{ "info": { "author": "Chris Mutel", "author_email": "cmutel@gmail.com", "bugtrack_url": null, "classifiers": [], "description": "Overview\n========\n\nThis 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.\n\nUsage\n=====\n\nThe easiest way is to call `autocorrelate.py` with the name and path of the shapefile, e.g.::\n\n\tpython autocorrelate.py path/to/file/filename.shp\n\nTo use in other python programs::\n\n\tfrom lcia_autocorrelation.ac_shapefile import AutocorrelationShapefile\n\tac = AutocorrelationShapefile(\"filepath\")\n\tac.global_autocorrelation()\n\nAutocorrelation calculations are made using the PySAL library; multiple measures of autocorrelation are possible.\n\nLocal Indicators of Spatial Autocorrelation (LISA)\n==================================================\n\n`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 \u2013 LISA\". Geographical Analysis, 27, 93-115) uses these local indicators directly, to calculate a local measure of clustering or autocorrelation. The LISA statistic is:\n\n.. math::\n\n\tI_{i} = \\frac{Z_{i}}{}\\sum_{j}W_{ij}Z_{j}\n\n.. math::\n\n\tI = \\sum_{i}\\frac{I_{i}}{N}\n\nWhere *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**.\n\nWe use the `PySAL library `_ to calculate `LISA statistics `_.\n\nInstallation\n============\n\nUsing pip::\n\n\tpip install lcia-autocorrelation\n\nUsing easy_install::\n\n\teasy_install lcia-autocorrelation\n\nRequirements\n------------\n\nThe following packages are required\n\n* numpy\n* scipy\n* pysal\n* rtree\n* osgeo\n* django\n* progressbar\n\nCopyright and License\n=====================\n\nThis code was written by Chris Mutel [3] during his studies at ETH Zurich [4], and is copyright 2011 ETH Zurich. 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