SpatialEpi (version 1.2.3)

NYleukemia: Upstate New York Leukemia Data

Description

Census tract level (n=281) leukemia data for the 8 counties in upstate New York from 1978-1982, paired with population data from the 1980 census. Note that 4 census tracts were completely surrounded by another unique census tract; when applying the Bayesian cluster detection model in bayes_cluster, we merge them with the surrounding census tracts yielding n=277 areas.

Usage

data(NYleukemia)

Format

List with 5 items:

geo	table of the FIPS code, longitude, and latitude of the geographic centroid of each census tract
data	table of the FIPS code, number of cases, and population of each census tract
spatial.polygon	object of class SpatialPolygons (See SpatialPolygons-class) containing a map of the study region
surrounded	row IDs of the 4 census tracts that are completely surrounded by the surrounding census tracts
surrounding	row IDs of the 4 census tracts that completely surround the surrounded census tracts

References

Turnbull, B. W. et al (1990) Monitoring for clusters of disease: application to leukemia incidence in upstate New York American Journal of Epidemiology, 132, 136--143

See Also

scotland, pennLC

Examples

# NOT RUN {
## Load data and convert coordinate system from latitude/longitude to grid
data(NYleukemia)
map <- NYleukemia$spatial.polygon
population <- NYleukemia$data$population
cases <- NYleukemia$data$cases
centroids <- latlong2grid(NYleukemia$geo[, 2:3])

## Identify the 4 census tract to be merged into their surrounding census tracts.  
remove <- NYleukemia$surrounded
add <- NYleukemia$surrounding

## Merge population and case counts
population[add] <- population[add] + population[remove]
population <- population[-remove]
cases[add] <- cases[add] + cases[remove]
cases <- cases[-remove]

## Modify geographical objects accordingly
map <- SpatialPolygons(map@polygons[-remove], proj4string=CRS("+proj=longlat +ellps=WGS84"))
centroids <- centroids[-remove, ]

## Plot incidence in latitude/longitude
plotmap(cases/population, map, log=TRUE, nclr=5)
points(grid2latlong(centroids), pch=4)
# }