The standard methods for regression analyses of clustered riverine larval habitat data of 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 intra-cluster correlation estimates can overstate precision in forecasted 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 larval ecosystem habitats sampled from 2 pre-established epidemiological sites in Togo were identified and recorded from July 2009 to June 2010. Initially the data was 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). This 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/GIS. Univariate and non-linear 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 test 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, ABR- stratified 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 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 habitats based on spatiotemporal field-sampled count data.

Noise simulations are an important part of noise studies that investigate the impact of noise sources on the environment. In noise simulation, noise levels at receiver points are calculated based on the noise propagation paths between the receiver and source points. These paths are derived from the height of the terrain. In current calculation approaches implemented in noise simulation software, 3D polylines are used as input to describe the height of the terrain. These 3D polylines are semi-automatically generated to meet the highly demanding computing performance of simulation software. In addition, previous research showed that the reconstruction of appropriate height lines as used in noise simulation is very difficult to automate, if not impossible As a solution, this research investigates how noise propagation paths between receiver and source points can directly be generated from a Triangulate Irregular Network (TIN) without creating the height lines. This would allow us to use the automatically generated TIN as input for noise simulation instead of the height lines. In addition, a TIN enables better control of the quality of the data than height lines do. This study uses the 3D noise modeling guidelines of Common Noise Assessment Methods in Europe (CNOSSOS-EU). Algorithms have been developed and implemented in a prototype to generate and validate the paths between receiver and source points using a TIN that includes the buildings as well as the noise absorption properties of the terrain. The prototype is successfully tested on two scenarios from the Netherlands. Since CNOSSOS-EU guidelines were used, the prototype is applicable to the entire European Union and can be the first step in improving the automation of 3D noise modeling using currently available techniques and data.