Spatial point pattern recognition and feature extraction based on singular spatial chromatic tessellation

To extend the diversity of identifiable spatial point patterns, this study is based on the spatial chromatic model (SCM) and investigates the correspondence between spatial chromatic codes derived from singular spatial chromatic tessellations and spatial point patterns. The results show that both the magnitude and statistical characteristics of the spatial chromatic codes can effectively indicate the distribution patterns of spatial points. The proposed method is capable of identifying not only common point pattern characteristics, such as randomness and clustering, but also special configurations including collinearity, cocircularity, and symmetry. Moreover, it facilitates the integration of point pattern recognition with other spatial analysis functions provided by SCM, enabling the analysis and processing of entities and their spatial relationships within a unified framework. The findings of this study provide new insights and analytical approaches for spatial point pattern recognition.