GIS-oriented classification modeling for management in organizational systems with a heterogeneous structure of spatial elements

The article presents theoretical approaches to formalizing problems of optimizing the management of complex organizational systems, taking into account GIS-based classification modeling. It is shown that models of complex systems with spatial characteristics can be classified as stochastic due to the wide variability of input parameters and their random distribution (both in space and time). At the same time, it is clarified that spatial characteristics can be considered, in fact, both geographic reference and any other attribute information about the objects of the system under consideration. The problem of presenting a model of a complex organizational system of an agricultural profile is solved, taking into account the hierarchy of characteristics affecting the system. It is clarified that a feature of the system under consideration is the dependence of stability not only on the structure and parameters of the system (as for linear systems), but also on the magnitude of the initial deviation of the system from the equilibrium position, based on the phase space method, widely used in the theory of automatic control. The problem of finding the optimal (equilibrium) state of a complex organizational system of an agricultural profile is formalized, the choice of significant characteristics and their combined influence on the target variable are justified. 3 main types of input variables are defined. It has been studied that, taking into account the Pareto efficiency when predictors influence each other, the constructed model will make it possible to find optimal solutions in a multicriteria system, taking into account the ranking of the significance and weight of features. The possibility of complicating this problem is noted by the fact that with GIS-oriented classification modeling, the heterogeneous structure of spatial elements can solve the inverse problem - finding the system at a minimum in the case where the optimal option is considered to be the absence of influence on the system of individual input parameters when leveled by other input features.

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