GeoXp-package | R Documentation |
This package is a tool for researchers in spatial statistics, spatial econometrics, geography, ecology etc allowing to link dynamically statistical plots with elementary maps. This coupling consists in the fact that the selection of a zone on the map results in the automatic highlighting of the corresponding points on the statistical graph or reversely, the selection of a portion of the graph results in the automatic highlighting of the corresponding points on the map. GeoXp includes tools from different areas of spatial statistics including geostatistics as well as spatial econometrics and point processes. Besides elementary plots like boxplots, histograms or simple scatterplos, GeoXp also couples with maps Moran scatterplots, variogram cloud, Lorentz Curves,... In order to make the most of the multidimensionality of the data, GeoXp includes some dimension reduction techniques such as PCA.
Package: | GeoXp |
Type: | Package |
Version: | 2.0.0 |
Date: | 2023-04-05 |
License: | GPL Vesion 2 or later |
In the version 2.0.0, GeoXp has adopted the sf
object proposed by E. Pebezma
in sf
package. The main advantage of using this structure object is on one hand,
a sf
object contains spatial coordinates and behaves as a data.frame
.
On the map, the coordinates of sites are represented by
using the function st_coordinates
and st_point_on_surface
included in sf
package, which calculates
longitude
(for x-axis) and latitude
(for y-axis), applied on a sf Object.
In GeoXp, we can find three main groups of functions:
- functions using only one variable: the interest variable
is designed by argument name.var
, a character corresponding to a column of the data.frame
included in sf.obj
, i.e. the Spatial Class object. It can be a numeric variable (histomap()
,
densitymap()
, angleplotmap
...) or a factor variable (or character) (barmap()
,...).
- functions using both several variables: the variables of interest are designed by argument
names.var
, a vector of character corresponding to columns of the data.frame
included in sf.obj
. It can be two numeric variables (dblehistomap
, dbledensitymap
),
one numeric variable and one factor (histobarmap()
, polyboxplotmap()
),
several numeric variables (plot3dmap
, pcamap()
and clustermap()
).
- functions using both a variable and a spatial weight matrix created as a nb
or listw
object
(see package spdep
).
In the case where sf.obj
is a SpatialPolygonDataFrame
, user will have the opportunity to draw
the polygons of Spatial unit by using the Draw Saptial contours
button in the Tk window. User can also give another sf object as background map with option carte
.
Among options which are common to each function, users have the
possibility to give a criteria
, vector of boolean of size the number of Spatial units,
with TRUE on specific sites. These sites are then represented by a green croice on the map
by clicking on preselected sites
button on the Tk window.
Moreover, users have the possibility to make bubbles and add some graphs
(histogram, barplot or scattermap). The potential variables are included
in the data.frame
of the SpatialObject
. Users can choose a proportional
symbol mapping: in function plot, we give value
var^{0.5}
. User can choose if a legend has to appear on
the map. He could choose then three values represented by bubbles of
corresponding sizes.
Finally, users can choose to represent the graphical with different colors
using argument col
. In the case of factors (as function barmap
), users could choose
if a legend with corresponding colors will appear on the map. Users can also modify the representation of
selected sites on map with argument pch
.
Recent functions barnbmap
and histnbmap
give the opportunity to analyse spatial
weight matrix build using functions included in spdep
package.
Christine Thomas-Agnan, Yves Aragon, Anne Ruiz-Gazen, Thibault Laurent, Laurianne Robidou Maintainer: Thibault Laurent <thibault.laurent@univ-tlse1.fr>
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.
Roger S.Bivand, Edzer J.Pebesma, Virgilio Gomez-Rubio (2009), Applied Spatial Data Analysis with R, Springer.
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