EconPapers    
Economics at your fingertips  
 

Spatial association between regionalizations using the information-theoretical V-measure

Jakub Nowosad and Tomasz Stepinski

No rcjh7, EarthArXiv from Center for Open Science

Abstract: There is a keen interest in inferring spatial associations between different variables spanning the same study area. We present a method for quantitative assessment of such associations in the case where spatial variables are either in the form of regionalizations or in the form of thematic maps. The proposed index of spatial association – called the V-measure – is adapted from a measure originally developed in computer science, where it was used to compare clusterings, to spatial science for comparing regionalizations. The V-measure is rooted in the information theory and, at its core, it is equivalent to mutual information between the two regionalizations. Here we re-introduce the V-measure in terms of spatial variance analysis instead of information theory. We identify three different contexts for application of the V-measure, comparative, associative, and derivative, and present an example of an application for each of them. In the derivative context, the V-measure is used to select an optimal number of regions for clustering-derived regionalizations. In effect, this also constitutes a novel way to determine the number of clusters for non-spatial clustering tasks as well. The advantage of V-measure over the Mapcurves method is discussed. We also use the insight from deriving the V-measure in terms of spatial variance analysis to point out a shortcoming of the Geographical Detector – a method to quantify associations between numerical and categorical spatial variables. The open-source software for calculating the V-measure accompanies this paper.

New Economics Papers: this item is included in nep-cmp and nep-ure
Date: 2018-04-19
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
https://osf.io/download/5ad8dbee9aa0a6000fd91d7b/

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:osf:eartha:rcjh7

DOI: 10.31219/osf.io/rcjh7

Access Statistics for this paper

More papers in EarthArXiv from Center for Open Science
Bibliographic data for series maintained by OSF ().

 
Page updated 2020-01-17
Handle: RePEc:osf:eartha:rcjh7