Temporal Pattern Analysis and Tourist Flow Dynamics: Leveraging High-Granularity Data From The Smartdest Platform

Understanding how visitor flows evolve throughout the day is essential for effective destination management, particularly in the context of smart tourism initiatives. This study analyzes intra-day temporal patterns of tourist activity using high-granularity data provided by the SmartDest platform, developed to support data-driven tourism research across the Macaronesia region. The research examines variations in visitor flows and identifies recurrent peak periods across key tourism indicators. A methodological framework combining descriptive statistics, temporal aggregation, and peak detection analysis is applied to harmonized datasets with hourly resolution. The proposed approach is empirically demonstrated through a real-world pilot implementation at a high-pressure tourism site, enabling the identification of distinct daily visitation rhythms and concentrated load periods. The results demonstrate the feasibility and operational value of high-frequency tourism data for monitoring visitor dynamics that remain invisible in traditionally aggregated statistics.