Using statistical techniques to conduct the geo-environmental compartmentalization of Serra de Martins-RN, Brazil
The systemic approach has been widely disseminated, with significant acceptance and applicability in geographic science, especially in Physical Geography. The geosystemic approach often refers only to the simple grouping of thematic information on the natural elements, using map overlay and visual i...
| Autores: | , |
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| Formato: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2020 |
| País: | Brasil |
| Recursos: | Universidade Federal de Uberlândia (UFU) |
| Repositorio: | Sociedade & natureza (Online) |
| Idioma: | portugués inglés |
| OAI Identifier: | oai:ojs.www.seer.ufu.br:article/46691 |
| Acesso em linha: | https://seer.ufu.br/index.php/sociedadenatureza/article/view/46691 |
| Access Level: | acceso abierto |
| Palavra-chave: | Análise sistêmica Serra de Martins Análise Multivariada Análise de Agrupamentos Systemic analysis Multivariate analysis Analysis of Clusters |
| Resumo: | The systemic approach has been widely disseminated, with significant acceptance and applicability in geographic science, especially in Physical Geography. The geosystemic approach often refers only to the simple grouping of thematic information on the natural elements, using map overlay and visual interpretation as the main techniques for individualization and spatialization of homogeneous units. Thus, this article aims to present the geoenvironmental compartmentalization of Serra de Martins-RN, performed with support in the systemic approach and using multivariate cluster analysis as a technique for identification and spatialisation of geoenvironmental units. The methodological procedures used were: data treatment and analysis, unit taxonomy, physical-geographical descriptions, tabulation, and cartography. For the application of statistical techniques, a set of data was used involving geological, geomorphological, pedological, phytogeographical and land cover variables, from which the most significant ones were selected applying the Principal Component Analysis technique from the minimum variance. The method allowed to identify and to spatialize six Classes of Facies. In the end, it was verified that, although it requires a more in depth knowledge in data manipulation and in the use of complex techniques, the application of statistical treatment was objective and effective for the geoenvironmental compartmentalization. |
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