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Geodiversity index weighted by multivariate statistical analysis

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Abstract

Although there are differences in geodiversity definitions, there is a consensus that the term covers elements of abiotic nature like geology, geomorphology, soils, among others. However, the attempt to evaluate these geodiversity elements revealed the need to create methodologies that can quantify them. In this sense, the goal of this study was to propose and implement a method capable of measuring geodiversity through a weighted index using multivariate statistics (principal component analysis) to rank a data set and indicate the real influence of each variable on geodiversity distribution. As a case study, we used the Guarulhos municipality (São Paulo state, Brazil). The results indicated that the variable “soils” was the one that most influenced the geodiversity index, due to its higher spatial variance, followed by the layers “geology,” “hydrography,” and “relief.” The Geoindex map pointed out that the sites of greatest geodiversity were those where there was high variability of latosol, cambisol, and gleysols, as well as where the lithology pointed to rocks of the Ferrous Formation, granitoid, metapelites, and restricted floodplains adjacent to areas with a high density of lineaments. Hill relief and high-density drainage patterns also influenced the increase of the Geoindex. This map also pointed out that areas of natural heritage, such as waterfalls, rocky outcrops, and lookouts, appeared associated respectively with high, medium, and low values of geodiversity.

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This study was financially supported by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior.

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dos Santos, F.M., de La Corte Bacci, D., Saad, A.R. et al. Geodiversity index weighted by multivariate statistical analysis. Appl Geomat 12, 361–370 (2020). https://doi.org/10.1007/s12518-020-00303-w

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