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Decomposing Malaria Mosquito Aquatic Habitat Data into Spatial Autocorrelation Eigenvectors in a SAS/GIS |
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| Authors: | Benjamin G Jacob Daniel A Griffith Robert J Novak |
| Institution: | Department of Medicine William C. Gorgas Center for Geographic Medicine; School of Social Sciences University of Texas at Dallas; Department of Medicine William C. Gorgas Center for Geographic Medicine |
| Abstract: | We present a geostatistical approach that accounts for spatial autocorrelation in malaria mosquito aquatic habitats in two East African urban environments. QuickBird 0.61 m data, encompassing visible bands and the near infra‐red (NIR) bands, were selected to synthesize images of Anopheles gambiae s.l. aquatic habitats in Kisumu and Malindi, Kenya. Field sampled data of An. gambiae s.l. aquatic habitats were used to determine which ecological covariates were associated with An. gambiae s.l. larval habitat development. A SAS/GIS® spatial database was used to calculate univariate statistics, correlations and perform Poisson regression analyses on the An. gambiae s.l. aquatic habitat datasets. Semivariograms and global autocorrelation statistics were generated in ArcGIS®. The spatially dependent models indicate the distribution of An. gambiae s.l. aquatic habitats exhibits weak positive autocorrelation in both study sites, with aquatic habitats of similar log‐larval counts tending to cluster in space. Individual anopheline habitats were further evaluated in terms of their covariations with spatial autocorrelation by regressing them on candidate spatial filter eigenvectors. This involved the decomposition of Moran's I statistic into orthogonal and uncorrelated map pattern components using a negative binomial regression. The procedure generated synthetic map patterns of latent spatial correlation representing the geographic configuration of An. gambiae s.l. aquatic habitat locations in each study site. The Gaussian approximation spatial filter models accounted for approximately 13% to 32% redundant locational information in the ecological datasets. Spatial statistics generated in a SAS/GIS® module can capture spatial dependency effects on the mean response term of a Poisson regression analysis of field and remotely sampled An. gambiae s.l. aquatic habitat data. |
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