<![CDATA[Spatial data refers to data that contains information related to the geographical characteristics of a region. As spatial data evolves into large-scale datasets, efficient analytical methods are required for processing the data. One such method suitable for analyzing large-scale spatial data is spatial fuzzy clustering. This method allows for the adjustment of cluster weights based on data likelihood, making it more capable of capturing the actual local variations present in spatial data. In this study, two types of spatial fuzzy clustering methods were evaluated through simulation: the method with a spatial penalty, Spatial Fuzzy Clustered Regression (SFCR), and the method without a spatial penalty, Fuzzy Geographically Weighted Clustering Regression (FGWCR). SFCR is a method that combines spatial clustering and regression modeling simultaneously, resulting in more efficient computation time. FGWCR produces clusters by considering both spatial proximity and attribute similarity, making it effective for spatial data analysis. The data were designed to form six clusters during the simulation process. The simulation results showed that the SFCR method was more capable of accurately capturing data variation and cluster distribution. The R² values for SFCR at a fuzziness degree of 2 and under weak, moderate, and strong spatial autocorrelation were 99.7%, 99.6%, and 99.5%, respectively, while the R² values for FGWCR were 98.5%, 98.6%, and 98.1%. Model performance was evaluated using RMSE, where lower RMSE values indicate better performance. The RMSE values for the SFCR method at a fuzziness degree of 2 and under weak, moderate, and strong spatial autocorrelation were 0.30, 0.289, and 0.298, respectively, while the RMSE values for the FGWCR method were 0.659, 0.541, and 0.551.]]>
Copyrights © 2025
