Abstract
Mobile location data are a major form of Big Data that hold many possibilities for study and insight into human behaviour. This research used mobile location data to investigate the differences in the activity patterns of tourists in Maui, Hawai’i. Mobile data used in this study were app-based location data collected as a stream of mobile phone locations with a timestamp. Tourists were clustered using K-Means based on time spent at attraction types. Different travel experiences were analyzed based on traveler’s accommodation choices, the average distance travelled from accommodation to attraction, and vacation length, which all varied significantly between the tourist clusters. This work provided a new use for K-means clustering with mobile location data to provide insightful information to marketing professionals and tourism management bodies.
Highlights
CHAPTER 1: IntroductionInformation on lodging types and tourist attractions was previously available mainly using surveys (Gursoy, Jurowski, and Uysal 2002; Collins, Rose, and Hess 2012; Phithakkitnukoon et al 2015; Chen et al 2018; Hasnat and Hasan 2018; Kirilenko, Stepchenkova, and Hernandez 2019), this form of data collection did not have the capability of capturing the nuances in travel experiences
These apps are embedded with a Software Development Kit (SDK) that collects a stream of times and locations from devices during app use along with a 25-digit identifier that is unique to each device
Mobile data have been used in an attempt to discover further insight into how Points of interest (POIs) are used by tourists, but it has never been used to create a tourist segmentation system based on POI use
Summary
Information on lodging types and tourist attractions was previously available mainly using surveys (Gursoy, Jurowski, and Uysal 2002; Collins, Rose, and Hess 2012; Phithakkitnukoon et al 2015; Chen et al 2018; Hasnat and Hasan 2018; Kirilenko, Stepchenkova, and Hernandez 2019), this form of data collection did not have the capability of capturing the nuances in travel experiences. Using mobile data in a tourism experience application may allow marketing professionals to pinpoint consumer behaviour and establish determining factors at a level of granularity that has been previously inaccessible. The use of mobile data in this study provided greater insight into tourist behaviour and motivation based on quantitative techniques rather than relying on qualitative surveys used to determine what might impact activity motivation. This work demonstrated the ability of mobile data to better inform tourism management bodies to better strategize their advertising and marketing based on the results – with the idea that this model could be replicated in other tourism regions
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