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Spatial Analysis of Recreation Opportunity Spectrum and Travel/Tourism-Generated

Revenues: A Case of West Virginia

Ishwar Dhami Division of Resource Management

Jinyang DengRecreation, Parks, and Tourism Resources Program

West Virginia University

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.

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Introduction• Three settings

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Setting Component

Physical Remoteness

Size

Evidence of humans

Social User density

Managerial Managerial regimentation and noticeability

Introduction-Physical SettingRemoteness Size Structures

Primitive >3 mi from all roads 5000 acres None

Semi-primitive Non-motorized

<3 mi from all roads and > ½ mile from unimproved roads

2500 acres Minimal

Semi-primitive motorized

<½ mile from unimproved roads > ½ mile from improved roads

2500 acres Minimal

Roaded Natural < ½ mile from improved roads

None Scattered (Public ownership)

Rural < ½ mile from improved roads

None Readily apparent (Private Ownership)

Urban < ½ mile from improved roads

None Dominant (Developed areas)

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Source: Pierskalla et al., 2009

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)

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Methods-Data and Software

Travel spending 2010 Dean Runyan Associates (2010)

Software: ArcGIS, Geoda

Data Source

Roads U.S census 2010 TIGER/Line 2012

Land ownership U.S Geological Survey 2012

Developed areas U.S Census 2010

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Methods- GIS Modeling

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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.

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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.

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Results -ROS Adjusted for Remoteness

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Results -ROS Adjusted for Size

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Results- Evidence of human/structures

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Results- Final ROS

Class %

SPNM 2.5

SPM 7.2

RN 7.1

R 79.8%

U 3.3

Results- ROS for Pocahontas County

Class %

SPNM 11.8

SPM 13.6

RN 36.5

R 37.7

U 0.4

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Variables

Moran’s I value P-value

Travel spending (2010) 0.01 0.25

SPNM0.34 0.00

SPM0.52 0.00

RN0.50 0.00

R0.41 0.00

U0.01 0.23

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Results- spatial autocorrelation

Results- LISA Cluster Map

SPNM

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Results- LISA Cluster Map

SPM

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RN

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Results- LISA Cluster Map

Rural

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Results- LISA Cluster Map

Results- OLS model

Variables Coefficient P-value

Intercept -6.98* 0.06

SPNM 4.69 0.84

SPM 3.78 0.76

RN 0.78 0.92

R 0.29 0.91

U 13.89*** 0.00

F-value 3.13 0.01

Adjusted R square

Moran’s I

Lagrange Multiplier (lag)

Lagrange Multiplier (error)

0.24

0.76

0.04 0.06

0.44

0.850.81

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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).

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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.

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• 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

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• 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

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• 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

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Thank You!