This paper aims at detecting spatio-temporal clustering in fire sequences
using space?time scan statistics, a powerful statistical framework
for the analysis of point processes. The methodology is applied to
active fire detection in the state of Florida (US) identified by
MODIS (Moderate Resolution Imaging Spectroradiometer) during the
period 2003?06. Results of the present study show that statistically
significant clusters can be detected and localized in specific areas
and periods of the year. Three out of the five most likely clusters
detected for the entire frame period are localized in the north of
the state, and they cover forest areas; the other two clusters cover
a large zone in the south, corresponding to agricultural land and
the prairies in the Everglades. In order to analyze if the wildfires
recur each year during the same period, the analyses have been performed
separately for the 4 years: it emerges that clusters of forest fires
are more frequent in hot seasons (spring and summer), while in the
southern areas, they are widely present during the whole year. The
recognition of overdensities of events and the ability to locate
them in space and in time can help in supporting fire management
and focussing on prevention measures.