Measuring Competitiveness at NUTS3 Level and Territorial Partitioning of the Italian Provinces

In this paper we propose a dashboard of indicators of territorial attractiveness at NUTS3 level in the framework of the EU Regional Competitiveness Index (RCI). Then, the Fuzzy C-Medoids Clustering model with multivariate data and contiguity constraints is applied for partitioning the Italian provinces (NUTS3). The novelty is the territorial level analized, and the identification of the elementary indicators at the basis of the construction of the eleven composite competitiveness pillars. The positioning of the Italian provinces is deeply analyzed. The clusters obtained with and without contraints are compared. The obtained partition may play an important role in the design of policies at the NUTS3 level, a route already considered by the Italian government. The analysis developed and the related set of indicators at NUTS3 level constitute an information base that could be effectively used for the implementation of the National Recovery and Resilience Plan (NRRP)

Bagged Clustering and its application to tourism market segmentation

Aim of the paper is to propose a segmentation technique based on the Bagged Clustering (BC) method. In the partitioning step of the BC method, B bootstrap samples with replacement are generated by drawing from the original sample.The fuzzy C-medoids Clustering (FCMdC) method is run on each bootstrap sam- ple, obtaining (B × C) medoids and the membership degrees of each unit to the different clusters.The sec- ond step consists in running a hierarchical clustering algorithm on the (B × C) medoids. The best partition of the medoids is obtained investigating properly the dendrogram.Then each unit is assigned to each cluster based on the membership degrees observed in the partitioning step.The effectiveness of the sug- gested procedure has been shown analyzing a suggestive tourism segmentation problem. Weanalyze two sample of tourists, each one attending adifferent cultural attraction, enlightening differences among clusters in socio-economic characteristics and in the motivational reasons behind visit behavior. © 2013 Elsevier Ltd. All rights reserved

Bagged fuzzy clustering for fuzzy data: An application to a tourism market.

Segmentation has several strategic and tactical implications in marketing products and services. Despite hard clustering methods having several weaknesses, they remain widely applied in marketing studies. Alternative segmentation methods such as fuzzy methods are rarely used to understand consumer behaviour. In this study, we propose a strategy of analysis, by combining the Bagged Clustering (BC) method and the fuzzy C-means clustering method for fuzzy data (FCM-FD), i.e., the Bagged fuzzy C-means clustering method for fuzzy data (BFCM-FD). The method inherits the advantages of stability and reproducibility from BC and the flexibility from FCM-FD. The method is applied on a sample of 328 Chinese consumers revealing the existence of four segments (Admirers, Enthusiasts, Moderates, and Apathetics) of the perceived images of Western Europe as a tourist destination. The results highlight the heterogeneity in Chinese consumers' place preferences and implications for place marketing are offered

Me, My Girls, and the Ideal Hotel: Segmenting Motivations of the Girlfriend Getaway Market Using Fuzzy C-Medoids for Fuzzy Data.

Segmenting the motivation of travelers using the push and pull framework remains ubiquitous in tourism. This study segments the girlfriend getaway (GGA) market on motivation (push) and accommodation (pull) attributes and identifies relationships between these factors. Using a relatively novel clustering algorithm, the Fuzzy C-Medoids clustering for fuzzy data (FCM-FD), on a sample of 749 women travelers, three segments (Socializers, Enjoyers, and Rejoicers) are uncovered. The results of a multinomial fractional model show relationships between the clusters of motivation and accommodation attributes as well as sociodemographic characteristics. The research highlights the importance of using a gendered perspective in applying well established motivation models such as the push and pull framework. The findings have implications for both destination and accommodation management

A Kemeny Distance-Based Robust Fuzzy Clustering for Preference Data

We propose two robust fuzzy clustering techniques in the context of preference rankings to group judges into homogeneous clusters even in the case of contamination due to outliers or, more generally, noisy data. The two fuzzy C-Medoids clustering methods, based on the same suitable exponential transformation of the Kemeny distance, belong to two different approaches and differ in the way they introduce the fuzziness in the membership matrix, the one based on the “m” exponent and the other on the Shannon entropy. As far as the Kemeny distance is concerned, it is equivalent to the Kendall distance in the case of complete rankings but differs from the latter in the way of handling tied rankings. Simulations prove that our methods are able to recover the natural structure of the groups neutralizing the effect of possible noises and outliers. Two applications to real datasets are also provided

Spatial-temporal clustering based on B-splines: robust models with applications to COVID-19 pandemic

Robust fuzzyC-Medoids clustering models based on B-splines with spatial penalty term have been proposed to cluster Italian regions according to the daily time-series of the cumulative COVID-19 cases over population (per 10000 inhabitants) and of the cumulative COVID-19 deaths over population (per 10000 inhabitants), spanning from 2020-02-24 to 2021-02-08. Both spatial and time components have been efficiently embedded in the model. Furthermore the use of B-splines coefficients allows to reduce consistently the computational burdern

A Regression Tree-Based Analysis of the European Regional Competitiveness

Regional competitiveness is considered a key factor of development. In this work, with the aim of analysing the main drivers of the competitiveness, a Regression Tree analysis has been performed for the Eurostat Regional Competitiveness Index (RCI) as response variable by taking the 74 basic indicators used in the 2019 RCI edition as explanatory variables. Being a non-parametric method, suitable for the analysis of large data sets via a recursive partitioning procedure, the Regression Tree allowed to identify (a) the 12 most influential indicators, out of the initial 74, for the overall 2019 RCI, and (b) a classification of the 268 European regions into 15 homogeneous groups. Interestingly, the groups are ranked by their predicted RCI values which correspond to the mean observed RCI values within the groups themselves. The almost perfect correlation between the Eurostat RCI and the predicted RCI within groups confirms the key role of the 12 selected indicators as determinants of the 2019 RCI. These evidences could help policy makers to address their strategies towards focused objectives in line with the specific needs of the territories, characterized by an intrinsic heterogeneity and complexity

A Three-Way Approach for Defining Competitiveness Indexes of the European Regions (NUTS-2)

In the analysis of the competitiveness of the European regions a three-way approach is used to define indexes which prove to be a valid alternative and integration to the Regional Competitiveness Index (RCI). The STATIS method, which is a generalization of the Principal Component Analysis for three-way data, is applied to take into account the data structure in three macro-pillars (Basic, Efficiency, Innovation) which represent different facets of the competitiveness. STATIS searches for a common “compromise" of the macro-pillars by explicitly taking into account the similarity structure between both European regions and competitiveness indicators. The projection of the NUTS-2 European regions in a low-dimensional space spanned by the compromise allows to analyse similarities as well as disparities at regional level and provide useful and easy-to-read map to visualise both the regions and the indicators. The results largely reflect the 2019 edition of the RCI and its sub-indexes provided by Eurostat with the great advantage of not using an exogenous weighting system necessary in defining the RCI

Clustering of financial time series

This paper addresses the topic of classifying financial time series in a fuzzy framework proposing two fuzzy clustering models both based on GARCH models. In general clustering of financial time series, due to their peculiar features, needs the definition of suitable distance measures. At this aim, the first fuzzy clustering model exploits the autoregressive representation of GARCH models and employs, in the framework of a partitioning around medoids algorithm, the classical autoregressive metric. The second fuzzy clustering model, also based on partitioning around medoids algorithm, uses the Caiado distance, a Mahalanobis-like distance, based on estimated GARCH parameters and covariances that takes into account the information about the volatility structure of time series. In order to illustrate the merits of the proposed fuzzy approaches an application to the problem of classifying 29 time series of Euro exchange rates against international currencies is presented and discussed, also comparing the fuzzy models with their crisp version. © 2012 Elsevier B.V. All rights reserved

Optimal design of air quality networks detecting warning and alert conditions

Air pollution, monitoring network, warning and alert levels,