This study was aimed to investigate the development of tourism in Tainan. This study comprised two parts, including an analysis of the number of visitors to Tainan and a network analysis of tourist attractions.
Firstly, based on the statistics released by Tourism Bureau of Tainan City Government, this study explained the changes in the number of visitors to famous tourist attractions in recent years. Results showed that Nankunshen Daitan Temple, Madou Daitan Temple, and Chihkan Tower are the top three tourist attractions in terms of the total number of visitors, suggesting that the religious type of attractions is relatively more popular among tourists. This study further classified tourist attractions into three groups, including religious, artistic, and recreational. Nankunshen Daitan Temple ranked first in the religious group, Chimei Museum was at the top in the artistic group, and Guanziling Hot Spring Area topped the recreational group. In terms of tourism growth, Beimen Visitor Center enjoyed the largest growth in the number of visitors over the past years.
Secondly, this study applied social network analysis to investigate the sightseeing routes recommended by the government. A total of 23 itineraries recommended Tourism Bureau of Tainan City Government were included in the analysis. Using UCINET as the instrument, this study created the social network diagram of tourist attractions and calculated the closeness centrality and betweenness centrality between tourist attractions to understand the representativeness of each tourist attraction of each sightseeing route. Further, this study compared the analysis results with the actual status obtained in the first stage and then proposed suggestions for improvement.
This study utilized UCINET, the most widely adopted software for social network analysis, to draw a relationship network of tourist attractions with data arrays. The relations between tourist attractions were determined based on collected sightseeing routes. In the relationship matrix, 1 indicates presence of a link between two tourist attractions, and 0 indicates absence of a link between two tourist attractions. Based on the matrix, this study used the centrality metric to calculate degree centrality, closeness centrality, and betweenness centrality of each tourist attraction for further discussion and analysis.
The results offered insights into the network connectivity between popular tourist attractions in Tainan City and might be helpful for the public department in allocation of resources and adjustment of suggested itineraries.