@article{
 title = {Multivariate analysis of incomplete mapped data},
 type = {article},
 year = {2003},
 pages = {411-422},
 volume = {7},
 id = {acc3adb5-edaf-3e18-8680-5f0cb4711239},
 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 = {Dray2003b},
 source_type = {article},
 short_title = {Multivariate analysis of incomplete mapped data},
 private_publication = {false},
 abstract = {Classical multivariate analyses are based on matrix algebra and enable the analysis of a table containing measurements of a set of variables for a set of sites. Incomplete mapped data consist of measurements of a set of variables recorded for the same geographical region but for different zonal systems and with only a partial sampling of this zone. This kind of data cannot be analysed with usual multivariate methods because there is no common system of sites for all variables. We propose a new approach using GIS technology and NIPALS, an iterative multivariate method, to analyse the spatial patterns of this kind of data. Moreover, an extension of our method is that it can be used for areal interpolation purposes. We illustrate the method in analysing data concerning the distribution of roe deer weights over several years in a reserve.},
 bibtype = {article},
 author = {Dray, Stéphane and Pettorelli, N and Chessel, D},
 journal = {Transactions in GIS},
 keywords = {Multivarié,PLS,Spatial}
}

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