arXiv subjects

Florent Bonneu

Publications and source records attributed to Florent Bonneu.

6 recordsLinked to original sources

arXiv2023

A spatio-temporal hybrid Strauss hardcore point process for forest fire occurrences

We propose a new point process model that combines, in the spatio-temporal setting, both multi-scaling by hybridization and hardcore distances. Our so-called hybrid Strauss hardcore point process model allows different types of interaction, at different spatial and/or temporal scales, that might be of interest in environmental and biological applications. The inference and simulation of the model are implemented using the logistic likelihood approach and the birth-death Metropolis-Hastings algorithm. Our model is illustrated and compared to others on two datasets of forest fire occurrences respectively in France and Spain.

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arXiv2020

A spatio-temporal multi-scale model for Geyer saturation point process: application to forest fire occurrences

Because most natural phenomena exhibit dependence at multiple scales like locations of earthquakes or forest fire occurrences, spatio-temporal single-scale point process models are unrealistic in many applications. This motivates us to construct generalizations of classical Gibbs models. In this paper, we extend the Geyer saturation point process model to the spatio-temporal multi-scale framework. The simulation process is carried out through a birth-death Metropolis-Hastings algorithm. In a simulation study, we compare two common methods for statistical inference in Gibbs models: the pseudo-likelihood and logistic likelihood approaches that we tailor to this model. Finally, we illustrate this new model on forest fire occurrences modeling in Southern France.

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arXiv2020

On spatial and spatio-temporal multi-structure point process models

Spatial and spatio-temporal single-structure point process models are widely used in epidemiology, biology, ecology, seismology... . However, most natural phenomena present multiple interaction structure or exhibit dependence at multiple scales in space and/or time, leading to define new spatial and spatio-temporal multi-structure point process models. In this paper, we investigate and review such multi-structure point process models mainly based on Gibbs and Cox processes.

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arXiv2020

Point-process based Bayesian modeling of space-time structures of forest fire occurrences in Mediterranean France

Due to climate change and human activity, wildfires are expected to become more frequent and extreme worldwide, causing economic and ecological disasters. The deployment of preventive measures and operational forecasts can be aided by stochastic modeling that helps to understand and quantify the mechanisms governing the occurrence intensity. We here develop a point process framework for wildfire ignition points observed in the French Mediterranean basin since 1995, and we fit a spatio-temporal log-Gaussian Cox process with monthly temporal resolution in a Bayesian framework using the integrated nested Laplace approximation (INLA). Human activity is the main direct cause of wildfires and is indirectly measured through a number of appropriately defined proxies related to land-use covariates (urbanization, road network) in our approach, and we further integrate covariates of climatic and environmental conditions to explain wildfire occurrences. We include spatial random effects with Matérn covariance and temporal autoregression at yearly resolution. Two major methodological challenges are tackled: first, handling and unifying multi-scale structures in data is achieved through computer-intensive preprocessing steps with GIS software and kriging techniques; second, INLA-based estimation with high-dimensional response vectors and latent models is facilitated through intra-year subsampling, taking into account the occurrence structure of wildfires.

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arXiv2016

Predicting the intensity of partially observed data from a revisited kriging for point processes

We consider a stationary and isotropic spatial point process whose a realisation is observed within a large window. We assume it to be driven by a stationary random field $U$. In order to predict the local intensity of the point process, $λ(x|U)$, we propose to define the first- and second-order characteristics of a random field, defined as the regularized counting process, from the ones of the point process and to interpolate the local intensity by using a kriging adapted to the regularized process.

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arXiv2015

Predicting the intensity of partially observed data from a revisited kriging for point processes

We consider a stationary and isotropic spatial point process, whose a realisation is observed within a large window. In order to predict its local intensity, we propose to define the first- and second-order characteristics of a random field, defined as the regularized counting process, from the ones of the point process and to interpolate the intensity by using a revisited kriging of the regularized process.

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