Distribution and Determining of Tourist Attractions and Modeling of Tourist Cities for the City of Isfahan-Iran

American Journal of Economics and Business Administration 1 (2): 160-166, 2009 ISSN 1945-5488 © 2009 Science Publications Distribution and Determining of Tourist Attractions and Modeling of Tourist Cities for the City of Isfahan-Iran Seifolddini-Faranak, M. Shabani Fard and Hosseini Ali Department of Geography and Urban Planning, University of Tehran, Iran Abstract: Problem statement: Cities have different attractions and usually have tourists. But Management and development sustainable tourist activities needs planning. Approach: This research used survey method and taxonomy analysis to study the spatial pattern of tourist attractions in Isfahan city. Isfahan city is chosen because of numerous tourist attractions and its importance at national and regional level and also its multi role. The research was done from March to November 2008. Results: The result of statistical analysis shows that tourist attractions in Isfahan can be classified into four groups. Conclusion: On the basis of the result of study, the spatial pattern of tourist attractions is obtained that can be used for the management and planning based on the result a spatial model of tourism consumption in large cities was developed. Key words: Tourist attractions, spatial tourist, spatial patterns, spatial distribution, Isfahan INTRODUCTION Table 1: Primary elements Activity place Leisure setting Cities have always been major destinations for Cultural facilities Physical characteristics tourists. The increase in the number of short trips to the Museums and art galleries Historical street pattern cities shows that these destinations are one of the major Theaters and cinemas Interesting buildings [5-10] Concert halls Ancients monuments and statues tourist attractions . Convention centers Parks and green areas Cities in developing countries have multi- Other visitor attractions Waterfronts (harbor, canal, river) functions. Cities in developing countries are the gates Sport facilities Socio-cultural features for the entrance to the country, centers for staying and Indoor and outdoor Liveliness of the place origins for the trips to rural areas and other tourist Amusement facilities Language attractions. Cities have important role for tourist Night clubs Local customs and costumes [2-21] Casinos and bingo halls Cultural heritage attractions . Organized events Friendliness To understand tourism in cities, we need to Festivals Security consider cities as a product of tourism; a container that Secondary elements Additional elements [22-28] Accommodation Accessibility includes human activities and tourism activities Catering facilities Transportation and parking has classified tourist centers into following elements Shopping Tourist information (maps, (Table 1). Markets sings, guides) Tourism has become an important economic source for planners and authorities in the field of city planning. In this research, tourist attractions and their spatial It is one of the sources of competition in terms of distribution are analyzed on the basis of indicators that investments, priority of goals, spatial organization of are associated directly with tourist activities. Taxonomy tourist spaces and establishing suitable commercial analysis is used to classify tourist attractions into homogenous clusters [27] . structure for tourist activities [6-18]. The important The case study for this study is Isfahan city which activity in planning and development of tourist [8,9] is the center of Isfahan province. Isfahan is an activities is their classification and prioritization . important asset and unique in terms of existence of In this research, classification of tourist attractions historical, cultural and religious buildings in Iran, and their spatial pattern are determined on the basis of middle East and Isfahan has an important industrial demand for tourists. There was a need to define the and cultural role at province and national level. indicators of demand and supply for tourist activities. Isfahan is well known as tourist pole and as a multi To apply evaluation methods and to use taxonomy. function city [15]. Corresponding Author: Seifolddini- Faranak, Department of Geography and Urban Planning, University of Tehran, No. 309, Azin.Al, Ghods .St, Enghelab.Ave, Theran, Iran 160 Am. J. of Economics and Business Administration, 1 (2): 160-166, 2009 Literature review: The tourism literature has increased space use patterns of urban tourists, using the historic in the past few decades. city of Leuven in Belgium as a pilot study. Studies have been done in the field of consumption Yet another study focusing on the differential and spatial distribution of tourists on the basis of consumption of tourist sights is [3] work on groups of variables and different methods. Christian pilgrims of different denominations in [29] Study by Raveh and Shoval studied Jerusalem Jerusalem. Bowman’s work was followed up by and Tel Aviv. The percentage of visit, average length of Shachar and Shoval [29] who identified segmented tourist stay, average number of visits was used to classify spaces based on the different national and religious tourist attractions. On the basis of these variables, four groups visiting the city. More recently, Shoval [32] re- classes of tourist attractions were obtained [13-29]. [4] examined their findings using a statistical model Cooper , who investigated the spatial behavior of consisting of 10 variables representing tourist and trip tourists on the Channel Island of Jersey, identified characteristics of visitors to Jerusalem. He concluded differences in the spatial patterns of tourists according that religious difference was only one of the reasons to two variables: stage in Life cycle and socio- explaining the differential consumption of tourists in economic status. He found, that low-income tourists tended to visit only the major tourist attractions, Jerusalem and that the most influential variables whereas higher income tourists visited lower-order (less explaining the spatial consumption of individual visited) attractions, as well. Chadefaud (1981) tourists are those related to the character of the trip, investigated the time–space patterns of pilgrims and such as, length of stay in the city, main purpose of visit tourists to Lourdes and presented detailed maps and number of visits to the city. showing the activity spaces of organized groups and In summary, the literature are not abundant with individual tourists; the former were more concentrated researches on the subject of this study and the existing and the latter were more dispersed. Chadefaud offers ones in most cases did not analyze the situations in two explanations for this finding: (1) tourists in large and multifunctional tourist cities. organized groups tend to be older and, therefore, it is harder for them to explore the city by foot and (2) MATERIALS AND METHODS organized groups tend to consist [24] More of pilgrims whereas a larger share of the individual visitors are Survey method was used to collect data. The tourists and not just pilgrims. Debbage [11] , who data for this study was collected from inbound examined the spatial behavior of tourists in a resort in individuals tourists who had visited the city of Isfahan the Bahamas basing himself on Plog’s [25] tourist in Iran, using questionnaires. The questionnaires were typology, found that differences in the spatial behavior administered before they left of the hotels and tourist of tourists resulted from differences in their personality center’s, information were used. The questionnaires structure [25] Prentice [24] found that the more affluent were filled between March to September 2008. Five sectors of English society are more highly represented hundred tourists were the sample of study. Each among visitors to heritage attractions and, in general, questionnaire included a list of the tourist sights visited [25] the visitors to these attractions tend to be older . Light in city, along with a section covering their personal and Prentice (1994) reported similar findings regarding details and trip characteristics (Table 2). Also tourist heritage attractions in Wales [26,7] . [3] attractions were defined; Taxonomy model is one of the Dietvorst , in his study of Enkhuizen, a small ranking methods used in different fields. This method historic town in the Netherlands, identified several has been since 18 the century [19-30] Recently, using this distinct tourist spaces based on differences in the main model has become common. According to a number of purpose of the visit to the town [3]. Montanari and indicators, The Method classifies data into homogenous Muscar"a [28] . outlined nine typical time-space profiles classes [1-17] . of tourists to Venice derived from a mix of the main Taxononomy method is one of the methods of purpose of their visit as well as other trip characteristics such as length of stay and previous visits to the city. classification for planning and decision making. This Pearce [7] analyzed the characteristics, structure and method is able to classify data on the basis of functioning of three tourist districts in Paris. In his indicators. Using these data, homogenous clusters can analysis he presented the differences of visitor patterns be defined and classified. between international to French tourists. Jansen- In this research, to classify tourists, tourists, (n) Verbeke and Lievois [20] highlighted both the theoretical tourist attractions which have (m) indicators were and applied potential of the analysis of different time- defined: 161 Am. J. of Economics and Business Administration, 1 (2): 160-166, 2009 Table 2: Characteristics of respondents x11 x 12 x 13 ..... x 1m Variable Classifications Numbers Percentage x x x ..... x 21 22 23 2m Length of 1-3 days 102 25.50 xx= x x ..... x stay 4-7 days 175 43.75 31 32 33 3m M More than 8 days 123 30.75 Purpose Recreation visiting friends 226 56.50 xn1 x n2 x n3 ..... x nm of visit and relatives 80 20.00 Work-business 76 19.00 In the above matrix, every column n×m is related Others 19 4.50 to one observation and m indicators. Sender Male 260 65.00 Female 140 35.00 In the second stage table of standardized data are Education Elementary 71 17.75 formed.

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