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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.

Online documentation is availablehere.

• Directional LISA, inference and visualization as rose diagram

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

• Income mobility measures
• Alignment-based sequence analysis methods

• Directional LISA
• Markov based methods
• Rank Markov methods
• Mobility measures
• Rank based methods
• Sequence methods (Optimal matching)

Install the stable version released on thePython Package Index from the command line:

pip install giddy

Install the development version onpysal/giddy:

pip install git+https://github.com/pysal/giddy

• scipy>=1.8
• libpysal>=4.8
• mapclassify>=2.5
• esda>=2.4
• quantecon>=0.7

PySAL-giddy is under active development and contributors are welcome.

If you have any suggestion, feature request, or bug report, please open a newissue on GitHub. To submit patches, please follow the PySAL developmentguidelines and open apull request. Once your changes get merged, you’ll automatically be added to theContributors List.

If you are having issues, please talk to us in thediscord channel.

The project is licensed under theBSD license.

@software{wei_kang_2024_10520458,  author       = {Wei Kang and                  Sergio Rey and                  James Gaboardi and                  Philip Stephens and                  Nicholas Malizia and                  Stefanie Lumnitz and                  Levi John Wolf and                  Charles Schmidt and                  Jay Laura and                  Eli Knaap},  title        = {pysal/giddy},  publisher    = {Zenodo},  doi          = {10.5281/zenodo.1322825},  url          = {https://doi.org/10.5281/zenodo.1322825}}

Award #1421935New Approaches to Spatial Distribution Dynamics