SavedAdd your journal

Journals / Güncel Turizm Araştırmaları Dergisi / 2026 / Cilt: 10 Sayı: 2

Visualizing Global Destinations: A Comparative Analysis of Visual Representations and Tourism Images

Küresel Destinasyonları Görüntülemek: Görsel Temsil ve Turizm İmgelerinin Karşılaştırmalı Analizi

Hatice Erdoğan(TEKİRDAĞ NAMIK KEMAL ÜNİVERSİTESİ)0000-0002-0597-7430
Pages
442–461
Publication Date
2026-09-11
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Findings indicate that the most emphasized category is the city skyline, followed by historical buildings.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Visual content analysis is one of the most effective methods for exploring destination image, as it enables a deeper understanding of how destinations are perceived and represented.
📊
Key Findings & Data:In line with the research design, a total of 1,000 photographs were collected and analyzed using MAXQDA software and the Latent Dirichlet Allocation (LDA) model.
💡
Conclusion & Impact:Findings indicate that the most emphasized category is the city skyline, followed by historical buildings.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Turizm İmgesi · Görsel Temsil · Turizm Coğrafyası

Abstract

Visual content analysis is one of the most effective methods for exploring destination image, as it enables a deeper understanding of how destinations are perceived and represented. Accordingly, this study employs visual content analysis to examine the online images of global destinations. Ten locations were selected as research areas, and their representations within visual contents were analyzed. The main objective of the study is to reveal the online image of these tourist destinations, to examine how they are expressed through photographs, and to conduct comparative evaluations. In line with the research design, a total of 1,000 photographs were collected and analyzed using MAXQDA software and the Latent Dirichlet Allocation (LDA) model. Six categories (city skyline, historical buildings, natural areas, transportation networks, local products, and symbols) and five themes (natural, cultural, magnificent, glamorous, and picturesque) were identified. Findings indicate that the most emphasized category is the city skyline, followed by historical buildings. These two categories, which dominate the global destination image, demonstrate that human-made elements have a stronger representation than natural attractions. The thematic classification yielded parallel results. When examined by location, both similar and differentiated visual images were observed. The results highlight that destination image is constructed through visual representations, and that strengthening these representations enhances the overall tourism image. The study concludes that integrating data science with tourism geography can contribute significantly to destination branding and image management.

Özet

Görsel içerik analizi, destinasyon imajını keşfetmenin en etkili yollarından biridir. Destinasyon imajını anlamayı mümkün kıldığı için bu çalışmada görsel içerik analizi yapılmıştır. Küresel destinasyonların çevrimiçi imajı incelenmiştir. 10 lokasyon araştırma alanı olarak belirlenmiş, bunların içeriklerde nasıl temsil edildiği analiz edilmiştir. Doğal olarak bu çalışmanın amacı turistik alanın çevrimiçi imajını ortaya koymak, görseller ile nasıl ifade edildiğini incelemek ve karşılaştırma yapmaktır. Araştırma tasarımı gereği öncelikle 1000 fotoğraf temin edilmiştir. MAXQDA ve LDA modeli ile incelenmiş, tema ve kategoriler belirlenmiştir. Altı kategori (şehir silüeti, tarihi yapılar, doğal alanlar, ulaşım hatları, yerel ürünler ve semboller) ve beş tema (doğal, kültürel, görkemli, ışıltılı ve pitoresk) tespit edilmiştir. Araştırma sonuçlarına göre en fazla vurgu şehir silüeti kategorisindedir. Onu tarihi yapılar takip etmektedir. Küresel destinasyon imajında en fazla etkiye sahip olan bu iki başlık beşeri unsurların doğal çekiciliklere nazaran daha güçlü bir temsile sahip olduğunu göstermektedir. Temalara göre belirlenen sınıflandırmalarda da aynı sonuçlara ulaşılmıştır. Sonuçlar lokasyonlar özelinde incelendiğinde benzer imajlar ve farklılaşan görüntüler ile karşılaşılmıştır. Netice itibariyle destinasyon imajının görsel temsiller ile üretildiği ve bu temsillerin dikkate alınmasıyla turizm imajının güçlendiğinin altı çizilmiştir. Veri bilimi ile turizm coğrafyası entegrasyonuyla turizm markalaşması ve imaj yönetimi alanına katkı sağlanabileceği belirtilmiştir.

🔖

Cite This Article

Erdoğan, H. (2026). Visualizing Global Destinations: A Comparative Analysis of Visual Representations and Tourism Images. Güncel Turizm Araştırmaları Dergisi, Cilt: 10 Sayı: 2, 442–461. https://doi.org/10.32572/guntad.1808292

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (49)

  1. Albers, P. C., & James, W. R. (1983). Tourism and the changing photographic image of the great lakes Indians. Annals of Tourism Research, 10(1), 123-148. https://doi.org/10.1016/0160-7383(83)90119-6
  2. An, W., & Alarcón, S. (2021). From netnography to segmentation for the description of the rural tourism market based on tourist experiences in Spain. Journal of Destination Marketing and Management, 19, 1-13. https://doi.org/10.1016/j.jdmm.2020.100549
  3. Bae, S. H., & Yun, H. J. (2017). Spatiotemporal distribution of visitors’ Geotagged landscape photos in rural areas. Tourism Planning & Development, 14(2) 167-180. https://doi.org/10.1080/21568316.2016.1204356
  4. Bærenholdt, J. O., Haldrup, M., & Urry, J. (2004). Performing Tourist Places (1. Baskı). Routledge.
  5. Bouchon, F. A. L. (2014). Truly Asia and global city? Branding strategies and contested identities in Kuala Lumpur. Place Branding and Public Diplomacy, 10(1), 6-18. https://doi.org/10.1057/pb.2013.21
  6. Bayram, M., Keleş, Y., & Bayram, Ü. (2017). Çevrimiçi Fotoğraflar Üzerinden Görsel Destinasyon İmajının İncelenmesi: Erzincan Doğa Sporları Örneği. Erzincan Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, Ulusal Alternatif Turizm Kongresi Özel Sayısı (IV), 15-26. https://izlik.org/JA22MB54PK
  7. Chinnapakjarusiri, N., Rafiyya, A., & Kasrisom, A. (2024). Enhancing International Tourist Brand Awareness in the Digital Economy: A Case Study of Bangkok in Thailand (SSRN Scholarly Paper No. 4652057). Social Science Research Network. https://papers.ssrn.com/abstract=4652057
  8. Crompton, J. L. (1979). An assessment of the image of Mexico as a vacation destination and the influence of geographical location upon that image. Journal of Travel Research, 14(4), 18-23. https://doi.org/10.1177/004728757901700404
  9. Doğanay, H., & Zaman, S. (2024). Türkiye Turizm Coğrafyası (10.Baskı). Pegem Akademi.
  10. Euromonitor International. (2024). Euromonitor International reveals world’s Top 100 City Destinations for 2024. https://www.euromonitor.com/press/press-releases/december-2024/euromonitor-international-reveals-worlds-top-100-city-destinations-for-2024
  11. Garrod, B., & Kosowska, A. (2012). Destination image consistency and dissonance: a content analysis of Goa’s destination image in brochures and guidebooks. Tourism Analysis, 17(2), 167-180. https://doi.org/10.3727/108354212X13388995267823
  12. Gilbert, P. K. (2002). Imagined Londons (1.Baskı). SUNY Press.
  13. Govers, R. (2012). Brand Dubai and its competitors in the Middle East: An image and reputation analysis. Place Branding and Public Diplomacy, 8(1), 48-57. https://doi.org/10.1057/pb.2011.30
  14. Guillet, B. D., Lee, A., Law, R., & Leung, R. (2011). Factors affecting outbound tourists’ destination choice: The case of Hong Kong. Journal of Travel & Tourism Marketing, 28(5), 556-566. https://doi.org/10.1080/10548408.2011.588120
  15. Güneş, E. (2018). Destinasyon kalite unsurlarının analizi: Antalya örneği. Seyahat ve Otel İşletmeciliği Dergisi, 15(2), 423-442. https://doi.org/10.24010/soid.453194
  16. Hassan, T., Carvache-Franco, M., Carvache-Franco, W., & Carvache-Franco, O. (2022). Motivations as predictors of religious tourism: The Muslim pilgrimage to the city of Mecca. Journal of Cultural Heritage Management and Sustainable Development, 14(3), 419-435. https://doi.org/10.1108/JCHMSD-01-2022-0005
  17. Hosany, S., Ekinci, Y., & Uysal, M. (2006). Destination image and destination personality: An application of branding theories to tourism places. Journal of Business Research, 59(5), 638-642. https://doi.org/10.1016/j.jbusres.2006.01.001
  18. Hsu, C. H., & Song, H. (2013). Destination image in travel magazines: A textual and pictorial analysis of Hong Kong and Macau. Journal of Vacation Marketing, 19(3), 253-268. https://doi.org/10.1177/1356766712473469
  19. Hunt, J. D. (1975). Image as a factor in tourism development. Journal of Travel Research. 13(3), 1-7. https://doi.org/10.1177/004728757501300301
  20. Jani, D., & Hwang, Y.-H. (2011). User-generated destination image through weblogs: a comparison of pre- and post-visit images. Asia Pacific Journal of Tourism Research, 16(3), 339-356. https://www.tandfonline.com/doi/abs/10.1080/10941665.2011.572670
  21. Kong, W. H., du Cros, H., & Ong, C.-E. (2015). Tourism destination image development: A lesson from Macau. International Journal of Tourism Cities, 1(4), 299-316. https://doi.org/10.1108/IJTC-08-2014-0016
  22. Li, J. J., Kim, W. G., & Wong, I. A. (2017). Does destination perception differ based on traveler type? A case of the world gambling capital: Macau. Tourism Planning & Development, 14(1), 15-30. https://doi.org/10.1080/21568316.2016.1152289
  23. Li, J., Xu, L., Tang, L., Wang, S., & Li, L. (2018). Big data in tourism research: A literature review. Tourism Management, 68, 301-323. https://doi.org/10.1016/j.tourman.2018.03.009.
  24. Li, N., Meng, F., & Martin, D. (2023). The influence of travel photo editing on tourists’ experiences. Tourism Management, 98, 1-3. https://doi.org/10.1016/j.tourman.2023.104762
  25. Li, Y. R., Lin, Y. C., Tsai, P. H., & Wang, Y. Y. (2015). Traveller-generated contents for destination image formation: Mainland China travellers to Taiwan as a case study. Journal of Travel & Tourism Marketing, 32(5), 518-533. https://doi.org/10.1080/10548408.2014.918924
  26. Lian, T., & Yu, C. (2017). Representation of online image of tourist destination: A content analysis of Huangshan. Asia Pacific Journal of Tourism Research, 22(10), 1063-1082. https://doi.org/10.1080/10941665.2017.1368678
  27. María Munar, A. (2011). Tourist‐created content: Rethinking destination branding. International Journal of Culture Tourism and Hospitality Research, 5(3), 291-305. https://doi.org/10.1108/17506181111156989
  28. Mazlum, H. (2024). Kültür turizmi bağlamında festival afişlerinin içerik ve biçim analizi: Balıkesir örneği. Journal of Humanities and Tourism Research, 14(4), 524-538. https://izlik.org/JA47PG96MS
  29. Milman, A. (2011). The symbolic role of postcards in representing a destination image: the case of Alanya, Turkey. International Journal of Hospitality & Tourism Administration, 12(2), 144-173. https://doi.org/10.1080/15256480.2011.564495
  30. Milman, A. (2012). Postcards as representation of a destination image: The case of Berlin. Journal of Vacation Marketing, 18(2), 157-170. https://doi.org/10.1177/1356766711435975
  31. Mou, N., Jiang, Q., Zhang, L., Niu, J., Zheng, Y., Wang, Y., & Yang, T. (2022). Personalized tourist route recommendation model with a trajectory understanding via neural networks. International Journal of Digital Earth, 15(1), 1738-1759. https://doi.org/10.1080/17538947.2022.2130456
  32. Özgüç, N. (2015). Turizm Coğrafyası Özellikler ve Bölgeler (8. Baskı). Çantay Kitabevi.
  33. Pan, S. (2009). The role of TV commercial visuals in forming memorable and impressive destination images. Journal of Travel Research, 50(2), 151-185. https://doi.org/10.1177/0047287509355325
  34. Pennington, J. W., & Thomsen, R. C. (2010). A semiotic model of destination representations applied to cultural and heritage tourism Marketing. Scandinavian Journal of Hospitality and Tourism, 10(1), 33-53. https://doi.org/10.1080/15022250903561895
  35. Qi, S., & Chen, N. (Chris). (2019). Understanding Macao’s destination image through user-generated content. Journal of China Tourism Research, 15(4), 503-519. https://doi.org/10.1080/19388160.2019.1577199
  36. Rabbiosi, C. (2015). Renewing a historical legacy: Tourism, leisure shopping and urban branding in Paris. Cities, 42, 195-203. https://doi.org/10.1016/j.cities.2014.07.001
  37. Sertkan, M., Neidhardt, J., & Werthner, H. (2020). From pictures to travel characteristics: deep learning-based profiling of tourists and tourism destinations. In J. Neidhardt and W. Wörndl (Ed.), Information and Communication Technologies in Tourism (pp. 142-153). Springer International Publishing. https://doi.org/10.1007/978-3-030-36737-4_12
  38. Sheng, F., Zhang, Y., Shi, C., Qiu, M., & Yao, S. (2022). Xi’an tourism destination image analysis via deep learning. Journal of Ambient Intelligence and Humanized Computing, 13(11), 5093-5102. https://doi.org/10.1007/s12652-020-02344-w
  39. Snow, N. (2025). Oh, the places we’ll meet: Türkiye’s rise as a global destination. In O. Güner (Ed.), Türkiye's Public Diplomacy Ecosystem A Reflection on Evolving Practices (pp. 25-38). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-81916-2_3
  40. Spyrou, E., & Mylonas, P. (2016). Analyzing Flickr metadata to extract location-based information and semantically organize its photo content. Neurocomputing, 172, 114-133. https://doi.org/10.1016/j.neucom.2014.12.104
  41. Stepchenkova, S., & Zhan, F. (2013). Visual destination images of Peru: Comparative content analysis of DMO and user-generated photography. Tourism Management, 36, 590-601. https://doi.org/10.1016/j.tourman.2012.08.006
  42. Suttikun, C., Chang, H. J., Acho, C. S., Ubi, M., Bicksler, H., Komolsevin, R., & Chongsithiphol, S. (2018). Sociodemographic and travel characteristics affecting the purpose of selecting Bangkok as a tourist destination. Tourism and Hospitality Research, 18(2), 152-162. https://doi.org/10.1177/1467358416637254
  43. Syed Jaafar, S. M. R., Ismail, H. N., & Md Khairi, N. D. (2022). Tourists’ real-time destination image of Kuala Lumpur. International Journal of Tourism Cities, 8(1), 7-29. https://doi.org/10.1108/IJTC-01-2021-0019
  44. Şahin, İ., & Güzel, F. Ö. (2018). Destinasyon deneyimi bileşenleri: Antalya ve İstanbul destinasyonları üzerinde karşılaştırmalı bir araştırma. Anatolia: Turizm Araştırmaları Dergisi, 29(1), 1-13. https://doi.org/10.17123/atad.435713
  45. TDK. (2024). İmge. Türk Dil Kurumu. https://sozluk.gov.tr/?ara=imge
  46. Wang, X., Mou, N., Zhu, S., Yang, T., Zhang, X., & Zhang, Y. (2024). How to perceive tourism destination image? A visual content analysis based on inbound tourists’ photos. Journal of Destination Marketing & Management, 33, 1-15. https://doi.org/10.1016/j.jdmm.2024.100923
  47. WP Travel. (2025, Ağustos 29). Most visited cities in the world: statistics with total arrivals data. WP Travel. https://wptravel.io/most-visited-cities-in-the-world/
  48. Xiao, X., Fang, C., & Lin, H. (2020). Characterizing tourism destination image using photos’ visual content. ISPRS International Journal of Geo-Information, 9(12), 730. https://doi.org/10.3390/ijgi9120730
  49. Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., & Torralba, A. (2018). Places: A 10 million image database for scene recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(6), 1452-1464. https://doi.org/10.1109/TPAMI.2017.2723009