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A circular spatial scan window and a maximum spatial cluster size of 50% of the cases were used so that both small and large clusters could be detected.
A maximum spatial cluster size of 10% of the population 15 years and younger was used.
Using a maximum spatial cluster size of ≤10% of total population, the clusters for all cases and indigenous cases were identical to the analysis using a maximum spatial cluster size of ≤50% of total population.
The scan was set at a maximum spatial cluster size of 25% of the population under study.
This cluster analysis was performed by using the default maximum spatial cluster size of <50% of the total population.
The maximum spatial cluster size was chosen to correspond to 7% of the German population (5,777,219).
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The global Getis-Ord G method showed significant (p ≤ 0.05) maximum spatial clustering of FSAs with high SIRs at 3.3 km.
For the local cluster analyses, we used the distance band identified at the global clustering step that showed maximum spatial clustering of FSAs with high non-smoothed SIRs (see Global clustering (Getis-Ord General G) subsection).
The highest statistically significant positive Z-score was observed at 3.3 km (Z = 2.34, p = 0.019), signifying maximum spatial clustering of FSAs with high SIRs at this distance band (Fig. 5).
Although Toronto is a large city (area of approximately 630 km), maximum spatial clustering of FSAs with high SIRs was detected at 3.3 km, which suggests that clustering of S. Enteritidis infections was localized to relatively small areas within the city.
Since preliminary analyses identified very large space-time clusters containing a number of statistically significant sub-clusters, the maximum spatial window for the analysis was set as a circle with a 100 km radius.
More suggestions(15)
maximum spatial vegetation
maximum spatial frequency
maximum spatial correlation
maximum spatial resolution
maximum spatial precision
maximum overall cluster
maximum spatial extent
maximum spatial overlap
maximum spatial diversity
maximum spatial scanning
maximum spatial variability
maximum spatial reuse
maximum probable cluster
maximum spatial separation
maximum spatial error
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