A year ago, we explored the potential of big data to revolutionize tourism statistics. Building on our findings, this blog dives deeper into the methods, analyses, conclusions, and the critical question: is web scraping a solution or another challenge?
Key Analyses and Insights
The analysis of scraped data provided valuable insights into tourism trends and operational efficiencies. Seasonal variations in accommodation prices were evident, with summer months showing the highest averages. Regional differences in pricing also emerged, reflecting diverse market demands and tourist preferences.
Integrating scraped data with official statistical registries brought additional clarity and accuracy to property classifications. This process helped to align platform-specific categories with standardized classifications, addressing notable discrepancies and enabling a more comprehensive understanding of the accommodation sector.
Innovative techniques such as image deduplication were instrumental in refining datasets. By employing advanced algorithms like SIFT, we identified duplicate listings across platforms, ensuring data accuracy and reliability even in cases where textual information was insufficient. This approach underscored the importance of combining multiple methodologies to achieve robust results.
Web Scraping: Opportunity or Obstacle?
Web scraping has been central to our project, enabling us to collect data from booking platforms. The data included variables such as accommodation types, locations, prices, and amenities. Yet, scraping emerged as both a solution and a challenge.
Booking platforms have implemented robust anti-scraping measures, including CAPTCHA, IP blocking, and dynamic content loading. These changes require constant script updates, which increases both complexity and costs. Additionally, frequent updates to website structures and search result presentations posed continuous challenges to maintaining scraping tools. Furthermore, the process raises significant ethical and legal considerations. Web scraping often prompts debates about the legality of extracting data from platforms and the ethical boundaries of such practices.
Despite these hurdles, the analytical value of scraped data has been significant. The data revealed critical trends, such as the seasonality of accommodation prices in Poland and Bulgaria, with peaks occurring in July and August. Yet, one must ask: could this added value not be made more stable and reliable through formal agreements with platform owners?
Is Web Scraping the Best Path Forward?
Web scraping undoubtedly provides granular and real-time data, enriching tourism analysis by addressing occasional gaps in traditional surveys. These gaps often arise due to respondents' inability to recall details or legal exemptions from reporting despite being part of the accommodation database. However, its reliance on constant updates to scraping scripts, coupled with anti-scraping measures implemented by platforms, makes it resource intensive. Moreover, the ethical and legal concerns surrounding data ownership and usage cannot be ignored.
This brings us to a critical reflection: could formal agreements with platform owners pave a more stable and efficient path? Establishing collaborations would alleviate the technical and legal hurdles and provide access to consistent, high-quality data, fostering mutual benefits for researchers and platform providers alike. While web scraping has its merits, the exploration of direct partnerships and alternative access methods appears increasingly essential.
Ultimately, while web scraping remains a powerful tool, its limitations necessitate the exploration of alternative data access methods.
Future Steps
To move forward effectively, our future steps must address two key areas: advancing analytical and methodological capabilities and fostering collaborations with industry partners.
Efforts will focus on refining the tools and methods used to extract and analyse tourism data. By implementing advanced machine learning models, we aim to improve the accuracy of imputed values and uncover hidden patterns within the data. Expanding the scope of data sources to include additional booking platform data, such as reviews, will also enhance the richness of the datasets. Furthermore, promoting standardized frameworks for scraping and integration will improve efficiency and help manage the challenges posed by dynamic content on platforms.
Establishing formal partnerships with booking platforms will ensure the long-term sustainability of data collection efforts. Collaborations could provide direct access to anonymized datasets, reducing the reliance on resource-intensive scraping methods. Addressing legal and ethical considerations will be a cornerstone of these partnerships, ensuring compliance with regulations while fostering mutual benefits for researchers and platform owners. These agreements will pave the way for stable, high-quality data sources that enhance the reliability of tourism statistics.
Conclusion
Web scraping has proven its potential to enrich tourism statistics by uncovering critical trends and enhancing data quality. However, its challenges underline the need for collaboration, innovation, and ethical frameworks. By advancing analytical methodologies and fostering industry partnerships, we can strike a balance between web scraping's benefits and limitations, paving the way for a more robust future in tourism analytics.
Published January 2025