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

Abdollah Jalilian () and Jorge Mateu ()
Additional contact information
Abdollah Jalilian: Razi University
Jorge Mateu: Universitat Jaume I

Advances in Data Analysis and Classification, 2023, vol. 17, issue 1, No 3, 42 pages

Abstract: 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.

Keywords: Classification; Deep learning; Dissimilarity; Inhomogeneity; Interactions; Spatial point processes; 62M45; 62H11; 62H30 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11634-021-00485-0 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:advdac:v:17:y:2023:i:1:d:10.1007_s11634-021-00485-0

Ordering information: This journal article can be ordered from
http://www.springer. ... ds/journal/11634/PS2

DOI: 10.1007/s11634-021-00485-0

Access Statistics for this article

Advances in Data Analysis and Classification is currently edited by H.-H. Bock, W. Gaul, A. Okada, M. Vichi and C. Weihs

More articles in Advances in Data Analysis and Classification from Springer, German Classification Society - Gesellschaft für Klassifikation (GfKl), Japanese Classification Society (JCS), Classification and Data Analysis Group of the Italian Statistical Society (CLADAG), International Federation of Classification Societies (IFCS)
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().