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Assessing similarities between spatial point patterns with a Siamese neural network discriminant model

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Abstract

Identifying structural differences among observed point patterns from several populations is of interest in several applications. We use deep convolutional neural networks and employ a Siamese framework to build a discriminant model for distinguishing structural differences between spatial point patterns. In a simulation study, and using a one-shot learning classification, we show that the Siamese network discriminant model outperforms the common dissimilarities based on intensity and K functions. The model is then used to analyze similarities between spatial point patterns of 130 species in a tropical rainforest study plot observed at different time instances. The simulation study and data analysis show the adequacy and generality of a Siamese network discriminant model in the classification of spatial point patterns.

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Authors and Affiliations

  1. Department of Statistics, Razi University, Kermanshah, 67149-67346, Iran

    Abdollah Jalilian

  2. Department of Mathematics, Universitat Jaume I, E-12071, Castellón, Spain

    Jorge Mateu

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  1. Abdollah Jalilian
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  2. Jorge Mateu
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Correspondence to Abdollah Jalilian.

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Jalilian, A., Mateu, J. Assessing similarities between spatial point patterns with a Siamese neural network discriminant model. Adv Data Anal Classif 17, 21–42 (2023). https://doi.org/10.1007/s11634-021-00485-0

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