Space–time 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.
- 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
Install giddy by running:
$ pip install giddy
- scipy
- numpy
- libpysal
- esda
- mapclassify
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 on GitHub. To submit patches, please follow the PySAL development guidelines and open a pull request. Once your changes get merged, you’ll automatically be added to the Contributors List.
If you are having issues, please talk to us in the gitter room.
The project is licensed under the BSD license.
Award #1421935 New Approaches to Spatial Distribution Dynamics