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Unsupervised hierarchical clustering approach for tourism market segmentation based on crowdsourced mobile phone data

(2018) SENSORS. 18(9).
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Flamenco: FLAnders Mobile Enacted Citizen Observations
Abstract
Understanding tourism related behavior and traveling patterns is an essential element of transportation system planning and tourism management at tourism destinations. Traditionally, tourism market segmentation is conducted to recognize tourist’s profiles for which personalized services can be provided. Today, the availability of wearable sensors, such as smartphones, holds the potential to tackle data collection problems of paper-based surveys and deliver relevant mobility data in a timely and cost-effective way. In this paper, we develop and implement a hierarchical clustering approach for smartphone geo-localized data to detect meaningful tourism related market segments. For these segments, we provide detailed insights into their characteristics and related mobility behavior. The applicability of the proposed approach is demonstrated on a use case in the Province of Zeeland in the Netherlands. We collected data from 1505 users during five months using the Zeeland app. The proposed approach resulted in two major clusters and four sub-clusters which we were able to interpret based on their spatio-temporal patterns and the recurrence of their visiting patterns to the region.
Keywords
tourism management, big data analytics, smartphones, human mobility, behavioural clustering, market segmentation, crowdsourcing, BLUETOOTH TRACKING DATA, RURAL-AREAS, BIG DATA

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Citation

Please use this url to cite or link to this publication:

MLA
Rodriguez Echeverría, Jorge, Ivana Semanjski, Sidharta Gautama, et al. “Unsupervised Hierarchical Clustering Approach for Tourism Market Segmentation Based on Crowdsourced Mobile Phone Data.” Ed. Emanuele Lattanzi & Valerio Freschi. SENSORS 18.9 (2018): n. pag. Print.
APA
Rodriguez Echeverría, J., Semanjski, I., Gautama, S., Van de Weghe, N., & Ochoa, D. (2018). Unsupervised hierarchical clustering approach for tourism market segmentation based on crowdsourced mobile phone data. (E. Lattanzi & V. Freschi, Eds.)SENSORS, 18(9).
Chicago author-date
Rodriguez Echeverría, Jorge, Ivana Semanjski, Sidharta Gautama, Nico Van de Weghe, and Daniel Ochoa. 2018. “Unsupervised Hierarchical Clustering Approach for Tourism Market Segmentation Based on Crowdsourced Mobile Phone Data.” Ed. Emanuele Lattanzi and Valerio Freschi. Sensors 18 (9).
Chicago author-date (all authors)
Rodriguez Echeverría, Jorge, Ivana Semanjski, Sidharta Gautama, Nico Van de Weghe, and Daniel Ochoa. 2018. “Unsupervised Hierarchical Clustering Approach for Tourism Market Segmentation Based on Crowdsourced Mobile Phone Data.” Ed. Emanuele Lattanzi and Valerio Freschi. Sensors 18 (9).
Vancouver
1.
Rodriguez Echeverría J, Semanjski I, Gautama S, Van de Weghe N, Ochoa D. Unsupervised hierarchical clustering approach for tourism market segmentation based on crowdsourced mobile phone data. Lattanzi E, Freschi V, editors. SENSORS. 2018;18(9).
IEEE
[1]
J. Rodriguez Echeverría, I. Semanjski, S. Gautama, N. Van de Weghe, and D. Ochoa, “Unsupervised hierarchical clustering approach for tourism market segmentation based on crowdsourced mobile phone data,” SENSORS, vol. 18, no. 9, 2018.
@article{8573026,
  abstract     = {Understanding tourism related behavior and traveling patterns is an essential element of transportation system planning and tourism management at tourism destinations. Traditionally, tourism market segmentation is conducted to recognize tourist’s profiles for which personalized services can be provided. Today, the availability of wearable sensors, such as smartphones, holds the potential to tackle data collection problems of paper-based surveys and deliver relevant mobility data in a timely and cost-effective way. In this paper, we develop and implement a hierarchical clustering approach for smartphone geo-localized data to detect meaningful tourism related market segments. For these segments, we provide detailed insights into their characteristics and related mobility behavior. The applicability of the proposed approach is demonstrated on a use case in the Province of Zeeland in the Netherlands. We collected data from 1505 users during five months using the Zeeland app. The proposed approach resulted in two major clusters and four sub-clusters which we were able to interpret based on their spatio-temporal patterns and the recurrence of their visiting patterns to the region.},
  articleno    = {2972},
  author       = {Rodriguez Echeverría, Jorge and Semanjski, Ivana and Gautama, Sidharta and Van de Weghe, Nico and Ochoa, Daniel},
  editor       = {Lattanzi, Emanuele and Freschi, Valerio},
  issn         = {1424-8220},
  journal      = {SENSORS},
  keywords     = {tourism management,big data analytics,smartphones,human mobility,behavioural clustering,market segmentation,crowdsourcing,BLUETOOTH TRACKING DATA,RURAL-AREAS,BIG DATA},
  language     = {eng},
  number       = {9},
  pages        = {17},
  title        = {Unsupervised hierarchical clustering approach for tourism market segmentation based on crowdsourced mobile phone data},
  url          = {http://dx.doi.org/10.3390/s18092972},
  volume       = {18},
  year         = {2018},
}

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