A Simplified Approach to Big Data Applications in Tourism: Monitoring Weekly Visitor Patterns of a Heritage Site Using Google Popular Times

Abstract

This study introduces a simplified big data application approach to monitor weekly visitor patterns in the example of heritage sites using semi-structured data. A descriptive research design was adopted by employing exploratory data analysis on hourly visitor intensity data retrieved from Google’s Popular Times feature to monitor weekly visitor patterns in the case of Beylerbeyi Palace, Istanbul. Data were collected between March and April 2025. Visitor patterns were identified through systematic graphical analysis and validated by quantitative indicators, including measures of central tendency and slope values of intensity curves. The results revealed six distinct visitor patterns that included weekday low peak, weekend high peak, morning low intensity, afternoon high intensity, positive slope, and negative slope, capturing both daily and weekly levels. Instead of employing complex analysis, by applying a novel approach to a single heritage site as a pilot study, it provides preliminary evidence that semi-structured big data can be effectively used to monitor visitor patterns in a cost-efficient and replicable way and emphasises the practical usefulness of a simple, digitally supported method for tracking visitor activity. Future studies could expand this preliminary approach to multiple sites or integrate it with AI-based analytical tools to improve pattern identification and support visitor flow prediction or management strategies.

Keywords

• Visitor Monitoring
• Big Data in Tourism
• Visitor Pattern Analysis
• Heritage Sites
• Google Popular Times

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