Contextual neural gas for spatial clustering and analysis

This study aims to introduce and discuss contextual Neural Gas (CNG), a variant of the Neural Gas algorithm, which explicitly accounts for spatial dependencies within spatial data. The main idea of the CNG is to map spatially close observations to neurons, which are close with respect to their rank distance. Thus, spatial dependency is incorporated independently from the attributes of the data and the process of incorporation is less sensitive to local variations in the input data. To discuss and compare the performance of the CNG and GeoSOM, this study draws from a series experiments, which are based on two artificial and one real-world dataset. The experimental results of the artificial datasets show that the CNG produces more homogenous clusters, a better ratio of positional accuracy, and a lower quantization error than the GeoSOM. The results of the real-world dataset illustrate that the resulting patterns of the CNG are theoretically more sound and coherent than the GeoSOM’s, which emphasizes its applicability for geographic analysis tasks.