Metadata-Version: 2.1
Name: giddy
Version: 2.3.1
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
        ---------------
        
        ```
        @misc{wei_kang_2019_3351744,
          author       = {Wei Kang and
                          Sergio Rey and
                          Philip Stephens and
                          Nicholas Malizia and
                          Levi John Wolf and
                          Stefanie Lumnitz and
                          James Gaboardi and
                          jlaura and
                          Charles Schmidt and
                          eli knaap and
                          Andy Eschbacher},
          title        = {pysal/giddy: giddy 2.2.1},
          month        = jul,
          year         = 2019,
          doi          = {10.5281/zenodo.3351744},
          url          = {https://doi.org/10.5281/zenodo.3351744}
        }
        ```
        
        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: docs
Provides-Extra: tests
