Locally weighted minimum contrast estimation for spatio-temporal log-Gaussian Cox processes
A local version of spatio-temporal log-Gaussian Cox processes is proposed by using Local
Indicators of Spatio-Temporal Association (LISTA) functions plugged into the minimum …
Indicators of Spatio-Temporal Association (LISTA) functions plugged into the minimum …
Local inhomogeneous second-order characteristics for spatio-temporal point processes occurring on linear networks
Point processes on linear networks are increasingly being considered to analyse events
occurring on particular network-based structures. In this paper, we extend Local Indicators of …
occurring on particular network-based structures. In this paper, we extend Local Indicators of …
GPS data on tourists: A spatial analysis on road networks
This paper proposes a spatial point process model on a linear network to analyse cruise
passengers' stop activities. It identifies and models tourists' stop intensity at the destination …
passengers' stop activities. It identifies and models tourists' stop intensity at the destination …
Spatio-Temporal-Network Point Processes for Modeling Crime Events with Landmarks
Self-exciting point processes are widely used to model the contagious effects of crime
events living within continuous geographic space, using their occurrence time and locations …
events living within continuous geographic space, using their occurrence time and locations …
Dealing with location uncertainty for modeling network-constrained lattice data
Á Briz-Redón - Spatial Statistics, 2024 - Elsevier
The spatial analysis of traffic accidents has long been a useful tool for authorities to
implement effective preventive measures. Initial studies were conducted at the areal level …
implement effective preventive measures. Initial studies were conducted at the areal level …
Extended Laplace approximation for self-exciting spatio-temporal models of count data
NJ Clark, PM Dixon - Spatial Statistics, 2023 - Elsevier
Self-exciting models are statistical models of count data where the probability of an event
occurring is influenced by the history of the process. In particular, self-exciting spatio …
occurring is influenced by the history of the process. In particular, self-exciting spatio …
Convolutional Non-Homogeneous Poisson Process and its Application to Wildfire Ignition Risk Quantification for Power Delivery Networks
To quantify wildfire ignition risks on power delivery networks, the current practice
predominantly relies on the empirically calculated fire danger indices, which may not well …
predominantly relies on the empirically calculated fire danger indices, which may not well …
Semi-parametric Spatio-Temporal Hawkes Process for Modelling Road Accidents in Rome
We propose a semi-parametric spatio-temporal Hawkes process with periodic components
to model the occurrence of car accidents in a given spatio-temporal window. The overall …
to model the occurrence of car accidents in a given spatio-temporal window. The overall …
Flexible Parametric Inference for Space-Time Hawkes Processes
E Siviero, G Staerman, S Clémençon… - arXiv preprint arXiv …, 2024 - arxiv.org
Many modern spatio-temporal data sets, in sociology, epidemiology or seismology, for
example, exhibit self-exciting characteristics, triggering and clustering behaviors both at the …
example, exhibit self-exciting characteristics, triggering and clustering behaviors both at the …
stopp: An R Package for Spatio-Temporal Point Pattern Analysis
N D'Angelo, G Adelfio - arXiv preprint arXiv:2408.15052, 2024 - arxiv.org
stopp is a novel R package specifically designed for the analysis of spatio-temporal point
patterns which might have occurred in a subset of the Euclidean space or on some specific …
patterns which might have occurred in a subset of the Euclidean space or on some specific …