17 research outputs found
Asymptotic models and inference for extremes of spatio-temporal data
Recently there has been a lot of effort to model extremes of spatially
dependent data. These efforts seem to be divided into two distinct groups: the study of
max-stable processes, together with the development of statistical models within this
framework; the use of more pragmatic, flexible models using Bayesian hierarchical
models (BHM) and simulation based inference techniques. Each modeling strategy
has its strong and weak points. While max-stable models capture the local behavior
of spatial extremes correctly, hierarchical models based on the conditional independence
assumption, lack the asymptotic arguments the max-stable models enjoy. On
the other hand, they are very flexible in allowing the introduction of physical plausibility
into the model. When the objective of the data analysis is to estimate return
levels or kriging of extreme values in space, capturing the correct dependence structure
between the extremes is crucial and max-stable processes are better suited for
these purposes. However when the primary interest is to explain the sources of
variation in extreme events Bayesian hierarchical modeling is a very flexible tool
due to the ease with which random effects are incorporated in the model. In this
paper we model a data set on Portuguese wildfires to show the flexibility of BHM in
incorporating spatial dependencies acting at different resolutions
Religious affiliation modulates weekly cycles of cropland burning in Sub-Saharan Africa
Research ArticleVegetation burning is a common land management practice in Africa, where fire is used
for hunting, livestock husbandry, pest control, food gathering, cropland fertilization, and
wildfire prevention. Given such strong anthropogenic control of fire, we tested the hypotheses
that fire activity displays weekly cycles, and that the week day with the fewest fires
depends on regionally predominant religious affiliation.We also analyzed the effect of land
use (anthrome) on weekly fire cycle significance. Fire density (fire counts.km-2) observed
per week day in each region was modeled using a negative binomial regression model, with
fire counts as response variable, region area as offset and a structured random effect to
account for spatial dependence. Anthrome (settled, cropland, natural, rangeland), religion
(Christian, Muslim, mixed) week day, and their 2-way and 3-way interactions were used as
independent variables. Models were also built separately for each anthrome, relating
regional fire density with week day and religious affiliation. Analysis revealed a significant
interaction between religion and week day, i.e. regions with different religious affiliation
(Christian, Muslim) display distinct weekly cycles of burning. However, the religion vs. week
day interaction only is significant for croplands, i.e. fire activity in African croplands is significantly
lower on Sunday in Christian regions and on Friday in Muslim regions. Magnitude of
fire activity does not differ significantly among week days in rangelands and in natural
areas, where fire use is under less strict control than in croplands. These findings can contribute
towards improved specification of ignition patterns in regional/global vegetation fire
models, and may lead to more accurate meteorological and chemical weather forecastinginfo:eu-repo/semantics/publishedVersio
Non-linear time series models
The last three decades have seen quite dramatic changes the way we modeled time dependent data. Linear processes have been in the center stage in modeling time series. As far as the second order properties are concerned, the theory and the methodology are very adequate.However, there are more and more evidences that linear models are not sufficiently flexible and rich enough for modeling purposes and that failure to account for non-linearities can be very misleading and have undesired consequences
Statistical aspects of health and the environment : proceedings of the SPRUCE conference, Enschede ITC, Netherlands, 8 - 12 September 1997
Spatial point processes applied to the study of forest fires in Portugal
The aim of this work is to analyse the behaviour of forest fi res in Portugal using statistical techniques applied to spatial point processes. We present a short overview on the most commonly used summary statistics for spatial point processes under homogeneity and inhomogeneity assumptions. The data set consists of records of 6295 forest fires larger than 100 hectares, observed in Portugal during the years 1975 through 2005
Quantification of annual wildfire risk; A spatio-temporal point process approach.
Policy responses for local and global firemanagement depend heavily on the proper understanding of the fire extent as well as its spatio-temporal variation across any given study area. Annual fire risk maps are important tools for such policy responses, supporting strategic decisions such as location-allocation of equipment and human resources. Here, we define risk of fire in the narrow sense as the probability of its occurrence without addressing the loss component. In this paper, we study the spatio-temporal point patterns of wildfires and model them by a log Gaussian Cox processes. Themean of predictive distribution of randomintensity function is used in the narrow sense, as the annual fire risk map for next year