Foley, Peter
(2012)
Using Geovisual Analytics to investigate
the performance of Geographically
Weighted Discriminant Analysis.
Masters thesis, National University of Ireland Maynooth.
Abstract
Geographically Weighted Discriminant Analysis (GWDA) is a method for prediction
and analysis of categorical spatial data. It is an extension of Linear
Discriminant Analysis (LDA) that allows the relationship between the predictor
variables and the categories to vary spatially. This is also referred to spatial
non-stationarity. If spatial non-stationarity exists, GWDA should model the relationship
between the categories and predictor variables more accurately, thus
resulting in a lower classification uncertainty and ultimately a higher classification
accuracy. The GWDA output also requires interpretation to understand which
variables are important in driving the classification in different geographical regions.
This research uses interactive visualisations from the field of geovisual
analytics to investigate the performance of GWDA in terms of classification accuracy,
classification uncertainty and spatial non-stationarity. The methodology
is demonstrated in a case study that uses GWDA to examine the relationship
between county level voting patterns in the 2004 US presidential election and five
socio-economic indicators. This research builds on existing techniques to interpret
the GWDA output and provides additional insight into the processes driving the
classification. It also demonstrates a practical application of geovisual analytic
tools.
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