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An Application of K-Means Clustering on Tourist Activity in Maui Using Mobile Location Data

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thesis
posted on 24.05.2021, 17:57 by Anna Meg Sheilds Brooker
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.

History

Language

eng

Degree

Master of Spatial Analysis

Program

Spatial Analysis

Granting Institution

Ryerson University

LAC Thesis Type

Thesis