Splot Statistics
In addition to visualizing Global autocorrelation statistics, splot has options to visualize local autocorrelation statistics. We compute the local Moran m. Then, we plot the spatial lag and the Donatns variable in a Moran Scatterplot. This tool identifies statistically significant spatial clusters of high values (hot spots) and low values (cold spots). It creates an Output Feature Class with a z-score, p-value, and confidence level bin field (GiBin) for each feature in the Input Feature Class. The splot package allows you to create both static plots ready for publication and interactive visualizations for quick iteration and spatial data exploration. The primary goal of splot is to enable you to visualize popular PySAL objects and gives you different views on your spatial analysis workflow. Splot - This program is the primary method to view spectra. We can actually look at some statistics from sections of a spectrum using m.
Abstract: The paper advocates the use of a statistical tool dedicated to theexploration of data samples populated by several sources of events. This newtechnique, called sPlot, is able to unfold the contributions of the differentsources to the distribution of a data sample in a given variable. The sPlottool applies in the context of a Likelihood fit which is performed on the datasample to determine the yields of the various sources.
Submission history
From: Muriel Pivk [view email][v1] Tue, 17 Feb 2004 16:26:42 UTC (17 KB)
[v2]Fri, 24 Jun 2005 14:27:27 UTC (37 KB)
[v3] Fri, 2 Sep 2005 13:35:57 UTC (37 KB)
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Split Statistics
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Plot Statistics
