Kyunghee Lee* - Texas A&M Univ
Michael A Schuett, Ph.D - Texas A&M Univ
National park visitation is influenced by many social, demographic, and economic factors. Research has found that the recent economic recession has led to lower visitation at many national parks. Socio-economic variables are important indicators in predicting future trends and provide beneficial information about potential park visitors for managers and planners. From a practitioner perspective, recreation agencies require multi-scale levels information in order to address visitor and facility needs. While site-based research or using disaggregated models are helpful to satisfy specific purposes for a park, they often do not provide this information in spatially distributed data on a statewide or regional level. Recreation planners and managers need recreation demand forecasts at different level of spatial aggregations. This presentation develops a model of U.S. national park visitation demand using Geographically Weighted Regression (GWR). This model estimates the strength of the relationship between visitation and selected social, demographic and economic factors. Methodologically, ordinary regression models yield only a single estimate in a relationship. In comparison, GWR allows an estimate of the spatial variation of this relationship. Several private (ESRI's Consumer Spending data) and public data sources (Census 2010; American Community survey, 2006-2010) were used in the model to create reliably aggregated data. Seven explanatory variables describing socio-economic, recreation related spending patterns, and level of urbanization were hypothesized to influence the change in recreation demand for spatially varying relationships across the study area. Implications of the results for researchers and managers will be discussed as well as future research directions.