Space-time clustering analysis performance of an aggregated dataset: The case of wildfires in Portugal. (October 2015)

• Record Type: Journal Article
• Authors: Pereira, Mário G.; Caramelo, Liliana; Orozco, Carmen Vega; Costa, Ricardo; Tonini, Marj
• Is Part Of: Environmental modelling & software. Volume 72(2015:Oct.)
• Page Start: 239
• Page End: 249
• Publication Date: 2015-10
• Subjects: Forest fires -- Point pattern -- Space–time permutation scan statistics -- Cluster analysis; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2015.05.016
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: This study focuses on the use of space–time permutation scan statistics (STPSS) to assess both the existence and the statistical significance of clusters on aggregated datasets. The investigated case study is represented from the Portuguese Rural Fire Database (PRFD) where the fire occurrences are georeferenced to an administrative unit level. The main goals are: ( i ) assessing the robustness of the STPSS to correctly detect clusters on aggregated datasets; ( ii ) testing the existence of space–time clustering in the PRFD; and ( iii ) characterizing the detected clusters. A synthetic database was designed to assess the potential bias introduced by aggregation of the data on the performance of the STPSS method. Results confirmed the ability of the STPSS to correctly identify clusters, regarding their number, location, and spatio-temporal dimensions and provided recommendations about the parameters setting of the scanning window. Finally, a discussion of the identified clusters on the PRFD is presented. Graphical abstract: Highlights: We used space–time permutation scan statistics (STPSS). Assessment of STPSS over an aggregated synthetic dataset. STPSS is able to correctly detect significant space–time fire clusters in Portugal. Detection performance depends on the characteristics of the scanning window and database. Detected clusters were characterized by socioeconomic and environmental factors.