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Identifying tourism attractiveness based on intra-destination tourist behaviour: evidence from Wi-Fi data
Current Issues In Tourism
Abstract
Knowledge of tourism attractiveness is crucial for tourists to choose preferred destinations and helps tourism managers configure attractive resources to better meet tourist demands. However, small-scale (e.g. intra-destination) studies of tourism attractiveness are scarce, despite their unique significance in understanding tourist demand at the destination level. Based on time geography theory, this study proposes a new method called the stay-time-based tourism attractiveness index (STTAI), which can be used to identify tourism attractiveness within a destination using Wi-Fi data. Specifically, we tracked Wi-Fi probe requests from 670,000 travellers over a month in the Shichahai scenic area, a famous cultural tourism destination in Beijing. After segmenting tourists from the Wi-Fi data, we employed the STTAI to identify locations with frequent and extended tourist stays as activity stations with high tourism attractiveness. Further, this study examined the environmental impact on identified tourism attractiveness using regression analysis.
... The Shichahai Scenic Area study [27] demonstrated sophisticated data collection capabilities, analyzing 670,000 tourist Wi-Fi probe requests. Their findings revealed clear correlations between location attractiveness and tourist dwell time, particularly near historical sites. ...
... The system successfully processed over 72 million connection records throughout the 20-month study period, demonstrating robust scalability comparable to Florence's comprehensive study [30] which analyzed 56 million connections. Third, our findings on spatial distribution patterns align with research from the Shichahai Scenic Area [27] where tourist concentrations correlated strongly with cultural and historical sites. The successful relocation of access points from low-traffic areas to more active zones demonstrates the system's effectiveness in supporting dynamic urban management, similar to the adaptive approach used in Bologna's university area study [19]. ...
Sri Chiang Mai Smart City faces challenges in tourism management due to its limited understanding of tourist behavior patterns and inefficient resource management at tourist attractions. The study aims to analyze tourist movement patterns and preferences across key locations, develop a privacy-preserving data collection framework, and provide data-driven recommendations for tourism resource allocation. The system analyzes Wi-Fi data from 11 strategic locations along the Mekong River using spatial and temporal analysis techniques. Tourist data is protected with SHA-256 encryption and tokenization of MAC addresses. Analysis of over 72 million connection records from May 2022 to December 2023 revealed significant patterns in tourist behavior. Tourist numbers showed dramatic increases during cultural events, with a 127.87% increase during the Songkran festival from April 13 to 15 compared to regular days in the same month at Boeng Wiang Courtyard. Four locations consistently showed higher visitor levels: Sri Chiang Mai Community Health Park, Naga Courtyard, In front of Had Pathum Temple, and Boeng Wiang Courtyard. Temporal analysis identified peak hours between 17:00 and 21:00, especially during the Songkran and longboat racing festivals. The findings enabled data-driven decisions in tourism resource allocation, event planning, and infrastructure development. However, the study is limited to tourists who connect to public Wi-Fi, potentially excluding those using mobile data or no digital devices. Despite these limitations, the system demonstrates how smart city infrastructure can effectively support smart tourism management through privacy-preserving data collection and analysis, contributing to the development of sustainable smart tourism initiatives.
... In recent years, numerous tourist tracking technologies that can be adapted to different spatial scales have been developed. Wi-Fi data, in particular, offer new opportunities for measuring tourist behavior in small-scale spaces [6]. This content will be explored in detail in Section 2. ...
... Several studies have increasingly focused on the application of Wi-Fi in the analysis of crowd-staying behavior. For instance, Li et al. proposed a stay-time-based method to identify the tourism attractiveness in small-scale tourist destinations [6]. ...
Tourist preferences for public spaces in historic districts can reflect whether renovated spaces and functional structures meet tourism demands. However, conventional big data lack the spatio-temporal accuracy needed to support a refined, dynamic study of small-scale public spaces inside historic districts. This paper, therefore, proposes using a Wi-Fi probe to evaluate the spatio-temporal dynamics of tourists’ spatial preferences in historic districts. We conducted a one-week measurement in the Xiaohe Street Historic Block in Hangzhou, China. Three indicators—visit time preference, aggregation preference, and stay preference—were used to examine the dynamic change in tourists’ spatial preferences, with 15 min as the time unit and public spaces with a radius of 25 m as the spatial unit. Our research demonstrates that, compared with conventional big data, the Wi-Fi probe offers a more reasonable and accurate method to measure tourists’ spatial preferences in historic districts at a smaller time and spatial granularity. The research findings can be applied to evaluate the effectiveness of spatial regeneration and diagnose renewal-related issues in historic districts. It can also serve as a foundation for more precise planning of public spaces in historic districts, as well as the modification of functional structures.
... Cycling tourism can be a form and way of discovering the attractiveness of city centers. The authors consider the digital identification of cyclists' parking spaces on bicycle routes in Krakow (Banet, 2022) and Beijing (Li, 2023). The analysis shows that cyclists make stops of various lengths in the immediate vicinity of tourist attractions, both natural and anthropogenic. ...
... Cycling tourism can be a form and way of discovering the attractiveness of city centers. The authors consider the digital identification of cyclists' parking spaces on bicycle routes in Krakow (Banet, 2022) and Beijing (Li, 2023). The analysis shows that cyclists make stops of various lengths in the immediate vicinity of tourist attractions, both natural and anthropogenic. ...
... Movement pattern analysis typically consists of three dimensions: inter-destination, intra-destination, and intra-attraction tourist behaviors [10][11][12]. ...
Exploring the spatial distribution of tourist attractions and comprehending the spatio-temporal behaviors of tourists within tourist attractions can provide local planning agencies, destination marketing organizations, and government departments with essential evidence for decision-making processes. This study examines the spatio-temporal behavior patterns of tourists in the Kushan Scenic Area by analyzing GPS trajectory data acquired from social media platforms. The investigation primarily utilizes three research methodologies: grid analysis, Markov chain, and K-means clustering. The grid analysis results reveal three spatial distribution patterns within the scenic area, while the outcomes from the Markov chain and K-means clustering delineate six tourist movement patterns, along with three choices regarding travel time. This finding holds significant practical implications for enhancing the attractiveness of scenic areas, optimizing spatial layout, and improving tourists’ experiences.
The definition of a tourism data space (TDS) in the Balearic Islands is a complex process that involves identifying the types of questions to be addressed, including analytical tools, and determining the type of information to be incorporated. This study delves into the functional requirements of a Balearic Islands’ TDS based on the study of scientific research carried out in the field of tourism in the Balearic Islands and drawing comparisons with international scientific research in the field of tourism information. Utilizing a bibliometric analysis of the scientific literature, this study identifies the scientific requirements that should be met for the development of a robust, rigorous, and efficient TDS. The goal is to support excellent scientific research in tourism and facilitate the transfer of research results to the productive sector to maintain and improve the competitiveness of the Balearic Islands as a tourist destination. The results of the analysis provide a structured framework for the construction of the Balearic Islands’ TDS, outlining objectives, methods to be implemented, and information to be considered.
The increasing popularity of Wi-Fi enabled smart devices, such as cell phones, poses unprecedented opportunities for modeling large-scale human movement trajectories. While these datasets enable the extended spatiotemporal scales and refined granularities for human dynamics research, the lack of semantic information hinders the understanding of the processes and rationales behind human movements. In this study, we employ Wi-Fi detectors to identify track points along travelers' movement trajectories. Also, we propose a semantic space-time matrix (SSTM) method to derive the semantic information from the movement trajectories. The temporalities and activity types of track points are aggregated to understand the mobility patterns of different travelers. We have further applied the method to a small tourist community, the Shichahai scenic area of Beijing, China, with the Wi-Fi data collected from 463,874 individuals over two weeks. The derived semantic information has been further validated by a field survey, justifying the feasibility of Wi-Fi data for movement tracking and semantic analysis. The Wi-Fi data and the proposed analytical method can shed new insights into human dynamics research by enriching the context of individual travel.
This article contributes to advancing the knowledge on the phenomenon of the most popular short‐term rental platforms, Airbnb. By implementing a geographically weighted regression (GWR) and its multiscale form, MGWR, we examine the relationship between Airbnb locations and the core elements of urban tourism including hotels, food and beverages (F&B) venues, as well as access to public transport. This article’s contributions are twofold: methodological and empirical. First, the results show that incorporating localities improve overall model performance. It allows us to account for the nuance of each area of interest as the MGWR performs slightly better than the GWR in the case of spatially sparse data. Second, both models show that Airbnbs collocate with hotels supported by various amenities, but Airbnbs also go beyond traditional hotel zones. This analysis highlights and extends the latter where Airbnb concentrations are those for which there are strong associations with F&B establishments and access to public transports. This suggests that Airbnbs might benefit local businesses outside the reach of major tourist zones. However, there is further work to be done to understand whether the economic benefit to the local economy is worth the associated social costs raised by previous studies.
This article demonstrates how large-scale tourist mobility data can be linked with network science approaches to better understand tourism destinations and their interactions. By analyzing a mobile positioning dataset that captures the nationality and movement patterns of foreign tourists to South Korea, we employ a few metrics to quantify the network properties of tourism destinations, aiming to reveal the collective dynamics of tourist movements and key differences across nationalities. According to the results, the number of inbound tourists to destinations follows a log-normal distribution, which indicates a notable heterogeneity of destination attractiveness. Although this finding holds across different nationalities, we find that tourists from different countries tended to visit different places in South Korea. A community detection algorithm partitions South Korea into several tourism regions, each covering a set of destinations that are closely connected by tourist flows. The implications for transportation development and regional tourism planning are discussed.
Urban community tourism (UCT) is a tourism product that promotes the sustainable development of urban communities. This paper constructs an index system of the influencing factors of UCT development (UCTD), which includes five secondary and 15 tertiary indicators. The weights of the indicators are calculated through expert consultation and questionnaire surveys. Based on the index system, this paper empirically evaluates five typical urban tourism communities in Beijing (i.e., Nanluoguxiang, Shichahai, Dashilan, 798 Art District, and Sanlitun) using expert scoring and online comments. We further analyze the impact mechanism of UCTD qualitatively. The findings show that among the secondary indicators, cultural atmosphere and built environment have higher weights than location conditions, specialty shops, and community participation. Among all the tertiary indicators, eight of them (i.e., cultural themes, ornamental value, frequency of cultural activities, store longevity and popularity, cultural institutions, abundance of distinctive buildings, scenic integrity, and community managers’ willingness to support UCTD) account for 80% of the total weight. In terms of the impact mechanism, 15 factors further affect UCTD through demand for authentic travel experiences, reachability perception, travel shopping demand, and stakeholder coordination.
Understanding crowd behaviors in a large social event is crucial for event management. Passive WiFi sensing, by collecting WiFi probe requests sent from mobile devices, provides a better way to monitor crowds compared with people counters and cameras in terms of free interference, larger coverage, lower cost, and more information on people’s movement. In existing studies, however, not enough attention has been paid to the thorough analysis and mining of collected data. Especially, the power of machine learning has not been fully exploited. In this paper, therefore, we propose a comprehensive data analysis framework to fully analyze the collected probe requests to extract three types of patterns related to crowd behaviors in a large social event, with the help of statistics, visualization, and unsupervised machine learning. First, trajectories of the mobile devices are extracted from probe requests and analyzed to reveal the spatial patterns of the crowds’ movement. Hierarchical agglomerative clustering is adopted to find the interconnections between different locations. Next, k-means and k-shape clustering algorithms are applied to extract temporal visiting patterns of the crowds by days and locations, respectively. Finally, by combining with time, trajectories are transformed into spatiotemporal patterns, which reveal how trajectory duration changes over the length and how the overall trends of crowd movement change over time. The proposed data analysis framework is fully demonstrated using real-world data collected in a large social event. Results show that one can extract comprehensive patterns from data collected by a network of passive WiFi sensors.
Urban open places with a public service function (e.g., urban parks) are likely to be populated in peak hours and during public events. To mitigate the risk of overcrowding and even events of stampedes, it is of considerable significance to realize a real-time full coverage estimate of the population density. The main challenge has been the limited deployment of crowd surveillance detectors in open public spaces, leading to incomplete data coverage and thus impacting the quality and reliability of the density estimation. To remedy this issue, this paper proposes a modified inverse distance weighting (IDW) method, named the inverse distance weighting based on path selection behavior (IDWPSB) method. The proposed IDWPSB method adjusts the distance decay effect according to visitors’ path selection behavior, which better characterizes the human dynamics in open spaces. By implementing the model in a real-world road network in the Shichahai scenic area in Beijing, China, the study shows a decrease in the absolute deviation by 17.62% comparing the results between the new method and the traditional IDW method, justifying the effectiveness of the new method for spatial interpolation in open public places. By considering the behavioral factor, the proposed IDWPSB method can provide insights into public safety management with the increasing availability of data derived from location-based services.
Drawing on theories of stimulus overload and social interference, this study proposes a multidimensional way to measure tourists’ perceptions of crowding. Three dimensions of crowding are discussed: neutral, personal, and social. A second-order structural equation model is used to analyze the relationships among crowding perceptions, destination attractiveness, and satisfaction by surveying tourists at Mount Sanqingshan National Park (n = 424). The results of a covariance-based SEM show that tourists’ perception of crowding (TPC) has negative effects on reported attractiveness of the destination. However, TPC dose not directly affects their satisfaction. Results of this study refine the multidimensional connotation of TPC and provide another dimension to evaluate the quality of tourists’ experiences apart from the universal construct of satisfaction. The paper then goes on to discuss the importance of managing TPC.
Empirical research focusing on time–space tourist activity has increased in recent years, since new technological tools make it possible as never before to accurately track tourists’ movements. Nonetheless, the published studies are inconsistent when assessing spatiotemporal tourist behaviour (STTB), hindering the development of a solid research base that permits replication of and comparison amongst studies. The incipient conceptualization of STTB leads to the present inconsistency of assessment indicators used in empirical research, which will probably be stressed in the future. Continuous technological evolution will allow the collection of new kinds of data, whose analysis will certainly result in a better understanding of STTB, especially if structured by a consistent taxonomy. Aiming to address this gap, the theoretical underpinnings of the multidimensional concept of STTB are reviewed and a conceptual model is proposed, based on a behavioural perspective and for the urban intra-destination context, as a comprehensive framework of analysis. Further, the systematization of the indicators used in previous research to assess STTB, its influential factors, and other methodological aspects regarding its assessment, as well as practical implications and future research directions are discussed.
This study develops a unique model capturing antecedents of place attractiveness in tourism hotspot crowding contexts. A structural equation model reveals three density dimensions: one destination image variable and two avoidance versus approach reactions that influence assessments of crowding attitude and destination appraisals. Perceived density dimensions affect destination appraisals with varying intensities and valences. Both positive and negative sentiments are present – the former as excitement, fun and conviviality resulting from people-watching and socialising, and the latter as discomfort and resentment resulting from personal space violations and reduced feelings of uniqueness. Many tourist types are included in this study in historic town centres and villages in iconic fjord landscapes in Norway. Cruise passengers are more crowding tolerant than are self-organised travellers.
This paper investigates the Cyber-Physical behavior of a user in a large indoor shopping center by leveraging anonymized (opt in) Wi-Fi association and browsing logs recorded by the center operators. Our analysis shows that many users exhibit high correlation between their cyber activities and physical context. To find this correlation , we propose a mechanism to semantically label a physical space with rich categorical information from Wikipedia concepts and compute a contextual similarity that represents a customer's activities with the mall context. We further show the use of cyber-physical contextual similarity in two different applications: user behavior classification and future location prediction. The experimental results demonstrate that the users' contextual similarity significantly improves the accuracy of such applications.
Understanding underlying behavioral mechanism of tourist destination choices is the first and foremost step in establishing effective and appropriate management measures for a scenic area. Few studies have used high-resolution and fine-grained spatio-temporal data to develop sophisticated statistical models for uncovering micro-scale tourist movements. This study combined Global Positioning Systems (GPS) and conventional questionnaire survey data of 312 respondents from Gulangyu, China, a small island with a total area of 1.87 km2, to develop multinomial logit (MNL) models for identifying factors that affect tourists’ destination choices. A market segmentation analysis for different ticket purchasers was then performed. The Watson and Westin pooling test demonstrated that destination choices vary significantly between joint- and single-ticket purchasers. Joint-ticket purchasers are shown to be a more regular group whose mobility behavior can be better predicted and modeled compared with single-ticket purchasers. This finding, of exceptional merit, is a significant and substantial contribution to current literature. Several policy implications were discussed based on the modeling results. The proposed modeling, findings, and discussions provide the basis for policy instruments and will be useful for the enhanced planning of on-site movement, improved regulation of tourist flows, and rationalized design and management of tourist destinations.
To better understand the location patterns of different types of restaurants across the United States, we investigate the relationship between neighborhood sociodemographic characteristics and restaurant location using a unique data set from 2013 covering 30,772 U.S. zip codes. The estimation results from negative binomial regression models confirm the significant impacts of various sociodemographic factors (e.g., population density, median age, median household income, average household size, educational attainment, gender distribution, housing tenure, neighborhood urbanization) on restaurant location. We also project future restaurant growth potential based on model estimates and projected changes in sociodemographic characteristics by 2020. The results are analyzed, and several metropolitan areas in Texas and Florida are identified as having high potential for growth. Lastly, implications are provided for restaurant real estate practitioners.
This methodological paper discusses the implications of differing approaches for tracking tourists’ movement through destinations and then describes an innovative new method that utilises smartphone-based Global Positioning Systems (GPS) technology and a mobile app to track tourists through regional destinations and attractions over an extended period. The method was developed by the Tasmanian Sensing Tourist Travel project team and is among the largest tracking studies ever conducted in tourism, both spatially and temporally and in terms of sample size. It tracked the movement of 472 tourists in real time via an app with three integrated surveys and GPS tracking. The research team was faced with multiple challenges given the ambitious nature of the project, including: app design, securing tracking hardware, forging partnerships with providers, participant recruitment, and the ethical and legal dimensions of both the tracking and the provision of mobile data as an incentive to participate. This paper discusses the lessons from the project and a holistic study design model to support continuous methodological progress in this growing research area.
There is little knowledge available on the spatial behaviour of urban tourists, and yet tourists generate an enormous quantity of data (Big Data) when they visit cities. These data sources can be used to track their presence through their activities. The aim of this paper is to analyse the digital footprint of urban tourists through Big Data. Unlike other papers that use a single data source, this article examines three sources of data to reflect different tourism activities in cities: Panoramio (sightseeing), Foursquare (consumption), and Twitter (being connected). Tourist density in the three data sources is compared via maps, correlation analysis (OLS) and spatial self-correlation analysis (Global Moran's I statistic and LISA). Finally the data are integrated using cluster analysis and combining the spatial clusters identified in the LISA analysis in the different data sources. The results show that the data from the three activities are partly spatially redundant and partly complementary, and allow the characterisation of multifunction tourist spaces (with several activities) and spaces specialising in one or various activities (for example, sightseeing and consumption). The case study analysed (Madrid) reveals a significant presence of tourists in the city centre, and increasing specialisation from the centre outwards towards the periphery. The main conclusion of the paper is that it is not sufficient to use one data source to analyse the presence of tourists in cities; several must be used in a complementary manner.
Novel digital data sources allow us to attain enhanced knowledge about locations and mobilities of people in space and time. Already a fast-growing body of literature demonstrates the applicability and feasibility of mobile phone-based data in social sciences for considering mobile devices as proxies for people. However, the implementation of such data imposes many theoretical and methodological challenges. One major issue is the uneven spatial resolution of mobile phone data due to the spatial configuration of mobile network base stations and its spatial interpolation. To date, different interpolation techniques are applied to transform mobile phone data into other spatial divisions. However, these do not consider the temporality and societal context that shapes the human presence and mobility in space and time. The paper aims, first, to contribute to mobile phone-based research by addressing the need to give more attention to the spatial interpolation of given data, and further by proposing a dasymetric interpolation approach to enhance the spatial accuracy of mobile phone data. Second, it contributes to population modelling research by combining spatial, temporal and volumetric dasymetric mapping and integrating it with mobile phone data. In doing so, the paper presents a generic conceptual framework of a multi-temporal function-based dasymetric (MFD) interpolation method for mobile phone data. Empirical results demonstrate how the proposed interpolation method can improve the spatial accuracy of both night-time and daytime population distributions derived from different mobile phone data sets by taking advantage of ancillary data sources. The proposed interpolation method can be applied for both location- and person-based research, and is a fruitful starting point for improving the spatial interpolation methods for mobile phone data. We share the implementation of our method in GitHub as open access Python code.
Smartphones are configured to automatically send WiFi probe message transmissions (latter called WiFi probes) to surrounding environments to search for available networks. Prior studies have provided evidence that it is possible to uncover social relationships of mobile users by studying time and location information contained in these WiFi probes. However, their approaches miss information about transfer patterns between different locations. In this paper, we argue that places where mobile users have been to should not be considered in isolation. We propose that semantic trajectory should be used to model mobile users and semantic trajectory patterns can well characterize users’ transfer patterns between different locations. Then, we propose a novel semantic trajectory similarity measurement to estimate similarity among mobile users. We deploy WiFi detectors in a university to collect WiFi probes and extract mobile users’ semantic trajectories from the dataset. Through experimental evaluation, we demonstrate that the proposed semantic trajectory similarity measurement is effective. Furthermore, we experimentally show that the proposed trajectory similarity measurement can be used to exploit underlying social networks existing in the university, as well as infer specific type of social relationships between a pair of mobile users by further studying their matching trajectory points.
This chapter discusses new developments and potential analytic approaches to travel demand modeling using behavioral big data, with the ultimate goal of generating customer-based knowledge through tourists’ feedback and information traces. The first section introduces the concept of generating big data in travel demand, by describing the different customer-based data that can be obtained from explicit and implicit tourists’ feedback. The second section looks at empirical problems that one finds when examining travel demand through big data, the third section presents several research avenues that can be opened and the fourth section provides some closing thoughts.
We investigate the effect of synoptic-scale weather patterns on observed
maximum daily 8-hour average (MDA8) surface ozone over the eastern United
States during 1980–2012 in summer (June–August, JJA). Zonally averaged, the
relative standard deviation (SD) of daily MDA8 JJA ozone shows a bimodal
structure, with peaks at 28–32 and 40–45° N,
and we show that those regions are most influenced by the
variability in daily weather. We apply empirical orthogonal functions (EOFs)
to understand the causes of this structure. The first three leading EOF
patterns explain 53 % of the total variance in detrended surface ozone,
displaying (1) a widespread response of ozone in the eastern United States
associated with north–south movement of jet wind latitude, (2) a north–south
pattern linked to the Bermuda High system when its west boundary is located
along the east coast, and (3) an east–west pattern characteristic of a
westward extension of the Bermuda High and an enhanced Great Plains low
level jet (GPLLJ). The northern peak of ozone relative SD can be explained
by polar jet activity, while the southern peak appears related to
variability in the Bermuda High and GPLLJ. We define a new metric polar jet
frequency as the total number of days the jet traverses the Midwest and
northeast each summer. In the Midwest and northeast, we find that the
correlation coefficient r between detrended mean JJA MDA8 ozone and the polar
jet frequency ranges between −0.76 and −0.93 over 1980–2012 depending on the
time period selected, suggesting that polar jet frequency could provide a
simple metric to predict ozone variability in future climate regimes. In the
southeast, the influence of the Bermuda High on mean JJA MDA8 ozone depends
on the location of its west edge. For those summers when the average
position of the west edge is located west of ~ 85.4°
W, a westward shift in the Bermuda High west edge increases ozone in the
southeast by ~ 1 ppbv deg−1 in longitude. For all
summers, a northward shift in the Bermuda High west edge increases ozone
over the entire eastern United States by 1–2 ppbv deg−1 in latitude.
None of the synoptic patterns identified in this study show a significant
trend from 1980 to 2012, confirming that the observed ozone decrease over
the eastern United States during this time period is mainly caused by
emission controls. Our work underscores the impact of synoptic patterns on
ozone variability and suggests that a combination of changing local and
synoptic meteorology together with trends in background ozone will determine
the influence of climate change on US ozone air quality in future decades.
The observed relationships of US surface ozone and synoptic circulations
in this study can also be used to validate models of atmospheric chemistry.
Community-based tourism (CBT) has often been cited as an alternative to mass tourism and an approach for tourism to become more sustainable. If developed well, CBT can become a poverty alleviation mechanism and a way to access improvements in quality of life, providing empowerment and greater economic benefit to individuals in local communities. Despite the plethora of literature on CBT and evaluation of models, there is little analysis of the facilitators and barriers to achieving it. Through the use of case studies in both academic and grey literature, this paper serves as an instructive review of the CBT literature to synthesize the key elements of success and the challenges.
Community-based tourism is a sustainable form of tourism development where tourists visit residential communities to interact with local lives and cultures for an enhanced travel experience. Identifying and tracking tourist activities in community-based tourism is particularly challenging, as tourists have shared activity spaces with residents. The paper proposes a new method to study the space–time patterns of the tourist flow using Wi-Fi data. Specifically, we have tracked Wi-Fi probe requests over six months in the Shichahai scenic area, a famous community-based tourist attraction in Beijing, China. After deriving the tourist flow from the Wi-Fi data, we have applied the empirical orthogonal function (EOF) method to the identification of the spatial aggregation pattern and the temporality of the tourist flow. A follow-up explanatory analysis examines the environmental impacts, such as weather conditions, air quality, and travel days, on the space–time patterns. The study is among the first to employ Wi-Fi data to study travel behaviours in community-based tourism. The proposed method can shed insights into a better understanding of tourist behaviours in open-space, tourism-oriented urban communities.
We demonstrate how digital traces of city-bike trips may become useful to identify urban space attractiveness. We exploit their unique feature – stopovers: short, non-traffic-related stops made by cyclists during their trips. As we demonstrate with the case study of Kraków (Poland), when applied to a big dataset, meaningful patterns appear, with hotspots (places with long and frequent stopovers) identified at both the top tourist and leisure attractions as well as emerging new places. We propose a generic method, applicable to any spatiotemporal city-bike traces, providing results meaningful to understand the general urban space attractiveness and its dynamics. With the proposed filtering (to mitigate a selection bias) and empirical cross-validation (to rule-out false-positive classifications) results effectively reveal spatial patterns of urban attractiveness. Valuable for decision-makers and analysts to enhance understanding of urban space consumption patterns by tourists and residents.
Tourist expenditure is studied from many perspectives, including that of the two primary types of budget—monetary and temporal—that a tourist must manage in a destination. Yet, despite the scholarly interest in tourist expenditure, no studies to date have sought to quantify how spend type affects tourist satisfaction.
Since money and time are finite, it is important to understand how they contribute to tourist satisfaction. The aim of the present research is therefore to analyze how tourist behavior in a cultural destination, taking into account the real-time spending of both monetary and time budgets, contributes to satisfaction-formation. A sample of 957 touristic activities conducted in the City of Granada (Spain) was analyzed from a multilevel perspective, measuring tourist satisfaction with the activity in real time.
The study identifies several factors of interest for stakeholders of cultural tourism from a new conceptual and methodological approach in tourism satisfaction studies.
A destination country is a new geographical area where tourists enjoy many different experiences. It is possible to analyses the tourism efficiency of a country and to evaluate whether the country is harvesting maximum tourism output by managing its diverse range of physical and human resources effectively. This study tried to measure the tourism efficiency for the country level across 170 countries of the globe based on the various resources possessed by them. The study also found that pollution levels of the country can adversely affect tourism efficiency. This perspective of ranking countries made certain small countries to feature in the top of the list concerning tourism efficiency.
The technological revolution and the widespread of social media have allowed people to generate tremendous amounts of data every day. Social networks provide users with access to information. This paper aims to determine the attractiveness of various tourism sites by investigating the behaviour of users through social media. The database involves geo-tagged photos located in six cities serving as a relevant artistic and cultural hub in Italy. Photos downloaded from Flickr, a data-sharing platform. Data analysis was conducted using Mathematica and Machine Learning models approach. The results of our study show maps of the users’ behaviour identify the annual trend of photographic activity in cities and highlight the effectiveness of the proposed methodology that is able to provide with place and user information. The study underline how the analysis of social data can to create a predictive model to formulate tourism scenarios. At the end, general tourism marketing strategies are discussed.
The accurate measurement of human activity with high spatial and temporal granularity is crucial for understanding the structure and function of the built environment. With increasing mobile ownership, the Wi-Fi ‘probe requests’ generated by mobile devices can act as a cheap, scalable and real-time source of data for establishing such measures. The two major challenges we face in using these probe requests for estimating human activity are: filtering the noise generated by the uncertain field of measurement and clustering anonymised probe requests generated by the same devices together without compromising the privacy of the users. In this paper, we demonstrate that we can overcome these challenges by using class intervals and a novel graph-based technique for filtering and clustering the probe requests which in turn, enables us to reliably measure real-time pedestrian footfall at retail high streets.
Human identification and monitoring are critical in many applications, such as surveillance, evacuation planning. Human identification and monitoring are not an easy task in the case of a large and densely populated crowd. However, none of the existing solutions consider seamless localization, identification, and tracking of the crowd for surveillance in both indoor and outdoor environments with significant accuracy. In this paper, we propose a novel and real-time surveillance system (named, SmartISS) which identifies, tracks and monitors individuals' wireless equipment(s) using their MAC ids. Our trackers/sensing units (PSUs) are the portable entities comprising of Smartphone/Jetson-TK1/PC which are enough to capture users' devices probe requests and locations without users' active cooperation. PSUs upload collected traces on the cloud server periodically where cloud server keeps finding the suspicious person(s). To retrieve the updated information, we propose an algorithm (named, LLTR) to select the optimal number of PSUs for finding the latest location(s) of the suspicious person(s). To validate and to show the usability of SmartISS, we develop a real prototype testbed and evaluate it extensively on a real-world dataset of 117,121 traces collected during the technical festival held at IIT Roorkee, India. SmartISS selects PSUs with an average selection accuracy of 95.3%.
In this paper, the research gaps in sustainable tourism development were addressed by examining residents' perceptions of the sustainability of community-based tourism based on tourism area life cycle theory. The survey questionnaire was distributed to the residents of six Taiwanese communities, and it was designed to determine the residents' perceptions of the economic, socio-cultural, environmental, and life satisfaction sustainability of tourism. In total, 849 usable questionnaires were collected. The analytical results further elucidated the sustainability of nature-based tourism and suggested that the economic, socio-cultural, and environmental sustainability varied significantly in the consolidation, development, and involvement stages of community-based tourism development. The four dimensions of sustainability were evaluated according to the pre- and post-development perceptions of tourism sustainability, and significantly different results were obtained. The study concludes that the residents’ perceptions differed across the developmental stages; thus, managers should consider the development opportunities and adopt appropriate strategies across different development stages.
This research investigates the determinants of the attractiveness of urban tourism in two recognized tourist and heritage cities, Québec City and Bordeaux. In an analysis based on a tripartite theoretical model of attractiveness, a statistical comparative analysis was done in both cities to examine visitors’ perceptions using a questionnaire survey completed by about 500 visitors. This analysis produced three main findings. First, a factor analysis demonstrated that tourists recognize attractiveness on four levels (context, tourist belt, complementary attractions and nucleus). Second, elements linked to the tourist belt, such as public spaces and the urban environment, are viewed as the most important. Third, mean value comparisons revealed that tourists who consult the Internet and social media have heightened sensitivity to nucleus, complementary attraction and context levels. To stimulate tourism attractiveness, cities’ communications should thus emphasize the elements associated with the tourist belt, and effectively utilize Internet and social media to convey its caracteristics. Because urban public areas play an essential role in tourism attractiveness, it is also recommended that cities incorporate tourism into their planning strategy.
Hand, foot and mouth disease (HFMD) remains an increasing public health concern. The spatiotemporal variation of HFMD can be represented from multiple-perspectives, and it may be driven by different dominant factors. In this study, the HFMD cases in children under the age of five years in each county in Henan province, China, from 2009 to 2013 were assessed to explore the integrative spatiotemporal patterns of HFMD and investigate their driving factors. The empirical orthogonal function was applied to identify representative spatiotemporal patterns. Then, GeoDetector was used to quantify the determinant powers of driving factors to the disease. The results indicated that the most prominent spatiotemporal pattern explained 56.21% of the total variance, presented in big cities, e.g. capital city and municipal districts. The dominant factors of this pattern were per capita gross domestic product and relative humidity, with determinant powers of 62% and 42%, respectively. The secondary spatiotemporal pattern explained 10.52% of the total variance, presented in the counties around big cities. The dominant factors for this pattern were the ratio of urban to rural population and precipitation, with determinant powers of 26% and 41%, respectively. These findings unveiled the key spatiotemporal features and their determinants related to the disease; this will be helpful in establishing accurate spatiotemporal preventing of HFMD.
Even at an early stage, diverse big data have been applied to tourism research and made an amazing improvement. This paper might be the first attempt to present a comprehensive literature review on different types of big data in tourism research. By data sources, the tourism-related big data fall into three primary categories: UGC data (generated by users), including online textual data and online photo data; device data (by devices), including GPS data, mobile roaming data, Bluetooth data, etc.; transaction data (by operations), including web search data, webpage visiting data, online booking data, etc. Carrying different information, different data types address different tourism issues. For each type, a systematical analysis is conducted from the perspectives of research focuses, data characteristics, analytic techniques, major challenges and further directions. This survey facilitates a thorough understanding of this sunrise research and offers valuable insights into its future prospects.
Purpose
This paper outlines the policy directives in Guyana as it relates to community-based tourism and argues that the development of this niche can only be driven by clear policies which speak to community empowerment and institutional strengthening.
Design/methodology/approach
The paper is exploratory in nature and utilized document analysis as the primary means of data collection.
Findings
Community-based tourism presents an opportunity to advance the goals of government to include communities in the economic growth and development agenda. The vision for community-based tourism is community empowerment that develops the industry in line with the needs and aspirations of host communities However, this cannot be fully realised without the supporting role of government via effective policy development and implementation.
Originality/value
It is anticipated that this research will serve as a valuable reference tool for researchers, policy makers and other relevant bodies with an interest in community based tourism and the policy implications.
While social network analysis techniques have increasingly been applied in tourism research, limited effort hasbeen devoted to attractions networks within a tourism destination. This study identified the spatial structure ofthe tourist attraction system in Seoul, South Korea. Based on anchor-point theory, social network analysistechniques with spatial statistics, such as local indicator of spatial autocorrelation (LISA), using GeographicInformation Systems (GIS), were employed. Chinese Free Independent Tourist (FIT) data were used to computethe centrality measures from the 2015 International Visitor Survey. Results indicate that multiple anchor-points(i.e. attractions) can exist within a tourism destination. In addition, the spatial distribution patterns of thecentralities were hierarchically structured and differentiated depending on the length of stay. These findings highlight the importance of examining the spatial structure of tourist attraction networks to better establishcompetitive tourism destination planning, development, and management strategies.
Surface waters off eastern Tasmania are a global warming hotspot. Here, mean temperatures have been rising over several decades at nearly four times the global average rate, with concomitant changes in extreme temperatures – marine heatwaves. These changes have recently caused the marine biodiversity, fisheries and aquaculture industries off Tasmania's east coast to come under stress. In this study we quantify the long-term trends, variability and predictability of marine heatwaves off eastern Tasmania. We use a high-resolution ocean model for Tasmania's eastern continental shelf. The ocean state over the 1993–2015 period is hindcast, providing daily estimates of the three-dimensional temperature and circulation fields. Marine heatwaves are identified at the surface and subsurface from ocean temperature time series using a consistent definition. Trends in marine heatwave frequency are positive nearly everywhere and annual marine heatwave days and penetration depths indicate significant positive changes, particularly off southeastern Tasmania. A decomposition into modes of variability indicates that the East Australian Current is the dominant driver of marine heatwaves across the domain. Self-organising maps are used to identify 12 marine heatwave types, each with its own regionality, seasonality, and associated large-scale oceanic and atmospheric circulation patterns. The implications of this work for marine ecosystems and their management were revealed through review of past impacts and stakeholder discussions regarding use of these data.
In the development of historic areas, functions change following socioeconomic transformation. Nowadays, tourism is popular in historic areas. We examined this to understand the internal motivation, spatial logic, and functional transformation of a historic area to sustainably maintain its vitality. For Shichahai in Beijing, the selected study area, space syntax and kernel density analyses were conducted to understand the development vitality and determine the inner order of space and function in tourism development. The results clarified the inertia retained by historic genes to provide vitality in tourism.
This article proposes a methodology to mine valuable information about the usage of a facility (e.g. building, open public spaces, etc.), based only on Wi-Fi network connection history. Data are collected at Concordia University in Montréal, Canada. Using the Wi-Fi access log data, we characterize activities taking place within a building without any additional knowledge of the building itself. The methodology is based on identification and generation of pertinent variables derived by Principal Component Analysis (PCA) for clustering (i.e. PCA-guided clustering) and time-space activity identification. K-means clustering algorithm is then used to identify 7 activity types associated with buildings in the context of a campus. Based on the activity clusters' centroids, a search algorithm is proposed to associate activities of the same types over multiple days. The spatial distribution of the computed activities and building plans are then compared, which shows a more than 85% match for the weekdays.
This paper presents Beanstalk, an interactive platform to assist communities in easily running systematic analysis of mobility patterns of tourists at their destinations, contributing in new ways in visualizing spatio-temporal mobility data for forecasting, tracking trends, detecting patterns and noticing anomalies. The approach takes advantage of a combination of passive Wi-Fi tracking and ground truth data provided by tourism authorities. By analyzing a large dataset for a medium sized European island, we provide evidence of the accuracy and effectiveness of this low-cost method in inferring topological characteristics of tourist behavior and relevant typologies of trip itineraries. This helps decision makers in the touristic sector to plan and manage actions geared towards improving the sustainability and competitiveness of their touristic regions. In particular, we argue that in a world where sensing data is becoming inexpensive, there is an opportunity to use this approach to deliver data back to local communities which are empowered to act and leverage this information.
Despite the fact that emotional solidarity, social distance, and contact theory have been widely used to
explain resident tourist interaction, these three concepts were never considered in tandem in investigating
the intergroup attitude within a domestic tourism setting. To understand what type of interaction
improves emotional solidarity and reduces social distance, and how emotional solidarity affects social
distance, this study sought to measure the factors explaining emotional solidarity and social distance by
focusing on the frequency and the nature of interaction in domestic tourism. Study hypotheses proposed
that frequency of interaction and different types of activities that residents engage in with visitors
explain both emotional solidarity and social distance, and emotional solidarity in return explains social
distance. All hypotheses were partially supported with the findings, confirming the validity of the contact
theory in a domestic tourism setting and underscoring the importance of interaction in residents' attitudes
toward tourists.
Tourism is evidently one of critical contributors to air pollution, while, air pollution seems to drive tourists away. This paper aims to explore whether air pollution, especially the carbon dioxide emission, would affect the business cycle of the demand for tourism at a popular scenic spot in Taiwan, i.e., the Sun Moon Lake. Macroeconomic variables and additional weather patterns are also considered as potential determinants of the demand for tourism in our Markov regime-switching model. The empirical results serve as evidence that the effects of air pollution and rainfall on the demand for tourism depend significantly on the phases of business cycle, and show that, during the peaks, monthly numbers of tourists traveling at the Sun Moon Lake would fall by 25,725 people as the number of bad-air-quality day increases by one. The finding can provide policymakers an insight into the evaluation of the trade-off between industrial development and environmental protection.
Time geography is a constraints-oriented approach to understanding human activities. Time geography highlights the necessary (though not sufficient) spatial and temporal conditions required for human activities. It also provides an elegant framework for incorporating these conditions into individualistic and ecological analyses and models of human behavior. Two central concepts in time geography are the space–time path and prism. The path represents actual mobility (real or simulated) and the prism represents potential mobility in space with respect to time. Developed as a conceptual framework in the 1960s by Torsten Hägerstrand, analytical time geography has emerged in response to growing capabilities to collect and analyze data on mobile objects and human activities. There is a rich collection of measures and procedures for analyzing space–time paths and prisms in planar space, constrained by networks and through velocity fields. There are also methods for representing virtual interaction via information and communication technologies. Emerging are methods for mobility data mining and knowledge discovery.
Visitor tracking is frequently used in tourism planning for large sites, but is far less common at individual attractions, despite a body of literature examining the detrimental impact of crowding on visitor experience. This study used handheld geographic positioning system (GPS) units to track 931 groups of visitors around a single tourist attraction to determine where they went and how long they dwelt at particular locations. The tracking data were combined with survey data to discover whether different types of visitors behaved differently when exploring the attraction. The majority of visitors followed similar routes revealing a strong ‘main path inertia’ with over half missing exhibits away from the perceived main route. Different group types varied in how long they dwelt at different locations and in how long they spent at the attraction altogether.
New streams of data are being generated by a range of in-situ instrumentation, mobile sensing, and social media that can be integrated and analyzed to better understand urban activity and mobility patterns. While several studies have focused on understanding flows of people throughout a city, these data can also be used to create a more spatially and temporally granular picture of local population, and to forecast localized population given some exogenous environmental or physical conditions. Effectively modeling population dynamics at high spatial and temporal resolutions would have significant implications for city operations and policy, strategic long-term planning processes, emergency response and management, and public health.
Tourism has emerged as a major driving force in the growth and expansion of rural settlements. After several studies revealed spatial differentiation of touristization among rural settlements, studies were conducted to explain this phenomenon. However, most of these studies explained spatial differentiation of rural touristization in a qualitative way. More robust and detailed quantitative results are needed to evaluate the relative roles of different factors. In this study, which takes Yesanpo tourism as a case study, the Geo-detector method was introduced to evaluate determining factors of rural touristization. Results show that “distance to core entry”, “tourist number and sojourn time”, and “distance to the nearest scenic area” have had a strong effect on the rural touristization in Yesanpo, whereas “distance to river”, “elevation”, “distance to main road”, and “slope” have had a weak influence. The latter did, however, contribute a lot to touristization when interacting with “distance to core entry”, “tourist number and sojourn time”, and “distance to the nearest scenic”, indicating the importance of these four factors. Higher rural touristization occurred in the zone near the core entry, with many tourists, long sojourn times, and proximity to the scenic area.
This study analyses residents' perceptions and attitudes towards tourism development and community integration in tourism planning in an island tourism destination whose economy is widely influenced by the presence of big external investors. Findings reveal that residents believe that tourism planning should be more sensitive to residents and tradition, and be able to guarantee a higher level of heritage proximity in order to achieve a more indigenous/endogenous-oriented development. Exploratory factor analysis and hierarchical and non-hierarchical cluster analysis were conducted. Four clusters were identified (‘enthusiastics’, ‘moderate supporters’, ‘critics’, and ‘indifferents’), with significant differences in terms of employment reliance on tourism, length of residence, contact with tourists in everyday life, and level of education. Conversely, they did not differ based on gender, age, employment status, or geographical proximity to the tourist area, thus providing some contradictory insights when compared with previous studies. From a theoretical point of view, the findings seem to suggest that studies devoted to the investigation of residents’ view and attitude toward tourism should concentrate more on the personal values of respondents and less on their socio-demographic characteristics, which often render the findings of cluster analysis very site-specific and hard to generalise. Managerial implications are discussed, and suggestions for further research are provided.
In the era of mass tourism, phenomena such as sustainable tourism, responsible tourism, rural tourism, eco-tourism, pro-poor tourism and community-based tourism are now increasingly under the research microscope. If established and managed correctly, these alternative forms of tourism have the potential to contribute towards sustainable community development and provide visitors with unique experiences. Few scholars have qualitatively addressed the growth of community-based tourism in South-East Asia. This study investigated the value of a community-based tourism project at Banteay Chhmar in north-west Cambodia, specifically the discourse of the project's members and the broader community. The objective of the study was to provide an ethnographic account of the community member's attitudes, opinions and beliefs concerning the perceived value and contribution of the project towards community development in their locality. The study initially relied on a literature review to conceptualise community-based tourism. Unstructured in-depth interviews were conducted with a total of 30 stakeholders of the Banteay Chhmar Community-Based Tourism Project. A narrative identifies the development of the project and provides samples of the respondents’ discourses. Findings from a thematic analysis of stakeholder responses indicated support of tourism and the overall operation of the project. Opinions were divided concerning the future growth of tourism and likely negative impacts on resident livelihoods. While some felt the project was relatively insular and lacked transparency, most identified the project's value as enhanced social capital, pride and a better sense of community. The findings show the project has positively contributed towards community development; however, before it can be considered a best practice example of community-based tourism, its financial sustainability, business practices and community support need to improve. The overall study serves as a valuable insight into themes promoting the merits and complications of community-based tourism, specifically in South-East Asia.抽象在大众旅游时代, 诸如可持续旅游、负责任旅游、乡村旅游、生态旅游、扶贫旅游和社区旅游等现象正逐步暴漏在研究的“显微镜”下接受检视。这些可替代旅游形式如果如果规划并管理得当, 就有潜力对社区可持续发展做出贡献, 并且给游客提供独特的旅游体验。很少有学者从定性方面考察东南亚社区旅游的发展。本研究考察了柬埔寨西北部位于班迭奇马的一个社区旅游项目的价值, 特别是考察了该项目成员和广大社区对该项目的看法。本研究的目的是, 针对该项目的感知价值以及对当地社区发展的贡献, 对社区成员的态度、看法及信念进行了一个民族志的描述。本研究首先利用文献综述概括了社区旅游, 然后对该项目总计 30 个利益相关者进行了一些非结构性深度访谈。深度访谈的描述识别了该项目的发展并提供了受访者观点的样本。对利益相关者访谈文本的主题分析揭示了社区对旅游业的支持及该项目的总体运行情况。这些看法分为两部分༚旅游业的未来发展和旅游发展对居民生计活动的可能负面影响。尽管有些人认为该项目有失偏颇并且缺乏透明度, 但绝大部分人认识到了该项目的价值, 即提升了社区的社会资本、自豪感和更紧密的社区意识。该研究表明该项目积极地促进了社区的发展。但是, 该项目在成为社区旅游的最佳实践范例之前, 需要改善其财务的持续性、业务运行方式和社区支持度。本研究整体而言对发挥社区旅游 (特别是东南亚地区社区旅游) 的长处, 解决社区旅游的难题提供了有价值的看法。
New sources of geotagged information derived from social media like Twitter show great promise for geographic research in tourism. This paper describes an approach to analyze geotagged social media data from Twitter to characterize spatial, temporal and demographic features of tourist flows in Cilento - a regional tourist attraction in southern Italy. It demonstrates how the analysis of geotagged social media data yields more detailed spatial, temporal and demographic information of tourist movements in comparison to the current understanding of tourist flow patterns in the region. The insights obtained from our case study illustrate the potential of the proposed methodology yet attention should be paid to biases in the data as well as methodological limitations when drawing conclusions from analytical results.