On the similarity of tree‐ring patterns

Assessing the influence of semi‐synchronous growth changes on the Gleichläufigkeitskoeffizient for big tree‐ring data sets

Datos Bibliográficos

ID8353082
AutoresR M Visser (0000-0001-6966-1729, Saxion University of Applied Sciences Handelskade 75, NL‐ 7417 DH Deventer The Netherlands, autor de correspondencia)
Año2021
Volumen63
Número1
Páginas204-215
Fecha de publicación2021-02-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaArchaeometry (JOURNAL)
Identificadores de la revistaISSN: 0003-813X • E-ISSN: 1475-4754
EditorialWiley (PUBLISHER • GB)
DOI10.1111/arcm.12600
OpenAlexW3042318109
IdiomaEN
Citas recibidas4
Referencias citadas28

The Gleichläufigkeitskoeffizient (GLK), or the percentage of parallel variation (%PV), is an often used non‐parametric similarity measure in dendrochronological research. However, when analysing big data sets using the GLK, this measure has some issues. The main problem is that it includes not only synchronous but also semi‐synchronous growth changes. These are years in which the growth in one of the compared series does not change in two subsequent years. This influences the GLK, often only slightly, but the larger the data set the stronger the effect. The similarity between tree‐ring series can be more objectively expressed by replacing the GLK with the synchronous (SGC) and semi‐synchronous growth changes (SSGC). The calculation is similar, since GLK = SGC + SSGC/2. Large values of the SSGC are indicative of possible anomalies or even errors. The SGC is much better suited than the GLK to describe similarity. The SGC should therefore be used to analyse big data sets, for clustering and/or dendroprovenance studies. It is recommended to combine the SGC with parametric measures

Big data · Cluster analysis · Data mining · Data set · Image (mathematics) · Measure (data warehouse) · Parametric statistics · Ring (chemistry) · Series (stratigraphy) · Set (abstract data type) · Similarity (geometry) · Similarity measure · Statistics · Tree (set theory) · Artificial Intelligence · Chemistry · Computer Science · Geology · Landslides and related hazards · Mathematics · Plant Water Relations and Carbon Dynamics · Tree-ring climate responses