@article{
 title = {Matching data sets from two different spatial samples},
 type = {article},
 year = {2002},
 pages = {867-874},
 volume = {13},
 id = {0bf3a629-7f11-3f93-91d4-1ee3c80cbe09},
 created = {2010-11-03T21:13:25.000Z},
 file_attached = {true},
 profile_id = {976aa121-3316-304c-8340-7ca54d70abe6},
 last_modified = {2017-03-16T14:38:37.564Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Dray2002},
 source_type = {article},
 short_title = {Matching data sets from two different spatial samp},
 private_publication = {false},
 abstract = {Methods for coupling two data sets (species composition and environmental
variables for example) are well known and often used in ecology.
All these methods require that variables of the two data sets have
been recorded at the same sample stations. But if the two data sets
arise from different sample schemes, sample locations can be different.
In this case, scientists usually transform one data set to conform
with the other one that is chosen as a reference. This inevitably
leads to some loss of information. We propose a new ordination method,
named spatial-RLQ analysis, for coupling two data sets with different
spatial sample techniques. Spatial-RLQ analysis is an extension of
co-inertia analysis and is based on neighbourhood graph theory and
classical RLQ analysis. This analysis finds linear combinations of
variables of the two data sets which maximize the spatial cross-covariance.
This provides a co-ordination of the two data sets according to their
spatial relationships. A vegetation study concerning the forest of
Chizé (western France) is presented to illustrate the method.},
 bibtype = {article},
 author = {Dray, Stéphane and Pettorelli, N and Chessel, D},
 journal = {Journal of Vegetation Science},
 keywords = {Co-Inertie,Multivarié,RLQ,Spatial}
}

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