Comparing the Performance of Spatial and Non-Spatial Self-Organizing Neural Networks in Clustering Socio-Economic Data of Isfahan Census Blocks

The increasing volume and dimensions of spatial data have made self-organizing neural networks a prominent tool for analyzing large and multi-dimensional datasets. Clustering, an approach for extracting knowledge from big data, aims to group similar data into clusters. This research focused on clustering socio-economic data of census blocks associated with urban sustainable development using self-organizing neural networks with and without spatial parameters referred to as SOM and Geo-SOM, respectively. Both algorithms employ the same clustering process but differ in the inclusion of spatial parameters, specifically the geographic coordinates of block centroids, in the Geo-SOM algorithm. The SOM and Geo-SOM algorithms were trained and applied to cluster the data. The resulting clusters exhibited distinct dissimilarities, demonstrating that clustering census block data solely based on non-spatial attributes leads to heterogeneous and incongruent clusters, whereas incorporating spatial parameters yields homogeneous and congruent clusters. Evaluation of the results using Silhouette coefficient indicated that Geo-SOM outperformed SOM in clustering the data with average Silhouette coefficients of -0.02 and 0.27 for SOM and Geo-SOM, respectively. Comparison of the outcomes highlighted the positive impact of incorporating spatial parameters on clustering socio-economic data.