Cluster-mapping procedure for tourism regions based on geostatistics and fuzzy clustering: example of Polish districts

Majewska, Justyna; Truskolaski, Szymon

doi:10.1080/13683500.2018.1467883

Cited by 18 publications

(7 citation statements)

References 71 publications

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“…Transpose the matrix N and generate the matrix T N . Create the norm relationship of the Minkowski distance between the tourists' interests and the tourist attractions, shown in the Formula (9) and Formula (10). Calculate the function ) ( , T N  between the matrix N and the matrix T .…”

Section: Tourist Attraction Reachability Space Model Based On Interes...mentioning

confidence: 99%

“…The researchers in [9] used the partitioning clustering method to find the nearest tourism destination according to the extracted geotagged photograph-location data. The researchers in [10] studied the clustermapping procedures for tourism regions based on the fuzzy-clustering method. This method proved to increase the identification accuracy of the tourism clusters.…”

Section: Introductionmentioning

confidence: 99%

“…As seen in [1][2][3][4][5][6][7], clustering algorithms have been used in tourism research for POI extraction, data mining, algorithm modeling, transportation behavior, etc. The other clustering methods in [8][9][10][11][12][13][14][15] indicated that spatial and attribute data of tourist attractions were the main targets that were used to generate proper tourism categories, extract tourist preferences, and recommend appropriate tourist destinations. The studies concerning tourist-attraction data extraction and tour-route algorithms that were used in [16][17][18][19][20][21][22][23][24][25][26][27][28][29] focused on three specific aspects.…”

Section: Introductionmentioning

confidence: 99%

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Tour-Route-Recommendation Algorithm Based on the Improved AGNES Spatial Clustering and Space-Time Deduction Model

Zhou

Tian

2022

IJGI

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This study designed a tour-route-planning and recommendation algorithm that was based on an improved AGNES spatial clustering and space-time deduction model. First, the improved AGNES tourist attraction spatial clustering algorithm was created. Based on the features and spatial attributes, city tourist attraction clusters were formed, in which the tourist attractions with a high degree of correlation among attributes were gathered into the same cluster. It formed the precondition for searching tourist attractions that would match tourist interests. Using tourist attraction clusters, this study also developed a tourist attraction reachability model that was based on tourist-interest data and geospatial relationships to confirm each tourist attraction’s degree of correlation to tourist interests. A dynamic space-time deduction algorithm that was based on travel time and cost allowances was designed in which the transportation mode, time, and costs were set as the key factors. To verify the proposed algorithm, two control algorithms were chosen and tested against the proposed algorithm. Our results showed that the proposed algorithm had better results for tour-route planning under different transportation modes as compared to the controls. The proposed algorithm not only considered time and cost allowances, but it also considered the shortest traveling distance between tourist attractions. Therefore, the tourist attractions and tour routes that were suggested not only met tourist interests, but they also conformed to the constraint conditions and lowered the overall total costs.

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Section: Tourist Attraction Reachability Space Model Based On Interes...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Tour-Route-Recommendation Algorithm Based on the Improved AGNES Spatial Clustering and Space-Time Deduction Model

Zhou

Tian

2022

IJGI

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“…Theoretical and applied aspects of tourist services market development are widely studied by such scientists as: Khalid, et al (2020), Gorban (2017), Guryanova & Prokopovich (2013), Chi et al (2020), Lohmatov (2011), Aleksushin et al (2020), Spalding & Parrett (2019), Moiseeva (2018). The processes of spatial organization and spatial polarization of the tourist services market were studied by Avramchikova (2018), Bhola-Paul (2015), Brandão et al (2019), Kisswani et al (2020), Gorina (2016), Majewska & Truskolaski (2019), Krasavceva (2014), Maltseva (2016), Zarezadeh et al (2019), Mitrofanov (2013), Çakmak et al (2019).…”

Section: Introductionmentioning

confidence: 99%

The Imperatives of the Development of the Tourist Services Market in Spatial Polarization of the Regional Tourist System

Трусова¹,

Insurance²,

Кирилов³

et al. 2020

GTG

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The article deals with the imperatives of the development of the tourist services market, which determines the parameters of the aggregate value of the sub-indices of the meso-and local levels in the spatial polarization of the regional tourist system. The methodology of spatial polarization of the regional tourist system, which provides convergence of the infra-structural space of tourist services, formation of an innovative nucleus, minimization of manifestation of destructive factors, balancing of interests of regions and the country as a whole is substantiated. It has been proposed the scientific-methodical approach to the definition of the target landmarks for the development of the tourist services market, which implements polarized tourist space in terms of quantitative and qualitative parameters, makes it possible to analyze the destructive determinants of the periphery and determinants-producers of the tourist center of activity, evaluation and forecasting of the safe level of the hierarchy of the regional tourist system, determination of clear impulses for the development of the tourist services market and density of interaction of the periphery with the center. Revenues from the sale of tourist services of local level enterprises have been analyzed. A comprehensive approach to the assessment of the concentration of the aggregate value of the security index of the regional tourist system, which is differentiated by the components of the regional security potential and threats, guarantees the development of the tourist services market and modifies the tourist system on the meso-level as a whole.

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“…Using the innovation technologies in the tourism sphere is key research among the present tourism and economics science scholars. We can see them in the work of Justyna M., Szymon T. [2] as Cluster-mapping procedure for tourism regions based on geostatistics and fuzzy clustering: example of Polish districts, Keith D. [3] Economic geographies of tourism: one 'turn' leads to another. Tourism Geographies.…”

Section: Introductionmentioning

confidence: 99%

Innovative Activity as a Means of Developing the Economic Potential of the Tourism Industry

Bakhramovich¹

2018

JIM

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This article gives an overview of the peculiarities of the use of innovations in tourism and their role in improving the investment attractiveness of the tourism industry of the Republic of Uzbekistan. And also the article highlights different approaches of creating favorable investment conditions for the creation of innovative tourism products. In the article the role of tourism has been demonstrated in the development of regional economies and the current condition of tourism in the region is analyzed with different approaches and methods. Thus, the distinctive feature of the development of innovative activities in tourism from classical tourist activities is the creation of a new or improvement of the existing tourist product, the improvement of transport, hotel and other services, the development of new markets, the introduction of advanced information and telecommunications technologies and modern forms of organizational and managerial activity.

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Cluster-mapping procedure for tourism regions based on geostatistics and fuzzy clustering: example of Polish districts

Cited by 18 publications

References 71 publications

Tour-Route-Recommendation Algorithm Based on the Improved AGNES Spatial Clustering and Space-Time Deduction Model

Tour-Route-Recommendation Algorithm Based on the Improved AGNES Spatial Clustering and Space-Time Deduction Model

The Imperatives of the Development of the Tourist Services Market in Spatial Polarization of the Regional Tourist System

Innovative Activity as a Means of Developing the Economic Potential of the Tourism Industry

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