Analyzing the Impact of COVID-19 on Travel and Search Distances for Prominent Landmarks: Insights from Google Trends, X, and Tripadvisor
Abstract
1. Introduction
- Spatial patterns of online searches on Google for the chosen landmarks;
- The distance between the locations of tweets mentioning these landmarks and the landmarks;
- The distance between users’ review activity areas on Tripadvisor and the location of the landmarks.
- Period 1 (March 2019–February 2020): “Business as usual”. Few to no travel restrictions;
- Period 2 (March 2020–February 2021): “Pandemic onset”. On 11 March 2020, COVID-19 was declared a global pandemic [14], followed by an increasing number of travel restrictions, both domestic and international;
- Period 3 (March 2021–February 2022): “Pandemic stabilization”. Following the development of COVID-19 vaccines, a significant share of the population is vaccinated. For instance, in the US, 100 million people had been vaccinated by 12 March 2021. The travel restrictions are gradually lifted for eligible travelers e.g., those with “vaccine passports”;
- Period 4 (March 2022–February 2023): “New normal”. Travel restrictions are being removed for all travelers for the majority of destinations.
2. Literature Review
3. Data and Methods
3.1. Google Trends
3.2. X
- Tweets mentioning landmark names, such as ”Niagara Falls“ or ”Machu Picchu”. This was achieved by incorporating the relevant search terms in the API scripts.
- Tweets with geolocation information, which was implemented by including ”has:geo“ in the API search parameters.
- Tweets posted between 1 March 2019 and 28 February 2023.
3.3. Tripadvisor
4. Results
4.1. Google Trends Index Values
4.2. Tweeting Distances
4.3. Tripadvisor Travel Distance
5. Discussion
5.1. The Pandemic and Its Effect on Travel and Search Distances to Landmarks
5.2. UGC Data Quality
5.3. Contribution Recovery
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A. Tweet Filter Process
References
- Gallego, I.; González-Rodríguez, M.R.; Font, X. International air travel attitude and travel planning lead times across 45 countries in response to the COVID-19 pandemic. Tour. Manag. Perspect. 2022, 44, 101037. [Google Scholar] [CrossRef]
- Castillo-Villar, F.R. Urban icons and city branding development. J. Place Manag. Dev. 2016, 9, 255–268. [Google Scholar] [CrossRef]
- Abdullah, M.; Ali, N.; Hussain, S.A.; Aslam, A.B.; Javid, M.A. Measuring changes in travel behavior pattern due to COVID-19 in a developing country: A case study of Pakistan. Transp. Policy 2021, 108, 21–33. [Google Scholar] [CrossRef]
- Shamshiripour, A.; Rahimi, E.; Shabanpour, R.; Mohammadian, A. How is COVID-19 reshaping activity-travel behavior? Evidence from a comprehensive survey in Chicago. Transp. Res. Interdiscip. Perspect. 2020, 7, 100216. [Google Scholar] [CrossRef]
- See, L.; Mooney, P.; Foody, G.; Bastin, L.; Comber, A.; Estima, J.; Fritz, S.; Kerle, N.; Jiang, B.; Laakso, M.; et al. Crowdsourcing, Citizen Science or Volunteered Geographic Information? The Current State of Crowdsourced Geographic Information. ISPRS Int. J. Geo Inf. 2016, 5, 55. [Google Scholar] [CrossRef]
- Estellés-Arolas, E.; González-Ladrón-de-Guevara, F. Towards an integrated crowdsourcing definition. J. Inf. Sci. 2012, 38, 189–200. [Google Scholar] [CrossRef]
- Krumm, J.; Davies, N.; Narayanaswami, C. User-Generated Content. IEEE Pervasive Comput. 2008, 7, 10–11. [Google Scholar] [CrossRef]
- Anwari, N.; Tawkir Ahmed, M.; Rakibul Islam, M.; Hadiuzzaman, M.; Amin, S. Exploring the travel behavior changes caused by the COVID-19 crisis: A case study for a developing country. Transp. Res. Interdiscip. Perspect. 2021, 9, 100334. [Google Scholar] [CrossRef]
- Fan, X.; Lu, J.; Qiu, M.; Xiao, X. Changes in travel behaviors and intentions during the COVID-19 pandemic and recovery period: A case study of China. J. Outdoor Recreat. Tour. 2023, 41, 100522. [Google Scholar] [CrossRef]
- González-Reverté, F.; Gomis-López, J.M.; Díaz-Luque, P. Reset or temporary break? Attitudinal change, risk perception and future travel intention in tourists experiencing the COVID-19 pandemic. J. Tour. Futures 2022. ahead-of-print. [Google Scholar] [CrossRef]
- Lee, H.Y.; Leung, K.Y.K. Island ferry travel during COVID-19: Charting the recovery of local tourism in Hong Kong. Curr. Issues Tour. 2022, 25, 76–93. [Google Scholar] [CrossRef]
- Höpken, W.; Eberle, T.; Fuchs, M.; Lexhagen, M. Improving Tourist Arrival Prediction: A Big Data and Artificial Neural Network Approach. J. Travel Res. 2020, 60, 998–1017. [Google Scholar] [CrossRef]
- Owuor, I.; Hochmair, H.H. Analysing the effect of COVID-19 on the localness of visitors to Florida state parks and New York attractions using online reviews, tweets, and SafeGraph travel patterns. J. Locat. Based Serv. 2024, 18, 118–138. [Google Scholar] [CrossRef]
- WHO. Rolling Updates on Coronavirus Disease (COVID-19). Available online: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/events-as-they-happen (accessed on 20 January 2024).
- Chen, K.; Steiner, R. Longitudinal and spatial analysis of Americans’ travel distances following COVID-19. Transp. Res. Part D Transp. Environ. 2022, 110, 103414. [Google Scholar] [CrossRef]
- Fatmi, M.R. COVID-19 impact on urban mobility. J. Urban Manag. 2020, 9, 270–275. [Google Scholar] [CrossRef]
- Yabe, T.; Tsubouchi, K.; Fujiwara, N.; Wada, T.; Sekimoto, Y.; Ukkusuri, S.V. Non-compulsory measures sufficiently reduced human mobility in Tokyo during the COVID-19 epidemic. Sci. Rep. 2020, 10, 18053. [Google Scholar] [CrossRef]
- Vannoni, M.; McKee, M.; Semenza, J.C.; Bonell, C.; Stuckler, D. Using volunteered geographic information to assess mobility in the early phases of the COVID-19 pandemic: A cross-city time series analysis of 41 cities in 22 countries from March 2nd to 26th 2020. Glob. Health 2020, 16, 85. [Google Scholar] [CrossRef]
- Jacobsen, G.D.; Jacobsen, K.H. Statewide COVID-19 Stay-at-Home Orders and Population Mobility in the United States. World Med. Health Policy 2020, 12, 347–356. [Google Scholar] [CrossRef]
- Gössling, S.; Scott, D.; Hall, C.M. Pandemics, tourism and global change: A rapid assessment of COVID-19. J. Sustain. Tour. 2021, 29, 1–20. [Google Scholar] [CrossRef]
- Kuo, H.-I.; Chen, C.-C.; Tseng, W.-C.; Ju, L.-F.; Huang, B.-W. Assessing impacts of SARS and Avian Flu on international tourism demand to Asia. Tour. Manag. 2008, 29, 917–928. [Google Scholar] [CrossRef]
- Li, J.; Nguyen, T.H.H.; Coca-Stefaniak, J.A. Coronavirus impacts on post-pandemic planned travel behaviours. Ann. Tour. Res. 2021, 86, 102964. [Google Scholar] [CrossRef]
- Zhong, C.; Morphet, R.; Yoshida, M. Twitter mobility dynamics during the COVID-19 pandemic: A case study of London. PLoS ONE 2023, 18, e0284902. [Google Scholar] [CrossRef]
- Han, Z.; Fu, H.; Xu, F.; Tu, Z.; Yu, Y.; Hui, P.; Li, Y. Who Will Survive and Revive Undergoing the Epidemic. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 2021, 5, 1–20. [Google Scholar] [CrossRef]
- Jay, J.; Heykoop, F.; Hwang, L.; Courtepatte, A.; de Jong, J.; Kondo, M. Use of smartphone mobility data to analyze city park visits during the COVID-19 pandemic. Landsc. Urban Plan. 2022, 228, 104554. [Google Scholar] [CrossRef]
- Abdullah, M.; Dias, C.; Muley, D.; Shahin, M. Exploring the impacts of COVID-19 on travel behavior and mode preferences. Transp. Res. Interdiscip. Perspect. 2020, 8, 100255. [Google Scholar] [CrossRef]
- Gallego, I.; Font, X. Changes in air passenger demand as a result of the COVID-19 crisis: Using Big Data to inform tourism policy. J. Sustain. Tour. 2021, 29, 1470–1489. [Google Scholar] [CrossRef]
- Xiang, Z.; Du, Q.; Ma, Y.; Fan, W. Assessing reliability of social media data: Lessons from mining TripAdvisor hotel reviews. Inf. Technol. Tour. 2017, 18, 43–59. [Google Scholar] [CrossRef]
- Uğur, N.G.; Akbıyık, A. Impacts of COVID-19 on global tourism industry: A cross-regional comparison. Tour. Manag. Perspect. 2020, 36, 100744. [Google Scholar] [CrossRef]
- Mary, S.R.; Pour, M.H. A model of travel behaviour after COVID-19 pandemic: TripAdvisor reviews. Curr. Issues Tour. 2022, 25, 1033–1045. [Google Scholar] [CrossRef]
- Setia, P.; Dede Yoga, P.; Geri Yesa, E.; Nensi Fitria, D.; Wiwin, S. Impact of COVID-19 pandemic on tourism in Indonesia. Curr. Issues Tour. 2022, 25, 2422–2442. [Google Scholar] [CrossRef]
- Alba, C.; Pan, B.; Yin, J.; Rice, W.L.; Mitra, P.; Lin, M.S.; Liang, Y. COVID-19’s impact on visitation behavior to US national parks from communities of color: Evidence from mobile phone data. Sci. Rep. 2022, 12, 13398. [Google Scholar] [CrossRef]
- Bokelmann, B.; Lessmann, S. Spurious patterns in Google Trends data—An analysis of the effects on tourism demand forecasting in Germany. Tour. Manag. 2019, 75, 1–12. [Google Scholar] [CrossRef]
- Irem, Ö.; Ulrich, G. Forecasting Tourism Demand with Google Trends for a Major European City Destination. Tour. Anal. 2016, 21, 203–220. [Google Scholar] [CrossRef]
- Önder, I. Forecasting tourism demand with Google trends: Accuracy comparison of countries versus cities. Int. J. Tour. Res. 2017, 19, 648–660. [Google Scholar] [CrossRef]
- Zayed, B.A.; Talaia, A.M.; Gaaboobah, M.A.; Amer, S.M.; Mansour, F.R. Google Trends as a predictive tool in the era of COVID-19: A scoping review. Postgrad. Med. J. 2023, 99, 962–975. [Google Scholar] [CrossRef]
- Prilistya, S.K.; Permanasari, A.E.; Fauziati, S. The Effect of the COVID-19 Pandemic and Google Trends on the Forecasting of International Tourist Arrivals in Indonesia. In Proceedings of the 2021 IEEE Region 10 Symposium (TENSYMP), Jeju, Republic of Korea, 23–25 August 2021; pp. 1–8. [Google Scholar]
- Li, J.; Xu, L.; Tang, L.; Wang, S.; Li, L. Big data in tourism research: A literature review. Tour. Manag. 2018, 68, 301–323. [Google Scholar] [CrossRef]
- Gandomi, A.; Haider, M. Beyond the hype: Big data concepts, methods, and analytics. Int. J. Inf. Manag. 2015, 35, 137–144. [Google Scholar] [CrossRef]
- Hargittai, E. Potential Biases in Big Data: Omitted Voices on Social Media. Soc. Sci. Comput. Rev. 2020, 38, 10–24. [Google Scholar] [CrossRef]
- Samper-Escalante, L.D.; Loyola-González, O.; Monroy, R.; Medina-Pérez, M.A. Bot Datasets on Twitter: Analysis and Challenges. Appl. Sci. 2021, 11, 4105. [Google Scholar] [CrossRef]
- Bryce, J.; Klang, M. Young people, disclosure of personal information and online privacy: Control, choice and consequences. Inf. Secur. Tech. Rep. 2009, 14, 160–166. [Google Scholar] [CrossRef]
- Zhao, B.; Sui, D.Z. True lies in geospatial big data: Detecting location spoofing in social media. Ann. GIS 2017, 23, 1–14. [Google Scholar] [CrossRef]
- Gardner, Z.; Leibovici, D.; Basiri, A.; Foody, G. Trading-off Location Accuracy and Service Quality: Privacy Concerns and User Profiles. In Proceedings of the 2017 International Conference on Localization and GNSS (ICL-GNSS), Nottingham, UK, 27–29 June 2017; pp. 1–5. [Google Scholar]
- Zheng, X.; Han, J.; Sun, A. A Survey of Location Prediction on Twitter. IEEE Trans. Knowl. Data Eng. 2018, 30, 1652–1671. [Google Scholar] [CrossRef]
- Ma, S.; Kirilenko, A. How Reliable Is Social Media Data? Validation of TripAdvisor Tourism Visitations Using Independent Data Sources. In Information and Communication Technologies in Tourism 2021: Proceedings of the ENTER 2021 eTourism Conference, Virtual, 19–22 January 2021; Springer: Cham, Switzerland, 2021; pp. 286–293. [Google Scholar]
- Hecht, B.; Hong, L.; Suh, B.; Chi, E.H. Tweets from Justin Bieber’s Heart: The Dynamics of the Location Field in User Profiles. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Vancouver, BC, Canada, 11–16 May 2024; pp. 237–246. [Google Scholar]
- Ajao, O.; Hong, J.; Liu, W. A survey of location inference techniques on Twitter. J. Inf. Sci. 2015, 41, 855–864. [Google Scholar] [CrossRef]
- Funder, D.C.; Ozer, D.J. Evaluating Effect Size in Psychological Research: Sense and Nonsense. Adv. Methods Pract. Psychol. Sci. 2019, 2, 156–168. [Google Scholar] [CrossRef]
- da Silva Lopes, H.; Remoaldo, P.C.; Ribeiro, V.; Martín-Vide, J. Effects of the COVID-19 Pandemic on Tourist Risk Perceptions—The Case Study of Porto. Sustainability 2021, 13, 6399. [Google Scholar] [CrossRef]
- Dai, F.; Wang, D.; Kirillova, K. Travel inspiration in tourist decision making. Tour. Manag. 2022, 90, 104484. [Google Scholar] [CrossRef]
- Ahmad, A.; Jamaludin, A.; Zuraimi, N.S.M.; Valeri, M. Visit intention and destination image in post-COVID-19 crisis recovery. Curr. Issues Tour. 2020, 24, 2392–2397. [Google Scholar] [CrossRef]
- Lu, J.; Xiao, X.; Xu, Z.; Wang, C.; Zhang, M.; Zhou, Y. The potential of virtual tourism in the recovery of tourism industry during the COVID-19 pandemic. Curr. Issues Tour. 2021, 25, 441–457. [Google Scholar] [CrossRef]
- Owuor, I.; Hochmair, H.H.; Paulus, G. The Effect of COVID-19 on the Origins of Florida State Park Visitors and Online Reviewers. AGILE GIScience Ser. 2022, 3, 50. [Google Scholar] [CrossRef]
- Neuburger, L.; Egger, R. Travel risk perception and travel behaviour during the COVID-19 pandemic 2020: A case study of the DACH region. Curr. Issues Tour. 2020, 24, 1003–1016. [Google Scholar] [CrossRef]
- Lee, I. Big data: Dimensions, evolution, impacts, and challenges. Bus. Horiz. 2017, 60, 293–303. [Google Scholar] [CrossRef]
- Lee, J.; Benjamin, S.; Childs, M. Unpacking the Emotions behind TripAdvisor Travel Reviews: The Case Study of Gatlinburg, Tennessee. Int. J. Hosp. Tour. Adm. 2022, 23, 347–364. [Google Scholar] [CrossRef]
- Nadeau, J.; Wardley, L.J.; Rajabi, E. Tourism destination image resiliency during a pandemic as portrayed through emotions on Twitter. Tour. Hosp. Res. 2021, 22, 60–70. [Google Scholar] [CrossRef]
- Yao, Y.; Jia, G.; Hou, Y. Impulsive travel intention induced by sharing conspicuous travel experience on social media: A moderated mediation analysis. J. Hosp. Tour. Manag. 2021, 49, 431–438. [Google Scholar] [CrossRef]
- Sakaki, T.; Okazaki, M.; Matsuo, Y. Tweet Analysis for Real-Time Event Detection and Earthquake Reporting System Development. IEEE Trans. Knowl. Data Eng. 2013, 25, 919–931. [Google Scholar] [CrossRef]
- Fontugne, R.; Cho, K.; Won, Y.; Fukuda, K. Disasters Seen through Flickr Cameras. In Proceedings of the Special Workshop on Internet and Disasters, Tokyo, Japan, 6–9 December 2011; p. 5. [Google Scholar]
- Fox, L. Tripadvisor Says It’s Catching More Fake Reviews. Available online: https://www.phocuswire.com/tripadvisor-fraudent-reviews-report#:~:text=Tripadvisor%20has%20revealed%20that%20approximately,points%20on%20its%202020%20figure (accessed on 10 October 2024).
- Juhász, L.; Hochmair, H. Comparing the Spatial and Temporal Activity Patterns between Snapchat, Twitter and Flickr in Florida. GI_Forum 2019, 1, 134–147. [Google Scholar] [CrossRef]
- Dube, K.; Nhamo, G.; Swart, M. COVID-19, Tourist Destinations and Prospects for Recovery; Springer: Berlin/Heidelberg, Germany, 2023. [Google Scholar]
- Li, L.; Tao, Z.; Lu, L. Understanding differences in rural tourism recovery: A critical study from the mobility perspective. Curr. Issues Tour. 2023, 26, 2452–2466. [Google Scholar] [CrossRef]
- Hajilo, M.; Pennington-Gray, L.; Tahmasbi, S.; Gheshlagh, S.I. Understanding spatial tourism destination recovery in Iran based on a destination attribute recovery index for COVID-19. J. Contingencies Crisis Manag. 2024, 32, e12536. [Google Scholar] [CrossRef]
- Koens, K.; Postma, A.; Papp, B. Management Strategies for Overtourism: From Adaptation to System Change. In Overtourism; Routledge: Abingdon-on-Thames, UK, 2019; pp. 149–160. [Google Scholar]
- El-Said, O.; Aziz, H. Virtual Tours a Means to an End: An Analysis of Virtual Tours’ Role in Tourism Recovery Post COVID-19. J. Travel Res. 2021, 61, 528–548. [Google Scholar] [CrossRef]
- Shevtsov, A.; Oikonomidou, M.; Antonakaki, D.; Pratikakis, P.; Kanterakis, A.; Fragopoulou, P.; Ioannidis, S. Discovery and Classification of Twitter Bots. SN Comput. Sci. 2022, 3, 255. [Google Scholar] [CrossRef]
Data Source | Data Unit | Data Location | User Home Location | Temporal Information |
---|---|---|---|---|
Google Trends | Search rate over time by region | Country | Unknown | Date range |
X | Tweet | Individual tweet | Unknown | Posted time |
Tripadvisor | Review | POI location | User profile/history | Visit and posted time |
Landmark | Eiffel Tower | Leaning Tower of Pisa | Uluru | Sydney Opera House | Taj Mahal | Angkor Wat |
---|---|---|---|---|---|---|
Countries | 52 | 54 | 49 | 56 | 51 | 24 |
Landmark | Statue of Liberty | Niagara Falls | Machu Picchu | Christ the Redeemer | Victoria Falls | Table Mountain |
Countries | 52 | 51 | 52 | 59 | 50 | 24 |
Landmark | Eiffel Tower | Leaning Tower of Pisa | Uluru | Sydney Opera House | Taj Mahal | Angkor Wat |
---|---|---|---|---|---|---|
Collected | 30,186 | 4212 | 6313 | 10,792 | 25,975 | 10,096 |
Retained | 27,576 | 3837 | 4225 | 9151 | 19,616 | 8478 |
Landmark | Statue of Liberty | Niagara Falls | Machu Picchu | Christ the Redeemer | Victoria Falls | Table Mountain |
Collected | 29,079 | 53,876 | 17,105 | 2907 | 7817 | 11,270 |
Retained | 22,754 | 46,928 | 15,418 | 1877 | 6186 | 7946 |
Landmark | Eiffel Tower | Leaning Tower of Pisa | Uluru | Sydney Opera House | Taj Mahal | Angkor Wat |
---|---|---|---|---|---|---|
Match (%) | 56.0 | 62.5 | 73.1 | 61.5 | 53.7 | 45.0 |
Mismatch (%) | 27.4 | 23.6 | 10.9 | 23.6 | 30.0 | 37.3 |
Missing (%) | 16.6 | 13.9 | 16.0 | 14.9 | 16.3 | 17.7 |
N | 5658 | 2664 | 499 | 2983 | 2857 | 3115 |
Landmark | Statue of Liberty | Niagara Falls | Machu Picchu | Christ the Redeemer | Victoria Falls | Table Mountain |
Match (%) | 64.2 | 63.2 | 56.8 | 60.5 | 52.2 | 56.4 |
Mismatch (%) | 21.6 | 22.4 | 26.3 | 21.9 | 35.2 | 31.7 |
Missing (%) | 14.2 | 14.4 | 16.9 | 17.6 | 12.6 | 11.9 |
N | 4027 | 2498 | 1323 | 4073 | 673 | 2186 |
Landmark | Eiffel Tower | Leaning Tower of Pisa | Uluru | Sydney Opera House | Taj Mahal | Angkor Wat |
---|---|---|---|---|---|---|
Collected | 5658 | 2664 | 499 | 2983 | 2857 | 3115 |
Filtered | 3169 | 1664 | 365 | 1836 | 1535 | 1403 |
Landmark | Statue of Liberty | Niagara Falls | Machu Picchu | Christ the Redeemer | Victoria Falls | Table Mountain |
Collected | 4027 | 2498 | 1323 | 4073 | 673 | 2186 |
Filtered | 2584 | 1578 | 751 | 2463 | 351 | 1233 |
2019 | 2020 | 2021 | 2022 | 2019 | 2020 | 2021 | 2022 | ||||
---|---|---|---|---|---|---|---|---|---|---|---|
Eiffel Tower | Delay | 1.2 | 4.4 | 1.7 | 1.2 | Statue of Liberty | Delay | 1.2 | 3.3 | 0.9 | 1.1 |
Reviews | 2592 | 147 | 82 | 348 | Reviews | 2150 | 141 | 90 | 203 | ||
% Visit | 98.2 | 98.0 | 100.0 | 98.9 | % Visit | 61.6 | 58.2 | 78.9 | 60.1 | ||
Leaning Tower of Pisa | Delay | 1.1 | 2.6 | 2.5 | 1.0 | Niagara Falls | Delay | 1.0 | 2.1 | 0.4 | 0.9 |
Reviews | 1171 | 216 | 121 | 156 | Reviews | 1223 | 121 | 57 | 177 | ||
% Visit | 46.6 | 17.6 | 14.9 | 39.1 | % Visit | 75.6 | 71.9 | 78.9 | 74.0 | ||
Uluru | Delay | 0.7 | 1.8 | 1.5 | 0.9 | Machu Picchu | Delay | 1.4 | 5.1 | 1.5 | 1.3 |
Reviews | 235 | 48 | 35 | 47 | Reviews | 572 | 59 | 36 | 84 | ||
% Visit | 78.7 | 87.5 | 91.4 | 85.1 | % Visit | 35.1 | 20.3 | 47.2 | 39.3 | ||
Sydney Opera House | Delay | 1.2 | 2.4 | 1.6 | 0.6 | Christ the Redeemer | Delay | 1.3 | 2.1 | 1.9 | 1.1 |
Reviews | 1479 | 182 | 40 | 135 | Reviews | 1818 | 319 | 142 | 184 | ||
% Visit | 79.9 | 83.0 | 85.0 | 73.3 | % Visit | 23.3 | 18.5 | 8.5 | 23.9 | ||
Taj Mahal | Delay | 1.4 | 2.3 | 1.2 | 1.3 | Table Mountain | Delay | 1.8 | 0.7 | 3.6 | 2.2 |
Reviews | 1196 | 158 | 54 | 127 | Reviews | 375 | 50 | 9 | 799 | ||
% Visit | 76.3 | 71.5 | 92.6 | 80.3 | % Visit | 73.9 | 66.0 | 55.6 | 44.2 | ||
Angkor Wat | Delay | 1.0 | 2.3 | 2.9 | 1.3 | Victoria Falls | Delay | 0.9 | 3.7 | 1.7 | 0.5 |
Reviews | 1104 | 163 | 14 | 122 | Reviews | 271 | 27 | 9 | 44 | ||
% Visit | 81.0 | 71.8 | 50.0 | 78.7 | % Visit | 73.8 | 85.2 | 77.8 | 81.8 |
Landmark | Local Mean (SD) (2019) | Local Mean (SD) (2020) | Global Mean (SD) (2019) | Global Mean (SD) (2020) | Local Mean Decrease (%) | Global Mean Decrease (%) |
---|---|---|---|---|---|---|
Eiffel Tower | 52.12 (13.48) | 24.70 (7.84) | 63.31 (8.02) | 43.24 (4.02) | 52.61 | 31.70 |
Leaning Tower of Pisa | 45.90 (16.07) | 19.64 (10.77) | 50.31 (4.65) | 40.55 (10.31) | 57.21 | 19.40 |
Uluru | 48.33 (16.09) | 26.02 (6.53) | 33.63 (11.43) | 19.57 (3.63) | 46.16 | 41.81 |
Sydney Opera House | 35.00 (4.86) | 11.51 (4.18) | 30.60 (3.37) | 19.64 (8.37) | 67.11 | 35.82 |
Taj Mahal | 62.21 (9.76) | 39.30 (5.83) | 64.46 (7.34) | 43.15 (4.79) | 36.83 | 33.06 |
Angkor Wat | 54.69 (13.21) | 16.02 (6.89) | 62.98 (7.04) | 34 (4.75) | 70.71 | 46.01 |
Statue of Liberty | 15.92 (3.77) | 10.34 (3.17) | 51.92 (7.69) | 41.25 (7.87) | 35.05 | 20.55 |
Niagara Falls | 50.46 (20.60) | 28.36 (12.51) | 37.63 (7.86) | 27.36 (5.51) | 43.80 | 27.29 |
Machu Picchu | 57.65 (10.31) | 30.98 (16.11) | 42.42 (4.73) | 26.96 (3.98) | 46.26 | 36.45 |
Christ the Redeemer | 23.31 (6.75) | 18.75 (5.94) | 17.71 (2.60) | 15.58 (3.35) | 19.56 | 12.03 |
Table Mountain | 29.21 (11.41) | 14.49 (8.31) | 26.35 (5.07) | 15.94 (3.91) | 50.39 | 39.51 |
Victoria Falls | 51.81 (16.47) | 22.25 (13.86) | 29.69 (12.25) | 18.66 (4.08) | 57.05 | 37.15 |
Landmark | N (2019) | N (2020) | Mean (SD) (2019) | Mean (SD) (2020) | Effect Size | Relative Increase in Mean (%) |
---|---|---|---|---|---|---|
Eiffel Tower | 9425 | 2596 | 1863 (3457) | 4713 (3989) | 0.47 | 153.0 |
Leaning Tower of Pisa | 1231 | 444 | 1732 (3424) | 3796 (4219) | 0.33 | 119.2 |
Uluru | 1551 | 525 | 2479 (4242) | 4661 (5892) | 0.18 | 88.0 |
Sydney Opera House | 3306 | 1024 | 1347 (4165) | 1570 (4396) | 0.04 | 16.6 |
Taj Mahal | 4758 | 3389 | 3548 (5070) | 4491 (5251) | 0.19 | 26.6 |
Angkor Wat | 2828 | 727 | 661 (2207) | 935 (2674) | 0.02 | 41.5 |
Statue of Liberty | 6190 | 3028 | 1830 (3393) | 3037 (4009) | 0.24 | 66.0 |
Niagara Falls | 12,708 | 6835 | 495 (1847) | 868 (2417) | 0.16 | 75.4 |
Machu Picchu | 4880 | 2089 | 1918 (3683) | 3201 (4356) | 0.27 | 66.9 |
Christ the Redeemer | 574 | 530 | 4106 (5139) | 7360 (4200) | 0.40 | 79.3 |
Table Mountain | 2529 | 1373 | 2220 (4647) | 2559 (4937) | 0.05 | 15.3 |
Victoria Falls | 1463 | 1007 | 2231 (4193) | 2360 (4074) | 0.14 | 5.8 |
Landmark | N (2019) | N (2021) | Mean (SD) (2019) | Mean (SD) (2021) | Effect Size | Mean Decrease (%) | Mean (SD) (2022) |
---|---|---|---|---|---|---|---|
Eiffel Tower | 2592 | 82 | 5719 (4444) | 4303 (3562) | −0.171 | 24.76 | 4668 (3785) |
Leaning Tower of Pisa | 1171 | 121 | 3403 (4128) | 1103 (2082) | −0.444 | 67.58 | 2538 (2082) |
Uluru | 235 | 35 | 6124 (5768) | 3383 (3727) | −0.235 | 44.76 | 7627 (6516) |
Sydney Opera House | 1479 | 40 | 10282 (6525) | 3863 (6126) | −0.493 | 62.43 | 9441 (6980) |
Taj Mahal | 1196 | 54 | 6203 (4306) | 2222 (3450) | −0.550 | 64.17 | 4556 (4455) |
Angkor Wat | 1104 | 14 | 7987 (4697) | 5433 (4704) | −0.308 | 31.98 | 7836 (4904) |
Statue of Liberty | 2150 | 90 | 5226 (3572) | 3464 (3538) | −0.333 | 33.72 | 4854 (2854.) |
Niagara Falls | 1223 | 57 | 3727 (3945) | 2093 (2769) | −0.318 | 43.83 | 3513 (3247) |
Machu Picchu | 572 | 36 | 6419 (4398) | 4494 (3454) | −0.228 | 29.99 | 5521 (3346) |
Christ the Redeemer | 1818 | 142 | 4292 (4265) | 1688 (2887) | −0.327 | 60.67 | 3677 (4026) |
Table Mountain | 375 | 9 | 7714 (4760) | 8635 (3242) | 0.136 | -11.94 | 8146 (4523) |
Victoria Falls | 271 | 9 | 9262 (4226) | 9823 (5509) | 0.139 | -6.06 | 7700 (4930) |
Landmarks | 2019 | 2020 | 2021 | 2022 | Total |
---|---|---|---|---|---|
Eiffel Tower | 4518 | 298 | 169 | 673 | 5658 |
Leaning Tower of Pisa | 1837 | 337 | 200 | 290 | 2664 |
Uluru | 352 | 56 | 40 | 51 | 499 |
Sydney Opera House | 2404 | 277 | 51 | 251 | 2983 |
Taj Mahal | 2236 | 273 | 81 | 267 | 2857 |
Angkor Wat | 2432 | 348 | 35 | 300 | 3115 |
Statue of Liberty | 3329 | 226 | 134 | 338 | 4027 |
Niagara Falls | 1976 | 180 | 81 | 261 | 2498 |
Machu Picchu | 983 | 93 | 77 | 170 | 1323 |
Christ the Redeemer | 3053 | 473 | 214 | 333 | 4073 |
Victoria Falls | 514 | 43 | 21 | 95 | 673 |
Table Mountain | 665 | 79 | 18 | 1424 | 2186 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Cao, J.; Hochmair, H.H.; Kirilenko, A.; Owuor, I. Analyzing the Impact of COVID-19 on Travel and Search Distances for Prominent Landmarks: Insights from Google Trends, X, and Tripadvisor. Geographies 2024, 4, 641-660. https://doi.org/10.3390/geographies4040035
Cao J, Hochmair HH, Kirilenko A, Owuor I. Analyzing the Impact of COVID-19 on Travel and Search Distances for Prominent Landmarks: Insights from Google Trends, X, and Tripadvisor. Geographies. 2024; 4(4):641-660. https://doi.org/10.3390/geographies4040035
Chicago/Turabian StyleCao, Jiping, Hartwig H. Hochmair, Andrei Kirilenko, and Innocensia Owuor. 2024. "Analyzing the Impact of COVID-19 on Travel and Search Distances for Prominent Landmarks: Insights from Google Trends, X, and Tripadvisor" Geographies 4, no. 4: 641-660. https://doi.org/10.3390/geographies4040035
APA StyleCao, J., Hochmair, H. H., Kirilenko, A., & Owuor, I. (2024). Analyzing the Impact of COVID-19 on Travel and Search Distances for Prominent Landmarks: Insights from Google Trends, X, and Tripadvisor. Geographies, 4(4), 641-660. https://doi.org/10.3390/geographies4040035