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Geographically Weighted Non-negative Principal Components Analysis for Exploring Spatial Variation in Multidimensional Composite Index. GeoComputation 2019

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Version 2 2019-12-01, 23:13
Version 1 2019-09-16, 12:28
conference contribution
posted on 2019-12-01, 23:13 authored by Narumasa Tsutsumida, Daisuke Murakami, Takahiro Yoshida, Tomoki Nakaya, Binbin Lu, Paul Harris
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.



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