geostatsp (version 1.2.1)

gambiaUTM: Gambia data

Description

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.

Usage

data(gambiaUTM)

format

A SpatialPointsDataFrame, with column pos being the binary response for a malaria diagnosis, as well as other child-level indicators such as netuse and treated being measures of bed net use and whether the nets were treated. The column green is a village-level measure of greenness. A UTM coordinate reference system is used, where coordinates are in metres.

source

http://www.leg.ufpr.br/doku.php/pessoais:paulojus:mbgbook:datasets. For further details on the malaria data, see Thomson et al. (1999).

References

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.

Examples

data("gambiaUTM")

plot(gambiaUTM, main="gambia data")

# 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)