Graphical modelling and partial characteristics for multitype and multivariate-marked spatio-temporal point processes

Matthias Eckardt, Jonatan A. González and Jorge Mateu

Computational Statistics & Data Analysis, 2021, vol. 156, issue C

Abstract: A method for dealing with multivariate analysis of marked spatio-temporal point processes is presented by introducing different partial point characteristics, and by extending the spatial dependence graph model formalism. The approach yields a unified framework for different types of spatio-temporal data, including both, purely qualitatively (multivariate) cases and multivariate cases with additional quantitative marks. The proposed graphical model is defined through partial spectral density characteristics; it is highly computationally efficient and reflects the conditional similarity amongst sets of spatio-temporal sub-processes of either points or marked points with identical discrete marks. Two applications, on crime and forestry data, are presented.

Keywords: Fourier transform; Quantitative marks; Spatial dependence graph model (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:156:y:2021:i:c:s0167947320302309

DOI: 10.1016/j.csda.2020.107139

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