A robust hierarchical clustering for georeferenced data

Abstract

The detection of spatially contiguous clusters is a relevant task in geostatistics since near located observations might have similar features than distant ones. Spatially compact groups can also improve clustering results interpretation according to the different detected subregions. In this paper, we propose a robust metric approach to neutralize the effect of possible outliers, i.e. an exponential transformation of a dissimilarity measure between each pair of locations based on non-parametric kernel estimator of the direct and cross variograms (Fouedjio, 2016) and on a different bandwidth identification, suitable for agglomerative hierarchical clustering techniques applied to data indexed by geographical coordinates. Simulation results are very promising showing very good performances of our proposed metric with respect to the baseline ones. Finally, the new clustering approach is applied to two real-word data sets, both giving locations and top soil heavy metal concentrations.

On the Interplay of Regional Mobility, Social Connectedness and The Spread of COVID-19 in Germany

“…Contrasting our approach, one may tackle the regression task in Section 3 by incorporating the spatial dependencies directly in the correlation structure, as is done in the literature on spatial econometric models (LeSage & Pace, 2009). We could also employ novel clustering algorithms that naturally exploit different proximity dimensions, such as the geographical and social space, to identify similar districts while taking into account spatial dependencies (D’Urso & Vitale, 2020; D’Urso et al., 2019). Furthermore, the research questions posed in this article would greatly benefit from an examination through the lens of analytical sociology (Hedström & Bearman, 2011).…”

Optimal Coordination of Over-Current Relays in Microgrids Using Unsupervised Learning Techniques

“…Hierarchical clustering algorithms allow for identifying homogeneous groups of data location, from a similarity matrix [27]. Finding the hierarchical structure involves calculating the distance between each pair of points and then using these distances to join pairs of points.…”

Community mobility in the European regions during COVID-19 pandemic: A partitioning around medoids with noise cluster based on space–time autoregressive models

“…Starting from the AR distance for time series processes ( Piccolo, 1990 ), several authors have proposed clustering algorithms for time series based on distance measure between coefficients (see, for example, Maharaj, 1999 , Otranto, 2008 , Otranto, 2010 , D’Urso et al, 2013a , D’Urso et al, 2013b , D’Urso et al, 2015 , D’Urso et al, 2016 , D’Urso et al, 2017 ). Notice that, in the literature, other clustering approaches which manage the spatial–temporal information in a different manner have been proposed by Disegna et al, 2017 , D’Urso et al, 2019 , D’Urso and Vitale, 2020 , D’Urso et al, 2021 .…”