NDLI: Multivariate Spatial Outlier Detection Using Robust Geographically Weighted Methods

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Multivariate Spatial Outlier Detection Using Robust Geographically Weighted Methods

Content Provider	Springer Nature Link
Author	Harris, Paul Brunsdon, Chris Charlton, Martin Juggins, Steve Clarke, Annemarie
Copyright Year	2013
Abstract	Outlier detection is often a key task in a statistical analysis and helps guard against poor decision-making based on results that have been influenced by anomalous observations. For multivariate data sets, large Mahalanobis distances in raw data space or large Mahalanobis distances in principal components analysis, transformed data space, are routinely used to detect outliers. Detection in principal components analysis space can also utilise goodness of fit distances. For spatial applications, however, these global forms can only detect outliers in a non-spatial manner. This can result in false positive detections, such as when an observation’s spatial neighbours are similar, or false negative detections such as when its spatial neighbours are dissimilar. To avoid mis-classifications, we demonstrate that a local adaptation of various global methods can be used to detect multivariate spatial outliers. In particular, we account for local spatial effects via the use of geographically weighted data with either Mahalanobis distances or principal components analysis. Detection performance is assessed using simulated data as well as freshwater chemistry data collected over all of Great Britain. Results clearly show value in both geographically weighted methods to outlier detection.
Starting Page	1
Ending Page	31
Page Count	31
File Format	PDF
ISSN	18748961
Journal	Mathematical Geosciences
Volume Number	46
Issue Number	1
e-ISSN	18748953
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2013-09-26
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Non-stationarity Mahalanobis distance Principal components analysis Co-kriging cross-validation Freshwater acidification Anomaly detection Earth Sciences Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences Geotechnical Engineering & Applied Earth Sciences Hydrogeology
Content Type	Text
Resource Type	Article
Subject	Earth and Planetary Sciences Mathematics

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