The standard methods for regression analyses of clustered riverine larval habitat data of Simulium damnosum s.l.a major black-fly vector of onchoceriasis,postulate models relating observational ecological-sampled para...The standard methods for regression analyses of clustered riverine larval habitat data of Simulium damnosum s.l.a major black-fly vector of onchoceriasis,postulate models relating observational ecological-sampled parameter estimators to prolific habitats without accounting for residual intra-cluster error correlation effects.Generally,this correlation comes from two sources:(1)the design of the random effects and their assumed covariance from the multiple levels within the regression model and(2)the correlation structure of the residuals.Unfortunately,inconspicuous errors in residual intracluster correlation estimates can overstate precision in forecasted S.damnosum s.l.riverine larval habitat explanatory attributes regardless how they are treated(e.g.independent,autoregressive,Toeplitz,etc.).In this research,the geographical locations for multiple riverine-based S.damnosum s.l.larval ecosystem habitats sampled from two preestablished epidemiological sites in Togo were identified and recorded from July 2009 to June 2010.Initially,the data were aggregated into PROC GENMOD.An agglomerative hierarchical residual cluster-based analysis was then performed.The sampled clustered study site data was then analyzed for statistical correlations using monthly biting rates(MBR).Euclidean distance measurements and terrain-related geomorphological statistics were then generated in ArcGIS.A digital overlay was then performed also in ArcGIS using the georeferenced ground coordinates of high and low density clusters stratified by annual biting rates(ABR).The data was overlain onto multitemporal sub-meter pixel resolution satellite data(i.e.QuickBird 0.61m wavbands).Orthogonal spatial filter eigenvectors were then generated in SAS/Geographic Information Systems(GIS).Univariate and nonlinear regression-based models(i.e.logistic,Poisson,and negative binomial)were also employed to determine probability distributions and to identify statistically significant parameter estimators from the sampled data.Thereafter,Durbin–Watson statistics were used to test the null hypothesis that the regression residuals were not autocorrelated against the alternative that the residuals followed an autoregressive process in AUTOREG.Bayesian uncertainty matrices were also constructed employing normal priors for each of the sampled estimators in PROC MCMC.The residuals revealed both spatially structured and unstructured error effects in the high and low ABR-stratified clusters.The analyses also revealed that the estimators,levels of turbidity,and presence of rocks were statistically significant for the high-ABR-stratified clusters,while the estimators distance between habitats and floating vegetation were important for the low-ABR-stratified cluster.Varying and constant coefficient regression models,ABRstratified GIS-generated clusters,sub-meter resolution satellite imagery,a robust residual intra-cluster diagnostic test,MBR-based histograms,eigendecomposition spatial filter algorithms,and Bayesian matrices can enable accurate autoregressive estimation of latent uncertainity affects and other residual error probabilities(i.e.heteroskedasticity)for testing correlations between georeferenced S.damnosum s.l.riverine larval habitat estimators.The asymptotic distribution of the resulting residual adjusted intra-cluster predictor error autocovariate coefficients can thereafter be established while estimates of the asymptotic variance can lead to the construction of approximate confidence intervals for accurately targeting productive S.damnosum s.l.habitats based on spatiotemporal field-sampled count data.展开更多
This paper surveys the current state of teaching spatial statistics in the United States(US),with commentary about the future teaching of such a course.It begins with a historical overview,and proposes what constitute...This paper surveys the current state of teaching spatial statistics in the United States(US),with commentary about the future teaching of such a course.It begins with a historical overview,and proposes what constitutes suitable content for a contemporary spatial statistics course.It notes that contemporary university-level spatial statistics courses are mostly taught across myriad units,including biology/ecology,climatology,economics(as spatial econometrics),environmental studies,epidemiology/public health,forestry,geography,geosciences/earth sciences,geospatial information sciences,mathematics,quantitative social science,soil science,and statistics.It discusses the diffusion of this course across the US,which began in the mid-1980s.One result it reports is a model spatial statistics course offering.展开更多
Scatterplots are essential tools for data exploration. However, this tool poorly scales with data-size, with overplotting and excessive delay being the main problems. Generalization methods in the attribute domain foc...Scatterplots are essential tools for data exploration. However, this tool poorly scales with data-size, with overplotting and excessive delay being the main problems. Generalization methods in the attribute domain focus on visual manipulations, but do not take into account the inherent nature of information redundancy in most geographic data. These methods may also result in alterations of statistical properties of data. Recent developments in spatial statistics, particularly the formulation of effective sample size and the fast approximation of the eigenvalues of a spatial weights matrix, make it possible to assess the information content of a georeferenced data-set, which can serve as the basis for resampling such data. Experiments with both simulated data and actual remotely sensed data show that an equivalent scatterplot consisting of point clouds and fitted lines can be produced from a small subset extracted from a parent georeferenced data-set through spatial resampling. The spatially simplified data subset also maintains key statistical properties as well as the geographic coverage of the original data.展开更多
基金This work was produced by the US National Institute of Health/Fogarty International Center under SR01TW008508.
文摘The standard methods for regression analyses of clustered riverine larval habitat data of Simulium damnosum s.l.a major black-fly vector of onchoceriasis,postulate models relating observational ecological-sampled parameter estimators to prolific habitats without accounting for residual intra-cluster error correlation effects.Generally,this correlation comes from two sources:(1)the design of the random effects and their assumed covariance from the multiple levels within the regression model and(2)the correlation structure of the residuals.Unfortunately,inconspicuous errors in residual intracluster correlation estimates can overstate precision in forecasted S.damnosum s.l.riverine larval habitat explanatory attributes regardless how they are treated(e.g.independent,autoregressive,Toeplitz,etc.).In this research,the geographical locations for multiple riverine-based S.damnosum s.l.larval ecosystem habitats sampled from two preestablished epidemiological sites in Togo were identified and recorded from July 2009 to June 2010.Initially,the data were aggregated into PROC GENMOD.An agglomerative hierarchical residual cluster-based analysis was then performed.The sampled clustered study site data was then analyzed for statistical correlations using monthly biting rates(MBR).Euclidean distance measurements and terrain-related geomorphological statistics were then generated in ArcGIS.A digital overlay was then performed also in ArcGIS using the georeferenced ground coordinates of high and low density clusters stratified by annual biting rates(ABR).The data was overlain onto multitemporal sub-meter pixel resolution satellite data(i.e.QuickBird 0.61m wavbands).Orthogonal spatial filter eigenvectors were then generated in SAS/Geographic Information Systems(GIS).Univariate and nonlinear regression-based models(i.e.logistic,Poisson,and negative binomial)were also employed to determine probability distributions and to identify statistically significant parameter estimators from the sampled data.Thereafter,Durbin–Watson statistics were used to test the null hypothesis that the regression residuals were not autocorrelated against the alternative that the residuals followed an autoregressive process in AUTOREG.Bayesian uncertainty matrices were also constructed employing normal priors for each of the sampled estimators in PROC MCMC.The residuals revealed both spatially structured and unstructured error effects in the high and low ABR-stratified clusters.The analyses also revealed that the estimators,levels of turbidity,and presence of rocks were statistically significant for the high-ABR-stratified clusters,while the estimators distance between habitats and floating vegetation were important for the low-ABR-stratified cluster.Varying and constant coefficient regression models,ABRstratified GIS-generated clusters,sub-meter resolution satellite imagery,a robust residual intra-cluster diagnostic test,MBR-based histograms,eigendecomposition spatial filter algorithms,and Bayesian matrices can enable accurate autoregressive estimation of latent uncertainity affects and other residual error probabilities(i.e.heteroskedasticity)for testing correlations between georeferenced S.damnosum s.l.riverine larval habitat estimators.The asymptotic distribution of the resulting residual adjusted intra-cluster predictor error autocovariate coefficients can thereafter be established while estimates of the asymptotic variance can lead to the construction of approximate confidence intervals for accurately targeting productive S.damnosum s.l.habitats based on spatiotemporal field-sampled count data.
文摘This paper surveys the current state of teaching spatial statistics in the United States(US),with commentary about the future teaching of such a course.It begins with a historical overview,and proposes what constitutes suitable content for a contemporary spatial statistics course.It notes that contemporary university-level spatial statistics courses are mostly taught across myriad units,including biology/ecology,climatology,economics(as spatial econometrics),environmental studies,epidemiology/public health,forestry,geography,geosciences/earth sciences,geospatial information sciences,mathematics,quantitative social science,soil science,and statistics.It discusses the diffusion of this course across the US,which began in the mid-1980s.One result it reports is a model spatial statistics course offering.
文摘Scatterplots are essential tools for data exploration. However, this tool poorly scales with data-size, with overplotting and excessive delay being the main problems. Generalization methods in the attribute domain focus on visual manipulations, but do not take into account the inherent nature of information redundancy in most geographic data. These methods may also result in alterations of statistical properties of data. Recent developments in spatial statistics, particularly the formulation of effective sample size and the fast approximation of the eigenvalues of a spatial weights matrix, make it possible to assess the information content of a georeferenced data-set, which can serve as the basis for resampling such data. Experiments with both simulated data and actual remotely sensed data show that an equivalent scatterplot consisting of point clouds and fitted lines can be produced from a small subset extracted from a parent georeferenced data-set through spatial resampling. The spatially simplified data subset also maintains key statistical properties as well as the geographic coverage of the original data.
