

x

x

x

x

x

(c)











fety

Verygood

Rule 1Satisfied

Rule 2Satisfied

Rule 5Satisfied

GoodRule 11Neutral

Rule 4Satisfied

PoorRule 6Neutral

Rule 7Neutral

Verypoor

Verypoor

Poor Good Verygood

Level of prices

x

x

x x x x

x

x

x

(a)

Safe

ty

Verygood

GoodRule 10Neutral

PoorRule 13


Rule 11Neutral

Verypoor

Rule 8Neutral

Verypoor

Poor Good Verygood

Level of prices

x

x

x x x

x

xx

x

x

x

(b)

Safe

ty

Verygood

Rule 3Satisfied

Good

Poor

Verypoor

Verypoor

Poor Good Verygood

Level of prices

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

(c)

Safe

ty

Verygood

Good

Poor

Verypoor

Rule 14Dissatisfied

Verypoor

Poor Good Verygood

Level of prices

x

x

x

x

x

x

x

x

x

x

x

x

x

xx

(d)



5 Conclusion











References





























Volume 2014




Journal of











Journal of


Function Spaces






Algebra










U

120583

D1D2 D3




120572as follows

119863120572= 119909 isin 119880 | 120583

119863 (119909) ge 120572 (3)


1199021is 1198861 (4)









1199021is 1198861and 119902

2is 1198862 (5)


119903 If 1199021is 1198861and 119902

2is 1198862

Then 119910 is 1198871

(6)


1is 1198861and 119902








119876 = 1199021 1199022 119902

119899 (7)





119886119901= 119886119901

1 119886119901

2 119886

119901

119895119901 (8)




119861 = 1198871 1198872 119887

119894 (9)





be as follows

119903ℎ

1 1199021is 11988611199011

and 1199022is 11988621199012


997888rarr 119910 is 119887ℎ

(10)



119877 = 1199031 1199032 119903

119894 (11)


119903ℎ= 119903ℎ

1 119903ℎ

2 119903

ℎ

119896ℎ (12)




119865ℎ= 119886minus1(119887ℎ) 119886minus2(119887ℎ) 119886

minus119899(119887ℎ) (13)


ℎand







141 49 11 201

Percentage 7015 2438 547 10000























Safe

ty

Verygood

Rule 1Satisfied

Rule 3Satisfied

Good Rule 5Neutral

Rule 2Satisfied


Rule 4 Neutral

Verypoor

Rule 6Dissatisfied

Rule 8Dissatisfied


x

x

x

x










































Safe

ty

Verygood

Rule 3Satisfied

Rule 7Satisfied

GoodRule 2

SatisfiedRule 6

Satisfied

Poor

Verypoor


Good

Level of prices

x

x

x

x

x

x

x

x

(a)

Safe

ty

Verygood

Rule 5Satisfied

GoodRule 9Neutral

Rule 4Satisfied

PoorRule 8Neutral

Verypoor

Rule 11Dissatisfied


Good

Level of prices

x

x

x

x

x

x

x

(b)

Safe

ty

Verygood

Good


Verypoor

Rule 10Dissatisfied

Rule 13Dissatisfied


Good

Level of prices

x

x

x

x

x

x

x

x

x

(c)











fety

Verygood

Rule 1Satisfied

Rule 2Satisfied

Rule 5Satisfied

GoodRule 11Neutral

Rule 4Satisfied

PoorRule 6Neutral

Rule 7Neutral

Verypoor

Verypoor

Poor Good Verygood

Level of prices

x

x

x x x x

x

x

x

(a)

Safe

ty

Verygood

GoodRule 10Neutral

PoorRule 13


Rule 11Neutral

Verypoor

Rule 8Neutral

Verypoor

Poor Good Verygood

Level of prices

x

x

x x x

x

xx

x

x

x

(b)

Safe

ty

Verygood

Rule 3Satisfied

Good

Poor

Verypoor

Verypoor

Poor Good Verygood

Level of prices

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

(c)

Safe

ty

Verygood

Good

Poor

Verypoor

Rule 14Dissatisfied

Verypoor

Poor Good Verygood

Level of prices

x

x

x

x

x

x

x

x

x

x

x

x

x

xx

(d)



5 Conclusion











References





























Volume 2014




Journal of











Journal of


Function Spaces






Algebra












be as follows

119903ℎ

1 1199021is 11988611199011

and 1199022is 11988621199012


997888rarr 119910 is 119887ℎ

(10)



119877 = 1199031 1199032 119903

119894 (11)


119903ℎ= 119903ℎ

1 119903ℎ

2 119903

ℎ

119896ℎ (12)




119865ℎ= 119886minus1(119887ℎ) 119886minus2(119887ℎ) 119886

minus119899(119887ℎ) (13)


ℎand







141 49 11 201

Percentage 7015 2438 547 10000























Safe

ty

Verygood

Rule 1Satisfied

Rule 3Satisfied

Good Rule 5Neutral

Rule 2Satisfied


Rule 4 Neutral

Verypoor

Rule 6Dissatisfied

Rule 8Dissatisfied


x

x

x

x










































Safe

ty

Verygood

Rule 3Satisfied

Rule 7Satisfied

GoodRule 2

SatisfiedRule 6

Satisfied

Poor

Verypoor


Good

Level of prices

x

x

x

x

x

x

x

x

(a)

Safe

ty

Verygood

Rule 5Satisfied

GoodRule 9Neutral

Rule 4Satisfied

PoorRule 8Neutral

Verypoor

Rule 11Dissatisfied


Good

Level of prices

x

x

x

x

x

x

x

(b)

Safe

ty

Verygood

Good


Verypoor

Rule 10Dissatisfied

Rule 13Dissatisfied


Good

Level of prices

x

x

x

x

x

x

x

x

x

(c)











fety

Verygood

Rule 1Satisfied

Rule 2Satisfied

Rule 5Satisfied

GoodRule 11Neutral

Rule 4Satisfied

PoorRule 6Neutral

Rule 7Neutral

Verypoor

Verypoor

Poor Good Verygood

Level of prices

x

x

x x x x

x

x

x

(a)

Safe

ty

Verygood

GoodRule 10Neutral

PoorRule 13


Rule 11Neutral

Verypoor

Rule 8Neutral

Verypoor

Poor Good Verygood

Level of prices

x

x

x x x

x

xx

x

x

x

(b)

Safe

ty

Verygood

Rule 3Satisfied

Good

Poor

Verypoor

Verypoor

Poor Good Verygood

Level of prices

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

(c)

Safe

ty

Verygood

Good

Poor

Verypoor

Rule 14Dissatisfied

Verypoor

Poor Good Verygood

Level of prices

x

x

x

x

x

x

x

x

x

x

x

x

x

xx

(d)



5 Conclusion











References





























Volume 2014




Journal of











Journal of


Function Spaces






Algebra






















Safe

ty

Verygood

Rule 1Satisfied

Rule 3Satisfied

Good Rule 5Neutral

Rule 2Satisfied


Rule 4 Neutral

Verypoor

Rule 6Dissatisfied

Rule 8Dissatisfied


x

x

x

x










































Safe

ty

Verygood

Rule 3Satisfied

Rule 7Satisfied

GoodRule 2

SatisfiedRule 6

Satisfied

Poor

Verypoor


Good

Level of prices

x

x

x

x

x

x

x

x

(a)

Safe

ty

Verygood

Rule 5Satisfied

GoodRule 9Neutral

Rule 4Satisfied

PoorRule 8Neutral

Verypoor

Rule 11Dissatisfied


Good

Level of prices

x

x

x

x

x

x

x

(b)

Safe

ty

Verygood

Good


Verypoor

Rule 10Dissatisfied

Rule 13Dissatisfied


Good

Level of prices

x

x

x

x

x

x

x

x

x

(c)











fety

Verygood

Rule 1Satisfied

Rule 2Satisfied

Rule 5Satisfied

GoodRule 11Neutral

Rule 4Satisfied

PoorRule 6Neutral

Rule 7Neutral

Verypoor

Verypoor

Poor Good Verygood

Level of prices

x

x

x x x x

x

x

x

(a)

Safe

ty

Verygood

GoodRule 10Neutral

PoorRule 13


Rule 11Neutral

Verypoor

Rule 8Neutral

Verypoor

Poor Good Verygood

Level of prices

x

x

x x x

x

xx

x

x

x

(b)

Safe

ty

Verygood

Rule 3Satisfied

Good

Poor

Verypoor

Verypoor

Poor Good Verygood

Level of prices

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

(c)

Safe

ty

Verygood

Good

Poor

Verypoor

Rule 14Dissatisfied

Verypoor

Poor Good Verygood

Level of prices

x

x

x

x

x

x

x

x

x

x

x

x

x

xx

(d)



5 Conclusion











References





























Volume 2014




Journal of











Journal of


Function Spaces






Algebra













































Safe

ty

Verygood

Rule 3Satisfied

Rule 7Satisfied

GoodRule 2

SatisfiedRule 6

Satisfied

Poor

Verypoor


Good

Level of prices

x

x

x

x

x

x

x

x

(a)

Safe

ty

Verygood

Rule 5Satisfied

GoodRule 9Neutral

Rule 4Satisfied

PoorRule 8Neutral

Verypoor

Rule 11Dissatisfied


Good

Level of prices

x

x

x

x

x

x

x

(b)

Safe

ty

Verygood

Good


Verypoor

Rule 10Dissatisfied

Rule 13Dissatisfied


Good

Level of prices

x

x

x

x

x

x

x

x

x

(c)











fety

Verygood

Rule 1Satisfied

Rule 2Satisfied

Rule 5Satisfied

GoodRule 11Neutral

Rule 4Satisfied

PoorRule 6Neutral

Rule 7Neutral

Verypoor

Verypoor

Poor Good Verygood

Level of prices

x

x

x x x x

x

x

x

(a)

Safe

ty

Verygood

GoodRule 10Neutral

PoorRule 13


Rule 11Neutral

Verypoor

Rule 8Neutral

Verypoor

Poor Good Verygood

Level of prices

x

x

x x x

x

xx

x

x

x

(b)

Safe

ty

Verygood

Rule 3Satisfied

Good

Poor

Verypoor

Verypoor

Poor Good Verygood

Level of prices

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

(c)

Safe

ty

Verygood

Good

Poor

Verypoor

Rule 14Dissatisfied

Verypoor

Poor Good Verygood

Level of prices

x

x

x

x

x

x

x

x

x

x

x

x

x

xx

(d)



5 Conclusion











References





























Volume 2014




Journal of











Journal of


Function Spaces






Algebra



























Safe

ty

Verygood

Rule 3Satisfied

Rule 7Satisfied

GoodRule 2

SatisfiedRule 6

Satisfied

Poor

Verypoor


Good

Level of prices

x

x

x

x

x

x

x

x

(a)

Safe

ty

Verygood

Rule 5Satisfied

GoodRule 9Neutral

Rule 4Satisfied

PoorRule 8Neutral

Verypoor

Rule 11Dissatisfied


Good

Level of prices

x

x

x

x

x

x

x

(b)

Safe

ty

Verygood

Good


Verypoor

Rule 10Dissatisfied

Rule 13Dissatisfied


Good

Level of prices

x

x

x

x

x

x

x

x

x

(c)











fety

Verygood

Rule 1Satisfied

Rule 2Satisfied

Rule 5Satisfied

GoodRule 11Neutral

Rule 4Satisfied

PoorRule 6Neutral

Rule 7Neutral

Verypoor

Verypoor

Poor Good Verygood

Level of prices

x

x

x x x x

x

x

x

(a)

Safe

ty

Verygood

GoodRule 10Neutral

PoorRule 13


Rule 11Neutral

Verypoor

Rule 8Neutral

Verypoor

Poor Good Verygood

Level of prices

x

x

x x x

x

xx

x

x

x

(b)

Safe

ty

Verygood

Rule 3Satisfied

Good

Poor

Verypoor

Verypoor

Poor Good Verygood

Level of prices

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

(c)

Safe

ty

Verygood

Good

Poor

Verypoor

Rule 14Dissatisfied

Verypoor

Poor Good Verygood

Level of prices

x

x

x

x

x

x

x

x

x

x

x

x

x

xx

(d)



5 Conclusion











References





























Volume 2014




Journal of











Journal of


Function Spaces






Algebra










Safe

ty

Verygood

Rule 3Satisfied

Rule 7Satisfied

GoodRule 2

SatisfiedRule 6

Satisfied

Poor

Verypoor


Good

Level of prices

x

x

x

x

x

x

x

x

(a)

Safe

ty

Verygood

Rule 5Satisfied

GoodRule 9Neutral

Rule 4Satisfied

PoorRule 8Neutral

Verypoor

Rule 11Dissatisfied


Good

Level of prices

x

x

x

x

x

x

x

(b)

Safe

ty

Verygood

Good


Verypoor

Rule 10Dissatisfied

Rule 13Dissatisfied


Good

Level of prices

x

x

x

x

x

x

x

x

x

(c)











fety

Verygood

Rule 1Satisfied

Rule 2Satisfied

Rule 5Satisfied

GoodRule 11Neutral

Rule 4Satisfied

PoorRule 6Neutral

Rule 7Neutral

Verypoor

Verypoor

Poor Good Verygood

Level of prices

x

x

x x x x

x

x

x

(a)

Safe

ty

Verygood

GoodRule 10Neutral

PoorRule 13


Rule 11Neutral

Verypoor

Rule 8Neutral

Verypoor

Poor Good Verygood

Level of prices

x

x

x x x

x

xx

x

x

x

(b)

Safe

ty

Verygood

Rule 3Satisfied

Good

Poor

Verypoor

Verypoor

Poor Good Verygood

Level of prices

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

(c)

Safe

ty

Verygood

Good

Poor

Verypoor

Rule 14Dissatisfied

Verypoor

Poor Good Verygood

Level of prices

x

x

x

x

x

x

x

x

x

x

x

x

x

xx

(d)



5 Conclusion











References





























Volume 2014




Journal of











Journal of


Function Spaces






Algebra










fety

Verygood

Rule 1Satisfied

Rule 2Satisfied

Rule 5Satisfied

GoodRule 11Neutral

Rule 4Satisfied

PoorRule 6Neutral

Rule 7Neutral

Verypoor

Verypoor

Poor Good Verygood

Level of prices

x

x

x x x x

x

x

x

(a)

Safe

ty

Verygood

GoodRule 10Neutral

PoorRule 13


Rule 11Neutral

Verypoor

Rule 8Neutral

Verypoor

Poor Good Verygood

Level of prices

x

x

x x x

x

xx

x

x

x

(b)

Safe

ty

Verygood

Rule 3Satisfied

Good

Poor

Verypoor

Verypoor

Poor Good Verygood

Level of prices

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

(c)

Safe

ty

Verygood

Good

Poor

Verypoor

Rule 14Dissatisfied

Verypoor

Poor Good Verygood

Level of prices

x

x

x

x

x

x

x

x

x

x

x

x

x

xx

(d)



5 Conclusion











References





























Volume 2014




Journal of











Journal of


Function Spaces






Algebra












References





























Volume 2014




Journal of











Journal of


Function Spaces






Algebra
















Volume 2014




Journal of











Journal of


Function Spaces






Algebra









Documents

Research Article The Development of a Tourism Attraction ...downloads.hindawi.com/journals/mpe/2015/643842.pdf · tourist attraction, a town, a certain region in a country, an entire