PySAL-giddy for exploratory spatiotemporal data analysis
============================================
[](https://github.com/pysal/giddy/actions/workflows/tests.yml)
[](https://codecov.io/gh/pysal/giddy)
[](https://gitter.im/pysal/giddy)
[](https://badge.fury.io/py/giddy)
[](https://doi.org/10.5281/zenodo.1322825)
[](https://mybinder.org/v2/gh/pysal/giddy/main)
[](https://pepy.tech/project/giddy)
Giddy is an open-source python library for exploratory spatiotemporal data analysis and the analysis of geospatial distribution dynamics. 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 U.S. state per-capita incomes from 1929 to 2004 at a fifteen-year interval.*
Documentation
--------------------
Online documentation is available [here](http://pysal.org/giddy/).
Features
------------
- Directional LISA, inference and visualization as rose diagram
[](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
[](notebooks/RankBasedMethods.ipynb)
- Income mobility measures
- Alignment-based sequence analysis methods
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 git+https://github.com/pysal/giddy
```
#### 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/main/LICENSE.txt).
BibTeX Citation
---------------------
```
@software{wei_kang_2023_7693957,
author = {Wei Kang and
Sergio Rey and
Philip Stephens and
James Gaboardi and
Nicholas Malizia and
Stefanie Lumnitz and
Levi John Wolf and
Charles Schmidt and
Jay Laura and
Eli Knaap},
title = {pysal/giddy: v2.3.4},
month = mar,
year = 2023,
publisher = {Zenodo},
version = {v2.3.4},
doi = {10.5281/zenodo.7693957},
url = {https://doi.org/10.5281/zenodo.7693957}
}
```
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)
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"description": "PySAL-giddy for exploratory spatiotemporal data analysis\n============================================\n\n[](https://github.com/pysal/giddy/actions/workflows/tests.yml)\n[](https://codecov.io/gh/pysal/giddy)\n[](https://gitter.im/pysal/giddy)\n[](https://badge.fury.io/py/giddy)\n[](https://doi.org/10.5281/zenodo.1322825)\n[](https://mybinder.org/v2/gh/pysal/giddy/main)\n[](https://pepy.tech/project/giddy)\n\nGiddy is an open-source python library for exploratory spatiotemporal data analysis and the analysis of geospatial distribution dynamics. 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.\n\n*Below are six choropleth maps of U.S. state per-capita incomes from 1929 to 2004 at a fifteen-year interval.*\n\n\n\nDocumentation\n--------------------\n\nOnline documentation is available [here](http://pysal.org/giddy/).\n\n\nFeatures\n------------\n\n- Directional LISA, inference and visualization as rose diagram\n\n[](notebooks/DirectionalLISA.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[](notebooks/RankBasedMethods.ipynb)\n\n- Income mobility measures\n- Alignment-based sequence analysis methods\n\n\nExamples\n-------------\n\n* [Directional LISA](notebooks/DirectionalLISA.ipynb)\n* [Markov based methods](notebooks/MarkovBasedMethods.ipynb)\n* [Rank Markov methods](notebooks/RankMarkov.ipynb)\n* [Mobility measures](notebooks/MobilityMeasures.ipynb)\n* [Rank based methods](notebooks/RankBasedMethods.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 git+https://github.com/pysal/giddy\n```\n\n#### Requirements\n\n- scipy>=1.3.0\n- libpysal>=4.0.1\n- mapclassify>=2.1.1\n- esda>=2.1.1\n- quantecon>=0.4.7\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/main/LICENSE.txt).\n\n\nBibTeX Citation\n---------------------\n\n```\n@software{wei_kang_2023_7693957,\n author = {Wei Kang and\n Sergio Rey and\n Philip Stephens and\n James Gaboardi and\n Nicholas Malizia and\n Stefanie Lumnitz and\n Levi John Wolf and\n Charles Schmidt and\n Jay Laura and\n Eli Knaap},\n title = {pysal/giddy: v2.3.4},\n month = mar,\n year = 2023,\n publisher = {Zenodo},\n version = {v2.3.4},\n doi = {10.5281/zenodo.7693957},\n url = {https://doi.org/10.5281/zenodo.7693957}\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)\n",
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