How Citizens Utilize In-State Recreation Resources: A Geographically Weighted Regression Analysis of Floridian’s TripAdvisor Reviews

Authors: Lijuan Su*, Dept. of Tourism, Recreation, and Sport Management, University of Florida, Svetlana Stepchenkova, Dept. of Tourism, Recreation, and Sport Management, University of Florida, Andrei P Kirilenko, Dept. of Tourism, Recreation, and Sport Management, University of Florida
Topics: Tourism Geography, Spatial Analysis & Modeling, Recreational and Sport Geography
Keywords: Floridian, Geographically Weighted Regression model (GWR), Travel Distance, TripAdvisor
Session Type: Paper
Day: 4/6/2019
Start / End Time: 3:05 PM / 4:45 PM
Room: Congressional A, Omni, West
Presentation File: No File Uploaded


Florida has abundant and diverse recreation and tourism resources including beaches, theme parks, and natural landscapes. This study aims to understand how Floridians utilize recreation resources, specifically how they visit attractions within the state. Travel distance is an important measure of resource utilization, but little is known about what factors affect travel distance. This study plans to investigate how the social demographic and economic factors affect Floridians’ travel distance and the spatial variations of travel distance at the county level. This study collected 431, 829 Floridians’ TripAdvisor reviews on in-state recreation resources. These reviews commented on 42,257 tourism properties within Florida, and reviewer’s travel distance ranged from 0.00 to 903.13 kilometers. The preliminary regression findings indicated that the distance between reviewers’ hometown to the state center, median age, country ethnic composition, and poverty rate affected the average travel distance of each county. This study also reports on the spatial variations of Floridian’s travel distance by establishing a geographically weighted regression model. This study contributes to understanding what factors affect travel distance and the spatial patterns of recreation resource utilization, which serves as an important tool to predict tourists’ behaviors.

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