RUSSIAN JOURNAL OF EARTH SCIENCES VOL. 10, ES2003, doi:10.2205/2007ES000236, 2008
[3] Functionality of analytical GIS is mainly focused on solving two types of problems: (1) exploration of multidisciplinary geographic information (GI) and evaluation of links between its components and (2) search of multivariate dependencies in GI, prediction, detection and identification of target stationary and dynamic parameters of examined environment. For solving these tasks three methods are widely used: Visual research, Analytical transformations and Plausible inference [Gitis and Ermakov, 2004].
[4] The goals of visual research are the determination of the spatial image of parameters and objects of an examined territory and cartographic measurements. For example, network GIS of IITP RAS allows dynamic operating of cartographic layers, filling-in maps, size of pictograms, thickness of lines, parameters of 3D screen animation, acquisition of multilayered sections, measuring of grid-fields values and vector data attributes, dynamically display on a map groups of objects in a moving time interval (for example, earthquake epicenters), dynamically highlight areas on a map, identical by their parameters to standards, chosen by a user etc.
[5] Analytical transformations help to acquire new thematic and spatio-temporal GI parameters
by predetermined operators. The most important are the following types of transformations: (1)
Grid layers
Grid layer, (2) Grid layers and Vector layers
Attributes of a vector layer,
(3) Vector layer
Grid layer, (4) Vector layers
Attributes of a vector layer.
[6] Methods of plausible inference allow determination of previously unknown operators of analytical transformation. Plausible inference tasks include: evaluation of dependencies and relations between an examined environment's parameters and its substances; prediction of target parameters of an environment; detection of target objects; prediction of spatio-temporal processes. Solving these tasks requires using of methods of multidimensional statistic analysis, pattern recognition, imitational modeling and artificial intelligence.
[7] All analytical operations are accompanied by interactive visualization. Close interaction of analytical and visual research methods lays the foundation of spatial and spatio-temporal cognitive modeling and significantly simplifies understanding of the researched material, accordingly increasing the efficiency of tasks solutions.
Citation: 2008), Distributed network analytical GIS, Russ. J. Earth Sci., 10, ES2003, doi:10.2205/2007ES000236.
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