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Identifying and mapping forest-based ecotourism areas in West Virginia – Incorporating visitors' preferences

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  • Dhami, Ishwar
  • Deng, Jinyang
  • Burns, Robert C.
  • Pierskalla, Chad

Abstract

This study identifies and maps forest-based ecotourism areas in West Virginia by incorporating visitors' preferences. Relative weights for ecotourism destination criteria were obtained using the Analytic Hierarchy Process based on a survey of 777 participants. The study classified the state into five levels of naturalness with areas under Class I being the least natural and Class V being the most natural. The presence of wildlife was found to be the most important criterion and slope was found to be the least. Results also showed significant variations in visitors' preferences. Areas under Class IV and Class V in both weighted and unweighted ecotourism maps covered more than half of the state's area, suggesting high prospects for promoting forest-based ecotourism in the state. The results further showed that each class changed in size when visitors' preferences were applied. The ecotourism maps created provide useful insights for visitors, destination managers, and decision makers.

Suggested Citation

  • Dhami, Ishwar & Deng, Jinyang & Burns, Robert C. & Pierskalla, Chad, 2014. "Identifying and mapping forest-based ecotourism areas in West Virginia – Incorporating visitors' preferences," Tourism Management, Elsevier, vol. 42(C), pages 165-176.
    • Handle: RePEc:eee:touman:v:42:y:2014:i:c:p:165-176
    DOI: 10.1016/j.tourman.2013.11.007
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    References listed on IDEAS

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    Cited by:

    1. Can Bülent Karakuş, 2024. "Assessment of ecotourism potentiality based on GIS-based fuzzy logarithm methodology of additive weights (F-LMAW) method for sustainable natural resource management," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(10), pages 27001-27055, October.
    2. Qin Huang & Chen Zhou & Manchun Li & Yu Ma & Song Hua, 2024. "An Approach for Mapping Ecotourism Suitability Using Machine Learning: A Case Study of Zhangjiajie, China," Land, MDPI, vol. 13(8), pages 1-22, August.
    3. Manijeh Talebi & Baris Majnounian & Majid Makhdoum & Ehsan Abdi & Mahmoud Omid, 2021. "Predicting areas with ecotourism capability using artificial neural networks and linear discriminant analysis (case study: Arasbaran Protected Area, Iran)," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(6), pages 8272-8287, June.
    4. Yekani Motlagh, Elgar & Hajjarian, Marzieh & Hossein Zadeh, Omid & Alijanpour, Ahmad, 2020. "The difference of expert opinion on the forest-based ecotourism development in developed countries and Iran," Land Use Policy, Elsevier, vol. 94(C).
    5. Abel González-Ramiro & Gil Gonçalves & Alonso Sánchez-Ríos & Jin Su Jeong, 2016. "Using a VGI and GIS-Based Multicriteria Approach for Assessing the Potential of Rural Tourism in Extremadura (Spain)," Sustainability, MDPI, vol. 8(11), pages 1-15, November.
    6. Lie Wang & Chuanhao Sun & Mo Wang, 2024. "Optimization Strategies for Waterfront Plant Landscapes in Traditional Villages: A Scenic Beauty Estimation–Entropy Weighting Method Analysis," Sustainability, MDPI, vol. 16(16), pages 1-16, August.
    7. Wu, Shou-Tsung & Chen, Yeong-Shyang, 2016. "Examining eco-environmental changes at major recreational sites in Kenting National Park in Taiwan by integrating SPOT satellite images and NDVI," Tourism Management, Elsevier, vol. 57(C), pages 23-36.

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