Metadata-Version: 1.0
Name: py_amoeba
Version: 0.2.3
Summary: UNKNOWN
Home-page: https://bitbucket.org/cmutel/py-amoeba
Author: Chris Mutel
Author-email: cmutel@gmail.com
License: BSD 2-clause; LICENSE.txt
Description: Overview
        ========
        
        This is a python module for calculating global (Moran's I [1]) and local spatial autocorrelation [1.5] using the AMOEBA algorithm [2]. This code works on shapefiles, although a base class is provided to allow the examination of other objects, e.g. from a spatial database.
        
        Usage
        =====
        
        The easiest way is to call `autocorrelate.py` with the name and path of the shapefile, e.g.::
        
        	python autocorrelate.py path/to/file/filename.shp
        
        To use in other python programs::
        
        	from lcia_autocorrelation.ac_shapefile import AutocorrelationShapefile
        	ac = AutocorrelationShapefile("filepath")
        	ac.global_autocorrelation()
        
        Autocorrelation calculations are made using the PySAL library; multiple measures of autocorrelation are possible.
        
        Local Indicators of Spatial Autocorrelation (LISA)
        ==================================================
        
        `Moran's I <http://en.wikipedia.org/wiki/Moran's_I)>`_ is a single statistic for global 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:
        
        .. math::
        
        	I_{i} = \frac{Z_{i}}{}\sum_{j}W_{ij}Z_{j}
        
        .. math::
        
        	I = \sum_{i}\frac{I_{i}}{N}
        
        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>`_.
        
        Installation
        ============
        
        Using pip::
        
        	pip install lcia-autocorrelation
        
        Using easy_install::
        
        	easy_install lcia-autocorrelation
        
        Requirements
        ------------
        
        The following packages are required
        
        * numpy
        * scipy
        * pysal
        * rtree
        * osgeo
        * django
        * progressbar
        
        Copyright and License
        =====================
        
        This code was written by Chris Mutel [3] during his studies at ETH Zurich [4], and is copyright 2011 ETH Zurich. The license is 2-clause BSD.
        
Platform: UNKNOWN
