This paper proposes a spatial point process model on a linear network to analyse cruise passengers' stop activities. It identifies and models tourists' stop intensity at the destination as a function of their main determinants. For this purpose, we consider data collected on cruise passengers through the integration of traditional questionnaire-based survey methods and GPS tracking data in two cities, namely Palermo (Italy) and Dubrovnik (Croatia). Firstly, the density-based spatial clustering of applications with noise algorithm is applied to identify stop locations from GPS tracking data. The influence of individual-related variables and itinerary-related characteristics are considered within a framework of a Gibbs point process model. The proposed model describes spatial stop intensity at the destination, accounting for the geometry of the underlying road network, individual-related variables, contextual-level information, and the spatial interaction among stop points. The analysis succeeds in quantifying the influence of both individual-related variables and trip-related characteristics on stop intensity. An interaction parameter allows for measuring the degree of dependence among cruise passengers in stop location decisions.

Nicoletta D'Angelo, Antonino Abbruzzo, Mauro Ferrante, Giada Adelfio, Marcello Chiodi (2023). GPS data on tourists: a spatial analysis on road networks. ASTA. ADVANCES IN STATISTICAL ANALYSIS, 108, 477-499 [10.1007/s10182-023-00484-w].

GPS data on tourists: a spatial analysis on road networks

Nicoletta D'Angelo
;
Antonino Abbruzzo;Mauro Ferrante;Giada Adelfio;Marcello Chiodi
2023-11-03

Abstract

This paper proposes a spatial point process model on a linear network to analyse cruise passengers' stop activities. It identifies and models tourists' stop intensity at the destination as a function of their main determinants. For this purpose, we consider data collected on cruise passengers through the integration of traditional questionnaire-based survey methods and GPS tracking data in two cities, namely Palermo (Italy) and Dubrovnik (Croatia). Firstly, the density-based spatial clustering of applications with noise algorithm is applied to identify stop locations from GPS tracking data. The influence of individual-related variables and itinerary-related characteristics are considered within a framework of a Gibbs point process model. The proposed model describes spatial stop intensity at the destination, accounting for the geometry of the underlying road network, individual-related variables, contextual-level information, and the spatial interaction among stop points. The analysis succeeds in quantifying the influence of both individual-related variables and trip-related characteristics on stop intensity. An interaction parameter allows for measuring the degree of dependence among cruise passengers in stop location decisions.
3-nov-2023
Nicoletta D'Angelo, Antonino Abbruzzo, Mauro Ferrante, Giada Adelfio, Marcello Chiodi (2023). GPS data on tourists: a spatial analysis on road networks. ASTA. ADVANCES IN STATISTICAL ANALYSIS, 108, 477-499 [10.1007/s10182-023-00484-w].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/608494
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