June, 20-24, 2011
PARTICIPANTS' TALKS |
Uncertainty
in PM10 Spatial Modelling
We will talk about a
collaborative project between the
RESEARCH BACKGROUND AND AIMS
Moose, Alces alces, is the most significant game animal in
In order to advance the collection of data for monitoring moose
populations, Finnish Game and Fisheries Research Institute launched a specific
moose observation card for hunting groups in early 1970s. Since then,
monitoring methods have been based on observation cards’ data, various
ground and aerial counts and different population indices. However, the methods
applied to estimate the totals have been rather ad hoc in nature, and there has
been a lot of dispute over the results between different groups of interest.
Therefore, a more scientifically based and transparent estimation procedure
would be highly welcomed.
Our main goal is to develop a statistical computation method or model,
which optimally uses information of several – direct or indirect –
sources of data of the status and changes in moose populations. It is also
possible to obtain other significant improvements, like yielding accuracy
estimates for population size point estimates and learning more not only about
the size but also the age and sex structure of local moose populations. In the
future, it is possible to develop the model to gain even more comprehensive
estimates related to forecasting forest damages and traffic accidents depending
on different decision making policies of annual and areal hunting allocation.
METHODS
Since the development of a moose population can be considered as a
dynamic process, an obvious solution for an assessment tool is a population
dynamics model. Population dynamics model is divided in smaller sub-models for,
e.g., breeding, sex assignment, hunting and survival processes. All the
sub-processes are modeled in a Bayesian framework for four sub-populations (states):
adult males, adult females, male calves and female calves.
The key data sources are linked to the Bayesian population model through
separate likelihood functions. The most influential likelihood function
connects moose observation cards’ daily numbers of adult moose
observations to the corresponding standing stock. These numbers are assumed to
be binomially distributed with unknown population size as parameter N and also unknown observation
probability as parameter p. Other likelihoods are set for hunters’
estimates of the population sizes, sex ratio estimates based on hunters’
observations, annual catch (or observations) per effort indices and harvest
structure indices, which are known to predict changes in the population size. The
dynamic model with all its side dishes is implemented in R environment, and is
run with WinBUGS program using R2WinBUGS software
package.
RESULTS AND CONCLUSIONS
For now, our population dynamics model works fine and produces plausible
results at least for the southern half of
The results from winter track counts and aerial counts are examples of
yet unexploited sources of data. Modeling these procedures would yield in new,
hopefully informative likelihoods. When it comes to the population model, the
movement of the moose could be taken into account in a more realistic manner,
and also adding a third age class of “yearlings” into the model
might lead us closer to the truth.
Plant Species
Variability in the Allt a’Mharcaidh
Catchment, Scotland
Identifying spatial
variability in plant species diversity is important for assessing changes in
species distribution and identifying the drivers of these changes. The survey technique used to measure
species abundance may not be appropriate to detect this variation or the
sampling effort may not be cost effective if the vegetation diversity does not
vary. Plant species in the Allt a’Mharcaidh catchment
in the Cairngorms National Park, Scotland are monitored as part of the
This paper investigates the
advantages of the Multinomial Mixed Logit Model
(MMLM) with respect to other discrete choice models applied to panel data econometric
studies. Implementation of MMLM allows a greater flexibility and a better
representation of individual heterogeneity w.r.t. Logit, Probit and Nested Logit models. This follows from a random utility
maximization assumption, subject to the identification of an unknown mixing
distribution for random parameters. However, considerable estimation complexity
arises from the use of MMLM under classical estimation.
Try to menage
this increment in computational complexity opens an interesting analysis over
the implementation of Bayesian methods. Bayesian methods present a good alternative
in dealing with computational complexity given by the MMLM implemented within
the classical approach. Several studies and empirical applications of Bayesian
algorithms for MMLM have been proposed. The first one, elaborated by Train, is
a parametric Bayesian approach. Assuming Normal distribution for random
parameters, this algorithm estimates unknown mean and variance-covariance
matrices from
Extensions of the algorithm proposed
by De Blasi, James, Lau (2010), allow for different distributional assumptions
but generate implementation troubles. In this respect, the
use of retrospective sampler (cfr. Roberts, Papaspilopoulos (2006)) or slice sampler (cfr.
Bayesian
Methodology for Estimation of Bio-geophysical Parameters from Remotely Sensed Data
This work focuses on the use
of Bayesian methodologies for inversion purposes: the estimation of
bio-geophysical parameters from remotely sensed data. The retrieval of
bio-geophysical parameters from remotely sensed data falls within the category
of inverse problems where, from a vector of measured values, m, one wishes to infer the set of ground
parameters, x, that gave rise to
them. The inverse problem is a typically ill-posed problem. It presents many
difficulties due to the non-linearity between remote sensing measurements and
ground parameters, and generally because more than one value of x could produce the same measured vector
m. Multi-sources information, such as
different polarization, frequencies and sensors are fundamental to the
development of operationally useful inversion systems in which the interference
among different parameters in the sensor response can be disentangled (Satalino et al. 1999). In this context, Bayesian
methodologies offer a convenient tool of combining two or more disparate
sources of information, models and data (Dubois et al., 1985). The work
describes the development of a general model starting from a theoretical model with
the inclusion of sensor noise and model errors through a Bayesian approach (Notarnicola et al., 2007). Some case studies will be
presented:
-
Different sensor combination for soil moisture
estimation from SAR images;
-
Different polarization combination for estimation of
plant water content from SAR images;
-
Comparison with other inversion approaches such as
Neural Networks.
References
1.
Dubois P. C., J. van Zyl,
and T. Engman, “Measuring soil moisture with
imaging radars,” IEEE Trans. Geosci. Remote
Sensing, vol. 33, July 1995.
2.
C. Notarnicola, M. Angiulli, F. Posa, “Use of Radar and Optical Remotely Sensed Data for Soil
Moisture Retrieval on Vegetated Areas”, IEEE Transactions on Geoscience and Remote
Sensing, vol.44, no.4, April 2006, p.925-935 .
3.
Satalino et al. (1999) The potential of multi-angle C-band SAR data for soil
moisture retrieval. In Proceedings of the
International Geoscience and Remote Sensing Symposium,
IGARSS 1999 (GE-18) (pp.288-295).
Hierarchical spatio-temporal models permit to
consider and estimate many sources of variability. In many environmental
problems, different features characterizing spatial locations can be found. For
example in a Region, monitoring sites can be classified as urban and rural.
Differences in these classifications can show differences either in mean levels
or in the spatio-temporal dependence structure. When
these differences are not included in the model structure, model performances
and the spatial predictions may lead to poor results.
The aim of this work consists in the comparison of several hierarchical spatio-temporal models, namely: a set of group-specific
models, a model that does not include groups, a model that includes differences
in the mean levels between groups and finally a model that includes different
spatial correlation structures between groups. Comparisons allow to detect and capture the actual differences between groups
if they exist.
The application presented concerns Ozone data in the Emilia-Romagna
Region in which 31 monitoring sites can be classified according to their
relative position with respect to traffic emissions.
Characterizing
Semiparametric
Bayesian Approaches to Mixed-Effects Models for Outcome Measures in the Treatment
of Acute Myocardial Infarction
Studies of variations in health care utilization and outcome involve the
analysis of multilevel data, considering in particular prediction of a specific
response, and estimate of covariates effect and components of variance. Those
studies quantify the role of contributing factors including patients and
providers characteristics and may assess the relationship between health-care
process and outcomes.
We consider Bayesian generalized linear mixed models to analyze data on patients
admitted with ST-elevation myocardial infarction (STEMI) diagnosis in Regione Lombardia hospitals.
Clinical registries and administrative databanks were used to predict both
in-hospital survival and ST resolution probability. We fit logit
models for the in-hospital survival and ST-resolution probability with grouping
effect (the hospital), under a semiparametric prior. In
particular, random effects with dependent Dirichlet process prior are assumed,
allowing to include specific hospital-covariates and
then enriching the dependence structure among the related random measures.
A Bayesian Modelling
and Simulation Concept for Knowledge Update in Adaptive Management of
A fully adaptive decision-making takes into account that more
information about climate and forest state is forthcoming and may change the
optimal decision. Therefore, we develop a modelling concept that applies
Bayesian updating of beliefs about future climate change, but otherwise build
on a set of forest management outputs under different climate change scenarios.
We apply the concept in a hypothetical
decision-making problem considering four species to be selected
for forest plantation in European temperate zone. We thus consider three models of slow, moderate and
high change in climate state such that the realized climate state at any time
is defined to be the predicted true climate model plus a shock normally
distributed with mean zero and a model specific variance, . The results may illustrate the value of knowledge update
and the need to switch the decisions for an optimal adaptive forest management.
Moreover, we show that, using Bayes’ theorem, revealing of the true
climate is just a matter of time and the more divergence are the climate
models, the faster is recognition of the true underlying one. However, the
outcome of the entire concept is highly sensitive to the initial beliefs on
different climate models where these beliefs may change after new observation
of climate states and via Bayesian belief updating. The economic value of such
an adaptive and updating approach would be positive and higher if a reasonable
change in climate state occurs asking consequently for a change in optimal
decision.
Models and Data for the Analysis of Traffic Flows
The idea behind this presentation is to analyze vehicular traffic flow in
the area of Milano through techniques that concern, directly or indirectly, the traffic analysis. First of all we
focus on a traffic model generated by the idea that diffusion of vehicles in a urban network is like the flow of a fluid through a
porous medium (Della Rossa, D’Angelo
and Quarteroni, 2010) where traffic flow spans
two-dimensional regions whose size (macroscale) is greater
than the characteristic size of the network arcs (microscale).
Starting from a stochastic lattice gas model with simple constitutive laws, a
distributed two-dimensional model of traffic flow is derived, in the form of a
non-linear diffusion-advection equation for the particle density. The equation
is formally equivalent to a non-linear Darcy’s filtration law. In
particular, it contains two parameters describing the morphology of urban area
that can be seen as the porosity and the permeability tensor of the network,
while a different parameter identifies the principal direction of traffic flow.
Once the model parameters have been identified, then
the model permit to determine the density of traffic in the considered network.
The innovative scenario we want to develop goes in the opposite direction.
From a given traffic density in a real situation we want to estimate the model parameters
that reproduce this density. In this way we could be able, for example, to
investigate how the analysis of real traffic would change altering these
parameters. We do not have direct
access to vehicular traffic data, but we do have information on telephone
traffic; indeed (Secchi, Vantini
and Vitelli, 2010) the Telecom Italia dataset was
analyzed. This dataset describes the intensity of telephone traffic (as a
function of time) in a large number of spatially distributed points in the area
of
We aim to use the info provided by this analysis of telephone data to
validate the model of traffic flow in complex network described above and to
identify these parameters. At that point we will in the position to simulate
different real scenarios to investigate how traffic flow in the area of
Integrated Environmental Risks Assessment in
Croatian Coastal River Basins