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2.
Ronald E. Gangnon 《Environmental and Ecological Statistics》2010,17(1):55-71
The spatial scan statistic is a widely applied tool for cluster detection. The spatial scan statistic evaluates the significance
of a series of potential circular clusters using Monte Carlo simulation to account for the multiplicity of comparisons. In
most settings, the extent of the multiplicity problem varies across the study region. For example, urban areas typically have
many overlapping clusters, while rural areas have few. The spatial scan statistic does not account for these local variations
in the multiplicity problem. We propose two new spatially-varying multiplicity adjustments for spatial cluster detection,
one based on a nested Bonferroni adjustment and one based on local averaging. Geographic variations in power for the spatial
scan statistic and the two new statistics are explored through simulation studies, and the methods are applied to both the
well-known New York leukemia data and data from a case–control study of breast cancer in Wisconsin. 相似文献
3.
Ecological counts data are often characterized by an excess of zeros and spatial dependence. Excess zeros can occur in regions
outside the range of the distribution of a given species. A zero-inflated Poisson regression model is developed, under which
the species range is determined by a spatial probit model, including physical variables as covariates. Within that range,
species counts are independently drawn from a Poisson distribution whose mean depends on biotic variables. Bayesian inference
for this model is illustrated using data on oak seedling counts.
Received: May 2004 / Revised: December 2004 相似文献
4.
Anderson Ribeiro Duarte Luiz Duczmal Sabino José Ferreira André Luiz F. Cançado 《Environmental and Ecological Statistics》2010,17(2):203-229
The geographic delineation of irregularly shaped spatial clusters is an ill defined problem. Whenever the spatial scan statistic
is used, some kind of penalty correction needs to be used to avoid clusters’ excessive irregularity and consequent reduction
of power of detection. Geometric compactness and non-connectivity regularity functions have been recently proposed as corrections.
We present a novel internal cohesion regularity function based on the graph topology to penalize the presence of weak links
in candidate clusters. Weak links are defined as relatively unpopulated regions within a cluster, such that their removal
disconnects it. By applying this weak link cohesion function, the most geographically meaningful clusters are sifted through
the immense set of possible irregularly shaped candidate cluster solutions. A multi-objective genetic algorithm (MGA) has
been proposed recently to compute the Pareto-sets of clusters solutions, employing Kulldorff’s spatial scan statistic and
the geometric correction as objective functions. We propose novel MGAs to maximize the spatial scan, the cohesion function
and the geometric function, or combinations of these functions. Numerical tests show that our proposed MGAs has high power
to detect elongated clusters, and present good sensitivity and positive predictive value. The statistical significance of
the clusters in the Pareto-set are estimated through Monte Carlo simulations. Our method distinguishes clearly those geographically
inadequate clusters which are worse from both geometric and internal cohesion viewpoints. Besides, a certain degree of irregularity
of shape is allowed provided that it does not impact internal cohesion. Our method has better power of detection for clusters
satisfying those requirements. We propose a more robust definition of spatial cluster using these concepts. 相似文献
5.
A declared need is around for geoinformatic surveillance statistical science and software infrastructure for spatial and spatiotemporal hotspot detection. Hotspot means something unusual, anomaly, aberration, outbreak, elevated cluster, critical resource area, etc. The declared need may be for monitoring, etiology, management, or early warning. The responsible factors may be natural, accidental, or intentional. This proof-of-concept paper suggests methods and tools for hotspot detection across geographic regions and across networks. The investigation proposes development of statistical methods and tools that have immediate potential for use in critical societal areas, such as public health and disease surveillance, ecosystem health, water resources and water services, transportation networks, persistent poverty typologies and trajectories, environmental justice, biosurveillance and biosecurity, among others. We introduce, for multidisciplinary use, an innovation of the health-area-popular circle-based spatial and spatiotemporal scan statistic. Our innovation employs the notion of an upper level set, and is accordingly called the upper level set scan statistic, pointing to a sophisticated analytical and computational system as the next generation of the present day popular SaTScan. Success of surveillance rests on potential elevated cluster detection capability. But the clusters can be of any shape, and cannot be captured only by circles. This is likely to give more of false alarms and more of false sense of security. What we need is capability to detect arbitrarily shaped clusters. The proposed upper level set scan statistic innovation is expected to fill this need 相似文献
6.
Annalina Sarra Eugenia Nissi Sergio Palermi 《Environmental and Ecological Statistics》2012,19(2):219-247
Indoor radon is an important risk factor for human health. Indeed radon inhalation is considered the second cause of lung cancer after smoking. During the last decades, in many countries huge efforts have been made in order to measuring, mapping and predicting radon levels in dwellings. Various researches have been devoted to identify those areas within the country where high radon concentrations are more likely to be found. Data collected through indoor radon surveys have been analysed adopting various statistical approaches, among which hierarchical Bayesian models and geostatistical tools are worth noting. The essential goal of this paper regards the identification of high radon concentration areas (the so-called radon prone areas) in the Abruzzo Region (Italy). In order to accurately pinpoint zones deserving attention for mitigation purpose, we adopt spatial cluster detection techniques, traditionally employed in epidemiology. As a first step, we assume that indoor radon measurements do not arise from a continuous spatial process; thus the geographic locations of dwellings where the radon measurements have been taken can be viewed as a realization of a spatial point process. Following this perspective, we adopt and compare recent cluster detection techniques: the simulated annealing scan statistic, the case event approach based on distance regression on the selection order and the elliptic spatial scan statistic. The analysis includes data collected during surveys carried out by the Regional Agency for the Environment Protection of Abruzzo (ARTA) in 1,861 random sampled dwellings across 277 municipalities of the Abruzzo region. The radon prone areas detected by the selected approaches are provided along with the summary statistics of the methods. Finally, the methodologies considered in this paper are tested on simulated data in order to evaluate their power and the precision of cluster location detection. 相似文献
7.
Luiz Duczmal Ricardo Tavares Ganapati Patil André L. F. Cançado 《Environmental and Ecological Statistics》2010,17(2):183-202
We propose a novel tool for testing hypotheses concerning the adequacy of environmentally defined factors for local clustering
of diseases, through the comparative evaluation of the significance of the most likely clusters detected under maps whose
neighborhood structures were modified according to those factors. A multi-objective genetic algorithm scan statistic is employed
for finding spatial clusters in a map divided in a finite number of regions, whose adjacency is defined by a graph structure.
This cluster finder maximizes two objectives, the spatial scan statistic and the regularity of cluster shape. Instead of specifying
locations for the possible clusters a priori, as is currently done for cluster finders based on focused algorithms, we alter
the usual adjacency induced by the common geographical boundary between regions. In our approach, the connectivity between
regions is reinforced or weakened, according to certain environmental features of interest associated with the map. We build
various plausible scenarios, each time modifying the adjacency structure on specific geographic areas in the map, and run
the multi-objective genetic algorithm for selecting the best cluster solutions for each one of the selected scenarios. The
statistical significances of the most likely clusters are estimated through Monte Carlo simulations. The clusters with the
lowest estimated p-values, along with their corresponding maps of enhanced environmental features, are displayed for comparative analysis. Therefore
the probability of cluster detection is increased or decreased, according to changes made in the adjacency graph structure,
related to the selection of environmental features. The eventual identification of the specific environmental conditions which
induce the most significant clusters enables the practitioner to accept or reject different hypotheses concerning the relevance
of geographical factors. Numerical simulation studies and an application for malaria clusters in Brazil are presented. 相似文献
8.
Zero-inflated models with application to spatial count data 总被引:1,自引:2,他引:1
Deepak K. Agarwal Alan E. Gelfand Steven Citron-Pousty 《Environmental and Ecological Statistics》2002,9(4):341-355
Count data arises in many contexts. Here our concern is with spatial count data which exhibit an excessive number of zeros. Using the class of zero-inflated count models provides a flexible way to address this problem. Available covariate information suggests formulation of such modeling within a regression framework. We employ zero-inflated Poisson regression models. Spatial association is introduced through suitable random effects yielding a hierarchical model. We propose fitting this model within a Bayesian framework considering issues of posterior propriety, informative prior specification and well-behaved simulation based model fitting. Finally, we illustrate the model fitting with a data set involving counts of isopod nest burrows for 1649 pixels over a portion of the Negev desert in Israel. 相似文献
9.
This paper extends the spatial local-likelihood model and the spatial mixture model to the space-time (ST) domain. For comparison,
a standard random effect space-time (SREST) model is examined to allow evaluation of each model’s ability in relation to cluster
detection. To pursue this evaluation, we use the ST counterparts of spatial cluster detection diagnostics. The proposed criteria
are based on posterior estimates (e.g., misclassification rate) and some are based on post-hoc analysis of posterior samples
(e.g., exceedance probability). In addition, we examine more conventional model fit criteria including mean square error (MSE).
We illustrate the methodology with a real ST dataset, Georgia throat cancer mortality data for the years 1994–2005, and a
simulated dataset where different levels and shapes of clusters are embedded. Overall, it is found that conventional SREST
models fair well in ST cluster detection and in goodness-of-fit, while for extreme risk detection the local likelihood ST
model does best. 相似文献
10.
G. P. Patil J. A. Bishop W. L. Myers C. Taillie R. Vraney Denice Wardrop 《Environmental and Ecological Statistics》2004,11(2):139-164
Geographical surveillance for hotspot detection and delineation has become an important area of investigation both in geospatial ecosystem health and in geospatial public health. In order to find critical areas based on synoptic cellular data, geospatial ecosystem health investigations apply recently discovered echelon tools. In order to find elevated rate areas based on synoptic cellular data, geospatial public health investigations apply recently discovered spatial scan statistic tools. The purpose of this paper is to conceptualize a joint role for these together in the spirit of a cross-disciplinary cross-fertilization to accomplish more effective and efficient geographical surveillance for hotspot detection and delineation, and early warning system. 相似文献
11.
Ratio estimation of the parametric mean for a characteristic measured on plants sampled by a line intercept method is presented and evaluated via simulation using different plant dispersion patterns (Poisson, regular cluster, and Poisson cluster), plant width variances, and numbers of lines. The results indicate that on average the estimates are close to the parametric mean under all three dispersion patterns. Given a fixed number of lines, variability of the estimates is similar across dispersion patterns with variability under the Poisson pattern slightly smaller than varia-bility under the cluster patterns. No variance estimates were negative under the Poisson pattern, but some estimates were negative under the cluster patterns for smaller numbers of lines. Variance estimates become closer to zero similarly for all spatial patterns as the number of lines increases. Ratio estimation of the parametric mean in line intercept sampling works better, from the viewpoint of approximate unbiasedness and variability of estimates, under the Poisson pattern with larger numbers of lines than other combinations of spatial patterns, plant width variances and numbers of lines. 相似文献
12.
Detecting population declines is a critical task for conservation biology. Logistical difficulties and the spatiotemporal variability of populations make estimation of population declines difficult. For statistical reasons, estimates of population decline may be biased when study sites are chosen based on abundance of the focal species. In this situation, apparent population declines are likely to be detected even if there is no decline. This site-selection bias is mentioned in the literature but is not well known. We used simulations and real population data to examine the effects of site-selection biases on inferences about population trends. We used a left-censoring method to detect population-size patterns consistent with site-selection bias. The site-selection bias is an important consideration for conservation biologists, and we offer suggestions for minimizing or mitigating it in study design and analysis. Article impact statement: Estimates of population declines are biased if studies begin in large populations, and time-series data show a signature of such an effect. 相似文献
13.
Whether general environmental exposures to endocrine disrupting chemicals (including pesticides and dioxin) might induce decreased
sex ratios (male/female ratio at birth) is discussed. To address this issue, the authors looked for a space-time clustering
test which could detect local areas of significantly low risk, assuming a Bernoulli distribution. As a matter of fact, if the endocrine disruptor hypothesis holds true, and if the
sex ratio is a sentinel health event indicative of new reproductive hazards ascribed to environmental factors, then in a given
region, either a cluster of low male/female ratio among newborn babies would be expected in the vicinity of polluting municipal
solid waste incinerators (MSWIs) (supporting the dioxin hypothesis), or local clusters would be expected in some rural areas
where large amounts of pesticides are sprayed.
Among cluster detection tests, the spatial scan statistic has been widely used in various applications to scan for areas
with high rates, and rarely (if ever) with low rates. Therefore, the goal of this paper was to check the properties of the
scan statistics under a given scenario (Bernoulli distribution, search for clusters with low rates) and to assess its added
value in addressing the sex ratio issue.
This study took place in the Franche-Comté region (France), mainly rural, comprising three main MSWIs, among which only one
had high dioxin emissions level in the past. The study population consisted of 192,490 boys and 182,588 girls born during
the 1975–1999 period.
On the whole, the authors conclude that: (i) spatial and space-time scan statistics provide attractive features to address
the sex ratio issue; (ii) sex ratio is not markedly affected across space and does not provide a reliable screening measure
for detecting reproductive hazards ascribed to environmental factors. 相似文献
14.
Accounting for rate instability and spatial patterns in the boundary analysis of cancer mortality maps 总被引:1,自引:0,他引:1
Pierre Goovaerts 《Environmental and Ecological Statistics》2008,15(4):421-446
Boundary analysis of cancer maps may highlight areas where causative exposures change through geographic space, the presence
of local populations with distinct cancer incidences, or the impact of different cancer control methods. Too often, such analysis
ignores the spatial pattern of incidence or mortality rates and overlooks the fact that rates computed from sparsely populated
geographic entities can be very unreliable. This paper proposes a new methodology that accounts for the uncertainty and spatial
correlation of rate data in the detection of significant edges between adjacent entities or polygons. Poisson kriging is first
used to estimate the risk value and the associated standard error within each polygon, accounting for the population size
and the risk semivariogram computed from raw rates. The boundary statistic is then defined as half the absolute difference
between kriged risks. Its reference distribution, under the null hypothesis of no boundary, is derived through the generation
of multiple realizations of the spatial distribution of cancer risk values. This paper presents three types of neutral models
generated using methods of increasing complexity: the common random shuffle of estimated risk values, a spatial re-ordering
of these risks, or p-field simulation that accounts for the population size within each polygon. The approach is illustrated
using age-adjusted pancreatic cancer mortality rates for white females in 295 US counties of the Northeast (1970–1994). Simulation
studies demonstrate that Poisson kriging yields more accurate estimates of the cancer risk and how its value changes between
polygons (i.e., boundary statistic), relatively to the use of raw rates or local empirical Bayes smoother. When used in conjunction
with spatial neutral models generated by p-field simulation, the boundary analysis based on Poisson kriging estimates minimizes
the proportion of type I errors (i.e., edges wrongly declared significant) while the frequency of these errors is predicted
well by the p-value of the statistical test.
相似文献
Pierre GoovaertsEmail: |
15.
We derive some statistical properties of the distribution of two Negative Binomial random variables conditional on their total. This type of model can be appropriate for paired count data with Poisson over-dispersion such that the variance is a quadratic function of the mean. This statistical model is appropriate in many ecological applications including comparative fishing studies of two vessels and or gears. The parameter of interest is the ratio of pair means. We show that the conditional means and variances are different from the more commonly used Binomial model with variance adjusted for over-dispersion, or the Beta-Binomial model. The conditional Negative Binomial model is complicated because it does not eliminate nuisance parameters like in the Poisson case. Maximum likelihood estimation with the unconditional Negative Binomial model can result in biased estimates of the over-dispersion parameter and poor confidence intervals for the ratio of means when there are many nuisance parameters. We propose three approaches to deal with nuisance parameters in the conditional Negative Binomial model. We also study a random effects Binomial model for this type of data, and we develop an adjustment to the full-sample Negative Binomial profile likelihood to reduce the bias caused by nuisance parameters. We use simulations with these methods to examine bias, precision, and accuracy of estimators and confidence intervals. We conclude that the maximum likelihood method based on the full-sample Negative Binomial adjusted profile likelihood produces the best statistical inferences for the ratio of means when paired counts have Negative Binomial distributions. However, when there is uncertainty about the type of Poisson over-dispersion then a Binomial random effects model is a good choice. 相似文献
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17.
This paper presents a scan statistic, progressive upper level set (PULSE) scan statistic, for geospatial hotspot detection
and its software implementation. Like ULS, the PULSE scan statistic is based on the arbitrarily shaped scan window and can
be adapted for a network setting. PULSE is a refinement of the upper level set (ULS) scan statistic. Like some other likelihood
based scanning devices, the ULS scan statistic identifies maximum likelihood estimate (MLE) zones that tend to be ‘stringy’
and sprawling. Its search path increases possibility of inclusion of extraneous cells in its MLE zones and, to a smaller extent,
of exclusion of cells that belong to a true hotspot from its MLE zone. The PULSE scan statistic achieves improvement over
the ULS scan statistic in two ways. First, it begins its search for a most likely zone with a large population of candidate
zones obtained by modifying the ULS tree structure and continues its search using a genetic algorithm. Secondly, to reduce
chances of generating an MLE that is excessively stringy and that includes extraneous cells in the MLE zone, PULSE uses cardinality
and compactness of zones along with their likelihoods as the fitness function in the genetic algorithm and uses several pertinent
criteria including evenness of intra-zone cellular response ratios to determine the MLE zone. To reduce computation, Gumbel
distribution of extreme values is used to determine the p-value of the MLE zone. Better results come at the cost of increased processing time. An evaluative performance study is presented. 相似文献
18.
Md. Monir Hossain Andrew B. Lawson Bo Cai Jungsoon Choi Jihong Liu Russell S. Kirby 《Environmental and Ecological Statistics》2013,20(1):91-107
We propose a space-time stick-breaking process for the disease cluster estimation. The dependencies for spatial and temporal effects are introduced by using space-time covariate dependent kernel stick-breaking processes. We compared this model with the space-time standard random effect model by checking each model’s ability in terms of cluster detection of various shapes and sizes. This comparison was made for simulated data where the true risks were known. For the simulated data, we have observed that space-time stick-breaking process performs better in detecting medium- and high-risk clusters. For the real data, county specific low birth weight incidences for the state of South Carolina for the years 1997–2007, we have illustrated how the proposed model can be used to find grouping of counties of higher incidence rate. 相似文献
19.
Recent years have witnessed the growth of new information technologies and their applications to various disciplines. The
goal of this paper is to demonstrate how the two innovative methods, upper level set scan (ULS) hotspot detection and the
multicriteria prioritization scheme, facilitate population health and break new ground in public health surveillance. It is
believed that the social environment (i.e. social conditions and social capital) is one of the determinants of human health.
Using infant health data and 10 additional indicators of social environment in the 159 counties of Georgia, ULS identified
52 counties that are in double jeopardy (high infant mortality and a high rate of low infant birth weight). The multicriteria
ranking scheme suggested that there was no conspicuous spatial cluster of ranking orders, which improved the traditional decision
making by visual geographic cluster. Both hotspot detection and ranking methods provided an empirical basis for re-allocating
limited resources and several policy implications could be drawn from these analytic results. 相似文献
20.
Hans J. Skaug 《Environmental and Ecological Statistics》2006,13(2):199-211
We model the points of the detection along the transect line by a Markov modulated Poisson process (MMPP). The MMPP can accommodate
the spatial cluster structure typical of many line transect surveys. The basic idea is that animal density switches between
a low and a high level according to a latent Markov process. The MMPP is attractive from a mathematical point of view, as
it provides an explicit expression for the likelihood function and other important quantities. We focus on estimating the
level of overdispersion in the number of detected animals, as this is important for quantifying the precision of the line
transect estimator of animal abundance. The approach is illustrated using both simulated data and data from a minke whale
sighting survey conducted in the North Atlantic.
Received: August 2004 / Revised: August 2005 相似文献