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Development Prototype: Puget Sound Ecosystem Portfolio Model

Recreation Visits Model

Recreation Visits Model – models changes in State Park beach visitation based on scenarios of population distributions in Puget Sound

Recreational opportunities can be considered a major contributor to human well-being in Puget Sound. In the future, however, changes in land use and population will likely alter the value of nearshore recreation. Yet the changes in recreational use are unknown, and will likely depend on the pattern of future urban growth. Increased population and land use change may stress recreation resources, but allow more people access, effectively increasing net visitation. To try and understand these dynamics we model current recreational use of coastal State Parks in Puget Sound using a dataset of observed park attendance from 2001 - 2008. We then use our model and the ENVISION land use scenarios to estimate the likely future recreational use, and the difference in recreation use between scenarios, to understand how the form of urban growth may affect human use of the nearshore environment through recreational visits.

We model recreational visitation as a function of environmental condition, park characteristics, and recreational demand. The full set of park characteristics developed included area, beach length, annual precipitation, number of concessions, number of activities, shellfishing opportunities, and number of campsites. Environmental condition was described by e. coli counts which may act as a proxy for perceived environmental quality. We estimated travel cost by determining the travel time from each zip code to each State Park, via a road and ferry networked dataset. With data on the origins and destinations of travelers we constructed a demand function that relates the visitation rate (# visits/zip code population) to the distance traveled (p < 0.001, r2 = .67, Figure 1). We then used this function to aggregate population within Puget Sound for each park according to the empirical distance decay relationship.  

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Figure 1: Demand function relating the visitation rate (# visits per 1000 population) by mean travel distance from zip code origins to state parks within Puget Sound. Red data points represent zip codes within Puget Sound. This data with origins and destinations is a subset of the larger dataset, and used to determine the distance decay to weight the population around a state park for the negative binomial model.



Results from our model can be seen in Table 1. Using the 57 State Parks in the dataset, our model explains nearly 70% of the null deviance. Out of the variables initially analyzed, seven were retained in the best model through an information theoretic approach. The variables that increased visitation include the number of campsites at a park, the park size, and the number of possible activities at the park. Variables that negatively affected visitation include a dummy describing accessibility only through personal watercraft, the weighted population from the relationship in Figure 1, the travel time to a park from Seattle, and the water quality (e. coli counts). This model was used to forecast future recreation at each State Park by using the projected population of the ENVISION future scenarios to change the weighted population variable in our model. Differences between scenarios were compared for the years 2030 and 2060.

 

Table 1: Negative binomial regression results for the visitation rate to State Parks with coastal access to Puget Sound, in relation to travel cost and park characteristics. Dependent variable is mean visits per year.

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