Cluster recognition in spatial-temporal sequences: the case of forest firesReport as inadecuate

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, Volume 16, Issue 4, pp 653–673

First Online: 01 July 2012Received: 30 September 2011Revised: 23 May 2012Accepted: 14 June 2012


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.

KeywordsForest fires Cluster analysis Point patterns Space-time scan statistics Permutation model  Download fulltext PDF

Author: Carmen Vega Orozco - Marj Tonini - Marco Conedera - Mikhail Kanveski


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