A Negative Binomial with a Non-Homogenous Gamma Distributed Mean for Robustifying Pseudo R2 Regression Values of Immature Vector and Nuisance Mosquito Count Data for Optimally Discerning Un-Geosampled Waste Tires in a Subtropical Oviposition Site in SAS®/GIS Utsing Worldview-3 Visble and Near Infra-Red Data in Hillsborough County, Florida

Abstract

Refuse vehicle tires on undeveloped land plots near human dwellings may be a public health threat, as they can provide a suitable habitat for vector and nuisance mosquito (Diptera: Culicidae) population growth. These tires are currently found only through ground-based searches, so interpolated spectral signature of a geo-referenceable, known, positive tire may help expedite discriminating unknown waste tire geolocations. However, frequentistic and nonfrequentistic quantification of bioenvironmental explanatorial time series covariates statistically significant to mosquito hyperproductivitt in waste tire habitats is needed to limit the search criteria of the signature. This study aimed to develop an iteratively interpolative geo-spectral biosignature for detecting unknown, un-geosampled waste tires conducive to mosquito propagation. After constructing various regression models, we found that the field geo-sampled mosquito count data featured deviations from the assumptions of regression modeling (i.e., collinear and heteroskedastic parameters). Thus, a negative binomial paradigm was utilized to assuage the violations of regression analysis and to robustify the model's R 2 value. Based on the results of the linear analyses, a spectral signature of a productive habitat was created from multispectral band imagery from WorldView-3 satellite sensor data. The signature was then applied in Hillsborough County, FL to remotely determine the eco-geographical geo-locations of anthropogenic waste tire habitats. The signature model exhibited a sensitivity of 83% and a specificity of 87%. In conclusion, the regression and signature models constructed here provided a parsimonious yet accurate estimation of undiscovered waste tire habitats that may yield many mosquitoes. [5] composed geo-optical algorithms for decomposing sub-meter spatial resolution (i.e., panchromatic Quickbird 0.61m IFOV data) imagery of rice field environments in order to geo-locate undiscovered productive aquatic larval habitats of malaria mosquito vector of Anopheles arabiensis (Diptera: Culicidae). The reference biosignatures of these habitats generated from the unmixing algorithmic geomteri-optical models were then used to perform an ordinary krig-based interpolation in ArcGIS ® [5]. Likewise, Jacob et al. [6] geo-predicted seasonal trailing vegation, discontinuous, infrequently canopied, turbid water, seasonal black -fly vector of onchocercisasis, Simulium damnosum s. l. (Diptera: Simuliidae) by extracting spectral end members of canopy shaded riverine sites featuring black Precambrian rock from QuickBird imagery [6]. The end members were decomposed to orthogonal eigenevctors render a specified graphical