Raw and pre-processed cruise passengers' GPS tracking datasets

The Global Positioning System (GPS) enables the precise collection of spatio-temporal data in real time, significantly enhancing our understanding of human mobility. GPS tracking data are spatially and temporally precise and can be supplemented using other sources. In ``Cruise passengers' behavior at the destination: Investigation using GPS technology'' by De Cantis et al. (2016) the application of this technology is exemplified to gather insightful information on spatio-temporal behaviour of cruise passengers at their destination. The study was the first to use GPS technology for analyzing the cruise tourism segment, setting a precedent in the field. Selected cruise ship passengers participated in a survey by completing initial and final questionnaires and carrying GPS data loggers during their visit. These loggers recorded geographic coordinates (latitude and longitude) along with timestamps at about ten-second intervals. The passengers were selected using a pseudo-systematic sampling strategy, where about one out of every twenty passengers were sampled during the specified survey period. Beyond simply presenting the raw GPS data, this article also offers pre-processed data. A specially designed algorithm was employed to eliminate outliers and noise points and to impute missing values by mean of dynamic moving medians. This algorithm detects and imputes noise points in GPS data by considering both temporal and spatial distances, effectively identifying abnormal observations caused by equipment failures or environmental interference. Its efficacy was demonstrated through tests conducted with data on cruise passengers' behavior in Palermo city (Italy). Despite these advancements, processing GPS data for the study of tourism phenomena remains challenging. There are numerous potential metrics derivable from such data, which are crucial for understanding tourist behavior at destinations. Making these data freely available represents a significant contribution to the collaborative development of pre-processing methodologies and GPS data analysis techniques for the analysis of tourist behavior, and of human mobility in general. The dataset holds high value for comprehending human mobility patterns and can be applied across various fields, including urban planning, transportation management, and tourism research. By systematically sampling and recording geographic coordinates along with timestamps, the dataset provides a robust foundation for the analysis of tourist mobility.