This paper proposes a spatiotemporal clustering algorithm and its implementation in the R package spotoroo. This work is motivated by the catastrophic bushfires in Australia throughout the summer of 2019-2020 and made possible by the availability of satellite hotspot data. The algorithm is inspired by two existing spatiotemporal clustering algorithms but makes enhancements to cluster points spatially in conjunction with their movement across consecutive time periods. It also allows for the adjustment of key parameters, if required, for different locations and satellite data sources. Bushfire data from Victoria, Australia, is used to illustrate the algorithm and its use within the package. [ABSTRACT FROM AUTHOR]
Abstract: This paper provides a novel Exceptional Object Analysis for Finding Rare Environmental Events (EOAFREE). The major contribution of our EOAFREE method is that it proposes a general Improved Exceptional Object Analysis based on Noises (IEOAN) algorithm to efficiently detect and rank exceptional objects. Our IEOAN algorithm is more general than already known outlier detection algorithms to find exceptional objects that may be not on the border; and experimental study shows that our IEOAN algorithm is far more efficient than directly recursively using already known clustering algorithms that may not force every data instance to belong to a cluster to detect rare events. Another contribution is that it provides an approach to preprocess heterogeneous real world data through exploring domain knowledge, based on which it defines changes instead of the water data value itself as the input of the IEOAN algorithm to remove the geographical differences between any two sites and the temporal differences between any two years. The effectiveness of our EOAFREE method is demonstrated by a real world application – that is, to detect water pollution events from the water quality datasets of 93 sites distributed in 10 river basins in Victoria, Australia between 1975 and 2010. [Copyright &y& Elsevier]