Algorithm for Cartography: Adding Out-domain Knowledge to the Level of Complexity Can Improve the Evolutionary Nature of Multilevel Genotype GIS

Abstract

By calculating the ecological efficacy in genotype-phenotype GIS, it can be concluded that evolution is the main attribute that explains the robustness and accessibility of genotype GIS. In this paper, we examine the definition of out-domain knowledge and how to enhance the level of complexity of genotype geographic system systems, through a multi-level computing model depending on road network growth characteristics in the description of the map-making process, realizing the mathematical logic expression, from data structures to regulatory networks to information clustering analysis. Our results suggest that historical archival information managed through spatiotemporal labels has many links to data in its seemingly unrelated socio-geographic information systems. Therefore, data showing high correlation and phenotypic abundance after location mapping are strongly cohesive, and common phenotypes are close to each other in genotype space. All of these properties are remarkable. Furthermore, evolutionary properties both increase with the number of genes in the genotype. The results show that increasing the complexity level of out-domain knowledge and increasing genome size can enhance both properties.