Clustering multivariate spatial data based on local measures of spatial autocorrelation

Luca Scrucca

No 20/2005, Quaderni del Dipartimento di Economia, Finanza e Statistica from Università di Perugia, Dipartimento Economia

Abstract: A growing interest in clustering spatial data is emerging in several areas, from local economic development to epidemiology, from remote sensing data to environment analyses. However, methods and procedures to face such problem are still lacking. Local measures of spatial autocorrelation aim at identifying patterns of spatial dependence within the study region. Mapping these measures provide the basic building block for identifying spatial clusters of units. If this may work satisfactorily in the univariate case, most of the real problems have a multidimensional nature. Thus, we need a clustering method based on both the multivariate data information and the spatial distribution of units. In this paper we propose a procedure for exploring and discover patterns of spatial clustering. We discuss an implementation of the popular partitioning algorithm known as K-means which incorporates the spatial structure of the data through the use of local measures of spatial autocorrelation. An example based on a set of variables related to the labour market of the Italian region Umbria is presented and deeply discussed.

Date: 2005-10-01
References: View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
http://www2.ec.unipg.it/quaderni/spatcluster.pdf (application/pdf)
Our link check indicates that this URL is bad, the error code is: 500 Can't connect to www2.ec.unipg.it:80 (A connection attempt failed because the connected party did not properly respond after a period of time, or established connection failed because connected host has failed to respond.)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pia:wpaper:20/2005

Access Statistics for this paper

More papers in Quaderni del Dipartimento di Economia, Finanza e Statistica from Università di Perugia, Dipartimento Economia Contact information at EDIRC.
Bibliographic data for series maintained by Ubaldo Pizzoli ().