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Tourism composite spatial indicators through variography and geographically weighted principal components analysis

Author

Listed:
  • Monica Palma

    (Università del Salento)

  • Sandra De Iaco

    (Università del Salento
    National Biodiversity Future Center)

  • Claudia Cappello

    (Università del Salento)

  • Veronica Distefano

    (Università del Salento)

Abstract

The definition of an index to synthesize the tourism appeal of a holiday destination is complex due to the effect of different aspects, such as the economic, socio-demographic, cultural, geographical ones regarding both demand and supply-side. In this paper, several spatially-referenced factors, related to the tourism attractiveness, are analyzed through the geographically weighted principal components analysis (GWPCA). However, the automatic setting of its kernel bandwidth, often used in practice, provides sometimes a not satisfactory result, since it is too small, with possible abrupt variation in the spatial domain, or too large, disregarding the spatial dependence. For this reason, a new approach, based on variography and minimum spatial correlation distance characterizing the covariates, is proposed for the GWPCA. A comparison with respect to the outputs of the GWPCA, based on automatic fitting, is discussed. Moreover, tourism composite spatial indicators are developed in order to support the policy makers in planning possible actions to boost tourism over the region of interest.

Suggested Citation

  • Monica Palma & Sandra De Iaco & Claudia Cappello & Veronica Distefano, 2024. "Tourism composite spatial indicators through variography and geographically weighted principal components analysis," Annals of Operations Research, Springer, vol. 342(3), pages 1687-1705, November.
    • Handle: RePEc:spr:annopr:v:342:y:2024:i:3:d:10.1007_s10479-023-05329-y
    DOI: 10.1007/s10479-023-05329-y
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    References listed on IDEAS

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    1. Daniele Trogu & Michele Campagna, 2018. "Towards Spatial Composite Indicators: A Case Study on Sardinian Landscape," Sustainability, MDPI, vol. 10(5), pages 1-21, April.
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    5. Alfredo Cartone & Paolo Postiglione, 2021. "Principal component analysis for geographical data: the role of spatial effects in the definition of composite indicators," Spatial Economic Analysis, Taylor & Francis Journals, vol. 16(2), pages 126-147, April.
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