Designing tourist experiences amidst air pollution: A spatial analytical approach using social media

In this study, we propose a spatial analytical framework to better understand tourist experiences from geotagged social media data in Beijing in 2013. Based on text analytics, deep learning classifiers, and econometric analysis, we investigated the effects of air pollution on tourists' experiences in terms of their behavioral, emotional, and health outcomes. Results indicate that a higher PM2.5 concentration led to a broader travel scope within Beijing with activities closer to the city center. Tourists reported fewer positive sentiments and more health issues due to increasing air pollution. Further, a comparison of residents and tourists revealed differential pollution sensitivity and adaptation strategies. We also developed a Web-GIS–based platform integrating various models to enable tourism planners to design better tourism experiences.