DOI: 10.14704/nq.2018.16.3.1185

A Visual Coding Method for Geographic Statistics Based on the Pattern Recognition Feature of Optic Nerve in Cerebral Cortex

Zuofei Tan, Zhaoxia Wang, Shenglin Li, Qinghui Ren, Bo Song


The human visual system can easily identify a variety of objects, all thanks to its powerful pattern recognition capability. One theory holds that the brain’s visual recognition mechanism is mainly achieved by single neurons and complex neurons located in area V1 of the cerebral cortex. Both types of cells decompose and synthesize the visual signals from sensory organs to extract their pattern features (Riesnhuber et al., 1991). However, in the information visualization field where logic is quite complicated, the visual recognition system of human beings has great limitations and can only effectively recognize complex visual patterns after the complex information is pretreated by a set of scientific visual coding methods. In the context of geographic statistics, based on the single neurons and complex neurons model and Gestalt psychology, this paper proposes a visual coding method based on aggregation and subdivision (AS method) to visualize geographic statistics. The simulation test results show that the AS method can deliver a good mapping relationship between geographic locations and a good rectangular aspect ratio and thus can achieve high visual perception efficiency.


Visual Cortex, Pattern Recognition, Single Neurons and Complex Neurons Model, Gestalt Psychology, Visual Coding, Cartogram, Tree graph

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Supporting Agencies

This work was supported by the project of Facility Asset Visualization and Decision Analysis System under No. AS214R002. We thank the anonymous reviewers whose comments helped improve the manuscript.

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