Journal article

A classification technique for local multivariate clusters and outliers of spatial association

Daniele Oxoli, Soheil Sabri, Abbas Rajabifard, Maria A Brovelli

Transactions in GIS | Wiley | Published : 2020


The detection of spatial clusters and outliers is critical to a number of spatial data analysis techniques. Many techniques embed spatial clustering components with the aim of exploring spatial variability and patterns in a data set, caused by the spatial association that generally affects most spatial data. A frontier challenge in spatial data analysis is to extend techniques—originally designed for univariate analysis—to a multivariate context, in order to be able to cope with the increasing complexity and variety of modern spatial data. This article proposes an exploratory procedure to detect and classify clusters and outliers in a multivariate spatial data set. Cluster and outlier detect..

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Awarded by PRIN Project-Italian Ministry of Education, University and Research (MIUR)

Funding Acknowledgements

URBAN-GEO BIG DATA; PRIN Project-Italian Ministry of Education, University and Research (MIUR)-ID, Grant/Award Number: 20159CNLW8