Density-based spatial clustering and ordering points approach for characterizations of tourist behaviour
- Author
- Jorge Rodriguez Echeverría (UGent) , Ivana Semanjski (UGent) , Casper Van Gheluwe (UGent) , Daniel Ochoa, Harm IJben and Sidharta Gautama (UGent)
- Organization
- Abstract
- Knowledge about the spots where tourist activity is undertaken, including which segments from the tourist market visit them, is valuable information for tourist service managers. Nowadays, crowdsourced smartphones applications are used as part of tourist surveys looking for knowledge about the tourist in all phases of their journey. However, the representativeness of this type of source, or how to validate the outcomes, are part of the issues that still need to be solved. In this research, a method to discover hotspots using clustering techniques and give to these hotspots a data-driven interpretation is proposed. The representativeness of the dataset and the validation of the results against existing statistics is assessed. The method was evaluated using 124,725 trips, which have been gathered by 1505 devices. The results show that the proposed approach successfully detects hotspots related with the most common activities developed by overnight tourists and repeat visitors in the region under study.
- Keywords
- tourism management, hotspot, crowdsourcing, big data analytics, human mobility, behavioural clustering, clustering evaluation, SEGMENTATION, ALGORITHM, DBSCAN, AREAS
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8680953
- MLA
- Rodriguez Echeverría, Jorge, et al. “Density-Based Spatial Clustering and Ordering Points Approach for Characterizations of Tourist Behaviour.” ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, vol. 9, no. 11, 2020, doi:10.3390/ijgi9110686.
- APA
- Rodriguez Echeverría, J., Semanjski, I., Van Gheluwe, C., Ochoa, D., IJben, H., & Gautama, S. (2020). Density-based spatial clustering and ordering points approach for characterizations of tourist behaviour. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, 9(11). https://doi.org/10.3390/ijgi9110686
- Chicago author-date
- Rodriguez Echeverría, Jorge, Ivana Semanjski, Casper Van Gheluwe, Daniel Ochoa, Harm IJben, and Sidharta Gautama. 2020. “Density-Based Spatial Clustering and Ordering Points Approach for Characterizations of Tourist Behaviour.” ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 9 (11). https://doi.org/10.3390/ijgi9110686.
- Chicago author-date (all authors)
- Rodriguez Echeverría, Jorge, Ivana Semanjski, Casper Van Gheluwe, Daniel Ochoa, Harm IJben, and Sidharta Gautama. 2020. “Density-Based Spatial Clustering and Ordering Points Approach for Characterizations of Tourist Behaviour.” ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 9 (11). doi:10.3390/ijgi9110686.
- Vancouver
- 1.Rodriguez Echeverría J, Semanjski I, Van Gheluwe C, Ochoa D, IJben H, Gautama S. Density-based spatial clustering and ordering points approach for characterizations of tourist behaviour. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION. 2020;9(11).
- IEEE
- [1]J. Rodriguez Echeverría, I. Semanjski, C. Van Gheluwe, D. Ochoa, H. IJben, and S. Gautama, “Density-based spatial clustering and ordering points approach for characterizations of tourist behaviour,” ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, vol. 9, no. 11, 2020.
@article{8680953,
abstract = {{Knowledge about the spots where tourist activity is undertaken, including which segments from the tourist market visit them, is valuable information for tourist service managers. Nowadays, crowdsourced smartphones applications are used as part of tourist surveys looking for knowledge about the tourist in all phases of their journey. However, the representativeness of this type of source, or how to validate the outcomes, are part of the issues that still need to be solved. In this research, a method to discover hotspots using clustering techniques and give to these hotspots a data-driven interpretation is proposed. The representativeness of the dataset and the validation of the results against existing statistics is assessed. The method was evaluated using 124,725 trips, which have been gathered by 1505 devices. The results show that the proposed approach successfully detects hotspots related with the most common activities developed by overnight tourists and repeat visitors in the region under study.}},
articleno = {{686}},
author = {{Rodriguez Echeverría, Jorge and Semanjski, Ivana and Van Gheluwe, Casper and Ochoa, Daniel and IJben, Harm and Gautama, Sidharta}},
issn = {{2220-9964}},
journal = {{ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION}},
keywords = {{tourism management,hotspot,crowdsourcing,big data analytics,human mobility,behavioural clustering,clustering evaluation,SEGMENTATION,ALGORITHM,DBSCAN,AREAS}},
language = {{eng}},
number = {{11}},
pages = {{23}},
title = {{Density-based spatial clustering and ordering points approach for characterizations of tourist behaviour}},
url = {{http://doi.org/10.3390/ijgi9110686}},
volume = {{9}},
year = {{2020}},
}
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