Spatiotemporal Contingencies in Tourists’ Intradiurnal Mobility Patterns

Abstract

Tourists' activity patterns result from complex interactions between time-space constraints and cognitive, social, cultural, and emotional factors. Accordingly, tourists' intradestination activity is studied today from multiple perspectives. Yet knowledge regarding the interrelationships between these factors is limited. The current research aims to contribute to the bridging of this gap, by studying tourists' activity patterns and the time-space resource allocation decisions they reflect. Using a smartphone application, 384 tourists' activity days in the cities of Tel-Aviv and Jerusalem were tracked and characterized in terms of resource allocation patterns, identifying three distinct behavioral patterns. The effects of various personal and external factors on group membership were then assessed. The results uncover complex relationships between various decision-making dimensions, where interdimensional interactions occur at multiple spatial scales and decision-making instances. These findings suggest that activity patterns emerge from a decision time-space in which effects are contingent upon each other across scales and behavioral dimensions.

High-frequency forecasting from mobile devices’ bigdata: an application to tourism destinations’ crowdedness

“…Tourism studies evolved from mainly focusing on the spatial analysis (Lew and McKercher, 2006;Xia et al, 2009) to incorporate a detailed time dimension (Shoval and Ahas, 2016;Shoval and Isaacson, 2007). As data record methods were providing higher frequencies, researchers were able to tackle more topics, as intradiurnal activities' analysis (Birenboim et al, 2013;Grinberger and Shoval, 2019). The currently available real-time or very HF data, as the big data used in this paper, allows new conceptualizations with short-time horizons.…”

Modelling the intra-destination behaviour of cruise visitors based on a three-dimensional approach

“…Previous research on intra-destination behaviour has mainly been based on analysing spatio-temporal flows or mobility patterns, while, until now, participation patterns have been studied in a secondary stage (Chantre-Astaiza et al, 2019;Grinberger & Shoval, 2019;Xu et al, 2019). The main theoretical contribution of this paper is therefore the proposal of a consistent three-dimensional framework that integrates spatial movement, temporal consumption and interaction with attractions.…”

“…In this way, the concentration and flow distribution within nodes is illustrated, offering information on the functional spatial cohesion of the tourism phenomenon. The participation and activity patterns suggested by Grinberger and Shoval (2019) and Zoltan and McKercher (2015), meanwhile, have been considered to classify cruise visitors depending on how they interact with attractions: as mere spectators/space-sitters (Urry, 1990;Walmsley & Jenkins, 1991) or co-creators of their experience/spacesearchers (Ek et al, 2008;Perkins & Thorns, 2001;Walmsley & Jenkins, 1991), which directly affect the spatio-temporal flows. Hence, this is the first study which sheds light on the spatio-temporal mobility and participation patterns of cruise visitors from active to passive involvement in order to classify homogenous intra-destination behaviour patterns analytically and holistically.…”

Passive Wi-Fi visitor analytics and food truck engagement: a feasibility study

“…In recent years, different Bluetooth and Wi-Fi tracking methods have been widely used within the tourism and hospitality industry (Lee et al, 2019;Hardy, 2020;Shoval and Isaacson, 2010). Tracking technologies have also been used in many studies to analyse the movement of tourists from small scales like festival sites (Versichele et al, 2014) and theme parks (Birenboim et al, 2013), to larger locations such as cities (Grinberger and Shoval, 2018;Shoval et al, 2011), islands (Li et al, 2019) and even entire regions (Baggio and Scaglione, 2017). For example, some researchers have analysed location data obtained from telecommunication providers to deliver a monitoring tool for the tourism industry (Raun et al, 2016;Ahas et al, 2007Ahas et al, , 2008Ahas et al, , 2010.…”