2026-05-30T09:46:12Zhttps://riuma.uma.es/rest/oai/requestoai:riuma.uma.es:10630/228012026-02-03T11:27:44Zcom_10630_2254col_10630_37953
RIUMA. Repositorio Institucional de la Universidad de Málaga
author
Ruiz-Reina, Miguel Ángel
2021-09-07T07:41:08Z
2021-09-07T07:41:08Z
2021-08-24
Ruiz Reina, M. Á. (2021). Spatio-temporal clustering: Neighbourhoods based on median seasonal entropy. Spatial Statistics, 45, 100535. https://doi.org/https://doi.org/10.1016/j.spasta.2021.100535
https://hdl.handle.net/10630/22801
https://doi.org/10.1016/j.spasta.2021.100535
In this research, a new uncertainty clustering method has been developed and applied to the spatial time series with seasonality. The new unsupervised grouping method is based on Neighbourhoods and Median Seasonal Entropy. This classification method aims to discover similar behaviours for a time series group and find a dissimilarity measure concerning a reference series r. The Neighbourhood’s Internal Verification Coefficient criterion makes it possible to measure intra-group similarity. This clustering criterion is flexible for spatial information. Our empirical approach allows us to measure accommodation decisions for tourists who visit Spain and decide to stay either in hotels or in tourist apartments. The results show the existence of dynamic seasonal patterns of behaviour. These insights support the decisions of economic agents.
eng
Attribution-NoDerivatives 4.0 Internacional
Turismo - Economía
Spatio-temporal clustering: Neighbourhoods based on median seasonal entropy.
journal article
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