Linked Life Data

18930703

Source:http://linkedlifedata.com/resource/pubmed/id/18930703

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2008-12-2
pubmed:abstractText
This research illustrates a geostatistical approach for modeling the spatial distribution patterns of Anopheles arabiensis Patton (Patton) aquatic habitats in two riceland environments. QuickBird 0.61 m data, encompassing the visible bands and the near-infra-red (NIR) band, were selected to synthesize images of An. arabiensis aquatic habitats. These bands and field sampled data were used to determine ecological parameters associated with riceland larval habitat development. SAS was used to calculate univariate statistics, correlations and Poisson regression models. Global autocorrelation statistics were generated in ArcGISfrom georeferenced Anopheles aquatic habitats in the study sites. The geographic distribution of Anopheles gambiae s.l. aquatic habitats in the study sites exhibited weak positive autocorrelation; similar numbers of log-larval count habitats tend to clustered in space. Individual rice land habitat data were further evaluated in terms of their covariations with spatial autocorrelation, by regressing them on candidate spatial filter eigenvectors. Each eigenvector generated from a geographically weighted matrix, for both study sites, revealed a distinctive spatial pattern. The spatial autocorrelation components suggest the presence of roughly 14-30% redundant information in the aquatic habitat larval count samples. Synthetic map pattern variables furnish a method of capturing spatial dependency effects in the mean response term in regression analyses of rice land An. arabiensis aquatic habitat data.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jan
pubmed:issn
1873-6254
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
109
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
17-26
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
Describing Anopheles arabiensis aquatic habitats in two riceland agro-ecosystems in Mwea, Kenya using a negative binomial regression model with a non-homogenous mean.
pubmed:affiliation
Department of Medicine, William C. Gorgas Center for Geographic Medicine, University of Alabama at Birmingham, 845 19th Street South, Birmingham, AL 35294, United States. bjacob@uab.edu
pubmed:publicationType
Journal Article, Research Support, N.I.H., Extramural