1. 1 Spatial Analysis of Recreation Opportunity Spectrum and Travel/Tourism-Generated Revenues: A Case of West Virginia Ishwar Dhami Division of Resource Management Jinyang Deng Recreation, Parks, and Tourism Resources Program West Virginia University

2. Introduction Recreation Opportunity Spectrum (ROS) is a planning framework developed in late 1970’s (Clark and Stankey 1979). The objective of ROS is to help managers to identify, classify, and manage supply of recreational opportunities in an area. Preferred setting, preferred activities, preferred experience. 2

3. Introduction Three settings 3 Setting Component PhysicalRemoteness Size Evidence of humans SocialUser density ManagerialManagerial regimentation and noticeability

4. Introduction-Physical Setting RemotenessSizeStructures Primitive>3 mi from all roads5000 acresNone Semi-primitive Non-motorized ½ mile from unimproved roads 2500 acresMinimal Semi-primitive motorized ½ mile from improved roads 2500 acresMinimal Roaded Natural< ½ mile from improved roads NoneScattered (Public ownership) Rural< ½ mile from improved roads NoneReadily apparent (Private Ownership) Urban< ½ mile from improved roads NoneDominant (Developed areas) 4 Source: Pierskalla et al., 2009

5. Rationale Recreational resources: a major pulling factor to promote the tourism industry. Assumed to be the most important assets for development in rural areas (Baehler,1995; Snepenger et al., 1995). Rural areas with more natural and artificial resources experience higher rates of economic growth (McGranahan, 1999; Deller et al., 2001) 5

6. Methods-Data and Software Travel spending 2010 Dean Runyan Associates (2010) Software: ArcGIS, Geoda DataSource RoadsU.S census 2010 TIGER/Line 2012 Land ownershipU.S Geological Survey 2012 Developed areasU.S Census 2010 6

7. 7 Methods- GIS Modeling

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11. Methods-Spatial Autocorrelation Global spatial autocorrelation (Moran’s I) was calculated to determine the clustering of ROS classes. Local Indicators of Spatial Association (LISA) was used to examine the spatial distribution of clustered variables. 11

12. Methods-Spatial Regression The relationship between travel spending and the ROS classes was first estimated using Ordinary Least Square (OLS). Lagrange multiplier (LM) diagnostics on the OLS for the spatial lag dependence or the spatial error dependence were used to determine spatial dependency. 12

13. 13 Results - ROS Adjusted for Remoteness

14. 14 Results -ROS Adjusted for Size

15. 15 Results - Evidence of human/structures

16. 16 Results - Final ROS Class% SPNM2.5 SPM7.2 RN7.1 R79.8% U3.3

17. Results - ROS for Pocahontas County Class% SPNM11.8 SPM13.6 RN36.5 R37.7 U0.4 17

18. Variables Moran’s I valueP-value Travel spending (2010)0.010.25 SPNM 0.340.00 SPM 0.520.00 RN 0.500.00 R 0.410.00 U 0.010.23 18 Results - spatial autocorrelation

19. Results- LISA Cluster Map SPNM 19

20. Results- LISA Cluster Map SPM 20

21. RN 21 Results- LISA Cluster Map

22. Rural 22 Results- LISA Cluster Map

23. Results- OLS model VariablesCoefficientP-value Intercept-6.98*0.06 SPNM4.690.84 SPM3.780.76 RN0.780.92 R0.290.91 U13.89***0.00 F-value3.130.01 Adjusted R square Moran’s I Lagrange Multiplier (lag) Lagrange Multiplier (error) 0.24 0.76 0.04 0.06 0.44 0.85 0.81 23

24. Discussion and Conclusion Most of the areas in West Virginia are Rural, followed by SPNM and RN. Hot spots for SPNM, SPM and RN are found in the eastern or central eastern part of state. Majority of areas in western part of state (mostly rural) are suitable for culture based tourism. Areas in eastern part of the state are suitable for both nature and culture based tourism (SPNM, SPM, RN and Rural). 24

25. Discussion and Conclusion 5.35% of SPM and 1.10% of SPNM fall under private ownership. Private land ownership can promote different kinds of recreational activities. 25

26. 2.45% of the state could cater to tourists who value wilderness (SPNM). 14.36% of the state could be suitable who value wilderness and amenities (SPM and RN). Areas under Rural (79.8%) are suitable for tourists who value amenities and accessibility. Discussion and Conclusion 26

27. Regression analysis: Visitors’ travel spending were significantly associated with the urban class. Counties with more of the other ROS classes but less of the urban areas were found to have less visitors spending. Discussion and Conclusion 27

28. Information to the visitors on the type of ROS available in the area. Helps to determine the management practice that would generate certain class. Information on existing recreation opportunities to assist them in making decisions on appropriate land uses. Dealing with size of the ROS classes changes in the area and trend of visitors could provide better planning of tourism. Discussion and Conclusion 28

29. Thank You!