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PeakLocator 1.0, a web tool to compare extreme value areas among maps
Author(s)
Language
English
Obiettivo Specifico
1VV. Altro
Status
Published
JCR Journal
JCR Journal
Peer review journal
Yes
Title of the book
Issue/vol(year)
5 / 61 (2018)
Issued date
2018
Abstract
We present here a simple web application, PeakLocator 1.0 (hereafter referred to as PL1.0), for the
analysis of gridded geo-located maps. In the present version of the code, the maps can contain up to
10 different variables with different units, not necessarily measured at the same locations, as well as
the same variable recurrently measured in the time. The aim of PL1.0 is to identify regions where
values lie outside the standard deviation from average values. The degree of spatial correspondence
between these regions is reflected in the “fitting index” associated to the overlapping area. Here we
demonstrate some possible applications of PL1.0 using published datasets, although its potential
applicability extends to wide range of topics where the common demand is the comparison of two
or more variables mapped over a common area or over areas partially overlapping. PL1.0 is freely
accessible through a web interface and runs on any platform.
analysis of gridded geo-located maps. In the present version of the code, the maps can contain up to
10 different variables with different units, not necessarily measured at the same locations, as well as
the same variable recurrently measured in the time. The aim of PL1.0 is to identify regions where
values lie outside the standard deviation from average values. The degree of spatial correspondence
between these regions is reflected in the “fitting index” associated to the overlapping area. Here we
demonstrate some possible applications of PL1.0 using published datasets, although its potential
applicability extends to wide range of topics where the common demand is the comparison of two
or more variables mapped over a common area or over areas partially overlapping. PL1.0 is freely
accessible through a web interface and runs on any platform.
Type
article
File(s)
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Name
Granieri et al 2018.pdf
Size
2.14 MB
Format
Adobe PDF
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