Classification of Events Using Local Pair Correlation Functions for Spatial Point Patterns
Impacto
Scholar |
Otros documentos de la autoría: González, Jonatan A.; Rodríguez-Cortés, Francisco Javier; Romano, Elvira; Mateu, Jorge
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Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7037
comunitat-uji-handle3:10234/8635
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https://doi.org/10.1007/s13253-021-00455-1 |
Metadatos
Título
Classification of Events Using Local Pair Correlation Functions for Spatial Point PatternsFecha de publicación
2021-05-12Editor
American Statistical Association; International Biometrics Society; Springer VerlagISSN
1085-7117; 1537-2693Cita bibliográfica
González, J.A., Rodríguez-Cortés, F.J., Romano, E. et al. Classification of Events Using Local Pair Correlation Functions for Spatial Point Patterns. JABES (2021). https://doi.org/10.1007/s13253-021-00455-1Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
http://www.springer.com/statistics/life+sciences%2C+medicine+%26+health/journal/13253Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
Spatial point pattern analysis usually concerns identifying features in an observation window where there is also noise. This identification traditionally begins with studying the second-order properties of the point ... [+]
Spatial point pattern analysis usually concerns identifying features in an observation window where there is also noise. This identification traditionally begins with studying the second-order properties of the point pattern, and it may be done locally by using local second-order characteristics (LISA). Some properties of this local structure solve the problem of classification into feature and clutter points. This paper proposes an estimator for local pair correlation LISA functions, discusses some of its properties and considers a particular distance to measure dissimilarities. Two classification procedures to separate feature from clutter points are described. One of them adopts multidimensional scaling and support vector machines, and the other employs bagged clustering. Simulations demonstrate the performance of the method, and it is applied to a dataset concerning earthquakes in a seismic nest located in Colombia. [-]
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