Forest fire sequences can be modelled as a stochastic point process
where events are characterized by their spatial locations and occurrence
in time. Cluster analysis permits the detection of the space/time
pattern distribution of forest fires. These analyses are useful to
assist fire-managers in identifying risk areas, implementing preventive
measures and conducting strategies for an efficient distribution
of the firefighting resources. This paper aims to identify hot spots
in forest fire sequences by means of the space-time scan statistics
permutation model (STSSP) and a geographical information system (GIS)
for data and results visualization. The scan statistical methodology
uses a scanning window, which moves across space and time, detecting
local excesses of events in specific areas over a certain period
of time. Finally, the statistical significance of each cluster is
evaluated through Monte Carlo hypothesis testing. The case study
is the forest fires registered by the Forest Service in Canton Ticino
(Switzerland) from 1969 to 2008. This dataset consists of geo-referenced
single events including the location of the ignition points and additional
information. The data were aggregated into three sub-periods (considering
important preventive legal dispositions) and two main ignition-causes
(lightning and anthropogenic causes). Results revealed that forest
fire events in Ticino are mainly clustered in the southern region
where most of the population is settled. Our analysis uncovered local
hot spots arising from extemporaneous arson activities. Results regarding
the naturally-caused fires (lightning fires) disclosed two clusters
detected in the northern mountainous area.