RT Journal Article
T1 Stochastic smoothing of point processes for wildlife-vehicle collisions on road networks
A1 Borrajo García, María Isabel
A1 Comas Rodríguez, Carles
A1 Costafreda-Aumedes, Sergi
A1 Mateu, Jorge
K1 Bandwidth selection
K1 Covariates
K1 First-order intensity
K1 Kernel estimation
K1 Linear network
K1 Spatial point pattern
K1 Wildlife-vehicle accidents
AB Wildlife-vehicle collisions on road networks represent a natural problem between human populations and the environment, that affects wildlife management and raise a risk to the life and safety of car drivers. We propose a statistically principled method for kernel smoothing of point pattern data on a linear network when the first-order intensity depends on covariates. In particular, we present a consistent kernel estimator for the first-order intensity function that uses a convenient relationship between the intensity and the density of events location over the network, which also exploits the theoretical relationship between the original point process on the network and its transformed process through the covariate. We derive the asymptotic bias and variance of the estimator, and adapt some data-driven bandwidth selectors to estimate the optimal bandwidth. The performance of the estimator is analysed through a simulation study under inhomogeneous scenarios. We present a real data analysis on wildlife-vehicle collisions in a region of North-East of Spain
PB Springer
YR 2021
FD 2021
LK http://hdl.handle.net/10347/28996
UL http://hdl.handle.net/10347/28996
LA eng
NO Stochastic Environmental Research and Risk Assessment 36, 1563–1577 (2022).  https://doi.org/10.1007/s00477-021-02072-3
NO Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature
DS Minerva
RD 23 abr 2026