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            "License :: OSI Approved :: BSD License",
            "Programming Language :: Python",
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        "description": "GeospatIal Distribution DYnamics (giddy) in PySAL\n=================================================\n\n[![Build Status](https://travis-ci.org/pysal/giddy.svg?branch=master)](https://travis-ci.org/pysal/giddy)\n[![Coverage Status](https://coveralls.io/repos/github/pysal/giddy/badge.svg?branch=master)](https://coveralls.io/github/pysal/giddy?branch=master)\n[![Gitter room](https://badges.gitter.im/pysal/giddy.svg)](https://gitter.im/pysal/giddy)\n[![Documentation Status](https://readthedocs.org/projects/giddy/badge/?version=latest)](https://giddy.readthedocs.io/en/latest/?badge=latest)\n[![PyPI version](https://badge.fury.io/py/giddy.svg)](https://badge.fury.io/py/giddy)\n[![DOI](https://zenodo.org/badge/91390088.svg)](https://zenodo.org/badge/latestdoi/91390088)\n\n\nGiddy is an open-source python library for the analysis of dynamics of\nlongitudinal spatial data. Originating from the spatial dynamics module\nin [PySAL (Python Spatial Analysis Library)](http://pysal.org/), it is under active development\nfor the inclusion of newly proposed analytics that consider the\nrole of space in the evolution of distributions over time.\n\n*Below are six choropleth maps of US state per-capita incomes from 1929 to 2004 at a fifteen-year interval.*\n\n![us_qunitile_maps](figs/us_qunitile_maps.png)\n\nDocumentation\n-------------\n\nOnline documentation is available [here](https://giddy.readthedocs.io).\n\n\nFeatures\n--------\n- Directional LISA, inference and visualization as rose diagram\n\n[![rose_conditional](figs/rose_conditional.png)](notebooks/directional.ipynb)\n\n*Above shows the rose diagram (directional LISAs) for US states incomes across 1969-2009 conditional on relative incomes in 1969.*\n\n- Spatially explicit Markov methods:\n    - Spatial Markov and inference\n    - LISA Markov and inference\n- Spatial decomposition of exchange mobility measure (rank methods):\n    - Global indicator of mobility association (GIMA) and inference\n    - Inter- and intra-regional decomposition of mobility association and inference\n    - Local indicator of mobility association (LIMA)\n        - Neighbor set LIMA and inference\n        - Neighborhood set LIMA and inference\n\n[![us_neigborsetLIMA](figs/us_neigborsetLIMA.png)](notebooks/Rank%20based%20Methods.ipynb)\n\n- Income mobility measures\n\nExamples\n--------\n\n* [Directional LISA](notebooks/directional.ipynb)\n* [Markov based methods](notebooks/Markov%20Based%20Methods.ipynb)\n* [Rank based methods](notebooks/Rank%20based%20Methods.ipynb)\n* [Mobility measures](notebooks/Mobility%20measures.ipynb)\n* [Rank-based Markov methods](notebooks/Rank_Markov.ipynb)\n* [Sequence methods (Optimal matching)](notebooks/Sequence.ipynb)\n\nInstallation\n------------\n\nInstall the stable version released on the [Python Package Index](https://pypi.org/project/giddy/) from the command line:\n\n```\npip install giddy\n```\n\nInstall the development version on [pysal/giddy](https://github.com/pysal/giddy):\n\n```\npip install https://github.com/pysal/giddy/archive/master.zip\n```\n\n#### Requirements\n\n- libpysal\n- esda\n- mapclassify\n\nContribute\n----------\n\nPySAL-giddy is under active development and contributors are welcome.\n\nIf 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\u2019ll automatically be added to the [Contributors List](https://github.com/pysal/giddy/graphs/contributors).\n\nSupport\n-------\n\nIf you are having issues, please talk to us in the [gitter room](https://gitter.im/pysal/giddy).\n\nLicense\n-------\n\nThe project is licensed under the [BSD license](https://github.com/pysal/giddy/blob/master/LICENSE.txt).\n\n\nBibTeX Citation\n---------------\n\n```\n@misc{wei_kang_2019_3351744,\n  author       = {Wei Kang and\n                  Sergio Rey and\n                  Philip Stephens and\n                  Nicholas Malizia and\n                  Levi John Wolf and\n                  Stefanie Lumnitz and\n                  James Gaboardi and\n                  jlaura and\n                  Charles Schmidt and\n                  eli knaap and\n                  Andy Eschbacher},\n  title        = {pysal/giddy: giddy 2.2.1},\n  month        = jul,\n  year         = 2019,\n  doi          = {10.5281/zenodo.3351744},\n  url          = {https://doi.org/10.5281/zenodo.3351744}\n}\n```\n\nFunding\n-------\n\n<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)",
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