  The scan statistic is widely used in spatial cluster detection applications
of inhomogeneous Poisson processes. However, real data may present substantial
departure from the underlying Poisson process. One of the possible departures
has to do with zero excess. Some studies point out that when applied to data
with excess zeros, the spatial scan statistic may produce biased inferences. In
this work, we develop a closed-form scan statistic for cluster detection of
spatial zero-inflated count data. We apply our methodology to simulated and
real data. Our simulations revealed that the Scan-Poisson statistic steadily
deteriorates as the number of zeros increases, producing biased inferences. On
the other hand, our proposed Scan-ZIP and Scan-ZIP+EM statistics are, most of
the time, either superior or comparable to the Scan-Poisson statistic.
