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Article

The Study of Tourist Movements in Tourist Historic Cities: A Comparative Analysis of the Applicability of Four Different Tools

by
Ana Muñoz-Mazón
1,*,†,
Laura Fuentes-Moraleda
1,
Angela Chantre-Astaiza
2 and
Marlon-Felipe Burbano-Fernandez
3
1
Business Administration Department, Rey Juan Carlos University, Vicalvaro Campus, Paseo Artilleros sn, 28032 Madrid, Spain
2
Departamento de Ciencias del Turismo, Universidad del Cauca, Popayán 190001, Colombia
3
Departamento de Telemática, Universidad del Cauca, Popayán 190001, Colombia
*
Author to whom correspondence should be addressed.
†
Vicalvaro Campus, Paseo Artilleros sn, 28032 Madrid, Spain.
Sustainability 2019, 11(19), 5265; https://doi.org/10.3390/su11195265
Submission received: 30 July 2019 / Revised: 31 August 2019 / Accepted: 16 September 2019 / Published: 25 September 2019
(This article belongs to the Special Issue Sustainability and Visitor Management in Tourist Historic Cities)
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Abstract

:
This paper presents the results of the application of four different tools (tourist card, questionarie, GPS and NFC) with the objective to study the movement of tourists in a tourist historic city (Popayán, Colombia). Given the need for these types of cities to manage tourism in a sustainable way, and considering that the management of tourist flows is a key aspect to achieve this, the aim was to find out which of the tools applied provides more precise data on the movement of tourists in the destination. For this, information was collected on the movement of tourists with four different tools, applying each tool in four different years (2011, 2012, 2013 and 2015) during the same time period (Holy Week). For the analysis of tourist movements, the Markov chain was obtained for each period. In order to study the generation of routes geo-location was used in each case. The results show that even though GPS technology provided more information on the visited places, NFC technology facilitates more extensive information. In addition, NFC technology allowed the extraction of important information about the places visited, showing a wide number of sites visited and, therefore, providing greater value for the study. Finally, the results of the study provide a better understanding of how destination management organizations could develop more suitable alternatives of the customer services systems, the delivery of tourist information and the identification of sites with heavy use. Conclusively, this study helps to identify how to take better advantage of the marketing strategies through different tools that analyses tourism movements.

1. Introduction

The rapid growth of cultural tourism in historic cities can turn these destinations into unsustainable places [1], provoking cycle changes and even introducing them in phases of decline [2]. This situation irremediably demands proactive policies on the part of those responsible for destinations, sustainable strategies and control measures to anticipate and prevent these cycle changes [1]. There is no doubt that the impact of tourist activity in the historic city has direct and indirect positive effects such as the generation of employment, the conservation of heritage, the stimulation of commercial activity and urban services [3]. But it is also necessary to be aware of the negative impacts among which are the massification of space, agglomerations in certain attractions [4] and of local services, producing in some cases an effect of “musealization” [5].
Within the control measures to anticipate and prevent decline, knowing and managing tourist flows is a very important element for destination managers [6]. The study of the movement of tourists makes it possible to understand their spatial-temporal behaviour in the destination and their interactions with space [7] which provides a better understanding of the visitor [8], optimizes the carrying capacity [4] and improves the use of resources, and thus facilitating both their management and organization [9]. Additionally, this type of analysis makes it possible to more efficiently define the marketing strategies of these destinations [10].
As mentioned above, the analysis of visitor movement usually encompasses spatial and temporal considerations [11] and the combination of both (space-time) [12]. Spatial elements include direction, location and distance. Temporal elements include time of arrival and duration, and the space-time element includes velocity. Likewise, movement can also be seen as the process of changing location in time, space and time being the main characteristics of time, as presented by K. Hummel and Hess (2010) [13].
The relevance of the study of tourist movements in destinations is reflected in the number of studies that have previously focused on this topic. As a result of these studies, several tools are known today that allow both the capture of data and the process of analysis of the same [8]. Nevertheless, the related literature nearly ignores historical cities, whose peculiar features illuminate the relation among tourism spatial organization, the quality of its products, and the general dynamics of regional economies. In relation to the use of space and the mobility of visitors, no studies have been found that address in a comparative way, the results of the use of different tools for collecting information on the movement of tourists in the same destination. This type of study would help decision making in these destinations, facilitating the implementation of certain technologies to undertake the study of visitor movements [14]. However, most of the works that have studied the movement of tourists at destination do so using one or two data collection tools [15,16].
There are two main raisons that justify this study. The first one is the importance of the tourist mobility management for the sustainable development of historic cities. The second one is the study of the existing tools from a comparative perspective for the monitoring of the tourist flows in the cultural destinations, in order to decide which fits better to a historic touristic city. For this study Popayan historic city has been chosen as case study. It is a small city in an initial step of its tourism development that can be representative of other cities that, with the same characteristics (Colombian small city, inhabitants: 280.000, seasonal tourist activity, cultural resources based, public-private local DMO) are facing the challenge of managing the visitors from the beginning in order to be more sustainable.
For this reason, in this paper we propose four tools for the study of visitor movements in a historic tourist city in order to determine which tool provides the best results. Our contribution is twofold: it deepens the knowledge about the study of tourist movements by providing a comparative analysis of the results of the use of four different tools in a historic city. It also facilitates the decision making of historic cities that wish to know how their tourists move in the destination in the most efficient way.

2. Tools for the Study of the Movement of Tourists in Historic Tourist Cities

Knowledge about how tourists interact with the heritage area of a city is crucial to manage the impact and, at the same time, to create sustainable historic and tourist cities [17]. Although the cultural tourism boom has undoubtedly generated many benefits, the growth of cultural tourism has also begun to cause problems: the large number of visitors attracted by major cultural attractions has become a concern, especially in historic city centres [18]. The representatives of the DMO (Destination Management Organizations) must understand what is the movement of tourists in the destination, and therefore, what is the consumption they make of the tourist space and resources in order to be more sustainable [19]. This is especially important in the case of historic cities, characterized by a high influx of tourists in their main resources and especially in their historic centers [17,20].
The technologies have allowed the advance in the tools that allow the collection of information on the movement of visitors in the destinations. In recent years we have witnessed substantial advances in tracking technologies (GPS, GIS, RFID, APPS) that have led to the emergence of new research [10,21]. These investigations reflect the results of the use of different tools for tracking tourists, from the more traditional ones such as site-based interviews to the use of other tools with technological support. Below are the most relevant tools, the results of which have been previously considered by academic literature.

2.1. Interviews and Surveys

Interviews are a traditional tool supported in many cases by a pre-developed questionnaire. It is characterised by a high response rate, since, if the tourist collaborates, a large amount of data can be collected about his visit to a destination. Smallwood [8] mention that, one of the benefits of this technique is, that researches can verify the information provided by the visitor. Like interviews, surveys are also considered traditional tools for capturing tourist information. They continue to be an essential tool for visitor research, complemented by other monitoring methods, which can provide useful information to explain travel patterns such as Wolf [22]. For Parroco [23], one of the main problems of tourism surveys relates to the mobile nature of tourists, which with this tool is difficult to measure; while Terrier [24] mentions that the survey is a good way to mesure tourist flow; thes surveys can be done is the homes of respondents, at the places they visit or at points along the way when they are travelling. Household surveys are carried out after the tourists return home, the most complete information is requested, since the trip to completed, can be done through telephone interviews, correspondence or home visits.

2.2. Direct Observation

Direct observation is another of the traditional tools for the study of visitors, Thornton, Williams, and Shaw [25] summarize it as identify, follow, observe, and map, which, in practical terms, implies that the researcher accompanies the person to observe it directly. O’Connor [26], mention that this tool can provide a lot of information, but it depends on human interpretation to derive the travel itineraries of people, which make this a tedious process, prone to errors and costly for the number of supervisors needed for adequate coverage of the study area. Likewise, Zakrisson and Zillinger [27], emphasize that there are several risks from the use of this tool, such as misinterpretation, lack of registration when people move very fast and the influence in the behavior of the tourist by the presence of the researcher.

2.3. Video Surveillance

Video surveillance, like direct observation, can provide the information needed to study tourist movements, but it depends on human interpretation to derive people’s travel itineraries, which is complex, error-inducing and costly because it must be added to the investment in surveillance equipment [26].

2.4. Tourist Card

Tourist cards are instruments usually used by DMOs (Destination Management Organizations) with the aim of facilitating visits to different resources and improving the experience in the destination [28]. The tourist cards (also called ‘destination card’, ‘city card’, ‘city pass’, ‘tourist pass’, ‘guest card’, ‘visitor card’, or ‘welcome card’) allow this access to the resources of the destination in a more economical way than if the tourist pays each of the accesses separately [29]. The main benefits of applying tourist cards are the improvement of the visitor experience, increase the consumption of products and services, promote those resources less visited and balance the distribution of tourist flows in the resources of the destination, monitor experiences, promote the use of public transport, lengthen stays, among others [29]. Zoltan and Mckercher [30] used the destination card to analyze the spatial distribution and activities carried out by tourists in the Canton of Ticino (Switzerlan). The authors reflect in this work how the use of this type of tools can help the decision making of the destinations as they help to better understand their markets.
Another example of the use of tourist card is the Dubrovnik’s Old City. The City has attempted to diversify tourist interest points by introducing the ‘Dubrovnik Card’ [31]. This card gives visitors access to multiple sites and not just the main cultural ‘honey pot’ attractions such as the City Walls [32].

2.5. GIS and GPS

Among the most “technological” tools is the GIS (Geographic Information System). This tool has previously been used in several studies [4,33,34] alone or in combination with GPS (Global Position System), as Grinberger [35]. GPS consists of specialized software and hardware that makes use of the public satellite network, with the ability to provide the position by triangulation of satellite signals. It is very popular as an individual device and has been widely incorporated into smartphones, thanks to the ability to incorporate these mapping applications [11,15,36,37,38,39,40]; Zheng, Specifically, in the case of historic cities, historic cities or historic centres it has been used by Tchetchik [41], Grinberger [35]; Aranburu [19] and Sugimoto [42]. Likewise, it is common to find works that combine GPS with another information collection system, as in the case of Bauder and Freytag [39] and East et al. [43] where information is combined with visitor surveys.

2.6. Mobile Networks

Other work has relied on information provided by mobile networks [44,45] and in some cases has combined the method with the application of GIS [46]. The tool based on records of activities in mobile networks generates the information originated by the use of these phones, can become very complex due to the dependence of mobile operators to provide the positioning data of the same, given the active competition between them and hardware and software suppliers, which generate professional secrets that lead them to hide data and XXgures, which becomes one of the main problems of this tool, along with the invasion of privacy of the phone user, as mentioned in [38]. For Vaccari et al. (2009) [47], mobile phone calls can inform how many people are present in a given area and how many enter or leave it. This tool presents two major problems, the invasion of privacy, since it needs to follow the signal from the mobile phone to obtain the location of the individual cell of the phone company, estimate the travel speed and travel time. The analysis of records of activities in mobile networks, are carried out by means of a GIS tool (Geographic Information System), which allows to study the distribution of activity in urban space and time to assess the density of users in cities and their movements across the territory as in [46]; this information is very useful, given its accuracy, compared with questionnaires and accommodation statistics for the analysis of tourist movements.

2.7. Bluetooth

Bluetooth has also been used as a tool for collecting information on the movement of tourists in previous works [48,49,50,51,52]. The main advantage of this tool is that it is not exclusive to smart phones, since phones with lower features may have it, as [53] propose, and which allow signals to be emitted between these devices that can be monitored.

2.8. Social Networks

Social networks as tool for capturing tourist information when visiting a destination, are supported by the publication of tweets with geographic location. This information is available on the Internet, which represents an advantage, having a low cost for access to information and not invading the privacy of the user, and from which you can obtain data of place, date and time, three important elements to reconstruct movements of a person in an specific area. In [54], the study case was developed in Barcelona metropolitan area (Spain) is presented, where this tool is used to reconstruct the trajectory that tourists followed when moving through several locations, based on the social network twitter. Also the work of Encalada [55] use the more than 17,000 photographs published in the social network “Panoramio” Lisbon visitors to analyze the spatial distribution of tourists. In the work of Chua [56] the spatial, temporal and demographic aspects of tourist flows are characterized by geotagged tweets posted by individuals in the region of Cilento region which includes different heritage sites declared by UNESCO (e.g., Paestum, Punta Licosa, Capo Palinuro). Shao et al. [7] also use China’s most popular social network, Sina-Weibo, to study visitor behavior in Huangshan City. His et al’s work [57] also uses the Flickrin social network to analyze the geographical preferences of international tourists through their geo-tagged pictures.

2.9. Travel Stories

The travel stories published on the web also constitute a tool based on the use of the stories that travellers write on the Internet, about their tourist experiences in a destination. Tourists often voluntarily write the report of their tour after a day or their entire trip, which becomes an advantage, as their activities are not affected by observation, whether by monitoring with some device, direct observation or interviews. Internet sites, such as blogs, become orderly sources for information about travel experiences, as people there freely write down their experiences and problems during their journey, as presented by [58].

2.10. NFC

NFC (Near Field Communication) technology is also another tool that enables travellers to be tracked electronically or to markup information about places with advantages from QR codes [59,60]. Atzori [61] make a review of various technologies that are used in traceability, which in the future may impact various fields, one of them is the NFC technology for its great usefulness for information capture, being a technology that is being included in mobile phones and is gaining much acceptance thanks to its easy interaction. It is known a case of use of NFC in tourist cards; Basili [62] that propose a mobile application as an assistant providing functions and benefits of a tourist card with services according to location. This integrates several technologies in addition to NFC that allows the tourist to make payments at destination, in addition to other information services that enhance their experience at the destination, before, during and after their visit.

2.11. Alge System

Alge systems are information capture systems that are based on the placement of bands on the ankles of participants (similar to those used in sports races), so that these are captured by sensors located along a network of routes, to capture all possible combinations of movements through them. Among their main limitations or disadvantages is the possible loss of data, due to the exhaustion of the batteries of the sensors and the bands, equally, these sensors do not provide as detailed information compared to GPS as analyzed in [26].
These studies, focused on specific places, have a mainly qualitative focus, that is, they seek to know the behaviour patterns of tourists during their trips. They use one or two tools to obtain data. However, the works that have used different tools for this type of studies are more limited. The comparative approach provided using several tools in the same destination makes it possible to determine which is the most suitable for the study of tourist movements in a destination that meets certain characteristics. This makes it easier for destination managers to make decisions based on the key variables they consider.

2.12. Classification of Tools for the Study of the Movement of Visitors

In general, the methods for the analysis of tourists can be classified according to different variables. For example [15] classify methods as observational or non-observational. Observational methods involve tracking a subject by means of direct surveillance or remote sensing, while non-observational methods rely on self-reported information to recount the subject’s sequence of movements. For this work, we have considered as a classificatory variable the intervention of the tourist and the level of incorporation or support in technology. Figure 1 as presented in [63] shows the classification of the tools previously described. The first variable corresponding to the interaction or intervention of the tourist, located on the X axis, relates the necessary actions that are requested of the tourist to carry out in a conscious way and in collaboration with the study, investing time and effort, if it is the case; or on the contrary without the direct intervention of the tourist, not beyond his own activity within the destination. The second variable correspondig to the level of incorporation or support in technology, located on the Y axis, is refers to the need for the use or intervention of a greater or lesser number o electronic devices, communications networks or processing services for the capture of information.
The tools located in the first quadrant relate a higher level of technology incorporation and a lower level of tourist interaction or collaboration; here are located: geolocalized tweets, records of activities in mobile networks, the Global Positioning System (GPS) of mobile phones (Smartphone), geolocalized photographs available on the Internet, Bluetooth technology incorporated in mobile phones, travel stories published on the web, video surveillance and the Alge system. The second quadrant relates those tools that demand greater technological support or infrastructure, and a greater effort from tourists to generate data; here are located the NFC technology (communication of nearby fields) and the Global Positioning System (GPS) in independent device and tourist cards. The third quadrant, relates the tool that demands a lower level of interaction or collaboration of the tourist, not beyond giving consent to participate in the study, which in some cases may be null and require little infrastructure or technological support, here is located the direct observation. Finally, the fourth quadrant, relates those tools that require a higher level of interaction or collaboration of the tourist and a lower incorporation or technological infrastructure, which in some cases becomes null; here are located: interviews, travel journals and surveys.

3. Methods

The methodologies used for the analysis of visitor movements in previous works are very varied [64]. One of the methods for classifying the data obtained is that of discrete models or in events such as the Markov chains [12,65,66,67]. The Markov Chains are statistical methods for the analysis of data, which represent by means of a matrix, a discrete model of probabilities of occurrence of events.
Other previous works have used Markov Chains as a way of analyzing trends and results, as a series of events that are linked together by a dependency. For example, Xia et al. [12] use them for the analysis of the spatial-temporal movement of the tourist. Previously Mednick [65] modelled the behaviour of tourist trips in the United States, using these chains to predict the probabilities of different travel patterns. In addition, Lusseau [67] focuses on the biological effects of tourist boat movements. Zheng et al. [68], use this model to estimate the statistics of visitors travelling from one region to another, to investigate the topological characteristics of tourist routes. This type of analysis has also been widely used in transportation issues as part of tourism activity, as seen in [69,70] or [71]. Its application is also highlighted in some analyses of the movements of people walking, as mentioned in [72,73]. In this work, for the analysis of tourist movements, the Markov Chain is extracted for each period and the movements at route generation level are analyzed, based on the geolocalized coordinates in each case. The Markov chains present certain advantages for the study of tourist movements because they allow the analysis of movement data by proposing discrete models, allowing the modelling of the spatial-temporal movement of tourists at a macro level and calculating their probabilities to visit a given tourist site, as was done by [12].
As far as the visualization of results is concerned, different tools are available as a way of representing the movement in a graphic way. Among the most used alternatives of visualization, are those generated by the Geographic Information Systems (GIS), which according to their capacities, can show static views in which they represent, fixed dynamic points with points in movement, being this an alternative for analysis of relevant movements, representing the movements on two dimensions or three dimensions as it is presented by [35,74,75,76,77,78,79]. Mckercher, Lau, and McAdam highlight the value of GIS as an alternative for data entry and analysis, providing graphic representation and modes of managing them in the search for patterns, both at the level of aspects of space and time; They generating analysis on maps based on GPS tools delivered to tourists during their journey, first taking a 2D representation on a map with densities and then a 3D map with the activities, which allows various levels of analysis. [80,81]. Most of the studies on visualization come from geography; however, the focus should not be lost from tourism, in terms of tourist movement and consumption of a destination, as highlighted by [82].
For this research we used the GPS Visualizer tool [83] and the plotsat R plotting functionalities of the package used, we generated the coordinate maps on Google Maps to visualize more clearly the visits and periods. The moveHMM package [84] of R, specialized in analysis of movements based on GPS coordinates (longitude and latitude), based on Markov Chains and more punctually, on hidden Markov chains (HMM), is used for the analysis of movements based on coordinate paths. Additionally, the diagrams and analyses have been carried out with the statistical software R [85], used for its programming what was proposed in [86,87]; as well as for the descriptive analysis, what was used in social sciences by [88,89]; the visualization alternatives proposed in [90,91]; analysis of correlograms of [92,93]; visual analysis of movements of Michelot, Langrock, and Patterson (2016a) [84,94,95,96].

3.1. Definition of fieldwork

This work focuses on the application of the four tools with the aim of analyzing the flow of visitors in a historic city, specifically in the city of Popayán (Colombia). It is the capital of the Department of Cauca, founded in 1537, located in the south of the country, 596 km from Bogota D.C., close to Ecuador, with an average temperature of 19/grades all year round, with a territorial extension of 512 km2 and an approximate population of 280 thousand inhabitants. According to [97], the white colour of the architecture of the historical centre of the city, is constituted in a symbolic mark that identifies Popayán with the name of “White City of Colombia”, from century XVI, epoch in which the city was an important “axis of the Spanish colonial power, exercising by then an outstanding civilizing work in its environment, so much to economic level as political and cultural”. The main tourist attraction are the Holy Week processions, an event that year after year attracts countless visitors, which have not been able to be measured for different reasons, linked to the organization of the sector, despite the existence of a management body of the destination, which although it exists, still lacks the necessary experience for the proper planning and development of activities that can contribute to establishing an official figure of tourists visiting the region. The city is characterized for being a small city (280.000 habs aprox), in phase of tourist development, with cultural tourist vocation and with a highly seasonal activity.

3.2. Facts

The data collection was carried out in four different periods (2011, 2012, 2013 and 2015, see Table 1) in the same season (Easter), the date on which seasonal tourism is presented at the destination. For all periods, a survey-based procedure to collect information from tourists, contributed to identify the tourists profile visiting the historic city of Popayán, this being especially a tourist who is in the age range between 30 to 50 years. It is important to mention that for the execution of the fieldwork we counted on the cooperation of the different public and private agents of the destination. In the following table the detail by period can be observed. In total, the sequence of visits for all periods was extracted, adding up to a total of 1346 movements and generating an additional exit state, called “E” (state in which the sequence goes to an end ), with a total of 490 times. The following table reflects the periods and the tools used.
Table 2 contains the identifiers of the visited sites codificaion, the list presents the sites that were visited in some of the periods, with their respective estimated coordinates, and these are used for the generation of Markov Chains.
In relation to the process of informing visitors of the procedure, each visitor involved was told that the data declared in the survey would be used with research goals, that they would not be marketed to third parties and do not involve any personal data. Movements and surveys are anonymous and assigned an ID. The data collected does not contain individualized or private personal information, but part of the anonymous profile, in compliance with the management of personal information management rules. Additionally, the visitors surveyed had the opportunity to not declare (not answer) any special data, so that some of the variables present unanswered results. The questions that presented the most unanswered answers were expenditure, age and means of information about the destination. At no time was contact information processed, so one could have the peace of mind that they would not be contacted in the future and would not be subject to commercial strategies.
In the case of applications with smartphones, you had the option to close the application or turn off the phone, in case you did not want at some particular time, to record motion data. Although in the surveys are recorded, some visitors in the range of minors, never took data from minors; for all cases it was ensured that, without the respective accompaniment of an adult, no data would be recorded and the data recorded would correspond to a group or greater companion. For all of these cases, the data from said surveys were made sure that they corresponded to a group in which an adult requested or delegated the use of a tourist card or mobile device; in other cases, these records were discarded if the accompaniment of an adult was not certain.
A subject and his or her survey information are not processed in isolation, nor is information cross-checked with any other source. The case study was conducted at different times in the historic city of Popayán in Colombia, and is therefore subject to Colombian law.

4. Results

Below is the analysis of tourist movements under the Markov Chains, which are extracted by observing the case as one and then, the analysis of movements based on coordinate routes, which is analyzed both individually by period, as well as jointly to visualize the variations versus partial analyses. In this case, an event of a Markov Chain, is the visit of a tourist to a specific place. Each tourist is identified in a unique way and visited or declared visits to specific sites. From these visits a chain of visited sites is constructed, in which the order of the visit is highly relevant. From this sequence of visits, the transition matrix is constructed, which is the probability that a tourist visits a site X, according to the conditional probability that comes from a site Y. The transition matrices can be accompanied by transition graphs, which are representations in nodes (a node is a visited state or site), and their transition (lines between two), with their respective probability, which correspond to the transition matrix. For your analysis, since you will always have a finite sample of movements, you must analyze whether the values of the matrix, tend to a probability value (i.e., stabilize), or tend to zero. This is studied by means of a convergence analysis, wich makes it possible to simule the trantition matrix, from some initial data and nally, to propose the transition final matrix. Likewise, an initial probability matrix can be established, which is the probability that a tourist starts in a site X, which is extracted from the sequence, taking into account the place or site from where each tourist started.

4.1. Construction of the Chain of Sequence of Visits

The sequence is extracted from the use of tourist cards for the 2011 period, from the visits declared in the 2012 case survey, from the route of visits extracted from the use of the mobile application with GPS in 2013 and from the use of the NFC mobile phone in 2015. From the sequence of movements the following matrix of transitions is extracted, presented in Table 3, with a confidence interval of 0.95 based on the standard error matrix of Table 4.
The generated matrix is of size 27, with the states that were presented (according to IDs 13, 14, 23, 24, 26, 27, 28, 29, 3, 30, 31, 32, 36, 38, 39, 4, 41, 42, 48, 5, 51, 52, 53, 55, 57, 6, E), which is resized in two parts for visualization. This implies that the transition probability between state X to state Y, P(X|Y ), is given by the calculated probability, which is at the crossroads between both states; for example, the transition probability from state with ID32, to state with ID29, is P(32|29) = 0.01546392, or from state 3 to state 6, is P(3|6) = 0.181818182.
The transition matrix, in turn, is represented by the graph in Figure 1. In this case, each of the IDs of the states (sites) is a vertex (node), which is linked to other vertex(s), by means of edges (links) that are directed, that is, with direction of the arrows, which represent the jump from one state to another, with its probability of transition.
The purpose of Figure 2 is to show, in a visual way that from any (or most) state, it is possible to move to another as part of the irreducibility explored below.

4.2. Descriptive Analysis

Figure 3 corresponds to the graphical view as a tabulation of the sites visited in relation to the previous table, highlighting the three museums among the four most visited sites.
Since the visits are supported in a single identifier, Figure 4 shows in an integrated way, the number of visitor identification against the number of visitis it has had in the figure. The codification used for the 2012 periods onwards is 5 digits, starting with the last two digit of the year. Of this, it is found that for all periods, the declaration of more sites visited by identification of visitors, was for the period 2015, followed by the period 2012. It should be remembered that, for the period 2015, the option managed is a map with NFC tags, which when consulted, provides more information, and in the case 2012, was as a statement in the survey. This gives indications that the way to stimulate the possible declaration of visits is more indicated in a strategy that delivers value to the visitor, based on providing information about the destination, instead of delivering comecial benefits, as in the case of 2011, or the exploration of the destination without delivery of information as in 2013 and 2011.

4.3. Movement Patterns

In order to establish the possible patterns of movement, analyses of both the matrix of movement transitions and the initial probabilities should be made. A count is made at the end of each state, understanding as states of departure, those that are in the first column and of arrival, each one of the crosses in the row, with the column of arrival. Since there are individually 6 establishments (38, 39, 41, 51, 52 and 53) located in the same place known as Payanés Corner, an additional RP state is created, resulting from the sum of all its transitions and probabilities that will be called RP from now on.
Performing an analysis you can find:
  • According to the initial vector, the only start options for possible patterns are states (sites) 13, 14, 23, 26, 29, 3, 30, 32, 36, 4, 42, 5, 55, 57 and 6.
  • The states in their order, to which most other states arrive (transition to it) (excluding E), 55 (16 transitions) followed by states 23, 29, 30 and 57 (with 12 transitions) and PR (11 states). This means that the sites of Rincón Payanés, Museo Arquidiocesano, followed by the Quingos, Museo de Historia Natural, Museo Nacional Guillermo Valencia and Panteón de los próceres, are the most visited.
  • The states in their order, which most reach others (transition others from it) (excluding E), are RP arriving at 15 transitions, 29 and 6 arriving at 14 transitions, 32 with 13 transitions, and 57 with 12 transitions. This means in its order Rincón Payanés, Museo Historia Natural, Museo Casa Mosquera, Manos de Oro and Panteón de los próceres.
  • It is then the strategic importance of these sites, highlighting the Rincon Payanés, the Museum of Natural History and Pantheon of the heroes, which are of greater transition from and to other sites or states.
  • The probabilities of transitions between higher states for tourist sites of non-commercial activity are: between 13 and 14 (p = 0.615384615) (Museo Negret Y MIAMP - Museo Guillermo León Valencia), between 3 and 14 (p = 0.54545454545) (Cámara de Comercio del Cauca - Museo Guillermo León Valencia), between 32 and 55 (p = 0.46907216) (Manos de Oro - Museo Arquidiocesano), between 42 and 55 (p = 0.40909091) (Expocauca - Museo Arquidiocesano), between 42 and 55 (p = 0.40909091) (Expocauca -Museo Arquidiocesano). The case of the state known as Rincón Payanés, is found repeatedly, to be a set of craft shops, which was expected to be located a few meters. Of these transitions, the only one that has no obvious geographical proximity is the relationship between 3 to 14 (p = 0.545454545) (Cauca Chamber of Commerce - Museo Guillermo León Valencia).
  • It should be noted that in the Chamber of Commerce, there is the Tourist Information Point of the city.
Exploring alternatives of possible patterns of sites to visit, in Table 5, the possibilities of transitions of states are reflected, that is to say, the transitions with non null value.

4.4. Analysis of Movements Based on Coordinate Paths

Figure 5 represents, in a unified way (all periods), the graphic representation of the coordinates of the tourist visits. It is remembered that the coordinates of the X and Y axes correspond to the latitude and longitude of each visit made, and the stroke between points represents the movement or moment of jump between each site, according to the instant of time (date and time) in which it took place. There is a greater concentration in the coordinates of what is called the historical centre and a specific range of the routes, the volumes of visits can be reaffirmed with the results of Figure 3, previously presented, which indicate the counts of visits of each site according to its ID for all periods.
In Figure 6, all periods plotted on a map are shown, as follows hybrid, showing the total routes developed for all periods. In the upper right corner, you will find the nomenclature of each period, being red for 2011, green for 2012, blue for 2013 and violet for 2015. In this it can be noted that only for 2012 there is a point far from the city, being for the other periods in an area of greater concentration, eminently related to the historic centre and surroundings. This central zone can be revised in an enlarged way in Figure 7.
Figure 8 shows a representation of the magnitude of the visits, presenting a greater radius, the sites with greater visits. It can be noted that the area of greatest activity is the centre, confirming the raised with the Markov Chains, and then by way of expansion is Figure 9.
The purpose of these representations, diagrams and maps generated is to confirm and provide visualization support to the analysis of patterns previously carried out, given that they are their representation in space, which also allows evidence of some other elements or patterns that mathematically cannot be evidenced.

4.5. Statistical Tests and Model Validation

Tourist movements can be modeled at the micro or macro level according to Xia and Arrowsmith [12,98,99,100]. At the micro level the movements are represented by a continuous stochastic process ( X t ) t E T , where T = [ 0 , ∞ ) and that takes values in a space of states S. The S states consist of several georeferenced spatial points representing the locations of people’s movements, so called continuous state space. On the other hand, in a tourist destination such as a city it is possible to model spatial—temporal movement in a state space S as the set of the different places of interest, therefore, S is discrete. At the macro level tourist movements are represented by a stochastic discrete process ( X t ) t E T , where T = 1 , 2 , 3 , . . . and takes values in a space of states S. In this way: S = A 1 , A 2 , A 3 … A k . So A i where i = 2 , 3 , . . . k represents the tourist attractions and A k represents the state “OUT” which is the outer space region S. For this research this space of states S is presented in Table 2, and the movement of tourist represented in a Markov Chain according to Section 4.1.
In order to validate this modeling, the null hypothesis to be contrasted is the independence between the factors and the alternative hypothesis as the dependence between the factors. In a general the value of X 2 calculated is compared with the tabulated value of X 2 , the latter indicates the value of a given confidence level and ( n − 1 ) ∗ ( k − 1 ) degrees of freedom, where n number of rows of the matrix and k number of columns of the matrix. If the calculated value is greater than the table value of X ( n − 1 ) ( k − 1 ) 2 , it will mean that the differences between the observed frequencies and the theoretical or expected frequencies are very high, so it is concluded that there is dependence between the factors or attributes analyzed. In short: H 1 = X 2 > X ( n − 1 ) ( k − 1 ) 2 means to reject null hypothesis (dependence between variables) and H 2 = X 2 < X ( n − 1 ) ( k − 1 ) 2 means to accept null hypothesis (independence between variables). Thus, if the results of the validation are in relation to the equation H 1 , it will indicate if the null hypothesis was rejected, thus existing the dependence between variables, otherwise, equation H 2 , the null hypothesis will be accepted, existing independence between the variables. However this works best for low values of n and k. For higher samples and multiple combinations of n and k a more elaborate treatment is wanted.
To test the model proposed we use the verifyMarkovProperty function from markovchain package [101] of R: this verifies whether the Markov property holds for the given secuence of events. “The test implemented in the package looks at triplets of successive observations (this implies more freedom degrees). If x 1 , x 2 , … , x N is a set of observations and n i j k is the number of times t 1 ≤ t ≤ N − 2 such that x t = i , x t + 1 = j , x x + 2 = k , then if the Markov property fulfilled n i j k indicates a binomial distribution with parameters n i j and p j k . A standard χ 2 test can check, since ∑ i ∑ j ∑ k n i j k − n i j p j k ^ n i j p j k ^ ∼ χ 2 | S | 3 where | S | is the cardinality of the state space” according to Spedicato in [101]. The calculated chi-square value for this research is X 2 = 1939 . 842 with degrees of freedom d f = 17 , 576 and p-value = 1, Since the p-value shown is 1, we do not reject the null hypothesis that the sequence follows the Markov property on independence between variables so the approach of the construction of a transitions matrix as in Table 3 is correct.

5. Discussion and Conclusions

The preservation and conservation of the historic site is directly related to the actions of urban mobility management, so in the case of visitor mobility, it is essential to have the right tools. Knowing which areas are under tourist pressure effectively contributes to tourism and city management and competitiveness, providing decision-makers with improved tools to design better, smarter, and sustainable strategies; also contributes to optimise tourists’ experience, which should be, ultimately, the goal of a smart tourism destination [55]. The application of different tools during different years in the same period and in the same historical city allows us to conclude the following aspects:
Firstly, that the GPS (in standalone device, used in 2013) provides more accurate results. However, the NFC technology (used in 2015) offers more results, in terms of declaration of visits, by providing value with delivery of information. Secondly, the survey (used in the 2012 period) requires a higher level of interaction or collaboration of the tourist and a lower incorporation or technological infrastructure. It was the only tool used that collected records of visits after they had taken place, which showed that many of the data collected were not accurate because it was based, first, on the willingness of the tourist to answer the questions in the questionnaire, and second, on the ability of the person to remember the times (exact or not) of the sites visited at the destination. Thirdly, the tourist card (used in 2011) allows by means of incentives to the tourists, to capture their data of profile and the exact registry of date and hour, in which it visited the different tourist sites of the city of Popayán. This type of results coincides with those obtained in later works [29,30]. Compared to the other tools, the card requires greater interaction of the tourist, since it is the tourist who takes the decision to accept it and present it in each associated establishment. One of the advantages is that this type of card does not require more supporting technology, since being a plastic card with a serial number; it does not demand more technology to be registered in each of the establishments. Likewise, it presents advantages related to a greater cooperation between different agents and also the greater visit of less known resources, which facilitates the distribution of flows outside the historic centre of the cities. In general, the results allow us to confirm that in the case of historic cities with an influx of tourists it is important to assess the availability of technologies and the use they can make of them to analyse the flow of their visitors.
In the case of cities with the capacity to incorporate technologies in the data collection tools, they will obtain captures of a greater number of data or greater precision; among the tools with these characteristics, the most convenient to obtain the best information on tourist movements will be those that require low participation or interaction of the tourist so that data capture is as transparent as possible. In this sense, for future studies it is important to consider tools that incorporate NFC technology, which, according to our study, is the one that produced the best results. Other previous studies [62,102,103,104,105,106] also confirmed that the use of NFC offers other services related to information supply, mobile payment, mobile ticketing, device pairing, access authorization, management of loyalty, bonus and membership cards. More recent works such as [107] confirm that in the case of MICE events, the use of NFC technology with a mobile application allows the experience to be improved for both the user and the organizer. Also, the Near Field Communication (NFC) technology has been applied previously in airports [108] and hotels [109]. In the touristic-cultural field we also can find applications such as museums [108,110] or in tourism destinations. Many destinations have implemented NFC technologies to improve the activity sustainability, to increase guest loyalty, boost the image and brand of the destination and to ultimately improve turnover [103]. In France a number of NFC field trials have been made. In June 2010 the first pilot study for the general public was launched in the city of Nice under the name “Cityzi” [102]. In Spain, the historic city of Cáceres was pioneer in the use of NFC technology applied to tourism from 2011. Also, in the Spanish city of Córdoba the NFC technology has been used [111] to help the user to find the location of interest points within the city and navigate through them. The users assessed very positively the simplicity of its use and the help that the system provides for surfing in urban environments, finding quite attractive that they just need their mobile device, as a support or alternative to traditional techniques. According with the authors in [111], touristic cities would require systems based on the NFC technology in order to provide its touristic offer with extra added value, given the social and economic impact of the tourism revenue in those kinds of cities. In Italy the NFC technology has been implemented in different regions [108,111] offering a wide range of services (information supply, mobile payment, mobile ticketing, device pairing, location based services, access 508 authorization, management of loyalty, bonus and membership cards). Destinations and attractions have the opportunity to know more about the tourist mobility and also, to enhance the consumer experience, through for example, the creation of new customer touchpoints through the use of NFC technology in different areas (information, ticketing or for access control) [103]. Even more, there are some authors [112] that highlight the future implication of NFC technology in within Smart City concepts.
To achieve these goals, it is necessary the good cooperation with and between the stakeholders in the destinations [103]. The advantages provided by NFC technology are very much recognized for the previous applications, reflected in the literature: first, because enables de sustainable tourism activity because it allows tourism destination a deeper knowledge about the tourism movements, what facilitates de management of the carrying capacity. Second because NFC encourages paperless travel, making the mobile phone all a tourist might need when travelling to certain, technologically advanced destinations [102]; third, because it is possible the connection with social media networks, such as Facebook, that are widely used both by consumers and companies and also because its high level of security [113]. It is important also to recognise the main limitations of the NFC technology applied in tourism destinations [108]; (i) the first one is related to the high fragmentation of the tourism sector, characterized by the existence of many small companies that often lack sufficient resources to invest in new technologies and infrastructure; (ii) secondly, this technology include a wide variety of heterogeneous and diversified services and this could be a barrier for the implementation of this offer; (iii) finally, local tourism destinations have few financial resources to invest in improving tourist offerings.
As a recommendation and according to the results presented, when historic cities are subject to the risk of increasing their flow of visitors that can concentrate in specific points, affecting their load capacity and putting at risk the sustainability of the cultural heritage (architectural and urban), as proposed by [114], it is necessary a better management that considers that its heritage is a non-renewable good. The sustainability of a tourist destination is directly related to the tourist reception capacity, a concept that according to Hernandez [115], is also applied to historic cities that are considered heritage ecosystems (more complex to manage) and have the challenge to manage the tourist activities in a responsible and sustainable way. Likewise, the author states that in order to do this it is necessary to manage the visitor flow in a proper way, which will allow protecting the patrimonial spaces experiencing tourist saturation, putting in value those of no tourist use, creating an infrastructure to welcome visitors, and improving the visitor’s satisfaction. To achieve this, the DMO (Destination Management Organization) must select the best tools available to collect precise and detailed data, which according to this study, are the mobile phones with GPS and NFC technologies, since they are much more accurate to identify the tourist movement patterns in a destination. To sum up, this work evidences two fundamental aspects: first, the added value of NFC technology compared to other technologies, in the study of tourist movement patterns in the historic city of Popayán. Furthermore, it can also be concluded that it is possible to model the spatio-temporal movement of tourists at the macro level, by using the MC methodology. This methodology using Markov chains can analyse trends and the results of a series of related events, since they depend on each other in a first order.

6. Practical Implications, Limitations and Future Research Lines

For destinations with similar characteristics to those of the city of Popayán, that are starting the stage of developing their sector of tourism and count on cultural attractions of interest to visitors, it is fundamental to use a methodology of tourist movement analysis which allows them to manage the flows more efficiently, and also to optimize the carrying capacity of the resources; that way, they can achieve a more sustainable tourist activity. Additionally, this allows a better collection of information about the tourists they receive (number, profile and movements), which facilitates the definition of the marketing rules in the destination.
One of the limitations of this study is the distribution of the samples, more specifically, the one regarding the tourists who used NFC, since due to the restrictions of the device, it represented a small number. Nevertheless, given the exploratory character of this research, it is considered big enough, as the proportion of the samples regarding foreign and national visitors is maintained, and the priority is the recorded movements. This is in agreement with the recommendations to assess technological systems using any interface novelty (in this case, the interaction with the mobile device), as proposed by J. Nielsen & Landauer (1993) [116], and K. Baxter, Courage, & Caine (2015) [117] for experimentation with this type of technologies.
Another limitation of this study has to do with the number of participants in 2013, when just mobile telephones with GPS(Global Positioning System) were the chosen tool for data collection: not all the tourists were willing to allow all their movements during their visit to the destination to be tracked. This limited the scope of this study for this period.
Any future research should test the advances in the use of the NFC technologies applied in tourism destinations not only cultural, in order to generalize these findings to a wider context. Also, it could be of interest to analyze the visitor’s and other stakeholders perceived value of the application of new technologies from both points of view.

Author Contributions

Conceptualization, L.F.-M.; Formal analysis, L.F.-M. and A.C.-A.; Investigation, A.C.-A.; Methodology, M.-F.B.-F., A.C.-A.; Resources, A.C.-A., M.-F.B.-F.; Supervision, L.F.-M. and A.M.-M.; Validation, L.F.-M. and A.M.-M.; Visualization, L.F.-M., M.-F.B.-F.; Writing – original draft, A.C.-A., L.F.-M. and M.-F.B.-F.; Writing – review and editing, A.C.-A., L.F.-M., A.M.-M. and M.-F.B.-F.

Funding

This research was funded by [Universidad del Cauca], grant number [501100005682].

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Russo, A.P. The “vicious circle” of tourism development in heritage cities. Ann. Tour. Res. 2002, 29, 165–182. [Google Scholar] [CrossRef]
  2. Butler, R. The Tourism Area Life Cycle; Channel View Publications: Bristol, UK, 2006; Volume 1. [Google Scholar]
  3. Fernández, D.B.; Castillo, L.A.; Salomao, E.M.A. Tourist use of historic cities: Review of international agreements and literature. Int. J. Humanit. Stud. 2014, 1, 1–11. [Google Scholar]
  4. Lau, G.; McKercher, B. Understanding tourist movement patterns in a destination: A GIS approach. Tour. Hosp. Res. 2006, 7, 39–49. [Google Scholar] [CrossRef]
  5. Hernández, G.M. Turismo y conjuntos monumentales. In Capacidad de Acogida Turística y Gestión de Flujos de Visitantes; Tirant lo Blanch: Valencia, Spain, 2003. [Google Scholar]
  6. Shoval, N.; Isaacson, M. Tracking tourists in the digital age. Ann. Tour. Res. 2007, 34, 141–159. [Google Scholar] [CrossRef]
  7. Shao, H.; Zhang, Y.; Li, W. Extraction and analysis of city’s tourism districts based on social media data. Comput. Environ. Urban Syst. 2017, 65, 66–78. [Google Scholar] [CrossRef]
  8. Smallwood, C.B.; Beckley, L.E.; Moore, S.A. An analysis of visitor movement patterns using travel networks in a large marine park, north-western Australia. Tour. Manag. 2012, 33, 517–528. [Google Scholar] [CrossRef]
  9. Tiwari, S.; Kaushik, S.; Jagwani, P. Location based recommender systems: Architecture, trends and research areas. In Proceedings of the IET International Conference on Wireless Communications and Applications (ICWCA 2012), Kuala Lumpur, Malaysia, 8–10 October 2012. [Google Scholar]
  10. Zheng, W.; Huang, X.; Li, Y. Understanding the tourist mobility using GPS: Where is the next place? Tour. Manag. 2017, 59, 267–280. [Google Scholar] [CrossRef] [Green Version]
  11. Orellana, D.; Bregt, A.K.; Ligtenberg, A.; Wachowicz, M. Exploring visitor movement patterns in natural recreational areas. Tour. Manag. 2012, 33, 672–682. [Google Scholar] [CrossRef]
  12. Xia, J.C.; Zeephongsekul, P.; Arrowsmith, C. Modelling spatio-temporal movement of tourists using finite Markov chains. Math. Comput. Simul. 2009, 79, 1544–1553. [Google Scholar] [CrossRef]
  13. Hummel, K.A.; Hess, A. Movement activity estimation for opportunistic networking based on urban mobility traces. In Proceedings of the 2010 IFIP Wireless Days, Venice, Italy, 20–22 October 2010; pp. 1–5. [Google Scholar]
  14. Bryon, J.; Russo, A.P. The tourist historic city. Ann. Tour. Res. 2003, 30, 492–494. [Google Scholar] [CrossRef]
  15. Shoval, N.; Isaacson, M. Tourist Mobility and Advanced Tracking Technologies; Routledge: Oxford, UK, 2009. [Google Scholar]
  16. Schaick, J.; Spek, S.C. Urbanism on Track: Application of Tracking Technologies in Urbanism; IOS Press: Amsterdam, The Netherlands, 2008; Volume 1. [Google Scholar]
  17. Martinez-Garcia, E.; Raya-Vilchez, J.; Galí, N. Factors Affecting Time Spent Visiting Heritage City Areas. Sustainability 2018, 10, 1824. [Google Scholar] [CrossRef]
  18. Richards, G.; Raymond, C. Creative tourism. ATLAS News 2000, 23, 16–20. [Google Scholar]
  19. Aranburu, I.; Plaza, B.; Esteban, M. Sustainable Cultural Tourism in Urban Destinations: Does Space Matter? Sustainability 2016, 8, 699. [Google Scholar] [CrossRef]
  20. García-Hernández, M.; de la Calle-Vaquero, M.; Yubero, C. Cultural heritage and urban tourism: Historic city centres under pressure. Sustainability 2017, 9, 1346. [Google Scholar] [CrossRef]
  21. Zheng, W.; Zhou, R.; Zhang, Z.; Zhong, Y.; Wang, S.; Wei, Z.; Ji, H. Understanding the tourist mobility using GPS: How similar are the tourists? Tour. Manag. 2019, 71, 54–66. [Google Scholar] [CrossRef]
  22. Wolf, I.D.; Hagenloh, G.; Croft, D.B. Visitor monitoring along roads and hiking trails: How to determine usage levels in tourist sites. Tour. Manag. 2012, 33, 16–28. [Google Scholar] [CrossRef]
  23. Parroco, A.M.; Vaccina, F.; De Cantis, S.; Ferrante, M. Multi-destination trips and tourism statistics: Empirical evidences in Sicily. Econ. Open-Access Open-Assess. E-J. 2012, 6, 1–27. [Google Scholar] [CrossRef]
  24. Terrier, C. Tourist flows and inflows: on measuring instruments and the geomathematics of flows. In Transport Survey Methods: Keeping up with a Changing World; Emerald Group Publishing Limited: Bingley, UK, 2009; pp. 219–241. [Google Scholar]
  25. Thornton, P.R.; Shaw, G.; Williams, A.M. Tourist group holiday decision-making and behaviour: The influence of children. Tour. Manag. 1997, 18, 287–297. [Google Scholar] [CrossRef]
  26. O’Connor, A.; Zerger, A.; Itami, B. Geo-temporal tracking and analysis of tourist movement. Math. Comput. Simul. 2005, 69, 135–150. [Google Scholar] [CrossRef]
  27. Zakrisson, I.; Zillinger, M. Emotions in motion: Tourist experiences in time and space. Curr. Issues Tour. 2012, 15, 505–523. [Google Scholar] [CrossRef]
  28. Zoltan, J.; Masiero, L. The relation between push motivation and activity consumption at the destination within the framework of a destination card. J. Destin. Mark. Manag. 2012, 1, 84–93. [Google Scholar] [CrossRef]
  29. Angeloni, S. A tourist kit ‘made in Italy’: An ‘intelligent’system for implementing new generation destination cards. Tour. Manag. 2016, 52, 187–209. [Google Scholar] [CrossRef]
  30. Zoltan, J.; McKercher, B. Analysing intra-destination movements and activity participation of tourists through destination card consumption. Tour. Geogr. 2015, 17, 19–35. [Google Scholar] [CrossRef]
  31. DubrovnikCard. Available online: http://www.dubrovnikcard.com/ (accessed on 30 September 2019).
  32. Centre, UNESCO World Heritage. UNESCO World Heritage Centre—Document—Report on the UNESCO/ICOMOS Reactive Monitoring mission to the Old City of Dubrovnik, Croatia, 27 October–1 November, 2015. Available online: https://whc.unesco.org/en/documents/141053/ (accessed on 30 September 2019).
  33. Van der Knaap, W.G. GIS-oriented analysis of tourist time-space patterns to support sustainable tourism development. Tour. Geogr. 1999, 1, 56–69. [Google Scholar] [CrossRef]
  34. Connell, J.; Page, S.J. Exploring the spatial patterns of car-based tourist travel in Loch Lomond and Trossachs National Park, Scotland. Tour. Manag. 2008, 29, 561–580. [Google Scholar] [CrossRef] [Green Version]
  35. Grinberger, A.Y.; Shoval, N.; McKercher, B. Typologies of tourists’ time–space consumption: A new approach using GPS data and GIS tools. Tour. Geogr. 2014, 16, 105–123. [Google Scholar] [CrossRef]
  36. rmen, J.; Thorhauge, A.M. Smartphone log data in a qualitative perspective. Mob. Media Commun. 2015, 3, 335–350. [Google Scholar]
  37. Medzini, A.; Meishar-Tal, H.; Sneh, Y. Use of mobile technologies as support tools for geography field trips. Int. Res. Geogr. Environ. Educ. 2015, 24, 13–23. [Google Scholar] [CrossRef]
  38. Ahas, R.; Aasa, A.; Mark, Ü.; Pae, T.; Kull, A. Seasonal tourism spaces in Estonia: Case study with mobile positioning data. Tour. Manag. 2007, 28, 898–910. [Google Scholar] [CrossRef]
  39. Bauder, M.; Freytag, T. Visitor mobility in the city and the effects of travel preparation. Tour. Geogr. 2015, 17, 682–700. [Google Scholar] [CrossRef]
  40. De Cantis, S.; Ferrante, M.; Kahani, A.; Shoval, N. Cruise passengers’ behavior at the destination: Investigation using GPS technology. Tour. Manag. 2016, 52, 133–150. [Google Scholar] [CrossRef]
  41. Tchetchik, A.; Fleischer, A.; Shoval, N. Segmentation of visitors to a heritage site using high-resolution time-space data. J. Travel Res. 2009, 48, 216–229. [Google Scholar] [CrossRef]
  42. Sugimoto, K.; Ota, K.; Suzuki, S. Visitor Mobility and Spatial Structure in a Local Urban Tourism Destination: GPS Tracking and Network analysis. Sustainability 2019, 11, 919. [Google Scholar] [CrossRef]
  43. East, D.; Osborne, P.; Kemp, S.; Woodfine, T. Combining GPS & survey data improves understanding of visitor behaviour. Tour. Manag. 2017, 61, 307–320. [Google Scholar]
  44. Raun, J.; Ahas, R.; Tiru, M. Measuring tourism destinations using mobile tracking data. Tour. Manag. 2016, 57, 202–212. [Google Scholar] [CrossRef]
  45. Zhao, X.; Lu, X.; Liu, Y.; Lin, J.; An, J. Tourist movement patterns understanding from the perspective of travel party size using mobile tracking data: A case study of Xi’an, China. Tour. Manag. 2018, 69, 368–383. [Google Scholar] [CrossRef]
  46. Bastianoni, S.; Pulselli, R.; Romano, P.; Pulselli, F. Dynamics and evolution of urban patterns: the evidence of the Mobile Landscape project. Des. Nat. IV Comp. Des. Nat. Sci. Eng. 2008, 4, 253. [Google Scholar]
  47. Vaccari, A.; Liu, L.; Biderman, A.; Ratti, C.; Pereira, F.; Oliveirinha, J.; Gerber, A. A holistic framework for the study of urban traces and the profiling of urban processes and dynamics. In Proceedings of the 2009 12th International IEEE Conference on Intelligent Transportation Systems, St. Louis, MO, USA, 4–7 October 2009; pp. 1–6. [Google Scholar]
  48. Ros, M.; D’Souza, M.; Postula, A.; MacColl, I. Location based services with personal area network for community and tourism applications. In Proceedings of the IET International Communication Conference on Wireless Mobile and Computing (CCWMC 2011), Shanghai, China, 14–16 November 2011. [Google Scholar]
  49. Georgeta-Irina, P. An insight into m-tourism. Rev. Econ. 2013, 65, 51–63. [Google Scholar]
  50. Versichele, M.; De Groote, L.; Bouuaert, M.C.; Neutens, T.; Moerman, I.; Van de Weghe, N. Pattern mining in tourist attraction visits through association rule learning on Bluetooth tracking data: A case study of Ghent, Belgium. Tour. Manag. 2014, 44, 67–81. [Google Scholar] [CrossRef] [Green Version]
  51. Delafontaine, M.; Versichele, M.; Neutens, T.; Van de Weghe, N. Analysing spatiotemporal sequences in Bluetooth tracking data. Appl. Geogr. 2012, 34, 659–668. [Google Scholar] [CrossRef] [Green Version]
  52. Gerval, J.P.; Le Ru, Y. Fusion of multimedia and mobile technology in audioguides for museums and exhibitions. In Fusion of Smart, Multimedia and Computer Gaming Technologies; Springer: New York, NY, USA, 2015; pp. 173–205. [Google Scholar]
  53. Moussouri, T.; Roussos, G. Conducting visitor studies using smartphone-based location sensing. J. Comput. Cult. Herit. (JOCCH) 2015, 8, 12. [Google Scholar] [CrossRef]
  54. Gabrielli, L.; Rinzivillo, S.; Ronzano, F.; Villatoro, D. From tweets to semantic trajectories: mining anomalous urban mobility patterns. In Proceedings of the International Workshop on Citizen in Sensor Networks, Barcelona, Spain, 19 September 2013; pp. 26–35. [Google Scholar]
  55. Encalada, L.; Boavida-Portugal, I.; Cardoso Ferreira, C.; Rocha, J. Identifying tourist places of interest based on digital imprints: Towards a sustainable smart city. Sustainability 2017, 9, 2317. [Google Scholar] [CrossRef]
  56. Chua, A.; Servillo, L.; Marcheggiani, E.; Moere, A.V. Mapping Cilento: Using geotagged social media data to characterize tourist flows in southern Italy. Tour. Manag. 2016, 57, 295–310. [Google Scholar] [CrossRef]
  57. Su, S.; Wan, C.; Hu, Y.; Cai, Z. Characterizing geographical preferences of international tourists and the local influential factors in China using geo-tagged photos on social media. Appl. Geogr. 2016, 73, 26–37. [Google Scholar] [CrossRef]
  58. Tussyadiah, I.P.; Fesenmaier, D.R. Interpreting Tourist Experiences from First-Person Stories: A Foundation for Mobile Guides. In Proceedings of the Fifteenth European Conference on Information Systems, ECIS 2007, St. Gallen, Switzerland, 7–9 June 2007; pp. 2259–2270. [Google Scholar]
  59. Borrero, F.; Sanjuán, P.; Ramirez-Gonzalez, G. Gamification techniques in tourism, application test, Casa Mosquera Museum. Sistemas & Telemática 2015, 13, 63–76. [Google Scholar]
  60. Dickinson, J.E.; Ghali, K.; Cherrett, T.; Speed, C.; Davies, N.; Norgate, S. Tourism and the smartphone app: capabilities, emerging practice and scope in the travel domain. Curr. Issues Tour. 2014, 17, 84–101. [Google Scholar] [CrossRef]
  61. Atzori, L.; Iera, A.; Morabito, G. The internet of things: A survey. Comput. Netw. 2010, 54, 2787–2805. [Google Scholar] [CrossRef]
  62. Basili, A.; Liguori, W.; Palumbo, F. NFC smart tourist card: Combining mobile and contactless technologies towards a smart tourist experience. In Proceedings of the 2014 IEEE 23rd International WETICE Conference, Parma, Italy, 23–25 June 2014; pp. 249–254. [Google Scholar]
  63. Chantre-Astaiza, A.; Fuentes-Moraleda, L.; Muñoz-Mazón, A.; Ramirez-Gonzalez, G. Science Mapping of Tourist Mobility 1980–2019. Technological Advancements in the Collection of the Data for Tourist Traceability. Sustainability 2019, 11, 4738. [Google Scholar] [CrossRef]
  64. Xia, J. Modelling the Spatial-Temporal Movement of Tourists. Ph.D. Thesis, Mathematical and Geospatial Sciences, RMIT University, Melbourne, Australia, 2007. [Google Scholar]
  65. Mednick, H. A Markov chain model of travel patterns of US visitors to Ontario. J. Leis. Res. 1975, 7, 246–255. [Google Scholar] [CrossRef]
  66. Tobler, W. Movement modeling on the sphere. Geogr. Environ. Model. 1997, 1, 97–103. [Google Scholar]
  67. Lusseau, D. Effects of tour boats on the behavior of bottlenose dolphins: using Markov chains to model anthropogenic impacts. Conserv. Biol. 2003, 17, 1785–1793. [Google Scholar] [CrossRef]
  68. Zheng, Y.T.; Li, Y.; Zha, Z.J.; Chua, T.S. Mining travel patterns from GPS-tagged photos. In Proceedings of the International Conference on Multimedia Modeling, Taipei, Taiwan, 5–7 January 2011; pp. 262–272. [Google Scholar]
  69. Yamamoto, T.; Kitamura, R.; Kishizawa, K. Sampling alternatives from colossal choice set: Application of Markov Chain Monte Carlo algorithm. Transp. Res. Rec. 2001, 1752, 53–61. [Google Scholar] [CrossRef]
  70. Kikuchi, A.; Kitamura, R.; Yamamoto, T.; Fujii, S. Simulating spatial choices with quasi-continuous representation of urban space: An application of MCMC algorithms. In Proceedings of the Transportation Research Board 82nd Annual Meeting, Washington, DC, USA, 12–16 January 2003. [Google Scholar]
  71. Tabatabaee, N.; Ziyadi, M. Bayesian approach to updating Markov-based models for predicting pavement performance. Transp. Res. Rec. 2013, 2366, 34–42. [Google Scholar] [CrossRef]
  72. Spitzer, F. Principles of Random Walk, 2nd ed.; Graduate Texts in Mathematics; Springer: New York, NY, USA, 2001. [Google Scholar]
  73. Harabagiu, S.; Hickl, A.; Lacatusu, F. Negation, contrast and contradiction in text processing. In Proceedings of the 21st National Conference on Artificial Intelligence, Boston, MA, USA, 16–20 July 2006; Volume 6, pp. 755–762. [Google Scholar]
  74. Wolf, I.D.; Stricker, H.K.; Hagenloh, G. Interpretive media that attract park visitors and enhance their experiences: A comparison of modern and traditional tools using GPS tracking and GIS technology. Tour. Manag. Perspect. 2013, 7, 59–72. [Google Scholar] [CrossRef]
  75. McKercher, B.; Shoval, N.; Ng, E.; Birenboim, A. Using GPS data to compare first-time and repeat visitors to Hong Kong. Tour. Geogr. 2012, 14, 147–161. [Google Scholar] [CrossRef]
  76. Paulino Valldepérez, I. Tourism Destination Zoning in Rural Regions: A Consumer-Based Approach in Terres de l’Ebre. 2013. Available online: https://dugi-doc.udg.edu/handle/10256/8808 (accessed on 30 September 2019).
  77. Blasco, D.; Guia, J.; Prats, L. Emergence of governance in cross-border destinations. Ann. Tour. Res. 2014, 49, 159–173. [Google Scholar] [CrossRef]
  78. Chen, Z.; Shen, H.T.; Zhou, X. Discovering popular routes from trajectories. In Proceedings of the 2011 IEEE 27th International Conference on Data Engineering, Hannover, Germany, 11–16 April 2011; pp. 900–911. [Google Scholar]
  79. Zhao, J.; Forer, P.; Sun, Q.; Simmons, D. Multiple-view strategies for enhanced understanding of dynamic tourist activity through geovisualization at regional and national scales. Cartogr. Geogr. Inf. Sci. 2013, 40, 349–360. [Google Scholar] [CrossRef]
  80. Mckercher, B.; Lau, G. Movement patterns of tourists within a destination. Tour. Geogr. 2008, 10, 355–374. [Google Scholar] [CrossRef]
  81. McAdam, D. The value and scope of geographical information systems in tourism management. J. Sustain. Tour. 1999, 7, 77–92. [Google Scholar] [CrossRef]
  82. Beeco, J.A.; Brown, G. Integrating space, spatial tools, and spatial analysis into the human dimensions of parks and outdoor recreation. Appl. Geogr. 2013, 38, 76–85. [Google Scholar] [CrossRef]
  83. GPS Visualizer. Available online: https://www.gpsvisualizer.com/ (accessed on 30 September 2019).
  84. Michelot, T.; Langrock, R.; Patterson, T.A. moveHMM: An R package for the statistical modelling of animal movement data using hidden Markov models. Methods Ecol. Evol. 2016, 7, 1308–1315. [Google Scholar] [CrossRef]
  85. R: The R Project for Statistical Computing. Available online: https://www.r-project.org/ (accessed on 30 September 2019).
  86. Schumacker, R.; Tomek, S. Understanding Statistics Using R; Springer Science & Business Media: New York, NY, USA, 2013. [Google Scholar]
  87. Kabacoff, R. R In Action: Data Analysis and Graphics with R; Kabacoff, R., Ed.; Manning Publications Co.: Shelter Island, NY, USA, 2015. [Google Scholar]
  88. Vinod, H.D. Advances in Social Science Research Using R; Springer: New York, NY, USA, 2010. [Google Scholar]
  89. Pagano, R.R. Understanding Statistics in the Behavioral Sciences; Cengage Learning: Boston, MA, USA, 2012; Volume 1. [Google Scholar]
  90. Friendly, M.; Kwan, E. Effect ordering for data displays. Comput. Stat. Data Anal. 2003, 43, 509–539. [Google Scholar] [CrossRef] [Green Version]
  91. Friendly, M. A brief history of data visualization. In Handbook of Data Visualization; Springer: New York, NY, USA, 2008; pp. 15–56. [Google Scholar]
  92. Friendly, M. Corrgrams: Exploratory displays for correlation matrices. Am. Stat. 2002, 56, 316–324. [Google Scholar] [CrossRef]
  93. Wei, T.; Simko, V.; Levy, M.; Xie, Y.; Jin, Y.; Zemla, J. corrplot: Visualization of a Correlation Matrix. 17 October 2017. Available online: https://cran.r-project.org/web/packages/corrplot/corrplot.pdf (accessed on 21 September 2019).
  94. Loecher, M.; Ropkins, K. RgoogleMaps and loa: Unleashing R graphics power on map tiles. J. Stat. Softw. 2015, 63. [Google Scholar] [CrossRef]
  95. Cheshire, J.; Lovelace, R. Spatial data visualisation with R. In Geocomputation: A Practical Primer; SAGE: London, UK, 2015; p. 3. [Google Scholar]
  96. Kahle, D.; Wickham, H. ggmap: Spatial Visualization with ggplot2. R J. 2013, 5, 144–161. [Google Scholar] [CrossRef] [Green Version]
  97. Quintero, F.G. La ciudad colonial y sus textualidades contemporáneas: El color blanco en Popayán. Un estudio de semiótica cultural urbana. Revista Nexus Comunicación 2014. [Google Scholar] [CrossRef]
  98. Xia, J.C.; Evans, F.H.; Spilsbury, K.; Ciesielski, V.; Arrowsmith, C.; Wright, G. Market segments based on the dominant movement patterns of tourists. Tour. Manag. 2010, 31, 464–469. [Google Scholar] [CrossRef] [Green Version]
  99. Xia, J.; Arrowsmith, C. Managing Scale Issues in Spatio-temporal Movement of Tourists Modelling. In Proceedings of the International Congress on Modelling and Simulation (MODSIM05), Melbourne, Australia, 12–15 December 2005; Zerger, A., Argent, R.M., Eds.; pp. 162–169. [Google Scholar]
  100. Arrowsmith, C.; Zanon, D.; Chhetri, P. Chapter 4—Monitoring Visitor Patterns of Use in Natural Tourist Destinations. In Taking Tourism to the Limits; Ryan, C., Page, S.J., Aicken, M., Eds.; Elsevier: Oxford, UK, 2005; pp. 33–52. [Google Scholar]
  101. Spedicato, G.A. Discrete Time Markov Chains with R. R J. 2017, 9, 84–104. [Google Scholar] [CrossRef]
  102. Pesonen, J.; Horster, E. Near field communication technology in tourism. Tour. Manag. Perspect. 2012, 4, 11–18. [Google Scholar] [CrossRef]
  103. Egger, R. The impact of near field communication on tourism. J. Hosp. Tour. Technol. 2013, 4, 119–133. [Google Scholar] [CrossRef]
  104. Ramírez-González, G.; Muñoz Organero, M.; Kloos, C.D.; Astaiza, A.C. Exploring NFC Interactive Panel. In Proceedings of the 5th Annual International Conference on Mobile and Ubiquitous Systems: Computing, Networking, and Services (Mobiquitous ’08), Dublin, Ireland, 21–25 July 2008; ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering): Brussels, Belgium, 2008; p. 32. [Google Scholar]
  105. Palumbo, F.; Dominici, G. Unraveling the complexity of tourist experience with NFC technology and mobile wallets. In Chaos, Complexity and Leadership 2013; Springer: New York, NY, USA, 2015; pp. 189–196. [Google Scholar]
  106. Imbachi, J.L.C.; Jacome, D.L.N.; González, G.R. Mobile payments system employing NFC technology under the Android operating system. Sistemas & Telemática 2015, 13, 77–87. [Google Scholar]
  107. Silva-Pedroza, D.; Marin-Calero, R.; Ramirez-Gonzalez, G. NFC Evaluation in the Development of Mobile Applications for MICE in Tourism. Sustainability 2017, 9, 1937. [Google Scholar] [CrossRef]
  108. Tecnología NFC Servirá Para Promover el Turismo en España. Available online: https://www.fayerwayer.com/2011/04/tecnologia-nfc-servira-para-promover-el-turismo-en-espana/ (accessed on 30 September 2019).
  109. NFC Phones Replace Room Keys and Eliminate Check-In at Swedish hotel NFC World. 2010. Available online: https://www.nfcworld.com/2010/11/03/34886/nfc-keys-hotel-sweden/ (accessed on 30 September 2019).
  110. Ceipidor, U.B.; Medaglia, C.; Volpi, V.; Moroni, A.; Sposato, S.; Carboni, M.; Caridi, A. NFC technology applied to touristic-cultural field: A case study on an Italian museum. In Proceedings of the 2013 5th International Workshop on Near Field Communication (NFC), Zurich, Switzerland, 5 February 2013; pp. 1–6. [Google Scholar]
  111. Borrego-Jaraba, F.; Luque Ruiz, I.; Gómez-Nieto, M.Á. A NFC-based pervasive solution for city touristic surfing. Pers. Ubiquitous Comput. 2011, 15, 731–742. [Google Scholar] [CrossRef]
  112. Ronay, E.; Egger, R. NFC smart city: Cities of the future—A scenario technique application. In Information and Communication Technologies in Tourism 2014; Springer: New York, NY, USA, 2013; pp. 565–577. [Google Scholar]
  113. Agredo, E.A.C.; Martínez-Acosta, L.C.; Chantre, A.; Ramirez-Gonzalez, G. Pervasive NFC-based solution for the analysis of tourism data in an environment of smart cities. Sistemas & Telemática 2015, 13, 41–60. [Google Scholar] [Green Version]
  114. Vinuesa, M.A.T. Turismo y desarrollo sostenible en las ciudades históricas con patrimonio arquitectónico- monumental. Estudios turísticos 1998, 137, 5–53. [Google Scholar]
  115. Hernández, M.G. Turismo y medio ambiente en ciudades históricas. De la capacidad de acogida turística a la gestión de los flujos de visitantes. Anales de Geografía de la Universidad Complutense 2000, 2000, 131–148. [Google Scholar]
  116. Nielsen, J.; Landauer, T.K. A mathematical model of the finding of usability problems. In Proceedings of the INTERACT’93 and CHI’93 Conference on Human Factors in Computing Systems, Amsterdam, The Netherlands, 24–29 April 1993; pp. 206–213. [Google Scholar]
  117. Baxter, K.; Courage, C.; Caine, K. Understanding Your Users: A Practical Guide to User Research Methods; Morgan Kaufmann: Burlington, MA, USA, 2015. [Google Scholar]
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Figure 1. Classification information capture tools. Source: [63].
Figure 1. Classification information capture tools. Source: [63].
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Figure 2. Transitions graph of the transition matrix of the Markov Chain—All periods. Source: Own elaboration using R.
Figure 2. Transitions graph of the transition matrix of the Markov Chain—All periods. Source: Own elaboration using R.
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Figure 3. Distribution of the count of visits to sites (states) of all periods. Source: Own elaboration.
Figure 3. Distribution of the count of visits to sites (states) of all periods. Source: Own elaboration.
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Figure 4. Number of visits per visitor identification for all periods. Source: Own elaboration.
Figure 4. Number of visits per visitor identification for all periods. Source: Own elaboration.
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Figure 5. Visual map of visits by coordinates - All periods. Source: Own elaboration.
Figure 5. Visual map of visits by coordinates - All periods. Source: Own elaboration.
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Figure 6. Display map of all periods on hybrid map. Source: Own elaboration.
Figure 6. Display map of all periods on hybrid map. Source: Own elaboration.
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Figure 7. Extended map display of all periods on street map. Source: Own elaboration.
Figure 7. Extended map display of all periods on street map. Source: Own elaboration.
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Figure 8. Visualization map of the intensity of the general visit of the city. Source: Own elaboration.
Figure 8. Visualization map of the intensity of the general visit of the city. Source: Own elaboration.
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Figure 9. Visualisation map of the intensity of visits to the historic centre. Source: Own elaboration.
Figure 9. Visualisation map of the intensity of visits to the historic centre. Source: Own elaboration.
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Table
Table 1. Description of the instruments used by period. Source: Own elaboration.
Table 1. Description of the instruments used by period. Source: Own elaboration.
Static Information, Collected by Means of a Survey in the Four Periods
AmplitudTourist profile. Variables; age, nationality, company, previous visits,
motivation, age, expenditure, gender, means of transport, means of information.
DepthNumber of visits to the 36 resources identified within the tourist offer
Dynamic information, collected differently in the four periods
YearINSTRUMENTINFORMATION RECOLECTIONNº Movements and DATA ANALYSIS
2011Tourist Cards (System that stores the records of visits. Tourist database). Inventiveness was provided when the tourist presented it.Definition of points of observation for delivery or reception of the tourist card, called PAT (Point of Attention of the Tourist).120 movements recorded. The system developed ensures that surveys and movements are processed and stored properly. The information is first captured on paper and then digitized.
2012Survey. Tool to process the survey. Survey database. Applied at the exit points of the destination.Definition of identification points of tourist, for the delivery and realization of 420 surveys. It is decided to carry it out at the points of entry and exit of the destination.821 movements recorded. We have a database that allows surveys and movements to be processed and stored properly. The information is first captured on paper and then digitized.
2013GPS Mobile phones with GPS and information capture application. Application for information downloads. Database. Provides the position of the tourist through the triangulation of satellite signals.Mobile phones are delivered to visitors who accessed the site.304 movements recorded. There is a database that allows surveys to be processed and stored properly. The information is first captured on paper and then digitized. The movements are stored in files in the mobile that are extracted.
2015NFC (Near Field Communication). Mobile phones with NFC. City map with NFC tags. Information download tool. Database. Allows the tourist to expand information on the resources visited.Mobile phones with NFC support and map with NFC tags are delivered.104 movements recorded. There is a database that allows surveys to be processed and stored properly. The information is first captured on paper and then digitized. Movements are stored in files on the mobile that are extracted.
Table
Table 2. List with ID of sites visited in all periods. Source: Own elaboration.
Table 2. List with ID of sites visited in all periods. Source: Own elaboration.
ID PlaceLOCATIONLONGITUDELATITUDE
3Cámara de Comercio del Cauca−76.60677552.4420285
4Policía de Turismo – Terminal−76.60844862.4513106
5Centro Comercial Campanario−76.59469442.4593543
6Museo Casa Mosquera−76.605012.44293
13Museo Negret Y MIAMP−76.6097262.4424412
14Museo Guillermo León Valencia−76.60928142.442345
23Los Quingos Restaurante Típico−76.60060212.4404079
24Jengibre Restaurante y Cafetería−76.59866722.4516348
26Aplanchados Doña Chepa−76.604053082.44401423
27Restaurante y Pizzería El Recuerdo−76.59808282.4521989
28Wipala Galería Café – Bar−76.60189632.4424512
29Museo Historia Natural Unicauca−76.6011782.4430614
30Museo Nacional Guillermo Valencia−76.60513842.4431587
31Miscelánea La Torre del Reloj−76.6072612.44159708
32Manos de Oro (Corseda)−76.604374952.4407985
36El Taller de Esperanza Polanco−76.609369222.44325854
38Rincón Payanés (Café La Nigua)−76.598979682.44349838
39Rincón Payanés (Cerámicas Tierra y Fuego)−76.598915312.44347962
41Rincón Payanés (Artesanías Dennis)−76.598936772.44348498
42Expocauca−76.609051382.44882575
48Granja Integral Mama Lombriz−76.55601742.51045209
51Rincón Payanés (Arte y Fuego)−76.598996452.44342267
52Rincón Payanés (Anthera Accesorios)−76.599032662.44367859
53Rincón Payanés (Muñecas de Trapo)−76.598922.44367993
55Museo Arquidiocesano−76.60440212.4417669
57Panteón de los próceres−76.60636172.4428647
Table
Table 3. Initial Markov Chain Transitions Matrix—All Periods. Source: Own elaboration.
Table 3. Initial Markov Chain Transitions Matrix—All Periods. Source: Own elaboration.
131423242627282933031323638
130.000000000.6153846150.025641030.0000000000.000000000.025641030.000000000.102564100.000000000.0512820510.0000000000.000000000.076923080.00000000
140.058823530.0000000000.019607840.0000000000.039215690.000000000.000000000.078431370.000000000.2549019610.0000000000.000000000.078431370.00000000
230.015384620.0000000000.000000000.0000000000.030769230.046153850.000000000.076923080.000000000.0307692310.0000000000.276923080.000000000.00000000
240.000000000.0000000000.000000000.0000000000.000000000.000000000.000000000.050000000.000000000.3000000000.0000000000.250000000.000000000.00000000
260.000000000.0000000000.045454550.1363636360.000000000.000000000.000000000.090909090.000000000.0454545450.0000000000.045454550.000000000.00000000
270.000000000.0000000000.000000000.0000000000.000000000.000000000.250000000.000000000.000000000.0000000000.0000000000.250000000.000000000.00000000
280.400000000.0000000000.000000000.0000000000.000000000.000000000.000000000.100000000.000000000.1000000000.1000000000.000000000.000000000.00000000
290.000000000.0000000000.068181820.0000000000.000000000.000000000.022727270.000000000.000000000.2954545450.0113636360.125000000.000000000.01136364
30.000000000.5454545450.000000000.0000000000.000000000.000000000.000000000.000000000.000000000.1818181820.0000000000.000000000.000000000.00000000
300.000000000.0000000000.000000000.0000000000.026143790.000000000.000000000.013071900.000000000.0000000000.0000000000.209150330.000000000.00000000
310.125000000.1250000000.000000000.0000000000.000000000.000000000.000000000.000000000.000000000.0000000000.0000000000.000000000.000000000.00000000
320.000000000.0000000000.010309280.0103092780.000000000.000000000.010309280.015463920.000000000.0000000000.0051546390.000000000.000000000.00000000
360.000000000.0000000000.111111110.2222222220.000000000.000000000.000000000.000000000.000000000.0000000000.0000000000.000000000.000000000.00000000
380.000000000.0000000000.000000000.0000000000.000000000.000000000.000000000.000000000.000000000.0000000000.0000000000.000000000.000000000.00000000
390.000000000.0000000001.000000000.0000000000.000000000.000000000.000000000.000000000.000000000.0000000000.0000000000.000000000.000000000.00000000
40.000000000.0000000000.000000000.0000000000.000000000.000000000.000000000.000000000.000000000.0000000000.0000000000.000000000.000000000.00000000
410.000000000.0000000000.100000000.0000000000.000000000.000000000.050000000.000000000.000000000.0000000000.0000000000.000000000.000000000.00000000
420.000000000.0000000000.000000000.0000000000.000000000.000000000.000000000.000000000.000000000.0000000000.0000000000.000000000.000000000.00000000
480.000000000.0000000000.000000000.0000000000.000000000.000000000.000000000.000000000.000000000.0000000000.0000000000.000000000.000000000.00000000
50.051724140.0000000000.000000000.0000000000.000000000.000000000.000000000.189655170.000000000.0517241380.0000000000.051724140.000000000.01724138
510.000000000.0000000000.150000000.0000000000.000000000.000000000.100000000.000000000.000000000.0000000000.0000000000.000000000.000000000.00000000
520.000000000.0000000000.333333330.0000000000.000000000.000000000.166666670.000000000.000000000.0000000000.0000000000.000000000.000000000.00000000
530.000000000.0000000000.000000000.0000000000.000000000.000000000.000000000.000000000.000000000.0000000000.0000000000.000000000.000000000.00000000
550.000000000.0062695920.000000000.0031347960.000000000.000000000.000000000.018808780.000000000.0062695920.0000000000.053291540.000000000.00000000
570.000000000.0000000000.022988510.1264367820.011494250.000000000.000000000.034482760.000000000.0229885060.0574712640.034482760.011494250.00000000
60.065573770.0983606560.016393440.0081967210.040983610.000000000.000000000.090163930.000000000.2868852460.0000000000.073770490.000000000.00000000
E0.038854810.0122699390.079754600.0000000000.016359920.000000000.000000000.071574640.022494890.1186094070.0000000000.192229040.002044990.00000000
394414248551525355576E
130.000000000.000000000.0000000000.000000000.0000000000.0000000000.0256410260.000000000.000000000.076923080.000000000.0000000000.00000000
140.000000000.000000000.0000000000.000000000.0000000000.0000000000.0000000000.000000000.000000000.058823530.039215690.0392156860.33333333
230.000000000.000000000.0000000000.000000000.0000000000.0000000000.0000000000.030769230.000000000.153846150.015384620.0307692310.29230769
240.000000000.000000000.0000000000.000000000.0000000000.0000000000.0000000000.000000000.000000000.100000000.200000000.0500000000.05000000
260.000000000.000000000.0000000000.000000000.0000000000.0000000000.0000000000.000000000.000000000.090909090.090909090.0000000000.45454545
270.000000000.000000000.0000000000.000000000.0000000000.0000000000.0000000000.000000000.000000000.000000000.000000000.0000000000.50000000
280.000000000.000000000.0000000000.000000000.0000000000.2000000000.0000000000.000000000.000000000.100000000.000000000.0000000000.00000000
290.000000000.000000000.1704545450.011363640.0000000000.0227272730.0454545450.000000000.011363640.113636360.022727270.0000000000.06818182
30.000000000.000000000.0000000000.000000000.0000000000.0909090910.0000000000.000000000.000000000.000000000.000000000.1818181820.00000000
300.000000000.000000000.0065359480.052287580.0000000000.0000000000.0065359480.000000000.000000000.320261440.091503270.0392156860.23529412
310.000000000.000000000.0000000000.000000000.0000000000.1250000000.0000000000.000000000.000000000.125000000.000000000.0000000000.50000000
320.000000000.000000000.0103092780.041237110.0051546390.0979381440.0000000000.000000000.000000000.469072160.020618560.0103092780.29381443
360.000000000.000000000.0000000000.000000000.0000000000.0000000000.0000000000.000000000.000000000.000000000.333333330.0000000000.33333333
380.000000000.000000000.0000000000.000000000.5000000000.0000000000.0000000000.000000000.000000000.000000000.000000000.0000000000.50000000
390.000000000.000000000.0000000000.000000000.0000000000.0000000000.0000000000.000000000.000000000.000000000.000000001.0000000000.00000000
40.000000000.000000000.0000000000.000000000.0000000000.0000000000.0000000000.000000000.000000000.000000000.000000001.0000000000.00000000
410.000000000.000000000.0000000000.050000000.0000000000.0000000000.5000000000.150000000.050000000.050000000.000000000.0000000000.05000000
420.000000000.000000000.0000000000.000000000.0000000000.0000000000.0454545450.000000000.000000000.409090910.045454550.0000000000.50000000
480.000000000.000000000.0000000000.000000000.0000000000.0000000000.0000000000.000000000.000000000.000000000.000000000.0000000001.00000000
50.000000000.000000000.0000000000.000000000.0000000000.0000000000.0000000000.000000000.000000000.137931030.017241380.1379310340.34482759
510.000000000.000000000.0000000000.000000000.0000000000.0000000000.0000000000.200000000.200000000.100000000.000000000.0000000000.25000000
520.083333330.000000000.0000000000.000000000.0000000000.1666666670.1666666670.000000000.000000000.000000000.000000000.0000000000.08333333
530.000000000.000000000.3333333330.000000000.0000000000.0000000000.1666666670.500000000.000000000.000000000.000000000.0000000000.00000000
550.000000000.000000000.0000000000.000000000.0000000000.0094043890.0000000000.000000000.000000000.000000000.131661440.0344827590.73667712
570.000000000.000000000.0000000000.000000000.0000000000.0229885060.0000000000.000000000.000000000.011494250.000000000.0114942530.63218391
60.000000000.000000000.0000000000.016393440.0000000000.0081967210.0000000000.000000000.000000000.213114750.040983610.0081967210.03278689
E0.000000000.002044990.0000000000.004089980.0000000000.0511247440.0000000000.000000000.000000000.204498980.012269940.1717791410.00000000
Table
Table 4. Initial Markov Chain Standard Error Values Matrix—All Periods. Source: Own elaboration.
Table 4. Initial Markov Chain Standard Error Values Matrix—All Periods. Source: Own elaboration.
131423242627282933031323638
130.0000000000.1256148590.0256410260.0000000000.0000000000.025641030.0000000000.0512820510.0000000000.0362618860.0000000000.000000000.044411560.00000000
140.0339617810.0000000000.0196078430.0000000000.0277296780.000000000.0000000000.0392156860.0000000000.0706970840.0000000000.000000000.039215690.00000000
230.0153846150.0000000000.0000000000.0000000000.0217571320.026646940.0000000000.0344010460.0000000000.0217571320.0000000000.065271400.000000000.00000000
240.0000000000.0000000000.0000000000.0000000000.0000000000.000000000.0000000000.0500000000.0000000000.1224744870.0000000000.111803400.000000000.00000000
260.0000000000.0000000000.0454545450.0787295820.0000000000.000000000.0000000000.0642824350.0000000000.0454545450.0000000000.045454550.000000000.00000000
270.0000000000.0000000000.0000000000.0000000000.0000000000.000000000.2500000000.0000000000.0000000000.0000000000.0000000000.250000000.000000000.00000000
280.2000000000.0000000000.0000000000.0000000000.0000000000.000000000.0000000000.1000000000.0000000000.1000000000.1000000000.000000000.000000000.00000000
290.0000000000.0000000000.0278351110.0000000000.0000000000.000000000.0160706090.0000000000.0000000000.0579434040.0113636360.037688920.000000000.01136364
30.0000000000.2226808860.0000000000.0000000000.0000000000.000000000.0000000000.0000000000.0000000000.1285648690.0000000000.000000000.000000000.00000000
300.0000000000.0000000000.0000000000.0000000000.0130718950.000000000.0000000000.0092432260.0000000000.0000000000.0000000000.036972900.000000000.00000000
310.1250000000.1250000000.0000000000.0000000000.0000000000.000000000.0000000000.0000000000.0000000000.0000000000.0000000000.000000000.000000000.00000000
320.0000000000.0000000000.0072897610.0072897610.0000000000.000000000.0072897610.0089280970.0000000000.0000000000.0051546390.000000000.000000000.00000000
360.0000000000.0000000000.1111111110.1571348400.0000000000.000000000.0000000000.0000000000.0000000000.0000000000.0000000000.000000000.000000000.00000000
380.0000000000.0000000000.0000000000.0000000000.0000000000.000000000.0000000000.0000000000.0000000000.0000000000.0000000000.000000000.000000000.00000000
390.0000000000.0000000001.0000000000.0000000000.0000000000.000000000.0000000000.0000000000.0000000000.0000000000.0000000000.000000000.000000000.00000000
40.0000000000.0000000000.0000000000.0000000000.0000000000.000000000.0000000000.0000000000.0000000000.0000000000.0000000000.000000000.000000000.00000000
410.0000000000.0000000000.0707106780.0000000000.0000000000.000000000.0500000000.0000000000.0000000000.0000000000.0000000000.000000000.000000000.00000000
420.0000000000.0000000000.0000000000.0000000000.0000000000.000000000.0000000000.0000000000.0000000000.0000000000.0000000000.000000000.000000000.00000000
480.0000000000.0000000000.0000000000.0000000000.0000000000.000000000.0000000000.0000000000.0000000000.0000000000.0000000000.000000000.000000000.00000000
50.0298629450.0000000000.0000000000.0000000000.0000000000.000000000.0000000000.0571831860.0000000000.0298629450.0000000000.029862940.000000000.01724138
510.0000000000.0000000000.0866025400.0000000000.0000000000.000000000.0707106780.0000000000.0000000000.0000000000.0000000000.000000000.000000000.00000000
520.0000000000.0000000000.1666666670.0000000000.0000000000.000000000.1178511300.0000000000.0000000000.0000000000.0000000000.000000000.000000000.00000000
530.0000000000.0000000000.0000000000.0000000000.0000000000.000000000.0000000000.0000000000.0000000000.0000000000.0000000000.000000000.000000000.00000000
550.0000000000.0044332710.0000000000.0031347960.0000000000.000000000.0000000000.0076786510.0000000000.0044332710.0000000000.012925100.000000000.00000000
570.0000000000.0000000000.0162553280.0381221240.0114942530.000000000.0000000000.0199086300.0000000000.0162553280.0257019310.019908630.011494250.00000000
60.0231838290.0283942760.0115919140.0081967210.0183284260.000000000.0000000000.0271854490.0000000000.0484924570.0000000000.024590160.000000000.00000000
E0.0089139040.0050091810.0127709570.0000000000.0057841050.000000000.0000000000.0120983230.0067824640.0155741780.0000000000.019826910.002044990.00000000
394414248551525355576E
130.000000000.000000000.0000000000.0000000000.0000000000.0000000000.0256410260.000000000.000000000.044411560.0000000000.0000000000.00000000
140.000000000.000000000.0000000000.0000000000.0000000000.0000000000.0000000000.000000000.000000000.033961780.0277296780.0277296780.08084521
230.000000000.000000000.0000000000.0000000000.0000000000.0000000000.0000000000.021757130.000000000.048650430.0153846150.0217571320.06705998
240.000000000.000000000.0000000000.0000000000.0000000000.0000000000.0000000000.000000000.000000000.070710680.1000000000.0500000000.05000000
260.000000000.000000000.0000000000.0000000000.0000000000.0000000000.0000000000.000000000.000000000.064282430.0642824350.0000000000.14373989
270.000000000.000000000.0000000000.0000000000.0000000000.0000000000.0000000000.000000000.000000000.000000000.0000000000.0000000000.35355339
280.000000000.000000000.0000000000.0000000000.0000000000.1414213560.0000000000.000000000.000000000.100000000.0000000000.0000000000.00000000
290.000000000.000000000.0440111740.0113636360.0000000000.0160706090.0227272730.000000000.011363640.035934970.0160706090.0000000000.02783511
30.000000000.000000000.0000000000.0000000000.0000000000.0909090910.0000000000.000000000.000000000.000000000.0000000000.1285648690.00000000
300.000000000.000000000.0065359480.0184864520.0000000000.0000000000.0065359480.000000000.000000000.045751630.0244552770.0160097370.03921569
310.000000000.000000000.0000000000.0000000000.0000000000.1250000000.0000000000.000000000.000000000.125000000.0000000000.0000000000.25000000
320.000000000.000000000.0072897610.0145795210.0051546390.0224685510.0000000000.000000000.000000000.049172120.0103092780.0072897610.03891667
360.000000000.000000000.0000000000.0000000000.0000000000.0000000000.0000000000.000000000.000000000.000000000.1924500900.0000000000.19245009
380.000000000.000000000.0000000000.0000000000.5000000000.0000000000.0000000000.000000000.000000000.000000000.0000000000.0000000000.50000000
390.000000000.000000000.0000000000.0000000000.0000000000.0000000000.0000000000.000000000.000000000.000000000.0000000000.0000000000.00000000
40.000000000.000000000.0000000000.0000000000.0000000000.0000000000.0000000000.000000000.000000000.000000000.0000000001.0000000000.00000000
410.000000000.000000000.0000000000.0500000000.0000000000.0000000000.1581138830.086602540.050000000.050000000.0000000000.0000000000.05000000
420.000000000.000000000.0000000000.0000000000.0000000000.0000000000.0454545450.000000000.000000000.136363640.0454545450.0000000000.15075567
480.000000000.000000000.0000000000.0000000000.0000000000.0000000000.0000000000.000000000.000000000.000000000.0000000000.0000000000.70710678
50.000000000.000000000.0000000000.0000000000.0000000000.0000000000.0000000000.000000000.000000000.048765980.0172413790.0487659850.07710579
510.000000000.000000000.0000000000.0000000000.0000000000.0000000000.0000000000.100000000.100000000.070710680.0000000000.0000000000.11180340
520.083333330.000000000.0000000000.0000000000.0000000000.1178511300.1178511300.000000000.000000000.000000000.0000000000.0000000000.08333333
530.000000000.000000000.2357022600.0000000000.0000000000.0000000000.1666666670.288675130.000000000.000000000.0000000000.0000000000.00000000
550.000000000.000000000.0000000000.0000000000.0000000000.0054296260.0000000000.000000000.000000000.000000000.0203158020.0103969430.04805552
570.000000000.000000000.0000000000.0000000000.0000000000.0162553280.0000000000.000000000.000000000.011494250.0000000000.0114942530.08524366
60.000000000.000000000.0000000000.0115919140.0000000000.0081967210.0000000000.000000000.000000000.041795240.0183284260.0081967210.01639344
E0.000000000.002044990.0000000000.0028920520.0000000000.0102249490.0000000000.000000000.000000000.020449900.0050091810.0187426410.00000000
Table
Table 5. Transition alternatives between states simplified. Source: Own elaboration.
Table 5. Transition alternatives between states simplified. Source: Own elaboration.
FROMTO
1314 23 27 29 30 36 55 RP
1413 23 26 29 30 36 55 57 6
2313 26 27 29 30 32 55 57 6 RP
2429 30 32 55 57 6 RP E
2623 24 29 30 32 55 57 RP
2728 32 RP E
2813 29 30 31 5 55 RP
2923 28 30 31 32 38 42 5 55 57 RP
314 30 5 6 RP
3014 26 29 30 31 32 42 55 57 6 RP
3113 14 5 55 RP E
3223 24 28 29 31 42 48 5 55 57 6 RP
3623 24 57 RP
3848 RP
46 RP
4255 57 RP
48RP
513 29 30 32 38 55 57 6 RP
5514 24 29 30 32 5 57 6 RP
613 14 23 24 26 29 30 32 42 5 55 57 6 RP

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Muñoz-Mazón, A.; Fuentes-Moraleda, L.; Chantre-Astaiza, A.; Burbano-Fernandez, M.-F. The Study of Tourist Movements in Tourist Historic Cities: A Comparative Analysis of the Applicability of Four Different Tools. Sustainability 2019, 11, 5265. https://doi.org/10.3390/su11195265

AMA Style

Muñoz-Mazón A, Fuentes-Moraleda L, Chantre-Astaiza A, Burbano-Fernandez M-F. The Study of Tourist Movements in Tourist Historic Cities: A Comparative Analysis of the Applicability of Four Different Tools. Sustainability. 2019; 11(19):5265. https://doi.org/10.3390/su11195265

Chicago/Turabian Style

Muñoz-Mazón, Ana, Laura Fuentes-Moraleda, Angela Chantre-Astaiza, and Marlon-Felipe Burbano-Fernandez. 2019. "The Study of Tourist Movements in Tourist Historic Cities: A Comparative Analysis of the Applicability of Four Different Tools" Sustainability 11, no. 19: 5265. https://doi.org/10.3390/su11195265

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