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

Abstract

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.

A Cartographic Analysis Using Spatial Filter Logistic Model Specifications for Implementing Mosquito Control in Kenya

“…gambiae s.l. habitat data analyses is |MC/MC max | > 0.25 (where MC max is the maximum MC for a given surface partitioning), because then each candidate eigenvector represents a level of spatial autocorrelation which can account for redundant information (see Jacob et al, 2008aJacob et al, , 2008bJacob et al, , 2008c. Here, the absolute value restriction resulted in both PSA and NSA eigenvectors forming the candidate set.…”

“…gambiae s.l. habitat explanatory predictor variables, a modified geographically weighted matrix provides measures of spatial linkages and covariates associated to productive habitats (Jacob et al, 2008a(Jacob et al, , 2009.…”

“…explanatory variables with a set of proxy variables, rather than to identify a global autocorrelation parameter for a spatially dependent process. Global statistics summarize standard error and other parameter uncertainty estimates from multiple sampled georeferenced habitat locations making it difficult for spatial assessment of predictive autoregressive error at a single sampled habitat site (Jacob et al, 2009d;Jacob et al, 2008d;Jacob et al, 2008a).…”

Quantifying stochastic error propagation in Bayesian parametric estimates using non-linear parameters of Anopheles gambiae s.l. habitats

“…concrete or brick). This sampling strategy has been used in previous publications , 2008a,b,c, Muturi et al 2007a,b, 2008, Mwangangi et al 2007.…”

“…gambiae s.l. aquatic habitat samples and, therefore, often fail to distinguish between different spatial patterns and scales (Diggle et al 2002, Jacob et al 2008a.…”