Using GIS to Incorporate the Spatial Dimensions of Nature and Human Interaction

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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