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+This is a python module for calculating global (Moran's I ) and local spatial autocorrelation [1.5] using the AMOEBA algorithm . This code works on shapefiles, although a base class is provided to allow the examination of other objects, e.g. from a spatial database.
+Autocorrelation calculations are made using the PySAL library; multiple measures of autocorrelation are possible.
+Moran's I (http://en.wikipedia.org/wiki/Moran's_I) is a single statistic for glboal autocorrelation. However, the calculation of Moran's I involves summing the individual cross products of each spatial unit. Local indicators of spatial association (LISA) (Anselin, L. (1995). "Local indicators of spatial association – LISA". Geographical Analysis, 27, 93-115) uses these local indicators directly, to calculate a local measure of clustering or autocorrelation. The LISA statistic is:
+Where *I* is the autocorrelation statistic, *Z* is the deviation of the variable of interest from the average, and *W* is the spatial weight linking **i** to **j**.
+We use the PySAL library (http://code.google.com/p/pysal/) to calculate LISA statistics (http://pysal.org/users/tutorials/autocorrelation.html#local-indicators-of-spatial-association).
+This code was written by Chris Mutel  during his studies at ETH Zurich , and is copyright 2011 ETH Zurich. The license is 2-clause BSD.
+ print "Can't understand arguments - please give filename e.g. \n\tpython autocorrelate.py path/to/file/filename.shp"