Keywords

1 Introduction

The spatial structure of a destination is constantly shaped and reshaped by the movements of tourists. In this sense, spatio-temporal behavioural patterns in the destination are defined by the movements of visitors moving from one attraction to another (Vu et al., 2018).

In the last decade, there has been an increase in research on this issue, but studies are still limited and the methodologies employed are multiple and varied, lacking uniformity and complementarity (Zheng et al., 2022). Against this background, the need for a more developed and nuanced theoretical framework, as well as the application, clustering and improvement of research techniques in this field is evident. The present study aims to contribute in providing a theoretical-methodological framework to this field of study and proposes as its main objective to study the use of WiFi sensorics to track and record tourists’ movement patterns.

The origins of research on the spatio-temporal behaviour of tourists at destination level date back to the 1980s. Hartmann (1988) pioneered the analysis of American and Canadian visitor flows in Munich in order to describe their behavioural patterns, using various methods such as direct observation and questionnaires. From this, a literature on the mobility and travel patterns of tourists visiting a destination began to take shape. In terms of purposes, most of them have focused on internal (tourist's own) or external (destination's) factors (Karagöz et al., 2022). The former encompass the study of those behaviours related to the capabilities and socio-demographic and psychological factors that influence the tourist's own decision-making. In this line, studies have been conducted to find differences in movement patterns according to the role of tourist or resident (Murphy, 1992), time of year (Keul & Küheberger, 1997) or nationality (Caldeira & Kastenholz, 2015), as well as in relation to psychological aspects (Fennell, 1996), establishing demand classifications (Galí & Donaire, 2006).

Other research focuses on factors external to the tourist and inherent to the destination itself, such as the distribution and characteristics of the tourist offer. Thus, trips from accommodation to attractions are considered first-order movements (Neutens et al., 2011), since tourist attractions are a source of motivation (Salazar et al., 2001), in addition to the distribution of supply, accessibility and transport infrastructure, or the demographic and economic environment itself (Lew & McKercher, 2006; Xiao-Ting & Bi-hu, 2012).

Similarly, when managing destinations and tourist demand, it is important to examine behaviour through their movement within a destination. In relation to this, there have been studies that have analysed the evolution of the tourist space (Lepan, 2013), as well as its spatial delimitation (Bauder, 2015) and the urban transformations that are related to these movements (Freytag & Bauder, 2018). Similarly, others have addressed tourist flows in the analysis and management of the destination, with a view to decentralising and decongesting overcrowded spaces (Cavaillès et al., 2016).

Tourism flows are analysed at different scales. Under the present research and for a better understanding of this variable, the following classification of tourist flows according to the scale of analysis is provided in a novel way (Fig. 1), being the meso level the one that corresponds to this study.

Fig. 1
An illustration of scale-wise tourist flow. It has 3 concentric rings labeled, micro, meso, and macro in order from the center to the edge involving flows of tourist attraction, intradestination, and that from sending region to receiving destination, in order. Each element has a short description.

Tourist flows according to scale. Own elaboration

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It should be added that most of the meso-scale research has been carried out in urban destinations. Regarding the tracking instruments used (Fig. 2), whatever the scale, these have been based on both quantitative and qualitative techniques, with mixed methodologies being very common. Likewise, both the instruments used to collect mobility data and those used to analyse them have evolved in line with technological advances.

Fig. 2
A table of 4 columns and 3 rows. It gives the respective entries of instruments, references, and scale of analysis for 2 types, namely, analogs and digital. Former has 4 instruments including direct observation and travel diaries, while the latter has 5 including G P S tracking and Wi-Fi sensors.

Tracking tools for the analysis of tourism flows. Own elaboration

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This paper employs Wi-Fi sensors to track tourists’ movement patterns. With the rapid development and diffusion of smartphones, Wi-Fi packet sensors provide new possibilities for obtaining flow data (Advani et al., 2020; Martchouk et al., 2011). These sensors are designed to detect and record all Wi-Fi enabled electronic devices within an average radius of about 40 m. The detected devices are identified by the use of a Wi-Fi packet sensor. The detected devices are identified by an anonymous tag encrypted from the MAC address of the device. In addition to the encrypted MAC address, Wi-Fi packet sensors also record the timestamp, packet sensor ID, received signal strength indication (RSSI) and device vendor ID. Multiple detections and the above attributes allow observing the routes of any individual carrying a detectable device (Gao & Schmocker, 2021).

With regard to Wi-Fi sensors, although numerous studies have been carried out in the field of engineering, others have focused on military operations, environmental monitoring in the face of climate change and natural disasters, agriculture, energy efficiency, home automation, means of transport or smart cities. However, there is little research associated with the use of sensor technology in tourist destinations, although, with the incorporation of smart destinations, there is an integration of technological infrastructures and user devices to improve the tourist experience and destination management (Buonincontri & Micera, 2016). This sensor technology could be very useful for collecting real-time data (number of visitors, movement of people, hotel occupancy, vehicle traffic, energy consumption, among others) (Femenia-Serra & Ivars-Baidal, 2021).

2 Methodology

This research focuses on the movements of tourists at the meso level, which is called Tourist Flow Destination (TFD). According to Foronda et al. (2022, 3), the TFD is “the quantified flow of visitors who, during their stay at the destination, move from one geographical point to another to visit a tourism product, consume a service or satisfy a basic or tourist need, either on foot, in their own vehicle or through the means of transport and mobility systems (public and private) offered by the destination”.

Likewise, with respect to the instruments for data extraction, Wi-Fi sensors will be used. It should be clarified that a Wireless Sensor Network (WSN) is a set of nodes composed of a microcontroller, various sensors, communication devices and, in some cases, actuators, which allow collecting and transmitting data from and influencing the physical environment in which they are applied. Each technology is exploited for its range and bandwidth characteristics. Wireless sensor networks (WSN) have different fields of application because they give the possibility to develop the nodes in a customised way for use in each specific area, as well as to adapt the network topology to the needs of the problem.

The case study where wifi tracking is applied is the Santa Cruz neighbourhood, one of the most emblematic places in Seville. Its ad hoc urban development, at the beginning of the twentieth century, was carried out to increase the attractiveness of the tourist offer, being “the first of the medieval historic quarters to undergo renovation for tourist purposes” (Moreno Garrido, 2005). Even today, it is the urban space with the highest intensity of tourist use in the city.

The study area of this neighbourhood is delimited with those points that have been considered most relevant from the point of view of congestion and traffic of people (residents and visitors), taking into account the access points (entrances and exits), as well as the main stops for visitors to the neighbourhood. Although 10 WiFi sensor points are planned, there are currently 4 of them, which provide real-time data on the capacity, affluence and flow of visitors in the area.

2.1 Phases of Analysis

2.1.1 Measurement and Observation Phase

The installation of the Wifi Location and Presence Analytics sensors is carried out by the company Galgus. This infrastructure connects the devices providing location analytics. Mobile devices use MAC (Media Access Control) address randomisation to be less traceable and protect personal information, but this interferes with traditional WiFi device counting methods. The company's patented device fingerprinting algorithm (Pérez-Hernández et al., 2023) overcomes MAC randomisation to accurately count WiFi devices without compromising the Intelligent Peripheral Interface (IPI). With these sensors, the study allows for location analytics (position estimation of nearby devices, heat maps, tracking, etc.) and presence (counting, segmentation, dwell times, gauging control, typical movements within the installation, recurrence estimation, etc.) using exclusively WiFi, through devices connected (associated) and not-connected (not associated) to the network. These WiFi frames of the “Probe Request” subtype are emitted by the devices when they are searching for nearby networks. They are frames sent by devices automatically and in bursts of several frames at a time, with the time interval and the number of frames per interval being configurable (Freudiger, 2015). Probe requests are interesting because they are the only frames sent by the device without being connected to an AP (Access Point) and they are the only frames sent by a device on all channels.

The Grafana OSS (Open-Source Software) operating system is a data analysis and visualisation software for processing time series data. A time series data set consists of several data points, each together with a time stamp. These data sets can be used to draw graphs and draw conclusions as the data changes over time (Salituro, 2020).

2.1.2 Prototyping Phase

All the information is processed and analysed from different areas. Contursa is the company responsible for the coordination to process and analyse the information, build and design the prototype solutions that will be incorporated in the implementation phase. In the future, the datasets extracted from the sensors will also be accessible from an open data portal so that users can work with them and offer possible solutions.

2.1.3 Implementation Phase

In advance, there are a multitude of actions, technological or otherwise, which may be recurrent in order to provide answers to questions such as:

  • The identification of tourist routes established by tourist guides and free tours, where entry and exit points are recognised, as well as stops for explanations of the main tourist attractions (Foronda et al., 2022).

  • The normative development of strategies related to the regulation of mobility in the destination, the regulation and control of the accommodation offer, the activation of peripheral points of attraction, the restriction of affluence in tourist resources and public spaces, among the most frequent (Mendoza et al., 2019).

3 Analysis and Results

In accordance with the methodology for accessing information, a series of analyses have been constructed, which are presented as preliminary results in the Santa Cruz neighbourhood of Seville.

Counting users by zones provides information on the flow of people, in a given area, by accesses, time slots, etc. and extracting ratios such as the number of people according to the distance from the WiFi. This information is used to obtain data on average occupancy, peak times, etc. This data is displayed in real time and recorded in the database to obtain historical reports.

Dwell time and Profiling. Emerging urban IoT infrastructures enable novel ways of detecting how urban spaces are used. However, the data produced by these systems are context-independent, making it difficult to discern what patterns and anomalies in such data mean. The profile of users has been classified according to time spent:

  • Passers-by, who pass through the space for less than 1 h. In this sense, they walk on public roads, have no fixed abode and are visiting or passing through. The streets in this neighbourhood are narrow, without vehicles and there are no traffic lights.

  • Visitors. The statistical concept of visitor is not used verbatim. In this case, the visitors counted are those who travel through the neighbourhood between 1 and 5 h, a duration that is used, for example, for cruise ship stopovers, short-haul flights or GPS tracking experiences (Dane, 2018).

  • Long duration, users stay more than 5 h, either because they stay overnight in tourist accommodation or because they live in that neighbourhood.

Network segmentation is another variable within the architecture that divides the network into several sub-networks. This allows traffic flow to be controlled at short (over −60 dBm), intermediate (between −61 and −80 dBm) and far (less than −81 dBm) distances, which facilitates monitoring, increases throughput, identifies technical problems and, most importantly, improves security. As a future line, this segmentation can build micro-perimeters to set alarms for over-capacity.

Visitor tracking. Tracking provides information on the behaviour of visitors within the neighbourhood, identifying where they move. This technique identifies through continuous tracking where tourists spend the most time and at what times they are most likely to be visited, as well as tracking between nodes to establish the routes travelled.

In later phases, the aim is to carry out flow analysis with contextual intelligence, both with WiFi sensor technology and with other technological tools developed by intelligent systems. All this, providing relevant recommendations and suggestions to tourists, improving their experience and satisfaction during the trip, as well as providing destination managers with a greater understanding of the behaviour and needs of tourists, allowing them to make more informed decisions, personalise the tourist experience, optimise resource management and promote the destination more effectively in relation to the prediction of crowds and prescription of actions, alarms for exceeding capacity or warnings when someone enters or leaves a restricted area.

4 Conclusions

The study has demonstrated the use of WiFi sensors as a tool to track and record the spatio-temporal behaviour of tourists and, specifically, the TFD. Through this technology and the data obtained, it has been possible to count people in real time in the areas in range. It has also made it possible to identify the user profile according to the time spent, the most frequented routes and spaces and network segmentation. With such information, more valuable insights are provided for destination planning, service design and management (Lew & McKercher, 2006), as well as for decentralising and decongesting crowded spaces (Shoval, 2018). However, as a future line of research, it is intended to deepen the categorisation of users based on the movement patterns detected and taking into account not only the length of stay, but also other socio-demographic issues that allow us to distinguish more precisely the movements between tourists and residents.

Likewise, the study demonstrates the effectiveness of Wi-Fi sensors as a tool for data extraction compared to other digital instruments such as GPS, since both tracking accuracy and applicability on a larger scale are greater and at a lower cost, as has already been demonstrated in previous studies (Foronda et al., 2022; Gao & Schmocker, 2021). Although this research has relied on 4 WiFi sensor points, which is a limitation, the results demonstrate their usefulness in view of the possibility of installing a greater number of points in other tourist areas.

Thus, as future research, there is a need to continue to deepen the use of this tool in combination with other technological solutions, which should be integrated under a holistic perspective aligned with Location Intelligence. With this, it would be possible to build a theoretical-methodological framework that would allow the design, evaluation and improvement of tourist destinations, both analytically and empirically, especially in the case of cities, which carry a large amount of data generated in the context of “smart” evolution.