A Spatial Filtering Specification for an Auto‐negative Binomial Model of Anopheles arabiensis Aquatic Habitats

This research accounts for spatial autocorrelation by including latent map pattern components as predictor variables in a malaria mosquito aquatic habitat model specification. The data used to derive the model was from a digitized grid-based algorithm, generated in an ArcInfo database, using QuickBird visible and near-infrared (NIR) data. The Feature Extraction (FX) Module in ENVI 4.4® was used to categorize individual pixels of field sampled aquatic habitats into separate spectral classes, convert remotely sensed raster layers to vector coverages, and classify output layers to vector format as ESRI shapefiles. These data were used to construct a geographic weights matrix for evaluation of field and remote sampled covariates of Anopheles arabiensis aquatic habitats, a major vector of malaria in East Africa. The principal finding is that synthetic map pattern variables, which are eigenvectors computed for a geographic weights matrix, furnish an alternative way of capturing spatial dependency effects in the mean response term of a regression model. The spatial autocorrelation components suggest the presence of roughly 11 to 28% redundant information in the aquatic habitat larval count samples. The presence of redundant information in the models suggest that the sampling configuration of the An. arabiensis aquatic habitats, in the study sites, may cause field and remote observations of aquatic habitats to be dependent, rather than independent, moving data analysis away from the classical statistical independence model. A Poisson regression model, with a non-constant, gamma-distributed mean, can decompose field and remote sampled An. arabiensis data into positive and negative spatial autocorrelation eigenvectors, which can assess the precision of a malaria mosquito aquatic habitat map and the significance of all factors associated with larval abundance and distribution in a riceland agroecosystem.