Exploring urban tourism crowding in Shanghai via crowdsourcing geospatial data

Abstract

Urban tourism is booming and, as a result, crowding is now recognized as a social constraint in many tourist cities. When related to sustainability, tourism crowding must be considered. However, the way tourists experience crowding is still a neglected topic in urban tourism research. In this study, we proposed a new approach to exploit tourism crowding from crowdsourcing geospatial data which goes beyond the scale, timeliness, and cost of traditional on-site questionnaire surveys. The new approach is based on analysis of 446,273 ‘check-in’ geotagged data from Weibo in Shanghai. The data provided a hotspot distribution of popular urban tourist attractions and a range of factors related to tourism crowding. These data provided deep insights into the relationship between crowdedness and popularity of tourist attractions. This empirical work can be extended to urban tourism crowding management environments for sustainable development of tourist attractions.

Keywords:

• tourism crowding
• crowdsourcing geospatial data
• urban tourism
• exploratory spatial data analysis
• popularity of tourist attractions

Additional information

Funding

This work was supported by Innovation Program of Shanghai Municipal Education Commission [14ZZ172]; Scholar Program of Shanghai Institute of Tourism [RS2015-B4].