Multivariate statistics in earth science by Dasapta Erwin Irawan

EN: A collection of papers in earth science with multivariate statistics approach made to support my paper in Indonesian language Unravel the hydrogeology of Pangalengan area using statistics visualisation. I wrote it on Authorea.

ID: Koleksi makalah di bidang ilmu kebumian dengan pendekatan statistik multivariabel. Koleksi ini saya buat untuk melengkapi makalah saya Mengurai sistem hidrogeologi daerah Pangalengan menggunakan visualisasi statistik yang ditulis menggunakan platform Authorea.

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      Multivariate statistics and visualisation in earth science


      This work has been published open access under Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at

      python, R, visualizations, cluster analysis, principal component analysis, multivariate statistics

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          It's not exist until it's shown on a map.

          Earth scientists are known to have a close relationship with maps. But since a long time a go, they also known to use statistics to support their claims. However, this strategy has not been applied extensively in Indonesia. More scientists still separate the spatial component with the statistical component in an geological analysis, for instance. Moreover, given the heterogenity of a geological environment, then the analysis is more complex than ever. Multivarible/multi parameters environment is the cause. This collection is another effort that I made to share multivariate statistics and it's various usage for earth science or geoscience. 

          I started with 79 papers in this case using several keywords listed in the description. I only selected documents with DOI link, to ensure that the doc is in form of a paper or an abstract. It covers mostly principal component analysis (PCA) and cluster analysis (CA) in soil, geochemistry, and water quality researches.

          Now anyone could apply multivariate statistics along with pretty and informative visualisation using free tools like R and Python, like this blog post showing an example of PCA using R

          As additional reference, I also recommend the visitor to have a look at Basic statistical concepts and methods for earth scientists, a USGS open report, writtern by Ricardo A. Olea in 2008.   

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