Spatially weighted functional clustering of river network data

Incorporating spatial covariance into clustering has previously been considered for functional data to identify groups of functions which are similar across space. However, in the majority of situations that have been considered until now the most appropriate metric has been Euclidean distance. Dire...

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Bibliographic Details
Published in:	Journal of the Royal Statistical Society Vol. 64; no. 3; pp. 491 - 506
Main Authors:	Haggarty, R. A., Miller, C. A., Scott, E. M.
Format:	Journal Article
Language:	English
Published:	England Blackwell Publishing Ltd 01.04.2015 John Wiley & Sons Ltd Oxford University Press BlackWell Publishing Ltd
Subjects:	Analysis Analysis of covariance Bee clustering Cluster analysis Clustering Computational methods Covariance Covariance matrices Data Data smoothing Euclidean space Functional data Hierarchical clustering Mathematical analysis Mathematical functions Mathematical models Networks Nitrates Original Pollution River networks Rivers Scotland Spatial analysis Spatial models Stations Statistics Streams Studies Tweed United Kingdom Water pollution Water quality British Isles, Scotland River networks Covariance Functional data Hierarchical clustering Water quality
ISSN:	0035-9254, 1467-9876
Online Access:	Get full text
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Summary:	Incorporating spatial covariance into clustering has previously been considered for functional data to identify groups of functions which are similar across space. However, in the majority of situations that have been considered until now the most appropriate metric has been Euclidean distance. Directed networks present additional challenges in terms of estimating spatial covariance due to their complex structure. Although suitable river network covariance models have been proposed for use with stream distance, where distance is computed along the stream network, these models have not been extended for contexts where the data are functional, as is often the case with environmental data. The paper develops a method of calculating spatial covariance between functions from sites along a river network and applies the measure as a weight within functional hierarchical clustering. Levels of nitrate pollution on the River Tweed in Scotland are considered with the aim of identifying groups of monitoring stations which display similar spatiotemporal characteristics.
Bibliography:	ArticleID:RSSC12082 'Spatially weighted functional clustering of river network data supplementary material'. istex:102100DC54C970FC192159D10003CCB357AC7419 ark:/67375/WNG-DCQ9K6GC-H Engineering and Physical Sciences Research Council SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-1 ObjectType-Feature-2 content type line 23
ISSN:	0035-9254 1467-9876
DOI:	10.1111/rssc.12082