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Area of Science:

  • Cognitive Psychology
  • Geospatial Analysis
  • Computational Social Science

Background:

  • Chess is a valuable tool for studying cognitive processes like decision-making.
  • Chess databases offer vast, analyzable datasets beyond game strategy.
  • Understanding geographical variations in personality has significant societal implications.

Purpose of the Study:

  • To develop a methodology for geospatial social analysis using chess game data.
  • To enable future research on geographical variations in personality inference.
  • To explore the societal implications of personality trait distribution.

Main Methods:

  • Utilizing big data from chess portals for comprehensive analysis.
  • Developing a methodology to link chess game data to geospatial social factors.
  • Applying analytical cause-and-effect thinking skills to large datasets.

Main Results:

  • A new methodology for geospatial social analysis using chess data has been established.
  • The study lays the groundwork for inferring personality traits from chess data.
  • The potential for analyzing geographical variations in personality is demonstrated.

Conclusions:

  • Chess big data can be leveraged for innovative geospatial social analysis.
  • The developed methodology facilitates understanding geographical personality differences.
  • This research has broad applications in social, educational, health, and economic contexts.