Abstract
Tourism is one of the most economically important industries. It is, however, vulnerable to disaster events. Geotagged social media data, as one of the forms of volunteered geographic information (VGI), has been widely explored to support the prevention, preparation, and response phases of disaster management, while little effort has been put on the recovery phase. This study develops a scientific workflow and methods to monitor and assess post-disaster tourism recovery using geotagged Flickr photos, which involve a viewshed based data quality enhancement, a space-time bin based quantitative photo analysis, and a crowdsourcing based qualitative photo analysis. The developed workflow and methods have also been demonstrated in this paper through a case study conducted for the Philippines where a magnitude 7.2 earthquake (Bohol earthquake) and a super typhoon (Haiyan) occurred successively in October and November 2013. In the case study, we discovered spatiotemporal knowledge about the post-disaster tourism recovery, including the recovery statuses and trends, and the photos visually showing unfixed damages. The findings contribute to a better tourism rehabilitation of the study area.
Highlights
In recent years, geotagged social media data, as one of the forms of volunteered geographic information (VGI) [1], has been put forward to the research frontier with regard to disaster management [2]
Not long after the disasters, there was effort to boost the tourism of the top destinations (e.g., Cebu and Bohol) by ensuring that they remained open for business with their respective ports of entry still accessible to tourists [47]
Taking the Flickr photos contributed by the non-locals in our case study as an example, it is hard to determine whether those non-local photo contributors are pure tourists or photographers who are less representative for the local tourism
Summary
In recent years, geotagged social media data, as one of the forms of volunteered geographic information (VGI) [1], has been put forward to the research frontier with regard to disaster management [2]. Such data are of the merits of being rich in data coverage and volume, cost-effective, and timely, which has been widely explored for the prevention, preparation, and response phases of the disaster management cycle [3,4]. Spatiotemporal characteristics of the tourism recovery in the study area have been discovered
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