Coupling maximum entropy modeling with geotagged social media data to determine the geographic distribution of tourists

International Journal of Geographical Information Science

Volumn 32, Issue 9, 2018, Pages 1699-1736

  Yan, Yingwei ^a   Kuo, Chiao Ling ^b   Feng, Chen Chieh ^c   Huang, Wei ^a   Fan, Hongchao ^d   Zipf, Alexander ^a  

^a UNIVERSITY OF HEIDELBERG   (Germany)

^b RESEARCH CENTER FOR HUMANITIES AND SOCIAL SCIENCES   (Taiwan)

^c NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

^d WUHAN UNIVERSITY   (China)

Author keywords

disaster and crisis management; Geotagged social media data; maximum entropy modeling; tourist geographic distribution; volunteered geographic information

Indexed keywords

CRISIS MANAGEMENT; DISASTER MANAGEMENT; GEOGRAPHICAL DISTRIBUTION; GIS; MAXIMUM ENTROPY ANALYSIS; SOCIAL MEDIA; TOURIST DESTINATION;

CALIFORNIA; SAN DIEGO; UNITED STATES;

EID: 85045750759     PISSN: 13658816     EISSN: 13623087     Source Type: Journal    
DOI: 10.1080/13658816.2018.1458989     Document Type: Article

References (51)

1. Avraham, E., 2015. Destination image repair during crisis: attracting tourism during the Arab Spring uprisings. Tourism Management, 47, 224–232. doi:10.1016/j.tourman.2014.10.003
2. Bakillah, M., Li, R.-Y., and Liang, S.H.L., 2015. Geo-located community detection in Twitter with enhanced fast-greedy optimization of modularity: the case study of typhoon Haiyan. International Journal of Geographical Information Science, 29 (2), 258–279. doi:10.1080/13658816.2014.964247
3. Bellard, C., et al., 2013. Will climate change promote future invasions? Global Change Biology, 19 (12), 3740–3748. doi:10.1111/gcb.12344
4. Bidinger, K., et al., 2012. Species distribution models for the alien invasive Asian Harlequin ladybird (Harmonia axyridis). Journal of Applied Entomology, 136 (1–2), 109–123. doi:10.1111/j.1439-0418.2010.01598.x
5. Cervone, G., et al., 2016. Using Twitter for tasking remote-sensing data collection and damage assessment: 2013 Boulder flood case study. International Journal of Remote Sensing, 37 (1), 100–124. doi:10.1080/01431161.2015.1117684
6. Chang, Y.-C., and Mastrangelo, C., 2011. Addressing multicollinearity in semiconductor manufacturing. Quality and Reliability Engineering International, 27 (6), 843–854. doi:10.1002/qre.1173
7. Cioccio, L., and Michael, E.J., 2007. Hazard or disaster: tourism management for the inevitable in Northeast Victoria. Tourism Management, 28 (1), 1–11. doi:10.1016/j.tourman.2005.07.015
8. Crooks, A., et al., 2013. #Earthquake: Twitter as a distributed sensor system. Transactions in GIS, 17 (1), 124–147. doi:10.1111/j.1467-9671.2012.01359.x
9. Cutter, S.L., Ash, K.D., and Emrich, C.T., 2014. The geographies of community disaster resilience. Global Environmental Change, 29, 65–77. doi:10.1016/j.gloenvcha.2014.08.005
10. de Albuquerque, J.P., et al., 2015. A geographic approach for combining social media and authoritative data towards identifying useful information for disaster management. International Journal of Geographical Information Science, 29 (4), 667–689. doi:10.1080/13658816.2014.996567
11. de Bruijn, J.A., et al., 2018. TAGGS: grouping tweets to improve global geoparsing for disaster response. Journal of Geovisualization and Spatial Analysis, 2 (1), 2. doi:10.1007/s41651-017-0010-6
12. Dunbar, P.K., and Weaver, C.S., 2008. US States and Territories national tsunami hazard assessment: historical record and sources for waves. Washington, DC, USA: US Department of Commerce, National Oceanic and Atmospheric Administration.
13. Elith, J., et al., 2006. Novel methods improve prediction of species’ distributions from occurrence data. Ecography, 29 (2), 129–151. doi:10.1111/j.2006.0906-7590.04596.x
14. Elith, J., et al., 2011. A statistical explanation of MaxEnt for ecologists. Diversity and Distributions, 17 (1), 43–57. doi:10.1111/j.1472-4642.2010.00725.x
15. Fohringer, J., et al., 2015. Social media as an information source for rapid flood inundation mapping. Natural Hazards and Earth System Sciences, 15 (12), 2725–2738. doi:10.5194/nhess-15-2725-2015
16. Fourcade, Y., et al., 2014. Mapping species distributions with MAXENT using a geographically biased sample of presence data: a performance assessment of methods for correcting sampling bias. Plos One, 9 (5), e97122. doi:10.1371/journal.pone.0097122
17. Gao, S., Janowicz, K., and Couclelis, H., 2017. Extracting urban functional regions from points of interest and human activities on location-based social networks. Transactions in GIS, 21 (3), 446–467. doi:10.1111/tgis.12289
18. García-Palomares, J.C., Gutiérrez, J., and Mínguez, C., 2015. Identification of tourist hot spots based on social networks: a comparative analysis of European metropolises using photo-sharing services and GIS. Applied Geography, 63 (Supplement C), 408–417. doi:10.1016/j.apgeog.2015.08.002
19. Garnett, R., and Stewart, R., 2015. Comparison of GPS units and mobile Apple GPS capabilities in an urban landscape. Cartography and Geographic Information Science, 42 (1), 1–8. doi:10.1080/15230406.2014.974074
20. Goodchild, M.F., and Glennon, J.A., 2010. Crowdsourcing geographic information for disaster response: a research frontier. International Journal of Digital Earth, 3 (3), 231–241. doi:10.1080/17538941003759255
21. Guo, Q., Kelly, M., and Graham, C.H., 2005. Support vector machines for predicting distribution of Sudden Oak Death in California. Ecological Modelling, 182 (1), 75–90. doi:10.1016/j.ecolmodel.2004.07.012
22. Hernandez, P.A., et al., 2006. The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography, 29 (5), 773–785. doi:10.1111/j.0906-7590.2006.04700.x
23. Hu, Y., et al., 2015. Extracting and understanding urban areas of interest using geotagged photos. Computers, Environment and Urban Systems, 54, 240–254. doi:10.1016/j.compenvurbsys.2015.09.001
24. Jaynes, E.T., 1957. Information theory and statistical mechanics. Physical Review, 106 (4), 620–630. doi:10.1103/PhysRev.106.620
25. Jose, S., et al. 2013. Invasive plant ecology. Boca Raton, London, New York: CRC Press.
26. Li, L., Goodchild, M.F., and Xu, B., 2013. Spatial, temporal, and socioeconomic patterns in the use of Twitter and Flickr. Cartography and Geographic Information Science, 40 (2), 61–77. doi:10.1080/15230406.2013.777139
27. Li, W., and Guo, Q., 2010. A maximum entropy approach to one-class classification of remote sensing imagery. International Journal of Remote Sensing, 31 (8), 2227–2235. doi:10.1080/01431161003702245
28. Lynett, P.J., et al., 2014. Assessment of the tsunami-induced current hazard. Geophysical Research Letters, 41 (6), 2048–2055. doi:10.1002/2013GL058680
29. Mack, B., and Waske, B., 2017. In-depth comparisons of MaxEnt, biased SVM and one-class SVM for one-class classification of remote sensing data. Remote Sensing Letters, 8 (3), 290–299. doi:10.1080/2150704X.2016.1265689
30. Mair, J., Ritchie, B.W., and Walters, G., 2016. Towards a research agenda for post-disaster and post-crisis recovery strategies for tourist destinations: a narrative review. Current Issues in Tourism, 19 (1), 1–26. doi:10.1080/13683500.2014.932758
31. Merow, C., and Silander, J.A., 2014. A comparison of Maxlike and Maxent for modelling species distributions. Methods in Ecology and Evolution, 5 (3), 215–225. doi:10.1111/2041-210X.12152
32. Moffett, A., Shackelford, N., and Sarkar, S., 2007. Malaria in Africa: vector species’ niche models and relative risk maps. Plos One, 2 (9), e824. doi:10.1371/journal.pone.0000824
33. Morakabati, Y., Page, S.J., and Fletcher, J., 2016. Emergency management and tourism stakeholder responses to crises. Journal of Travel Research, 56 (3), 299–316. doi:10.1177/0047287516641516
34. Pawar, S., et al., 2007. Conservation assessment and prioritization of areas in Northeast India: priorities for amphibians and reptiles. Biological Conservation, 136 (3), 346–361. doi:10.1016/j.biocon.2006.12.012
35. Peary, B.D.M., Shaw, R., and Takeuchi, Y., 2012. Utilization of social media in the East Japan earthquake and tsunami and its effectiveness. Journal of Natural Disaster Science, 34 (1), 3–18. doi:10.2328/jnds.34.3
36. Phillips, S.J., Anderson, R.P., and Schapire, R.E., 2006. Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190 (3), 231–259. doi:10.1016/j.ecolmodel.2005.03.026
37. Pouteau, R., et al., 2012. Support vector machines to map rare and endangered native plants in Pacific islands forests. Ecological Informatics, 9 (Supplement C), 37–46. doi:10.1016/j.ecoinf.2012.03.003
38. Qiao, H., et al., 2015. Marble Algorithm: a solution to estimating ecological niches from presence-only records. Scientific Reports, 5, 14232. doi:10.1038/srep14232
39. Rahmati, O., Pourghasemi, H.R., and Melesse, A.M., 2016. Application of GIS-based data driven random forest and maximum entropy models for groundwater potential mapping: a case study at Mehran Region, Iran. Catena, 137, 360–372. doi:10.1016/j.catena.2015.10.010
40. Ritchie, B.W., Mair, J., and Walters, G., 2014. Tourism crises and disasters. In: A.A., Lew, C.M., Hall, and A.M., Williams, eds. The Wiley Blackwell companion to tourism. Chichester, West Sussex, UK: John Wiley & Sons, Ltd, 611–622. doi:10.1002/9781118474648.ch49
41. Rittichainuwat, B.N., 2013. Tourists’ and tourism suppliers’ perceptions toward crisis management on tsunami. Tourism Management, 34, 112–121. doi:10.1016/j.tourman.2012.03.018
42. Steiger, E., Resch, B., and Zipf, A., 2016. Exploration of spatiotemporal and semantic clusters of Twitter data using unsupervised neural networks. International Journal of Geographical Information Science, 30 (9), 1694–1716. doi:10.1080/13658816.2015.1099658
43. Tsai, C.-H., and Chen, C.-W., 2010. An earthquake disaster management mechanism based on risk assessment information for the tourism industry-a case study from the island of Taiwan. Tourism Management, 31 (4), 470–481. doi:10.1016/j.tourman.2009.05.008
44. Wang, -J.-J., 2015. Flood risk maps to cultural heritage: measures and process. Journal of Cultural Heritage, 16 (2), 210–220. doi:10.1016/j.culher.2014.03.002
45. Wiens, J.A., et al., 2009. Niches, models, and climate change: assessing the assumptions and uncertainties. Proceedings of the National Academy of Sciences, 106 (Supplement 2), 19729–19736. doi:10.1073/pnas.0901639106
46. Wisz, M.S., et al., 2008. Effects of sample size on the performance of species distribution models. Diversity and Distributions, 14 (5), 763–773. doi:10.1111/j.1472-4642.2008.00482.x
47. Yan, Y., et al. 2017a. Monitoring and assessing post-disaster tourism recovery using geotagged social media data. ISPRS International Journal of Geo-Information, 6 (5), 144. doi:10.3390/ijgi6050144
48. Yan, Y., et al., 2017b. Potential distributional changes of invasive crop pest species associated with global climate change. Applied Geography, 82, 83–92. doi:10.1016/j.apgeog.2017.03.011
49. Zandbergen, P.A., and Barbeau, S.J., 2011. Positional accuracy of assisted GPS data from high-sensitivity GPS-enabled mobile phones. Journal of Navigation, 64 (3), 381–399. doi:10.1017/S0373463311000051
50. Zhang, L., et al. 2016. Prediction of potential geographic distribution of Lyme disease in Qinghai province with Maximum Entropy model. Chinese Journal of Epidemiology, 37 (1), 94–97. doi:10.3760/cma.j.issn.0254-6450.2016.01.020
51. Zook, M., et al., 2010. Volunteered geographic information and crowdsourcing disaster relief: a case study of the Haitian earthquake. World Medical & Health Policy, 2 (2), 7–33. doi:10.2202/1948-4682.1069