Flexible regression models over river networks

Many statistical models are available for spatial data but the vast majority of these assume that spatial separation can be measured by Euclidean distance. Data which are collected over river networks constitute a notable and commonly occurring exception, where distance must be measured along comple...

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Bibliographic Details
Published in:Journal of the Royal Statistical Society 2014-01, Vol.63 (1), p.47-63
Main Authors: O'Donnell, David, Rushworth, Alastair, Bowman, Adrian W., Marian Scott, E., Hallard, Mark
Format: Article
Language:English
Subjects:
Applied statistics
Computational methods
Construction
Covariance
Flexible regression
Freshwater
Kernels
Mathematical models
Modelling
Network
Networks
Original
Penalized splines
Regression
Regression analysis
River networks
Rivers
Smoothing
Spatial separation
Spatiotemporal models
Splines
Statistical analysis
Statistics
Studies
Water quality
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Summary:Many statistical models are available for spatial data but the vast majority of these assume that spatial separation can be measured by Euclidean distance. Data which are collected over river networks constitute a notable and commonly occurring exception, where distance must be measured along complex paths and, in addition, account must be taken of the relative flows of water into and out of confluences. Suitable models for this type of data have been constructed based on covariance functions. The aim of the paper is to place the focus on underlying spatial trends by adopting a regression formulation and using methods which allow smooth but flexible patterns. Specifically, kernel methods and penalized splines are investigated, with the latter proving more suitable from both computational and modelling perspectives. In addition to their use in a purely spatial setting, penalized splines also offer a convenient route to the construction of spatiotemporal models, where data are available over time as well as over space. Models which include main effects and spatiotemporal interactions, as well as seasonal terms and interactions, are constructed for data on nitrate pollution in the River Tweed. The results give valuable insight into the changes in water quality in both space and time.
ISSN:0035-9254
1467-9876
DOI:10.1111/rssc.12024