This data-set was used by Diggle, Moyeed, Rowlingson, and Thomson (2002) to demonstrate how the model-based geostatistics framework of Diggle et al. (1998) could be adapted to assess the source(s) of extrabinomial variation in the data and, in particular, whether this variation was spatially structured. The malaria prevalence data set consists of measurements of the presence of malarial parasites in blood samples obtained from children in 65 villages in the Gambia. Other child- and village-level indicators include age, bed net use, whether the bed net is treated, whether or not the village belonged to the primary health care structure, and a measure of 'greenness' using a vegetation index.
A SpatialPointsDataFrame, with column
pos being the binary response for a malaria
diagnosis, as well as other child-level indicators such as
being measures of bed net use and whether the nets were treated. The column
a village-level measure of greenness. A UTM coordinate reference system is used, where
coordinates are in metres.
http://www.leg.ufpr.br/doku.php/pessoais:paulojus:mbgbook:datasets. For further details on the malaria data, see Thomson et al. (1999).
Diggle, P. J., Moyeed, R. A., Rowlingson, R. and Thomson, M. (2002). Childhood Malaria in the Gambia: A case-study in model-based geostatistics. Journal of the Royal Statistical Society. Series C (Applied Statistics), 51(4): 493-506.
Diggle, P. J., Tawn, J. A. and Moyeed, R. A. (1998). Model-based geostatistics (with Discussion). Applied Statistics, 47, 299–350.
Thomson, M. C., Connor, S. J., D'Alessandro, U., Rowlingson, B., Diggle, P., Creswell, M. and Greenwood, B. (2004). Predicting malaria infection in Gambian children from satellite data and bed net use surveys: the importance of spatial correlation in the interpretation of results. American Journal of Tropical Medicine and Hygiene, 61: 2-8.
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data("gambiaUTM") plot(gambiaUTM, main="gambia data") ## Not run: # get the gambia data gambia = read.table( "http://www.leg.ufpr.br/lib/exe/fetch.php/pessoais:paulojus:mbgbook:datasets:gambia.txt", header=TRUE) # create projection without epsg code so rgdal doesn't need to be loaded library(sp) library(rgdal) theproj = CRSargs(CRS("+init=epsg:32628")) theproj = gsub("\+init=epsg:[[:digit:]]+ ", "", theproj) theproj = CRS(theproj) gambiaUTM = SpatialPointsDataFrame(gambia[,c("x","y")], data=gambia[,-(1:2)], proj4string = theproj) save(gambiaUTM, file="~/workspace/diseasemapping/pkg/geostatsp/data/gambiaUTM.RData", compress="xz") download.file("http://thematicmapping.org/downloads/TM_WORLD_BORDERS-0.3.zip", "borders.zip") unzip("borders.zip") worldBorders = readOGR(".", "TM_WORLD_BORDERS-0.3") africa = worldBorders[worldBorders$REGION ==2,] plot(gambiaUTM) plot(spTransform(africa, gambiaUTM@proj4string),add=TRUE) ## End(Not run)
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