Analysis of spatial distribution of touristic accommodation in Poland with the kernel density estimation of POIs

Mirosław Bełej

doi:10.31648/aspal.6818

Published: 2021-10-041

Vol. 20 No. 3 (2021)

Analysis of spatial distribution of touristic accommodation in Poland with the kernel density estimation of POIs

Mirosław Bełej

Acta Scientiarum Polonorum Administratio Locorum

Section: Articles

https://doi.org/10.31648/aspal.6818

Abstract

Motives: Using Points-of-Interest (POIs) data and GIS software, the spatial heterogeneity of different types of accommodation could cheap, easily and quick be analyzed.

Aim: The use of kernel density estimation (KDE) of Points-of-Interest data to shown spatial distribution of different types of accommodation in Poland.

Results: There is a close relationship between the type of accommodation and the type of tourist attraction.

Keywords:

accommodation, tourism geography, points-of-interest, KDE

Most read articles by the same author(s)

Radosław Cellmer, Aneta Beata Cichulska, Mirosław Bełej, The regional spatial diversity of housing prices and market activity – evidence from Poland , Acta Scientiarum Polonorum Administratio Locorum: Vol. 20 No. 1 (2021)

Download files

PDF

Citation rules

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), 159–171. https://doi.org/10.31648/aspal.6818

Cited by / Share

Licence

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

References

Anderson, T.K. (2009). Kernel density estimation and K-means clustering to profile road accident hotspots. Accident Analysis & Prevention, 41(3), pp. 359–364. doi:10.1016/j.aap.2008.12.014.
Crossref
Google Scholar

ArcGis (2021). How Kernel Density works – ArcGIS Pro. Documentation. https://pro.arcgis.com/en/pro-app/latest/tool-reference/spatial-analyst/how-kernel-density-works.htm, (date: 24.04.2021).
Google Scholar

Arimond, G., Elfessi, A. (2001). A Clustering Method for Categorical Data in Tourism Market Segmentation Research. Journal of Travel Research, 39(4), pp. 391–397. doi:10.1177/004728750103900405.
Crossref
Google Scholar

Assaf, A.G. (2012). Benchmarking the Asia Pacific tourism industry: A Bayesian combination of DEA and stochastic frontier. Tourism Management, 33(5), pp. 1122–1127. doi:10.1016/j.tourman.2011.11.021
Crossref
Google Scholar

Assaf, A.G., Tsionas, M., Oh, H. (2018). The time has come: Toward Bayesian SEM estimation in tourism research. Tourism Management, 64, pp. 98–109. doi:10.1016/j.tourman.2017.07.018.
Crossref
Google Scholar

Basu, S., Thibodeau, T.G. (1998). Analysis of Spatial Autocorrelation in House Prices. The Journal of Real Estate Finance and Economics, 17(1), pp. 61–85. doi:10.1023/A:1007703229507.
Crossref
Google Scholar

Boers, B., Cottrell, S. (2007). Sustainable Tourism Infrastructure Planning: A GIS-Supported Approach. Tourism Geographies, 9(1), pp. 1–21. doi:10.1080/14616680601092824.
Crossref
Google Scholar

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.
Crossref
Google Scholar

Cowell, P.J., Zeng, T.Q. (2003). Integrating uncertainty theories with GIS for modeling coastal hazards of climate change. Marine Geodesy, 26(1–2), pp. 5–18. doi:10.1080/01490410306700.
Crossref
Google Scholar

Domański, R. (2012). Złożoność przestrzeni ekonomicznej: elementy teorii [Complexity of Economic Space: Elements of Theory]. Zeszyty Naukowe / Uniwersytet Ekonomiczny w Poznaniu, 247, pp. 7–27.
Google Scholar

Gao, S., Janowicz, K., Couclelis, H. (2017). Extracting urban functional regions from points of interest and human activities on location-based social networks,Transactions in GIS, 21(3), pp. 446–467. doi:10.1111/tgis.12289.
Crossref
Google Scholar

Hall, C.M. (2013). Framing tourism geography: notes from the underground. Annals of Tourism Research, 43, pp. 601–623. doi:10.1016/j.annals.2013.06.007.
Crossref
Google Scholar

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. 3–16. doi:10.1016/j.tourman.2008.05.014.
Crossref
Google Scholar

Han, Z., Song, W. (2020). Identification and Geographic Distribution of Accommodation and Catering Centers. ISPRS International Journal of Geo-Information, 9(9), p. 546. doi:10.3390/ijgi9090546.
Crossref
Google Scholar

Jeffrey, D. (1985). Spatial and temporal patterns of demand for hotel accomodation: Time series analysis in Yorkshire and Humberside, UK. Tourism Management, 6(1), pp. 8–22. doi:10.1016/0261-5177(85)90051-2.
Crossref
Google Scholar

Jia, R., Khadka, A., Kim, I. (2018). Traffic crash analysis with point-of-interest spatial clustering, Accident Analysis & Prevention, 121, pp. 223–230. doi:10.1016/j.aap.2018.09.018.
Crossref
Google Scholar

Jin, C., Xu, J., Huang, Z. (2019). Spatiotemporal analysis of regional tourism development: A semiparametric Geographically Weighted Regression model approach. Habitat International, 87, pp. 1–10. doi:10.1016/j.habitatint.2019.03.011.
Crossref
Google Scholar

Kulshrestha, A., Krishnaswamy, V., Sharma, M. (2020). Bayesian BILSTM approach for tourism demand forecasting, Annals of Tourism Research, 83, p. 102925. doi:10.1016/j.annals.2020.102925.
Crossref
Google Scholar

Läuter, H. (1988). Silverman, B. W.: Density Estimation for Statistics and Data Analysis. Chapman & Hall, London – New York 1986, 175 pp., L12.—. Biometrical Journal, 30(7), pp. 876–877. doi:10.1002/bimj.4710300745.
Crossref
Google Scholar

Lee, Y.-J. A., Jang, S., Kim, J. (2020). Tourism clusters and peer-to-peer accommodation. Annals of Tourism Research, 83, p. 102960. doi:10.1016/j.annals.2020.102960.
Crossref
Google Scholar

Lewandowska-Gwarda, K. (2013). Rola przestrzeni w badaniach ekonomicznych [The role of space in economic researcH]. Acta Universitatis Nicolai Copernici Ekonomia, 44(1), pp. 145–158. doi:10.12775/AUNC_EKON.2013.011.
Crossref
Google Scholar

Liszewski, S. (1995). Przestrzeń turystyczna [Tourism space]. Turyzm, 5(2). http://dspace.uni.lodz.pl:8080/xmlui/handle/11089/27787, date: 28.04.2021.
Crossref
Google Scholar

Lu, C., Pang, M., Zhang, Y., Li, H., Lu, C., 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), p. 92. doi:10.3390/ijgi9020092.
Crossref
Google Scholar

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, p. 101597. doi:10.1016/j.compenvurbsys.2021.101597.
Crossref
Google Scholar

Navrátil, J. et al. (2012). The Location of Tourist Accommodation Facilities: A Case Study of the Sumava Mts. and South Bohemia Tourist Regions (CzechRepublic). Moravian Geographical Reports, 3(20), pp. 50–63.
Google Scholar

Perroux, F. (1950). Economic space: theory and applications. The Quarterly Journal of Economics, 64(1), pp. 89–104.
Crossref
Google Scholar

Renjith, S., Sreekumar, A., Jathavedan, M. (2018). Evaluation of Partitioning Clustering Algorithms for Processing Social Media Data in Tourism Domain, In: 2018 IEEE Recent Advances in Intelligent Computational Systems (RAICS). 2018 IEEE Recent Advances in Intelligent Computational Systems (RAICS), pp. 127–131. doi:10.1109/RAICS.2018.8635080.
Crossref
Google Scholar

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), p. 28. doi:10.3390/economies8020028.
Crossref
Google Scholar

Shariat-Mohaymany, A., Tavakoli Kashani, A., Nosrati, H., Kazemzadehazad, S. (2013). Development of head-on conflict model for overtaking maneuvers on two-lane rural roads using inductive loop detectors. Journal of Transportation Safety & Security, 5(4), pp. 273–284. doi:10.1080/19439962.2013.766290.
Crossref
Google Scholar

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), p. 259. doi:10.3390/ijgi9040259.
Crossref
Google Scholar

Tasyurek, M., Celik, M. (2020). RNN-GWR: A geographically weighted regression approach for frequently updated data. Neurocomputing, 399, pp. 258–270. doi:10.1016/j.neucom.2020.02.058.
Crossref
Google Scholar

Tobler, W.R. (1970). A Computer Movie Simulating Urban Growth in the Detroit Region. Economic Geography, 46, pp. 234–240. doi:10.2307/143141.
Crossref
Google Scholar

Ustawa z dnia 29 sierpnia 1997 r. o usługach hotelarskich oraz usługach pilotów wycieczek i przewodników turystycznych [Act of August 29, 1997 on hotel services and the services of tour leaders and tourist guides] (Poland). https://lexlege.pl/ustawa-o-uslugach-turystycznych/.
Google Scholar

Voltes-Dorta, A., Sánchez-Medina, A. (2020). Drivers of Airbnb prices according to property/room type, season and location: A regression approach. Journal of Hospitality and Tourism Management, 45, pp. 266–275. doi:10.1016/j.jhtm.2020.08.015.
Crossref
Google Scholar

Wall, G., Dudycha, D., Hutchinson, J. (1985). Point pattern analyses of accomodation in Toronto. Annals of Tourism Research, 12(4), pp. 603–618. doi:10.1016/0160-7383(85)90080-5.
Crossref
Google Scholar

Wei, W. (2012). Research on the Application of Geographic Information System in Tourism Management. Procedia Environmental Sciences, 12, pp. 1104–1109. doi:10.1016/j.proenv.2012.01.394.
Crossref
Google Scholar

Williams, A.M., Shaw, G. (2015). Tourism, Geography of’, In: Wright, J.D. (ed.) International Encyclopedia of the Social & Behavioral Sciences (Second Edition). Oxford: Elsevier, pp. 469–473. doi:10.1016/B978-0-08-097086-8.72082-4.
Crossref
Google Scholar

Włodarczyk, B. (2014). Space in tourism, tourism in space: On the need for definition, delimitation and classification. Tourism, 24(1), pp. 25–34. doi:10.2478/tour-2014-0003.
Crossref
Google Scholar

Wojdacki, K.P. (2014). Rozwój bazy hotelowej w Polsce – analiza czasowo-strukturalna [Development of the Hotel Base in Poland – Temporal and Structural Analysis]. Handel Wewnętrzny, 2, pp. 103–124.
Google Scholar

Wong, K.K.F., Song, H., Chon, K.S. (2006). Bayesian models for tourism demand forecasting. Tourism Management, 27(5), pp. 773–780. doi:10.1016/j.tourman.2005.05.017.
Crossref
Google Scholar

Woolard, J.W., Colby, J.D. (2002). Spatial characterization, resolution, and volumetric change of coastal dunes using airborne LIDAR: Cape Hatteras, North Carolina. Geomorphology, 48(1–3), pp. 269–287. doi:10.1016/S0169-555X(02)00185-X.
Crossref
Google Scholar

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, p. 103202. doi:10.1016/j.cities.2021.103202.
Crossref
Google Scholar

Yang, B. (2016). GIS based 3-D landscape visualization for promoting citizen’s awareness of coastal hazard scenarios in flood prone tourism towns. Applied Geography, 76, pp. 85–97. doi:10.1016/j.apgeog.2016.09.006.
Crossref
Google Scholar

Yu, W., Ai, T. (2014). The visualization and analysis of urban facility pois using network kernel density estimation constrained by multi-factors. Boletim de Ciencias Geodesicas, 20(4). https://revistas.ufpr.br/bcg/article/view/38958, date: 24.04.2021.
Crossref
Google Scholar

Zajadacz, A. (2014). Accessibility of tourism space from a geographical perspective. Turyzm/Tourism, 24(1), pp. 45–50. doi:10.2478/tour-2014-0005.
Crossref
Google Scholar

Zhu, L., Li, W., Guo, K., Shi, Y., Zheng, Y. (2017). The Tourism-Specific Sentiment Vector Construction Based on Kernel Optimization Function. Procedia Computer Science, 122, pp. 1162–1167. doi:10.1016/j.procs.2017.11.487.
Crossref
Google Scholar