A spatial point pattern is a collection of points irregularly located within a bounded area (2D) or space (3D) that have been generated by some form of stochastic mechanism. Examples of point patterns include locations of trees in a forest, of cases of a disease in a region, or of particles in a microscopic section of a composite material. Spatial Point pattern analysis is used mostly to determine the absence (completely spatial randomness) or presence (regularity and clustering) of spatial dependence structure of the locations. Methods based on the space domain are widely used for this purpose, while methods conducted in the frequency domain (spectral analysis) are still unknown to most researchers. Spectral analysis is a powerful tool to investigate spatial point patterns, since it does not assume any structural characteristics of the data (ex. isotropy), and uses only the autocovariance function, and its Fourier transform. There are some methods based on the spectral frameworks for analyzing 2D spatial point patterns. There is no such methods available for the 3D situation and, therefore, the aim of this work is to develop new methods based on spectral framework for the analysis of three-dimensional point patterns. The emphasis is on relating periodogram structure to the type of stochastic process which could have generated a 3D observed pattern. The results show that the methods based on spectral analysis developed in this work are able to identify patterns of three typical three-dimensional point processes, and can be used, concurrently, with analyzes in the space domain for a better characterization of spatial point patterns.
RESUMO. Este trabalho investiga o padrão da distribuição espacial de partículas de carboneto de silício em uma liga de alumínio (Al/SiC) utilizando análise espacial espectral bidimensional. Esta técnica se baseia na transformada de Fourier da função de autocovariância amostral. A análise espectralé uma ferramenta poderosa para investigar o padrão espacial de pontos, uma vez que não assume características estruturais dos dados antes da análise e utiliza-se apenas da função espectral. As coordenadas dos centros das partículas foram adquiridas através de tecnicas de processamento de imagem. O espectro polar do periodograma e métodos de Monte-Carlo são usados para testar a hipótese de completa aleatoriedade espacial das partículas. Os resultados mostram que as partículas de carboneto de silício apresentam distribuição espacial completamente aleatória na liga de alumínio e que a análise espacial espectral pode ser uma ferramenta alternativa para investigar padrões de configurações pontuais no espaço. Palavras-chave: espectro polar, processos pontuais, propriedade de materiais, distribuição espacial.
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