Gönüllü coğrafi veriler ile dağ bisikleti kullanımının zamansal ve mekânsal değişimlerinin modellenmesi: Datça Yarımadası örneği

Yıl 2022, Cilt: 8 Sayı: 2, 22 - 32, 30.12.2022

Ahmet Uslu

https://doi.org/10.53516/ajfr.1173904

Cited By: 2

Öz

Dağ bisikleti, popüler bir rekreasyon ve turizm aktivitesidir. Rekreasyonel ve korunan alanların sürdürülebilir yönetiminde dağ bisikleti kullanımının zamansal ve mekânsal verilerine ihtiyaç duyulmaktadır. Geleneksel veri toplama yöntemleri pahalı, zaman alıcı ve zamansal-mekânsal ayrıntılardan yoksundur. Günümüzde GNSS tabanlı mobil uygulamalardan elde edilen gönüllü coğrafi veriler, dağ bisikleti kullanımının zamansal ve mekânsal analizi için önemli bir veri kaynağı haline gelmiştir. Bu çalışmada gönüllü coğrafi veri perspektifine dayalı olarak, GNSS verileri aracılığıyla Datça Yarımadası’nda dağ bisikleti kullanımının zamansal ve mekânsal değişimlerinin modellenmesi amaçlanmıştır. Wikiloc platformunda paylaşılan 203 adet seyahat rotasının GNSS izleri kullanılarak oluşturulan grafikler ve yoğunluk haritaları ile dağ bisikleti kullanımı analiz edilmiştir. Çalışmanın sonuçları, kitle kaynaklı Wikiloc verilerinin, dağ bisikleti kullanımının zamansal ve mekânsal özelliklerini analiz etmek için veri kaynağı olarak kullanılabileceğini göstermiştir. Çalışma, gönüllü coğrafi verilerin avantajlarından yararlanarak rekreasyonel ve korunan alanların sürdürülebilir yönetimi için gelecekteki araştırmaların önünü açmaktadır.

Anahtar Kelimeler

Datça Yarımadası, Wikiloc, Gönüllü coğrafi veri, Dağ bisikleti, Zamansal ve mekânsal analiz

Modeling of temporal and spatial changes of mountain bike use with voluntary geographical data: the case of the Datça Peninsula

Öz

Mountain biking is a popular recreation and tourism activity. Temporal and spatial data of mountain bike usage are needed in the sustainable management of recreational and protected areas. Traditional data collection methods are expensive, time-consuming, and lack spatio-temporal details. Nowadays, voluntary geographical data obtained from GNSS-based mobile applications have become an important data source for temporal and spatial analysis of mountain bike usage. In this study, it is aimed to model the temporal and spatial changes of mountain bike use in the Datça Peninsula through GNSS data, based on a voluntary geographic data perspective. Mountain bike usage was analyzed with graphics and density maps created using GNSS traces of 203 travel routes shared on the Wikiloc platform. The results of the study showed that crowdsourced Wikiloc data can be used as a data source to analyze the temporal and spatial characteristics of mountain bike usage. The study paves the way for future research for the sustainable management of recreational and protected areas by taking advantage of voluntary geographic data.

Anahtar Kelimeler

Datça Peninsula, Wikiloc, Voluntary geographical data, Mountain biking, Temporal and spatial analysis

Kaynakça

• Arnberger, A., 2006. Recreation use of urban forests: an inter-area comparison. Urban forestry & urban greening, 4(3-4), 135-144.
• Barros, C., Moya-Gómez, B., García-Palomares, J. C., 2019. Identifying temporal patterns of visitors to national parks through geotagged photographs. Sustainability, 11(24), 6983.
• Barros, C., Moya-Gómez, B., Gutiérrez, J., 2020. Using geotagged photographs and GPS tracks from social networks to analyse visitor behaviour in national parks. Current Issues in Tourism, 23(10), 1291-1310.
• Campelo, M. B., 2015. Caracterização do BTT no Parque Natural de Sintra-Cascais: um contributo para a revisão da Carta de Desporto de Natureza do PNSC. Master’s Thesis, Lisbon University, Lisbon, Portugal.
• Campelo, M. B., Mendes, R. M. N., 2016. Comparing webshare services to assess mountain bike use in protected areas. Journal of outdoor recreation and tourism, 15, 82-88.
• Cessford, G., Muhar, A., 2003. Monitoring options for visitor numbers in national parks and natural areas. Journal for nature conservation, 11(4), 240-250.
• Chang, H. L., Chang, H. W., 2005. Comparison between the differences of recreational cyclists in national scenic bikeway and local bike lane. Journal of the Eastern Asia Society for Transportation Studies, 6, 2178-2193.
• Cole, D. N., Daniel, T. C., 2003. The science of visitor management in parks and protected areas: from verbal reports to simulation models. Journal for Nature Conservation, 11(4), 269-277.
• Conrow, L., Wentz, E., Nelson, T., Pettit, C., 2018. Comparing spatial patterns of crowdsourced and conventional bicycling datasets. Applied geography, 92, 21-30.
• ÇŞVİDB, 2022. Çevre, Şehircilik ve İklim Değişikliği Bakanlığı. https://ockb.csb.gov.tr/datca-bozburun-ozel-cevre-koruma-bolgesi-i-2747, Erişim: 03.08.2022.
• DK, 2022. Datça Kaymakamlığı. http://www.datca.gov.tr /cografikonum, Erişim: 03.08.2022.
• Elwood, S., 2008. Volunteered geographic information: future research directions motivated by critical, participatory, and feminist GIS. GeoJournal, 72(3), 173-183.
• Gutzwiller, K. J., D'Antonio, A. L., Monz, C. A., 2017. Wildland recreation disturbance: broad‐scale spatial analysis and management. Frontiers in Ecology and the Environment, 15(9), 517-524.
• GH, 2022. Google Haritalar. https://www.google.com/maps /place/Datça, Erişim: 24.07.2022.
• Heer, C., Rusterholz, H. P., Baur, B., 2003. Forest perception and knowledge of hikers and mountain bikers in two different areas in northwestern Switzerland. Environmental Management, 31(6), 709-723.
• Jestico, B., Nelson, T., Winters, M., 2016. Mapping ridership using crowdsourced cycling data. Journal of transport geography, 52, 90-97.
• Lee, J. Y., Tsou, M. H., 2018. Mapping spatiotemporal tourist behaviors and hotspots through location-based photo-sharing service (Flickr) data. In LBS 2018: 14th International Conference on Location Based Services (pp. 315-334). Springer, Cham.
• Mendes, R. M. N., Dias, P., Silva, C. P., 2014. Profiling MTB users’ preferences within protected areas through Webshare services. In Proceedings of the 7th International Conference on Monitoring and Management of Visitors in Recreational and Protected Areas, August 20-23, s. 166-168.
• Metsalu, T., Vilo, J., 2015. ClustVis: a web tool for visualizing clustering of multivariate data using principal component analysis and heatmap. Nucleic acids research, 43(W1), W566-W570. Mou, N., Zheng, Y., Makkonen, T., Yang, T., Tang, J. J., Song, Y., 2020. Tourists’ digital footprint: The spatial patterns of tourist flows in Qingdao, China. Tourism Management, 81, 104151.
• Norman, P., Pickering, C. M., 2017. Using volunteered geographic information to assess park visitation: comparing three on-line platforms. Applied geography, 89, 163-172.
• Norman, P., Pickering, C. M., 2019. Factors influencing park popularity for mountain bikers, walkers and runners as indicated by social media route data. Journal of environmental management, 249, 109413.
• Norman, P., Pickering, C. M., Castley, G., 2019. What can volunteered geographic information tell us about the different ways mountain bikers, runners and walkers use urban reserves?. Landscape and Urban Planning, 185, 180-190.
• OSM, 2022. OpenStreetMap. https://www.openstreetmap.org/# map=11/36.8112/27.6203, Erişim: 24.07.2022. Özcan, A., 2019. Karia Yolu: Datça Yarımadası Yürüyüş ve Gezi Rehberi. Amerikan Baskı, 1, 1-240.
• Ploner, A., Brandenburg, C., 2003. Modelling visitor attendance levels subject to day of the week and weather: a comparison between linear regression models and regression trees. Journal for Nature Conservation, 11(4), 297-308.
• Romanillos, G., Moya-Gómez, B., Zaltz-Austwick, M., Lamiquiz-Dauden, P. J., 2018. The pulse of the cycling city: visualising Madrid bike share system GPS routes and cycling flow. Journal of Maps, 14(1), 34-43.
• Santos, T., Nogueira Mendes, R., Farías-Torbidoni, E. I., Julião, R. P., Pereira da Silva, C., 2022. Volunteered geographical ınformation and recreational uses within metropolitan and rural contexts. ISPRS International Journal of Geo-Information, 11(2), 144.
• Song, X. P., Richards, D. R., He, P., Tan, P. Y., 2020. Does geo-located social media reflect the visit frequency of urban parks? A city-wide analysis using the count and content of photographs. Landscape and Urban Planning, 203, 103908.
• Tenerelli, P., Demšar, U., Luque, S., 2016. Crowdsourcing indicators for cultural ecosystem services: A geographically weighted approach for mountain landscapes. Ecological Indicators, 64, 237-248.
• Taylor, S., 2010. ‘Extending the dream machine’: understanding people's participation in mountain biking. Annals of Leisure Research, 13(1-2), 259-281.
• Türker, A., Özaltın Türker, G., Çelik, A., 2015. Dış mekân turizm ve rekreasyon faaliyetleri açısından Muğla ili iklim konforu analizi. Dokuz Eylul University Journal of Graduate School of Social Sciences, 17(4), 555-577.
• Uslu, A., 2021. Sosyal medya verileri kullanılarak milli park ziyaretlerinin mekânsal ve zamansal değişiminin modellenmesi: Beydağları sahil milli parkı örneği. Bartın Orman Fakültesi Dergisi, 23(2), 386-398.
• Xie, Z., Yan, J., 2008. Kernel density estimation of traffic accidents in a network space. Computers, environment and urban systems, 32(5), 396-406.
• Zheng, W., Huang, X., Li, Y., 2017. Understanding the tourist mobility using GPS: Where is the next place?. Tourism Management, 59, 267-280.
• WIKILOC, 2022. Dünyanın rotaları. https://tr.wikiloc.com/, Erişim: 23.07.2022. Wood, S. A., Guerry, A. D., Silver, J. M., Lacayo, M., 2013. Using social media to quantify nature-based tourism and recreation. Scientific reports, 3(1), 1-7.