Abstract
We present GeoXp, an R package implementing interactive graphics for exploratory spatial data analysis. We use a data set concerning public schools of the French Midi- Pyrénées region to illustrate the use of these exploratory techniques based on the coupling between a statistical graph and a map. Besides elementary plots like boxplots, histograms or simple scatterplots, GeoXp also couples maps with Moran scatterplots, variogram clouds, Lorenz curves and other graphical tools. In order to make the most of the multidimensionality of the data, GeoXp includes dimension reduction techniques such as principal components analysis and cluster analysis whose results are also linked to the map.
Highlights
Exploratory analysis of georeferenced data must take into account their spatial nature
The use of the coupling between a map and a statistical graph such as a histogram, a boxplot or a scattermatrix has already been advocated in the literature
The coupling is 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
Summary
Exploratory analysis of georeferenced data must take into account their spatial nature. Geographic Information Systems (GIS) are very elaborate cartographic tools but their statistical analysis capabilities are generally limited. When they include statistical techniques, they are very basic tools from descriptive statistics (boxplots, histograms, barcharts, etc.) but none of the state of the art tools specific to spatial data from geostatistics or spatial econometrics. We have chosen to illustrate only a selection of the different routines and the reader will find a comprehensive list of the GeoXp functions in the annex and more illustrations on the web site[4]
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