*5.5. Multi-Distance Spatial Cluster Analysis and Space–Time Cluster Analysis*

A multi-distance spatial cluster analysis tool in ArcGIS v10.6.1 was used to identify the maximum distance of the relationships between CSF notifications by applying the common transformation of Ripley's *K* function. Detailed information on the method for calculating the maximum distance of relationships, which yielded the highest Diff *K* value, was described in a previous study [5]. A space–time permutation technique was applied to examine the presence of space–time clusters in the area affected by CSF. The upper limit on the geographical size of the cluster was set to 26 km, the minimum time aggregation to seven days, and the maximum temporal cluster size to 50% of the total study period (default setting) [28]. A Monte Carlo process was implemented using 999 replications to test for the presence of candidate clusters (*p* < 0.05). Analyses were conducted in SaTScan software v9.6 (Kulldorff, Boston, MA, USA) [29]. The habitat of each cluster was visually assessed with the guide of Global Map Specifications to assess the pattern of land cover in the cluster identified [8].

**Author Contributions:** Conceptualization, N.I., M.I., Y.S., and K.M.; Methodology, N.I., S.I., and K.M.; Validation, Y.S., and K.M.; Formal Analysis, K.B. and S.I.; Data Curation, N.I., K.B., S.I., M.I., and K.M.; Writing—Original Draft Preparation, N.I. and K.B.; Writing—Review and Editing, M.I., Y.S., and K.M.; Supervision, K.M. All authors have read and agree to the published version of the manuscript.

**Funding:** This research received no external funding.

**Conflicts of Interest:** The authors declare no conflict of interest.
