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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.

========

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.

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File Name & Checksum SHA256 Checksum Help | Version | File Type | Upload Date |
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py_amoeba-0.2.3.tar.gz (8.0 kB) Copy SHA256 Checksum SHA256 | – | Source | Apr 30, 2013 |