Metadata-Version: 2.1
Name: giddy
Version: 2.3.2
Summary: GIDDY: GeospatIal Distribution DYnamics
Home-page: https://github.com/pysal/giddy
Maintainer: Wei Kang
Maintainer-email: weikang9009@gmail.com
License: 3-Clause BSD
Description: GeospatIal Distribution DYnamics (giddy) in PySAL
        =================================================
        
        ![.github/workflows/unittests.yml](https://github.com/pysal/giddy/workflows/.github/workflows/unittests.yml/badge.svg?branch=master)
        [![codecov](https://codecov.io/gh/pysal/giddy/branch/master/graph/badge.svg)](https://codecov.io/gh/pysal/giddy)
        [![Gitter room](https://badges.gitter.im/pysal/giddy.svg)](https://gitter.im/pysal/giddy)
        [![PyPI version](https://badge.fury.io/py/giddy.svg)](https://badge.fury.io/py/giddy)
        [![DOI](https://zenodo.org/badge/91390088.svg)](https://zenodo.org/badge/latestdoi/91390088)
        [![badge](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/pysal/giddy/master)
        
        Giddy is an open-source python library for the analysis of dynamics of
        longitudinal spatial data. Originating from the spatial dynamics module
        in [PySAL (Python Spatial Analysis Library)](http://pysal.org/), it is under active development
        for the inclusion of newly proposed analytics that consider the
        role of space in the evolution of distributions over time.
        
        *Below are six choropleth maps of US state per-capita incomes from 1929 to 2004 at a fifteen-year interval.*
        
        ![us_qunitile_maps](figs/us_qunitile_maps.png)
        
        Documentation
        -------------
        
        Online documentation is available [here](http://pysal.org/giddy/).
        
        
        Features
        --------
        - Directional LISA, inference and visualization as rose diagram
        
        [![rose_conditional](figs/rose_conditional.png)](notebooks/DirectionalLISA.ipynb)
        
        *Above shows the rose diagram (directional LISAs) for US states incomes across 1969-2009 conditional on relative incomes in 1969.*
        
        - Spatially explicit Markov methods:
            - Spatial Markov and inference
            - LISA Markov and inference
        - Spatial decomposition of exchange mobility measure (rank methods):
            - Global indicator of mobility association (GIMA) and inference
            - Inter- and intra-regional decomposition of mobility association and inference
            - Local indicator of mobility association (LIMA)
                - Neighbor set LIMA and inference
                - Neighborhood set LIMA and inference
        
        [![us_neigborsetLIMA](figs/us_neigborsetLIMA.png)](notebooks/RankBasedMethods.ipynb)
        
        - Income mobility measures
        
        Examples
        --------
        
        * [Directional LISA](notebooks/DirectionalLISA.ipynb)
        * [Markov based methods](notebooks/MarkovBasedMethods.ipynb)
        * [Rank Markov methods](notebooks/RankMarkov.ipynb)
        * [Mobility measures](notebooks/MobilityMeasures.ipynb)
        * [Rank based methods](notebooks/RankBasedMethods.ipynb)
        * [Sequence methods (Optimal matching)](notebooks/Sequence.ipynb)
        
        Installation
        ------------
        
        Install the stable version released on the [Python Package Index](https://pypi.org/project/giddy/) from the command line:
        
        ```
        pip install giddy
        ```
        
        Install the development version on [pysal/giddy](https://github.com/pysal/giddy):
        
        ```
        pip install https://github.com/pysal/giddy/archive/master.zip
        ```
        
        #### Requirements
        
        - scipy>=1.3.0
        - libpysal>=4.0.1
        - mapclassify>=2.1.1
        - esda>=2.1.1
        - quantecon>=0.4.7
        
        Contribute
        ----------
        
        PySAL-giddy is under active development and contributors are welcome.
        
        If you have any suggestion, feature request, or bug report, please open a new [issue](https://github.com/pysal/giddy/issues) on GitHub. To submit patches, please follow the PySAL development [guidelines](https://github.com/pysal/pysal/wiki) and open a [pull request](https://github.com/pysal/giddy). Once your changes get merged, you’ll automatically be added to the [Contributors List](https://github.com/pysal/giddy/graphs/contributors).
        
        Support
        -------
        
        If you are having issues, please talk to us in the [gitter room](https://gitter.im/pysal/giddy).
        
        License
        -------
        
        The project is licensed under the [BSD license](https://github.com/pysal/giddy/blob/master/LICENSE.txt).
        
        
        BibTeX Citation
        ---------------
        
        ```
        @software{wei_kang_2020_3887050,
          author       = {Wei Kang and
                          Sergio Rey and
                          Philip Stephens and
                          Nicholas Malizia and
                          James Gaboardi and
                          Stefanie Lumnitz and
                          Levi John Wolf and
                          Charles Schmidt and
                          Jay Laura and
                          Eli Knaap},
          title        = {pysal/giddy: Release v2.3.1},
          month        = jun,
          year         = 2020,
          publisher    = {Zenodo},
          version      = {v2.3.1},
          doi          = {10.5281/zenodo.3887050},
          url          = {https://doi.org/10.5281/zenodo.3887050}
        }
        ```
        
        Funding
        -------
        
        <img src="figs/nsf_logo.jpg" width="50"> Award #1421935 [New Approaches to Spatial Distribution Dynamics](https://www.nsf.gov/awardsearch/showAward?AWD_ID=1421935)
        
Keywords: spatial statistics,spatiotemporal analysis
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: GIS
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >3.5
Description-Content-Type: text/markdown
Provides-Extra: tests
Provides-Extra: docs
