Improving the resident–tourist relationship in urban hotspots

Abstract

High volumes of tourists often pose a threat to tourism and decrease the quality of life for local residents, particularly in attractive urban tourism places. Yet, to date only a few solution-oriented studies have attempted to alleviate the overtourism problems and to improve the resident-tourist relationship. This study aims to present potential solutions, based on data analytics. Combining venue-referenced social media data with topic modelling from a case study in Paris, this research reveals both similarities and differences in the temporal and spatial activity patterns of tourists and residents. Results offer strategic support to tourism planners on how to manage over-crowded urban tourism hotspots, which consequently facilitate the improvement of the resident–tourist relationship and improve destination attractiveness in the long run. Results further indicate that the exchange of social media-based information for residents and tourists are part of the practice-based solution for better sustainable tourism planning.

Keywords:

• Urban tourism hotspots sustainable tourism planning
• social media data
• data analytics
• topic modelling
• venue check-ins

Acknowledgement

The work was completed when Gang Li was on ASL in Chinese Academy of Sciences. We would also like to thank Deakin University's ASL fund and Xinjiang research fund with Chinese Academy of Sciences, the work is also partially supported by National Natural Foundation of China (71871090).

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This paper and research project (Project Account Code: ZJLQ) is funded by Research Grant of Hospitality and Tourism Research Centre (HTRC Grant) of the School of Hotel and Tourism Management, The Hong Kong Polytechnic University.

Notes on contributors

Huy Quan Vu

Huy Quan Vu is a senior lecturer at the Business school of Deakin University. His main interests are tourists behaviour analysis, tourism/hospitality data mining, and business intelligence.

Birgit Muskat

Birgit Muskat is a senior lecturer at the Research School of Management, Australian National University. Her main interests are Entrepreneurship, knowledge transfer, and innovation behaviour and experience management.

Gang Li

Gang Li is an associate professor at the school of information technology, Deakin University. His research interests are artificial intelligence, data privacy and tourism/hospitality data mining.

Rob Law

Rob Law is a professor at the school of hotel & tourism management, the Hong Kong Polytechnic University. His research interests are technology applications to tourism and information management.