Peer-Reviewed Journal Details
Mandatory Fields
Harris, P;Charlton, M;Fotheringham, AS
2010
December
Stochastic Environmental Research and Risk Assessment
Moving window kriging with geographically weighted variograms
Published
15 ()
Optional Fields
STATISTICAL ASSESSMENT SPATIAL PREDICTION MODELS INTERPOLATION GEOSTATISTICS DATASETS TRENDS
24
1193
1209
This study adds to our ability to predict the unknown by empirically assessing the performance of a novel geostatistical-nonparametric hybrid technique to provide accurate predictions of the value of an attribute together with locally-relevant measures of prediction confidence, at point locations for a single realisation spatial process. The nonstationary variogram technique employed generalises a moving window kriging (MWK) model where classic variogram (CV) estimators are replaced with information-rich, geographically weighted variogram (GWV) estimators. The GWVs are constructed using kernel smoothing. The resultant and novel MWK-GWV model is compared with a standard MWK model (MWK-CV), a standard nonlinear model (Box-Cox kriging, BCK) and a standard linear model (simple kriging, SK), using four example datasets. Exploratory local analyses suggest that each dataset may benefit from a MWK application. This expectation was broadly confirmed once the models were applied. Model performance results indicate much promise in the MWK-GWV model. Situations where a MWK model is preferred to a BCK model and where a MWK-GWV model is preferred to a MWK-CV model are discussed with respect to model performance, parameterisation and complexity; and with respect to sample scale, information and heterogeneity.
NEW YORK
1436-3240
10.1007/s00477-010-0391-2
Grant Details