1. Using GIS to Incorporate the Spatial Dimensions of Nature and Human Interaction Brent Read Dept. of Forest, Rangeland and Watershed Stewardship Colorado State University

2. Introduction • Study of interactions between humans and aquatic species. • Factors influencing where people will go for aquatic recreation. • Methods for creating a cost surface model. • Results (to date). • Conclusions.

3. Factors influencing where people will go for aquatic recreation. • Time • Monetary Costs • Origin/Destination

4. Factors influencing where people will go for aquatic recreation. • Time • The single most important factor in determining an individuals choice of destination (Juliao, 1999 and Bateman, Lovett & Brainard, 1999). • Time spent in travel vs. time spent at the site. • Types if travelers: (Chesire and Stabler 1976) • ‘Pure Visitor’ • ‘Transit Visitor’ • ‘Meanderer’

5. Factors influencing where people will go for aquatic recreation. • Money • Travel Expenses: Gas Consumption/Tollbooths • Fees • Permits

6. Factors influencing where people will go for aquatic recreation. • Origin • 57.4% of visitors travel from San Juan. • 30.7% of visitors travel from rural areas near the forest. • 11.9% of visitors travel from “Other” areas. (Kartchner 2002)

8. Factors influencing where people will go for aquatic recreation. • Destination (Nodes) • Site Characteristics • Established Picnic Facilities • Waterfalls • Pools • Flora/Fauna • Vistas • Congestion • Regulations

10. Creating a Time Surface Model • Roads were attributed according to road classification. • They were then attributed according to travel speed.

11. Creating a Time Surface Model • Values were calculated for conversion to raster. • Preliminary testing of the model was done, comparing results to data from www.mapblast.com Juliao (1999) Where: CCT – Cell Crossing Time P – Pixel Size (meters) TS – Traveling Speed (MPH)

13. Results (To Date) • GIS Travel Time Model predicts 76% of the visitors’ responses up to 60 minutes. • GIS Travel Time Model predicts 57% of the visitors’ responses up to 120 minutes. • GIS Monetary Cost Model predicts 26% of the visitors’ responses.

14. Results (To Date) • The GIS Model is significantly predicting the visitors’ responses for travel time. • The survey location is not significant. • Further testing must be done to determine other affects (i.e. road classification)

15. Conclusions • The GIS Travel Time Model is capable of predicting 76% of visitors travel time, and can be used to predict travel time for other sites. • The GIS Travel Expense Model poorly predicts travel expenses, and will not be useful ‘as is’ for prediction.

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