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.
Tourism composite spatial indicators through variography and geographically weighted principal components analysis
Distefano, Veronica
2024-01-01
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
