A generic regional spatiotemporal cooccurrence p

Authors: Mohammad Akbari Farhad Samadzadegan Robert Weibel

Publish Date: 2015/06/25

Volume: 17, Issue: 3, Pages: 249-274

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Abstract

Spatiotemporal cooccurrence patterns represent subsets of object types which are located together in both space and time Existing algorithms for cooccurrence pattern mining cannot handle complex applications such as air pollution in several ways First the existing models assume that spatial relationships between objects are explicitly represented in the input data while the new method allows extracting implicitly contained spatial relationships algorithmically Second instead of extracting cooccurrence patterns of only point data the proposed method deals with different feature types that is with point line and polygon data Thus it becomes relevant for a wider range of real applications Third it also allows mining a spatiotemporal cooccurrence pattern simultaneously in space and time so that it illustrates the evolution of patterns over space and time Furthermore the proposed algorithm uses a Voronoi tessellation to improve efficiency To evaluate the proposed method it was applied on a real case study for air pollution where the objective is to find correspondences of air pollution with other parameters which affect this phenomenon The results of evaluation confirm not only the capability of this method for cooccurrence pattern mining of complex applications but also it exhibits an efficient computational performance

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