ENVIRONMETRIKA
Dihimpun dan diabstraksikan: Smno.psl-ppsub.agst2012
The International Environmetrics Society
(TIES)
The International Environmetrics Society (TIES) is a nonprofit organization aimed to foster the development and use of
statistical and other quantitative methods in the environmental
sciences, environmental engineering and environmental
monitoring and protection.
To this end, the Society promotes the participation of
statisticians, mathematicians, scientists and engineers in the
solution of environmental problems and emphasizes the need for
collaboration and for clear communication between individuals
from different disciplines and between researchers and
practitioners.
The Society further promotes these objectives by conducting
meetings and producing publications, and by encouraging a
broad membership of statisticians, mathematicians, engineers,
scientists and others interested in furthering the role of statistical
and mathematical techniques in service to the environment.
Diunduh dari: http://www.environmetrics.org/ …… 1/9/2012
Environmetrics
Environmetrics, the official journal of The International Environmetrics Society
(TIES), an Association of the International Statistical Institute, is devoted to the
dissemination of high-quality quantitative research in the environmental sciences,
broadly construed. The journal welcomes pertinent and innovative submissions from
applied mathematics, engineering and signal processing, statistics, risk analysis, and
other quantitative disciplines. Articles must answer important scientific questions in
the environmental sciences or develop novel methodology with clear applications to
environmental science. New methodology should be illustrated with recent
environmental data.
Editors-in-Chief: Peter Guttorp and Walter W. Piegorsch
Impact Factor: 1.06
ISI Journal Citation Reports © Ranking: 2011: 41/92 (Mathematics
Interdisciplinary Applications); 45/116 (Statistics & Probability); 139/205
(Environmental Sciences)
Online ISSN: 1099-095X
Environmetrics
The official journal of The International Environmetrics Society (TIES).
Aims and Scope
It is a multidisciplinary journal that publishes refereed papers on the
development and application of quantitative methods in the
environmental sciences. The scope covers a broad range of statistical,
mathematical and engineering topics dealing with the analysis of
environmental changes and their impacts on humans and various life
forms and ecological relationships. Therefore, the journal welcomes a
wide diversity of applications in such areas as water and air quality,
regulation and control, risk and impact analysis, waste management,
transboundary pollution, health aspects of pollution, monitoring, field and
laboratory quality control and climatic changes. In addition to publishing
significant research and review papers, Environmetrics publishes book
reviews, software reviews, descriptions of data sources and notices of
general interest.
Diunduh dari: http://www.nrcse.washington.edu/ties/journal/journal.html…… 1/9/2012
. Modelling
species responses and other data
Analysing species response curves or modeling other data often involves the fitting
of standard statistical models to ecological data and includes simple (multiple)
regression, Generalised Linear Models (GLM), extended regression (e.g. Generalised
Least Squares [GLS]), Generalised Additive Models (GAM), and mixed effects
models, amongst others.
Tree-based models
Tree-based models are being increasingly used in ecology,
particularly for their ability to fit flexible models to complex data
sets and the simple, intuitive output of the tree structure.
Ensemble methods such as bagging, boosting and random forests
are advocated for improving predictions from tree-based models
and to provide information on uncertainty in regression models or
classifiers.
Tree-structured models for regression, classification and survival
analysis
Ordination methods, many of which are specialised techniques particularly suited
to the analysis of species data:
1. Principal Components (PCA) is used in the climate and climate change fields
is Empirical Orthogonal Function (EOF) analysis.
2. Redundancy Analysis (RDA)
3. Canonical Correspondence Analysis (CCA)
4. Detrended Correspondence Analysis (DCA)
5. Principal coordinates analysis (PCO)
6. Non-Metric multi-Dimensional Scaling (NMDS)
7. Coinertia analysis
8. Co-correspondence analysis to relate two ecological species data matrices
9. Canonical Correlation Analysis (CCoA - not to be confused with CCA, above)
10. Procrustes rotation providing functions to test the significance of the
association between ordination configurations (as assessed by Procrustes
rotation) using permutation/randomisation and Monte Carlo methods.
11. Constrained Analysis of Principal Coordinates (CAP)
12. Constrained Quadratic Ordination (CQO; formerly known as Canonical
Gaussian Ordination (CGO)
Diunduh dari: http://cran.r-project.org/web/views/Environmetrics.html…… 3/9/2012
. Cluster analysis
Cluster analysis aims to identify groups of samples within multivariate data sets. A
large range of approaches to this problem have been suggested, but the main
techniques are hierarchical cluster analysis, partitioning methods, such as k -means,
and finite mixture models or model-based clustering. In the machine learning
literature, cluster analysis is an unsupervised learning problem.
1. Hierarchical cluster analysis
2. Partitioning methods
3. Mixture models and model-based cluster analysis
Ecological theory
There is a growing number of packages and books that focus on the use of
R for theoretical ecological models.
Population dynamics
Estimating animal abundance and related parameters
This ection concerns estimation of population parameters (population size,
density, survival probability, site occupancy etc.) by methods that allow for
incomplete detection. Many of these methods use data on marked animals,
variously called 'capture-recapture', 'mark-recapture' or 'capture-markrecapture' data.
Modelling population growth rates:
It can be used to construct and analyse age- or stagespecific matrix population models.
It contains functions for simulating future forest conditions
under different silvicultural regimes using the growth model.
Diunduh dari: http://cran.r-project.org/web/views/Environmetrics.html…… 3/9/2012
. ENVIRONMETRICS & MODELLING
One of the ongoing endeavours of the School of Environmental Sciences
is the development of improved measures or metrics for quantifying
environmental patterns and processes.
Armed with an increasingly sophisticated set of metrics, environmental
scientists will be in a better position to discovery new patterns or trends
and their underlying causes.
Modelling is useful for the discovery of knowledge, but is also a
predictive tool for forecasting adaptation, mitigation and/or production
strategies in response to changing environments.
Environmetrics and modelling is a transdisciplinary research area and
several faculty are members of the Biophysics Interdisciplinary Group
(BIG).
Diunduh dari: http://www.uoguelph.ca/ses/content/environmetrics-modelling …… 4/9/2012
. Ill-conditioned information matrices, generalized linear models and
estimation of the effects of acid rain
Eric P. Smith1, Brian D. Marx
Environmetrics. Volume 1, Issue 1, pages 57–71, 1990
. The problem of acid rain deposition has generated much interest in the
modelling and estimation of the effects of acid rain. Recent studies in the
northeastern United States have focused on the question of trends in lake
acidity and the effects on aquatic organisms, especially fish.
One approach has been to model the presence or absence of fish species
as a function of relevant environmental variables.
As the number of these explanatory variables may be large, there is
concern about redundancies and collinearities. Because the model used is
a special case of generalized linear models, standard approaches to
assessment and adjustment for collinearity may be misleading.
Estimation of parameters in the generalized linear model involve an
interative method of solution. The important parameter is the information
matrix. Illconditioning of this matrix, as caused by collinearity has severe
effects on parameter and variance estimates.
To asssess the effects of collinearities, some new diagnostics are
presented. Two techniques for estimating parameters in the presence of
multicollinearity; the ridge estimator and the principal component method,
are extended to the generalized linear model.
Diunduh dari: http://onlinelibrary.wiley.com/doi/10.1002/env.3170010107/abstract …… 3/9/2012
. Model selection for environmental data
Jun Bai1, Anthony J. Jakeman2, Michael McAleer
Environmetrics. Volume 1, Issue 3, pages 211–254, 1990
. A practical approach is proposed for model selection and discrimination
among nested and non-nested probability distributions. Some existing
problems with traditional model selection approaches are addressed,
including standard testing of a null hypothesis against a more general
alternative and the use of some well-known discrimination criteria for nonnested distributions.
A generalized information criterion (GIC) is used to choose from two or
more model structures or probability distributions. For each set of random
samples, all model structures that do not perform significantly worse than
other candidates are selected.
The two-and three-parameter gamma, Weibull and lognormal distributions
are used to compare the discrimination procedures with traditional
approaches. Monte Carlo experiments are employed to examine the
performances of the criteria and tests over large sets of finite samples.
For each distribution, the Monte Carlo procedure is undertaken for various
representative sets of parameter values which are encountered in fitting
environmental quality data.
Diunduh dari: http://onlinelibrary.wiley.com/doi/10.1002/env.3170010301/abstract…… 3/9/2012
Asymptotic confidence intervals from a preliminary test estimator
S. E. Ahmed1, R. J. Kulperger
Environmetrics
Volume 1, Issue 3, pages 295–303, 1990
A preliminary test estimator is a method of combining information
from two experiments in a problem of estimating a parameter.
The asymptotics of this estimator in the case of estimating a
population mean is considered in a setting of a local alternative.
Asymptotic confidence intervals are obtained using the
asymptotic distribution.
Diunduh dari: http://onlinelibrary.wiley.com/doi/10.1002/env.3170010304/abstract …… 3/9/2012
Non-linear mixed regression models
Richard T. Burnett, W. H. Ross, Daniel Krewski
Environmetrics. Volume 6, Issue 1, pages 85–99, January/February 1995
In this paper we present an estimating equation approach to statistical
inference for non-linear random effects regression models for correlated data.
With this approach, the distribution of the observations and the random
effects need not be specified; only their expectation and covariance structure
are required.
The variance of the data given the random effects may depend on the
conditional expectation.
An approximation to the conditional expectation about the fitted value of the
random effects is used to obtain closed form expressions for the unconditional
mean and covariance of the data.
The proposed methods are illustrated using data from a mouse skin painting
experiment.
Diunduh dari: http://onlinelibrary.wiley.com/doi/10.1002/env.3170060108/abstract …… 3/9/2012
. A new model pdf for contaminants dispersing in the atmosphere
D. M. Lewis, P. C. Chatwin
Environmetrics. Special Issue: New Statistical Methods in Turbulent Diffusion
Volume 6, Issue 6, pages 583–593, November/December 1995
This paper illustrates the modelling of the probability density
function for the concentration of a scalar dispersing in the
atmosphere, using a simple modification of a two state
distribution relevant to the hypothetical case of no molecular
diffusion.
The low concentration values are modelled by means of an
exponential distribution, and the high concentration tails by
a generalized Pareto distribution.
The fits are generally better than those obtained using the
beta distribution considered in earlier work.
The new model links well with the α-β theory for
concentration moments in turbulent flows
Diunduh dari: http://onlinelibrary.wiley.com/doi/10.1002/env.3170060605/abstract…… 3/9/2012
Statistical modeling of sediment and oyster PAH contamination data
collected at a South Carolina estuary (complete and left-censored
samples)
R. E. Thompson1, E. O. Voit1,*, G. I. Scott
Environmetrics. Volume 11, Issue 1, pages 99–119, January/February 2000
. This paper presents an analysis of polycyclic aromatic hydrocarbon (PAH)
sediment and oyster contamination data collected at Murrells Inlet, South
Carolina. Murrells Inlet is a high salinity estuary located in a heavily
urbanized area south of Myrtle Beach, South Carolina.
In the first part, lognormal and Weibull distributions are determined that
best fit the data, as measured by P–P and Q–Q probability plots.
The results indicate that the Weibull gives an adequate fit for almost all the
PAH analytes considered. In fact, the Weibull almost always provides a
better fit to the data than the lognormal distribution.
The second part addresses issues associated with non-detection points, as
they are regularly encountered in environmental analyses.
In statistical terms, the existence of non-detection points corresponds to
data that are left-censored. Several statistical methods for estimating the
Weibull parameters from such left-censored data are explored.
The overall result is in agreement with recent findings reported by other
investigators: methods based on the underlying distribution of the data give
more consistent results than those obtained by commonly used substitution
methods.
Diunduh dari: http://onlinelibrary.wiley.com/doi/10.1002/%28SICI%291099095X%28200001/02%2911:1%3C99::AID-ENV391%3E3.0.CO;2-4/abstract …… 3/9/2012
Parametric empirical Bayes estimation for a class of extended log-linear
regression models
Wanzhu Tu1,*, Walter W. Piegorsch
Environmetrics. Volume 11, Issue 3, pages 271–285, May/June 2000
This paper presents a fully parametric empirical Bayes approach for the
analysis of count data, with emphasis on its application to environmental
toxicity data.
A hierarchical structure for the mean response is developed from a
generalized linear model, based on a Poisson distribution. The linear
predictor is embedded at the prior level of the hierarchy. This allows for
enhanced flexibility when accounting for extra-Poisson variation, which is
often displayed with count data from environmental bioassays.
The model expands upon the traditional log-linear model in two different
ways: (1) it extends the Poisson distributional assumption; and (2) it
incorporates an extended family of link functions that includes the log link
as a special case.
The main advantage of this approach is that it combines relative
computational simplicity with hierarchical modeling flexibility. In this
paper, we emphasize the model's development and the practical issues
related to the analysis.
We describe an application of the proposed model to data from an
environmental mutagenesis experiment.
Diunduh dari: http://onlinelibrary.wiley.com/doi/10.1002/%28SICI%291099095X%28200005/06%2911:3%3C271::AID-ENV407%3E3.0.CO;2-T/abstract…… 3/9/2012
Application of a local linear autoregressive model to BOD time series
Zongwu Cai, Ram C. Tiwari
Environmetrics
Volume 11, Issue 3, pages 341–350, May/June 2000
In this paper, we analyze the biochemical oxygen demand data collected over
two years from McDowell Creek, Charlotte, North Carolina, U.S.A., by fitting
an autoregressive model with time-dependent coefficients.
The local linear smoothing technique is developed and implemented to
estimate the coefficient functions of the autoregressive model.
A nonparametric version of the Akaike information criterion is developed to
determine the order of the model and to select the optimal bandwidth.
We also propose a hypothesis testing technique, based on the residual sum of
squares and F-test, to detect whether certain coefficients in the model are
really varying or whether any variables are significant.
The approximate null distributions of the test are provided.
The proposed model has some advantages, such as it is determined
completely by data, it is easily implemented and it provides a better
prediction.
Diunduh dari: http://onlinelibrary.wiley.com/doi/10.1002/%28SICI%291099095X%28200005/06%2911:3%3C341::AID-ENV421%3E3.0.CO;2-8/abstract…… 3/9/2012
Spatiotemporal models in the estimation of area
precipitation
Eduardo Severino, Teresa Alpuim
Environmetrics
Volume 16, Issue 8, pages 773–802, December 2005
Since area precipitation measurements are difficult to obtain because of the
large spatial and time variability of the precipitation field, the development of
statistical methods for the optimal combination of weather radar and rain
gauge measurements is a matter of great importance.
This work presents area rainfall prediction methods based on kriging and
cokriging techniques modified to account for the autoregressive temporal
structure of the gauge measurement process.
Hence, the suggested kriging-type predictor includes spatial observations
both at the present time and at k lagged time instants. Such predictors are
called of kth-order. Cokriging-type predictors developed in this article include
the mixed cokriging and linkage cokriging predictors.
Mixed cokriging combines 1st-order prediction and observations of a coprocess. The linkage cokriging predictor is appropriate to deal with
observations from any two different processes with proportional, yet
unknown, expected values. This will be the case for the spatiotemporal
models adopted in this work to describe rain gauges and radar
measurements. Its expression is the same as the simple cokriging, but the
usual conditions are replaced by a single linkage condition.
Finally, we apply these methods to a storm of mixed type that occurred in
1992, for 99 h, over the Alenquer River basin region located north of Lisbon.
Diunduh dari: http://onlinelibrary.wiley.com/doi/10.1002/env.733/abstract …… 3/9/2012
Evaluating the impact of pollution on plant–Lepidoptera relationships
Christian Mulder*, Tom Aldenberg, Dick de Zwart, Harm J. van Wijnen, Anton M.
Breure
Environmetrics
Volume 16, Issue 4, pages 357–373, June 2005
We monitored the biodiversity of plants, adult butterflies and leaf-miners in
a Dutch nature reserve over a period of six years (1994–1999) within the
International Co-operative Programme on Integrated Monitoring on Air
Pollution Effects (ICP-IM).
Butterfly abundance decreased steadily over the period, indicating a
negative diversity trend, while the number of leaf-mining larvae of
Microlepidoptera remained fairly constant. Also the concentration of
pollutants (NH4, NO3, SO4, Cd, Cu and Zn) was determined in air, leaves,
litter, throughfall and stemflow.
We have no reason to expect a negative impact of acidification in rainwater
or climate change, as temperature and ozone show no significant trends
across the six years.
It is shown that the nectar-plants of adult butterflies are much more
sensitive to heavy metals than the nectar-plants of moths and other
pollinating insects. It is hypothesized that the butterfly decline is a
secondary effect of heavy metal stress on local plants, not resulting in a
decrease in the number of host-plants, but in a selective pressure of
pollutants on the plant vigour, subsequently affecting their pollinators (p
< 0.001).
An alternative explanation, such as the possible coexistence of a direct
effect of xenobiotics on the adult Lepidoptera occurring in the study area, is
not supported by our data (p > 0.05).
Diunduh dari: http://onlinelibrary.wiley.com/doi/10.1002/env.706/abstract …… 3/9/2012
Bivariate distributed lag models for the analysis of temperatureby-pollutant interaction effect on mortality
Vito M. R. Muggeo
Environmetrics
Special Issue: Special Issue: Statistics for Environmental Decisions
Volume 18, Issue 3, pages 231–243, May 2007
This paper introduces Bivariate Distributed Lags Models (BDLMs) to
investigate synergic effect of temperature and airborne particles on
mortality.
These models seem particulary attractive since they allow to model
interactions between such environmental variables accounting for possible
delayed effects.
A B-spline framework is used to approximate the coefficient surface of the
temperature-by-pollutant interaction and possible alternatives are also
discussed.
A case study of mortality time-series data in Palermo, Italy, is presented to
illustrate the model.
Diunduh dari: http://onlinelibrary.wiley.com/doi/10.1002/env.829/abstract …… 3/9/2012
Environmental pollution analysis by dynamic structural equation models
Lara Fontanella, Luigi Ippoliti*, Pasquale Valentini
Environmetrics
Special Issue: Special Issue: Statistics for Environmental Decisions
Volume 18, Issue 3, pages 265–283, May 2007
As requested by the framework EU Directive on air quality assessment and
management (96/62/EC) and related ‘daughter’ directives, air quality
standards for specific pollutants are designed to protect public health and
environment.
Modeling is one of the main activities to evaluate air quality and to prepare
future control programs. Of course, this is not an easy task since a variety of
pollutants may undergo chemical reactions between themselves and with
other species.
Nevertheless, in this paper we attempt to discuss a framework to construct a
multivariate model which is able to capture the dynamical interactions
among pollutants and meteorological variables.
Specifically, assuming that latent or background effects underlying the
fluctuation of observations can be estimated, a dynamic structural equation
model is developed in a state-space form.
A research study on the Milan district for data provided by the Lombardia
Environmental Protection Agency (ARPA) is presented.
Diunduh dari: http://onlinelibrary.wiley.com/doi/10.1002/env.835/abstract…… 3/9/2012
Semiparametric zero-inflated Poisson models with application to animal
abundance studies
Monica Chiogna1,*, Carlo Gaetan
Environmetrics
Special Issue: Special Issue: Statistics for Environmental Decisions
Volume 18, Issue 3, pages 303–314, May 2007
This paper describes a framework for flexibly modeling zero-inflated data.
Semiparametric regression based on penalized regression splines for zeroinflated Poisson models is introduced. Moreover, an EM-type algorithm is
developed to perform maximum likelihood estimation.
As an illustration, a study of animal abundance is tackled. In fact,
abundance often shows excess of zeroes and is a complicated function of
the explanatory variables.
In particular, the relationships between avian abundance and
environmental variables indicating land use are tackled.
Diunduh dari: http://onlinelibrary.wiley.com/doi/10.1002/env.830/abstract…… 3/9/2012
Optimal design for detecting dependencies with an application in spatial
ecology
Werner G. Müller, Juan M. Rodríguez-Díaz, María J. Rivas López
Environmetrics
Special Issue: Spatio-Temporal Stochastic Modelling. (METMAV)
Volume 23, Issue 1, pages 37–45, February 2012
. The paper is concerned with further developing a spatial sampling method
based upon optimal design concepts motivated by an application in the area
of biodiversity monitoring.
Statistical techniques for detecting spatial patterns in the distribution of
species richness now have some long tradition in this field, specifically the
use of correlograms.
The issue of where (and when) to undertake observations has, but only
rarely, been treated. In this paper, we aim to extend the existing literature
with techniques of finding good designs to optimize the power of tests for
spatial dependence.
Special emphasis will be given to the difference in using the exact distribution
of Moran's and its normal approximation in this context. We uncover the
remarkable effect that the use of optimal designs tends to improve the
normal approximation.
Two illustrative artificial examples will be followed by a real case analysis from
the ecological literature.
Diunduh dari: http://onlinelibrary.wiley.com/doi/10.1002/env.1132/full …… 3/9/2012
DESIGNS FOR DETECTING SPATIAL DEPENDENCE
Daniela Gumprecht1, Werner G. Müller2, Juan M. Rodríguez-Díaz
Geographical Analysis. Volume 41, Issue 2, pages 127–143, April 2009
The aim of this article is to find optimal or nearly optimal designs for
experiments to detect spatial dependence that might be in the data.
The questions to be answered are: how to optimally select predictor
values to detect the spatial structure (if it is existent) and how to avoid
to spuriously detect spatial dependence if there is no such structure.
The starting point of this analysis involves two different linear regression
models: (1) an ordinary linear regression model with i.i.d. error terms—
the nonspatial case and (2) a regression model with a spatially
autocorrelated error term, a so-called simultaneous spatial
autoregressive error model.
The procedure can be divided into two main parts: The first is use of an
exchange algorithm to find the optimal design for the respective data
collection process; for its evaluation an artificial data set was generated
and used. The second is estimation of the parameters of the regression
model and calculation of Moran's I, which is used as an indicator for
spatial dependence in the data set.
The method is illustrated by applying it to a well-known case study in
spatial analysis.
Diunduh dari: http://onlinelibrary.wiley.com/doi/10.1111/j.1538-4632.2009.00736.x/abstract ……
3/9/2012
Spatial Autocorrelation in Ecological Studies: A Legacy of
Solutions and Myths
Marie-Josée Fortin1, Mark R.T. Dale
Geographical Analysis
Special Issue: A 40th Anniversary Celebration of A. Cliff and J. Ord, 1969, The
Problem of Spatial Autocorrelation
Volume 41, Issue 4, pages 392–397, October 2009
A major aim of including the spatial component in ecological studies is to
characterize the nature and intensity of spatial relationships between
organisms and their environment.
The growing awareness by ecologists of the importance of including
spatial structure in ecological studies (for hypothesis development,
experimental design, statistical analyses, and spatial modeling) is
beneficial because it promotes more effective research.
Unfortunately, as more researchers perform spatial analysis, some
misconceptions about the virtues of spatial statistics have been carried
through the process and years. Some of these statistical concepts and
challenges were already presented by Cliff and Ord in 1969.
Here, we classify the most common misconceptions about spatial
autocorrelation into three categories of challenges: (1) those that have no
solutions, (2) those where solutions exist but are not well known, and (3)
those where solutions have been proposed but are incorrect.
We conclude in stressing where new research is needed to address these
challenges.
Diunduh dari: http://onlinelibrary.wiley.com/doi/10.1111/j.1538-4632.2009.00766.x/abstract ……
3/9/2012
. Categorical structured additive regression for assessing habitat
suitability in the spatial distribution of mussel seed abundance†
M. P. Pata1,2, T. Kneib3, C. Cadarso-Suárez4,*, V. Lustres-Pérez2, E. FernándezPulpeiro
Environmetrics. Special Issue: Spatio-Temporal Stochastic Modelling. (METMAV)
Volume 23, Issue 1, pages 75–84, February 2012
Categorical regression models enable the investigation of regression
relationships between a polytomous response and a set of regressor
variables. Depending on whether the categories are ordered or nominal,
special categorical models such as cumulative and multinomial models
have been proposed in the statistical literature.
In this paper, we compare various categorical structured additive
regression (STAR) models for assessing habitat suitability in the spatial
distribution of mussel seed abundance in the Galician coast (northwest
Spain).
STAR models allow us to include nonlinear effects of continuous
covariates on the basis of penalized splines whereas spatial effects can
be represented via a Markov random field. Inference is based on a mixed
model representation that allows for the simultaneous estimation of
regression coefficients and smoothing parameters.
Although cumulative models may seem to be the most natural choice in
our application because of the ordinal nature of the response,
multinomial models provide more detailed information on covariate
effects as all effects are allowed to depend on the different categories of
mussel seed abundance.
The statistical procedures based on STAR models proved very useful in
revealing valuable information towards the application of adequate
management of this marine resource.
Diunduh dari: http://onlinelibrary.wiley.com/doi/10.1002/env.1140/full …… 3/9/2012
. Categorical structured additive regression for assessing habitat
suitability in the spatial distribution of mussel seed abundance†
M. P. Pata1,2, T. Kneib3, C. Cadarso-Suárez4,*, V. Lustres-Pérez2, E. FernándezPulpeiro
Environmetrics. Special Issue: Spatio-Temporal Stochastic Modelling. (METMAV)
Volume 23, Issue 1, pages 75–84, February 2012
Knowledge on the relationship between species distribution and
environmental factors is of crucial interest to ecologists (Guisan and
Zimmermann, 2000; Lehmann et al.2002; Moisen et al.2006).
For example, understanding the ecological processes that determine
distributional patterns can be used to develop resource-specific exploitation
plans (Underwood et al.2000).
For a long time, generalized linear models have been the most widely used
statistical model class for assessing the impact of environmental variables on
species distribution, but nowadays, generalized additive models (GAM)
become increasingly popular because of their ability to handle nonlinear
effects of continuous covariates.
This GAM is particularly relevant in ecological data where nonlinear covariate
effects may obey a better ecological interpretation.
However, GAMs still assume that the responses are conditionally independent
while spatial correlation is often present in ecological data. Structured
additive regression (STAR) models allow to overcome this restriction by
combining nonlinear covariate effects with the possibility to correct for
spatial autocorrelation via the inclusion of spatial effects.
The spatial effect can additionally be split into a correlated (structured) and
an uncorrelated (unstructured) part to separate overdispersion effects
caused by unobserved, spatially unstructured heterogeneity and spatial
autocorrelation
Diunduh dari: http://onlinelibrary.wiley.com/doi/10.1002/env.1140/full …… 3/9/2012
. Categorical structured additive regression for assessing habitat
suitability in the spatial distribution of mussel seed abundance†
M. P. Pata1,2, T. Kneib3, C. Cadarso-Suárez4,*, V. Lustres-Pérez2, E. FernándezPulpeiro
Environmetrics. Special Issue: Spatio-Temporal Stochastic Modelling. (METMAV)
Volume 23, Issue 1, pages 75–84, February 2012
STATISTICAL METHODOLOGY: CATEGORICAL STRUCTURED ADDITIVE
REGRESSION
Categorical regression models aim at modeling the relationship between regressor
variables and a polytomuous response Y ∈ {1, … ,k}. Depending on the nature of the
response, either cumulative regression models for ordered response categories or
multinomial regression models for unordered, nominal responses may be considered.
Although ordinal models may seem to be the most natural choice in our application
because of the ordinal structure of the response, we also opt for a multinomial
model as the latter enables the inclusion of category-specific covariate effects. In
particular, comparing results from both types of models yields a more informative
picture from a biological point of view.
For regression modeling, it is advantageous to represent the categorical response Y
in terms of dummy variables y(1), … ,y(k) representing the different response
categories such that :
Obviously, one of the dummy variables is redundant, and we can therefore identify
one of the categories as reference category and work only with the remaining q = k
−1 dummies.
The aim of a categorical regression model can now be formulated as explaining the
probabilities
on the basis of a set of category-specific predictors η(1), … ,η(q) and response
functions h(1), … ,h(q) (Fahrmeir and Tutz, 2001). Given the vector of probabilities
π = (π(1), … ,π(q)) ′ , the vector of dummy variables y = (y(1), … ,y(q)) ′ then follows a
multinomial distribution, yielding the basis for maximum likelihood estimation of the
regression coefficients. Different types of categorical regression models are obtained
by choosing specific predictors and response functions as detailed in the following
Diunduh dari: http://onlinelibrary.wiley.com/doi/10.1002/env.1140/full
…… 3/9/2012
sections.
. Categorical structured additive regression for assessing habitat
suitability in the spatial distribution of mussel seed abundance†
M. P. Pata1,2, T. Kneib3, C. Cadarso-Suárez4,*, V. Lustres-Pérez2, E. FernándezPulpeiro
Environmetrics. Special Issue: Spatio-Temporal Stochastic Modelling. (METMAV)
Volume 23, Issue 1, pages 75–84, February 2012
Multinomial STAR models
A multinomial logit model is obtained based on the response function
that extends the well-known binary logit model to the case of categorical responses.
It can be motivated from latent utilities associated with the different response
categories via the principle of maximum utility (McFadden, 1973), which is quite
common in brand choice modeling.
In a structured additive multinomial logit model adapted to our specific data
situation, the predictor is specified as :
where υ ′ α(r) corresponds to parametric effects α(r) of covariates υ,
are smooth, nonlinear functions of continuous covariates x1, … ,xl,
represents correlated spatial effects of regions s ∈ {1, … ,S},
and are
unstructured, uncorrelated spatial effects. The separation of the spatial effect into
two contributions reflects the interpretation of spatial effects as proxies for
unobserved covariates that either may be spatially varying with a strong spatial
structure or may vary only locally. Another reason to include unstructured spatial
effects is to adjust for overdispersion which, however, may also be caused by
missing covariates. All covariate effects are category specific such that a large
flexibility is achieved.
Diunduh dari: http://onlinelibrary.wiley.com/doi/10.1002/env.1140/full …… 3/9/2012
. Categorical structured additive regression for assessing habitat
suitability in the spatial distribution of mussel seed abundance†
M. P. Pata1,2, T. Kneib3, C. Cadarso-Suárez4,*, V. Lustres-Pérez2, E. FernándezPulpeiro
Environmetrics. Special Issue: Spatio-Temporal Stochastic Modelling. (METMAV)
Volume 23, Issue 1, pages 75–84, February 2012
. Cumulative STAR models
A cumulative categorical regression model is given by the response function
where F denotes an absolutely continuous cumulative distribution function. An
equivalent formulation of the model is
which reveals more clearly why the model is referred to as a cumulative model: It
defines a specific form for the cumulative distribution function of Y. Note that the
model assumes that the categories of the response are ordered such that the
expression Y ⩽r is indeed meaningful. The most popular choice for the cumulative
distribution function F results when considering the extreme value distribution, which
again leads to a logit type model.
The cumulative model can also be motivated from considering latent utilities (see for
example (Fahrmeir and Tutz, 2001)). The predictor is given by
, where − ∞ = θ(0) < … < θ(k) = ∞ is a set of ordered thresholds and
denotes a predictor that is defined in analogy to the predictor of the multinomial
model (except that all effects are constant across categories).
Diunduh dari: http://onlinelibrary.wiley.com/doi/10.1002/env.1140/full …… 3/9/2012
. Categorical structured additive regression for assessing habitat
suitability in the spatial distribution of mussel seed abundance†
M. P. Pata, T. Kneib, C. Cadarso-Suárez, V. Lustres-Pérez, E. Fernández-Pulpeiro
Environmetrics. Special Issue: Spatio-Temporal Stochastic Modelling. (METMAV)
Volume 23, Issue 1, pages 75–84, February 2012
The main aim of this paper was to propose a novel application of different categorical STAR
models to the field of marine resources. STAR models enable the inclusion of flexible nonlinear
effects of continuous covariates as well as spatial effects in the regression specification. In our
application, it turned out that flexible categorical regression models can be very useful tools for
fitting biological data such as mussel seed abundance.
Compared with the ordinal model, the multinomial model provides more insight into the factors
that influence the spatial distribution of mussel seed. As the multinomial model allows for different
covariate effects along the various response categories, we can identify whether factors
determining the presence of the resource are different from those determining its abundance,
which is very important in order to develop suitable exploitation plans.
Estimation in our models was based on an empirical Bayes procedure relying on mixed model
methodology. This approach allows to determine the functional form of covariate effects
simultaneously with the estimation of the function complexity (as represented by the smoothing
variances). An alternative approach is given by the Markov chain Monte Carlo simulation
techniques. Such models have also been proposed in the literature but are mostly based on probit
instead of logit specifications as these allow to recur to latent Gaussian models via the utility
approaches that we considered as a motivation for categorical regression models.
For the sake of illustration, we have only included two continuous covariates (tidal height and
magnetic course) in the STAR models considered in this paper. However, STAR models are of
course flexible enough to accommodate (i) more than two continuous and/or categorical
covariates (i.e., slope of the site, type of substrate) and (ii) complex interactions between them.
Interpretation of results drawn from smooth multinomial STAR models is not immediate. For such
models to be directly interpreted, employment of an effect measure, such as the OR, was
suggested in this paper. The use of such measure may help the researcher to better understand
the effect of the continuous covariates on the different categories of the response. It may be
worth pointing out that although the functional form of the OR for a given predictor does not
depend on the value used as reference point, the choice of this point does affect OR values and
must be taken into account in their interpretation.
Finally, an additional advantage of using STAR models for fitting mussel seed data lies in the
flexibility of incorporating temporal effects in a straightforward manner. In this way, it is possible to
offer flexible spatio-temporal models, which may provide essential information for carrying out
adequate management of this species and other marine resources with similar distribution
patterns.
Diunduh dari: http://onlinelibrary.wiley.com/doi/10.1002/env.1140/full …… 3/9/2012
. Generalized structured additive regression based on Bayesian P-splines
Andreas Brezger , Stefan Lang
Computational Statistics & Data Analysis. Volume 50, Issue 4, 24 February 2006,
Pages 967–991.
. Generalized additive models (GAM) for modeling nonlinear effects of
continuous covariates are now well established tools for the applied
statistician. A Bayesian version of GAM's and extensions to generalized
structured additive regression (STAR) are developed.
One or two dimensional P-splines are used as the main building block.
Inference relies on Markov chain Monte Carlo (MCMC) simulation
techniques, and is either based on iteratively weighted least squares (IWLS)
proposals or on latent utility representations of (multi)categorical regression
models.
The approach covers the most common univariate response distributions,
e.g., the binomial, Poisson or gamma distribution, as well as
multicategorical responses. For the first time, Bayesian semiparametric
inference for the widely used multinomial logit model is presented.
Two applications on the forest health status of trees and a space–time
analysis of health insurance data demonstrate the potential of the approach
for realistic modeling of complex problems. Software for the methodology is
provided within the public domain package BayesX.
Diunduh dari: http://www.sciencedirect.com/science/article/pii/S0167947304003214 …… 3/9/2012
Ordinal response regression models in ecology
Antoine Guisan, Frank E. Harrell
Journal of Vegetation Science
Volume 11, Issue 5, pages 617–626, October 2000
Although ordinal data are not rare in ecology, ecological studies have, until
now, seriously neglected the use of specific ordinal regression models.
Here, we present three models – the Proportional Odds, the Continuation
Ratio and the Stereotype models – that can be successfully applied to
ordinal data.
Their differences and respective fields of application are discussed.
Finally, as an example of application, PO models are used to predict spatial
abundance of plant species in a Geographical Information System.
It shows that ordinal models give as good a result as binary logistic models
for predicting presence-absence, but are additionally able to predict
abundance satisfactorily.
Diunduh dari: http://onlinelibrary.wiley.com/doi/10.2307/3236568/abstract …… 3/9/2012
. . Methodologic issues in linking aggregated environmental and
health data
Markku Nurminen and Tuula Nurminen
Environmetrics
Vol. 11, No. 1, 2000
Epidemiologic studies of environmental exposures and their impacts on
disease risk are an important and increasingly applied approach in public
health assessment. However, environmental epidemiology often uses data
that have been collected as temporal-spatial and demographic statistics, and
thus are only available for analysis at the level of aggregate information.
The need to conduct aggregate-level studies springs primarily from the
difficulty of obtaining high-quality, individual-level data on environmental
exposures and extraneous covariates.
This paper discusses the special characteristics of aggregate data and
explains why great care must be exercised when the links between
environment and health are analyzed.
Further, this paper recalls ways to select an appropriate data-analytic method
and strategy for epidemiologic studies, and to infer whether exposure to an
environmental risk factor leads to a specific health outcome. Applicable
statistical methods include ecologic analysis, time series analysis, multilevel
modeling, and quantiative risk assessment.
Finally, this paper outlines some methodologic requirements for linking
environment and health data.
Diunduh dari: http://markstat.net/en/images/stories/environmetrics.pdf …… 4/9/2012
. Bivariate splines for ozone concentration forecasting
Bree Ettinger, Serge Guillas, Ming-Jun Lai
Environmetrics. Volume 23, Issue 4, pages 317–328, June 2012
In this paper, we forecast ground level ozone concentrations over the
USA, using past spatially distributed measurements and the functional
linear regression model.
We employ bivariate splines defined over triangulations of the relevant
region of the USA to implement this functional data approach in which
random surfaces represent ozone concentrations.
We compare the least squares method with penalty to the principal
components regression approach. Moderate sample sizes provide good
quality forecasts in both cases with little computational effort. We also
illustrate the variability of forecasts owing to the choice of smoothing
penalty.
Finally, we compare our predictions with the ones obtained using thinplate splines. Predictions based on bivariate splines require less
computational time than the ones based on thin-plate splines and are
more accurate.
We also quantify the variability in the predictions arising from the
variability in the sample using the jackknife, and report that predictions
based on bivariate splines are more robust than the ones based on thinplate splines.
Diunduh dari: http://onlinelibrary.wiley.com/doi/10.1002/env.2147/abstract …… 4/9/2012
Threshold models for river flow extremes
Olivia Grigg, Jonathan Tawn
Environmetrics. Volume 23, Issue 4, pages 295–305, June 2012
We model extreme river flow data from five UK rivers with distinct
hydrological properties. The data exhibit significant and complex
nonstationarity, which we model using a nonlinear function of
hydrological covariates corresponding to soil saturation, latent flow of the
river and rainfall.
We additionally consider season as a covariate, although the hydrological
covariates explain most of the seasonal effect directly. The standard
approach to modelling data of this kind is to fix a threshold and to model
exceedances of this threshold using the generalised Pareto distribution.
We identify a number of problems with this approach in nonstationary
cases. To overcome these issues, we propose the use of a censored
generalised extreme value distribution for threshold exceedances.
The data analysis illustrates a number of features of model fit and in
particular the stability of the model parameters and return levels to
threshold choice.
Diunduh dari: http://onlinelibrary.wiley.com/doi/10.1002/env.2138/abstract …… 4/9/2012
Environmetrics to evaluate marine environment quality.
Spanos T, Simeonov V, Simeonova P, Apostolidou E, Stratis J
Environmental Monitoring and Assessment [2008, 143(1-3):215-225]
.
The environmetric data analysis of analytical datasets from sediment and
benthic organisms samples collected from different sampling sites along
the coast of Black Sea near to City of Varna, Bulgaria has given some
important indications about the bioindication properties of both type of
samples.
Various multivariate statistical methods like cluster analysis, principal
components analysis, source apportioning modeling and partial least
square (PLS) modeling were used in order to classify and interpret the
parameters describing the chemical content of the coastal sediments
(major components, heavy metals and total organic carbon) and benthic
organisms (heavy metals).
It has been shown that seriously polluted coastal zones are indicated in
the same way by all benthic species, although some specificity could be
detected for moderate polluted regions' e.g. polychaeta accumulated
preferably Co, Cr, Cu, and Pb; crustacea - As, Cd, and Ni; mollusca - Zn.
The identified latent factors responsible for the dataset structure are
clearly indicated and apportioned with respect to their contribution to the
total mass or total concentration of the species in the samples.
The linear regression and PLS models indicated that a reliable forecast
about the relation between naturally occurring chemical components and
polluting species accumulated in the benthic organisms is possible.
Diunduh dari: http://ukpmc.ac.uk/abstract/MED/17874195 …… 4/9/2012
Extreme Value Analysis
Saralees Nadarajah
Published Online: 15 SEP 2006
Encyclopedia of Environmetrics
Extreme value theory concerns the behavior of the extremes of a process
or processes. The fundamentals of this probability theory have been
known since about the beginning of the twentieth century, but the
relevant statistical methods for modeling extreme values emerged in the
literature only in the past two decades. In fact, since 1980 the literature
has seen a flood of applications of statistical extreme values, covering a
wide range of areas.
The application areas include: environmental sciences, including climate,
engineering and hydrology, performance assessment as in sports or
policing, astronomy, finance, chemometrics, mortality studies, and outlier
detection. Further references to specific applications are noted throughout
the rest of this entry.
The aim of this article is to review some fundamentals of extreme value
theory and relevant statistical methods. The emphasis will be on the latter
and the applications it has attracted in the literature so far. The entry is in
two parts. The first part considers univariate extremes and the remainder
is for multivariate extremes. Each part begins with a discussion of
fundamental theoretical results.
This is then followed by a discussion of relevant statistical models,
inference and simulation.
Diunduh dari: http://onlinelibrary.wiley.com/doi/10.1002/9780470057339.vae061/abstract ……
4/9/2012
Epidemic Models
Professor Eric Renshaw
Published Online: 15 SEP 2006
Encyclopedia of Environmetrics
The importance of studying disease outbreak and spread cannot be
overstated. In fourteenth century Europe the Black Death killed 25 million
people out of a population of 100 million; the Aztecs lost half their
population of 3.5 million from smallpox; whilst around 20 million people
died in the world pandemic of influenza in 1919.
Today the overriding epidemic concern is the spread of human
immunodeficiency virus (HIV) acquired immune deficiency syndrome
(AIDS), to the considerable detriment of the vast numbers of people
suffering from less mediaconscious diseases such as malaria,
schistosomiasis, filariasis, and hookworm disease.
Parallel problems in marine and agriculturally based environments take a
similar toll on plant, animal, and fish populations.
Human attempts to control such epidemiological disasters can themselves
lead to further problems e.g. the improper use of pesticides and
management strategies.
Increasing understanding of the underlying processes involved is therefore
one of the major environmental problems of our age, and the best way
forward is through the careful use of mathematical modeling.
Diunduh dari: http://onlinelibrary.wiley.com/doi/10.1002/9780470057339.vae046/abstract ……
4/9/2012
Testing for space–time interaction in conditional autoregressive models†
M.D. Ugarte, T. Goicoa, J. Etxeberria, A.F. Militino
Environmetrics
Special Issue: Spatio-Temporal Stochastic Modelling. (METMAV)
Volume 23, Issue 1, pages 3–11, February 2012
Data on disease incidence or mortality over a set of contiguous regions
have been commonly used to describe geographic patterns of disease,
helping epidemiologists and public health researchers to identify possible
etiologic factors. Nowadays, the availability of historical mortality registers
offers the possibility of going further, describing the spatio-temporal
distribution of risks.
The literature on spatio-temporal modeling of risks is very rich, and it is
mainly focused on the use of conditional autoregressive models from a fully
Bayesian perspective.
The complexity of the estimation procedure makes the Empirical Bayes
approach a plausible alternative. In this context, it is of interest to test for
interaction between space and time, as an absence of space–time
interactions simplifies modeling and interpretation.
In this work, a score test is derived as well as a bootstrap approximation of
its null distribution.
A parametric bootstrap test is also provided for comparison purposes.
Results are illustrated using brain cancer mortality data from Spain in the
period 1996–2005.
Diunduh dari: http://onlinelibrary.wiley.com/doi/10.1002/env.1126/abstract …… 4/9/2012
Spatio-temporal disease mapping using INLA†
Birgit Schrödle, Leonhard Held
Environmetrics
Special Issue: Handling Complexity and Uncertainty in Environmental Studies, Arising
from the TIES-GRASPA Joint Conference Held in Bologna in 2009
Volume 22, Issue 6, pages 725–734, September 2011
Spatio-temporal disease mapping models are a popular tool to describe
the pattern of disease counts.
They are usually formulated in a hierarchical Bayesian framework with
latent Gaussian model. So far, computationally expensive Markov chain
Monte Carlo algorithms have been used for parameter estimation which
might induce a large Monte Carlo error.
An alternative method using integrated nested Laplace approximations
(INLA) has recently been proposed. A major advantage of INLA is that it
returns accurate parameter estimates in short computational time.
Additionally, the deviance information criterion is provided for Bayesian
model choice.
This paper describes how several parametric and nonparametric models
and extensions thereof can be fitted to space–time count data using
INLA.
Particular emphasis is given to the appropriate choice of linear
constraints to ensure identifiability of the parameter estimates.
The models are applied to counts of Salmonellosis in cattle reported to
the Swiss Federal Veterinary Office 1991–2008.
Diunduh dari: http://onlinelibrary.wiley.com/doi/10.1002/env.1065/abstract …… 4/9/2012
An exponential family model for clustered multivariate
binary data
Geert Molenberghs, Louise M. Ryan
Environmetrics
Volume 10, Issue 3, pages 279–300, May/June 1999
This paper focuses on the analysis of clustered multivariate
binary data that arise from developmental toxicity studies.
In these studies, pregnant mice are exposed to chemicals to
assess possible adverse effects on developing fetuses.
Multivariate binary outcomes arise when each fetus in a litter is
assessed for the presence of malformations and/or low birth
weight.
We analyse the data using a multivariate exponential family
model which is flexible in terms of allowing response rates to
depend on cluster size.
Maximum likelihood estimation of model parameters and the
construction of score tests for dose effect are discussed.
Diunduh dari: http://onlinelibrary.wiley.com/doi/10.1002/%28SICI%291099095X%28199905/06%2910:3%3C279::AID-ENV352%3E3.0.CO;2-X/abstract …… 4/9/2012
Statistical modelling of ecosystem structure
Patricia B. Cerrito
Environmetrics
Volume 3, Issue 2, pages 169–181, 1992
In this paper, we provide an example demonstrating a means
of modelling the interaction of species in an ecosystem which
will enable us to determine long term growth trends of the
various species.
In most cases, the type of interaction between species in an
ecosystem is unknown. Therefore it is useful to use a
mathematical structure which makes almost no assumptions
as to the form of this interaction.
We use a topological semigroup to model the behavior
interaction. To do this, it will be shown how to define the
multiplicative structure of the semigroup. Also, it will be
demonstrated how to use kernel density estimation to predict
these interactions.
Diunduh dari: http://onlinelibrary.wiley.com/doi/10.1002/env.3170030203/abstract …… 4/9/2012
Dynamical model for indoor radon concentration
monitoring
Marek Brabec, Karel Jílek
Environmetrics
Special Issue: The 18th TIES Conference: Computational Environmetrics: Protection
of Renewable Environment and Human and Ecosystem Health
Volume 20, Issue 6, pages 718–729, September 2009
In this paper, we will deal with interesting example of natural risk modeling –
namely of indoor radon concentration, needed for proper exposure
assessment and appreciation of quality of preventive measures taken.
First, we will illustrate, how the traditional view based on fixed (i.e., timeinvariant) coefficient models can be misleading. Then we formulate a flexible
(nonparametric) regression dynamic model offering a doable alternative.
Situation is not entirely standard here, as the smoothed and weight-giving
variables are different. Nevertheless, estimation for our local regression
formulation is easily doable even for rather large data via extension of
standard local smoothing that we describe.
We illustrate on data coming from a rare intensive measurement campaign in
an occupied house with simultaneous measurements taken in different rooms.
Our model has nice physical interpretation of its parts. We also illustrate how
even such a simple model can produce behavior that throws some light on
radon experts' discussions about natural radon concentration circadial
movement phase. This is because our model acts as a linear but time-varying
filter that can change not only amplitude but also phase between different
rooms of the same house, suggesting that there might not be a universal
phase of (e.g., daily) radon variation. Instead, the phase might depend on
how close a particular indoor location is to the radon sources.
Finally, we present some ideas about how our model can be expanded in
future to cover more complicated situations and settings.
Diunduh dari: http://onlinelibrary.wiley.com/doi/10.1002/env.973/abstract …… 4/9/2012
A unified strategy for building simple air quality indices
Francesca Bruno, Daniela Cocchi
Environmetrics
Volume 13, Issue 3, pages 243–261, May 2002
Interest in air quality indices has been increasing in recent years. This is
strictly connected with the development and the easy availability of webcommunication and on-line information. By means of web pages it is indeed
possible to give quick and easy-to-consult information about air quality in a
specific area. We propose a class of air quality indices which are simple to
read and easy to understand by citizens and policy-makers.
They are constructed in order to be able to compare situations that differ in
time and space. In particular, interest is focused on situations where many
monitoring stations are operating in the same area. In this case, which
occurs frequently, air pollution data are collected according to three
dimensions: time, space and type of pollutant. In order to obtain a
synthetic value, the dimensions are reduced by means of aggregation
processes that occur by successively applying some aggregating function.
The final index may be influenced by the order of aggregation. The
hierarchical aggregation here proposed is based on the successive selection
of order statistics, i.e. on percentiles and on maxima. The variety of
pollutants measured in each area imposes a standardization due to their
different effects on the human health.
This evaluation comes from epidemiological studies and influences the final
value of the index. We propose to use simultaneously more than one index
of the selected class and to associate a measure of variability with every
index. Such measures of dispersion account for very important additional
information.
Copyright © 2002 John Wiley & Sons, Ltd.
Diunduh dari: http://onlinelibrary.wiley.com/doi/10.1002/env.512/abstract …… 4/9/2012
Distribution of the maximum in air pollution fields
Sofia Aberg, Peter Guttorp
Environmetrics
Volume 19, Issue 2, pages 183–208, March 2008
Air quality standards are set to protect public health. The values of
the standards are often based on health effect studies, without
any statistical considerations. In order to judge if a standard is
met measurements of ambient air quality are taken at monitoring
stations, and these measured values are used to decide whether
or not the standard has been violated.
In this paper we examine the statistical quality of some air quality
standards by taking both measurement error and variability of the
ambient field away from the monitoring sites into account.
In particular we study the distribution of the maximum of the
ambient field conditional on a measured monitoring value at the
value prescribed by the standard.
The distribution of the maximum is computed using a Rice method
and relies on a generalization of upcrossings of a level in one
dimension to two dimensions.
Copyright © 2007 John Wiley & Sons, Ltd.
Diunduh dari: http://onlinelibrary.wiley.com/doi/10.1002/env.866/abstract …… 4/9/2012
Spatial models for flood risk assessment
Marco Bee, Roberto Benedetti, Giuseppe Espa
Environmetrics
Special Issue: Special Issue on Spatial Data Methods for Environmental and
Ecological Processes
Volume 19, Issue 7, pages 725–741, November 2008
The problem of computing risk measures associated to flood
events is extremely important not only from the point of view of
civil protection systems but also because of the necessity for
the municipalities of insuring against the damages.
In this work we propose, in the framework of an integrated
strategy, an operating solution which merges in a conditional
approach the information usually available in this setup.
First, we use a logistic auto-logistic model (LAM) for the
estimation of the univariate conditional probabilities of flood
events. This approach has two fundamental advantages: it
allows to incorporate auxiliary information and does not require
the target variables to be independent. Then we simulate the
joint distribution of floodings by means of the Gibbs sampler.
Finally, we propose an algorithm to increase ex post the spatial
autocorrelation of the simulated events.
The methodology is shown to be effective by means of an
application to the estimation of the flood probability for two
partitions of the Italian territory with different spatial resolution.
Copyright © 2008 John Wiley & Sons, Ltd.
Diunduh dari: http://onlinelibrary.wiley.com/doi/10.1002/env.932/abstract …… 4/9/2012
Modeling monthly temperature data in Lisbon and Prague
Teresa Alpuim, Abdel El-Shaarawi
Environmetrics
Volume 20, Issue 7, pages 835–852, November 2009
This paper examines monthly average temperature series in two widely
separated European cities, Lisbon (1856–1999) and Prague (1841–2000).
The statistical methodology used begins by fitting a straight line to the
temperature measurements in each month of the year. Hence, the 12
intercepts describe the seasonal variation of temperature and the 12
slopes correspond to the rise in temperature in each month of the year.
Both cities show large variations in the monthly slopes.
In view of this, an overall model is constructed to integrate the data of
each city. Sine/cosine waves were included as independent variables to
describe the seasonal pattern of temperature, and sine/cosine waves
multiplied by time were used to describe the increase in temperature
corresponding to the different months.
The model also takes into account the autoregressive, AR(1), structure
that was found in the residuals. A test of the significance of the variables
that describe the variation of the increase in temperature shows that both
Lisbon and Prague had an increase in temperature that is different
according to the month. The winter months show a higher increase than
the summer months.
Copyright © 2009 John Wiley & Sons, Ltd.
Diunduh dari: http://onlinelibrary.wiley.com/doi/10.1002/env.964/abstract …… 4/9/2012
Comparative spatiotemporal analysis of fine particulate matter
pollution
W. Pang, G. Christakos , J-F Wang
Environmetrics
Special Issue: Spatio-Temporal Stochastic Modelling: Environmental and Health
Processes
Volume 21, Issue 3-4, pages 305–317, May - June 2010
The prime focus of this work is the comparative investigation, theoretical
and numerical, of spatiotemporal techniques used in air pollution studies.
Space-time statistics techniques are classified on the basis of a set of
criteria and the relative theoretical merits of each technique are
discussed accordingly.
The numerical comparison involves the applications of two representative
techniques. For this purpose, the popular spatiotemporal epistemic
knowledge synthesis and graphical user interface (SEKS-GUI) software of
spatiotemporal statistics is used together with a dataset of PM2.5 daily
measurements obtained at monitoring stations geographically distributed
over the state of North Carolina, USA.
The analysis offers valuable insight concerning the choice of an
appropriate spatiotemporal technique in air pollution studies.
Copyright © 2009 John Wiley & Sons, Ltd.
Diunduh dari: http://onlinelibrary.wiley.com/doi/10.1002/env.1007/abstract …… 4/9/2012
Iterated confirmatory factor analysis for pollution source apportionment†
William F. Christensen, James J. Schauer, Jeff W. Lingwall
Environmetrics
Special Issue: Special Issue on TIES Conference 2004
Volume 17, Issue 6, pages 663–681, September 2006
Many approaches for pollution source apportionment have been
considered in the literature, most of which are based on the chemical
mass balance equations. The simplest approaches for identifying the
pollution source contributions require that the pollution source profiles are
known. When little or nothing is known about the nature of the pollution
sources, exploratory factor analysis, confirmatory factor analysis, and
other multivariate approaches have been employed.
In recent years, there has been increased interest in more flexible
approaches, which assume little knowledge about the nature of the
pollution source profiles, but are still able to produce nonnegative and
physically realistic estimates of pollution source contributions.
Confirmatory factor analysis can yield a physically interpretable and
uniquely estimable solution, but requires that at least some of the rows of
the source profile matrix be known. In the present discussion, we discuss
the iterated confirmatory factor analysis (ICFA) approach.
ICFA can take on aspects of chemical mass balance analysis, exploratory
factor analysis, and confirmatory factor analysis by assigning varying
degrees of constraint to the elements of the source profile matrix when
iteratively adapting the hypothesized profiles to conform to the data. ICFA
is illustrated using PM2.5 data from Washington D.C., and a simulation
study illustrates the relative strengths of ICFA, chemical mass balance
approaches, and positive matrix factorization (PMF).
Copyright © 2006 John Wiley & Sons, Ltd.
Diunduh dari: http://onlinelibrary.wiley.com/doi/10.1002/env.782/abstract …… 4/9/2012
Environmetric approaches for lake pollution assessment.
Simeonova P, Lovchinov V, Dimitrov D, Radulov I
Laboratory for Environmental Physics, Georgi Nadjakov Institute of Solid State
Physics, Bulgarian Academy of Sciences, Tzarigradsko Chaussee 72, 1784, Sofia,
Bulgaria. poly-sim@issp.bas.bg
Environmental Monitoring and Assessment [2010, 164(1-4):233-248]
The application of multivariate statistical methods to high mountain lake
monitoring data has offered some important conclusions about the
importance of environmetric approaches in lake water pollution assessment.
Various methods like cluster analysis and principal components analysis
were used for classification and projection of the data set from a large
number of lakes from Rila Mountain in Bulgaria. Additionally, self-organizing
maps of Kohonen were constructed in order to solve some classification
tasks.
An effort was made to relate the maps with the input data in order to
detect classification patterns in the data set. Thus, discrimination chemical
parameters for each pattern (cluster) identified were found, which enables
better interpretation of the pollution situation.
A methodology for application of a combination of different environmetric
methods is suggested as a pathway to interpret high mountain lake water
monitoring data.
Diunduh dari: http://ukpmc.ac.uk/abstract/MED/19353283 …… 4/9/2012
Estimating constrained concentration–response functions
between air pollution and health
Helen Powell*, Duncan Lee, Adrian Bowman
Environmetrics
Volume 23, Issue 3, pages 228–237, May 2012
The health risks associated with short-term exposure to air pollution have
been the focus of much recent research, most of which has considered
linear concentration–response functions (CRFs) between ambient
concentrations of pollution and a health response.
A much smaller number of studies have relaxed this assumption of linearity
and allowed the shape of the function to be estimated from the data.
However, this increased flexibility has resulted in CRFs being estimated
that appear unfeasible, often showing decreases in the risk to health with
increasing concentrations. Therefore, this paper proposes a Bayesian
hierarchical model for estimating constrained CRFs in this context, which is
based on monotonic integrated splines.
These splines produce non-decreasing CRFs, owing to the associated
regression parameters being constrained to be non-negative, which we
ensure by modelling the latter with a ‘slab and spike’ prior.
The efficacy of our approach is assessed via simulation before being
applied to a study of ozone concentrations and respiratory disease in
Greater London between 2000 and 2005.
Copyright © 2012 John Wiley & Sons, Ltd.
Diunduh dari: http://onlinelibrary.wiley.com/doi/10.1002/env.1150/abstract …… 4/9/2012
Estimating constrained concentration–response functions between air
pollution and health
Helen Powell*, Duncan Lee, Adrian Bowman
Environmetrics
Volume 23, Issue 3, pages 228–237, May 2012
The health risks associated with short-term exposure to air pollution have
been the focus of much recent research, most of which has considered
linear concentration–response functions (CRFs) between ambient
concentrations of pollution and a health response.
A much smaller number of studies have relaxed this assumption of linearity
and allowed the shape of the function to be estimated from the data.
However, this increased flexibility has resulted in CRFs being estimated
that appear unfeasible, often showing decreases in the risk to health with
increasing concentrations. Therefore, this paper proposes a Bayesian
hierarchical model for estimating constrained CRFs in this context, which is
based on monotonic integrated splines.
These splines produce non-decreasing CRFs, owing to the associated
regression parameters being constrained to be non-negative, which we
ensure by modelling the latter with a ‘slab and spike’ prior.
The efficacy of our approach is assessed via simulation before being
applied to a study of ozone concentrations and respiratory disease in
Greater London between 2000 and 2005.
Copyright © 2012 John Wiley & Sons, Ltd.
Diunduh dari: http://onlinelibrary.wiley.com/doi/10.1002/env.1150/abstract …… 4/9/2012
Water quality monitoring using cluster analysis and linear models
A. Manuela Gonçalves, Teresa Alpuim.
Environmetrics
Volume 22, Issue 8, pages 933–945, December 2011
The development of statistical methodologies based on spatial and
temporal hydrological data is a very important tool in the monitoring of
surface water quality in a river basin.
This paper uses cluster analysis and linear models to describe hydrological
space–time series of quality variables and to detect changes in surface
water quality data collected in the River Ave hydrological basin, located in
the north-west region of Portugal.
This area receives many untreated effluent discharges from textile
industries, which result in extreme pollution. Because of this problematic
environmental situation, local authorities installed a network of 20
monitoring sites, producing monthly measurements of quality variables and
later began to operate three wastewater treatment plants (WTP) at the
end of the 1998 hydrological year.
In this work, we propose a two-step methodology to analyse these data
which use cluster analysis to classify the quality monitoring sites into
spatial homogeneous groups. Then we adjust linear models to the quality
variables associated with the clusters, taking into account the seasonal
variations throughout the year, different trends for each period of time
(before and after the installation of WTPs), and the hydro-meteorological
factor.
Finally, statistical tests are performed to evaluate the effective role of the
WTPs' performance.
Copyright © 2011 John Wiley & Sons, Ltd.
Diunduh dari: http://onlinelibrary.wiley.com/doi/10.1002/env.1112/abstract …… 4/9/2012
Comparing spatio-temporal models for particulate matter in Piemonte
Michela Cameletti, Rosaria Ignaccolo, Stefano Bande
Environmetrics
Volume 22, Issue 8, pages 985–996, December 2011
In the last two decades, increasing attention has been given to air
pollution around the world, mainly because of its impact on human health
and on the environment. In the Po valley (northern Italy), one of the most
troublesome pollutant is PM10 (particulate matter with an aerodynamic
diameter of less than 10 μm).
In order to assess PM10 concentration over an entire region,
environmental agencies need models to predict PM10 at unmonitored
sites. To choose among possible predictive models and then meet the
agencies' request, we focus on the class of Bayesian hierarchical models
as they provide a flexible framework for incorporating relevant covariates
as well as spatio-temporal interactions.
We consider six alternative models for PM10 concentration in Piemonte
region (north-western Po Valley), during the winter season October 2005–
March 2006. Our aim is to choose a model that is satisfactory in terms of
goodness of fit, interpretability, parsimony, prediction capability and
computational costs. In order to support this choice, we propose a
comparison approach based on a set of criteria summarized in a table
that can be easily communicated to non-statisticians.
The comparison findings allow to provide Piemonte environmental
agencies with an effective statistical model for building reliable PM10
concentration maps, equipped with the corresponding uncertainty
measure.
Copyright © 2011 John Wiley & Sons, Ltd.
Diunduh dari: http://onlinelibrary.wiley.com/doi/10.1002/env.1139/abstract …… 4/9/2012
Autologistic models for binary data on a lattice
John Hughes1, Murali Haran2,*, Petruţa C. Caragea
Environmetrics
Volume 22, Issue 7, pages 857–871, November 2011
The autologistic model is a Markov random field model for spatial binary
data. Because it can account for both statistical dependence among the
data and for the effects of potential covariates, the autologistic model is
particularly suitable for problems in many fields, including ecology, where
binary responses, indicating the presence or absence of a certain plant or
animal species, are observed over a two-dimensional lattice.
We consider inference and computation for two models: the original
autologistic model due to Besag, and the centered autologistic model
proposed recently by Caragea and Kaiser. Parameter estimation and
inference for these models is a notoriously difficult problem due to the
complex form of the likelihood function. We study pseudolikelihood (PL),
maximum likelihood (ML), and Bayesian approaches to inference and
describe ways to optimize the efficiency of these algorithms and the perfect
sampling algorithms upon which they depend, taking advantage of parallel
computing when possible.
We conduct a simulation study to investigate the effects of spatial
dependence and lattice size on parameter inference, and find that inference
for regression parameters in the centered model is reliable only for
reasonably large lattices (n > 900) and no more than moderate spatial
dependence.
When the lattice is large enough, and the dependence small enough, to
permit reliable inference, the three approaches perform comparably, and so
we recommend the PL approach for its easier implementation and much
faster execution.
Copyright © 2011 John Wiley & Sons, Ltd.
Diunduh dari: http://onlinelibrary.wiley.com/doi/10.1002/env.1102/abstract …… 4/9/2012
Regression estimator under inverse sampling to estimate
arsenic contamination
Mohammad Moradi, Mohammad Salehi, Jennifer Ann Brown, Naser Karimi
Environmetrics
Volume 22, Issue 7, pages 894–900, November 2011
The fate of arsenic introduced to the environment as a result of
the natural and human activities is an important issue. Surveys
of arsenic typically involve sampling from a large area.
Measuring arsenic concentrations in samples is expensive, and
any improvement in the survey design is welcome. One way to
improve efficiency in sampling is to make use of auxiliary
information. Surveys of environmental pollution can be classed
as surveys of rare populations, where there is a large area with a
small polluted subarea.
The rare population has many zeroes, or low, values, and
contaminated subareas have non-zero, or high, values.
Regression estimators or ratio estimators are undefined for those
samples containing only information from the non-rare (zerovalue) subpopulation (i.e., the non-contaminated subpopulation)
in simple random sampling. In this paper, we introduce the
modified regression estimators and their associated variance
estimators for sampling designs which are suitable for rare
populations, such as general inverse sampling and inverse
sampling with unequal selection probabilities.
We conducted a simulation study on the real rare population
arsenic contamination in Kurdistan. The simulation results
showed that the modified regression estimators are more
efficient than the previous estimators.
Diunduh dari: http://onlinelibrary.wiley.com/doi/10.1002/env.1116/abstract …… 4/9/2012
The role of statistics in the analysis of ecosystem services
R. I. Smith, J. McP. Dick, E. M. Scott
Environmetrics
Special Issue: Quantitative Approaches to Ecosystem Service Evaluation
Volume 22, Issue 5, pages 608–617, August 2011
Operationalising the holistic approach implicit in an ecosystem services
assessment is a challenge, incorporating social and economic
considerations alongside the physical, chemical and biological function of
ecosystems. The paper considers the role of statistics within a range of
frameworks proposed for the analysis of ecosystem services.
The use of different statistical techniques within the component parts of
an ecosystem services assessment framework are discussed, including (1)
data availability and sampling strategies, (2) statistical data analysis, (3)
geography and spatial models, (4) meta-analysis, (5) environmental
models, (6) societal models, (7) feedbacks and loop analysis, and (8)
graphical models including Bayesian belief networks.
Issues of value and the potential for a statistical contribution to
multivariate non-monetary valuation are considered.
We argue that statistics has an underpinning role by providing tools to
link together the component elements along with their uncertainties for a
thorough ecosystem services assessment, and should be an integral part
of this developing inter-disciplinary research area.
Copyright © 2011 John Wiley & Sons, Ltd.
Diunduh dari: http://onlinelibrary.wiley.com/doi/10.1002/env.1107/abstract …… 4/9/2012
Ambient Air Pollution and the Risk of Acute Ischemic Stroke
Gregory A. Wellenius, Mary R. Burger, Brent A. Coull, Joel Schwartz, Helen H. Suh, Petros
Koutrakis, Gottfried Schlaug, Diane R. Gold, Murray A. Mittleman.
Arch Intern Med. 2012;172(3):229-234.
The link between daily changes in level of ambient fine particulate matter (PM)
air pollution (PM <2.5 μm in diameter [PM2.5]) and cardiovascular morbidity
and mortality is well established. Whether PM2.5 levels below current US
National Ambient Air Quality Standards also increase the risk of ischemic
stroke remains uncertain.
We reviewed the medical records of 1705 Boston area patients hospitalized
with neurologist-confirmed ischemic stroke and abstracted data on the time of
symptom onset and clinical characteristics. The PM2.5 concentrations were
measured at a central monitoring station. We used the time-stratified casecrossover study design to assess the association between the risk of ischemic
stroke onset and PM2.5 levels in the hours and days preceding each event. We
examined whether the association with PM2.5 levels differed by presumed
ischemic stroke pathophysiologic mechanism and patient characteristics.
The estimated odds ratio (OR) of ischemic stroke onset was 1.34 (95% CI,
1.13-1.58) (P < .001) following a 24-hour period classified as moderate (PM2.5
15-40 μg/m3) by the US Environmental Protection Agency's (EPA) Air Quality
Index compared with a 24-hour period classified as good (≤15 μg/m3).
Considering PM2.5 levels as a continuous variable, we found the estimated odds
ratio of ischemic stroke onset to be 1.11 (95% CI, 1.03-1.20) (P = .006) per
interquartile range increase in PM2.5 levels (6.4 μg/m3). The increase in risk
was greatest within 12 to 14 hours of exposure to PM2.5 and was most strongly
associated with markers of traffic-related pollution.
These results suggest that exposure to PM2.5 levels considered generally safe
by the US EPA increase the risk of ischemic stroke onset within hours of
exposure.
Diunduh dari: http://archinte.jamanetwork.com/article.aspx?articleid=1108717 …… 4/9/2012
The influence of contextual variables on interpersonal spacing.
Worchel, Stephen.
Journal of Nonverbal Behavior vol. 10 issue 4 December 1986. p. 230 - 254
Four studies examined the effects of contextual variables on interpersonal
spacing. Contextual variables were defined as transitory factors that involved
the setting in which an interaction occurs; these variables were delineated
from personal and interpersonal characteristics. In each experimental setting,
white male subjects were allowed to choose the distance at which they
interacted with a stranger.
The first study found that subjects who had experienced social isolation prior
to the interaction chose greater distances than subjects who had not been
isolated. The second study found that subjects chose greater distances when
they believed their interaction would be observed by others than when the
interaction was private.
Results from the third study yielded an interaction between topic of
conversation and expected length of conversation with greatest distance being
chosen when subjects expected a long conversation to focus on a personal
topic. In the final study, room size and shape influenced interpersonal
distance; the interaction indicated that room size affected distance only in
rectangular rooms.
The results are discussed in terms of equilibrium model (Argyle & Dean, 1965).
It is argued that contextual variables affect intimacy, and that the equilibrium
model can explicate the effects of contextual as well as personal and
interpersonal variables.
Diunduh dari:
http://journals.ohiolink.edu/ejc/search.cgi?q=id:03007839/v04i0001/47_irapsrratm&pagesize=&ml
Interpersonal relationships and personal space: Research review
and theoretical model.
Sundstrom, Eric; Altman, Irwin.
Human Ecology vol. 4 issue 1 January 1976. p. 47 - 67
This article reviews research concerning interpersonal distance as
a function of interpersonal relationships, attraction, and reactions
to spatial invasion.
To integrate research findings, we propose a simple model, based
on the idea that people seek an optimal distance from others that
becomes smaller with friends and larger for individuals who do
not expect to interact.
The model describes comfort-discomfort as a function of
interaction distance in three situations: interacting friends,
interacting strangers, and strangers who do not expect
interaction.
These three personal space profiles are discussed in terms of
qualifying variables, such as seated vs. standing interaction, sex
composition of the dyad, intimacy of conversation topics, and
situational variables.
Diunduh dari:
http://journals.ohiolink.edu/ejc/search.cgi?q=id:03007839/v04i0001/47_irapsrratm&pagesize=&ml
t=y …… 4/9/2012
Relative vs. absolute statistical analysis of compositions: A comparative
study of surface waters of a Mediterranean river.
Otero, N.; Tolosana-Delgado, R.; Soler, A.; Pawlowsky-Glahn, V.; Canals, A.
Water Research vol. 39 issue 7 April, 2005. p. 1404-1414
Most hydrogeological research includes some sort of statistical study, which is
generally conducted on the raw measures of chemical variables, though there
are several theoretical and practical studies warning against this practice.
Arguments refer mainly to the positive character of this type of data, and to
the fact that they carry only information about the relative abundance of each
component on the whole, what makes techniques based on correlation, like
the widely used Principal Component Analysis (PCA), loose their meaning.
The solution proposed by Aitchison (1982, Journal of the Royal Statistical
Society, Series B 44(2), 139–177)—based on working with log-ratios of
observations—is equivalent to define a new distance between compositions
and to adapt usual statistical techniques to it. To illustrate its effect, our study
compares the performance of the biplot—a PCA graphical technique—
according to the usual Euclidean and to the Aitchison distance.
The study is conducted on a set of 14 molarities measured monthly through
the years 1997–1999 at 30 different stations along the Llobregat River and its
tributaries (Barcelona, NE Spain). Ordinary analysis, implicitly based on an
Euclidean distance, presents some deficiencies, mainly because it only
captures major ion variations and the inferred relationship between them
actually depends on other non-relevant variables, such as water mass.
An analysis based on compositional distances captures variations of all the
ions; it is robust against the inclusion of non-relevant variables in the
analysis; and it offers a way to build factors expressed as equilibrium
equations. In our case, two promising factors are extracted, showing the
different anthropogenic and geological pollution sources of the rivers.
Diunduh dari:
http://journals.ohiolink.edu/ejc/search.cgi?q=keywords:%22Distancing%22&category.facet=Earth
A spatial model to aggregate point-source and nonpoint-source water-quality data
for large areas. White, D.A.; Smith, R.A.; Price, C.V.; Alexander, R.B.; Robinson, K.W.
Computers and Geosciences vol. 18 issue 8 September, 1992. p. 1055-1073
More objective and consistent methods are needed to assess water quality
for large areas. A spatial model, one that capitalizes on the topologic
relationships among spatial entities, to aggregate pollution sources from
upstream drainage areas is described that can be implemented on land
surfaces having heterogeneous water-pollution effects.
An infrastructure of stream networks and drainage basins, derived from
1:250,000-scale digital-elevation models, define the hydrologic system in
this spatial model.
The spatial relationships between point- and nonpoint pollution sources and
measurement locations are referenced to the hydrologic infrastructure with
the aid of a geographic information system.
A maximum-branching algorithm has been developed to simulate the
effects of distance from a pollutant source to an arbitrary downstream
location, a function traditionally employed in deterministic water quality
models.
Diunduh dari:
http://journals.ohiolink.edu/ejc/search.cgi?q=keywords:%22Distancing%22&category.facet=Earth
Interpersonal relationships and personal space: Research review
and theoretical model.
Sundstrom, Eric; Altman, Irwin.
Human Ecology vol. 4 issue 1 January 1976. p. 47 - 67
This article reviews research concerning interpersonal distance as
a function of interpersonal relationships, attraction, and reactions
to spatial invasion.
To integrate research findings, we propose a simple model, based
on the idea that people seek an optimal distance from others that
becomes smaller with friends and larger for individuals who do not
expect to interact.
The model describes comfort-discomfort as a function of
interaction distance in three situations: interacting friends,
interacting strangers, and strangers who do not expect interaction.
These three personal space profiles are discussed in terms of
qualifying variables, such as seated vs. standing interaction, sex
composition of the dyad, intimacy of conversation topics, and
situational variables.
Diunduh dari:
http://journals.ohiolink.edu/ejc/search.cgi?q=keywords:%22Distancing%22&category.facet=Earth
%20Sciences&start=211 …… 4/9/2012
A method for spatial heterogeneity evaluation on landscape
pattern of farmland shelterbelt networks: A case study in
midwest of Jilin Province, China.
Shi, Xiaoliang; Li, Ying; Deng, Rongxin.
Chinese Geographical Science vol. 21 issue 1 February 2011. p. 48 - 56
On the basis of landscape ecology, combining the Spot 5 high resolution
satellite imagery with GIS, a method evaluating the spatial heterogeneity
of shelterbelts distribution at landscape scale is put forward in this paper.
The distance coefficients of reasonable and existing landscape indexes of
farmland shelterbelt networks were computed, and then through the
classification of the distance coefficients, and the establishment of
evaluation rules, the spatial heterogeneity of farmland shelterbelts was
evaluated.
The method can improve the evaluating system of previous studies on
shelterbelts distribution, resolve the disadvantages of lacking spatiality of
overall evaluation, and make the evaluation results have more directive
significance for shelterbelt management. Based on this method, spatial
heterogeneity of shelterbelt networks was evaluated in the midwest of Jilin
Province, China.
The results show that the regions with fewer shelterbelts and no closed
network account for 34.7% of the total area, but only 4.9% of the area
has relative reasonable pattern of shelterbelt networks. Many problems
exist in the distribution pattern of shelterbelts, therefore, much attention
should be paid to construct farmland shelterbelts in the study area.
Diunduh dari:
http://journals.ohiolink.edu/ejc/search.cgi?q=keywords:%22Distancing%22&category.facet=Earth%20Scie
nces&start=211 …… 4/9/2012
Segment transformation in urban tourism.
McKercher, Bob.
Tourism Management vol. 29 issue 6 December, 2008. p. 1215-1225
This study tests the proposition that market segments transform
unevenly as distance from the source increases. It builds on distance
decay theory by extending the concept to a sub-market or segmentspecific level. To date, no research has examined the transformation of
market segments with distance.
The study examines outbound travel by Hong Kong residents to urban
destinations in 11 countries/territories.
The study reveals that the aggregate market profile changes with
distance, becoming generally older, more affluent and better educated.
However, analysis of share differential of six segments identified
through Cluster analysis reveals substantial differences between
them.
Two segments show evidence of segment decay, two show evidence of
segment emergence, one shows a polarized segment transformation
structure and another shows no relationship between share and
distance.
Diunduh dari:
http://journals.ohiolink.edu/ejc/search.cgi?q=keywords:%22Distancing%22&category.facet=Earth
Characterizing Tourist Sensitivity to Distance. Nicolau, Juan L. Journal of Travel
Research vol. 47 issue 1 August 2008. p. 43-52
Literature suggests that the effect of distance on destination choice
can be positive or negative, contingent on individual characteristics.
The aim of this study was to objectively measure, identify, and
characterize tourist sensitivities to distance—individual by individual—
in a real context where real choices made by tourists are observed.
The empirical application is carried out on a sample of 2,127
individuals, and the operative formalization used to estimate the
individual sensitivities to distance follows a random-coefficient logit
model; to detect the determinant factors, a regression analysis is
used.
After obtaining the sensitivity to distance of each sampled individual,
the dimensions that appear to have an effect on it are income,
number of children, size of the city of residence, use of
intermediaries, transport mode, interest in discovering new places,
variety-seeking behavior, and motivations.
Diunduh dari:
http://journals.ohiolink.edu/ejc/search.cgi?q=keywords:%22Distancing%22&category.facet=Earth
Spatial modelling in irregularly shaped regions: kriging
estuaries.
Rathbun, Stephen L.
Environmetrics vol. 9 issue 2 March/April 1998. p. 109 - 129
Estuaries are among the earth’s most valuable and productive environmental
resources. To further our understanding of the impact of human activities on
estuaries, there is a need for appropriate statistical methods for analyzing
estuarine data.
Estuaries possess a number of features that must be considered during
spatial data analyses. Estuaries are irregularly shaped non-convex regions.
Therefore, Euclidean distance may not be an appropriate distance metric for
spatial analyses of estuaries, especially if the line segment connecting two
sites intercepts land. Furthermore, some environmental variables may take
deterministic values at estuarine boundaries. For example, shorelines are
saturated with dissolved oxygen, and the salinity at estuarine mouths should
be close to that of the ocean.
This paper considers methods for spatial modelling and prediction using
different distance metrics, and under fixed boundary conditions. These
methods are illustrated using data from Charleston Harbor, an estuary on the
coast of South Carolina, USA
© 1998 John Wiley & Sons, Ltd.
Diunduh dari:
http://journals.ohiolink.edu/ejc/search.cgi?q=keywords:%22Distancing%22&category.facet=Earth
Effects of Silviculture Using Best Management Practices on Stream Macroinvertebrate
Communities in Three Ecoregions of Arkansas, USA. McCord, Samuel B.; Grippo,
Richard S.; Eagle, Dennis M. Water, Air, and Soil Pollution vol. 184 issue 1-4
September 2007. p. 299 - 311
We examined aquatic macroinvertebrate assemblages in six Arkansas
low-order streams across three ecoregions. Samples were taken at
locations above and below silviculture sites using Best Management
Practices (BMPs) and were compared in winter and spring for 1 year
prior to logging and 2 years after treatments. Implementation at all sites
scored between 89 and 100% in compliance assessments using state
BMP guidelines.
Deficiencies were generally limited to engineering controls designed to
prevent soil erosion; however, no clear evidence of sedimentation was
observed in any of the study streams. Water quality variables were
similar between sites upstream and downstream of the harvests in all
survey periods.
Analysis of variance did not indicate reduced taxonomic richness that
could clearly be attributed to silviculture operations, but did reveal
several significant differences in relative abundance variables that could
be associated with negative impacts, primarily at a single site.
Euclidean distance indicated that macroinvertebrate assemblage
similarity between reference and treatment stations decreased after
treatments at two additional study sites. At most sites, however, there
was not an assemblage shift from organisms using coarse particulate
organic matter as the primary food source to those using fine particulate
organic matter downstream of the harvests.
Our results indicated that BMPs were moderately to strongly effective in
protecting water quality and biological integrity in five of the six study
streams.
Diunduh dari:
http://journals.ohiolink.edu/ejc/search.cgi?q=keywords:%22Distancing%22&category.facet=Earth
A method for spatial heterogeneity evaluation on landscape pattern of farmland
shelterbelt networks: A case study in midwest of Jilin Province, China. Shi, Xiaoliang;
Li, Ying; Deng, Rongxin. Chinese Geographical Science vol. 21 issue 1 February
2011. p. 48 - 56
On the basis of landscape ecology, combining the Spot 5 high resolution
satellite imagery with GIS, a method evaluating the spatial heterogeneity of
shelterbelts distribution at landscape scale is put forward in this paper.
The distance coefficients of reasonable and existing landscape indexes of
farmland shelterbelt networks were computed, and then through the
classification of the distance coefficients, and the establishment of evaluation
rules, the spatial heterogeneity of farmland shelterbelts was evaluated.
The method can improve the evaluating system of previous studies on
shelterbelts distribution, resolve the disadvantages of lacking spatiality of
overall evaluation, and make the evaluation results have more directive
significance for shelterbelt management.
Based on this method, spatial heterogeneity of shelterbelt networks was
evaluated in the midwest of Jilin Province, China.
The results show that the regions with fewer shelterbelts and no closed
network account for 34.7% of the total area, but only 4.9% of the area has
relative reasonable pattern of shelterbelt networks. Many problems exist in
the distribution pattern of shelterbelts, therefore, much attention should be
paid to construct farmland shelterbelts in the study area.
Diunduh dari:
http://journals.ohiolink.edu/ejc/search.cgi?q=keywords:%22Distancing%22&category.facet=Earth
%20Sciences&start=241 …… 4/9/2012
Concept to assess the human perception of odour by estimating short-time peak
concentrations from one-hour mean values. Reply to a comment by Janicke et al.
Schauberger, Günther; Piringer, Martin; Schmitzer, Rainer; Kamp, Martin; Sowa,
Andreas; Koch, Roman; Eckhof, Wilfried; Grimm, Ewald; Kypke, Joachim; Hartung,
Eberhard. Atmospheric Environment vol. 54 July, 2012. p. 624-628
Biologically relevant exposure to environmental pollutants often shows a nonlinear relationship. For their assessment, as a rule short term concentrations
have to be determined instead of long term mean values. This is also the case
for the perception of odour. Regulatory dispersion models like AUSTAL2000
calculate long term mean concentration values (one-hour), but provide no
information on the fluctuation from this mean. The ratio between a short term
mean value (relevant for odour perception) and the long term mean value
(calculated by the dispersion model), called the peak-to-mean value, is
usually used to describe these fluctuations. In general, this ratio can be
defined in different ways.
Janicke et al. (2012), in a comment to Schauberger et al. (2012) which
includes a statement that AUSTAL2000 uses a constant factor of 4, argue that
AUSTAL2000 does not apply a peak-to-mean factor and does not calculate
odour exceedance probabilities.
Instead it calculates the frequency of so-called odour-hours by applying the
relation between the 90-percentile of the instantaneous concentration and the
hourly mean (Janicke and Janicke, 2007a), not between some peak value and
the mean.
According to Janicke and Janicke (2007a), the 90-percentile of the
instantaneous concentration can in practice be estimated with sufficient
accuracy from the hourly mean by using a factor of 4.
Having so far replied to Janicke et al. (2012) we take additionally the
opportunity to elaborate a little more on the peak-to-mean concept, especially
pointing out that a constant factor independent of the stability of the
atmosphere, the distance from and the geometry of the source, is not
appropriate. On the contrary it shows a sophisticated structure which cannot
be described by only one single value.
Diunduh dari:
http://journals.ohiolink.edu/ejc/search.cgi?q=keywords:%22Distancing%22&category.facet=Earth
Least cost distance analysis for spatial interpolation. Greenberg, Jonathan A.; Rueda,
Carlos; Hestir, Erin L.; Santos, Maria J.; Ustin, Susan L. Computers and Geosciences
vol. 37 issue 2 February, 2011. p. 272-276
Spatial interpolation allows creation of continuous raster surfaces from a
subsample of point-based measurements. Most interpolation approaches
use Euclidean distance measurements between data points to generate
predictions of values at unknown locations. However, there are many
spatially distributed data sets that are not properly represented by
Euclidean distances and require distance measures which represent their
complex geographic connectivity.
The problem of defining non-Euclidean distances between data points has
been solved using the network-based solutions, but such techniques have
historically relied on a network of connected line segments to determine
point-to-point distances. While these vector-based solutions are
computationally efficient, they cannot model more complex 2- and 3dimensional systems of connectivity. Here, we use least-cost-path analyses
to define distances between sampled points; a solution that allows for
arbitrarily complex systems of connectivity to be interpolated.
We used least-cost path distances in conjunction with the inverse distance
weighting interpolation for a proof-of-concept interpolation of water
temperature data in a complex deltaic river system.
We compare our technique to Euclidean distance interpolation, and
demonstrate that our technique, which follows connectivity rules, yields are
more realistic interpolation of water temperature.
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%20Sciences&start=31 …… 4/9/2012
Diurnal variation of urban structure in terms of time distance: A spatio-temporal
analysis of an urban area. Itoh, Satoru. GeoJournal vol. 52 issue 3 November 2000.
p. 223 - 235
The purpose of this paper is to clarify the diurnal variations in structure of
an urban area from the viewpoint of time distance.
To accomplish this, for one entire day, and for the morning, noon, and
evening periods, time maps are delineated by using MDS; also, the indices
of accessibility and circuity are computed from the time distances.
As a result, the difference in shape between the time and actual maps
becomes clear especially in the morning and also in the evening. Both the
accessibility and circuity measured from the time distance show a
concentrically shaped pattern where the regional disparity is especially
distinct within the morning and evening periods.
The diurnal variations as described above are thought to occur against the
backdrop of the topological traffic conditions within the study area.
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http://journals.ohiolink.edu/ejc/search.cgi?q=keywords:%22Distancing%22&category.facet=Earth
Ecological Cost-Benefit Modelling of Herbivore Habitat Quality Degradation due to
Range Fragmentation. Lundqvist, Henrik. Transactions in GIS vol. 11 issue 5 October
2007. p. 745-763
Fragmentation of grazing ranges and ensuing rise in edge effects
decrease forage range quality for large herbivores. A method is proposed
to quantify, in ecological cost-benefit terms, the negative impact of
fragmentation by linear structures with special emphasis on summer
ranges of semi-domesticated reindeer ( Rangifer t. tarandus).
The method is also applicable to other terrestrial species and on different
scales. The term ‘reachability’ is introduced for this measurement, which
integrates forage quality, quantity and availability, as well as the costs of
the animal's movement in a variable landscape and across fragmenting
linear structures. The method uses a cost-distance algorithm, commonly
available in GIS software. Effective distances and reachability over large
areas are calculated from evenly distributed sample points.
Effects of varying sample point distance, fragmenting structure friction
weight and density, and edge effect depth were analysed for model
calibration. In an example the model was used for estimation of
reachability alteration due to linear structures in the summer ranges of
the Handölsdalen reindeer herding district in Sweden, where hourly GPS
positions of 10 free-ranging female reindeer were available.
In these data the reindeer population density appeared to decrease up
to 1 km away from roads, but no effect from hiking trails was detected.
The reachability model quantified a loss of 2.2–2.7% in range quality due
to range fragmentation.
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http://journals.ohiolink.edu/ejc/search.cgi?q=keywords:%22Distancing%22&category.facet=Earth
Landscape-based geostatistics: a case study of the distribution
of blue crab in Chesapeake Bay.
Jensen, Olaf P.; Christman, Mary C.; Miller, Thomas J.
Environmetrics vol. 17 issue 6 September 2006. p. 605 - 621
Geostatistical techniques have gained widespread use in ecology and
environmental science. Variograms are commonly used to describe and
examine spatial autocorrelation, and kriging has become the method of choice
for interpolating spatially-autocorrelated variables. To date, most applications
of geostatistics have defined the separation between sample points using
simple Euclidean distance. In heterogeneous environments, however, certain
landscape features may act as absolute or semi-permeable barriers.
This effective separation may be more accurately described by a measure of
distance that accounts for the presence of barriers. Here we present an
approach to geostatistics based on a lowest-cost path (LCP) function, in which
the cost of a path is a function of both the distance and the type of terrain
crossed.
The modified technique is applied to 13 years of survey data on blue crab
abundance in Chesapeake Bay. Use of this landscape-based distance metric
significantly changed estimates of all three variogram parameters. In this case
study, although local differences in kriging predictions were apparent, the use
of the landscape-based distance metric did not result in consistent
improvements in kriging accuracy.
Copyright © 2006 John Wiley & Sons, Ltd.
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%20Sciences&start=31 …… 4/9/2012
Effect of depression storage capacity on overland-flow generation for
rough horizontal surfaces: water transfer distance and scaling.
Darboux, F.; Davy, P.; Gascuel-Odoux, C.
Earth Surface Processes and Landforms vol. 27 issue 2 February 2002. p. 177 - 191
Overland-flow triggering on rough surfaces was investigated using an
understanding-oriented model.
The model was based on conditioned-walker technique and developed to
simulate and analyse the evolution of puddle connection on numerically
generated rough surfaces. The percolation theory gave a theoretical
framework to formalize model outputs and to study overland-flow scaling.
Overland-flow triggering appeared consistent with a percolation process.
A scale-change exponent was suggested. New insights based on the concept
of transfer distance of water were emphasized.
Transfer distance enabled us to analyse the water redistribution inside a field
and helped to define rainfall efficiency when infiltration occurred.
Copyright © 2002 John Wiley & Sons, Ltd.
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Uncertainties in real-time flood forecasting with neural
networks.
Han, Dawei; Kwong, Terence; Li, Simon.
Hydrological Processes vol. 21 issue 2 15 January 2007. p. 223 - 228
Although artificial neural networks (ANNs) have been applied in rainfall runoff
modelling for many years, there are still many important issues unsolved that
have prevented this powerful non-linear tool from wide applications in
operational flood forecasting activities.
This paper describes three ANN configurations and it is found that a dedicated
ANN for each lead-time step has the best performance and a multiple output
form has the worst result. The most popular form with multiple inputs and single
output has the average performance. In comparison with a linear transfer
function (TF) model, it is found that ANN models are uncompetitive against the
TF model in short-range predictions and should not be used in operational flood
forecasting owing to their complicated calibration process. For longer range
predictions, ANN models have an improved chance to perform better than the TF
model; however, this is highly dependent on the training data arrangement and
there are undesirable uncertainties involved, as demonstrated by bootstrap
analysis in the study.
To tackle the uncertainty issue, two novel approaches are proposed: distance
analysis and response analysis. Instead of discarding the training data after the
model’s calibration, the data should be retained as an integral part of the model
during its prediction stage and the uncertainty for each prediction could be
judged in real time by measuring the distances against the training data.
The response analysis is based on an extension of the traditional unit
hydrograph concept and has a very useful potential to reveal the hydrological
characteristics of ANN models, hence improving user confidence in using them in
real time.
Copyright © 2006 John Wiley & Sons, Ltd.
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http://journals.ohiolink.edu/ejc/search.cgi?q=keywords:%22Distancing%22&category.facet=Earth
Encyclopedia of Environmetrics
Volume 1
Abdel H. El-Shaarawi, Walter W. Piegorsch
Wiley, Dec 31, 2001 - 2672 pages
Environmetrics covers the development and application of
quantitative methods in the environmental sciences.
It provides essential tools for understanding, predicting, and
controlling the impacts of agents, both man-made and natural,
which affect the environment.
Basic and applied research in this area covers a broad range of
topics.
Primary among these are the quantitative sciences, such as
statistics, probability and applied mathematics, chemometrics, and
econometrics.
Applications are also important, for example in, ecology and
environmental biology, public health, atmospheric science, geology,
engineering, risk management, and regulatory/governmental policy
amongst others.
Diunduh dari:
G. P. Patil and C. R. Rao, eds., Handbook of Statistics, Vol. 12
© 1994 ElsevierScienceB.V. All rights reserved.
Environmetrics: An Emerging Science
(J. Stuart Hunter)
Environmetrics finds its origins in the search for the understanding
of the natural phenomena that surrounds mankind.
In antiquity these studies led to the creation of the earliest
instruments of measurement and to the beginning arts of
mathematical description.
Today's environmental studies combine the modern tools of physics
and chemistry with mathematical modeling of great sophistication.
But beyond measurement and mathematics, environmetrics has
become a unique 'n-science', a meeting ground for the ecologist,
the natural and social scientist, the engineer and statistician, and
ultimately the political scientist.
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….. dst………. environmetrika
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