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.
| Original language | English (US) |
|---|---|
| Article number | 107139 |
| Journal | Computational Statistics and Data Analysis |
| Volume | 156 |
| DOIs | |
| State | Published - Apr 2021 |
Bibliographical note
Funding Information:This research has been partially funded by grants UJI-B2018-04 and MTM2016-78917-R from UJI and the Spanish Ministry of Education and Science, Spain .
Publisher Copyright:
© 2020 Elsevier B.V.
Keywords
- Fourier transform
- Quantitative marks
- Spatial dependence graph model
ASJC Scopus subject areas
- Statistics and Probability
- Computational Mathematics
- Computational Theory and Mathematics
- Applied Mathematics
