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In an earlier paper [ 15], the authors used discriminant analysis, a supervised statistical technique, based on a training data-set that was formed by the authors using the aggregation of known GIs from different organisms.
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The ability of GIS to integrate spatial data from different sources with different formats, structures, projections or levels of resolution is a powerful aid in spatial analysis.
With the REDSS, all GIS and non-GIS components are integrated seamlessly and data from different sources can be queried simultaneously.
By leveraging GIS-based visualization of projects data, staff from different departments who are trying to make project planning decisions, can have access to and utilize useful information from other departments.
The core of the exhibit is GIS-driven thematic maps powered by authoritative data from different cities.
However, coming from different sources, interpolating and crossing data in GIS at the same resolution are factors of some bias during the processing and analysis.
In the context of GMP dispersal, cluster analysis can be used to integrate measurement data from different meteorological networks with different coverage in a GIS environment for defining representative climatic regions.
Nowadays geospatial data is used and analyzed most of the times within a Geographical Information System (GIS) that has capabilities such as assembling, storing, manipulating, displaying, and merging data from different sources (Giuliani et al. 2011).
Historical topographical maps and orthophotomaps from different periods were used to define landscape character types, which were mapped as polygons in a GIS.
GIS-based multicriteria decision analysis (MCDA) is considered to be an important spatial analysis method in the decision-making process that allows information derived from different sources to be combined (Feizizadeh and Blaschke, 2001).
On the other hand it may identify GIs with such different composition values that a compositionally similar donor is unlikely.
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