(Use example(nc.sids) to read the data set from shapefile, together with import of two different list of neighbours). The nc.sids data frame has 100 rows and 21 columns. It contains data given in Cressie (1991, pp. 386-9), Cressie and Read (1985) and Cressie and Chan (1989) on sudden infant deaths in North Carolina for 1974-78 and 1979-84. The data set also contains the neighbour list given by Cressie and Chan (1989) omitting self-neighbours (ncCC89.nb), and the neighbour list given by Cressie and Read (1985) for contiguities (ncCR85.nb). The data are ordered by county ID number, not alphabetically as in the source tables sidspolys is a "polylist" object of polygon boundaries, and sidscents is a matrix of their centroids.

nc.sids

Format

This data frame contains the following columns:

  • SP_ID SpatialPolygons ID

  • CNTY_ID county ID

  • east eastings, county seat, miles, local projection

  • north northings, county seat, miles, local projection

  • L_id Cressie and Read (1985) L index

  • M_id Cressie and Read (1985) M index

  • names County names

  • AREA County polygon areas in degree units

  • PERIMETER County polygon perimeters in degree units

  • CNTY_ Internal county ID

  • NAME County names

  • FIPS County ID

  • FIPSNO County ID

  • CRESS_ID Cressie papers ID

  • BIR74 births, 1974-78

  • SID74 SID deaths, 1974-78

  • NWBIR74 non-white births, 1974-78

  • BIR79 births, 1979-84

  • SID79 SID deaths, 1979-84

  • NWBIR79 non-white births, 1979-84

Source

Cressie, N (1991), Statistics for spatial data. New York: Wiley, pp. 386--389; Cressie, N, Chan NH (1989) Spatial modelling of regional variables. Journal of the American Statistical Association, 84, 393--401; Cressie, N, Read, TRC (1985) Do sudden infant deaths come in clusters? Statistics and Decisions Supplement Issue 2, 333--349; http://sal.agecon.uiuc.edu/datasets/sids.zip.

Examples

if (requireNamespace("rgdal", quietly = TRUE)) { library(rgdal) if (requireNamespace("spdep", quietly = TRUE)) { library(spdep) nc.sids <- readOGR(system.file("shapes/sids.shp", package="spData")[1]) proj4string(nc.sids) <- CRS("+proj=longlat +ellps=clrk66") row.names(nc.sids) <- as.character(nc.sids$FIPS) rn <- row.names(nc.sids) ncCC89_nb <- read.gal(system.file("weights/ncCC89.gal", package="spData")[1], region.id=rn) ncCR85_nb <- read.gal(system.file("weights/ncCR85.gal", package="spData")[1], region.id=rn) plot(nc.sids, border="grey") plot(ncCR85_nb, coordinates(nc.sids), add=TRUE, col="blue") plot(nc.sids, border="grey") plot(ncCC89_nb, coordinates(nc.sids), add=TRUE, col="blue") } }
#> OGR data source with driver: ESRI Shapefile #> Source: "/tmp/RtmpFoaGE6/temp_libpath5fb329aec9a3/spData/shapes/sids.shp", layer: "sids" #> with 100 features #> It has 22 fields