Fast Spatial Scan makes the automatic detection of anomalous
spatial clusters very efficient and effective. Given a massive
set of spatial or space-time count data (e.g. the number of
reported disease cases in each zip code on each day), it
searches through the dataset to find spatial regions with higher
than expected counts. This process has two steps: first it
infers the expected count for each spatial location using time
series analysis, then it uses an expectation-based spatial scan
statistic approach to find spatial regions where the counts are
significantly higher than expected. Randomization testing is
performed to compute the statistical significance of each
discovered cluster, enabling us to distinguish true clusters
from those due to chance. Spatial scan statistics are a powerful
statistical test for detection of significant spatial clusters.
Unlike many other cluster detection methods, they can be used
both to determine whether any statistically significant clusters
exist and to precisely pinpoint