R: Conditioned choropleth maps

CCmaps {maptools} | R Documentation

## Conditioned choropleth maps

### Description

Conditioned choropleth maps permit the conditioning of a map of a variable on the values of one or two other variables coded as factors or shingles. This function uses spplot after constructing multiple subsets of the variable of interest defined by the intervals given by the conditioning variables.

### Usage

    CCmaps(obj, zcol = NULL, cvar = NULL, cvar.names = NULL, ..., names.attr,
     scales = list(draw = FALSE), xlab = NULL, ylab = NULL,
     aspect = mapasp(obj, xlim, ylim), sp.layout = NULL, xlim = bbox(obj)[1, ],
     ylim = bbox(obj)[2, ])

### Arguments

`obj` | object of class SpatialPolygonsDataFrame
`zcol` | single variable name as string
`cvar` | a list of one or two conditioning variables, which should be of class factor or shingle
`cvar.names` | names for conditioning variables, if not given, the names of the variables in the `cvar` list
`...` | other arguments passed to spplot and levelplot
`names.attr` | names to use in panel, if different from zcol names
`scales` | scales argument to be passed to Lattice plots; use `list(draw = TRUE)` to draw axes scales
`xlab` | label for x-axis
`ylab` | label for y-axis
`aspect` | aspect ratio for spatial axes; defaults to "iso" (one unit on the x-axis equals one unit on the y-axis) but may be set to more suitable values if the data are e.g. if coordinates are latitude/longitude
`sp.layout` | NULL or list; see spplot
`xlim` | numeric; x-axis limits
`ylim` | numeric; y-axis limits

### Value

The function returns a SpatialPolygonsDataFrame object with the `zcol` variable and the partitions of the `cvars` list variables invisibly.

### Author(s)

Roger Bivand

### References

Carr D, Wallin J, Carr D (2000) Two new templates for epidemiology applications: linked micromap plots and conditioned choropleth maps. Statistics in Medicine 19(17-18): 2521-2538 Carr D, White D, MacEachren A (2005) Conditioned choropleth maps and hypothesis generation. Annals of the Association of American Geographers 95(1): 32-53 Friendly M (2007) A.-M. Guerry's Moral Statistics of France: challenges for multivariable spatial analysis. Statistical Science 22(3): 368-399

### See Also

`spplot`

### Examples

    nc.sids <- readShapeSpatial(system.file("shapes/sids.shp",
     package="maptools")[1], IDvar="FIPSNO",
     proj4string=CRS("+proj=longlat +ellps=clrk66"))
    nc.sids$ft.SID74 <- sqrt(1000)*(sqrt(nc.sids$SID74/nc.sids$BIR74) +
     sqrt((nc.sids$SID74+1)/nc.sids$BIR74))
    nc.sids$ft.NWBIR74 <- sqrt(1000)*(sqrt(nc.sids$NWBIR74/nc.sids$BIR74) +
     sqrt((nc.sids$NWBIR74+1)/nc.sids$BIR74))
    library(lattice)
    sh_nw4 <- equal.count(nc.sids$ft.NWBIR74, number=4, overlap=1/5)
    CCmaps(nc.sids, "ft.SID74", list("Nonwhite_births"=sh_nw4),
     col.regions=colorRampPalette(c("yellow1", "brown3"))(20),
     main="Transformed SIDS rates 1974-8")

[Package maptools version 0.9-3 Index]