Python Spatial Analysis Library
===============================

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[![LISA Maps of US County Homicide Rates](https://farm2.staticflickr.com/1699/23937788493_1b9d147b9f_z.jpg){width="100.0%"}](http://nbviewer.ipython.org/urls/gist.githubusercontent.com/darribas/657e0568df7a63362762/raw/pysal_lisa_maps.ipynb)

*Above: Local Indicators of Spatial Association for Homicide Rates in US
Counties 1990.*

PySAL, the Python spatial analysis library, is an open source
cross-platform library for geospatial data science with an emphasis on
geospatial vector data written in Python. It supports the development of
high level applications for spatial analysis, such as

> -   detection of spatial clusters, hot-spots, and outliers
> -   construction of graphs from spatial data
> -   spatial regression and statistical modeling on geographically
>     embedded networks
> -   spatial econometrics
> -   exploratory spatio-temporal data analysis

PySAL Components
================

-   **explore** - modules to conduct exploratory analysis of spatial and spatio-temporal data, including statistical testing on points, networks, and
    polygonal lattices.  Also includes methods for spatial inequality and distributional dynamics.
-   **viz** - visualize patterns in spatial data to detect clusters,
    outliers, and hot-spots.
-   **model** - model spatial relationships in data with a variety of
    linear, generalized-linear, generalized-additive, and nonlinear
    models.
-   **lib** - solve a wide variety of computational geometry problems:
    -   graph construction from polygonal lattices, lines, and points.
    -   construction and interactive editing of spatial weights matrices
        & graphs
    -   computation of alpha shapes, spatial indices, and
        spatial-topological relationships
    -   reading and writing of sparse graph data, as well as pure python
        readers of spatial vector data.

Installation
============

PySAL is available through
[Anaconda](https://www.continuum.io/downloads) (in the defaults or
conda-forge channel) and [Enthought
Canopy](https://www.enthought.com/products/canopy/). We recommend
installing PySAL from conda-forge:

``` {.sourceCode .bash}
conda install pysal
```

PySAL can be installed using pip:

``` {.sourceCode .bash}
pip install pysal
```

As of version 2.0.0 PySAL has shifted to Python 3 only.

Users who need an older stable version of PySAL that is Python 2
compatible can install version 1.14.3 through pip or \`conda\`:

``` {.sourceCode .bash}
conda install pysal==1.14.3
```

Documentation
=============

For help on using PySAL, check out the following resources:

-   [User
    Guide](http://pysal.readthedocs.org/en/latest/users/index.html)
-   [Tutorials and Short
    Courses](https://github.com/pysal/notebooks/blob/master/courses.md)

Development
===========

As of version 2.0.0, PySAL is now a collection of affiliated geographic
data science packages. Changes to the code for any of the subpackages
should be directed at the respective upstream repository, and not made
here. Infrastructural changes for the meta-package, like those for
tooling, building the package, and code standards, will be considered.

Development is hosted on [github](https://github.com/pysal/pysal).

Discussions of development as well as help for users occurs on the
[developer list](http://groups.google.com/group/pysal-dev) as well as
[gitter](https://gitter.im/pysal/pysal?).

Getting Involved
================

If you are interested in contributing to PySAL please see our
development guidelines \<https://github.com/pysal/pysal/wiki/GitHub-Standard-Operating-Procedures\>

Bug reports
===========

To search for or report bugs, please see PySAL\'s
[issues](http://github.com/pysal/pysal/issues).

License information
===================

See the file \"LICENSE.txt\" for information on the history of this
software, terms & conditions for usage, and a DISCLAIMER OF ALL
WARRANTIES.
