Coupling maximum entropy modeling with geotagged social media data to determine the geographic distribution of tourists
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...
Gespeichert in:
| Hauptverfasser: | , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
20 Apr 2018
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| In: |
International journal of geographical information science
Year: 2018, Jahrgang: 32, Heft: 9, Pages: 1699-1736 |
| ISSN: | 1365-8824 |
| DOI: | 10.1080/13658816.2018.1458989 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1080/13658816.2018.1458989
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| Verfasserangaben: | Yingwei Yan, Chiao-Ling Kuo, Chen-Chieh Feng, Wei Huang, Hongchao Fan & Alexander Zipf |
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| 520 | |a 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. This 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. | ||
| 650 | 4 | |a disaster and crisis management | |
| 650 | 4 | |a Geotagged social media data | |
| 650 | 4 | |a maximum entropy modeling | |
| 650 | 4 | |a tourist geographic distribution | |
| 650 | 4 | |a volunteered geographic information | |
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