Kernel Density Estimation: Examining the Effect of Kernel Function, Bandwidth, and Grid Cell Size in Mapping Earthquake-Prone in Indonesia

Identifying earthquake-prone areas is critical for disaster mitigation to reduce casualties and economic losses. This study applies Kernel Density Estimation (KDE) to analyze and rank earthquake-prone regions, focusing on variations in kernel functions, bandwidths, and grid sizes. The Indonesian Earthquake Catalog (1964–2023) is used as a case study. The results indicate that different kernel functions have unique strengths. The Epanechnikov kernel provides an even density distribution, particularly in low and medium categories, while the Gaussian kernel captures high concentrations effectively, especially in high and extreme categories. The Biweight kernel performs well in medium and high categories but less effectively identifies extreme density concentrations. Grid size also significantly impacts results; smaller grids (0.25 x 0.25) reveal detailed density patterns but may overemphasize localized concentrations, whereas larger grids (5 x 5) are suited for macro-scale analyses but can obscure finer variations. Bandwidth selection significantly affects density estimates. Smaller bandwidths (0.1) spread density widely, resulting in many grids in the low category but few in the medium, high, and extreme categories. Medium bandwidths (0.3) increase the proportion of medium and high categories, while larger bandwidths (0.5) produce the highest proportion of grids in the medium category, though extreme values remain limited. These variations demonstrate how bandwidth choices influence the balance between localized detail and broader distribution patterns. KDE effectively identifies earthquake-prone areas with varying densities and cluster significance, providing essential insights to prioritize disaster mitigation efforts and improve spatial planning in earthquake-prone regions. The combination of Gaussian kernel, 0.5 bandwidth, and 1x1 degree grid yields the greatest results in completely mapping earthquake risk.