نوع مقاله : مقاله علمی پژوهشی
نویسندگان
1 کارشناسی ارشد جغرافیا و برنامه‌ریزی توریسم، دانشکدة جغرافیا، دانشگاه تهران
2 دانشیار گروه جغرافیا، دانشکدة جغرافیا، دانشگاه تهران
3 استاد گروه جغرافیا، دانشکدة جغرافیا، دانشگاه تهران
چکیده
کلیدواژه‌ها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Introduction
So far, tourism has been viewed from different perspectives and in different fields. Recently, the behaviorists have attracted the attention of tourism development researchers to identify individual characteristics, more than ever. Supply of tourism facilities and equipment that is not based on the understanding the environment will not be satisfy the public. Accordingly, the development of tourism based on the visitor behavior is an effective and desirable solution. Tourism applications are strongly affected by behavioral -activation patterns of communities. Tourist behavior is always observed in time and space, and the study of spatial temporal behavior has become increasingly popular in recent years. Tourist behavior has been studied by researchers from various perspectives. Describing tourist spatial-temporal behavior patterns results in a better understanding of tourism activities. A better understanding of the tourist behavior patterns may, in turn, provide a scientific basis for industry practices, such as attraction management, product renewal and attraction marketing. This study is practically significant for upgrading the facilities and the ultimate improvement of the quality of tourist experiences. Time is considered to be one of the three main constraints on tourism demand. Recently, tourism scholars have paid increasing attention to the effects of time factors on tourist behavior. Time, space and context were considered to be three important domains of tourist experience. The concept of time geography was proposed and developed by the geographer Hagerstrand. Hagerstrand’s time geography offers a useful conceptual framework by which it is possible to study individual activity patterns under various constraints in a space–time context. The space–time path is the core concept of time-geography. It highlights the constraints imposed by activities that are finite in space and time, and the need to trade time for space when moving among activities. The space– time path represents the spatial movements of an individual over time, and offers an effective way of modelling the spatial-temporal characteristics of individual activities. By applying the concept of the space–time path, it is possible to capture and analyze tourist behavior information in both the temporal and spatial dimensions. This helps us improve our understanding about tourism behavior both theoretically and practically.
Methodology
This is a functional study through a heuristic method. This study focuses on the concepts of time geography and collecting data from Visitors of TheCultural and Historical Complex of Saadabad. Graph theory has been used to analyze direct and indirect relationships to depict identified behavior patterns.
This study focuses on visitors temporal-spatial behaviors and tries to recognize the spatial-temporal behavior structure patterns that can be used to update attractions facilities and improve the final quality of the visitor experience in theCultural and Historical Complex of Saadabad and similar attractions across the country. This research seeks to know the spatial- temporal behavior pattern of the visitors to TheCultural and Historical Complex of Saadabad and the factors affecting that.
Results and Discussion
The research results revealed nine clusters of temporal-spatial behavior patterns. The spatial behavior factors had the largest contribution to the clustering analysis. The most popular tourist spots of the Saadabad complex was Mellat Palace located in district C and the Green Palace located in district E, where most of the visitors stood for 30 minutes there. The main activity in the complex was also the visit of the Palace – Museums. To depict identified behavior patterns, in alpha levels of 0, 0.25, 0.5, 0.75 and 1 were assessed and the results of Alpha=0.25 were considered for appropriate behavior patterns.
Conclusion
This study focuses on intra-attraction tourist temporal-spatial behavior patterns and attempts to clarify the patterns using the concept of the space–time path of time geography. TheCultural and Historical Complex of Saadabad has been considered as a case study. Tourist spatial-temporal behavior can be described and clarified by some factors including temporal behavior, spatial behavior, activity choice and path characteristics. After a qualitative reasoning process, the results of the clustering analysis can be presented as visual images. Describing the intra-attraction tourist temporal-spatial behavior patterns can help us to better understand tourist activities and demand among attractions. The research results of the Cultural and Historical Complex of Saadabad revealed nine clusters of temporal-spatial behavior patterns, rather than a homogeneous social group. Spatial behavior factors made the largest contribution to the clustering analysis in this case. The results of the quantitative study suggest that a stay of at least 10 minutes and at most 45 minutes is the threshold for stay time for the identification of a stay point. The results of this study help people get a better understanding of intra-destination tourist behavior patterns in TheCultural and Historical Complex of Saadabad in a more accurate and structural way. A better understanding of tourist behavior patterns should provide a more scientific basis for industry practices, such as location of service, guide identification system and intra-attraction transportations. Therefore, this study has practical significance for the upgrading of attraction facilities and ultimately the improvement of the quality of tourist experiences. In a similar study in China, Huang Xiao-Ting and Wu Bi-Hu identified seven clusters of temporal-spatial behavior patterns in the Summer Palace. In the mentioned study, temporal behavior factors had the largest influence on the clustering analysis.
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