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Project description


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.


The easiest way is to call `` with the name and path of the shapefile, e.g.::

python path/to/file/filename.shp

To use in other python programs::

from lcia_autocorrelation.ac_shapefile import AutocorrelationShapefile
ac = AutocorrelationShapefile("filepath")

Autocorrelation calculations are made using the PySAL library; multiple measures of autocorrelation are possible.

Local Indicators of Spatial Autocorrelation (LISA)

`Moran's I <'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 <>`_ to calculate `LISA statistics <>`_.


Using pip::

pip install lcia-autocorrelation

Using easy_install::

easy_install lcia-autocorrelation


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.

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