%0 Conference Paper %A Tsutsumida, Narumasa %A Murakami, Daisuke %A Yoshida, Takahiro %A Nakaya, Tomoki %A Lu, Binbin %A Harris, Paul %D 2019 %T Geographically Weighted Non-negative Principal Components Analysis for Exploring Spatial Variation in Multidimensional Composite Index %U https://auckland.figshare.com/articles/conference_contribution/Geographically_Weighted_Non-negative_Principal_Components_Analysis_for_Exploring_Spatial_Variation_in_Multidimensional_Composite_Index/9850826 %R 10.17608/k6.auckland.9850826.v1 %2 https://auckland.figshare.com/ndownloader/files/17662430 %K non-negative PCA %K geographically weighted model %K multivariable spatial data %K multidimensional composite index %K Geospatial Information Systems %X
The objectives of this study are to develop geographically weighted (GW) non-negative principal components analysis (PCA) and to explore spatial variations of contributions to a multidimensional composite index (MCI) from spatial multidimensional data. As a case study, we produced a MCI for earthquake risk in Tokyo, Japan, in 2018 from the collapse risk of buildings, the fire risk, and the evacuation risk associated with earthquake. GW non-negative PCA was applied to these data to uncover spatial variation of non-negative weightings (eigenvectors). This study demonstrates GW non-negative PCA provides more informative outputs when considering local differences of contributions of multidimensional data to the composite index.
%I The University of Auckland