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Tracking Tourist Flows Through Wi-Fi Sensor Technology in Seville

In: Tourism and ICTs: Advances in Data Science, Artificial Intelligence and Sustainability

Author

Listed:
  • Irene N. Franco

    (University of Seville
    Andalusian Institute for Research and Innovation in Tourism (IATUR))

  • Concepción Foronda-Robles

    (University of Seville
    Andalusian Institute for Research and Innovation in Tourism (IATUR))

  • Federico Rollán

    (Smart Tourist Office-Sevilla City Office of Seville City Council)

  • Pino Canales

    (Smart Tourist Office-Sevilla City Office of Seville City Council)

Abstract

The study of tourism flows consists of understanding the spatial-temporal relationship of tourists with the space they visit, which has become a key aspect for the management of destinations. The advance of communication and information technologies nowadays allows the extraction and storage of a large amount of data of different types and at different scales, which can be very useful for decision-making. In this context, this study aims to use WiFi sensor technology to track and record the movement patterns of tourists. The methodology used focuses on the measurement and analysis of this variable through the extraction of real-time data from WiFi points in the Barrio de Santa Cruz, Seville. The results obtained demonstrate the viability of this instrument for analysing tourist flows at the destination as opposed to the use of other instruments that involve higher costs and/or limitations. Likewise, in terms of its applicability, the results show the need for its use, in combination with other tools and techniques, for the planning and management of tourist destinations.

Suggested Citation

  • Irene N. Franco & Concepción Foronda-Robles & Federico Rollán & Pino Canales, 2024. "Tracking Tourist Flows Through Wi-Fi Sensor Technology in Seville," Springer Proceedings in Business and Economics, in: Antonio J. Guevara Plaza & Alfonso Cerezo Medina & Enrique Navarro Jurado (ed.), Tourism and ICTs: Advances in Data Science, Artificial Intelligence and Sustainability, pages 15-24, Springer.
    • Handle: RePEc:spr:prbchp:978-3-031-52607-7_2
    DOI: 10.1007/978-3-031-52607-7_2
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