Implementing GPS logger data for visitor monitoring in the Karawanken–Karavanke UNESCO Global Geopark

Main Article Content

Sabrina Muscolino
Lilia Schmalzl
Julian Greiler

DOI

https://doi.org/10.71911/cii-p3-nt-2026313

Abstract

Human-Nature coexistence is an increasingly important component in the sustainable management of conservation areas. Pioneering conservation approaches are focused on integrating these two aspects rather than creating a strict separation. Disruptions to the balance between humans and nature can affect both natural resource conservation and the tourist experience. To avoid this, resource, infrastructure, and tourism flow management plans are needed. The HUMANITA project aims to develop innovative solutions to monitor tourism and its impact on the environment within selected conservation areas in Central Europe. It includes several methods to estimate tourism numbers, hotspots, and activities. We distributed 69 GPS loggers in the Karawanken–Karavanke UNESCO Global Geopark and created visitor density and intensity maps. This study demonstrates that GPS logger tools can support science-based management in conservation areas by providing quantitative insights for managing tourism flows. However, part of our objective is also to highlight key limitations, including low tourist participation, technical challenges, and concerns related to the high spatial resolution of the data in the UNESCO Global Geopark.

Keywords

GPS tracking, Ecotourism, Human-Nature Coexistence, CUAS

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References

[1] L. Schmalzl, D. Sitanyiova, A. Valetta, L. Stern Racan, and B. Megyeri, “Monitoring tourism impacts in conservation areas: Catalogue and toolbox of practical monitoring methods (Output 0.2.2 of Interreg CENTRAL EUROPE HUMANITA),” Carinthia University of Applied Sciences, 2026.

[2] U. Hasana, S. K. Swain, and B. George, “A bibliometric analysis of ecotourism: A safeguard strategy in protected areas,” Reg. Sustain., vol. 3, no. 1, pp. 27–40, 2022, doi: https://doi.org/10.1016/j.regsus.2022.03.001

[3] J. Chun, C.-K. Kim, G. S. Kim, J. Jeong, and W.-K. Lee, “Social big data informs spatially explicit management options for national parks with high tourism pressures,” Tour. Manag., vol. 81, p. 104136, 2020, doi: https://doi.org/10.1016/j.tourman.2020.104136

[4] M. Rogowski, “A method for overtourism optimisation for protected areas,” J. Outdoor Recreat. Tour., vol. 49, p. 100859, 2025, doi: https://doi.org/10.1016/j.jort.2025.100859

[5] D. Bertocchi, J. Van der Borg, and N. Camatti, “Tourism peaks on the Three Peaks : using big data to monitor where, when and how many visitors impact the Dolomites UNESCO World Heritage Site,” Riv. Geogr. Ital. CXXVIII 3 2021, pp. 59–81, 2021, doi: https://doi.org/10.3280/rgioa3-2021oa12532

[6] R. Knafou, Hypertourisme. Le tourisme à l’épreuve de sa démesure. Fondation Jean-Jaurès, 2026.

[7] J. A. Beeco, J. C. Hallo, W. ‘Rockie’ English, and G. W. Giumetti, “The importance of spatial nested data in understanding the relationship between visitor use and landscape impacts,” Appl. Geogr., vol. 45, pp. 147–157, 2013, doi: https://doi.org/10.1016/j.apgeog.2013.09.001

[8] J. C. Hallo, J. A. Beeco, C. Goetcheus, J. McGee, N. G. McGehee, and W. C. Norman, “GPS as a method for assessing spatial and temporal use distributions of nature-based tourists,” J. Travel Res., vol. 51, no. 5, pp. 591–606, 2012, doi: https://doi.org/10.1177/0047287511431325

[9] M. Bielański, K. Taczanowska, A. Muhar, P. Adamski, L.-M. González, and Z. Witkowski, “Application of GPS tracking for monitoring spatially unconstrained outdoor recreational activities in protected areas – A case study of ski touring in the Tatra National Park, Poland,” Appl. Geogr., vol. 96, pp. 51–65, 2018, doi: https://doi.org/10.1016/j.apgeog.2018.05.008

[10] M. J. Soeters, “Results and lessons learnt from experimenting with passive GPS data in a UNESCO Global Geopark,” 2025, Social Science Research Network, Rochester, NY: 5556264. doi: https://doi.org/10.2139/ssrn.5556264

[11] R. Mashkov and N. Shoval, “Using high-resolution GPS data to create a tourism Intensity-Density Index,” Tour. Geogr., vol. 25, no. 6, pp. 1657–1678, 2023, doi: https://doi.org/10.1080/14616688.2023.2276910

[12] D. E. Lundberg, “Caribbean tourism,” Cornell Hotel Restaur. Adm. Q., vol. 14, no. 4, pp. 30–45, 1974, doi: https://doi.org/10.1177/001088047401400407

[13] D. Orellana, A. K. Bregt, A. Ligtenberg, and M. Wachowicz, “Exploring visitor movement patterns in natural recreational areas,” Tour. Manag., vol. 33, no. 3, pp. 672–682, 2012, doi: https://doi.org/10.1016/j.tourman.2011.07.010

[14] J. Sun, S. Chen, Y. Huang, H. Rong, and Q. Li, “Harnessing GPS spatiotemporal big data to enhance visitor experience and sustainable management of UNESCO Heritage Sites: A case study of Mount Huangshan, China,” ISPRS Int. J. Geo-Inf., vol. 14, no. 10, 2025, doi: https://doi.org/10.3390/ijgi14100396

[15] A. Hardy and N. Shoval, “25 years of tourist tracking: a geographical perspective,” Tour. Geogr., vol. 27, no. 3–4, pp. 851–862, 2025, doi: https://doi.org/10.1080/14616688.2025.2462222

[16] L. Schmalzl, G. Hartmann, M. Jungmeier, D. Komar, and R. M. Schomaker, “Transnational water resource management in the Karawanken/Karavanke UNESCO Global Geopark,” Journal of Entrepreneurship, Management and Innovation, vol. 18, no. 3, pp. 7–36, 2022, doi: https://doi.org/10.7341/20221831

[17] F. Larroya, O. Díaz, O. Sagarra, P. Colomer Simón, S. Ferré, E. Moro, and J. Perelló, “Home-to-school pedestrian mobility GPS data from a citizen science experiment in the Barcelona area,” Sci. Data, vol. 10, no. 1, p. 428, 2023, doi: https://doi.org/10.1038/s41597-023-02328-3

[18] Python Software Foundation. [Online]. Available: https://www.python.org/downloads/release/python-314/

[19] J. C. Hallo, R. E. Manning, W. Valliere, and M. Budruk, “A case study comparison of visitor self-reported travel routes and GPS recorded travel routes,” in Proc. 2004 Northeast. Recreat. Res. Symp., Gen. Tech. Rep. NE-326. Newtown Square, PA, USA: U.S. Dep. Agric., For. Serv., Northeast. Res. Stn., 2005, pp. 172–177. [Online]. Available: https://research.fs.usda.gov/treesearch/9588. Accessed: Jan. 26, 2026.

[20] J. Wang, Y. Liu, and M.-P. Kwan, “Cross-validation between GPS-derived trajectories and activity-travel diaries for transport geography studies,” J. Transp. Geogr., vol. 126, p. 104239, 2025, doi: https://doi.org/10.1016/j.jtrangeo.2025.104239

[21] M. Bauder, “Using GPS supported speed analysis to determine spatial visitor behaviour,” Int. J. Tour. Res., vol. 17, no. 4, pp. 337–346, 2015, doi: https://doi.org/10.1002/jtr.1991

[22] Miyasaka, T., Oba, A., Akasaka, and Tsuchiya, T., “Sampling limitations in using tourists’ mobile phones for GPS-based visitor monitoring,” Journal of Leisure Research, vol. 49, no. 3–5, 2018, doi: https://doi.org/10.1080/00222216.2018.1542526

[23] G. Ankunda and C. Venter, “Studying transfers in informal transport networks using volunteered GPS data,” Travel Behav. Soc., vol. 39, p. 100936, 2025, doi: https://doi.org/10.1016/j.tbs.2024.100936

[24] J. Molloy et al., “The MOBIS dataset: a large GPS dataset of mobility behaviour in Switzerland,” Transportation, vol. 50, no. 5, pp. 1983–2007, 2023, doi: https://doi.org/10.1007/s11116-022-10299-4

[25] H. Dabbas and B. Friedrich, “Benchmarking machine learning algorithms by inferring transportation modes from unlabeled GPS data,” Transp. Res. Procedia, vol. 62, pp. 383–392, 2022, doi: https://doi.org/10.1016/j.trpro.2022.02.048

[26] S. Giri, R. Brondeel, T. El Aarbaoui, and B. Chaix, “Application of machine learning to predict transport modes from GPS, accelerometer, and heart rate data,” Int. J. Health Geogr., vol. 21, no. 1, p. 19, 2022, doi: https://doi.org/10.1186/s12942-022-00319-y

[27] N. E. H. Stappers, M. P. M. Bekker, M. W. J. Jansen, S. P. J. Kremers, N. K. Vries, J. Schipperijn, and D. H. H. van Kann, “Effects of major urban redesign on sedentary behavior, physical activity, active transport and health-related quality of life in adults,” BMC Public Health, vol. 23, no. 1, p. 1157, 2023, doi: https://doi.org/10.1186/s12889-023-16035-6

[28] R. Costa-Pereira, R. J. Moll, B. R. Jesmer, and W. Jetz, “Animal tracking moves community ecology: Opportunities and challenges,” J. Anim. Ecol., vol. 91, no. 7, pp. 1334–1344, 2022, doi: https://doi.org/10.1111/1365-2656.13698

[29] T. A. Wild, J. C. Koblitz, D. K. N. Dechmann, C. Dietz, M. Meboldt, and M. Wikelski, “Micro-sized open-source and low-cost GPS loggers below 1 g minimise the impact on animals while collecting thousands of fixes,” PLOS ONE, vol. 17, no. 6, p. e0267730, 2022, doi: https://doi.org/10.1371/journal.pone.0267730

[30] S. W. Forrest, M. R. Recio, and P. J. Seddon, “Moving wildlife tracking forward under forested conditions with the SWIFT GPS algorithm,” Anim. Biotelemetry, vol. 10, no. 1, p. 19, 2022, doi: https://doi.org/10.1186/s40317-022-00289-9

[31] D. Edwards and T. Griffin, “Understanding tourists’ spatial behaviour: GPS tracking as an aid to sustainable destination management,” J. Sustain. Tour., vol. 21, no. 4, pp. 580–595, 2013, doi: https://doi.org/10.1080/09669582.2013.776063

[32] M. Modsching, R. Kramer, U. Gretzel, and K. ten Hagen, “Capturing the beaten paths: A novel method for analysing tourists’ spatial behaviour at an urban destination,” in Information and Communication Technologies in Tourism 2006, M. Hitz, M. Sigala, and J. Murphy, Eds., Vienna: Springer, 2006, pp. 75–86. doi: https://doi.org/10.1007/3-211-32710-X_15.

[33] P. Sadeghian, A. Golshan, M. X. Zhao, and J. Håkansson, “A deep semi-supervised machine learning algorithm for detecting transportation modes based on GPS tracking data,” Transportation, vol. 52, no. 4, pp. 1745–1765, 2025, doi: https://doi.org/10.1007/s11116-024-10472-x

[34] Y. Zheng, L. Liu, L. Wang, and X. Xie, “Learning transportation mode from raw GPS data for geographic applications on the web,” in Proceedings of the 17th international conference on World Wide Web, in WWW ’08. New York, NY, USA: Association for Computing Machinery, 2008, pp. 247–256. doi: https://doi.org/10.1145/1367497.1367532

[35] S. Korpilo, T. Virtanen, T. Saukkonen, and S. Lehvävirta, “More than A to B: Understanding and managing visitor spatial behaviour in urban forests using public participation GIS,” J. Environ. Manage., vol. 207, pp. 124–133, 2018, doi: https://doi.org/10.1016/j.jenvman.2017.11.020

[36] M. Satomura, S. Shimada, Y. Goto, and M. Nishikori, “GPS measurements to investigate the reason why GPS is less accurate in mountain areas,” in A Window on the Future of Geodesy, F. Sansò, Ed., Berlin, Heidelberg: Springer, 2005, pp. 44–47. doi: https://doi.org/10.1007/3-540-27432-4_8

[37] L. Stamberger, C. J. van Riper, R. Keller, M. Brownlee, and J. Rose, “A GPS tracking study of recreationists in an Alaskan protected area,” Appl. Geogr., vol. 93, pp. 92–102, 2018, doi: https://doi.org/10.1016/j.apgeog.2018.02.011

[38] A. D’Antonio, C. Monz, S. Lawson, P. Newman, D. Pettebone, and A. Courtemanch, “GPS-based measurements of backcountry visitors in parks and protected areas: Examples of methods and applications from three case studies,” J. Park Recreat. Adm., vol. 28, no. 3, p. 42, 2010

[39] K. Taczanowska, M. Bielański, L.-M. González, X. Garcia-Massó, and J. L. Toca-Herrera, “Analyzing spatial behavior of backcountry skiers in mountain protected areas combining GPS tracking and graph theory,” Symmetry, vol. 9, no. 12, 2017, doi: https://doi.org/10.3390/sym9120317

[40] UNESCO, UNESCO Global Geoparks: Celebrating Earth Heritage, Sustaining Local Communities, SC.2015/WS/32. UNESCO, 2015. [Online]. Available: https://unesdoc.unesco.org/ark:/48223/pf0000243650

[41] R. K. Dowling, “Geotourism’s global growth,” Geoheritage, vol. 3, no. 1, pp. 1–13, 2011, doi: https://doi.org/10.1007/s12371-010-0024-7

[42] A. Duarte, V. Braga, C. Marques, and A. A. Sá, “Geotourism and territorial development: A systematic literature review and research agenda,” Geoheritage, vol. 12, no. 3, p. 65, 2020, doi: https://doi.org/10.1007/s12371-020-00478-z

[43] O. Cheablam, P. Tansakul, B. Nantakat, and S. Pantaruk, “Assessment of the geotourism resource potential of the Satun UNESCO Global Geopark, Thailand,” Geoheritage, vol. 13, no. 4, 2021, doi: https://doi.org/10.1007/s12371-021-00609-0

[44] R. K. Dowling, “Global geotourism—an emerging form of sustainable tourism,” Czech Journal of Tourism, vol. 2, no. 2, 2013, doi: https://doi.org/10.2478/cjot-2013-0004

[45] N. T. Farsani, C. Coelho, and C. Costa, “Geotourism and geoparks as novel strategies for socio-economic development in rural areas,” Int. J. Tour. Res., vol. 13, no. 1, pp. 68–81, 2011, doi: https://doi.org/10.1002/jtr.800

[46] R. Ólafsdóttir and R. Dowling, “Geotourism and geoparks—a tool for geoconservation and rural development in vulnerable environments: a case study from Iceland,” Geoheritage, vol. 6, no. 1, pp. 71–87, 2014, doi: https://doi.org/10.1007/s12371-013-0095-3.

[47] E. M. Rosado-González, J. M. M. Lourenço, N. M. Vaz, E. Silva, and A. A. Sá, “A literature review of geographical information systems applications in UNESCO Global Geoparks,” Geoheritage, vol. 15, no. 2, 2023, doi: https://doi.org/10.1007/s12371-023-00829-6

[48] E. Drápela , A. Boháč, H. Böhm, and K. Zágoršek, “Motivation and preferences of visitors in the Bohemian Paradise UNESCO Global Geopark,” Geosciences, vol. 11, no. 3, Art. no. 116, 2021, doi: https://doi.org/10.3390/geosciences11030116.

[49] Schmalzl, L., Kieliszek, Z., Grabner, U., and Berger, V., “Report on environmental impacts of tourism within the Karawanken Karavanke UNESCO Global Geopark.,” Carinthia University of Applied Sciences, EGTC Geopark Karawanken, Villach, Sittersdorf, 2023

[50] Republic of Slovenia Statistical Office, “Municipalities”, 2023. [Online]. Available: https://pxweb.stat.si/SiStat/en/Podrocja/Index/583/regional-overview/

[51] Statistics Austria, “Tourism”, 2023. [Online]. Available: https://www.statistik.at/en

[52] QGIS Development Team, QGIS Geographic Information System (Version 3.44.4). [Online]. Available: https://qgis.org/en/site/forusers/download.html

[53] Posit, RStudio: Integrated Development Environment for R (Version 2024.12.0).

[54] E. Parzen, “On estimation of a probability density function and mode,” Ann. Math. Stat., vol. 33, no. 3, pp. 1065–1076, 1962.

[55] T. Thimm and R. Seepold, “Past, present and future of tourist tracking,” Journal of Tourism Futures, vol. 2, no. 1, pp. 43–55, 2016, doi: https://doi.org/10.1108/JTF-10-2015-0045

[56] H.-J. L. Weber and M. Bauder, “Neue Methoden der Mobilitätsanalyse: Die Verbindung von GPS-Tracking mit quantitativen und qualitativen Methoden im Kontext des Tourismus,” Raumforschung und Raumordnung | Spatial Research and Planning, vol. 71, no. 2, 2013, doi: https://doi.org/10.1007/s13147-013-0218-y

How to Cite

Muscolino, S., Schmalzl, L., & Greiler, J. (2026). Implementing GPS logger data for visitor monitoring in the Karawanken–Karavanke UNESCO Global Geopark. Carinthia II - Part 3, 3(1), 42-54. https://doi.org/10.71911/cii-p3-nt-2026313