<em>Aromia bungii</em> has established persistent infestations across Saitama Prefecture, Japan, yet the influence of grid tessellation and spatial weight configuration on the detection of infestation clustering patterns remains poorly understood. This study evaluated whether rectangular (4,013 cells) and hexagonal (4,007 cells) tessellations, under four spatial weight configurations: Rook, Standard Queen, Distance-Weighted Queen, and Hexagonal KNN-6 yield consistent spatial autocorrelation results using 2,439 occurrence records spanning the years 2017–2024. Global Moran’s I (0.4525–0.5380), Geary’s C (0.5527–0.5970), and Getis-Ord G were all statistically significant (<em>p</em> = 0.001) across all configurations, confirming robust non-random clustering of <em>A. bungii</em> occurrence regardless of tessellation choice. Local autocorrelation analyses consistently identified two primary infestation hotspots: a cluster in the northern Kazo–Gyoda region and a localised cluster in the southeastern periphery of Saitama Prefecture across all configurations. Although hotspot locations were consistent across tessellation configurations, coldspot delineation was found to be unstable, with certain configurations producing inconsistent coldspot boundaries that may undermine the accurate identification of management priority zones. These findings provide a methodological basis for selecting appropriate tessellation structures in invasive species spatial analysis and offer spatially explicit evidence to support targeted surveillance and management planning for <em>A. bungii</em> in Saitama Prefecture.