Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
This study investigates the geographical dimension of tourist accommodation in Slovenia, Croatia, and Bosnia and Herzegovina and attempts to synthesize its spatial structure. The countries were chosen for the study due to their undoubted tourism qualities, their proximity in terms of location and, at the same time, the fundamental diversity of country size, population and access to the sea. The basis of this research was the use of point of interest (POIs), an open-source data, to analyse the spatial heterogeneity of the different types of accommodation. Kernel Density Estimation and Empirical Bayesian Kriging were used in the research.
Czasopismo
Rocznik
Tom
Strony
113--127
Opis fizyczny
Bibliogr. 27 poz.
Twórcy
autor
- University of Warmia and Mazury in Olsztyn, Faculty of Geoengineering, Institute of Spatial Economy and Geography, Department of Spatial Analysis and Real Estate Market
Bibliografia
- 1. ArcGis (2021) How Kernel Density works - ArcGIS Pro | Documentation. Available at: https://pro.arcgis.com/en/pro-app/latest/tool-reference/spatial-analyst/how-kernel-density-works.htm.
- 2. Bełej M. (2021). Analysis of spatial distribution of touristic accommodation in Poland with the kernel density estimation of POIs. Acta Scientiarum Polonorum. Administratio Locorum, 20(3), pp. 159-171. DOI: 10.31648/aspal.6818.
- 3. Cai L., Wen W., Wu B., Yang X. (2021). A coarse-to-fine user preferences prediction method for point-of-interest recommendation. Neurocomputing, 422, pp. 1-11. DOI: 10.1016/j.neucom.2020.09.034.
- 4. Carrascal Incera A., Fernández M.F. (2015). Tourism and income distribution: Evidence from a developed regional economy. Tourism Management, 48, pp. 11-20. DOI: 10.1016/j.tourman.2014.10.016.
- 5. Geofabrik (2022). Available at: http://download.geofabrik.de/ [access: 4.06.2022].
- 6. Gupta A., Kamble T. and Machiwal D. (2017). Comparison of ordinary and Bayesian kriging techniques in depicting rainfall variability in arid and semi-arid regions of north-west India. Environmental Earth Sciences, 76(15). Springer, pp. 1-16.
- 7. Hall C.M., Page S.J. (2009). Progress in Tourism Management: From the geography of tourism to geographies of tourism - A review. Tourism Management 30(1), pp. 3-DOI: 10.1016/j.tourman.2008.05.014.
- 8. Han Z., Song W. (2020). Identification and Geographic Distribution of Accommodation and Catering Centers. ISPRS International Journal of Geo-Information, 9(9): 546. DOI: 10.3390/ijgi9090546.
- 9. Jia R., Khadka A., Kim I. (2018). Traffic crash analysis with point-of-interest spatial clustering. Accident Analysis & Prevention, vol. 121, pp. 223-230. DOI: 10.1016/j.aap.2018.09.018.
- 10. Krivoruchko K. (2012). Empirical Bayesian Kriging Implemented in ArcGIS Geostatistical Analyst. Available at: https://www.esri.com/news/arcuser/1012/files/ebk.pdf [access: 4.06.2022].
- 11. Krivoruchko K. Gribov A. (2019). Evaluation of empirical Bayesian kriging. Spatial Statistics, 32. Elsevier: 100368. DOI: 10.1016/J.SPASTA.2019.100368.
- 12. Lee Y-JA., Jang S. and Kim J. (2020). Tourism clusters and peer-to-peer accommodation.
- 13. Annals of Tourism Research, 83: 102960. DOI: 10.1016/j.annals.2020.102960.
- 14. Lengyel A. (2016). Tourism, meditation, sustainability. ABSTRACT: Applied Studies in Agribusiness and Commerce, 10(1033-2016-84303): 81-92.
- 15. Liu T., Liao J., Wu Z., Wang Y., Wang J. (2020). Exploiting geographical-temporal awareness attention for next point-of-interest recommendation. Neurocomputing 400: 227-237. DOI: 10.1016/j.neucom.2019.12.122.
- 16. Lu Ch., Pang M., Zhang Y., Li H., Lu Ch., Tang X., Cheng W. (2020). Mapping Urban Spatial Structure Based on POI (Point of Interest) Data: A Case Study of the Central City of Lanzhou, China. ISPRS International Journal of Geo-Information, 9(2): 92. DOI: 10.3390/ijgi9020092.
- 17. Milias V., Psyllidis A. (2021). Assessing the influence of point-of-interest features on the classification of place categories. Computers, Environment and Urban Systems, 86:
- 18. 101597. DOI: 10.1016/j.compenvurbsys.2021.101597.
- 19. Navrátil J., Švec R., Pícha K. (2012). The Location of Tourist Accommodation Facilities: A Case Studyof the Sumava Mts. and South Bohemia Tourist Regions (Czech Republic). Moravian Geographical Reports 3(20): 50-63. Available at: https://www.researchgate.net/profile/Josef- Navratil/publication/283509021_The_Location_of_Tourist_Accommodation_Faciliti es_A_Case_Study_of_the_Sumava_Mts_and_South_Bohemia_Tourist_Regions_Czech_ Republic/links/563c6e7208ae405111a909b4/The-Location-of-Touri.
- 20. Oliver M.A., Webster R. (1990). Kriging: a method of interpolation for geographical information systems. International Journal of Geographical Information System, 4(3). Taylor & Francis: pp. 313-332.
- 21. Rodríguez Rangel M.C., Sánchez Rivero M., Ramajo Hernández J. (2020). A Spatial Analysis of Intensity in Tourism Accommodation: An Application for Extremadura (Spain). Economies 8(2): 28. DOI: 10.3390/economies8020028.
- 22. Silviu-Florin S. (2014) What is Tourism Geography? Geography Realm. Available at: https://www.geographyrealm.com/tourism-geography/.
- 23. Suárez-Vega R., Hernández J.M. (2020). Selecting Prices Determinants and Including Spatial Effects in Peer-to-Peer Accommodation. ISPRS International Journal of Geo- Information, 9(4): 259. DOI: 10.3390/ijgi9040259.
- 24. Vestal B.E., Carlson N.E., Ghosh D. (2021). Filtering spatial point patterns using kernel densities. Spatial Statistics, 41: 100487. DOI: 10.1016/j.spasta.2020.100487.
- 25. Williams A.M., Shaw G. (2015). Tourism, Geography of. In: J.D. Wright (ed.) International Encyclopedia of the Social & Behavioral Sciences (Second Edition). Oxford: Elsevier, pp. 469-473. Available at: https://www.sciencedirect.com/science/article/pii/B9780080970868720824.
- 26. Wu R., Wang J., Zhang D., Wang S. (2021). Identifying different types of urban land use dynamics using Point-of-interest (POI) and Random Forest algorithm: The case of Huizhou, China. Cities, 114: 103202. DOI: 10.1016/j.cities.2021.103202.
- 27. Yu W., Ai T. (2014). The visualization and analysis of urban facility pois using network kernel density estimation constrained by multi-factors. Boletim de Ciências Geodésicas, 20(4). Available at: https://revistas.ufpr.br/bcg/article/view/38958.
Uwagi
PL
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-6e249c74-7400-4101-bfa2-bb463a0c33b2