arraystatslib: doxygen update (discont algorithm) and author

git-svn-id: https://svn.osgeo.org/grass/grass/trunk@63913 15284696-431f-4ddb-bdfa-cd5b030d7da7

lib/arraystats/arraystatslib.dox

@@ -1,9 +1,62 @@
 /*! \page arraystatslib GRASS Array Statistics Library
 
-by GRASS Development Team (http://grass.osgeo.org)
+by Jean-Pierre Grimmeau, and GRASS Development Team (http://grass.osgeo.org)
 
 \tableofcontents
 
+\section discont The discont algorithm
+
+The discont algorithm systematically searches discontinuities in the slope
+of the cumulated frequencies curve, by approximating this curve through
+straight line segments whose vertices define the class breaks. This
+algorithm is inspired by techniques of automatic line generalization used
+in cartography [1]. The first approximation is a straight line which links
+the two end nodes of the curve. This line is then replaced by a
+two-segmented polyline whose central node is the point on the curve which
+is farthest from the preceding straight line. The point on the curve
+furthest from this new polyline is then chosen as a new node to create
+break up one of the two preceding segments, and so forth. The problem of
+the difference in terms of units between the two axes is solved by
+rescaling both amplitudes to an interval between 0 and 1. In the original
+algorithm, the process is stopped when the difference between the slopes
+of the two new segments is no longer significant. As the slope is the
+ratio between the frequency and the amplitude of the corresponding
+interval, i.e. its density, this effectively tests whether the frequencies
+of the two newly proposed classes are different from those obtained by
+simply distributing the sum of their frequencies amongst them in
+proportion to the class amplitudes. 
+
+The algorithm described above creates class breaks which each are
+identical to a specific observation. It is thus necessary to decide to
+which class these observations should be attributed. It seems logical to
+prefer the densest, i.e. the one with the strongest slope. The
+automatisation of this method allows to distinguish classes with high
+frequencies from those with low frequencies, but also to introduce
+subtleties and to delimit transition classes.
+
+This method, inspired by Jenks' algorithm [2], provides a good analysis of
+the distribution, but not necessarily cartographically satisfying class
+breaks. It is thus up to the cartographer to judge whether all the
+identified breaks are cartographically useful (or whether some should be
+combined) and whether any of the class amplitudes is too large. In the
+latter case, the class should be subdivided into equal intervals
+(arithmetic progression) as by definition, the classes resulting from the
+discont algorithm have a homogeneous interior distribution. If the general
+distribution of the data is close to the normal distribution, it is also
+possible to combine equiprobable class breaks [3] , with their advantage
+of regularity, with discont class breaks for the extremes which often have
+large amplitudes when using equiprobable class breaks.
+
+[1] Douglas, D.H. & Peucker, T.K. (1973) Algorithms for the reduction
+of the number of points required to represent a digitized line or its
+caricature, The Canadian Cartographer, 10, pp. 112-122.
+
+[2] Jenks, G.F. (1963) Generalisation in statistical mapping, Annals
+of the Association of American Geographers, 53, pp.15-26.
+
+[3] Grimmeau, J.P. (1977) Cartographie par plages et discontinuités
+spatiales, Paris, Espace géographique, VI, pp.49-58.
+
 \section listOfFunctios List of functions
 
 - AS_class_apply_algorithm()
@@ -16,5 +69,8 @@ by GRASS Development Team (http://grass.osgeo.org)
 - AS_eqdrt()
 - AS_basic_stats()
 
-*/
+\section arraystatslibAuthors Authors
 
+- Jean-Pierre Grimmeau at the Free University of Brussels (ULB)
+
+*/

lib/arraystats/discont.README.txt

@@ -1,49 +0,0 @@
-Discont
-Jean-Pierre Grimmeau - Université Libre de Bruxelles <grimmeau@ulb.ac.be>
-
-The discont algorithm systematically searches discontinuities in the slope
-of the cumulated frequencies curve, by approximating this curve through
-straight line segments whose vertices define the class breaks. This
-algorithm is inspired by techniques of automatic line generalization used
-in cartography [1]. The first approximation is a straight line which links
-the two end nodes of the curve. This line is then replaced by a
-two-segmented polyline whose central node is the point on the curve which
-is farthest from the preceding straight line. The point on the curve
-furthest from this new polyline is then chosen as a new node to create
-break up one of the two preceding segments, and so forth. The problem of
-the difference in terms of units between the two axes is solved by
-rescaling both amplitudes to an interval between 0 and 1. In the original
-algorithm, the process is stopped when the difference between the slopes
-of the two new segments is no longer significant. As the slope is the
-ratio between the frequency and the amplitude of the corresponding
-interval, i.e. its density, this effectively tests whether the frequencies
-of the two newly proposed classes are different from those obtained by
-simply distributing the sum of their frequencies amongst them in
-proportion to the class amplitudes. 
-
-The algorithm described above creates class breaks which each are
-identical to a specific observation. It is thus necessary to decide to
-which class these observations should be attributed. It seems logical to
-prefer the densest, i.e. the one with the strongest slope. The
-automatisation of this method allows to distinguish classes with high
-frequencies from those with low frequencies, but also to introduce
-subtleties and to delimit transition classes.
-
-This method, inspired by Jenks' algorithm [2], provides a good analysis of
-the distribution, but not necessarily cartographically satisfying class
-breaks. It is thus up to the cartographer to judge whether all the
-identified breaks are cartographically useful (or whether some should be
-combined) and whether any of the class amplitudes is too large. In the
-latter case, the class should be subdivided into equal intervals
-(arithmetic progression) as by definition, the classes resulting from the
-discont algorithm have a homogeneous interior distribution. If the general
-distribution of the data is close to the normal distribution, it is also
-possible to combine equiprobable class breaks [3] , with their advantage
-of regularity, with discont class breaks for the extremes which often have
-large amplitudes when using equiprobable class breaks.
-
-[1] Douglas, D.H. & Peucker, T.K. (1973) Algorithms for the reduction of the number of points required to represent a digitized line or its caricature, The Canadian Cartographer, 10, pp. 112-122.
-
-[2] Jenks, G.F. (1963) Generalisation in statistical mapping, Annals of the Association of American Geographers, 53, pp.15-26.
-
-[3] Grimmeau, J.P. (1977) Cartographie par plages et discontinuités spatiales, Paris, Espace géographique, VI, pp.49-58.