RT Journal Article
T1 Spatio-temporal clustering: Neighbourhoods based on median seasonal entropy.
A1 Ruiz-Reina, Miguel Ángel
K1 Turismo - Economía
AB 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.
PB Spatial Statistics
YR 2021
FD 2021-08-24
LK https://hdl.handle.net/10630/22801
UL https://hdl.handle.net/10630/22801
LA eng
NO 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
NO This research is associated with the group of Faculty of Economic and Business Sciences at the University of Malaga: “Social Indicators-SEJ157”. The research group has funded the professional editing service in English. Research Funders: “Funding for open access charge: Universidad de Málaga/CBUA”.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 19 ene 2026