Biological invasions represent one of the most significant threats to global biodiversity and agricultural systems, causing substantial ecological and economic damage worldwide. Among emerging invasive pests in East Asia, Aromia bungii, commonly known as the red-necked longhorn beetle, has become a serious threat in Japan. The species attacks several Prunus species, including ornamental cherry trees (Cerasus spp.), peach (Prunus persica), and plum (Prunus salicina). Because cherry trees play an important ecological and cultural role in Japan, the spread of this invasive beetle has raised growing concerns for landscape management and biodiversity conservation.
Since its first detection in Aichi Prefecture in 2012, A. bungii has expanded rapidly across urban and peri-urban areas. Understanding its spatial pattern is therefore essential for effective monitoring and early intervention. Spatial analysis can be applied to these processes. However, such analyses fundamentally depend on how spatial data are defined, including the geometry of the spatial grid, which can influence the results.

Thus, to examine how grid shape influences spatial analysis results, this study evaluates spatial autocorrelation measures using different tessellations of invasive species occurrence data. Specifically, we compared rectangular and hexagonal grids for analysing spatial patterns in A. bungii occurrence records in Saitama Prefecture, Japan.
Volunteer-based occurrence data for A. bungii were compiled from field surveys across Saitama Prefecture from 2017 to 2024, yielding 2, 439 confirmed presence records. Records were classified as confirmed presences if either adult beetle observations (Adult-yes = 1) or evidence of tree damage (Tree_damage = 1) was recorded. All records were georeferenced using latitude–longitude coordinates (WGS84) and then reprojected to UTM Zone 54N (EPSG:32654) to ensure metric accuracy in spatial calculations. We assembled the occurrence data into predefined grid cells to explore spatial patterns across the study area and to enable consistent spatial aggregation and neighbourhood-based analyses.
We calculated the occurrence of Aromia bungii incidence in Saitama from citizen science and have been verified by Center of Environmental Science in Saitama.

Two grid tessellation schemes were constructed over the study area. The rectangular grid consisted of 6,372 cells at a 1 km × 1 km resolution. To evaluate the effect of diagonal bias, the same rectangular grid was also analysed using queen contiguity, which includes eight neighbouring cells by incorporating both direct and diagonal neighbours. The hexagonal grid was generated using Shapely and GeoPandas
producing hexagonal cells with an equivalent spatial resolution to the rectangular grid. To keep the overall grid coverage comparable to the rectangular representation, 6,392 hexagonal cells were generated to cover the study area. The rectangular grid had a side length of 1.0 km, whereas the hexagonal grid had a slightly larger side length of 1.074 km. Each hexagonal cell has a circumradius (equal to side length) of 620~m.

The objective of this study is to examine the influence of tessellation choice on spatial autocorrelation results.We evaluated the results using global and local spatial autocorrelation measures, as they provide comprehensive overview clustering tendencies.
We investigated how the choice of tessellation geometries (rectangular vs hexagonal) influenced spatial autocorrelation analysis of \textit{A.bungi} occurrence in Saitama Prefecture, Japan. We compared rectangular rook contiguity, rectangular queen contiguity, distance-weighted queen contiguity, and hexagonal tessellation using Global and Local Indicators of Spatial Association (LISA) to investigate how neighbourhood structure and grid geometry affect the measurement of spatial clustering. Two grid tessellations, rectangular (4,013 cells,
1.0~km$^{2}$ each) and hexagonal (4,007 cells, 0.9987~km$^{2}$ each), were used and evaluated under four spatial weight configurations: Rook, Standard Queen, Distance-Weighted Queen, and Hexagonal KNN-6. To further investigate the influence of weight configuration, we designed a distance-weighted queen scheme by assigning diagonal neighbors a weight of 1/√2. Moran’s I was 0.4732 (p = 0.001). Comparing Moran’s I across configurations in descending order (rook, queen, distance-weighted queen, and hexagonal), we found that the degree of spatial autocorrelation can be influenced by the choice of spatial grid, although all values were positive and statistically significant.

Global spatial autocorrelation analysis confirmed significant and positive clustering of \textit{A. bungii} occurrences across all configurations Moran's I = 0.4525--0.5380, Geary's C = 0.5527--0.5970, Getis-Ord G with p = 0.001, demonstrating that the spatial agglomeration characteristic is robust to the choice of tessellation geometry and neighbourhood definition. Local spatial autocorrelation analyses consistently identified two primary hotspot zones at the northern (Kazo--Gyoda) and the southeastern (Soka) parts of Saitama Prefecture across all configurations, providing spatially explicit evidence of persistent infestation core that may serve as priority zones for targeted surveillance and countermeasure deployment.

In conclusion, we found that 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 A. bungii in Saitama Prefecture.