Modeling the geographic distribution of tourists at a tourist destination is crucial when it comes to enhancing the destination’s resilience to disasters and crises, as it enables the efficient allocation of limited resources to precise geographic locations. Seldom have existing studies explored the geographic distribution of tourists through understanding the mechanisms behind it. A recently published article proposes to couple maximum entropy modeling with geotagged social media data to determine the geographic distribution of tourists in order to facilitate disaster and crisis management at tourist destinations. As one of the most popular tourist destinations in the United States, San Diego was chosen as the study area to demonstrate the proposed approach. We modeled the tourist geographic distribution in the study area by quantifying the relationship between the distribution and five environmental factors, including land use, land parcel, elevation, distance to the nearest major road and distance to the nearest transit stop. The geographic distribution’s dependency on and sensitivity to the environmental factors were uncovered. The model was subsequently applied to estimate the potential impacts of one simulated tsunami disaster and one simulated traffic breakdown due to crisis events such as a political protest or a fire hazard. As such, the effectiveness of the approach has been demonstrated with specific disaster and crisis scenarios. The research outcome can facilitate all four phases of touristic disaster and crisis management, i.e. prevention, preparation, response and recovery phases; it can also help to save more lives, and avoid huge economic losses. To demonstrate its effectiveness for the stated purpose, the modeled tourist geographic distribution was applied to one simulated tsunami disaster, as well as one simulated traffic breakdown due to crisis events such as a political protest or a fire hazard. As such, the strength of the approach in estimating the potential impacts of disaster and crisis events has been displayed.
While this study focuses on the two simulated events, the tourist geographic distribution modeling in this study has the potential to support applications other than disaster and crisis management, such as urban planning, tourism marketing and promotion, and tourism planning. It also contributes to the GIS community by presenting a novel way of identifying touristic AOIs. In the future, the proposed approach can be further enhanced by exploring factors which lead to a more refined characterization of tourist geographic distribution; it is also possible to explore ways to take into account the temporal dimension when modeling tourist geographic distribution and to identify better methods to extract tourist users from other sources of social media data, such as Twitter.
(2018) : Coupling maximum entropy modeling with geotagged social media data to determine the geographic distribution of tourists. International Journal of Geographical Information Science (IJGIS), https://doi.org/10.1080/13658816.2018.1458989
Selected recent related earlier work:
- Novack T., R. Peters and A. Zipf (2018): Graph-Based Matching of Points-of-Interest from Collaborative Geo-Datasets. ISPRS Int. J. Geo-Inf. 2018, 7(3), 117; doi:10.3390/ijgi7030117
- Kuo, C.-L., T.C. Chan, I.-C. Fan, A. Zipf (2018): Efficient Method for POI/ROI Discovery Using Flickr Geotagged Photos. ISPRS Int. J. Geo-Inf. 2018, 7(3), 121; doi:10.3390/ijgi7030121.
- Westerholt, R., Steiger, E., Resch, B. and Zipf, A. (2016): Abundant Topological Outliers in Social Media Data and Their Effect on Spatial Analysis. PLOS ONE, 11 (9), e0162360. DOI:10.1371/journal.pone.0162360.
- Jonietz, D., Zipf, A. (2016,): Defining fitness-for-use for crowdsourced points of interest (POI). ISPRS Internat. Journal of Geo-Information. 2016. 5(9), 149; DOI:10.3390/ijgi5090149
- Rousell A. and Zipf A. (2017): Towards a landmark based pedestrian navigation service using OSM data. International Journal of Geo-Information, ISPRS IJGI, 6(3): 64.
- Yan, Y., M. Eckle, C.-L. Kuo, B. Herfort, H. Fan and A. Zipf (2017): Monitoring and Assessing Post-Disaster Tourism Recovery Using Geotagged Social Media Data.International Journal of Geo-Information, ISPRS IJGI. 6(5), 144; doi:10.3390/ijgi6050144
- Li, M., Westerholt, R., Fan, H., Zipf, A. (2016): Assessing spatiotemporal predictability of LBSN: A case study of three Foursquare datasets. GeoInformatica. Volume and issue pending. DOI:10.1007/s10707-016-0279-5.