GeoXp (version 1.6.2)

densitymap: Kernel density estimates and map

Description

The function densitymap draws kernel density estimates of the variable name.var with bkde and a map with sites of coordinate coordinates(sp.obj). Each site is associated to a value of name.var and there is interactivity between the two windows.

Usage

densitymap(sp.obj, name.var, kernel='triweight',
names.attr=names(sp.obj), criteria=NULL, carte=NULL, identify=FALSE, cex.lab=0.8, pch=16,
col="lightblue3", xlab="", ylab="", axes=FALSE, lablong="", lablat="")

Arguments

sp.obj

object of class extending Spatial-class

name.var

a character; attribute name or column number in attribute table

kernel

Smoothing kernel (see help(bkde) for list of options)

names.attr

names to use in panel (if different from the names of variable used in sp.obj)

criteria

a vector of boolean of size the number of spatial units, which permit to represent preselected sites with a cross, using the tcltk window

carte

matrix with 2 columns for drawing spatial polygonal contours : x and y coordinates of the vertices of the polygon

identify

if not FALSE, identify plotted objects (currently only working for points plots). Labels for identification are the row.names of the attribute table row.names(as.data.frame(sp.obj)).

cex.lab

character size of label

pch

16 by default, symbol for selected points

col

"lightblue3" by default, color of bars on the histogram

xlab

a title for the graphic x-axis

ylab

a title for the graphic y-axis

axes

a boolean with TRUE for drawing axes on the map

lablong

name of the x-axis that will be printed on the map

lablat

name of the y-axis that will be printed on the map

Value

In the case where user click on save results button, a vector of integer is created as a global variable in last.select object. It corresponds to the number of spatial units selected just before leaving the Tk window.

Details

The user can choose an interval on the density curve by mouse clicking on the graph on the extremities of interval or by specifying directly values. The sites selected by an interval are then represented on the map in red. The selection of sites on the map by `points' or `polygon' results in the drawing of the kernel densities of the subdistributions corresponding to this subset of sites. Finally, the user can modify the bandwith parameter with a cursor in the Tk window (parameter $alpha$). $alpha$ is the smoothing parameter for the kernel smooth : it represents the mean percentage of sample points involved in the local averaging (example : $alpha=20$ means that on average, $n x 0.2$ points are in any interval of length 2h where h is the usual bandwidth).

References

Thibault Laurent, Anne Ruiz-Gazen, Christine Thomas-Agnan (2012), GeoXp: An R Package for Exploratory Spatial Data Analysis. Journal of Statistical Software, 47(2), 1-23.

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. New York: Springer.

Wand M.P. et Jones M.C. (1995), Kernel Smoothing, Chapman \& Hall.

See Also

histomap, histobarmap, scattermap, densitymap

Examples

######
# data oldcol
require("maptools")
example(columbus)

# columbus is included in the Spatial-Class object
# a very simple use of histomap :
densitymap(columbus,"CRIME")

######
# data on price indices of real estate in France
data(immob)
row.names(immob)<-immob$Nom

# immob is a data.frame object. We have to create
# a Spatial object, by using first the longitude and latitude
# to create Spatial Points object ...
immob.sp = SpatialPoints(cbind(immob$longitude,immob$latitude))
# ... and then by integrating other variables to create SpatialPointsDataFrame
immob.spdf = SpatialPointsDataFrame(immob.sp, immob)
# For more details, see vignette('sp', package="sp")

# optional : we add some contours that don't correspond to the spatial unit
# but are nice for mapping
midiP <- readShapePoly(system.file("shapes/region.shp", package="GeoXp")[1])
cont_midiP<-spdf2list(midiP[-c(22,23),])$poly

# A basic call of densitymap function
densitymap(immob.spdf,"prix.vente", carte= cont_midiP, identify=TRUE,
xlab="housing price by square meter", cex.lab=0.6)