A Method of selecting potential development regions based on GPS and social network models – from the perspective of tourist behavior

ABSTRACT

Research into the sustainable development of scenic regions has drawn more interest from researchers even though it has been suggested that the field needs to consider the spatial–temporal behavioral characteristics of tourists. However, few investigations within the extant literature focus on intra-attraction based tourist behavior; indeed, current research only examines GPS visualization problems without exploring deeper applications for the development of scenic regions. In this paper, we propose a new method to select potential development regions based on the characteristic of tourist flow structures and the relationships between scenic regions supported by GPS and social network models. We choose Gulangyu (a world famous heritage site in Xiamen, China) as our study case. The GPS trajectories of 312 tourists have been recorded and the social network centrality index of 54 regions in Gulangyu have been calculated. Based on tourists’ spatial–temporal behavior characteristics and the relationships between scenic regions, we select two types of ‘potential development regions’; these, are those ‘potential development regions’ near core scenic spots and those with clustering characteristics. The main contribution of our proposed method lies in linking GPS techniques with social network models to support deeper forms of quantitative spatial analysis in tourism research.

KEYWORD:

• GPS
• social network model
• intra-attraction
• tourist behavior
• Gulangyu Island
• tourism marketing
• Asia Pacific

Acknowledgements

The authors acknowledge the financial support of the National Natural Science Foundation of China (NO. 41671141), Fujian Natural Science Foundation (NO. 2015J01226), the Project for Special Funding provided by the Fund for Scientific Research in Colleges and Universities (NO. 20720170046), the project of Xiamen Engineering Technology for Intelligent Maintenance of Infrastructures (TCIMI201805) and the project of Xiamen science and Technology Bureau (3502Z20183005). We are grateful to the Editor and anonymous referees for their valuable comments and guidance.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The authors acknowledge the financial support of the National Natural Science Foundation of China (grant number 41671141), Fujian Natural Science Foundation (grant number 2015J01226), the Project for Special Funding provided by the Fund for Scientific Research in Colleges and Universities (grant number 20720170046), the project of Xiamen Engineering Technology for Intelligent Maintenance of Infrastructures (grant number TCIMI201805) and the project of Xiamen science and Technology Bureau (grant number 3502Z20183005).