Abstract
Cartographic spatial analysis plays an important role in decision making process formed by a user-analyst during a dialogue with geographic information system. The validity of the analysis result is determined by the content of the analysis workspace constructed by the analyst. Fragments from sources of inconsistent spatial data are included in the work area. The quality of such area mostly is far from satisfactory, but significant for solving the researched problem. As a result, display defects occur. It negatively affects the perception of the map and complicates the analysis process. In this paper, we consider the problem of controlling the analysis process for visual anomalies representation occurred due to the use of inconsistent data. A particular task mental image defects impact and situational awareness of analyst are analyzed. Semantic orientation analysis concept and the need for using analysis contexts associated with it are studied. An analytic model is proposed. The control problem is formulated as the process of choosing the closest meaningful context when an abnormal level of work area defective objects number occurs. The proposed method for solving the problem is based on looking for context sequences during an analytic session that meets the requirement of semantic proximity.
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Acknowledgments
The reported study was funded by RFBR according to the research projects #19-07-00074, #20-01-00197.
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Belyakov, S., Bozhenyuk, A., Glushkov, A., Rozenberg, I. (2020). Spatial Analysis Management Using Inconsistent Data Sources. In: Silhavy, R. (eds) Artificial Intelligence and Bioinspired Computational Methods. CSOC 2020. Advances in Intelligent Systems and Computing, vol 1225. Springer, Cham. https://doi.org/10.1007/978-3-030-51971-1_31
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DOI: https://doi.org/10.1007/978-3-030-51971-1_31
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