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Space–time tourist flow patterns in community-based tourism: an application of the empirical orthogonal function to Wi-Fi data
Current Issues In Tourism
Abstract
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.
... For instance, Salas et al. applied Wi-Fi data to analyze urban mobility in an international tourist city [18]. Li et al. collected Wi-Fi data to identify the spatial aggregation pattern and the temporality of the tourist flow at a community tourist attraction [19]. Hu et al. used Wi-Fi data to investigate crowd activities in urban public green spaces [20]. ...
... Wi-Fi data, characterized by its high spatio-temporal granularity, can be used to collect real-time information on crowd behavior and activity patterns at specific locations. It has been primarily applied to study the aggregation patterns of crowds and their dynamics over time by counting visit numbers at specific locations [19][20][21]. Furthermore, it was possible to categorize crowd attributes according to the frequency of mobile device presence, allowing researchers to investigate differences in spatial distribution patterns among distinct crowds [22,23]. ...
... In previous studies, spatial granularity has been set with radii ranging from 15 m to 100 m. Our study set the spatial granularity at a radius of 25 m, considering the detection range of devices and the size of public spaces, which was the same for research conducted in community-scale settings [19,25]. In terms of temporal granularity, previous research has set time intervals ranging from 5 min to 2 h when measuring the spatial distribution dynamics of crowds. ...
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.
... WiFi data is collected passively through signals, which travel in the air, sent by mobile devices, therefore requiring no action on the part of the participant [36]. In addition, these data constitute an emerging source for studying small-scale tourism activities [37]. The WiFi technique is similar to Bluetooth, but appears to be more convenient and low-cost [20], since WiFi data has a high spatio-temporal granularity and is somewhat cost-effective in relation to equipment installation and data collection [37]. ...
... In addition, these data constitute an emerging source for studying small-scale tourism activities [37]. The WiFi technique is similar to Bluetooth, but appears to be more convenient and low-cost [20], since WiFi data has a high spatio-temporal granularity and is somewhat cost-effective in relation to equipment installation and data collection [37]. ...
... WiFi data has been used for analysis of crowd behavior and activity patterns, both indoors and outdoors [37], for example, to analyze the behavior of tourists in a tourism event, and therefore it is useful for tourism recommendation and emergency management [20]. In the field of tourism, in addition to being implemented in tourism events, it has also been implemented in community-based tourism. ...
The tourism industry has recently undergone a significant evolution because of the implementation of Information and Communication Technologies (ICT). These changes have raised new conceptions of destinations, thus giving rise to smart destinations. In this context, digital tracking of tourists has become of great interest. This article aims to analyze the research landscape on tourist tracking through a bibliometric study, which allows the evaluation of the performance of publications, recognition of the prominent SciVal topics, analysis of the evolution of the techniques, and recognition of their use as a tool that contributes to the development of smart destinations. Unlike prior studies in this area, in addition to the performance analysis of publications, this paper focuses only on empirical studies on tourist tracking, identifies the prominent SciVal topics, and analyzes the linkage thereof with the Sustainable Development Goals (SDGs). The bibliometric study was carried out based on articles on tourist tracking extracted from the Scopus database and published between 2007 and 2023. This research is mainly quantitative, based on research performance analysis (journals, authors, institutions, and countries) and science mapping (citation analysis, co-word analysis, co-authorship analysis). Results show the quality of the publications made in the 2007–2023 period and indicate that the most used techniques have been GPS devices, social media, and app-based GPS/GNSS. In addition, it was found that the evolution of the techniques occurred at the same time as the development of ICT and that the prominent SciVal topic that covers the largest number of publications is associated with destination management. It is concluded that the application of tourist tracking techniques contributes to the transformation of destinations into smart destinations, as they provide useful relevant information to tourism market stakeholders for the development of strategies that improve decision-making and allow for the sustainable management of destinations.
... Particularly, data can be passively collected in the form of Wi-Fi probe requests when a mobile phone initiates a connection to a Wi-Fi network. Thus, Wi-Fi data can identify the presence of nearby Wi-Fi users with high spatiotemporal granularities and relatively high cost efficiency (Li et al., 2023). Mobility data derived from Wi-Fi have been used for multiple objectives in different urban settings, such as estimating the real-time population in urban streets (Kontokosta & Johnson, 2017;Soundararaj et al., 2020), discovering social relationships in schools (Wang et al., 2017), and identifying tourist density (Wang et al., 2019) and mobility patterns Li et al., 2023) in tourist attractions. ...
... Thus, Wi-Fi data can identify the presence of nearby Wi-Fi users with high spatiotemporal granularities and relatively high cost efficiency (Li et al., 2023). Mobility data derived from Wi-Fi have been used for multiple objectives in different urban settings, such as estimating the real-time population in urban streets (Kontokosta & Johnson, 2017;Soundararaj et al., 2020), discovering social relationships in schools (Wang et al., 2017), and identifying tourist density (Wang et al., 2019) and mobility patterns Li et al., 2023) in tourist attractions. ...
... Therefore, probe requests can be used to track tourist locations and length of stay with high spatiotemporal granularities, given the appropriate deployment of Wi-Fi probes. Recently, this technology has been used for intra-destination tourism research in limited case studies, such as to derive the semantics of tourist movement , and identify space -time tourist flow patterns (Li et al., 2023). ...
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.
... Within this context, the notion of a tourist community emerges, understood as the group of people who live or stay together for a certain period of time in a certain place for reasons related to pleasure and relaxation within the framework of the practice of tourism (Dickinson et al., 2017). In this spirit, a tourist community is classified by the sharing of space and time, where the interrelationships between individuals (tourists) occur through casual encounters or because, without intending to, they belong to a group of visitors in a certain time window (Li et al., 2023). ...
... These attitudinal predispositions involve the creation of expectations regarding what each tourist expects to find at the specific destination (Rodríguez et al., 2019). Although tourist communities are configured according to the confluence of groups in the same unit of space and time (Li et al., 2023), each person can interact differently with the goods and elements that constitute the cultural and historical heritage of a specific destination (Rodríguez et al., 2018). ...
The city of Cartagena in Colombia is recognised worldwide as a tourist destination with great wealth in terms of historical and cultural heritage. Tourists from all over the world visit the city and have diverse experiences and opinions about the city. Advertising communication and the media contribute to the positioning of the city-brand with members of the tourist communities, understanding that each of them has separate places of origin, varied ages, and different consumption of media. The objective of this study was to investigate the perception that a selected tourist community has about Cartagena as a tourist destination, considering demographic factors and the recall of advertising communication about the city in different media. After analysing the results from a survey of more than 400 tourists during a three-month period in 2023, it is concluded that their perception of the city is structured by a combination of positive and negative opinions. The place of origin, the age range and the various media have different relations with regard to the recall of advertising messages about the city, which encourages the development of tourism promotional strategies based on demographic and psychographic factors.
... The scale of the research subject includes multiple levels, such as scenic spots [16,17], cities [18], provinces [19], and city clusters [20]. Based on this, scholars have further analyzed the influencing factors of tourist flow, pointing out that the spatial structure of tourist flow is closely related to factors such as tourism resource endowment, transportation level, and distance [21][22][23]. In addition, Yang Li et al. (2023) found that the total volume of telecommunications services, the number of employees in the tourism industry, the number of accommodation enterprises, the number of corporate legal entities, and the total amount of government investment in the tourism industry are important factors influencing the flow of tourist information [24]. ...
... Red tourism receives significant attention from Chinese government departments, leading to extensive online promotion and a substantial impact on the network attention of scenic areas. On the other hand, tourist flow is influenced by various factors such as resource endowment, transportation level, distance, and preferences [21][22][23]. Therefore, there is both correlation and dislocation between network attention and tourist flow. ...
Red tourism is a distinctive form of tourism in China. Its network attention serves as a typical indicator to measure the level of promotion and publicity for red tourism, as well as an important reflection of its influence. Understanding the network structure of red tourism is of significant importance for optimizing the spatial pattern of tourism and promoting the development of the tourism industry. Based on this, this study takes the classic red tourism attractions in Shaanxi province, China as an example and constructs a multi-source data network attention evaluation index. Additionally, it employs social network theory to explore the network attention and tourist flow characteristics of the case study area. Research shows that: (1) Overall, the network attention to case-based destinations is relatively low, and there are significant differences in network attention among different attractions. Spatially, the distribution of network attention is uneven. This is manifested by higher network attention to attractions in Yan’an city and lower network attention to attractions in other regions. (2) There are differences in the network attention of different types of attractions. High-level attractions have a higher level of online attention, while low-level attractions have a lower level of network attention. Additionally, archaeological sites tend to receive a higher level of online attention. (3) The network density of tourist flow is low, and the tourism connections between nodes are not closely linked. The linkage between core nodes and edge nodes in tourism is poor. Developed tourism routes only exist in core nodes. (4) Nodes such as Zaoyuan revolution site, Yangjialing revolution site, and Wangjiaping revolution site have a significant influence in the network structure. In addition, the integration and development between red nodes and non-red nodes have been achieved. (5) There is a correlation between network attention and tourist flow, as well as a ‘misplacement’ feature. Based on the characteristics of attractions, they can be divided into four types: bright-star attractions, cash-cow attractions, thin-dog attractions, and question attractions. Based on the above conclusions, this study proposes targeted development recommendations.
This study proposes an integrated multi-criteria decision-making (MCDM) method to evaluate the ecological safety of Chinese ice and snow tourism destinations. To improve current methods, we propose an effective evaluation technique that incorporates the DPSIR model, entropy method, Delphi method, rough set, and TOPSIS method in a challenging environment. The indicator weights for the data were determined using the entropy method. The rough set matrix was constructed based on the rough set theory, and positive and negative ideal solutions were then determined. The study aimed to calculate and rank the ecological safety of six ice and snow tourism destinations in China using the modified rough-TOPSIS method. The results show that the Aoshan area is critically unsafe, the Chongli area is relatively safe, and the remaining four areas are mostly safe. The paper presents an analysis of the factors that hinder ecological security in ice and snow tourism destinations using the obstacle degree model. The results show that R22 (0.7368) has the most significant impact on Shennongjia. Additionally, S14 (0.5727) directly impedes the ecological security of Yunshangcaoyuan. The presence of P11 (0.4658) in Beidahu hampers its ecological security development.
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.
Havacılık, otomotiv ve beyaz eşya gibi sektörlerde delik delme talaşlı ve talaşsız imalatın büyük çoğunluğunu oluşturur. Bu sektörlerde kullanılan metal aksamların üretilmesi esnasında yüzlerce delik delinmektedir. Özellikle ocak gibi ev eşyalarının metal parçalarında 1-5 mm gibi küçük çaplarda zımbalar kullanılarak delik elde edilmektedir. Yüksek üretim adetleri ve üretim proseslerinde karşılaşılan problemler sebebi ile bu zımbalar aşınmaktadır. Seri üretim esnasında aşınan zımbalar delinen deliklerde çapaklanmaya, ölçüsel farklılıklara ve üretim duruşlarına sebebiyet vermektedir. Bu sebeplerden dolayı delik delme ve kesme zımbalarının dayanımlarının arttırılması amacı ile tavlama, normalleştirme, su verme, temperleme, tavlama, yaşlandırma, karbürleme, nitrürleme ve kriyojenik işlem gibi uygulamalar yapılmaktadır. Bu yöntemlerden kriyojenik işlem ile kesme zımbalarının mikroyapısının iyileştiği, homojen karbür dağılımı ve ince taneli martensit yapıya dönüşmesi sayesinde sertlik, tokluk, aşınma direnci gibi birçok özelliklerinin iyileştiği bilinmektedir. Bu çalışmada ankastre ocakların metal parçalarının delinmesinde kullanılan Ø3 mm zımbalara 36 saat kriyojenik işlem ve 500 °C temperleme parametrelerinde kriyojenik işlem uygulanarak sertlik değişimleri incelenmiştir. Kriyojenik işlem uygulanan ve referans olarak işlemsiz seçilen üçer adet zımbanın makro, mikro analizleri sonrası sertlik ölçümleri yapıldıktan sonra kriyojenik işlemli zımbaların mekanik özelliklerinin iyileştiği tespit edilmiştir. Sertlik ölçüm sonuçlarına göre kriyojenik işlem görmüş zımbaların ortalama sertlik değerleri 774 HV0.3 ‘dan 801 HV0.3 değerine yükselerek % 3.37 artmıştır. Delik elde etmek için kullanılan zımbalara uygulanan kriyojenik işlemin malzeme mikro yapısını olumlu etkilediği ve seri üretimde zımba hasarlarının azaltılmasında avantaj sağlayacağı düşünülmektedir.
Beyaz eşya sektöründe yüksek üretim adedine sahip fırın ve ocaklar büyük oranda metal parçalardan oluşurlar. Özellikle dış kasa ve dayanım gerektiren parçaların bir çoğu metal malzemelerden imal edilmektedir. Bu parçaların kullanım yerlerine montaj edilebilmesi veya diğer ekipmanların bu parçalara sabitlenebilmesi için üzerlerine delik oluşturmak gerekir. Seri üretime uygunluk ve ölçüsel hassasiyet söz konusu olduğu için delik delme işlemleri hidrolik veya eksantrik preslerde sac ve metal kalıplar kullanılarak yapılmaktadır. Delme kalıplarında kullanılan delik delme zımbalarının ısı ve sürtünmeye karşı dirençli, sarma ve soğuk kaynak gibi etkilere karşı dayanıklı olması istenir. Bu sebeple delik delme zımbaları olarak sinterli karbür, toz metal ve titanyum nitrür kaplamalı gibi çeşitler tercih edilmektedir. Bu çalışmada ise 1.3343 HSS malzemeden imal edilen titanyum nitrür kaplı zımbalara 36 saat kriyojenik işlem ve 200 °C temperleme parametrelerinde kriyojenik işlem uygulanmıştır. Deneylerde kullanılan zımbaların kriyojenik işlem öncesi ve sonrası SEM, EDS analizleri yapılarak mikro ve makro görüntüleri elde edilmiştir. Ayrıca her bir zımbanın sertlik değerleri ölçülerek aritmetik ortalamalar tespit edilmiştir. Yapılan ölçümler neticesinde kriyojenik işlem görmüş zımbaların ortalama sertlik değerleri 774 HV0.3 ‘dan 792 HV0.3 değerine yükselerek % 2.32 artmıştır. Bu artışın mikroyapıda kalan ve yumuşak faz olan östenitin, sert faz olan martenzite dönüşmesinden kaynaklandığı düşünülmektedir. Ayrıca kriyojenik işlem ve temperleme uygulanması sonrası malzeme mikroyapısında ikincil sert karbür yapılarının çökelmesinin ortalama sertlik değerlerini arttırdığı düşünülmektedir.
With the ongoing development of information and communications technology, geospatial technologies have become increasingly important in monitoring, managing, and predicting tourism activities. These tools can also uncover tourism’s social, economic, cultural, environmental, and political impacts. In this viewpoint article, we discuss applications of cutting-edge geospatial analysis in tourism studies. Topics include opportunities from emerging geospatial data, a new typology of spatial analysis in tourism studies, spatial analysis with the 4-Ws approach, and humanistic geographic information systems. This paper offers methodological guidance for multi-scale geospatial analyses that are essential to tourism research.
Wireless tracking technologies is an emerging technology used in tourism destinations to better understand the movement of tourists and to develop more personalized products. However, little is understood of tourists’ perceptions of these wireless tracking technologies. This study investigated the impact inferences of manipulative intent, privacy concerns, perceived personalization, and personal innovativeness on tourists’ attitudes and intentions towards the technology and destinations. A total of 615 questionnaires were collected under three between-subject’s conditions, namely ‘wireless tracking only’, ‘Wi-Fi wireless tracking’, and ‘app-based tracking’. Structural equation modelling revealed that perceived personalization significantly predicted positive attitudes, while inferences of manipulative intent significantly deterred tourists. Theoretically, this study reveals the different ways in which different wireless tracking technologies are perceived and how these perceptions impact on the destination. Further, the results of this study can assist destination marketing organizations and governing bodies to improve their implementation of such technologies at a destination.
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.
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.
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.
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.
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.
Interest in the interactions between climate, weather and tourism has increased markedly in the last decade in response to climate change concerns; however, there is little empirical research on how tourists respond to the weather conditions they encounter. Thus, in this paper, we examine the impact of the weather on a sample of international tourists visiting New Zealand during the 2009–10 summer season. Based on weather-related changes made to tourists’ travel routes, the timing of their travel and activity participation while in New Zealand, respondents were segmented into three groups: those with ‘no travel changes’ during their trip, those with ‘some travel changes’ and those with ‘substantial travel changes’ to their trip. The results show a generally high level of changes made to trips, particularly in the less settled early summer season, and an interesting link with satisfaction. The research provides an empirical basis for a framework that would facilitate further research into the adaptive capacity of tourists towards climate variability and change.
The tourism sector is one of the largest and fastest growing global industries and is a significant contributor to national and local economies around the world. The interface between climate and tourism is multifaceted and complex, as climate represents both a vital resource to be exploited and an important limiting factor that poses risks to be managed by the tourism industry and tourists alike. All tourism destinations and operators are climate-sensitive to a degree and climate is a key influence on travel planning and the travel experience. This chapter provides a synopsis of the capacities and needs for climate services in the tourism sector, including current and emerging applications of climate services by diverse tourism end-users, and a discussion of key knowledge gaps, research and capacity-building needs and partnerships that are required to accelerate the application of climate information to manage risks to climate variability and facilitate successful adaptation to climate change.
This study examines a behavioral model using latent variables of place attachment, recreation involvement, conservation commitment and environmentally responsible behavior among tourists visiting wetlands. In total, 928 usable questionnaires were collected. Confirmatory factor analysis and structural equation modeling were applied to the data by using LISREL 8.70 for Windows. Analytical results, which further elucidate the behavioral models of nature-based tourism, suggest that place attachment, recreation involvement and conservation commitment critically impact environmentally responsible behavior. In this behavioral model, conservation commitment simultaneously and partially mediates the relationships between place attachment and environmentally responsible behavior and between recreation involvement and environmentally responsible behavior. A series of management implications are drawn, including the need to use this information via a visitor interpretation strategy, greater use of partnerships with local communities and businesses to spread the importance of wetlands and of environmentally friendly behavior, and the need to work with other wetlands to share the type of visitor motivations best suited to encourage environmentally friendly behavior. LISREL8.70
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.
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.
With the increasing demand of location-based services, Wi-Fi based localization has attracted great interest because it provides ubiquitous access in indoor environments. In this paper, we propose CiFi, deep convolutional neural networks (DCNN) for indoor localization with commodity 5GHz WiFi. First, by leveraging a modified device driver, we can extract phase data of channel state information (CSI), which is used to estimate angle of arriving (AOA). We then create estimated AOA images as input to the DCNN, to train the weights in the offline phase. The location of mobile device is predicted based on the trained DCNN and new CSI AOA \hl{images}. We implement the proposed CiFi system with commodity Wi-Fi devices in the 5GHz band and verify its performance with extensive experiments in two representative indoor environments.
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.
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.
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.
Sustainability is a concept that continues to evolve and perplex in tourism, one of the world's largest industries. Effective new theories and practices are constantly explored so as to incorporate sustainability into tourism frameworks. Due to their focus on participation processes, integration of resources, and responses to specific needs and contexts, ecomuseum principles can be very useful for the development of sustainable tourism. This paper examines the potential of using the principles of ecomuseology to support sustainable tourism development. The study adopted a mixed-methods approach involving local expert stakeholders at the case study site, the Rupununi region of Guyana, South America. The findings from this research suggest that the principles of ecomuseology possess considerable potential to support sustainable tourism development in the Rupununi and potentially other destinations internationally. In particular, this study illustrates how ecomuseological principles can be used to manage heritage resources and economic development by focusing on, for example, holistic interpretation and information sharing, placing equal attention on heritage resources, and monitoring changes to the region over time.
ENSO-like variability is examined using a set of univariate indices based on unfiltered monthly global sea surface temperature (SST), sea level pressure (SLP), outgoing longwave radiation (OLR), sea level, and the three-dimensional ocean temperature (OT) fields. These indices, many of which correspond to the leading principal components (PCs) of the respective global fields, are highly correlated with each other. In combination with their spatial regression patterns, they provide a comprehensive description of ENSO-like variability in the atmosphere and ocean across time scales ranging from months to decades, from 1950 onward. The SLP and SST indices are highly correlated with one another back to the late nineteenth century. The interdecadal-scale shifts in the prevailing polarity of ENSO that occurred in the 1940s, the 1970s, and around the year 2000 are clearly evident in low-pass-filtered time series of these indices.
On the basis of empirical mode decomposition, ENSO-like variability is partitioned into an interannual “ENSO cycle,” to which equatorial ocean wave dynamics imparts a distinctive equatorial signature, and a red noise background continuum, most prominent on the interdecadal time scale, which resembles the ENSO-like variability in some models in which the atmosphere is coupled to a slab ocean. The background continuum bears the imprint of the Pacific–North American (PNA) pattern, the leading mode of the Northern Hemisphere wintertime variability of the atmospheric circulation over the Pacific sector. The superposition of the ENSO cycle and the background continuum imparts a distinctive frequency dependence to the patterns of ENSO-like climate variability.
New sources of geolocated information, associated with big data and social networks, show great promise for geographical research, especially in the field of tourism geography. Photo-sharing services comprise one of these sources. The aim of this article is to demonstrate the potential of photo-sharing services for identifying and analyzing the main tourist attractions in eight major European cities: Athens, Barcelona, Berlin, London, Madrid, Paris, Rome and Rotterdam. Geotagged photographs on Panoramio were differentiated according to whether they had been taken by tourists or local residents, and their spatial distribution patterns were analyzed using spatial statistical techniques in a GIS. The results indicated the concentration and dispersion of photographs in each city and their main hot spots, and revealed marked differences between tourists' and residents' photographs, since the former showed higher spatial concentrations. In addition, differences were observed between cities; Barcelona and Rome presented a strong spatial concentration compared with London or Paris, which showed much greater dispersion.
There has been some discussion about the relationship between sustainable development and sustainable tourism, and the need to further explore the relationships between theory and practice. Using grounded theory, this paper reviews new and existing research on Beijing's historic hutong neighbourhoods in relation to the housing, heritage conservation and hutong tourism sectors. Results indicate that sustainability as a theoretical category has at least five extant properties. Sustainability is relative, dynamic, normative, contestable and reflexive. Sustainability is relative in the sense that proponents of different sectors will construct it differently. It is dynamic in the related sense that this construction changes over time. Sustainability is normative because it is value-laden; one important ideal being that a sector should strive to become ever more sustainable. Proponents of different sectors will assert their values, hence sustainability is contestable. Finally, sustainability is reflexive in that the normative ideal may not occur, thus the sustainability of a sector will be reflexive of the nature of its evolving real world development. The paper explores the substance of these properties, together with a review of the several stages through which hutong tourism has passed in its short 20-year life, including the emergence of governance systems.目的地可持续性框架的应用来探讨泰国在2004年印度洋海啸以后脆弱性的原因2004年印度洋海啸对多个旅游目的地的影响是对依赖旅游业的目的地社区的脆弱性的冲击和压力是一个惊人的提醒。但是，目的地脆弱性的因果驱动仍正在研究中。另外，很少研究是系统化地应用和测试新理论框架在评估真实世界问题例如对自然灾害的脆弱性的有用性。这个社会理论发展中的重要步骤经常被忽略。在该文章中我们通过`操作'新的目的地可持续性框架(DSF)来纠正这些基本的缺口。DSF被用来导向一个案例研究为基础的对比目的地脆弱性评估(DVA)，针对泰国的拷叻，巴东，皮皮岛这些海啸影响的目的地来更好地理解目的地脆弱性和在不同地方和发展内容下的进化。结论显示目的地脆弱性被一个多样的，多层次的，相互作用的因素的集合来创造和延续，这包括地理曝光，特定目的地的发展特点，社会结构和治理流程。这些因素和流程的基础是不断相互竞争的利益相关者日程和行为，历史嵌入的文化规范，体制的偏好和巩固和延续不平等的资源使用上的权利结构，这些都随着时间的推移在社会组织的多个规模下发挥出来。
A 2-yr set of profile data from Torrey Pines Beach, California, measured at monthly intervals has been statistically analyzed by using empirical eigenfunctions. The analysis separates the temporal and spatial dependence of the data, this separation permitting beach changes to be described objectively by a linear combination of corresponding time and space functions. Most of the variation in profile configuration can be accounted for by three eigenfunctions corresponding to the three largest eigenvalues. The largest eigenvalue corresponds to an eigenfunction called the'mean beach function,' which represents an average profile. A second eigenfunction, the 'bar-berm function,' has a large maximum at the location of the summer berm and a minimum at the location of the winter bar, indicating its relation to the seasonal onshore-offshore movement of sand. The third eigenfunction, the 'terrace function,' has a maximum at the location of the low-tide terrace. Results of this study indicate that the eigenfunctions are useful in the analysis of beach profile data and provide objective insight into the nature of the variations of the profile configuration. sent the variation of the beach profile configuration in terms of distance from. fixed data points and in terms of temporal changes in profile configuration over the period of study. No standard set of terms exists for describing beach profile features. However, certain features are sufficiently common in their occurrence to be recognized on most beaches and have been given names that are ill defined but are in general usage. Figure I (Inman, 1971) shows the morphological definition of some important beach features. This study will be concerned primarily with three of these features which are the basic parts of the beach profile; the berm and beach face, the terrace, and the bar. Movement or change in these three features accounts for most of the changes in beach profile configuration and con sequently is most significant in describing profile changes. Since these three features are so important in the profile changes, the morphologic term has also been applied to the appropriate statistical function.
Some current uses of empirical orthogonal functions (EOF) are briefly summarized, together with some relations with spectral and principal component analyses. Considered as a mean square estimation technique of unknown values within a random process or field, the optimization of error variance leads to a Fredholm integral equation. Its kernel is the autocorrelation function, which in many practical cases is only known as discrete values of interstation correlation coefficients computed from a sample of independent realizations.The numerical solution in one or two dimensions of this integral equation is approximated in a new and more general framework that requires, in practice, the a priori choice of a set of generating functions. Developments are provided for piecewise constant, facetlike linear, and thin plate type spline functions.The first part of the paper ends with a review of the mapping, archiving and stochastic simulating possibilities of the EOF method. A second part includes a case study concerning precipitation fields, previously worked out by optimal interpolation methods.
In the 1960s, spurred by jumbo jets, charter tours, and the growing affluence of the middle classes in Western industrial nations, tourism erupted on a grand scale. This was seen as offering a new opportunity for developing countries to secure foreign exchange and stimulate economic growth. Their sunny climates, sandy beaches, and exotic cultures attracted a stream of vacationers, and resorts multiplied to meet the demand. With the oil crisis and the recession of 1974-75, there was a pause in the growth of tourism. The end of the boom gave new urgency to existing concerns about whether tourism produced sufficient gains for developing countries to justify the investments required. In addition to doubts about whether tourism yielded economic returns commensurate with its economic costs, there was a general questioning of some of the basic assumptions about the relationship between development and economic growth. In the case of tourism, these doubts were reinforced by the belief that it brings larger adverse social and cultural effects than does development of other sectors. In December 1976 the World Bank and Unesco sponsored a seminar to discuss the social and cultural impacts of tourism on developing countries and to suggest ways to take account of these concerns in decision making. This report is a summary of those proceedings with written accounts of those seminars presented.
Measurements of beach profiles from southern California spanning a 5-year period have been examined for temporal changes in beach configuration. On an annual time scale the data suggest two distinct seasonal pivotal points separating eroding and accreting regions. Empirical eigenfunction analysis of combined onshore and offshore profiles shows a pivotal point for seasonal onshore/offshore sediment movement at a depth of 2-3 m below mean sea level and suggests another at a 6-m depth. Analysis of accurate reference rod measurements at 4-, 6-, 10-, 14-, and 20-m depths supports the presence of the 6-m pivotal point. A simple model of depth-dependent seasonal sand movement suggests that during initial winter storms, sand is eroded from both the foreshore and from depths of 6-10 m and is deposited in water depths from 2 to 6 m. During less energetic periods, sediment migrates both shoreward (to the beach face) and seaward (to depths of 10 m) from its winter site of deposition (water depths from 2-6 m). This observation of depth-dependent motion contradicts the simple single pivotal point model previously suggested for nearshore seasonal onshore/offshore sediment motion and emphasizes the complexity of nearshore sediment transport. A sediment budget for seasonal onshore/offshore transport, based on the dual pivotal point model, consists of exchanges of 85 m3/m of beach length across the 3-m pivotal point and 15 m3/m across the 6-m pivotal point. Over a longer time scale (i.e., the entire 5 years of study) the beaches showed no noet erosion or accretion, suggesting that this limited coastal region is stable over these short time scales.
This research was conducted in the rural village of Bigodi, Uganda. Bigodi is a village of approximately 385 adults and has been involved with tourism since 1991. Bigodi's primary attraction is a forested wetland managed by a local tourism cooperative. Tourists are guided through the wetland in search of primates and tropical birds. Village life and local culture are also experienced. This research investigated residents’ attitudes towards tourism in Bigodi. Data were collected and analyzed with qualitative methods. Results show residents have consistently positive attitudes towards tourism. Positive attitudes result from resident's belief that tourism creates community development, improves agricultural markets, generates income, and finally, that tourism brings random good fortune. Using the Theory of Reasoned Action, it was hypothesized that positive attitudes would lead to pro-tourism behavior. Observations of behavior over 6 months in Bigodi support this hypothesis. Implications for tourism development in poor rural areas are discussed.