TY - JOUR
T1 - Graphical modelling and partial characteristics for multitype and multivariate-marked spatio-temporal point processes
AU - Eckardt, Matthias
AU - González, Jonatan A.
AU - Mateu, Jorge
N1 - 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.
PY - 2021/4
Y1 - 2021/4
N2 - 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.
AB - 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.
KW - Fourier transform
KW - Quantitative marks
KW - Spatial dependence graph model
UR - http://www.scopus.com/inward/record.url?scp=85097110890&partnerID=8YFLogxK
U2 - 10.1016/j.csda.2020.107139
DO - 10.1016/j.csda.2020.107139
M3 - Article
AN - SCOPUS:85097110890
SN - 0167-9473
VL - 156
JO - Computational Statistics and Data Analysis
JF - Computational Statistics and Data Analysis
M1 - 107139
ER -
