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DEVELOPMENT OF THE DECISION SUPPORT INFORMATION SYSTEM ARCHITECTURE FOR SPATIAL PLANNING AND REGIONAL MANAGEMENT BASED ON HETEROGENEOUS SPATIAL DATA
Abstract
This paper describes the general architecture of the decision support system. This system is intended for solving problems of spatial planning and regional management. Spatial planning allows for the analysis and coordination of various activities in time and space, taking into account the state of the surrounding ecosystems. Application of spatial planning methods in the scale of a region allows to estimate complex influence of various activities (economic, environmental, military, etc.) both on natural and ecological systems, and on other activities. In addition, spatial planning methods allow to detect and prevent potential conflicts between major stakeholders operating within a region's borders. Currently, spatial planning and regional management decision makers use different software systems to analyse heterogeneous data. However, such systems are often imperfect and cannot meet the information needs of decision makers. We believe that in order to improve the efficiency of decision support it is necessary to carry out preliminary preparation of visualized information in accordance with the information needs of a particular user. For this purpose, we suggest using adaptive approaches to visualize heterogeneous data, including cognitive geovisualization. The architecture offered in the paper will increase the efficiency of decision making in the field of spatial planning and regional management. The architecture includes methods and tools of detection and identification of potential contradictions, associated with the activity of the main stakeholders of the region, operating common natural objects, resources and territories. To identify potential conflicts of nature users, the previously developed conceptual model of the subject area, semantic model of the subject area and technology for dynamic visualization of spatial data are used. The integrated approach to data processing and visualization proposed in this paper can be used to build cognitive user interfaces for decision support systems.
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