Spatial Analysis of Tourist Accommodation in Mainland Portugal: Insights from Open and Official Data
DOI:
https://doi.org/10.14198/INTURI.30105Palabras clave:
Turismo, Alojamiento, Puntos de Interés, OpenStreetMap (OSM), Datos de código abierto, Datos georreferenciadosResumen
The main objective of this study is to analyse the spatial structure of tourist accommodation in mainland Portugal. This study compares Point-Of-Interest (POI) from OpenStreetMap (OSM) with the Portuguese National Tourism Register (RNT), in order to assess whether open data can be an alternative to official data in countries where such access is difficult. Using geostatistical techniques such as Kernel Density Estimation (KDE) and Predominance Map, it evaluates the spatial distribution and regional disparities of various accommodation types. The results show a significant concentration along mainland Portugal's coastline and metropolitan areas and emerging clusters in inland regions. The research results show that official data is more important, but OSM data can be an alternative to this data, due to its free availability and quick accessibility. The study’s results will provide regional planning and development insights, enabling stakeholders to make informed decisions towards a more sustainable tourism industry.
Citas
Almendros-Jiménez, J. M., & Becerra-Terón, A. (2018). Analyzing the Tagging Quality of the Spanish OpenStreetMap. ISPRS International Journal of Geo-Information, 7(8), 323. https://doi.org/10.3390/ijgi7080323
Arimond, G., & Elfessi, A. (2001). A Clustering Method for Categorical Data in Tourism Market Segmentation Research. Journal of Travel Research, 39(4), 391–397. https://doi.org/10.1177/004728750103900405
Antunes, G., & Ferreira, J. (2021). SHORT-TERM RENTALS: HOW MUCH IS TOO MUCH – SPATIAL PATTERNS IN PORTUGAL AND LISBON. Tourism and Hospitality Management, 27(3), 581–603. https://doi.org/10.20867/thm.27.3.6
Arsanjani, J., Zipf, A., Mooney, P., & Helbich, M. (2015). An Introduction to OpenStreetMap in Geographic Information Science: Experiences, Research, and Applications (J. Arsanjani, A. Zipf, P. Mooney, & M. Helbich, Eds; pp. 1–15). Springer International Publishing. https://doi.org/10.1007/978-3-319-14280-7_1
Bento, R., Marques, C. P., & Guedes, A. (2022). Rural tourism in Portugal: Moving to the countryside. Journal of Maps, 18(1), 79–88. https://doi.org/10.1080/17445647.2022.2079430
Boers, B., & Cottrell, S. (2007). Sustainable Tourism Infrastructure Planning: A GIS-Supported Approach. Tourism Geographies, 9(1), 1–21. https://doi.org/10.1080/14616680601092824
Brouder, P., & Eriksson, R. H. (2013). TOURISM EVOLUTION: ON THE SYNERGIES OF TOURISM STUDIES AND EVOLUTIONARY ECONOMIC GEOGRAPHY. Annals of Tourism Research, 43, 370–389. https://doi.org/10.1016/j.annals.2013.07.001
Bustamante, A.; Laura, S.; Onaindia, E. (2019). Exploratory analysis of representativeness of tourism data in OpenStreetMap. In Proceedings of the 33 International Business Information Management (33 IBIMA 2019), Granada, Spain, 10–11 April 2019; pp.4161–4169.
Carrascal Incera, A., & Fernández, M. F. (2015). Tourism and income distribution: Evidence from a developed regional economy. Tourism Management, 48, 11–20. https://doi.org/10.1016/j.tourman.2014.10.016
Cellmer, R., Bełej, M., & Trojanek, R. (2024). Housing prices and points of interest in three Polish cities. Journal of Housing and the Built Environment. https://doi.org/10.1007/s10901-024-10124-7
Decreto-Lei n.º 39/2008. (2008). Diário da República, 1.ª série — N.º 48, 7 de março de 2008, 1444–1453. https://diariodarepublica.pt/dr/legislacao-consolidada/decreto-lei/2008-34454775
Decreto-Lei n.º 128/2014. (2014). Diário da República, 1.ª série — N.º 166, 29 de agosto de 2014. https://diariodarepublica.pt/dr/legislacao-consolidada/decreto-lei/2014-56917875
Decreto-Lei n.º 63/2015. (2015). Diário da República, 1.ª série — N.º 79, 23 de abril de 2015. https://diariodarepublica.pt/dr/detalhe/decreto-lei/63-2015-67059141
Decreto-Lei n.º 80/2017. (2017). Diário da República, 1.ª série — N.º 124, 30 de junho de 2017. https://diariodarepublica.pt/dr/detalhe/decreto-lei/80-2017-107596685
Decreto-Lei n.º 9/2021. (2021). Diário da República, 1.ª série — N.º 20, 29 de janeiro de 2021. https://diariodarepublica.pt/dr/detalhe/decreto-lei/9-2021-155732595
Encalada, L., Boavida-Portugal, I., Cardoso Ferreira, C., & Rocha, J. (2017). Identifying Tourist Places of Interest Based on Digital Imprints: Towards a Sustainable Smart City. Sustainability, 9(12), 2317. https://doi.org/10.3390/su9122317
Encalada-Abarca, L., Ferreira, C. C., & Rocha, J. (2022). Measuring Tourism Intensification in Urban Destinations: An Approach Based on Fractal Analysis. Journal of Travel Research, 61(2), 394–413. https://doi.org/10.1177/0047287520987627
Feng, R., & Morrison, A. M. (2002). GIS Applications in Tourism and Hospitality Marketing: A Case in Brown County, Indiana. Anatolia, 13, 127–143. https://doi.org/10.1080/13032917.2002.9687129
García-Palomares, J. C., Gutiérrez, J., & Mínguez, C. (2015). Identification of tourist hot spots based on social networks: A comparative analysis of European metropolises using photo-sharing services and GIS. Applied Geography, 63, 408–417. https://doi.org/10.1016/j.apgeog.2015.08.002
Guedes, A. S., & Jiménez, M. I. M. (2015). Spatial patterns of cultural tourism in Portugal. Tourism Management Perspectives, 16, 107–115. https://doi.org/10.1016/j.tmp.2015.07.010
Gupta, A., Kamble, T., & 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), 1–16. https://doi.org/10.1007/s12665-017-6814-3
Hennig, S. (2017). OpenStreetMap used in protected area management. The example of the recreational infrastructure in Berchtesgaden National Park. Eco.Mont (Journal on Protected Mountain Areas Research), 9(2), 30–41. https://doi.org/10.1553/eco.mont-9-2s30
Ji, H., Ji, H., Zheng, W., Zheng, W., Zhuang, X., & Lin, Z. (2021). Explore for a Day? Generating Personalized Itineraries That Fit Spatial Heterogeneity of Tourist Attractions. Information & Management. https://doi.org/10.1016/j.im.2021.103557
Jia, R., Khadka, A., & Kim, I. (2018). Traffic crash analysis with point-of-interest spatial clustering. Accident Analysis & Prevention, 121, 223–230. https://doi.org/10.1016/j.aap.2018.09.018
Lei n.º 62/2018. (2018). Diário da República, 1.ª série — N.º 160, 22 de agosto de 2018. https://diariodarepublica.pt/dr/detalhe/lei/62-2018-116152179
Leung, R., Vu, H. Q., & Rong, J. (2017). Understanding tourists’ photo sharing and visit pattern at non-first tier attractions via geotagged photos. Information Technology & Tourism, 17(1), 55–74. https://doi.org/10.1007/s40558-017-0078-3
Levin, N., Lechner, A. M., & Brown, G. (2017). An evaluation of crowdsourced information for assessing the visitation and perceived importance of protected areas. Applied Geography, 79, 115–126. https://doi.org/10.1016/j.apgeog.2016.12.009
Lim, K. H., Chan, J., Karunasekera, S., & Leckie, C. (2018). Tour Recommendation and Trip Planning Using Location-Based Social Media: A Survey. Knowledge and Information Systems. https://doi.org/10.1007/s10115-018-1297-4
McCarty, D. A., & Kim, H. W. (2023). A standardized European hexagon gridded dataset based on OpenStreetMap POIs. Data in Brief, 49, 109315. https://doi.org/10.1016/j.dib.2023.109315
Mira, M. D. R., Moura, A. A., Costa, V., & Pereira, R. (2025). Tourism, Economic Development, and Regional Inequality in Portugal: A Data-Driven Approach from 2012 to 2022. Tourism and Hospitality, 6(2), 110. https://doi.org/10.3390/tourhosp6020110
Mondzech, J.; Sester, M. (2011). Quality analysis of OpenStreetMap data based on application needs. Cartogr. Int. J. Geogr. Inf. Geovis. 46, 115–125. https://doi.org/10.3138/carto.46.2.115
Moreira, C. O. (2018). Portugal as a tourism destination: Paths and trends. Méditerranée, 130. https://doi.org/10.4000/mediterranee.10402
Nunes, H., Almeida, A., & Martins, C. (2016). Gathering data for professional tourism points of interest. Canadian Conference on Computer Science & Software Engineering. https://doi.org/10.1145/2948992.2948996
Ólafsdóttir, R., & Tverijonaite, E. (2018). Geotourism: A Systematic Literature Review. Geosciences, 8(7), 234. https://doi.org/10.3390/geosciences8070234
Önder, I., & Gunter, U. (2016). Forecasting tourism demand with Google Trends for a major European city destination. Tourism Analysis, 21(2–3), 203–220. https://doi.org/10.3727/108354216X14559233984773
Özenen Kavlak, M., Metin, T. C., Aksoy, T., Erdoğan, Ö., Korkmaz, C., Günok, E., Altunel, M. C., Çabuk, S. N., & Çabuk, A. (2025). Spatial Analysis of Academic Competence Level of Countries Regarding Tourism—Recreation Planning and Geographical Information Systems Relationship. GSI Journals Serie A: Advancements in Tourism Recreation and Sports Sciences, 8(1), 137–158. https://doi.org/10.53353/atrss.1541131
Pordata. (2024). Hóspedes nos alojamentos turísticos. Hóspedes Nos Alojamentos Turísticos: Total e Por Tipo de Estabelecimento. https://www.pordata.pt/portugal/hospedes+nos+alojamentos+turisticos+total+e+por+tipo+de+estabelecimento-2614
Reckziegel, M., Cheema, M. F., Scheuermann, G., & Janicke, S. (2018). Predominance Tag Maps. IEEE Transactions on Visualization and Computer Graphics, 24(6), 1893–1904. https://doi.org/10.1109/TVCG.2018.2816208
Santamaria-Granados, L., Mendoza-Moreno, J. F., & Ramirez-Gonzalez, G. (2020). Tourist Recommender Systems Based on Emotion Recognition—A Scientometric Review. Future Internet. https://doi.org/10.3390/fi13010002
Sehra, S., Singh, J., & Rai, H. (2017). Using Latent Semantic Analysis to Identify Research Trends in OpenStreetMap. ISPRS International Journal of Geo-Information, 6(7), 195. https://doi.org/10.3390/ijgi6070195
Shang, Y., Wen, C., Bai, Y., & Hou, D. (2022). A Novel Framework for Exploring the Spatial Characteristics of Leisure Tourism Using Multisource Data: A Case Study of Qingdao, China. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 6259–6271. https://doi.org/10.1109/JSTARS.2022.3196002
Silverman, B. W. (1978). Weak and strong uniform consistency of the kernel estimate of a density and its derivatives. The Annals of Statistics, 177–184. https://doi.org/10.1214/aos/1176344076
Silvestre, R. (2023). Portuguese state intervention(s) in the country market economy. In Z. Ranschburg (Ed.), The role of the state in contemporary market economies (Chapter 2). European Liberal Forum & Republikon Institute. https://republikon.hu/media/135963/The-role-of-the-state-2023-1-.pdf
Teles Da Mota, V., & Pickering, C. (2020). Using social media to assess nature-based tourism: Current research and future trends. Journal of Outdoor Recreation and Tourism, 30, 100295. https://doi.org/10.1016/j.jort.2020.100295
TravelBI. (2024). Receitas do Turismo. Receitas Do Turismo - Viagens e Turismo Na Balança de Pagamentos. https://travelbi.turismodeportugal.pt/turismo-em-portugal/receitas-do-turismo-dashboard/
Turismo de Portugal. (2022). Portugal 2022 Mercado em números. https://travelbi.turismodeportugal.pt/turismo-portugal/turismo-numeros-2022/
Turismo de Portugal. (2024a). Alojamento Local. Alojamento Local. https://travelbi.turismodeportugal.pt/alojamento/alojamento-local-dashboard/
Turismo de Portugal. (2024b). Empreendimentos Turísticos | Oferta. Empreendimentos Turísticos | Oferta (RNET - Registo Nacional de Empreendimentos Turísticos) | Dashboard. https://travelbi.turismodeportugal.pt/alojamento/empreendimentos-turisticos-oferta-rnet-registo-nacional-de-empreendimentos-turisticos-dashboard/
Turismo de Portugal. (2024c). Registo Nacional de Turismo (RNT). Registo Nacional de Alojamento Local. https://rnt.turismodeportugal.pt/RNT/Pesquisa_AL.aspx
Wei, J., Zhong, Y., & Fan, J. (2022). Estimating the Spatial Heterogeneity and Seasonal Differences of the Contribution of Tourism Industry Activities to Night Light Index by POI. Sustainability, 14(2), 692. https://doi.org/10.3390/su14020692
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, 85–97. https://doi.org/10.1016/j.apgeog.2016.09.006
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), Article 4. https://doi.org/10.1590/S1982-21702014000400050
Zhang, L., & Pfoser, D. (2019). Using OpenStreetMap point-of-interest data to model urban change—A feasibility study. PLOS ONE, 14(2), e0212606. https://doi.org/10.1371/journal.pone.0212606
Zhao, M., & Liu, J. (2021). Study on Spatial Structure Characteristics of the Tourism and Leisure Industry. Sustainability, 13(23), 13117. https://doi.org/10.3390/su132313117
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, 1162–1167. https://doi.org/10.1016/j.procs.2017.11.487
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